Public Disclosure Authorized...Vietnam Transport Knowledge Series Supported by AUSTRALIA–WORLD...

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AddressingClimateChangeinTransport

Volume2:PathwaytoResilientTransport

VietnamTransportKnowledgeSeriesSupportedbyAUSTRALIA–WORLDBANKGROUPSTRATEGICPARTNERSHIPINVIETNAMandNDCPARTNERSHIPSUPPORTFACILITY

AddressingClimateChangeinTransport

Volume2:PathwaytoResilientTransport

JungEunOh,XavierEspinetAlegre,RaghavPant,ElcoE.Koks,

TomRussell,RoaldSchoenmakers,andJimW.Hall

FINALREPORT

September2019

©2019TheWorldBank

1818HStreetNW,WashingtonDC20433

Telephone:202-473-1000;Internet:www.worldbank.org

This work is a product of the staff of The World Bank with external contributions. The findings,

interpretations and conclusions expressed in thiswork do not necessarily reflect the views of TheWorldBankanditsBoardofExecutiveDirectors.TheWorldBankdonotguaranteetheaccuracyofthedataincluedinthiswork.

Theboundaries,colors,denominationsandotherinformationshownonanymapinthisworkdonotimplyanyjudgementonthepartofTheWorldBankconcerningthelegalstatusofanyterritoryorthe

endorsementoracceptanceofsuchboundaries.

Nothinghereinshallconstituteorbeconsideredtobea limitationuponorwaiverof theprivileges

andimmunitiesofTheWorldBank,allofwhicharespecificallyreserved.

AllqueriesonrightsandlicensesshoudbeaddressedtothePublishingandKnowledgeDivision,TheWorld Bank, 1818 H Street NW, Washington DC 20433, USA; fax: 202-522-2625; email:[email protected].

Cover photo: A motorcycle crosses over a flooded road in rural Vietnam. Hanoi Photography –stock.adobe.com.

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Contents

FiguresandTables ................................................................................................................ vii

Foreword ............................................................................................................................... xi

Acknowledgments ................................................................................................................xiii

AbbreviationsandAcronyms...............................................................................................xvii

ExecutiveSummary .............................................................................................................. 21

Chapter1:Introduction ........................................................................................................ 29BackgroundandObjectives ........................................................................................................................ 29ScopeoftheStudy ...................................................................................................................................... 31StudyContributionsandLimitations .......................................................................................................... 33ReportStructure ......................................................................................................................................... 36

Chapter2:VietnamProfile:TransportNetworkandExposuretoNaturalHazard................. 39TransportNetworks .................................................................................................................................... 39

National-scaleroadinfrastructurenetworks ................................................................................... 39Province-scaleroadsnetworks......................................................................................................... 40Railwayinfrastructurenetwork........................................................................................................ 41Inlandwaterwayinfrastructurenetwork ......................................................................................... 41Maritimeinfrastructurenetwork ..................................................................................................... 42Airtransportinfrastructurenetwork................................................................................................ 42

ModelingFreightFlowonTransportNetworks ......................................................................................... 42Resultsoffreightflowmappingatnationalscale............................................................................ 42Resultsofprovince-scaleflowmapping ........................................................................................... 45Uncertaintiesinfreightflowmapping ............................................................................................. 48

NaturalHazardExposureofTransportNetworks ...................................................................................... 50National-scaleroadandrailwayshazardexposures........................................................................ 51Province-scalehazardexposureofroads ......................................................................................... 57

Chapter3:Vulnerability,Criticality,andRiskAssessment..................................................... 65VulnerabilityandCriticalityAssessment .................................................................................................... 65National-ScaleRoadNetworkCriticality..................................................................................................... 66National-ScaleRailwayCriticality ............................................................................................................... 69Province-ScaleRoadCriticality ................................................................................................................... 71

LaoCaiprovinceroadsdisruptions................................................................................................... 71BinhDinhprovinceroadsdisruptions............................................................................................... 72ThanhHoaprovinceroadsdisruptions............................................................................................. 73

RiskAssessment.......................................................................................................................................... 74National-scalenetworks................................................................................................................... 75Province-scaleanalysis..................................................................................................................... 81

UncertaintiesinCriticalitiesandRisks........................................................................................................ 86CriticalitiesandVulnerabilitiesofPorts...................................................................................................... 90

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Chapter4:AdaptationStrategyandAnalysis........................................................................ 93ScopeandPurposeofAdaptationAnalysis ................................................................................................ 93IdentifyingFailuresbyClimateChangeDrivenHazardThreats ................................................................. 94AdaptationOptionsandCosts.................................................................................................................... 95ResultsofAdaptationAnalysis ................................................................................................................... 97

National-scaleroadnetworkadaptationcostsandbenefits ........................................................... 98Province-scaleroadnetworkadaptationcostsandbenefits ......................................................... 104Decision-MakingunderUncertainty:SensitivityofCostsofAdaptation........................................ 111Rangesanduncertaintiesinadaptationestimates........................................................................ 112

Chapter5:ExploringMultimodalOptionsforNational-ScaleTransport.............................. 117IncludingMultimodalLinksandCosts ...................................................................................................... 118ResultsofPartialMultimodalShiftsinRoadFailureAnalysis .................................................................. 119RailFailureAnalysisResults ...................................................................................................................... 122

Chapter6:ConclusionsandPolicyRecommendations ........................................................ 127IncreasingVulnerabilitiesDuetoClimateChange ................................................................................... 127PrioritizingTransportNetworkResilienceforSystemicEconomicBenefit ............................................. 127MakingtheCaseforInvestmentinAdaptationtoBuildTransportNetworkResilience......................... 128StrongCaseforImprovingMultimodalTransportLinkagesandEnhancingEfficiencyacrossModes....129ImprovingExistingDataGapsforFurtherStudies ................................................................................... 129

AppendixA:MethodologyforTransportRiskandAdaptationAssessment ........................ 131MethodologicalFrameworkandImplementationStructure................................................................... 131

Keydefinitions ................................................................................................................................ 132Estimationofadaptationoptions................................................................................................... 135Uncertaintyanalysis ....................................................................................................................... 136

AppendixB:OverviewofDatasets...................................................................................... 141Naturalhazardsdataset................................................................................................................. 142

AppendixC:VulnerabilityResultsforPorts......................................................................... 145

AppendixD.CommunesandDistrictsbyExposureofRoadNetworkstoNaturalHazards . 151

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FiguresandTables

FIGURES

Figure E.1.Maximum Economic Losses and Benefit-Cost Ratios of National RoadNetworks Due to

TransportDisruptions ...................................................................................................................... 24

Figure1.1.VietnameseProvincesandNeighboringCountriesConsideredintheStudy ...................... 32

Figure2.1.MaximumAADFfortheNational-ScaleTransportNetwork ............................................... 45

Figure2.2.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson

LaoCaiProvincialRoads................................................................................................................... 46

Figure2.3.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson

BinhDinhProvincialRoads............................................................................................................... 47Figure2.4.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenueson

ThanhHoaProvincialRoads............................................................................................................. 48

Figure2.5.RangesofAADFFlowsbyMode .......................................................................................... 49

Figure2.6.RangesofNetRevenueFlowsAssignedtoRoadNetworksinLaoCai,BinhDinh,andThanh

Hoa ................................................................................................................................................... 50

Figure2.7.HazardExposureofNational-ScaleRoadsNetworkinVietnam.......................................... 54

Figure2.8.HazardExposureofNational-ScaleRailwayNetworkinVietnam ....................................... 55

Figure 2.9. Percentage Change in Exposure of National-Scale Road Network to 1,000-Year River

Floodingby2030.............................................................................................................................. 56

Figure 2.10. Percentage Change in Exposure of National-Scale Rail Network to 1,000-Year River

Floodingby2030.............................................................................................................................. 57

Figure2.11.HazardExposureofProvinceRoadsNetworkinLaoCai ................................................... 59

Figure2.12.PercentageChangeinExposureofLaoCaiRoadNetworkLinkstoLandslidesby2050 ... 60Figure2.13.HazardExposureofProvinceRoadsNetworkinBinhDinh............................................... 60

Figure 2.14. Percentage Change in Exposure of Binh Dinh Road Network Links to 1,000-year River

Floodingby2030.............................................................................................................................. 61

Figure2.15.HazardExposureofProvinceRoadsNetworkinThanhHoa ............................................. 62

Figure2.16.PercentageChange inExposureof ThanhHoaRoadNetwork Links to1,000-YearRiver

Floodingby2030.............................................................................................................................. 63

Figure3.1.National-ScaleRoadsCriticalityResults .............................................................................. 68

Figure3.2.National-ScaleRailwayCriticalityResults............................................................................ 70

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Figure3.3.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossforLaoCai

ProvinceRoadNetwork ................................................................................................................... 72

Figure3.4.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheBinh

DinhProvinceRoadNetwork ........................................................................................................... 73

Figure 3.5. Maximum Estimated Disruption in Net Revenue andMaximum Economic Loss for the

ThanhHoaProvinceRoadNetwork ................................................................................................. 74

Figure3.6.MaximumEstimatedRisksforNational-ScaleRoadNetworkLinks .................................... 76Figure 3.7. PercentageChange inMaximumFailureRisks ofNational-Scale RoadNetwork Links for

Future2030RiverFloodingunderClimateScenarios ...................................................................... 77

Figure3.8.MaximumEstimatedRisksforNational-ScaleRailwayNetwork......................................... 79

Figure3.9. PercentageChange inMaximumFailureRisksofNational-ScaleRailNetwork for Future

2030RiverFloodingunderClimateScenarios.................................................................................. 80

Figure3.10.MaximumRisksofLaoCaiProvince-ScaleRoadNetwork................................................. 81

Figure 3.11. PercentageChange inMaximumFailureRisks of LaoCai Province-ScaleRoadNetwork

LinksforFuture2050LandslideSusceptibilityunderClimateScenarios ......................................... 82

Figure3.12.MaximumRisksofBinhDinhProvince-ScaleRoadNetwork............................................. 83

Figure3.13.PercentageChangeinFailureRisksofBinhDinhProvince-ScaleRoadNetworkforFuture


Figure3.14.MaximumEstimatedRisksforThanhHoaProvince-ScaleRoadNetwork ........................ 85

Figure3.15.PercentageChangeinFailureRisksofThanhHoaProvince-ScaleRoadNetworkforFuture


Figure 3.16. Estimated Ranges of Daily Economic Losses for Individual Network Link Failures in

Vietnam’sNational-ScaleRoadandRailNetworks .......................................................................... 87

Figure 3.17. EstimatedRanges ofMinimumandMaximumRisks for Individual Link FailuresDue to

CurrentandFutureRiverFloodingonNational-ScaleNetworks ..................................................... 88

Figure3.18.EstimatedRangesofDailyEconomicLossesandRisksDue toCurrentandFutureRiver

FloodingofIndividualLinkFailuresontheLaoCaiRoadNetwork .................................................. 89


FloodingofIndividualLinkFailuresontheBinhDinhRoadNetwork .............................................. 89


FloodingofIndividualLinkFailuresontheThanhHoaRoadNetwork ............................................ 90

Figure4.1.MaximumTotalInvestmentover35YearsontheClimateAdaptationforIdentifiedLinksin

theNational-ScaleRoadNetworkinVietnam.................................................................................. 99Figure4.2.EstimatedMaximumBenefitsover35YearsandMaximumBCRsofAdaptationOptionsfor

IdentifiedLinksintheNational-ScaleRoadNetworkinVietnam .................................................. 100

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Figure 4.3. Comparisons of BCRs of Adaptation Options for National-Scale Road Network Links in

Vietnam.......................................................................................................................................... 102

Figure4.4.Investments,Benefits,andAdaptationBCROptionsforIdentifiedLinksintheLaoCaiRoad

Network ......................................................................................................................................... 105

Figure4.5.Investments,Benefits,andBCRsofAdaptationOptionsforIdentifiedLinksintheBinhDinh

RoadNetwork ................................................................................................................................ 107

Figure4.6.Investments,Benefits,andAdaptationBCRsOptionsforIdentifiedLinksintheThanhHoa

RoadNetwork ................................................................................................................................ 109

Figure4.7.ParameterSensitivityforNational-andProvince-ScaleRoads ......................................... 111

Figure4.8.AdaptationAnalysisResultsfortheNational-ScaleRoadNetworkinVietnam ................ 113

Figure4.9.RangesofAdaptationBCRsfortheNational-ScaleRoadNetworkinVietnam,Evaluatedfor

CurrentandFutureExtremeRiverFloodingScenarios .................................................................. 113

Figure4.10.AdaptationBCRsforProvince-ScaleRoadNetworks ...................................................... 115

Figure5.1.MultimodalLinksCreatedintheNetworkModel ............................................................. 118

Figure 5.2. Total Economic Impacts of National-Scale Road Links Failures When Considering 90

PercentFlowReroutingonRoadsand10PercentReroutingalongOtherNetworksviaMultimodal

Links ............................................................................................................................................... 120

Figure5.3.Redistributionof10PercentofMaximumRoadTonnageFlowsasAddedTonnagesonto

RailandWaterwayNetworksfollowingRoadNetworkLinkDisruptions ...................................... 122

Figure5.4. Total Economic Impacts of Rail Link FailureswhenConsideringReroutingOptions along

National-ScaleRoads,InlandWaterwaysandMaritimeNetworksviaMultimodalLinks ............. 123

Figure5.5.RedistributionofMaximumTonnageFlowsasAddedTonnagesontoNational-ScaleRoads

andWaterway(InlandandMaritime)NetworksfollowingRailNetworkLinkDisruptions ........... 124

Figure A.1. Graphical Representation of Infrastructure System-of-Systems Risk Assessment

Framework ..................................................................................................................................... 132

TABLES

TableE.1.ClimateAdaptationAnalysisforProvince-ScaleRoadNetworks.......................................... 25

Table2.1.RoadCategoriesbyAverageDailyVehicleTrafficCounts .................................................... 39

Table2.2.EstimatedTotalLengthsbyRoadCategoryintheNational-ScaleRoadNetworkModelfor

Vietnam............................................................................................................................................ 40

Table 2.3. Estimated Lengths by Road Category in the Province-Scale Road Network Model for

Vietnam............................................................................................................................................ 40

Table 2.4. Estimated Total Link Lengths and Percentages of Rail Routes Identified in the National-

ScaleRailNetworkModelforVietnam ............................................................................................ 41

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Table2.5.DailyTrafficVolumesbyCommodityandTransportMode.................................................. 44

Table2.6.LengthsofNationalRoadsandRailwayNetworksExposedtoDifferentTypesofHazards . 52

Table2.7.ExposureofProvinceRoadsNetworkstoHazards ............................................................... 58

Table3.1.SelectedNumbersofSingleLinkFailureScenariosforDifferentTransportNetworks........ 66

Table4.1.PotentialFailureTypesDuetoIncreasedHazardThreatsDrivenbyAdverseClimateChange

......................................................................................................................................................... 95

Table 4.2. Cost Summary of Initial Adaptation Investment for Building Prototype Climate Resilient

RoadsinVietnam ............................................................................................................................. 97

Table 4.3. Numbers of Single-Link Failure and Hazard Exposure/Adaptation Options Scenarios

EvaluatedforTransportNetworksinVietnam................................................................................. 98

Table4.4.TwentyNational-ScaleRoadsRanked inDescendingOrderofMaximumAdaptationBCRs

....................................................................................................................................................... 103

Table4.5.Top20LaoCaiRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs. 106

Table4.6.Top20BinhDinhRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs

....................................................................................................................................................... 108

Table4.7.Top20ThanhHoaRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs

....................................................................................................................................................... 110

Table B.1. Raw Datasets Collected from Different Data Sources for the Transport Risks Analysis

ModelinginVietnam...................................................................................................................... 141

TableB.2.NaturalHazardDatasetsAssembledfortheStudyNotes .................................................. 143TableC.1.AirportswithAADFFlowsandHazardExposureScenarioResults ..................................... 145

TableC.2.MajorMaritimePortswithAADFFlowsandHazardExposureScenarioResults ............... 146

TableC.3.MajorInlandWaterwayPortswithAADFFlowsandHazardExposureScenarioResults... 147

TableD.1.TwentyCommunesMostExposedtoFloodinginLaoCaiProvince................................... 151

TableD.2.TwentyCommunesMostExposedtoFloodinginBinhDinhProvince............................... 152

TableD.3.TwentyCommunesMostExposedtoFloodinginThanhHoaProvince............................. 153

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Foreword

ClimatechangeissettohaveprofoundeffectsonVietnam’sdevelopment.Withnearly60percentofits land area and 70 percent of population at risk ofmultiple natural hazards, Vietnam globally isamongthemostvulnerablecountries tobothchronicandextremeevents.Over thepast25years,

extremeweather events have resulted in 0.4 to 1.7 percent of GDP loss, which climate change ispredicted to steeply rise by 2050. At the same time, as Vietnam’s economy grows, the country isbecomingasignificantemitterofgreenhousegases.WhileVietnam’sabsolutevolumeofemissionsis

still small compared to that of larger and richer countries, emissions are growing rapidly anddisproportionate to its economy size. Vietnam is the 13thmost carbon intensive economy in theworld, measured in terms of emissions per GDP, and 4th among the low- and middle-income

countriesinEastAsia.

Thetransportsectorplaysacriticalroleintheserecenttrends.Asteepriseinincomeandeconomic

growthhasledtorapidmotorization:Thecountryofaround96millionpeopleisalsohometonearly40 million vehicles, including 35 million motorbikes. While car ownership is still relatively low inVietnam,asincomerisescarsarequicklyreplacingmotorbikes,especiallyinthelargestcities.Public

transportmodalshareremainspersistentlylow,partlyduetothelowlevelofnetworkdevelopmentand partly to the convenience and affordability of two-wheeler-based mobility. Thanks to itseconomicsuccessandrapidintegrationwiththeinternationaltrade,cargotransportationinVietnam

has seen remarkable growth in the recent years.Vietnam’s long coastal lines andextensive inlandwaterway network have been extensively used for themovement of goods; however, theirmodalsharevis-à-visroadtransportisdeclining.

Vietnam’stransportnetwork,whichhasseenanimpressiveexpansionoverthepasttwodecades,isincreasinglyvulnerabletotheintensifyingclimatehazards.Today,Vietnam’sroadnetworkextendsto

over 400,000 km,much ofwhichwas not built towithstand extreme hazard scenarios,which areexpectedtobecomemorefrequentduetoclimatechange.Withouteffortstoimprovetheresilienceofthebuiltnetwork,Vietnam’sachievements inprovidinguniversalaccessto itsruralcommunities

may be undermined. Moreover, resilience of connectivity is critical to the continued success ofVietnam’s economy, which heavily relies on external trade and would increasingly depend onseamlessrural-urbanlinkages.

Inthisanalyticalwork,AddressingClimateChangeinTransportforVietnam,carriedoutbytheWorldBankandseveralotherpartnerswithsupportfromtheMinistryofTransportofVietnam,thestudy

aimstosetoutavisionandstrategyforclimate-smarttransport,inordertominimizethecarbonfootprintofthesectorwhileensuringitsresilienceagainstfuturerisks.The

analytical findings and recommendations are presented in two

volumes of the report: Volume 1—Pathway to Low CarbonTransport and Volume 2—Pathway to ResilientTransport. The first volume provides how Vietnam

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canreduceitscarbonemissionbyemployingamixofdiversepoliciesandinvestments,undervaryinglevels of ambition and resources. The second volume provides a methodological framework to

analyzenetworkcriticalityandvulnerability,andtoprioritizeinvestmentstoenhanceresilience.

Thesetworeportvolumeshavebeenpreparedatacriticaltime,wheretheGovernmentofVietnam

isworkingtoupdateitsNationallyDeterminedContributionandsetoutitsnextmedium-termpublicinvestment plan for the period of 2021 to 2025.We hope that these findings can provide usefulinsightsandspecific recommendations towards thesecriticaldocuments, contributing toVietnam’s

achievementindevelopingalow-carbonandresilienttransportsector.

GuangzheChen OusmaneDioneGlobalDirector CountryDirector

TransportGlobalPractice Vietnam

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Acknowledgments

ThisreportwaspreparedbytheTransportGlobalPracticeandtheEastAsiaandPacificRegionoftheWorldBank.

ThepreparationofthereportwasledbyDr.JungEunOh(SeniorTransportEconomist)andDr.XavierEspinet Alegre (Transport Specialist) at The World Bank, along with Dr. Raghav Pant at OxfordInfrastructureAnalytics(OIA).TheteamincludesMs.MariaCordeiro,Dr.JasperCook,Mr.DucCong

Phan,Dr.NguyenThanhLong,Mr.ChiKienNguyenattheWorldBank,andDr.ElcoE.Koks,Mr.TomRussell, Mr. Roald Schoenmakers and Prof. Jim W. Hall at OIA. All crop production outputs aregratefullyacknowledgedfromDr.JawooKoo,Dr.ZheGuo,Dr.UlrikeWood-Sichra,andDr.YatingRu

atInternationalFoodPolicyResearchInstitute.

The team extends its appreciation for the guidance of Guangzhe Chen (Global Director, Transport

Practice), Franz R. Drees-Gross, (Director, Transport Practice), Ousmane Dione (Vietnam CountryDirector), AlmudWeitz (Transport Practice Manager, Southeast Asia and the Pacific), Achim Fock(VietnamOperationsManager),andMadhuRaghunath(VietnamInfrastructureProgramLeader).

TheteamconductedthestudyincollaborationwiththeGovernmentofVietnam,andappreciatesthestrongsupportandadvicegenerouslyprovidedbyMr.LeDinhTho,ViceMinisterofTransport,and

Mr.TranAnhDuong(DirectorGeneral),Ms.NguyenThiThuHang(DeputyDirector),Mr.VuHaiLuu(official),Ms. Doan Thi Hong Tham (official) andMr.Mai VanHien (official) at theDepartment ofEnvironment,MinistryofTransport (MoT). Several governmententitiesandorganizationsprovided

vital inputs: Directorate for Roads of Vietnam (DRVN); Vietnam InlandWaterways Administration(VIWA);VietnamMaritimeAdministration(VINAMARINE);CivilAviationAuthorityofVietnam(CAAV);VietnamRailwayAuthority(VRA);andVietnamRegister(VR).

Theworkbenefited from the adviceprovidedby the followingpeer reviewers: Stephen Ling (LeadEnvironmental Specialist), Cecilia M. Briceno-Garmendia (Lead Economist), Neha Mukhi (Senior

ClimateChangeSpecialist),andIanHalvdanRossHawkesworth(SeniorGovernanceSpecialist)attheWorldBank,andRobinBednall(FirstSecretary)andDuc-CongVu(SeniorInfrastructureManager)atthe Government of Australia. Kara Watkins (World Bank Communications Consultant) thoroughly

copyedited the report for consistency and readability, and Chi Kien Nguyen (Transport Specialist)reviewedtheVietnamesetranslationforaccuracy,forwhichtheteamisgrateful.

TheteamalsoappreciatestheexcellentproductionsupportprovidedbyNguyenThanhHang,NguyenMaiTrang,IraChairaniTriasdewi(administration),DangThiQuynhNga(operations),andNguyenHongNgan (communications). The team also appreciates the excellent production support provided by

Nguyen Thanh Hang, Nguyen Mai Trang, Ira Chairani Triasdewi (administration), Dang ThiQuynhNga(operations),andNguyenHongNgan(communications).

TheteamwouldliketothanktheGovernmentofAustraliaandNDCPartnership Support Facility for their generous supportand funding toward this analyticalwork andpublication.

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AbouttheAuthors

JungEun“Jen”OhisaseniortransporteconomistattheWorldBank.Withabackgroundintransport

engineeringandeconomics,Jenhasledanumberofinvestmentprojectsandtechnicalassistanceforvarious client countries inSoutheastAsia,CentralAsia, Sub-SaharanAfrica, andEurope, coveringabroad range of transport sector issues. Jen served as a transport cluster leader for Vietnam from

2016 to 2019, overseeing and coordinating a large portfolio of World Bank-financed projects inVietnam’s transport sector, and led several knowledge pieces on climate change in transport,transportconnectivity,urbanmobility,and infrastructure financing. JenholdsanMSc ineconomics

and a PhD in transportation systems, and has authored several articles, including a chapter in thebook,TheUrbanTransportCrisisinEmergingEconomies(2017),publishedbySpringerInternational.

XavierEspinetAlegre,atransportspecialistforthechiefeconomistforsustainabledevelopmentatthe World Bank, has extended expertise in transport planning and economics, climate changeadaptation, disaster riskmanagement, anddecision-making science.Hehasworked formore than

five years developing interdisciplinary and cross-sectoral solutions, as the Decision-Making underDeep Uncertainties approach, to challenges posed by climate change to transport planning inMozambique,SierraLeone,Albania,Vietnam,Cambodia,andSaintLucia,amongothers.Priortothe

WorldBank,heco-foundedResilientAnalytics,servingasatechnicallead,andworkedasaresearchassociateat the InstituteofClimateandCivil Systems.XavierholdsaPhD incivil systems fromtheUniversityofColoradoatBoulderandacivilengineeringdegreefromtheUniversitatPolitecnicade

CatalunyainSpain.

RaghavPantservesasaseniorpostdoctoralresearcherattheEnvironmentalChangeInstitute(ECI)at the University of Oxford, and is a founder and director of Oxford Infrastructure Analytics Ltd.

Raghav has more than 12 years of academic and professional experience on infrastructure andeconomic network risks modeling, and leads the resilience analysis research within the UKInfrastructure Transitions Research Consortium (ITRC). He has worked with the World Bank on

ongoing projects on transport risk analysis in Tanzania and Argentina. His research paper onvulnerabilityassessmentofGreatBritain’srailwayinfrastructurehasbeenawardedthe2016LloydsScienceofRiskPrizeinSystemsModeling.

Elco E. Koks works as a postdoctoral researcher in global network analysis at ECI andassistantprofessorat the Institute forEnvironmentalStudiesatVrijeUniversiteitAmsterdam.Elcohasmorethansevenyearsofexperienceworkingonseveraltopicsrelatedtodisasterimpactmodeling,climate

change, and water economics in the context of national (Knowledge for Climate) and EUresearchprojects (ENHANCEandTURAS).Hiskey researchdomain is themodeling

oftheeconomy-wideconsequencesofnaturaldisastersonbotharegionaland

interregional level, with a specific focus on industrial areasandcriticalinfrastructure.

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Tom Russell, a research software engineer at the Environmental Change Institute, University ofOxford,hasmorethansevenyearsofexperience inresearchsoftwaredevelopment,particularly in

spatial network analysis and data-driven design and interaction. Tom is creating the infrastructuresystems modeling integration framework for the Infrastructure Transitions Research Consortium(ITRC) and developing open-source tools for infrastructure risk analysis through the Oxford

Infrastructure Analytics–World Bank projects on transport risk analysis in Tanzania, Vietnam andArgentina.

Roald Schoenmakers works as a research software developer, actively collaborating with domainexpertstofacilitateintheirneedofsoftwaredevelopment.Heisalsoinvolvedinmaintaininglegacysoftware,toensureavailabilityofthemodelingplatformtotheITRCanditsindustrialpartners.Roald

has a background in engineering, with several years of experience in professional softwaredevelopmentforindustrialcontrolsystemsandembeddedapplications.

JimW.Hall,theprofessorofclimateandenvironmentalrisksattheUniversityofOxford,isafounderand director of Oxford Infrastructure Analytics Ltd. Jim leads the UKInfrastructure TransitionsResearch Consortium, which has developed the world’s first national infrastructure simulation

modelsforappraisalofnationalinfrastructureinvestmentandrisks.Hisbook,TheFutureofNationalInfrastructure:ASystemofSystemsApproach,waspublishedbyCambridgeUniversityPressin2016.He sits on the Expert Advisory Group for theNational Infrastructure Commissionand Chairs

theDAFNIDataandAnalyticsFacilityforNationalInfrastructure.HeisafellowoftheRoyalAcademyofEngineeringandforthelasttenyearshehasbeenamemberoftheUKindependentCommitteeonClimateChangeAdaptation.

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AbbreviationsandAcronyms

AADF AverageAnnualDailyFreight

AADT AverageAnnualDailyTraffic

BCR Benefit-CostRatio

CAAV CivilAviationAdministrationofVietnam

CBA Cost-BenefitAnalysis

CGE ComputationalGeneralEquilibrium

CI CapitalInvestments

CMIP5 CoupledModelIntercomparisonProjectPhase5

CVTS CommercialVehicleTrackingSystem

DMDU Decision-MakingUnderDeepUncertainty

DRVN DirectorateofRoadsforVietnam

EAD ExpectedAnnualDamages

EAEL ExpectedAnnualEconomicLosses

GCM GlobalClimateModel

GDP GrossDomesticProduct

GIS GeospatialInformationSystems

GLOFRIS GlobalFloodRiskwithImageScenarios

GSO GeneralStatisticsOfficeofVietnam

GVA GrossValueAdded

IFPRI InternationalFoodPolicyResearchInstitute

IMF InternationalMonetaryFund

IMHEN VietnamInstituteofMeteorology,HydrologyandClimateChange

IO Input-Output

JICA JapanInternationalCooperationAgency

km Kilometers

m Meters

MapSPAM SpatialProductionAllocationModel(MapSPAM)

MARD MinistryofAgricultureandRuralDevelopmentinVietnam

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MoNRE MinistryofNaturalResourcesandEnvironmentinVietnam

MoT MinistryofTransportinVietnam

MRIA Multi-RegionalImpactAssessment

MRIO Multi-RegionalInput-Outputdatasets

MT MetricTons

NPV NetPresentValue

OD Origin-Destination

OIA OxfordInfrastructureAnalytics

OSM OpenStreetMap

PDoT ProvincialDepartmentsofTransport

RCP RepresentativeConcentrationPathways

TDSI TransportandStrategyDevelopmentInstitute

TEU Twenty-FootEquivalentUnit

UN UnitedNations

UNCTAD UNConferenceonTradeandDevelopment

UNCOMTRADE UnitedNationsCommodityTradestatisticsdatabases

US$ UnitedStatesDollars

VINAMARINE VietnamMaritimeAdministration

VITRANSSIITheComprehensiveStudyontheSustainableDevelopmentofTransportSystemInVietnam

VIWA VietnamInlandWaterwayAdministration

VND VietnameseDong

VRAMS VietnamRoadAssetManagementSystem

WHO WorldHealthOrganization

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CURRENCYMEASURE

Currencyunit—US$

WEIGHTANDMEASURES

Metricsystem

BASELINEDATAYEAR

Commodityfreights—2009,2016

Macroeconomicdata—2012

Censusdata—2012

BusinessSurveydata—2012

EXCHANGERATES

US$1in2016=22,500VND

US$1in2012=22,000VND

US$1in1999=14,000VND

CURRENCYINFLATION

US$1in2016=US$1.5in1999

US$1in2016=US$1.05in2012

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ExecutiveSummary

Vietnam is one of the world’s fastest growing economies—with Gross Domestic Product (GDP)growthabove7percentinthefirstquarterof2018—andstrongforecastedgrowthfortheremainingyear(WorldBank2018).Vietnamisalsoprojectedtobeamongthefastestgrowingeconomiesover

the2016to2050period,capableofsustainingapotential5percentannualGDPgrowthrate (PwC2017).However,thisrapidgrowthisthreatenedbyextremeweathereventssuchasstorms,floods,typhoons,andlandslides.Vietnamrankshighasanaturaldisasterhotspot;twoormoremultihazard

eventspotentiallyexpose60percentofthelandareaand71percentofthepopulationtorisk(Dilleyetal2005)—whichcouldresultinannualaverageassetlossesamountingto1.5percentofGDP,andconsumptionlossesamountingto2percentofGDP(Hallegatteetal2016).

Climatechangewillexacerbatetheseextremehazards.TheMinistryofTransport(MoT) inVietnamaimstodevelopanationalstrategyforclimateresilienttransportandplans—aspartofthetransportsector’scontributiontotheNationallyDeterminedContributions(NDCs)—tomeettheParisClimate

Agreement targets. To this end, this report intends to support MoT on the development ofmultimodalnetwork-levelcriticalityandvulnerabilityanalysis,aswellasmethodsandtoolstoinformprioritization of investments in transport asset management to ensure integration of climate and

naturalriskconsiderations.

This report presents the results from theTransportMulti-Hazard Risk Analysis for Vietnam at twoscales—national and provincial—and draws upon policy recommendation to enhance climate

resilienceofthetransportsector.Thescopeofthenationalanalysiscoverskeynational-scaleroads,railways,civilaviation,inlandwaterways,andmaritimesystemsthatmakeupVietnam’smultimodaltransportationinfrastructure.Atthenationalscale,thefocusistounderstandtheeconomicimpacts

ofdisruptionstomultimodalfreighttransportationduetoinfrastructurefailures.

The scope of the province-scale analysis covers three specific provinces—Lao Cai, Binh Dinh, andThanh Hoa—and their road networks only, which are represented in greater granularity than the

national-scaleanalysis.Theroadnetworks includenational,provincial,district,andcommuneroadsandotherassets suchasbridgesandculverts,amongothers.At theprovince scale, the focus is tounderstand how road failures impact access to key locations within communes, thereby affecting

economicoutputgeneration.

Atbothnationalandprovincescales, twotypesofnaturalhazard—floodingand landslides—inducetransportfailures.Thefloodinghazardsconsidered inthisstudy includeriverflooding(i.e., flooding

causedby riversovertopping theirbanks), surfacewater flooding (i.e., flooding causedbyextremerainfall—also known as “pluvial” or “flash” flooding), and tidal flooding (i.e., flooding caused bytyphoon-induced storm surges). Besides looking at the present situation (the year 2016), we use

climatechangescenario-drivenmodeloutputsrepresentingfuturefloodingintheyears2025,2030,and 2050. The landslide hazards information includes model outputs for landslide susceptibilitypresented for current conditions (the year 2016) and future climate change scenarios in 2025 and

2050.

Theframeworkdevelopedforthisstudyoutlinesasystem-of-systemsmethodology.Eachtransport

mode(road,railways,inlandwaterways,maritime,andairlines)consideredinthisstudyistreatedas

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an infrastructure system, which is the collection and interconnection of all physical facilities andhumansystemsthatoperateinacoordinatedwaytoprovideinfrastructureservice(Halletal2016).

The infrastructure or transport service refers to the mobility for freight and passengers betweenlocations.Themultimodal transport infrastructure is thendefinedasa system-of-systems,which isthecollectionandinterconnectionofindividualtransportsystems.

The frameworkpresents the following typesof system-of-systemsassessmentsuseful fordecision-making:

• Criticalityassessmentasthemeasureofatransportlink’simportanceanddisruptiveimpactontherestofthetransportinfrastructure(Pantetal2015).

• Vulnerabilityassessmentasthemeasureofthenegativeconsequencescausedbyfailuresoftransportlinksfromexternalshockevents(Pantetal2016).

• Risk assessment as the product of the probability of a hazard and the consequences oftransportlinkfailures.

• Adaptationplanningasengineeringmeasurestakentoreducerisks.Inthecontextofclimatechange, the planned adaptation seeks to capitalize on the opportunities associated withclimatechange(Füssel2007).

Criticalities for the national-scale roads and railway networks are measured in terms of the total

economic impact metric, measured in United States dollars (US$) per day as the sum of the

macroeconomiclossesandtheincreasedfreightredistributioncostsincurredduetotheirfailures.

At the province scale, the study estimates road network criticalities in terms ofeconomic impactsestimated as the sum of the economic losses (net revenue losses) in US$ per day, due to lack of

accessible routes toward the commune centers, and the increases in rerouting costs where it ispossibletomaintainaccesstothecommunecentersafterthedisruptions.

Thestudyestimatestherisksofextremehazardfailuresbycombiningknowledgeofthehazardsand

impactsintoonemetric.ThenetworkrisksareestimatedintermsofanExpectedAnnualEconomicLoss (EAEL) metric. The losses signify the daily economic impacts, estimated in the criticalityassessment,multiplied by certain duration of disruption duringwhich the affected network link is

assumedtobeoutofoperation.

Theadaptationanalysisexploresthemeasuresintendedtoimprovethestructurereliabilityofroadassets to make them more resistant to climate change impacts. The adaptation options for a

particularroadassetarequantifiedintermsoftheircostsandbenefits.Thecostsincludetheinitialcostofinvestmenttoimplementtheadaptationoptions,alongwiththeroutineandperiodiccostsofmaintainingtheclimateresilienceoftheroadasset.Thebenefitsincludetheavoidedlosses,interms

of the damage (or rehabilitation) costs and the network-wide economic impacts of transport flowdisruptionsduetotheroadassets’ failure,weretheadaptationoptionnot implemented.Basedontheanalysis,thekeyfindingsandrecommendationsaresummarizedasfollows.

Vietnam’stransportsectorneedstoprepareforextremehazardsofincreasingintensitiesandwithincreasing frequency due to climate change. Our analysis shows that under climate changescenarios, exposures to extreme hazards would substantially increase for all transport sectors in

Vietnam.Theoverallmaximumkilometers(km)ofnationalroadsandrailwaysalongwithprovince-scaleroadnetworksexposedtoextremehazardlevelsincreasesunderclimatechangescenarios.Forexample,inLaoCai,roadlengthexposurestoextremelandslideschangefrom142kminthecurrent

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scenarios to 210 km in the future high-emission scenario for 2050, shown in RepresentativeConcentration Pathway (RCP) 8.5, or RCP8.5. In this scenario, extreme levels of flooding seen in

1,000-year events could start occurring in five-year events, making major inland, maritime, andairportsvulnerabletoriverfloodingwithincreasingfrequency,translatingtoincreasedriskexposure.

Systemic understanding of locations whose failures create increasing economic risks presents a

strongeconomiccaseforinvestinginbuildingclimateresilienceofVietnam’stransportnetworks.Themodelandanalysisresultshighlightsystemiccriticalitiesandhazard-specific,high-risk locationsin thenetworks,which canbeprioritized for furtherdetailed investigations into climate resilience.

ThemodelresultsinfigureE.1showlocationsofroadnetworkswhosefailurescanresultinveryhighdaily losses of up to US$1.9million per day, while railway failures can result in losses as high asUS$2.6millionperday.Whenfactoringsuchlosses,thewiderimplicationsofmacroeconomiclosses

due to transport disruptions become quite crucial. Generally, such effects are often ignored intransportanalysis,whichresultsinunderestimatingtheimpactsoftransportdisruptions.

ComparisonsofcurrentandfuturehazardrisksprominentlyhighlightthestrongcaseforVietnamto

invest in building climate-resilient national-scale roads and railways. Comparisons also stronglyindicate that access to economicopportunities in several provincial areaswill be severely affectedwithout investment in building climate-resilient roads. The expected annual economic losses by

2030—ameasureofrisks—duetotransportfailuresfromfutureclimatechange-drivenriverfloodingsignificantly increasebymorethan100percentatseveral locationsacrossnationalroads,railways,andprovinceroadnetworks.Suchincreasesarerecordedatallnetworklinkswithhighimpacts.The

analysisalsoshowsthethreeVietnameseprovinceswillexperienceadramaticincreaseinriskduetoclimate change scenarios,up to400percent (LaoCai), 900percent (BinhDinh), and4,000percent

(ThanhHoa)onroad linkswiththehighest losses.Additionally,severalnew instancesofsignificantlossesalsoemergeinfuturefloodingscenariosforthethreeprovinces.

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FigureE.1.MaximumEconomicLossesandBenefit-CostRatiosofNationalRoadNetworksDuetoTransportDisruptions

Vietnam’s roadnetworks require investments tooverhaulexisting roadassets tohigher climate-

resilientdesign standards. Though such investments couldbe costly, theirbenefitsoutweigh thecosts for priority network assets, making adaptation investments viable options for roads. Theanalysis has demonstrated that the national-scale and province-scale road networks all need

adaptation planning to protect against future climate-related hazards. The analysis shows theBenefit-CostRatios(BCR)ofadaptationinvestmentsintonational-scaleroadsaremostlygreaterthanone.Theanalysissuggeststhat,forsomenational-scaleroads,upgradingtoclimate-resilientdesigns

could cost up toUS$3.4million per kilometer—very high and comparable to costs of constructinghigh-standardnewhighways.But thehigheconomicbenefitsof such investments justify suchhighcosts.Whenthetop20roadlinkswithhighestmaximumBCRsareselected,theanalysisshowsthat

cumulativeclimateadaptationinvestmentsamounttoapproximatelyUS$95millioninitially,andover

A.Maximumtotaleconomicloss B.MaximumBCRofadaptationovertime

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35 years total approximately US$153 million. The cumulative benefits over 35 years of suchinvestments—estimated by adding the benefits from individual links—are substantial, ranging

betweenUS$651millionandUS$3.66billion.Whenlookingatadaptationtoflooding,thenumberoflinkswithBCR>1doubleswhenconsideringclimatechangeprojections.

Similarly, forprovince-scaleroadnetworksarelativelysmall,butsignificant,numberofassetshave

BCRs > 1.Many of these are bridges and local roads, which are crucial for accessing locations ofeconomic activities. For all road networks, the increasing BCRs under future climate-hazardsscenariosstrengthenthecaseforinvestinginclimateresiliencetoprotectagainfutureclimaterisks.

TableE.1summarizestheresultsoftheadaptationanalysisattheprovinciallevel.

TableE.1.ClimateAdaptationAnalysisforProvince-ScaleRoadNetworks

Vietnam’stransportnetworkscanbecomemoreresilientiftheyfunctionasintegratedmultimodal

systems, achieved by improving existing and creating new multimodal linkages. This study hasshowneconomicimpactsofdisruptionscanbereducedbybuildingadditionaltransportmultimodalconnectivity.Oftenknownasincreasinganetwork’s“redundancy,”thisphrasemightbeinterpreted

asbeingwastefuland inefficient.However,providingextraconnectivityandcapacitywillassist theeverydayflowofgoodsandpeople,aswellasprovidingnewopportunitiesforreroutingwhenmajordisruptionsoccur.

Thisstudyshowsaveryclearbenefitof redistributing flows fromtheroadnetworkto therailwaysandinlandandmaritimenetworks.Evena10percentshiftofroadfreights,especiallytorailways,can

lead to a 20 to 25 percent reduction of economic impacts. In addition, enhancing railway andwaterwayefficiencycanreducerisksforalreadycongestedroads.Moreover,existingunusedcapacity

intherailwaysaccommodatestheadditionalmodalshiftfromroads.Thepotentialrailwaylossesarealsosignificantlyreducedthroughtheavailabilityofmultimodaloptions,whichshouldbeconsideredinthefuture.

Itwouldbe important to invest in improvingdata gaps in future studies inorder tomakemorerobust economic case for investment in the transport network. Vietnam’s transport resilience

planning can benefit from a system-of-systems transport risk analysis. Importantly, creating cross-sectorandcross-organizationcollaborationshelpsincreasecapacitytoundertakefurtherstudiesandpursue continued efforts to improve the underlying datasets. The study has dealt with severely

Roadnetworkassets LaoCai BinhDinh ThanhHoa

PercentofassetswithBCR>1 15 2–3 2–3

NumberofassetswithBCR>1 190 220–330 530–800

AdaptationInvestmentover35years(US$millions) 12.9 1.6 2.3

AdaptationBenefitsover35years(US$millions) 16.4–22.5 14.2–31.4 7.8–22.3

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limited data, highlighted throughout the report. Some of these limitations exist in assembling andstandardizing:

• Topologicalrepresentationsofinfrastructurenetworkswithproperattributes

• Transportflowsthatrepresentlatestconditionsandtrends

• Transportationcostassignmentsreflectiveofexistingconditions

• Economicflowsdatashowingcurrentstructureofthecountryandregionaleconomies

• Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climatescenarios

• Seasonalityinformationofextremelevelsofhazardsandtransportnetworkflows

• Informationondisruptiondurationsandresponsebehaviors

• CostsofvariousadaptationoptionsmostrelevanttoVietnam.

Next steps: Creating a cross-sector and cross-organization collaboration involving various

governmentagenciesisextremelyimportantforincreasingcapacitytostandardizeandsharedata.While the model developed in this study can be readily adapted to accommodate furtherimprovements, this study addresses only some of the economic and social functions of transport

infrastructure.Notably,ithasnotexploredtheroleoftransportinfrastructureinenablingpassengertravelforworkorotherpurposes,orforlabormarketparticipation.Thatshouldbeincludedinfuturestudies, alongside other wider economic and social benefits of transport infrastructure. In these

recommendations we have already identified that prioritizing investments to improve transportnetwork resilience would require consideration of the costs and effectiveness of alternativeinterventions. Building on this study of transport network vulnerability and risks, we recommend

studyingthecostsandeffectivenessofalternativeinterventionsasthenextstep.

References

Dilley,Maxx,RobertS.Chen,UweDeichmann,ArthurL.Lerner-Lam,MargaretArnold,JonathanAgwe,PietBuys,OddvarKjevstad,BradfieldLyon,andGregoryYetman.2005.NaturalDisasterHotspots:A

GlobalRiskAnalysis.DisasterRiskAnalysisSeries.Washington,DC:WorldBank.http://documents.worldbank.org/curated/en/621711468175150317.

Füssel,Hans-Martin.2007.“AdaptationPlanningforClimateChange:Concepts,AssessmentApproaches,andKeyLessons.”SustainabilityScience2(2):265–75.doi:10.1007/s11625-007-0032-y.

Hall,JimW.,MartinoTran,Adrian.J.Hickford,andRobertJ.Nicholls,eds.2016.TheFutureofNationalInfrastructure:ASystem-of-SystemsApproach.Cambridge,UK:CambridgeUniversity

Press.

Hallegatte,Stephane,AdrienVogt-Schilb,MookBangalore,andJulieRozenberg.2016.Unbreakable:

BuildingtheResilienceofthePoorintheFaceofNaturalDisasters.Washington,DC:WorldBank.https://openknowledge.worldbank.org/handle/10986/25335.

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Pant,Raghav,JimW.Hall,andSimonP.Blainey.2016.“VulnerabilityAssessmentFrameworkforInterdependentCriticalInfrastructures:CaseStudyforGreatBritain’sRailNetwork.”European

JournalofTransportandInfrastructureResearch16(1):174–94.https://eprints.soton.ac.uk/385442/.

Pant,Raghav,JimW.Hall,Simon.P.Blainey,andJohnM.Preston.2015.“AssessingSinglePointCriticalityofMulti-ModalTransportNetworksattheNational-Scale.”Paperpresentedatthe25thEuropeanSafetyandReliabilityConference,Zurich,Switzerland,September2015.

https://eprints.soton.ac.uk/385456/.

PwC(PricewaterhouseCoopers).2017.TheWorldin2050—TheLongView:HowWilltheGlobal

EconomicOrderChangeby2050.PwCEconomics&PolicyTeamreport.London:PricewaterhouseCoopersLLP.https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf.

WorldBank.2018.“Vietnam’seconomicprospectimprovesfurtherwithGDPprojectedtoexpandby68percentin2018.”PressRelease,June14.https://www.worldbank.org/en/news/press-

release/2018/06/14/vietnams-economic-prospect-improves-further-with-gdp-projected-to-expand-by-68-percent-in-2018.

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Chapter1:Introduction

BackgroundandObjectives

TheSocialistRepublicofVietnam (henceforth referred toasVietnam) isoneof theworld’s fastest

growingeconomieswithGrossDomesticProduct(GDP)growthabove7percentinthefirstquarterof2018,withstrongforecastedgrowthfortheremainingyear(WorldBank2018).Thisfollowsfromaconsistent average GDP growth of 6.4 percent since the 2000s.1 Vietnam is also projected to be

amongthefastestgrowingeconomiesoverthe2016to2050period,capableofsustainingapotential5percentannualGDPgrowthrate(PwC2017).

However, such rapid growth is threatened by extremeweather hazards (storms, floods, typhoons,

landslides).PreviousreportssuggestVietnamalreadyrankshighasanaturaldisasterhotspotoftwoormoremulti-hazardevents,potentiallyexposing60percentlandareaand71percentpopulationtorisk(Dilleyetal2005)—whichcouldresultinannualaverageassetlossesamountingto1.5percentof

GDPandconsumptionlossesamountingto2percentofGDP(Hallegatteetal2016).Itispossiblethatclimate change will exacerbate these extreme hazards, even after factoring uncertainties indownscaled global climate model predictions (MoNRE 2009; World Bank 2011; Irish Aid 2017).

Recognizingthatthecountry’svulnerabilitytoimpactsofclimatechangecanimpedeitsgrowth,andas part of its Nationally Determined Contributions (NDCs) to meet the Paris Climate Agreementtargets, theMinistry of Transport (MoT) inVietnamaims to establish national transport strategies

andplansaspartof the transport sector’s contribution todeliveronNDC implementation. To thisend, this report supports MoT in the development of multimodal network level criticality andvulnerability analysis, as well as methods and tools to inform prioritization of investments in

transportassetmanagementtoensureintegrationofclimateandnaturalriskconsiderations.

In delivering the multimodal transport network criticality and vulnerability analysis to prioritizemaintenance, rehabilitation, and infrastructure upgrading, this study answers some key questions

relevanttotransportplanners,investors,andrelevantstakeholders,namely:

1. Whereandwhatare thekey transportnetwork locationsexposed todifferent typesof

extremenaturalhazards?

2. In which areas in the country are transport assets particularly exposed to hazards, as

predictedbymodeloutputsofcurrentandfutureclimatechange-drivennaturalhazard

scenarios?

3. Whatarethewidermacroeconomiclossesatthenationalscalewhenfreighttransportis

disruptedduetotransportfailures?

4. Whataretheimpactsonthelocalcommune-scaleeconomiesduetolackofaccesstokey

locationsofeconomicactivity,becauseoflocalroadfailures?

5. Whataretheimpactsoftransportdisruptionsintermsofincreasedtransportationcosts

ofreroutingtrafficformaintainingservicecontinuity?

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6. How resilient are the multimodal transport networks in providing continuous service

when individual transport modes are damaged or disrupted due to external natural

hazardshocks?

7. When considering the key climate resilience investments and strategies evaluated for

currentandfutureclimatescenarios,whatarethenetbenefitsofadaptation?

8. Whereandwhatarethekeytransportnetworklocationsprioritizedaccordingtohighest

netbenefitsofclimateadaptationmeasures?

9. What are the most robust climate resilience interventions or policies to reduce the

vulnerabilityofcriticalsegmentstofutureclimatechangeimpacts,takingaccountofthe

uncertaintiesandsensitivities?

In answering the above questions, some specific objectives satisfied in this report include the

following:

1. Creating multi-scale geospatial network flow models to represent the multimodaltransportinfrastructuresinVietnam,bothinthepresentandfuture

2. Modelingtrafficflowdisruptionsandflowreallocationswhennetworkassetsfailduetonaturaldisasters

3. Evaluating the potential economic and social impacts when the multimodal transportnetworksaredisruptedbynaturaldisasters

4. Creatingnetworkcriticalitymetricstosystemicallymeasureandidentifycriticalsegments

of the multimodal transport networks that lack or have built-in resilience and/orredundancy

5. Performingexhaustive scenario-based simulations to incorporatemultipleuncertaintiesin underlying model assumptions. This aligns with the decision making under deepuncertainty(DMDU)approaches(Espinetetal2015)

6. Incorporatingvariousclimate-resilienttransportinvestmentsandpoliciestoevaluatetheadaptation options and identify robust interventions or policies to reduce thevulnerabilityofcriticalsegmentstofutureclimatechangeimpacts,takingaccountoftheuncertaintiesandsensitivities.

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ScopeoftheStudy

Thisreportfocusesitsrecommendationsattwoscales:nationalandprovincial.Thefocusareaofthis

studyisthemainlandofVietnam,2whichonthewestandnorthshareslandborderswithneighboringcountries(LaoPDR,Cambodia,andChina)accessiblebyroad,and issurroundedbycoastal linesontheeastandsouth.Forthenational-scaleanalysis, inputdata isderivedfromprovincial,district,or

commune-level statistics where appropriate. The province-scale analysis focuses on three specificprovinces—LaoCai,BinhDinh,andThanhHoa—where information isderived fromcommune-levelstatistics.

Figure1.1providesarepresentationoftheregionalandinternationalboundariesofVietnamrelevanttothisstudy.ThethreeprovincesofLaoCai,BinhDinh,andThanhHoa,forwhichthedetailedroadnetworkanalysisisconducted,arehighlightedindarkgray.

The scope of the national analysis covers the key national-scale roads, railways, airline, inlandwaterways,andmaritimesystemsthatmakeupVietnam’smultimodaltransportationinfrastructure.At this scale the study focuses on understanding the economic impacts of disruptions to freight

transportationduetotransportfailures.

The scope of the provincial analysis is confined to road networks only, which are represented ingreater granularity than the national-scale roads and include national, provincial, district, and

communeroadsandotherassetssuchasbridgesandculverts,amongothers.Atthisscale,thestudyfocusesonunderstandinghowroadfailuresimpactaccesstokeylocationswithincommunes,therebyaffectingeconomicoutputgeneration.

Specifically, for all road networks considered in this study, adetailed adaptation analysismodel iscreated to quantify the benefits and costs of adaptation, identify the road network assets whereclimate-resilient investments should be prioritized, and provide estimates of the scales of

investmentsrequired.AnoverviewofallinfrastructurerelateddatasetsusedinthisstudyisgiveninappendixBofthisreport.

Atbothnationalandprovincialscales,transportfailuresareinducedbytwotypesofnaturalhazards:

floodingand landslides. The floodhazards considered in this study are river flooding (i.e., floodingcausedby riversovertopping theirbanks), surfacewater flooding (i.e., flooding causedbyextremerainfall—also known as “pluvial” or “flash” flooding) and tidal flooding (i.e., flooding caused by

typhoon-induced storm surges). Besides looking at the present situation (the year 2016), we useclimatechangescenario-drivenmodeloutputsrepresentingfuturefloodingintheyears2025,2030,and 2050. The landslide hazards information includes model outputs for subsets of landslides

quantified in termsof landslide susceptibility presented for current conditions (the year 2016) andfuture climatechange scenarios in2025and2050.Chapter2, section “NaturalHazardExposureofTransportNetworks”andappendixBprovidefurtherdetailsforfloodhazarddata.3

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Figure1.1.VietnameseProvincesandNeighboringCountriesConsideredintheStudy

.

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StudyContributionsandLimitations

Themaincontributionofthisstudyistoprovidedetailedspatialevidenceofsystemicvulnerabilities

and risks on the multimodal transport systems of Vietnam. Several study contributions would bevaluable to stakeholders in Vietnam such asMinistry of Transport (MoT),Directorate of Roads forVietnam (DRVN), Provincial Departments of Transport (PDoT), Civil Aviation Administration of

Vietnam (CAAV), Vietnam Maritime Administration (VINAMARINE), Vietnam Inland WaterwayAdministration(VIWA),TransportDevelopmentandStrategyInstitute(TDSI),MinistryofAgricultureandRuralDevelopment(MARD),andMinistryofNaturalResourcesandEnvironment(MoNRE).

Thisstudyincludesthefollowingspecificusefulcontributions:

1) Creatinguniquedatasetsandmodelingresources

a. First-of-a-kindrepresentationsoftopologicallyconnectedgeospatialnational-scaleroads,railways, inland waterways, and maritime multimodal transport networks with flowassignmentsforVietnam

b. Detailedagriculturecropsandcommodityor industrylevelnetworkflowsmappedonto

themultimodaltransportnetworks increaseunderstandingofthedomesticfreightflowpatternsinthecountry

c. Detailedtransportcostestimatesincreaseunderstandingofrealisticcriteriafortransportfreightflowassignments

d. Long-termgrowthforecastsincorporatedontothegeospatialnetworks

e. The study’s codebase, developed in Python programming language as an open-sourceresource,isavailabletothepublicathttps://github.com/oi-analytics/vietnam-transport.

Themethodologybehind the codehasbeendocumented indetail through this report,anda separateuserdocumenton thecompilationandcreationofunderlyingdataandcode is also available publicly at: https://vietnam-transport-risk-analysis.readthedocs.io/en/latest/.

f. Testing of the underlying codes to perform billions of computations of network flow

assignmentsandfailurescenarioswithoptimalperformance,afundamentalrequirementforastudyofthisscale.

2) Prioritizingcriticality-basednetworks

a. The key metrics created and analyzed in this study answer the previously highlightedquestionsrelevanttotransportstakeholders,whowanttoknowtheeffectsoftransportdisruptionsoncontinuedprovisionofserviceandtransportcosts.

b. Thedetailednetworkassessment conducted in this studyhelps identify locationsmost

important for thecontinuityof thetransportservice in thecountry,andwhose failurescanpotentiallyleadtolarge-scalesocio-economicconsequences.

c. The study’s ranking of critical assets, based on their disruptive potential, provides ameanstoprioritizeandsequenceriskreductionactivities.

d. Ranking of critical assets further supports the rationale for prioritizing investments toreduce vulnerability and build systemic resilience, forming the basis for long-termadaptationplanning(Thackeretal2018).

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3) Increasingtheunderstandingofhazardvulnerability

a. Thestudyspatiallydepictstheexposureoftransportnetworkstovariousnaturalhazardsat detailed spatial scales, enabling understanding of the potential severity of differentthreatstoinfrastructurenetworks.

b. Study results presented at national and provincial scales highlight areaswhere several

transportassetsaremorepronetobeexposedtohazards in thecurrentscenariosandhowthismightchangeinthefutureduetoclimatechangedrivenscenarios.

c. Thestudy’sspatial informationonextremehazardexposuresoftransportnetworkscaninformnationalandprovincialtransportplanningdecisionstoallocateadequatebudgetforshort-termandlong-termtransportriskreductions.

4) Developingaroadmapforadaptationplanning

a. Akeycontributionofthisstudyistoprovidethetoolstoundertakeadaptationanalysis

whereby the benefits and costs of different strategies designed to build transportstructuralresiliencecanbeassessedundervarioushazardscenarios.

b. Thestudypresentsvariousadaptationoptionsat thescaleof individual roadassets,aswell as the cost-benefit analyses metrics to deliver a detailed understanding ofinvestmentoptionsforeachassetunderdifferenthazardscenarios.

c. Thestudyperformskeyanalysistounderstandwhetherasset leveladaptationplanningshouldbedoneproactivelytoprotectagainstfutureclimatehazards.

d. The study catalogues and presents locations of all road assets with high benefits of

adaptationtoprovidedecision-makerswithinformationthatwillhelpprioritizeresourcestowardthemostcriticallyimportantassetsinthenetworks.

5) Assessingmultimodality

a. Thestudy’spresentationofkeymethodsandanalysesquantifiestheeffectsoftransportfailures,ifmultimodaloptionswereavailable.

b. As one of its key aims, the study’s multimodality assessment provides evidencesupportingtheneedforenhancedmultimodalconnectivityasanadaptationstrategy.

c. Understanding the benefits of multimodality at detailed geospatial network scales

provides a means to target network locations where multimodal options could bestrengthenedthroughmoreinvestments.

6) Assessinguncertainty

a. Themodels and analyses presented in this report havemany sources of uncertaintiescomingfromlackofproperdataonphysicaltransportnetworkstructuresandtheirusagestatistics, among others. These uncertainties have been accounted for throughout theanalysis.

b. Thestudyfollowsarobustdecision-makingapproach(Lempertetal2013)wherebythe

analysis quantifies the extreme ranges (minimum and maximum) of potential flows,criticalitiesandadaptation.Thisapproachprovidesadecision-makingtoolthataccountsforrobustperformanceofdifferentadaptationoptionsunderdifferentscenarios.

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Theanalysispresented in this reportoffersahigh-level indicativeassessmentof transport systemsand their natural hazard risks, providing a first-order screeningwhereby locations and assetswith

highriskscanbenarroweddownfor further investigation.Hence,certainconsiderationsshouldbemade in interpreting thiswork.Nevertheless,mostof these limitationsexist due to lackof properdata; with improvement in input data the underlying model is fully capable of correcting several

limitationsunderlinedbelow:

1) Physicalinfrastructurenetworkdataandrepresentations

a. Thetransportdatacreatedinthemodelservesasanetworkpresentationoffunctionalconnectivityof thetransportsystem,ratherthanadetailedmasterplanrepresentation

ofeachlocation’sdetailedspatiallayoutandattributes.Forexample,inthisanalysis,HoChiMinhportisrepresentedasapointinspace,whileinrealityitexistsoveralargearea.

b. Similarly, the represented road, railways, airports, ports, and multimodal links showgeneral travel routes. Any details provided for the physical width, number of lanes orlines,ornavigationalchannelsofthesesystemshavebeenestimated.

c. The representationof thenetworks’built-in functionalandphysical connectivity isalso

heavily contingent of the availability or lack of data. For example, if the available datadoes not show a road or rail connection that exists, the model will not be able torepresentthatconnection.

d. Themultimodallinksinthisanalysisrepresentderivedconnectivity,withasmanylinksaspossibleverifiedthroughsatelliteimagery.

2) Networkflowassignments

a. As explained later in the report, themodel estimates network flow assignments from

several sources anddatasets. Several assumptions have been taken in interpreting theflows.

b. Representationoffutureflowsandlossesareestimatedbasedonhigh-levelforecastsofgrowthforthewholecountry.

c. Hence,theflowandfailurevaluesintheanalysisshowthehigh-leveltrends,ratherthantheexactestimatesofactualflows.

3) Failureandhazardvulnerabilityanalysis

a. Griddeddatasetsshowhazardsatdifferentspatialresolutions,whicharesometimesverycoarse. Thus, the hazard information should not be used for detailed site-specificanalysis.

b. Derivedfromtheanalysisof transportnetworkexposurestohazards, themain insightsprovideusefuloverviewsofthelikelyhazardsaroundspecificlocations.

4) Adaptationanalysis

a. The adaptation analysis presented in this report reflects the underlying data of cost

estimates for different options under consideration. Therefore, the cost estimatesderivedfromdatasetsrepresentbestestimates.

b. The main insights from the adaptation analyses provide a means to understand howdifferentoptionscanbeevaluatedandwhichscenariosshouldbeconsidered.

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ReportStructure

Followingthisintroductorychapter,theremainderofthereportisstructuredasfollows:

Chapter 2 — Vietnam Profile: Transport Network and Exposure to Natural Hazard: Chapter 2describes the underlying transport network information assembled and created in this study, thetypes of hazards considered—with their source—climate change scenarios (if considered), spatial

resolutions, and spatial coverage. The chapter infers some information regarding the state of theinfrastructure from the underlying network data, which is compared with relevant literaturewhereverpossible.

Chapter3—Vulnerability,CriticalityandRiskAssessment: Chapter3presents the results for thetransport criticality assessmentof freight flowdisruptionson the assembled transport networks inVietnam.Thecriticalityassessmentconsidersanexhaustivesetof individualnetwork linkscenarios

exposedtohazardsforfailure.Inaddition,theassessmentconsidersimpactsintermsofmetricsthatgivea senseof thenetwork locationswith themost socio-economic impacts.A risk assessment toinferthehazardsthatcausetheimpactsfollows.Therangesofdisruptiveimpactsacrosseachhazard

arequantified foreachnetwork tobetterunderstandwhetherhazard impactswill increasedue tofutureclimatescenarios.

Chapter 4 — Adaptation Strategy and Analysis: Chapter 4 presents the adaptation options

considered for national-scale and province-scale road network assets, with a detailed benefit-costanalysisperformedforeachassetunderdifferenthazardscenarios.Inaddition,thechapterevaluatestherangesofadaptationoptionsandtheirbenefits,exploringtheadvantagesofadaptingtofuture

climatescenarios.

Chapter5—ExploringMultimodalOptionsforNational-ScaleTransport:Thischapterexplainsthedevelopmentof thestudy’smultimodal transportsystem.Following this, thechapterevaluates the

effectofmultimodalityonfailuresbyexploringmodalshiftsfromroadandrailnetworks.

Chapter6—ConclusionsandPolicyRecommendations: Chapter6presents recommendations forbuildingandenhancingtransportresiliencetoclimatechangeimpacts.

Notes

1.SeetheWorldBank’sonlinecountrypageforVietnam:http://www.worldbank.org/en/country/vietnam/overview.

2.Forthepurposesofthisstudy,theanalysiswasconfinedwithinthemainlandofVietnam,consideringthedataavailabilityandsignificanceoftrafficflows.

3.Thenaturalhazarddatasetsusedinthisstudyhavebeenobtainedfromthirdpartysources—eitherfromtheGovernmentofVietnamorthroughtheWorldBankpartners—andhavenotbeenalteredinanywaybytheauthors.

Page|37

ReferencesDilley,Maxx,RobertS.Chen,UweDeichmann,ArthurL.Lerner-Lam,MargaretArnold,JonathanAgwe,

PietBuys,OddvarKjevstad,BradfieldLyon,andGregoryYetman.2005.NaturalDisasterHotspots:A

GlobalRiskAnalysis.DisasterRiskAnalysisSeries.Washington,DC:WorldBank.http://documents.worldbank.org/curated/en/621711468175150317.

Espinet,Xavier,AmySchweikert,andPaulChinowsky.2015.“RobustPrioritizationFrameworkforTransport.”ASCE-ASMEJournalofRiskandUncertaintyinEngineeringSystems,PartA:CivilEngineering3(1).doi:10.1061/AJRUA6.0000852.

Hallegatte,Stephane,AdrienVogt-Schilb,MookBangalore,andJulieRozenberg.2016.Unbreakable:BuildingtheResilienceofthePoorintheFaceofNaturalDisasters.Washington,DC:WorldBank.

https://openknowledge.worldbank.org/handle/10986/25335.

IrishAid.2017.VietnamClimateActionReportfor2016.IrishAidResilienceandEconomicInclusion

Teamreport,Limerick,Ireland:IrishAid.https://www.irishaid.ie/media/irishaid/allwebsitemedia/30whatwedo/climatechange/Vietnam-Country-Climate-Action-Reports-2016.pdf.

Lempert,Robert,NidhiKalra,SuzannePeyraud,ZhiminMao,SinhBachTan,DeanCira,andAlexanderLotsch.2013.“EnsuringRobustFloodRiskManagementinHoChiMinhCity.”PolicyResearch

WorkingPaper6465,WorldBank,Washington,DC.http://documents.worldbank.org/curated/en/749751468322130916.

MoNRE(MinistryofNaturalResourcesandEnvironment).2009.ClimateChange,SeaLevelRisescenariosforVietnam.Hanoi:MinistryofNaturalResourcesandEnvironment,Vietnam.https://www.preventionweb.net/files/11348_ClimateChangeSeaLevelScenariosforVi.pdf.

PwC(PricewaterhouseCoopers).2017.TheWorldin2050—TheLongView:HowWilltheGlobalEconomicOrderChangeby2050.PwCEconomics&PolicyTeamreport.London:

PricewaterhouseCoopersLLP.https://www.pwc.com/gx/en/world-2050/assets/pwc-the-world-in-2050-full-report-feb-2017.pdf.

Thacker,Scott,ScottKelly,RaghavPant,andJimW.Hall.2018.“EvaluatingtheBenefitsofAdaptationofCriticalInfrastructurestoHydrometeorologicalRisks.”RiskAnalysis38(1):134–50.doi:10.1111/risa.12839.

WorldBank.2011.ClimateResilientDevelopmentinVietnam:StrategicDirectionsforTheWorldBank.Report.WorldBankSustainableDevelopmentDepartment,VietnamCountryOffice,

Vietnam.http://documents.worldbank.org/curated/en/348491468128389806.

WorldBank.2018.“Vietnam’seconomicprospectimprovesfurtherwithGDPprojectedtoexpandby

68percentin2018.”PressRelease,June14.https://www.worldbank.org/en/news/press-release/2018/06/14/vietnams-economic-prospect-improves-further-with-gdp-projected-to-expand-by-68-percent-in-2018.

Page|39

Chapter2:VietnamProfile:TransportNetworkandExposuretoNaturalHazard

TransportNetworks

Thissectiondescribesthecurrentnetworkswithfreightflowvolumesandcostsacrossnational-scale

roads, railways, inland waterways, maritime ports, and airline networks. Similarly, flows are alsoidentifiedonprovinceroadsnetworksintermsoftheaccesstoimportantcommunecenterswithinprovinces, uncertainties of flows due to rice-crop seasonality, and network cost assignments are

considered.

National-scaleroadinfrastructurenetworks

Thenational-scalenetworkmodelcreatedforthestudyextendstoabout30,900kmandconsistsof2,130nodesand2,512links.Ofthe30,900kmofthenetworkanalyzed,30,032kmor97.2percent,is

paved;amere868kmremainsunpaved.Roadshavebeenclassifiedinscaleof1to6,withagreatertraffic volume associated with higher road class (table 2.1). Table 2.2 presents the estimatedclassification.

Table2.1.RoadCategoriesbyAverageDailyVehicleTrafficCounts

Source:MoTVietnam2000.

Roadtrafficcount Roadcategory

>6000 1

3000–6000 2

1,000–3000 3

300–1,000 4

50–300 5

<=50 6

Page|40

Table2.2.EstimatedTotalLengthsbyRoadCategoryintheNational-ScaleRoadNetworkModelforVietnam

Source:Authors’estimationusingCVTSGPSdatamapping

Province-scaleroadsnetworks

Giventheunderlyinggeospatialdatasetshadthesameattributes,theprovince-scalenetworkmodelsused similar types of assumptions across all provinces. Table 2.3 shows the estimated lengths ofpaved and unpaved roads of different levels in each of the province road network models. The

estimatesindicatetheseprovincescontainmostlyunpavedroads.Table2.3.EstimatedLengthsbyRoadCategoryintheProvince-ScaleRoadNetworkModelforVietnam

Roadclass Length(km) Length(%)

1 1,656 5.35%

2 2,331 7.54%

3 4,951 16.02%

4 6,449 20.87%

5 8,561 27.70%

6 6,952 22.70%

Province Nodes Links RoadlevelPaved(km)

Unpaved(km)

Paved(%)

Unpaved(%)

National 579 0 14.71% 0.00%

Provincial 435 0 11.06% 0.00%

Local(district/commune)

65 1,342 1.65% 34.11%

Other 0 1,514 0.00% 38.48%

LaoCai 3,744 4,697

Total 1,079 2,857 27.42% 72.58%

National 341 0 6.31% 0.00%

Provincial 321 0 5.93% 0.00%


226 446 4.17% 8.25%

Other 0 4,075 0.00% 75.34%

BinhDinh 21,686 26,213

Total 887 4,522 16.41% 83.59%

National 921 0 4.58% 0.00%

Provincial 457 0 2.27% 0.00%


394 2,312 1.96% 11.49%

Other 5 16,025 0.03% 79.67%

ThanhHoa 66,863 91,304

Total 1,777 18,337 8.83% 91.17%

Page|41

Railwayinfrastructurenetwork

Therail infrastructurenetworkmodelwasderivedfromdataonstationpointlocationsandrail line

geometries provided from the Ministry of Transport (MoT) dataset, including the VITRANSS IIdatasetsfrom2009(JICAandMoT2010).Theresultingnetworkcontains313nodes,ofwhich234arestationsand324arelinks.Themodeledlengthoftherailnetworkisapproximately2,662kilometers.

Table 2.4 reports the lengths of somemain routes. Themain hub of the rail network lies aroundHanoi,withtheHanoitoHoChiMinhrouteasthelongestlineofthenetworkbyfar.

Due to an aging railway infrastructure largely comprised of a single-track system, railway usage isvery limited inVietnam,which limits train lengths in thedifficult andnarrow terrainsalong routes(Systra2018).

Table2.4.EstimatedTotalLinkLengthsandPercentagesofRailRoutesIdentifiedintheNational-ScaleRailNetworkModelforVietnam

Inlandwaterwayinfrastructurenetwork

The study identified inlandwaterwaynetworkmodel nodes fromdataobtained from theVietnamInlandWaterwayAdministration(VIWA)1andtheVITRANSSIIstudy.Theresultingnetworkcontains

131inlandwaterwayportnodesand502links.Themodeledlengthoftheinlandwaterwaynetworkis approximately 5,407 kilometers, concentrated along theMekongDelta in the south, or the Red

RiverDeltainthenorth,withaverysmallsectioninQuangBinh.

Railroutecode Routedescription Length(km) Length(%)

DSTN Hanoi–HoChiMinh 1,719 64.56%

YV-LC Hanoi–LaoCai 297 11.16%

HN-DD Hanoi–LangSon 171 6.42%

GL-HP Hanoi–HaiPhong 106 3.98%

K-HL BacGiang–QuangNinh 104 3.90%

DA-QT Hanoi–ThaiNguyen 58 2.16%

K-LX BacGiang–ThaiNguyen 55 2.07%

BH-VD Hanoi 39 1.47%

MP-LD LangSon 31 1.17%

CG-ND NgheAn 30 1.12%

CL-PL HaiDuong 17 0.65%

MM-PT BinhThuan 12 0.45%

PL-KK HaNam 9 0.34%

DT-QN BinhDinh 9 0.34%

L-TM Unknown 6 0.22%

Page|42

Maritimeinfrastructurenetwork

Thestudyidentifiedmaritimenetworkmodelnodesfromgeospatialportlocationdataobtainedfrom

the MoT, and maritime routes published by the Ministry of Natural Resources and Environment(MoNRE).Theresultingnetworkcontained45maritimeportsand206 links.Themodeled lengthofthemaritimenetworkisapproximately5,254kilometers,fordomestictransportationusesonly.The

VietnamMaritimeAdministration (VINAMARINE) classifies international hubs classified as Class 1Aports,anddomestichubsasClass1ports(MoT2015).

Airtransportinfrastructurenetwork

The study identified air transport infrastructure network model nodes from geospatial airport

locationdataobtainedfromtheMoT.BasedonapreviousMoTstudyoftheVITRANSSIImodel(JICAandMoT2010),thestudyidentifiedmainairportswithanyfreightflowsbetweenthemandcreatedstraight-line link geometries to join the paired airports. The resulting network contains 23 airport

nodes, of which 8 are connected via 10 links. While the share of air transport of total cargomovementinVietnamisincreasingrapidly,detaileddataforairfreightvolumeswerenotavailableatthe time of this study. Given this limitation, the study analyzed the air transport sector as a

standalonesector,ratherthanasamultimodaltransportoptioninVietnam.

ModelingFreightFlowonTransportNetworks

Thisstudymodeledandestimatedthefollowingtransportflowmetrics:

• Averageannualdailyfreightvolumes(AADF):Theestimatedvolumeoffreightintonsperdayonanaveragedaybetweendifferentnetworklocations;and

• Net revenue measures: The estimated daily net revenue of goods and services withaccesstoan important locationviaatransportnetwork.Thesemeasuresareestimatedtounderstandlocalroadaccesstoimportantlocationsatprovincescale.

Trafficflowswereconstructedbasedonavailableexistingdata,includinginformationreceivedfrom

relevantministries anddepartments inVietnam,data compiled in previous studies and reports on

transport planning and development in Vietnam, and open-source data and online information oftransport estimates. The study relied on previous origin-destination (OD)matrix provided byMoT,CVTSdatafrom2017, input-output(IO)matrix,trafficcountdatafromDRVN,cropproductiondata

from IFPRI (Koo et al 2018), and other socio-economic statistics, among others. The process ofassigningfreighttransportflowsisdescribedindetailinappendixA.

Resultsoffreightflowmappingatnationalscale

Theresultsoftheflowassignmentsprovideinsightsintothefollowingimportantquestions:

• Wherearethelocationsonthenetworklinkswiththemostflowconcentrations?

• Whichlinksandrouteshavethelargestconcentrationsofeconomicvalues?

First the results of the national-scaleODmatrix aremapped in figure 2.1 and shown in table 2.5,

whichpresentsthedifferentdailytonnagesforeachcommodityconsideredinthestudyandsplitofthesedaily tonnagesbyeach transportmode (road, railway, air, inlandwaterways, andmaritime).

Foreachcommodity,themodewiththehighestpercentageshareishighlighted(ingray)intable2.5.Theresultshowsthefollowing:

Page|43

• The fluctuations in the rice volumes show a significant change in the daily tonnagevolumes on the transport systems,which can have huge implications on the values offailureestimates.

• Inland waterways and roads are the two most dominant transport modes for thesecommodities.Dependinguponthefluctuationsinthericetransporttonnages,themodesharesbetweenthesetwotransportmodesvariesbetween44and48percent.

• Maritime transport is the next significant mode, with around 4 to 5 percent modalshares, followedbyrailways,witha1.9 to2.1percentmodalshare.Airlinetransport isalmostinsignificant.

• For commodities such as cement, coal, construction materials, fertilizer, fishery, andsugar, inland waterways represent the most dominant mode. Roads rank as thedominant mode for manufacturing, steel, meat, petroleum, and all crop commoditiesusedinthisstudy.

The tonnage volumes andmodal shares presented are a subset of the estimated annual tonnages

that focusonmajornational inter-provincial commodity flowsandarebasedon thebestavailable

origin-destinationdataofsomecommodities.ThisresultsinmodalsharefiguresthataresomewhatdifferentfromthosefromthestatisticsoftheGeneralStatisticsOffice(GSO)ofVietnam.2AccordingtotheGSOfiguresfor2015,roughly76.5percentoftradeflowsoccurredonroad,17.5percenton

inland waterways, 5.3 percent on maritime, 0.6 percent on railways, and 0.02 percent on airlinenetworks.Hence, theestimatedmodeshares in thisstudyunder-representthedominanceof roads,whileover-representingthecontributionsofinlandwaterwaysandrailways.

Themaps in figure 2.1 show the how daily transport flows in Vietnam happen at long distances,connectingtheregionofeconomicactivitiesinthesouth(MekongDelta)tothoseinthenorth(RedRiver Delta). For the national-scale road network, the high-volume freight routes run through the

nationalhighwaysandexpresswaysontheeasterncoast.Therailway flowsaremoreconcentratedtowardthenorthernsections.The inlandwaterwayshavekeyhigh-volumeroutes inthenorthandsouth,whilethecross-countrydomesticmaritimearethebusiestroutes.Basedontheavailabledata

for airlines, the connectivity between the Hanoi and Ho Chi Minh airports is most important forfreighttransport.

Page|44

Table2.5.DailyTrafficVolumesbyCommodityandTransportMode

Co

mm

od

ity

Ro

ad

Ra

ilw

ay

Air

Inla

nd

wa

te

rw

ay

sM

ariti

me

To

ta

l

To

ns/d

ay

Pe

rce

nt

sh

are

To

ns/d

ay

Pe

rce

nt

sh

are

To

ns/d

ay

Pe

rce

nt

sh

are

To

ns/d

ay

Pe

rce

nt

sh

are

To

ns/d

ay

Pe

rce

nt

sh

are

Ce

me

nt

34,534

30.93%

3,58

13.21

%1

0.00

%60

,626

54.31%

12,898

11.55%

11

1,6

40

Co

al

14,963

13.49%

2,02

21.82

%0

0.00

%83

,917

75.68%

9,97

79.00

%1

10

,87

9

Cons

truc

tion

ma

te

ria

ls10

8,72

123

.37%

8,42

31.81

%5

0.00

%34

6,18

974

.41%

1,90

40.41

%4

65

,24

1

Ferti

lizer

10,940

26.58%

1,97

34.79

%3

0.01

%27

,058

65.74%

1,18

52.88

%4

1,1

58

Fis

he

ry

7,13

836

.96%

300.16

%1

0.00

%11

,912

61.67%

234

1.21

%1

9,3

15

Ma

nu

factu

rin

g15

3,12

182

.40%

4,70

22.53

%15

20.08

%13

,933

7.50

%13

,921

7.49

%1

85

,82

8

Coffe

e A

rabi

ca57

99.61%

00.37

%0

0.00

%0

0.00

%0

0.03

%5

7

Ca

sh

ew

2,99

699

.61%

10.03

%0

0.00

%2

0.07

%9

0.29

%3

,00

8

Ca

ssa

va

19,416

92.85%

436

2.09

%1

0.00

%14

60.70

%91

24.36

%2

0,9

11

Mai

ze5,35

483

.90%

199

3.11

%12

0.18

%29

94.68

%51

88.12

%6

,38

2

Pe

pp

er

404

97.94%

30.71

%0

0.00

%4

1.07

%1

0.29

%4

13

Coffe

e ro

bust

a2,03

399

.64%

70.33

%0

0.00

%0

0.00

%1

0.03

%2

,04

0

Rubb

er2,57

098

.57%

200.76

%0

0.00

%2

0.07

%16

0.60

%2

,60

8

Sw

ee

t p

ota

to

es

1,03

459

.22%

874.98

%6

0.35

%38

722

.15%

232

13.31%

1,7

46

Te

as

162

77.75%

136.20

%0

0.03

%0

0.01

%33

16.02%

20

8

Me

at

50,940

72.79%

555

0.79

%48

0.07

%14

,019

20.03%

4,42

56.32

%6

9,9

87

Pe

tro

leu

m32

,532

71.49%

384

0.84

%8

0.02

%5,30

111

.65%

7,28

416

.01%

45

,50

9

Ste

el

33,446

77.08%

2,35

05.42

%2

0.01

%6,84

015

.76%

751

1.73

%4

3,3

89

Su

ga

r3,06

437

.98%

130.16

%0

0.00

%4,84

460

.06%

145

1.80

%8

,06

6

Wo

od

10,748

46.51%

325

1.40

%7

0.03

%11

,211

48.51%

820

3.55

%2

3,1

10

Ric

e (

min

)73

,557

70.96%

1,21

01.17

%9

0.01

%25

,762

24.85%

3,11

43.00

%1

03

,65

3

Ric

e (

ma

x)

361,47

859

.64%

8,04

31.33

%43

0.01

%20

4,78

133

.78%

31,799

5.25

%6

06

,14

4

To

ta

l to

ns (

min

)5

67

,72

94

4.8

7%

26

,33

22

.08

%2

54

0.0

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61

2,4

52

48

.41

%5

8,3

80

4.6

1%

1,2

65

,14

7

To

ta

l to

ns (

ma

x)

85

5,6

50

48

.41

%3

3,1

65

1.8

8%

28

70

.02

%7

91

,47

14

4.7

8%

87

,06

54

.93

%1

,76

7,6

38

Page|45

Figure2.1.MaximumAADFfortheNational-ScaleTransportNetwork

A. Total tonnage (Road) B. Total tonnage (Inland waterway)

C. Total tonnage (Maritime)

D. Total tonnage (Railway)

E. Total tonnage (Air)

Page|46

Resultsofprovince-scaleflowmapping

Trafficflowsandrevenuesonprovince-scaleroadnetworkshavebeencalculatedbasedontheIFPRIcropsdata (Kooetal2018),andcommune-level revenuestatistics fromGSO.3Figure2.2 (LaoCai),

figure 2.3 (Binh Dinh), and figure 2.4 (ThanhHoa) show heatmaps of flow concentrations due tomaximumdailycroptonnages(panelA)andmaximumdailynetrevenues(panelB),inUS$thousandsperday)beingdirectedtowardcommunecentersoftherespectiveprovinces.

Theresultspredominantlyhighlighttheeffectofroaddensityintheprovinces.ForLaoCai,wheretheroad network is not very dense, some road clusters (e.g., in the Bao Yen district) have moresignificantusagethanothers.Ontheotherhand,forThanhHoatheroadnetworkisverydense,with

manyrouteoptions,makingtheflowspatternsmoredistributivewithfewclustersofverysignificantroads. The roadnetwork is sparse in parts of BinhDinh,which createsmore significant clusters inlocationssuchasLaoCai,whilefewerclustersexistinareaswherethenetworkisdense,asinThanh

Hoa.

Theanalysisbycommodityhighlightsthenetrevenue’sdependenceonagricultureproductivity:

• ForLaoCai,thenon-agriculturefirmrevenuesdominatetheminimumandmaximumnetrevenuedistributionsonthenetwork,whichresultinlittlevariationsinflowpatterns.

• In Binh Dinh, the dependence on agriculture production results in some differencesbetweentheminimumandmaximumnetrevenueallocations.

• InThanhHoa,again,theamountofagricultureproductionaffectsthenetrevenue.

Figure2.2.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonLaoCaiProvincialRoads

A.MaximumtonsofcropflowsinLaoCai

B.MaximumnetrevenueofcropflowsinLaoCai

Page|47

Figure2.3.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonBinhDinhProvincialRoads

A.MaximumtonsofcropflowsinBinhDinh

B.MaximumnetrevenueofcropflowsinBinhDinh

Page|48

Figure2.4.MaximumCropFlowstowardNearestCommuneCentersandMaximumNetRevenuesonThanhHoaProvincialRoads

Uncertaintiesinfreightflowmapping

Thenational-scaleresultsshowthemaximumestimatesofflowunderthefollowingassumptions:

• Estimatedfromthehigh-levelODmatrices,dailyODtonnagesrepresentthehighestdailytonnagesallocatedonthetransportlinks.

• Basedonthehighestgeneralizedcostsoftheflows,flowassignmentsdependontravelspeedsandtransporttariffs.

Considering the range of OD tonnage, this report captures and presents the uncertainties in

estimated flows. Figures 2.5A through 2.5D show the ranges of daily freight flow estimates alongnetwork links for the national road, railway, inland waterway, and maritime sectors. The results

illustrate the flows, ranked from lowest to highest, based on themaximumestimated flows alonglinks.Accordingly,thex-axis is labeledas“percentilerank,”wherethepercentilerankof80impliesthat80percentoflinkshaveflowslowerthanthatlink.

Each of these figures gives a sense of the uncertainties of flow estimates along links. Theseuncertaintiesaredrivenmainlybytwofactors:a)fluctuations inODestimates,whichdependuponthe seasonality of production and transport; and b) changes in route choice due to fluctuating

generalized transport costs, which depend upon speeds and transport tariffs. The relativecontributions of these factors toward flow uncertainties can be measured by comparing theminimum and maximum flow assignment routes for the same OD pairs. If the minimum and

maximum flow assignment routes are the same, then flow assignments are only sensitive to thevolumesofODtonnages.Iftheminimumandmaximumflowassignmentroutesdiffer,thentheflowassignmentsaresensitivetothegeneralizedcostsoftransports.

A.MaximumtonsofcropflowsinThanhHoa

B.MaximumnetrevenueofcropflowsinThanhHoa

Page|49

The analysis for the national-scale road network shows a 28 percent difference between theminimumandmaximumflowsforthesameODpairs,suggesting28percentoftheflowissensitiveto

the transport costs. In figure 2.5A, some links show significant variability between minimum andmaximumaverageannualdailyfreight(AADF)flows,resultinginsomenoticeablespikesintheplot.Analysis shows small differences in the railway, inland waterway, and maritime network flows,

signifying that the flow uncertainties in figures 2.5B through 2.5D are driven by changing ODtonnagesratherthanchangesinthetransportcostsassignedtothesenetworks.Thisisexplainedbythegreater variability in attributesof the roadnetwork, suchas roads types, terrains, speeds, and

tariffs,whichleadstogreatersensitiveoftrafficflows.

Figure2.5.RangesofAADFFlowsbyMode

Similar to the national-scale network, flow assignments on the province-scale networks are alsouncertain due to changing values of net revenues generatedwithin communes, and the changing

transportcostsonroadsusedtogainaccesstothenearestcommunecenters.Figures2.6Athrough2.6C show the ranges of rankednet revenue flowestimated alongnetwork links for the province-scaleroadnetworksofLaoCai,BinhDinh,andThanhHoa.Whencomparedtoothernetworklinks,

most road links in each province produce lower values of assigned net revenue flows, due to theconcentrationofeconomicactivitiesnearahandfulofcommuneswithlimitedaccesstoroadroutes.

Theanalysisshowssmalldifferencesbetweenthechosenroutesintheminimumandmaximumnetrevenueflowassignmentsbetweenvillages,cropproductionsites,andthecommunecenters.ForLaoCai,only1.6percentoftheminimumandmaximumflowassignmentroutesdiffer,witha2.6percent

differenceforBinhDinh,andadifferenceof3.3percentforThanhHoa.Thus,thechangingtransportcostshaveverylittleinfluenceonthetransportpatternsandtheresultinguncertaintiesinthevalues

ofnet revenue flowsalong links,duemainly tomostcommutesbetweenorigins (villagesandcropproduction sites) and destinations (communes) covering small distances along local networkswithfewalternativeroutes.

A. Range of AADF flows on road links B. Range of AADF flows on railway links

C. Range of AADF flows on inland waterway links D. Range of AADF flows on maritime links

Page|50

Figure2.6.RangesofNetRevenueFlowsAssignedtoRoadNetworksinLaoCai,BinhDinh,andThanhHoa

NaturalHazardExposureofTransportNetworks

Thestudyassembledfourmaintypesofnaturalhazarddatasets:

• Landslide susceptibility zonation maps include classification of the types, areas, and

spatialdistributionofexistingandfuturelandslides.Thesemapsshowtherelativespatiallikelihoodfortheoccurrenceoflandslidesbytypeandvolume—orarea(Felletal2008).

• Flashfloodsusceptibilityzonationmapssimilarlyshowtherelativespatiallikelihoodthataheavyraineventwillbecomestationaryandprotractedoveranarea(Hapuarachchietal2011).

• Typhoon induced floodmaps estimate the increases in storm surgeheights inducedbypressuregradientscreatedbytyphoonsalongcoastalareas,whichtranslatetoover-landinundations(Lapidezetal2015).

• Fluvialfloodmapsshowinundationcausedbyriversovertoppingtheirbanks.

Of the above hazards, only the fluvial flood map data offered a country wide spatial coverage,whereas all other types of hazard map data covered only certain parts of the country. The study

assumedallhazardmapswithoutany future climatechange scenarios representhazardevents forthepresentrisks intheyear2016.Fromtheunderlyingdatasets, thestudyselectedspatialextentsandareaswherehazardlevelsexceededthethresholdneededtobeconsideredcatastrophicenough

tocausetransportfailures.

A. Net revenue flows on links in Lao Cai B. Net revenue flows on links in Binh Dinh

C. Net revenue flows on links in Thanh Hoa

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The study accessed some of the natural hazardsmaps with future climate changemodel outputs

developed by MoNRE, based on the internationally recognized Representative ConcentrationPathways(RCP)4.5and8.5scenarios(Meinshausenetal2011).RCP4.5scenariosassumethatglobalemissionspeakaroundtheyear2040andthendecline,whileRCP8.5scenariosassumenodeclinein

globalemissionsthroughthe21stcentury.

In addition, the study looked at flashflood and landslide susceptibility maps for fifteen northernprovinces generated for RCP4.5 and RCP8.5 scenarios in 2025 and 2050. The study also accessed

fluvialfloodmapsforthewholecountryshowingcurrentfloodingandfuturefloodingunderRCP4.5and RCP8.5 scenarios in 2030, from the GLOFRIS (Winsemius et al 2013) model. See appendix B,includingtableB.2,foradetaileddescriptionofthehazarddatasets.

Per the IPCC definition (IPCC 2014), the study conducted a hazard exposure analysis as the firstcomponentofavulnerabilityassessment.Onlyareasofhighandveryhigh landslideandflashfloodsusceptibilityaswellasareasofflooddepthsgreaterthanorequalto1meterwereconsideredinthe

study’s extreme hazard scenarios. The analysis aimed to identify the types of hazards to whichtransportassetsaremostexposedtoandthechangesinthehazardexposuresduetovariousclimatechangescenarios.

National-scaleroadandrailwayshazardexposures

Table2.6showstheoverallminimumandmaximumtotalkilometersofthenational-scaleroadandrailwaynetworks inVietnamthatareexposed todifferentextremehazard levels.Foreveryhazardwhere future climate change scenarios are considered, the lengths of road and railway networks

exposedtoextremehazardswouldincreaseduetoclimatechange.Forinstance,whileapproximately720kmto1,163kmofthenationalroadnetworkisexposedtoextremeriverfloodinginthecurrent

flooding scenarios, thenetwork lengthswould increase tobetween786kmand1,180kmunderafutureRCP4.5scenario.Inthecaseofriverflooding,theminimumexposurevaluescorrespondtothelowest return periods (two or five years), while themaximum exposure values correspond to the

1,000-yearreturnperiodfloodmaps.4Whiletheroadnetworksaremostexposedtoriverflooding,railwaysaremostexposedtolandslides,primarilyinthecoastalprovinces.

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Table2.6.LengthsofNationalRoadsandRailwayNetworksExposedtoDifferentTypesofHazards

Note:a.Theflashfloodsusceptibilitydatacoveronly15northernprovinces.b.Thelandslidesusceptibilitydataforcurrentscenarioscover8centraland15northernprovinces.Thefutureclimatescenarioscoveronly15northernprovinces.c.Thetyphoonfloodingdatacoveronly28coastalprovinces.

Nationalroadsexposures(kilometers)

Nationalrailexposures(kilometers)

HazardtypeClimatescenario

Year

Min. Max. Min. Max.

— 2016 188 188 3 3

2025 197 197 3 3RCP4.5

2050 222 222 3 3

2025 211 211 3 3

Flashfloodsusceptibilitya

RCP8.5

2050 235 235 3 3

— 2016 720 1,163 88 117

RCP4.5 2030 786 1,180 97 121Riverflooding

RCP8.5 2030 785 1,174 97 119

— 2016 896 896 189 189

2025 276 276 7 7RCP4.5

2050 320 320 9 9

2025 289 289 7 7

Landslidesusceptibilityb

RCP8.5

2050 334 334 10 10

Typhoonfloodingc — 2016 783 783 123 123

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By examining the spatial patterns of the hazard exposures presented in figure 2.7 and figure 2.8,additionalinsightscanbederivedbyexaminingthespatialpatternsofthehazardexposuresforthe

national-scale road and railway networks respectively.5 Each of the figures shows (at the district-level) thepercentagenetwork kilometers exposed to theworst-casesof current (2016) levels. Thespatialpatternsofextremehazardexposuresrevealthedistrictswithveryhighpercentages(greater

than40percent)of networksexposed toextremehazards, especially for landslides, river flooding,and typhoon flooding. Based on the above findings, the study analysis indicates districts withelevatedpercentagesofexposurefacehighpotentialfortransportfailures.

The study next considered the effects shown in climate change scenarios, examining the spatialchangestonational-scaleroadandrailwaynetworkscausedbyexposurestoextremeriverflooding.

Figure2.9 shows the change inpercentage kilometerswith road links exposures to the1,000-yearreturnperiodriverflooding.PanelAshowstheeffectsin2030undertheRCP4.5emissionscenarioand panel B illustrates the RCP 8.5 emission scenario. These changes compare to the figure 2.7b

result. Though theworst-case scenarios of river flooding in general showa very slight decrease inrisk,theresultssharedinfigure2.9and2.10providean importanttakeaway:climatechangecouldcausesomeregionstofaceasubstantialincreaseinnetworkflooding.Inconsiderationofadvanced

planningforclimatechange,suchlocationsshouldbeexaminedfurtherforpotentialfailureimpacts.

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Figure2.7.HazardExposureofNational-ScaleRoadsNetworkinVietnam

Note:PanelA=landslides,wherelandslidesusceptibilityishighandveryhigh;panelB=riverflooding,wherefloodingdepthexceeds1meterfora1,000-yearevent;panelC=typhoonflooding,wherefloodingdepthexceeds1meter;andpanelD=flashfloods,whereflashfloodsusceptibilityishighandveryhigh.

A. Extreme landslide susceptibility exposures B. Extreme 1,000-year river flooding exposures

C. Extreme typhoon flooding exposure D. Extreme flashflood susceptibility exposure

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Figure2.8.HazardExposureofNational-ScaleRailwayNetworkinVietnam

A. Extreme landslide susceptibility exposure

B. Extreme 1,00-year river flooding exposure

C. Extreme typhoon flooding exposure D. Extreme flashflood susceptibility exposure

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Figure2.9.PercentageChangeinExposureofNational-ScaleRoadNetworkto1,000-YearRiverFloodingby2030

A.Percentagechangeforfluvialflooding(RCP4.52030)

B.Percentagechangeforfluvialflooding(RCP8.52030)

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Figure2.10.PercentageChangeinExposureofNational-ScaleRailNetworkto1,000-YearRiverFloodingby2030

Province-scalehazardexposureofroads

Thestudyconductedsimilaranalysisfortheprovince-scaleroads.Table2.7showstheminimumand

maximumkilometersoftheprovince-scaleroadnetworksexposedtovariousextremehazardlevels,asspecifiedbytheunderlyinghazarddata.TheresultsshowthatLaoCaiandBinhDinhexperiencethehighestexposures toextreme landslides,while inThanhHoa, river flooding causes thehighest

exposures. Generally, the current and future climate change scenarios indicate minor differencesbetweentheworst-casehazardexposures.

A.Percentagechangeforfluvialflooding(RCP4.52030)

B.Percentagechangeforfluvialflooding(RCP8.52030)

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Table2.7.ExposureofProvinceRoadsNetworkstoHazards

Geospatial analyses highlight the locations where much higher percentages of road networkkilometers are exposed to extreme hazards. Figure 2.11 shows the commune-level percentages of

roads exposed to hazards in Lao Cai, where the spatial landslides are more prevalent morecommunesarepronetoflashfloodsandriverflooding.Asshowninfigure2.13,exposuresofroadstolandslidesare farmorepronounced throughoutBinhDinhandsimilarly inThanhHoa,as shown in

figure2.15. From thesemaps, the study identified thecommunes facing increasedmultiplehazardthreatsduetothehighpercentagesofroadsexposedoneormorehazards(seeappendixD).

Thestudyalsolookedatclimatechange-inducedexposurescenariosforhazardsaffectingprovincial

road networks. In Lao Cai, figure 2.12 shows changes in exposures to extreme landslides in 2050underRCP4.5andRCP8.5scenarios,whichresults inagreaternumberofcommunesexperiencingincreased percentages of roads exposed to extreme landslides, with amore pronounced increase

under the RCP 8.5 climate scenario. In figure 2.14, similar comparisons are seen in Binh Dinh forextreme1,000-year river floodingunder climate scenarios,with an increasedexposureof roads toextremeriverflooding.TheresultsforThanhHoa,showninfigure2.16,highlightsubstantialclusters

of communes where climate change causes a potentially significant (greater than 40 percent)increaseofroadsexposedtoextreme1,000-yearriverflooding(seeappendixD).

Roadhazardexposures(km.)

LaoCai BinhDinh ThanhHoaHazardtypeClimate

scenarioYear

Min. Max. Min. Max. Min. Max.

— 2016 88 88

2025 88 88 RCP4.5

2050 93 93

2025 90 90

Flashflood

susceptibility

RCP8.52050 132 132

— 2016 57 57 534 539 1,803 1,841

RCP4.5 2030 57 57 535 543 1,875 1,910Riverflooding

RCP8.5 2030 55 57 534 539 1,864 1,889

— 2016 142 142 801 801 921 921

2025 145 145 RCP4.5

2050 180 180

2025 163 163

Landslidesusceptibility

RCP8.52050 210 210

Typhoonflooding — 2016 315 315 592 592

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Figure2.11.HazardExposureofProvinceRoadsNetworkinLaoCai

A. Extreme landslide susceptibility exposures B. Extreme flashflood susceptibility exposures

C. Extreme river flooding

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Figure2.12.PercentageChangeinExposureofLaoCaiRoadNetworkLinkstoLandslidesby2050

Figure2.13.HazardExposureofProvinceRoadsNetworkinBinhDinh

A.RCP4.52050 B.RCP8.52050

A.Extremelandslidesusceptibilityexposures

B.Extremeriverfloodingexposures

C.Extremetyphoonfloodingexposures

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Figure2.14.PercentageChangeinExposureofBinhDinhRoadNetworkLinksto1,000-yearRiverFloodingby2030

A.RCP4.52030 B.RCP8.52030

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Figure2.15.HazardExposureofProvinceRoadsNetworkinThanhHoa

A.Extremelandslidesusceptibilityexposures B.Extremeflashfloodsusceptibilityexposures

C.Extremetyphoonfloodingexposures

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Figure2.16.PercentageChangeinExposureofThanhHoaRoadNetworkLinksto1,000-YearRiverFloodingby2030

Notes1.Dataaccessedat:http://cangben.viwa.gov.vn.

2.GSOtransportstatistics,availableat:https://www.gso.gov.vn/default_en.aspx?tabid=781.

3.GSOtransportstatistics,availableat:https://www.gso.gov.vn/default_en.aspx?tabid=778.

4.Novariationbetweentheminimumandmaximumvaluesresultsfromtheunderlyinghazarddata

havingonerealization.

5.Themapsinfigure3.9andfigure3.10provideasenseofthespatialextentsanddisaggregationof

the underlying hazard data, where flashflood exposures are clearly confined to northern areasbecausetheunderlyinghazarddataonlyexistedforthoseregions.Similarly,landslidesexposuresarelimited by the spatial coverages of the underlying data. As stated previously, only river flooding

datasetsofferedwhole-nationcoverage.

ReferencesIPCC(IntergovernmentalPanelonClimateChange).2014:ClimateChange2014:Impacts,Adaptation,

andVulnerability.PartB:RegionalAspects.WorkingGroupIIContributiontotheFifthAssessmentReportoftheIntergovernmentalPanelonClimateChange.EditedbyV.R.Barros,C.B.Field,D.J.Dokken,M.D.Mastrandrea,K.J.Mach,T.E.Bilir,M.Chatterjee,K.L.Ebi,Y.O.

Estrada,R.C.Genova,B.Girma,E.S.Kissel,A.N.Levy,S.MacCracken,P.R.Mastrandrea,andL.L.White.LondonandNewYork:CambridgeUniversityPress.https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-PartB_FINAL.pdf.

A.RCP4.52030 B.RCP8.52030

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Fell,Robert,JordiCorominas,ChristopheBonnard,LeonardoCascini,EricLeroi,andWilliamZ.Savage.2008.“GuidelinesforLandslideSusceptibility,HazardandRiskZoningforLandUse

Planning.”EngineeringGeology102(3):85–98.doi:10.1016/j.enggeo.2008.03.014.

Hapuarachchi,H.A.P.,Q.J.Wang,andT.C.Pagano.2011.“AReviewofAdvancesinFlashFlood

Forecasting.”HydrologicalProcesses25(18):2771–84.doi:10.1002/hyp.8040.

JICA(JapanInternationalCooperationAgency),MoT(MinistryofTransport,SocialistRepublicof

Vietnam),andTDSI(TransportDevelopmentandStrategyInstitute,Vietnam).2000.TheStudyontheNationalTransportDevelopmentStrategyIntheSocialistRepublicOfVietnam(VITRANSS)—TechnicalReportNo.3:TransportCostAndPricingInVietnam.Tokyo,Japan:JICA,ALMEC

Corporation,andPacificConsultantsInternational.http://open_jicareport.jica.go.jp/pdf/11596814.pdf.

Koo,J.,Z.Guo,U.Wood-Sichra,andY.Ru.2018(unpublished).SpatialDisaggregationof2015Sub-NationalCropProductionStatisticsinVietnam.InternaldataprovidedbyIFPRIforthestudy.Washington,DC:InternationalFoodPolicyResearchInstitute.

Lapidez,J.P.,J.Tablazon,L.Dasallas,L.A.Gonzalo,K.M.Cabacaba,M.M.A.Ramos,J.K.Suarez,J.Santiago,A.M.F.Lagmay,andV.Malano.2015.“IdentificationofStormSurgeVulnerableAreas

inthePhilippinesthroughtheSimulationofTyphoonHaiyan-inducedStormSurgeLevelsoverHistoricalStormTracks.NatHazardsEarthSystSci3(2):1473–81.doi:10.5194/nhess-15-1473-2015.

Meinshausen,M.,S.J.Smith,K.Calvin,J.S.Daniel,M.L.T.Kainuma,J-F.Lamarque,K.Matsumoto,S.A.Montzka,S.C.B.Raper,K.Riahi,A.Thomson,G.J.M.Velders,D.P.P.vanVuuren.2011.“The

RCPGreenhouseGasConcentrationsandtheirExtensionsfrom1765to2300.ClimaticChange109(1-2):213–41.doi:10.1007/s10584-011-0156-z.

JICA(JapanInternationalCooperationAgency)andMoT(MinistryofTransport,Vietnam).2010.TheComprehensiveStudyontheSustainableDevelopmentofTransportSysteminVietnam(VITRANSS

II).Tokyo:JapanInternationalCooperationAgency(JICA)andALMECCorporation.http://open_jicareport.jica.go.jp/700/700/700_123_11999943.html.

MoT(MinistryofTransport,Vietnam).2015.SeaportClassification.Hanoi:VINAMARINE(VietnamMaritimeAdministration),MinistryofTransport,Vietnam.http://www.vinamarine.gov.vn/Index.aspx?page=reportshipdetail&id=13.

Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: RailwayDemandAnalysis—Freight.ReportprovidedbySystraforthestudy.

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Chapter3:Vulnerability,Criticality,andRiskAssessment

This chapter brings the results of the analyses presented in Chapter 2, to analyze vulnerability,criticalityandriskassessmentofflowdisruptionsontheassembledtransportnetworksforVietnam.

Here,theanalysisconsidersanexhaustivesetofexposurescenariosforfailure,assessingthesocio-economic impacts through a variety ofmetrics to identify the network locationswith the greatestimpacts. For each network, the analysis quantifies the ranges of disruptive impacts across each

hazardtounderstandwhetherhazardimpactswillincreaseinfutureclimatescenarios.

VulnerabilityandCriticalityAssessment

After identifying the network links exposed to different extreme levels of hazards, a vulnerability

assessment can be conducted. Vulnerability assessments, completed within the context of thehazards, result in understanding the relative impacts of hazards on the continued transportavailability.Oncethevulnerabilityassessmentisdone,acriticalityassessmentcanprovideresultsin

ranking network elements, based on their relative impacts on the serviceability of the transportnetworks(ArgaJafino2017).

The criticality analysis performs an exhaustive analysis of the individual network links’ “what if

failure”scenarios.Havingbeenexposedtotheextremelevelsofhazards,theselinksexistinpotentialfailurelocations.Thecriticalityanalysisservesasusefulasafirststepinexploringandevaluatingthesystemicperformanceinageneralizedcontext, impartialtothenatureoftheexternalshockevent.

Theassessmentcanidentifythemostcritical links inthenetworks,whichcanthenbeusedtohelpselectasmallersetoflinksforfurtherhazardriskanalysis.Suchexploratoryanalysiscanbeusedtoexaminewhetherthemostcriticallinksidentifiedhavealsobeenexposedtohazards,thusproviding

astrongcasefordetailedsite-specificanalysis.

Thecriticalityassessmentaimstoanswerthefollowingquestions:

• Whichlinksandroutesalongthenetworksaremostimportanttothecontinuedserviceofnetworkflows?

• Whichindividuallinksfailuresresultinmacroeconomicdisruptiveimpacts,andwhatarethemagnitudesofthosedisruptiveimpacts?

• Whichindividuallinksfailureshavemostimpactsonthecostsofreroutingflows?

Inresponse,thestudycreatedandestimatedthefollowingcriticalitymetrics:

• Average annual daily freight (AADF) disruptions: The potential daily tonnage of freightflow disrupted by the failure of individual network links exposed to any hazardconsideredinthestudy.

• Totalmacroeconomic loss: The sumof thedirectand indirectmacroeconomic losses inUS$ per day, due to the losses of commodity flows that create economic supply and

demandimbalancesintheeconomy.Suchlossesarisefromindividuallinkswhosefailurecausestripisolations,wheretheonlyavailablerouteoptionalongtheorigin-destination(OD)routebecomesphysicallyinaccessiblethroughthefailededge.

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• Freight redistributioncostmetric:The totaldifferencebetween thepost-disruptionandpre-disruption generalized cost estimates of allOD flows rerouteddue to edge failure.

The freight redistribution costs are assigned to the edge whose failure causes thoseredistributions.Thesevaluesshowthemostcost-effectivelinksinthenetwork.

• Total economic impact metric: The overall economic criticality of network links ismeasuredasthesumoftheirmacroeconomiclossesandthefreightredistributioncostsincurredduetofailures.

Forthevulnerabilityandcriticalityassessments,theanalysisselectedindividualnetworklinksbased

onthespatialintersectionofnetworksandthehazardslistedintable3.1.Theselectionwaslimited

tothelandslideandflashfloodsusceptibilityareaswithhighandveryhighlikelihoodvaluesaswellasriverandtyphoonfloodingareaswithflooddepths≥1meter.Eachselectedtransportnetwork’slinkswasintersectedwithallselectedhazardstoidentifytheexhaustivesetofalluniquenodesandlinks

subjected to a hazard. The subsequent sections present and discuss only the following networkresults:

Table3.1.SelectedNumbersofSingleLinkFailureScenariosforDifferentTransportNetworks

Transportnetworkselected Uniquesinglelinkfailurescenarios

National-scaleroads 968

National-scalerailways 164

LaoCaiprovinceroads 1,299

BinhDinhprovinceroads 11,042

ThanhHoaprovinceroads 26,852

National-ScaleRoadNetworkCriticality

Figure 3.1 shows the national-scale road network criticality results, including themaximumAADFdisruption values in figure 3.1A. The result highlights the network locations with very high flows,where there are threats of systemic disruptions due to exposures to extreme hazards. The results

showaclusterofroadsaroundHoChiMinhandlargesectionsoftheQL1Atrans-Vietnamhighwaywith high AADF flows that could be disrupted by hazards. The highest ranges of potential AADFdisruptionssubstantial,between124,000to156,000tonsperday.

Figure 3.1B shows themaximum totalmacroeconomic losses inUS$millionper day, basedon theidentified cases, where some of the AADF disruptions reported in figure 3.1A result in immediatemacroeconomic losses. These trip isolations, where the commodities comprising the AADF mix

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cannot be transported, create supply and demand imbalances in themacroeconomic system. Theprocess of estimating thesemacroeconomic losses is explained in appendixA.This process can be

treatedasaworst-caseassumption;realisticallyadisruptionlastingadaywillprobablynotproducemacroeconomic losses. Therefore, the results can be interpreted to highlight the macroeconomicimportance of network links. Quite significantly, the results show that disruption of themajor link

towardHoChiMinh(DT743)couldpotentiallycreateanestimatedUS$0.074toUS$0.44millionperday in macroeconomic losses. If this link is disrupted for a significant time, such losses couldrealistically materialize. The macroeconomic loss results also, importantly, highlight that large

disruptions in tonnagemight not necessarily result in significant economic losses in every case. Insomecases,economicgainshaveresultedfromregionssubstitutingforproductioncapacitylossesinotherregions,leadingtoreducedmacroeconomicimpactsandevennetmacroeconomicgainspost-

disruption.

WherethedisruptedAADFflowscanbereroutedalongalternativeroutes,freightredistributioncostincreases along disrupted links create significant failure impacts on the national road network.

However, this comes at the price of increased transportation costs (figure 3.1c), where the valuesshowthesystemicfreightredistributioncostsonthewholenetworkreportedattheindividualfailedlinks. The highest rerouting costs can range between US$0.67 million per day (minimum flow

disruptionscenario) toUS$1.90millionperday (maximumflowdisruptionscenario).Mostof thesearethroughoutsectionsoftheQL1Atrans-Vietnamhighway,averysignificantrouteinthecountry.Inparticular, thesectionsof thishighwaythroughNgheAhtoThanhHoashowhighest impactson

reroutingcosts.

Theeconomicimpactsofroaddisruptionsareshowninfigure3.1D,whichcombinestheresultsfrom

figures3.1Band3.1C.Asdiscussed, the significant economic impacts are createdby redistributioncost increases, ratherthanmacroeconomic losses fromtrip isolations—witha fewexceptions,suchastheDT743link.

Basedon themodel results, it couldbeworthconsideringwhether thehighcostsof reroutingwillresultincompanies’non-useoftheroadswhilewaitingfordisruptedroadstobefixed,orpartialuseofthereroutingoptionsduringroutereconstruction.Thisbehaviorhasnotbeenexploredhere,but

thisanalysisprovidesameanstoconsidersuchpossibilities.

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Figure3.1.National-ScaleRoadsCriticalityResults

A.Maximumdailytonsdisrupted B.Maximummacroeconomiclosses

C.Maximumreroutingloss D.Maximumtotaleconomicloss

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National-ScaleRailwayCriticality

Figure3.2showstheresultsfortherailwaynetwork.Giventhesingleroutestructurealongmostof

the railwaynetwork, link failures impact significant routeswith veryhigh flows, resulting inworst-caseAADFdisruptionsbetween8,000to10,000tonsperday(figure3.2A).

FollowingthecasesofAADFdisruptions,figure3.2Bshowsmaximumtotalmacroeconomiclossesin

US$million per day, based on the assumption that the AADF losses reported result in immediateeconomicimpacts.Quitesignificantly,theresultsshowthatrailwaydisruptionscanpotentiallyresultinveryhigheconomiclosses,witheconomiclossesonthebusiestroutesrangingfromUS$2.3to2.6

million per day. These results show that though the overall railway network usage is much lesscompared to roads, use is significant in the regions vulnerable to disruptions. The high impactscausedbyrailwaydistributionscouldberesponsiblefordecliningrailwayusageinVietnam

Figure3.2Chighlightsthefreightredistributioncosts,withafewlinkscausingonlyminorimpactsonredistribution cost increases along the railway network; the highest rerouting costs, at mostUS$9,000perday,occuraroundtheHanoihub.

Theeconomic impactsof railwaydisruption (figure3.2D)arecreatedby themacroeconomic lossesresulting from trip isolation redistribution cost increases, rather than redistribution cost increasesalone.Basedonthemodelresults,therailwaynetworkshowsverylittleredundancyincopingwith

disruptions.

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Figure3.2.National-ScaleRailwayCriticalityResults

A.Maximumdailytonsdisrupted B.Maximummacroeconomiclosses

C.Maximumreroutingloss D.Maximumtotaleconomicloss

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Province-ScaleRoadCriticality

Attheprovincescale,thestudyestimatesroadnetworkcriticalitiesintermsof:

• Net revenue flow disruptions, which potentially arise from hazard induced disruptionsalongaccessibleroutestowardthecommunecenters.

• Economic impacts estimates,which represent the sumof theeconomic losses (thenetrevenue disrupted) due to the lack of accessible routes toward the commune centers,andtheincreasesinreroutingcosts,wherepost-disruptionaccesstocommunecentersismaintained.

Theresultsthatfollowhighlight,throughthenetrevenueflowdisruptionmaps,potentialdisruptionsto the network links most important in providing access to commune centers. Also, the maps

highlight any rerouting options around each disrupted network link. Taking such rerouting intoaccount, theresultsunderlinetheeconomic impactsofdisruptions.Crucially, theresultsshowthatthe existence of rerouting options decreases the impacts of flow disruptions. The economic loss

resultsprominently reveal thenetwork linksusedmostheavily fornet revenuegeneration,whosefailureswouldresultincompletelackofaccess.

To give a senseof theworst-case scenariosof failures, the sub-sections that follow showonly the

maximumnetrevenueflowdisruptionsandmaximumeconomicimpacts.

LaoCaiprovinceroadsdisruptions

Theheat-mapforLaoCaiprovince in figure3.3Ashowsthemaximumnetrevenueflowdisruptions

due to hazard-driven road failures. The spare concentration of roads in Lao—especially in theBaoYen,BatXat,andVanBandistricts—focuses flowdisruptionsalong fewerroutes.Potentially, thesenetrevenueflowdisruptionscancauseuptoUS$27,000perdayineconomiclosses.

Theeconomic impacts shown in figure3.3B reflecthow theexistenceof reroutingoptionsabsorbsmostofthepotentialdisruptionsinnetrevenueflows.However,somelinkfailurescausecompletelossofaccesstocommunecenters,resultinginworst-caseeconomiclossesofuptoUS$26,000perday.

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Figure3.3.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossforLaoCaiProvinceRoadNetwork

A.Maximumnetrevenuedisruption

inLaoCaiprovince

B.Maximumeconomicloss

inLaoCaiprovince

BinhDinhprovinceroadsdisruptions

In figure 3.4, heat-maps for Binh Dinh province show the maximum net revenue flow disruptions

(panelA)andmaximumeconomic impacts (panelB)causedbyhazard-drivenroadfailures.Mostofthe network is very redundant, leading to insignificant impacts due to disruptions. However, asignificant cluster in the Quy Nhon district, which has very high disruptive impacts, could see

potentialeconomicimpactsofUS$56,000perday.

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Figure3.4.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheBinhDinhProvinceRoadNetwork

A.MaximumnetrevenuedisruptioninBinhDinhprovince

B.MaximumeconomiclossinBinhDinhprovince

ThanhHoaprovinceroadsdisruptions

In figure 3.5, heat-maps for ThanhHoa province show themaximumnet revenue flow disruptions(panelA)andmaximumeconomicimpacts(panelB)resultingfromhazard-drivenroadfailures.Hereagain,most of the network is very redundant, leading to insignificant disruption-induced impacts.

However, significant clusters in the Bim Son districtwith the highest disruptive impacts could seepotentialeconomicimpactsofUS$67,000perday.

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Figure3.5.MaximumEstimatedDisruptioninNetRevenueandMaximumEconomicLossfortheThanhHoaProvinceRoadNetwork

A.MaximumnetrevenuedisruptioninThanhHoaprovince

B.MaximumeconomiclossinThanhHoaProvince

RiskAssessment

Following the assessment of network link criticalities, the risk of extreme hazard failures can beestimatedbycombiningtheknowledgeofthehazardsandimpactsintoonemetric.Themainaimoftheriskestimationistoidentify:

• Whichtypesofhazardshavethemostimpactsontransportlinks?

• Whatarethechangesinthehazardimpactsduetodifferentclimatechangescenarios?

The losses here signify the daily economic impacts, estimated in the criticality assessment, and

multipliedbycertaindurationofdisruption—duringwhichtheaffectednetworklinkisassumedtobeoutofoperation.Todifferentiatebetween the severitiesofeventswithdifferentprobabilities, the

analysisassumedthatthedurationofdisruptionwilldependuponwhatpercentageofthenetworklink’slengthisexposedtotheextremehazardforthatevent.

Limitation:The riskcalculation resultspresented in thesubsequentsectionsshowveryhighvalues

forthelandslideandtyphoonfloodingevents—andtosomeextenttheflashfloodevents—basedonthe assumption that these hazards have a probability equal to one. The analysis made theassumption in the absence of any probabilistic information for these hazards. Because only the

underlying river floodinghazards information isprobabilistic,only the results for the river floodingriskscapturethetruenatureofriskestimation.

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National-scalenetworks

Theresultsthatfollowforthenational-scaleroadandrailwaynetworkassumeamaximumduration

ofdisruptionof 10days,with a500m threshold lengthof exposureof anetwork link.Hence, if anetwork link length greater than 500mwas exposed to the chosen extreme hazard scenario, theresultsassumeda10-daydisruption.Belowthatlength,thedisruptiondurationdependeduponthe

fractionofnetworklengthexposedtothehazard.

National-scaleroadnetworkrisks

Figure3.6 shows themaximumvalueofeconomic riskson thenational-scale roadnetworkdue tocurrent 2016 hazards of, respectively, (A) landslide susceptibility, (B) river flooding, (C) typhoonflooding, and (D) flashflood susceptibility.The results aim to highlight the hazard-specific high-risk

locations on the network, which can be prioritized for further detailed investigations into climateresilience. Theanalysis shows that risksalongkey sectionsof theQL1A trans-Vietnamhighwayaremainly driven by landslides and typhoon flooding,while river flooding affects links around Ho Chi

Minh and Thua Then Hue, and flashfloods affect somemountain provinces due to the underlyinghazardsconcentratedonlyinthoseregions.

To understand the changes in the hazard impacts due to climate change scenarios, the analysiscompares road network risks due to future climate hazards with those due to the current dayhazards.Suchacomparisonispossibleonlyforriverfloodingbecauseitistheonlyhazardthatgives

fullnationalcoverageofdifferentprobabilistichazardsunderclimatechangescenarios.Bothclimatechangescenariosforriverfloodingseeasubstantialincreaseinthevalueofsystemicrisksoffuturefailure of national-scale road network links (figure 3.7). On almost all affected network links, the

potential risks of failure due to river flooding increase by at least 40 percent in the future 2030hazard scenarios, a substantial increase in risks that highlight a strong case to invest in buildingnetworksresilienttofutureclimatehazards.

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Figure3.6.MaximumEstimatedRisksforNational-ScaleRoadNetworkLinks

A.Landslidesusceptibilitymaximumrisks B.Riverfloodingmaximumrisks

C.Typhoonfloodingmaximumrisks D.Flashfloodsusceptibilitymaximumrisks

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Figure3.7.PercentageChangeinMaximumFailureRisksofNational-ScaleRoadNetworkLinksforFuture2030RiverFloodingunderClimateScenarios

A.RCP4.52030 B.RCP8.52030

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National-scalerailwaynetworkrisks

Similartotheresultsoftheprevioussection,figure3.8showsthemaximumvalueofeconomicriskson the national-scale rail network links due to current 2016 hazards of, respectively: (A) landslide

susceptibility, (B) river flooding, (C) typhoon flooding,and (D) flashfloodsusceptibility.Theanalysisshowsthat largesectionsoftherailwaysareatriskmainlydueto landslidesandtyphoonflooding,while river flooding affects a small section of links around Quang Nam and Thua Then Hue, and

flashfloodsaffectsomemountainprovincesduetotheunderlyinghazardsconcentratedonlyinthoseregions.

Fortherailwaynetworkaswell,therisksduetofutureclimatechangescenario-drivenriverflooding

hazardsin2030comparewiththoseduetothecurrentdayriverfloodinghazardsin2016(figure3.9).Underbothclimate change scenariosof river flooding, systemic risksofnational-scale railnetworkfailureincreasessubstantiallyinthefuture.Similartothenational-scaleroadnetworklinks,atalmost

allaffectednetworklinksthepotentialrisksoffailureduetoriverfloodingfaceasubstantialincreaseofat least40percent in the future2030hazardscenarios,highlightingastrongcase inVietnamtoinvestinbuildingnationalrailwaysresilienttofutureclimatehazards.

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Figure3.8.MaximumEstimatedRisksforNational-ScaleRailwayNetwork

A.Landslidesusceptibilitymaximumrisks B.Riverfloodingmaximumrisks

C.Typhoonfloodingmaximumrisks D.Flashfloodssusceptibilitymaximumrisks

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Figure3.9.PercentageChangeinMaximumFailureRisksofNational-ScaleRailNetworkforFuture2030RiverFloodingunderClimateScenarios

A.RCP4.52030 B.RCP8.52030

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Province-scaleanalysis

Theresultsfortheprovince-scaleroadnetworksassumeamaximumdisruptiondurationof10days,witha100mexposurethresholdlengthofanetworklink.Hence,ifanetworklinkgreaterthan100mwas exposed to the chosen extreme hazard scenario, the results assumed a 10-day disruption.

Belowthat length,thedurationofdisruptiondependeduponthefractionofthe lengthexposedtothehazard.

LaoCaiprovince-scaleroadsrisks

TheanalysishighlightskeysectionsofthenetworklinksintheVanBanandSaPadistrictsaffectedbylandslides,flashfloods,andriverflooding(figure3.10),whosefailuresresultsinhighrisksduetolack

ofaccesstotheirnearestcommunecenters.

Figure3.10.MaximumRisksofLaoCaiProvince-ScaleRoadNetwork

A.Landslidesusceptibilitymaximumrisks B.Flashfloodsusceptibilitymaximumrisks

C.Riverfloodingmaximumrisks

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TheanalysisshowsthatalongseveralroadsintheBatXat,LaoCai,VanBan,andBaoYendistrictsthefuture landslide-driven failure risks of large continuous sections of the road network increase

substantiallybyatleast40percent,withmoreseverechangesunderRCP8.5scenarios(figure3.11).Hence,strongly indicatingthat futureaccess toeconomicopportunities in thispartof theprovincewillbeseverelyaffectedwithoutinvestmentinbuildingclimate-resilientroads.

Figure3.11.PercentageChangeinMaximumFailureRisksofLaoCaiProvince-ScaleRoadNetworkLinksforFuture2050LandslideSusceptibilityunderClimateScenarios

A.RCP4.52050 B.RCP8.52050

BinhDinhprovince-scaleroadsrisks

TheanalysishighlightskeysectionsoftheroadnetworklinksintheTuyPhuocandQuyNhondistrictsaffectedbylandslides,typhoonflooding,andriverflooding(figure3.12),whosefailuresresultinhighrisksduetolargeareasofeconomicactivityexperiencingalackofaccesstotheirnearestcommune

centers.

ThechangingpercentagesofmaximumrisksduetoclimatechangeintheBinhDinhprovinceclearlyindicateincreasingrisksduetoroadlinkfailuresfromfutureclimatechangedrivenhazardscenarios,

withexpectedannuallossesinalmosteverycasesubstantiallyincreasedbyatleast40percent(figure3.13).Again,thereisastrongindicationthatinthefuture,accesstoeconomicopportunitieswillbeseverelyaffectedwithoutinvestmentinbuildingclimate-resilientroads.

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Figure3.12.MaximumRisksofBinhDinhProvince-ScaleRoadNetwork

A.Typhoonfloodingmaximumrisks B.Landslidesusceptibilitymaximumrisks


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Figure3.13.PercentageChangeinFailureRisksofBinhDinhProvince-ScaleRoadNetworkforFuture2030RiverFloodingunderClimateScenarios

A.RCP4.52030 B.RCP8.52030

ThanhHoaprovince-scaleroadsrisks

TheanalysishighlightskeysectionsofnetworklinksinBimSondistrictaffectedbylandslides,whose

failuresresults inhighrisksdueto largeareasofeconomicactivityexperiencinga lackofaccesstotheirnearestcommunecenters(figure3.14).

As shown in figure3.15, thechangingmaximumrisksdue toclimatechange in theThanhHoa isa

clearindicationofincreasingrisksduetoroadlinkfailuresfromfutureclimatechange-drivenhazardscenarios, with expected annual losses in almost every case substantially increased by at least 40percent. Again, there is a strong indication that access to economic opportunitiesmay be severely

affectedwithoutinvestmentinclimateresilience.

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Figure3.14.MaximumEstimatedRisksforThanhHoaProvince-ScaleRoadNetwork

A.Typhoonfloodingmaximumrisks B.Landslidesusceptibilitymaximumrisks


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Figure3.15.PercentageChangeinFailureRisksofThanhHoaProvince-ScaleRoadNetworkforFuture2030RiverFloodingunderClimateScenarios

A.RCP4.52030 B.RCP8.52030

UncertaintiesinCriticalitiesandRisks

Uncertaintiesareanaturalpartofestimatingthecriticalitiesandrisksof failuresoftransport links.

The estimated network link criticality uncertainties are sensitive to the modeled ranges of flowassignmentsandreroutingonthenetworksaswellastotheparametersinthemacroeconomiclossmodel.

The result shown in figure3.16Ahighlights thehighvariability in theestimated road losses,wherethelargestlossesvarybetweenUS$0.67andUS$1.9millionperday.Theselosses,mainlydrivenbythe increases intransportcosts fromreroutingofnetworkflows, follow individual link failures.The

resulting increased transport costs are primarily influenced by the volumes of rerouted freight.Hence,theuncertaintiesinestimatedlossesaremainlysensitivetothevolumesofdisruptedflows.

As discussed in the section “Rail Failure Analysis Results” in Chapter 5, the economic losses for

railways aremainly driven bymacroeconomic losses. Themacroeconomic loss estimates take intoaccount theabilityof themultiregionaleconomy to substitute for lost freight ina region,which inturndependsonthepercentageof regionaleconomicproductiondependentuponthe lost freight.

Substitutionsforahighfailurepercentagewillnotbesufficienttooffsetmacroeconomiclossesdueto freight disruptions; however, a low failure percentage allows a multiregional economy tosubstitute and offset macroeconomic losses. The large fluctuations in economic losses shown in

figure3.16Baremainlysensitivetothesesubstitutioneffects inthemacroeconomicmodel.Hence,losses for several links range between very small values (~0)—when the volumes of disruptedtonnagesdonotaffecttheregionaleconomies—andveryhighvalues(inexcessofUS$2millionper

day),becausethevolumesofdisruptedtonnagessubstantiallydisrupttheregionaleconomies’abilitytosubstitute.

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Figure3.16.EstimatedRangesofDailyEconomicLossesforIndividualNetworkLinkFailuresinVietnam’sNational-ScaleRoadandRailNetworks

A.Roads:

Rangeofeconomiclossesduetolinkfailures

B.Railways:

Rangeofeconomiclossesduetolinkfailures

The estimated risks are sensitive to the type of hazards, in addition to factors influencing thecriticalities. Thus, comparing ranges of hazard-specific risks across different scenarios of the same

hazardtypeshowshowthehazarduncertaintiesinfluencenetworkrisks.Suchacomparisonismadefor the case of national-scale road and railway network risks due to river flooding in current andfutureconditionsundertheRCP4.5andRCP8.5scenarios.

Figures3.17Aand3.17Bshowtherangesofcurrent(2016)andfuture(2030)climatechange-drivenriverfloodingrisks,calculatedastheExpectedAnnualEconomicLosses(EAEL)inUS$millionsfor10-

daymaximumdisruptionofindividualnetworklinks, inthenational-scaleroadandrailwaynetworkrespectively. The results show the uncertainties of failure risks due to climate change significantlyincrease under the RCP4.5 and RCP8.5 scenarios. Also, both climate scenarios show significant

increases in the maximum estimates of risks, due to increasing severity and frequency of futureextremeriverflooding.AnalysisfortheroadnetworkshowsthehighestEAELincreasesfromUS$0.44toUS$0.88million,anincreaseof100percent.Fortherailwaynetwork,thehighestEAELincreases

fromUS$1.4toUS$2.8million,asignificantincreaseof100percentinhighestlosses.

Figures 3.18 to 3.20 present the uncertainties in estimating criticalities and risks for the threeprovince-scalenetworks(LaoCai,BinhDinh,andThanhHoa).Theplotontheleftpanel(panelA)of

each figure shows the rangeofdailyeconomic impacts forall impacted links, rankedbymaximumdaily impacts.Theplotontherightpanel (panelB)showstheEAELforall linksaffectedbycurrentandfutureextremeriverflooding.

Eachcriticalityresultindicatesthedominanceofarelativelysmallpercentileoflinksineachprovince.HighlossesinLaoCaiareconcentratedinapproximatelythetop20percentoflinks,inBinhDinhtheyareconcentratedinapproximatelythetop10percentoflinks,andinThanhHoainapproximatelythe

top5percentoflinks.Thehighrangesoflossesoccurmainlyforthoselinkswhosefailuresresultincompletelackofaccesstocommunecenters.Themagnitudesofsuchlossesaremainlysensitivetothe changing net revenues assigned to these links, which in turn are sensitive to the changing

tonnagesofcrop(rice)productionsassumedintheflowestimationmodel.Theanalysisinfigure3.18showsthatforLaoCai,thehighestdailyeconomicimpactsrangefromUS$23,000toUS$26,000perday,forBinhDinh(figure3.19)thehighestdailylossesrangefromUS$47,000toUS$56,000perday,

andforThanhHoa(figure3.20),thehighestdailylossesrangefromUS$64,000toUS$67,000perday.

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Figure3.17.EstimatedRangesofMinimumandMaximumRisksforIndividualLinkFailuresDuetoCurrentandFutureRiverFloodingonNational-ScaleNetworks

A.Roads:Riverfloodingrisks

B.Railways:Riverfloodingrisks

However,theserangesofhighestlossesmightnotnecessarilyberecordedforthesamelinks,astheunderlyingdistributionsofnetrevenueallocatedtolinksvarywidely.

Theriskresults, inthecontextofriverflooding,showsignificantly increaseduncertaintiesoffailure

risksdue toclimatechange,underbothRCP4.5andRCP8.5 scenarios. Similar to thenational-scaleanalysis, both climate scenarios show significant increases in themaximum risk estimates, due toincreasing severity and frequency of extreme river flooding in the future. In both future climate

scenarios for LaoCai roadnetwork (figure3.18), the linkwith thehighestmaximumEAEL (aroundUS$20,000) showed a maximum EAEL of around US$4,000 in the current scenario—reflecting anincreaseofmorethan400percent.AnalysisfortheBinhDinhroadnetwork(figure3.19)showsthe

linkwiththehighestmaximumEAELofaroundUS$4,500inthefutureRCP4.5climatescenariohadamaximumEAEL inthecurrentscenario,approximatelyUS$450, indicatingan increaseofmorethan900percent. In the ThanhHoa roadnetwork, analysis shows the linkwith highestmaximumEAEL

(approximately US$11,900) in the future RCP 8.5 climate scenario had amaximum EAEL of aboutUS$300 in the current scenario, an increaseofmore than3,800percent.All threeprovinces showsubstantialincreasesinrisksduetoclimatechange-drivenfloodingforindividuallinks.

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Figure3.18.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheLaoCaiRoadNetwork

A.DailyeconomiclossesinLaoCai

B.RiverfloodingrisksinLaoCai

Figure3.19.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheBinhDinhRoadNetwork

A.DailyeconomiclossesinBinhDinh

B.RiverfloodingrisksinBinhDinh

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Figure3.20.EstimatedRangesofDailyEconomicLossesandRisksDuetoCurrentandFutureRiverFloodingofIndividualLinkFailuresontheThanhHoaRoadNetwork

A.DailyeconomiclossesinThanhHoa

B.RiverfloodingrisksinThanhHoa

CriticalitiesandVulnerabilitiesofPorts

The following section presents criticality and vulnerability assessment results for the air transport,

inlandwaterways,andmaritimesectorsatthemajorportsscales,asthesearethemost importantassetsonthesenetworks.

Theresultsaimtoprovideevidenceon:

• Themainportsinthreesectorsidentifiedwithknownexposurestoextremehazards.

• Theeffectofclimatechangeonthechangingrisksofexposure.

The resultsdemonstrate that, foreach sector, the risksof climate change-drivenhazardexposureswill increase.1 For example, themaximum hazard probability of flooding of the Ho ChiMinh port

increasesto0.2(1in5-yearflooding)undertheRCP4.5andRCP8.5scenarios, incomparisontothe0.04(1in25-yearflooding)probability.Fromthis,theanalysisconcludestheHoChiMinhportwillbeabout 5 timesmore prone to frequent flooding in the future, and observed similar trends for all

majorportsineverysector.

Basedonriskestimatesforairportsproducedwitholderstatistics,theanalysisrevealsthatonlyDa

Nang airport shows significant AADF flows exposed to extreme hazard. Since 2006 developmentaroundDaNanghas increased,andatpresentDaNangservesasamajorpassengerhub incentralVietnam.2Becauseofthis,impactsontheairlinesectorshouldbeinterpretedintermsofpassenger

usage,amuchmoresubstantialmetricthancargotransport.

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Resultsshowexposuretoextremehazards inthemajormaritimeportsofHoChiMinh,HaiPhong,and Can Tho, with analysis for the Ho ChiMinh and Can Tho ports clearly indicating that climate

change will increase the frequency of extreme hazards. The significance of these ports to theeconomy of Vietnam is immense, with the analysis showing that under worst-case scenariosapproximately 68,000 to 106,000 tons of cargo flows per day could be affected. Even though this

studyincludeddomesticflows,theseportshavefargreatersignificanceasmajorimportandexporthubsforVietnam.

In terms of the country’s inlandwaterway ports (a total of around 41), results suggest thatmajorinland hubs in An Giang, Hai Phong, Thai Binh, Quang Ninh, and Ho Chi Minh are identified asexposedtoextremehazards,withthefrequencyofhazardexposuresforAnGiangincreasinginthe

futureduetoclimatechange.Theimplicationsofdisruptionstotheseportscouldbeimmense,withanalysis indicating that under worst-case scenarios approximately 25,000 to 55,000 tons of cargoflowsperdaycouldbeaffected.

Notes1. Appendix C shows detailed results of vulnerability assessment of the airports, major identifiedmaritime ports, and major identified inland waterway ports. The analysis showed the 44 inlandwaterwayportsand10maritimeportswith significant commodity flowswereexposed toextreme

hazards,mostlytyphoonfloodingandriverflooding.

2.Source(inVietnamese):https://web.archive.org/web/20130729200548/http://www.mt.gov.vn/PrintView.aspx?ArticleID=10663.Reference

ArgaJafino,Bramka.2017.MeasuringFreightTransportNetworkCriticality:ACaseStudyinBangladesh.Delft,Netherlands:TUDelft(DelftUniversityofTechnology).

http://resolver.tudelft.nl/uuid:0905337b-cdf7-4f6e-9cf6-ac26e4252580.

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Chapter4:AdaptationStrategyandAnalysis

Thischapterpresentstheadaptationoptionsconsideredfornational-scaleandprovinces-scaleroadnetworkassets,evaluatingeachassettherangesofadaptationoptionsandtheassociatedcostsandbenefits. Following this, the chapter discusses results of a detailed cost-benefit analysis for each

networkassetunderdifferenthazardscenarios.

ScopeandPurposeofAdaptationAnalysis

Adaptationplanningisamuchlargerprocessthandiscussedinthisstudy.Ideally,adaptationoptionsshouldinvolveawiderangeofmeasuresincluding,amongothers,structural,institutional,andsocial

changes.Also,thetypesofadaptationoptionsdependuponthecontextofthespecificcountryandthe financing objectives, which govern the types of objectives outlined by different multidonor-funded development agencies (Ebinger and Vandycke 2015). This study recommends several

proposedguidelineson the steps required fordetailed site-specific adaptationoptions inVietnam,neighboring Lao PDR, and other countries, based on an understanding of local conditions (ICEM

2017a and 2017b; MoPWT Laos 2008; CSIR et al 2011). The reader can refer to these studies tounderstandthestep-by-stepguidelinesintheadaptationplanningandimplementationprocess.

Adaptation options explored in this study look specifically at measures intended to improve the

structurereliabilityofroadassetstomakethemmoreresilienttoclimatechangeimpacts.Thesecanalsobecalledclimateresiliencestrategies.Thefocushereisspecificallyonevaluatingthebenefitsofstructural engineeringoptions tomake the roadassets structurally “climateproof.” Therefore, the

approachadoptedhereconsistsofexploringengineeringoptionsofadaptationtargetedtoimproveor create climate-resilient engineering standards of design for various aspects of road assets—subsurface conditions, upgrading material specifications, cross section and standard dimensions,

drainage and erosion, and protective engineering structures (ADB 2011). The levels of hazardexposuresofroadassetsselectedherearethemostextremebasedontheir identifiedmagnitudes:selectedlandslidesandflashfloodssusceptibilitiesarehighandveryhigh;selectedriverandtyphoon

floodingaregreaterthan1meter.Hence,thestudyassumestheselevelsofhazardswillpotentiallycausecatastrophic failurestoroadassets, i.e.,physically, theroadassetswillbedamagedand losetheir ability to provide service and will require rehabilitation. This assumption implies that the

purposeoftheadaptationoptionistomakeroadassetsresilienttocatastrophicfailuresinordertoavoidrehabilitationandthusmaintainthecontinuityofserviceflows.

The generalized evaluation of adaptation options presented here creates a high-level indicative

assessment of transport systems and their climate resilience strategies; evaluation of adaptationoptionsisaverysite-specificproblem,anddependsonthedetailedinvestigationof,amongothers,local terrains, structuraldimensionsofassets,conditionsofassets,andspecific failuremechanisms

duetohazards.Estimatingadaptationcostsisaverysite-specificproblemandinparticulardifferenttypes of engineering-based adaptation options require detailed site investigation and structuraldesign calculations of road asset design standards. At best, this analysis presents a first-order

screening whereby the efficacy of different types of adaptation options can be compared andlocations and assets with high (or low) adaptation benefits can be narrowed down for furtherinvestigation.

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As discussed later, in Vietnam the vulnerability to slope stability of roads due to landslides andflashfloods is amajor issue.Unfortunately, the underlying data in this study does not provide any

information on road slopes inclines and areas, which would have been used to fully quantifyadaptationoptionstobuildclimateresiliencetolandslidesandflashfloods.

IdentifyingFailuresbyClimateChangeDrivenHazardThreats

Listedbelowaresomeofthetypesoffailuresofroadassetsduetohazards—landslidesandflooding(river,flash,andtyphoon)—inVietnam.Thisisnotanexhaustivelist,butratheralistofmaincauses

offailuresthataffectthecontinuityofroadusage.Inseveralinstancestwoormoreofthesefailuretypescouldmanifestsimultaneously:

• Erosionoftheroadpavement;

• Embankmentfailures:slopeerosion;

• Cutslopefailure;

• Drainagefailures:a)pavementdrainagefailure,b)crossdrainagefailureaffectingdams,culvertsandspillways,andc)riverbankerosion;

• Structural failures: Collapse of any of the road asset, especially critical assets such asbridgesandculverts.

While the several hazardmechanisms could trigger failures, the analysis identifies intense rainfall-

inducedlandslidesandfloodingastheprimarycauseofroadfailuresinVietnam.Forexample,istheanalysis recognizes that in Vietnam water is the single biggest trigger of slope movements, since

saturation following heavy rain reduces pore suction, adds to the weight of the slope mass, andreducesbothcohesionandfriction(GITEC2018).

Climatechangecanamplify theseverityof failuresdue to increased levelsofhazard threats.Table

4.1listssomefailuretypesthatcanintensifyduetoclimatechange.

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Table4.1.PotentialFailureTypesDuetoIncreasedHazardThreatsDrivenbyAdverseClimateChange

ClimateChange Impacts

Increaseinrainfall

andintensity

• Damagetoroads,under-groundandsurfacedrainagesystemsduetoflooding

• Increaseinscouringofroads,bridges,culverts,andsupportstructures• Erosionofembankmentsandroadinfrastructureduetoflooding• Triggeringofroadsideslopefailuresduetolandslides• Overloadingofdrainagesystemsduetoincreasedsedimentationfor

flooding• Deteriorationofstructuralintegrityofroadsandbridgesduetoincrease

insoilmoisturelevels• Washoutofgravelandearthroadsduetoflooding

Sealevelrise

• Generalovertoppingofroads,embankmentsandbridges• Damagetolow-lyingstructures,andbridgesduetoflooding,inundation

incoastalareas,andcoastalerosion• Damagetoembankmentsfromlandsubsidenceandlandslides• Scouringofroads,bridges,andbridgesupports

Highwinds• Damagetoroadpavementsandstructuresfromtreesandotherdebris• Damagetobridgedecksandsuspensions• Impactbywinddrivenwaveactiononembankments,bridgeabutments

Increaseintyphoonfrequency

• Directimpacterosionofroads,slopes,andembankmentsfromflashfloods

• Allrisksassociatedwithincreaseinrainfallintensityduetoincreaselandfall

Sources:JasperCook,ReCAP1;ADB2011;EbingerandVandycke2015;WorldBank2017.

AdaptationOptionsandCosts

IntheVietnam-specificcontext,therelevantadaptationinterventionscanbebroadlydescribedasacombinationofthefollowing:

1. Pavement strengthening: Unpaved roads made of earth and gravel are prone to rapiddeterioration due to soil moisture saturation from excess water. Hence, the pavementstrengtheningoptionaimstocreateamoreclimate-resilientsurfacebyinvestinginbitumen

sealingorconcretepaving,which increases thestrengthandprotectionof subsurface roadconditionstopreventsoilsaturation.Periodicmaintenanceofthepavementtopreserve itsstrengtheningcharacteristics shouldbeperformed toprevent soilmoisture saturation.Thisinterventionprotectsroadsagainstfloodingdisruptions.

2. Improvedpavementdrainage:Toavoiddisruptivepoolingofwaterandsedimentsonroads(or even bridges), this investment option is aimed at improving the road surface crossfall,puttingmoresidedrains,turn-outdrains,andscourchecks.Periodicmaintenanceclearsthedrainageandpreventscour.Thisinterventionprotectsroadsagainstfloodingdisruptions.

3. Earthwork protection: Toprevent structural collapseof slopedearthworks, this investment

optionworksbyadoptingdifferentclimate-resilientslopestrengtheningmeasures,includingimproved masonry standards (ADB 2011) to enhance embankments and cut road slopes.

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Periodic maintenance of the slopes should be performed to remove loose materials. Thisinterventionprotectstheroadsagainstlandslidedisruptions.

4. Slopeprotection:Aimedspecificallyatenhancingclimate-resilienceofroadslopestructures,

thisoptionincludesinstallinggabions,blockfacing,bio-engineeringofplantedvegetationtostrengthen slopes (ICEM 2017b), and improving face drainage against overtopping due toriver flooding. Investment in slope strengthening can be undertaken with periodicmaintenance.Thisinterventionprotectstheroadsagainstlandslidedisruptions.

5. Improved cross drainage (culverts): This investment option builds more climate-resilient

culvertsand/or increasesthesizeandprotectionstandardsofexistingculverts (streamandrelief), reflecting the increases in flood intensities due to climate change. Periodicmaintenance of the culverts is performed to remove debris and sediments to prevent

scouring.FloodvulnerabilityofculvertsinVietnamisamajorissue,withseveralinstancesofculvert and small bridge failures recordedduringmajor floodevents (CSCNDPC2017).Thisinterventionprotectstheroadsagainstfloodingdisruptions.

Implementationof theaboveoptionscreatesaclimate-resilient roadprotectedagainstall typesofnatural hazards risks. Hence, the study suggests a climate-resilient road design, which includes

combinations of the five options as described above. In simplistic terms, this study suggests thatbuildingaclimate-resilient road inVietnamrequires implementingoneormorepavementupgradeoptions, enhancing the pavement drainage, constructing embankments and cut-slopes, increasing

slope stability by investing in different measures, and improving cross-drainage by building moreculverts.

Forthepurposesofthisstudy,theanalysisgeneralizesasetoffixedadaptationoptionsacrossroads,dependingon:

• Roadclass,eithertothestandardofaclimate-resilientnationalroadortothestandardofaclimate-resilientdistrictroad.

• Road terrain, where a sample road is designed either for a flat terrain or amountainterrain.

• Bridge,whereasamplebridgeisdesignedtorepresentanytypeofbridgeinVietnam.2

In order to enhance a road’s climate resilience, the study selected four climate-resilient roadprototype designs: national mountain, national flat, district mountain, district flat, and bridge,

identifyingeveryat-riskroadassociatedwithoneoftheseprototypedesigns.

For each prototype road, the study designed a sample climate-resilient, 100-meter section bycreatingabillofquantities,whichspecifiestheamountsandunitcostsofquantitiesneededtouplift

the climate resilience of existing roads. The results of the study’s climate-resilient designassumptions, summarized in table 4.2, arrive at unit costs of investment (CI) in US$ millions perlengthforupgradingexistingnationalanddistrictroadstoprototypeclimate-resilientroadstandards.

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Table4.2.CostSummaryofInitialAdaptationInvestmentforBuildingPrototypeClimateResilientRoadsinVietnam

Prototyperoad TerrainCostofadaptationinvestment(CI)

(US$/km)

Flat 1,535,000Nationalroad• Two-lane,22.5meterswide Mountain 1,828,500

Flat 808,000Districtroad• One-lane,6.5meterswide Mountain 1,439,000

Bridge All 10,179,000

Source:WorldBankcalculations,withinputsprovidedbyDr.JasperCook,ResearchforCommunityAccessPartnership(ReCAP):http://research4cap.org.

ResultsofAdaptationAnalysis

In estimating the parameters of the adaptation net present value (NPV) calculations, the studyassumedthefollowing:

• All climate hazard scenarios models estimated hazard severity until the year 2050.3

Accordingly,thestudyconsidersa35-yeartimelineofanyadaptationintervention,from2016to2050.

• The study assumed a discount rate of 12 percent, based on the Vietnam Road AssetManagementSystem(VRAMS)cost-benefitanalysistoolassumption.

• The study assumed the adaptation options were implemented over the length of thenetworklinksaffectedbyhazards.Forexample,if10percentofaroadnetworklinkwasaffectedbyahazard,thenthecostswereestimatedforthis10percentsection.

• Thestudyassumedthefreightflowvolumesacrossthenetworkswouldgrowasperthe

International Monetary Fund (IMF) forecasted 6.5 percent GDP growth for Vietnam.4Thus, thestudyassumedeconomic impactswouldgrowby6.5percenteveryyearoverthetimelineoftheadaptationoption.

For each roadnetwork under consideration, the study performed adaptation analysis on each link

subjectedtoeachhazardmodelassembledforthestudy.Thus,ifasingleroadnetworklinkisatrisk

for typhoon flooding, river flooding, and landslides, the study estimates the costs and benefits ofadaptation options for each type of hazard risk under each model scenario (current, or climatechangeRCP4.5andRCP8.5,withthegiventime-horizon).Forexample,ifa1kmnationalroadlinkis

exposedtoextremeriverfloodingover100to500mof its length,dependinguponfloodingreturnperiods,thenthestudyestimatesthecostsandbenefitsofadaptationforachosenclimate-resilientdesignfor500mofthisroad.Ifthesameroadisexposedtoextremetyphoonfloodingover100to

200m of its length, depending upon typhoon intensities, then the study estimates another set ofcostsandbenefitsforbuildingclimateresilienceover200moftheroad.Thus,thestudyestimatesmultiple climate-resilient designs for the same roads, depending upon their hazard exposures and

risks.Table4.3liststheoverallnumbersofuniquehazardexposureandadaptationoptionsscenarioscalculatedforeachtypeofroadnetwork.

Page|98

Table4.3.NumbersofSingle-LinkFailureandHazardExposure/AdaptationOptionsScenariosEvaluatedforTransportNetworksinVietnam

Selectedtransportnetwork Uniquesingle-linkfailurescenariosUniquehazardexposure/adaptation

optionsscenarios

National-scaleroads 968 6,010

LaoCaiprovinceroads 1,299 5,049

BinhDinhprovinceroads 11,042 14,397

ThanhHoaprovinceroads 26,852 38,449

National-scaleroadnetworkadaptationcostsandbenefits

This section presents a number of results at the national-scale road network level, discussing theadaptationmetricsdescribedinearliersections.

Figure4.1Ashowsthemaximuminitialinvestments(inUS$millions)requiredforindividualroadlinkstomake them resilient to theirworst levels of climate hazards. These estimates dependupon thelengthandwidthof theroad linksexposedtotheextremehazards,which implies thatsomeroads

requirelargerinvestmentsbecauseoftheirverylargehazardexposures.TheresultsshowparticularroadslinksaroundHoChiMinhmightrequirementashighasanUS$55millioninvestmenttomakethem climate resilient. By adding themaintenance costs to the initial adaptation investments, the

resultsalsohighlightparticularroadlinksaroundHoChiMinhthatmightrequireashighinvestmentsasUS$105millionover35yearstomaintainclimateresilience.

Figure 4.1B shows the adaptation investment costs per kilometers, which gives a sense of the

comparativelevelsofinvestmentsrequiredtoupliftroadsfromcurrentstandardstoclimate-resilientstandards.Thespatialanalysisshowsthecostsofinvestmentsperkilometerofnationalroads,whichcan reachashighasUS$3.4million for initial investmentsandUS$6.4million for investmentsover

time.Duetothestudy’sassumedchosenoptionsforveryhighdesignstandardsmeanttoavoidanyfailures,theseinvestmentamountscomparetotheconstructionofanewexpressway,

Page|99

Figure4.1.MaximumTotalInvestmentover35YearsontheClimateAdaptationforIdentifiedLinksintheNational-ScaleRoadNetworkinVietnam

Figure 4.2A shows themaximumvalues of the estimatedbenefits over 35 years of investments inclimateresilienceofindividualroadlinksinthenational-scaleroadnetwork.Thesebenefitsrepresentthesumoftheavoidedannualexpectedrehabilitationcostsovertimeforindividualroadlinksaswell

as the systemic expected annual economic losses (EAEL) over time from 10-day disruptions oftransport freight flows caused by failures of individual road links. The results highlight the largebenefits of investing into building climate resilience, particularly along the expressway sections

towardtheeasterncoastline.

Figure4.2Bshowsthemaximumbenefit-costratios(BCRs)ofadaptationforallidentifiedroadlinks,

derived from the results of figures 4.1A and 4.2A. The BCRs highlights where climate resilienceinvestmentscouldbeprioritized—forroadlinkswithhighBCRs,muchgreaterthanone.Theresultshere show that the expressway link betweenNghe An and ThanhHoa has the highest BCR (197),

whichmakesitahighprioritylinkforclimateresilienceinvestments.

A.Maximuminvestmentovertimepersection B.Maximumtotalinvestmentperkmovertime

Page|100

Figure4.2.EstimatedMaximumBenefitsover35YearsandMaximumBCRsofAdaptationOptionsforIdentifiedLinksintheNational-ScaleRoadNetworkinVietnam

A.Maximumbenefitsovertime B.MaximumBenefit-CostRatio(BCR)

Page|101

Another key insight from the adaptation analysis considers howBCRsmight change under currentand future climate hazard scenarios. This helps make a case for proactively investing in climate

resilience for future levels of extreme hazards, rather than planning for current extreme hazardlevels. For the national-scale roadnetwork links, such a comparison ismadebetween current andfuture (RCP4.5andRCP8.5) scenariosof river flooding. Figure4.3Ashows themaximumBCRs for

currentlevelsofriverflooding,whileFigures4.3Band4.3CshowthemaximumBCRsforfuturelevelsofriverfloodingundertheRCP4.5andRCP8.5scenariosrespectively.AsubstantialupliftintheBCRsin the futurehazard scenariosmakes a strong case for investing intobuilding climate resilience to

future hazards in Vietnam. The increase in BCRs is duemainly to the increase in the future riverfloodingrisksunderbothemissionscenarios,showingthegreaterbenefitsfrominvestinginclimateresiliencetoprotectagainstpotentialfuturehigherlosses.

Table 4.4 provides the list of specific national road network links identified by their BCRs ofadaptation.Theselinksrepresentthetop20assetsrankedbymaximumBCRvalues,andalsoreport

theminimumBCRs.IfboththeminimumandmaximumBCRvaluesaregreaterthan1,thenthecaseforinvestingintosuchassetsisquiterobust,sinceeveryhazardscenarioillustratesthatinvestinginclimate resilience ismore beneficial than paying the adaptation costs. All 20 assets shown have a

robust case for investing in climate resilience. The analysis shows that for these 20 assets acumulativeclimateadaptationinvestmentamountstoapproximatelyUS$95millioninitially;over35years is the investmentswouldamount toapproximatelyUS$153million.However, thecumulative

benefits of such investments over 35 years—estimated by adding together the benefits fromindividual links—arequitesubstantial, rangingbetweenUS$651andUS$3,656million.Notably, thecombined benefits of investing in all roads concurrently will not necessarily equal the sum of

individualbenefitsbecausetheavoidedeconomiclossesarenotadditive,butratherdependontheestimationofsystemicnetworkimpacts.

Page|102

Figure4.3.ComparisonsofBCRsofAdaptationOptionsforNational-ScaleRoadNetworkLinksinVietnam

A.Current2016riverflooding

B.Future2030riverfloodingunder

RCP4.5emissionscenarios

C.Future2030riverfloodingunder

RCP8.5emissionscenarios

Page|103

Table4.4.TwentyNational-ScaleRoadsRankedinDescendingOrderofMaximumAdaptationBCRs

Nati

onal

highway

Rout

e na

me

Road

cl

ass

Max

imum

haz

ard

expo

sure

leng

th

(met

ers)

Initi

al in

vest

men

t (U

S$ m

illio

ns)

Tota

l inv

estm

ent

(US$

mill

ions

)

Bene

fit (U

S$ m

illio

ns)

BCR

Min

.M

ax.

Min

.M

ax.

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

21,

285

2.49

4.04

32.5

731

5.87

819

7

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

31,

943

3.00

4.73

36.3

332

0.16

814

9

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

24,

178

8.07

13.1

085

.70

333.

837

128

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

21,

414

2.74

4.45

50.3

020

3.75

1111

9

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

179

61.

792.

9829

.07

319.

6110

107

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

32,

303

3.56

5.61

11.2

113

7.31

210

4

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

33,

189

4.93

7.77

12.8

420

6.74

294

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

22,

688

5.19

8.43

23.4

417

1.94

385

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

31,

019

1.59

2.50

71.8

219

2.87

2977

1TT

H-D

NG

bor

der

- KH

A-N

TN b

orde

r2

1,44

52.

804.

5444

.28

133.

1310

76

DBV

BĐ

ường

bộ

ven

biển

2

3,72

17.

1811

.67

23.7

716

9.72

275

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

29,

609

18.5

330

.12

82.2

018

9.73

372

1TT

H-D

NG

bor

der

- KH

A-N

TN b

orde

r2

1,29

82.

524.

0816

.15

131.

754

70

1TT

H-D

NG

bor

der

- KH

A-N

TN b

orde

r2

1,84

93.

575.

809.

3312

7.84

262

1TT

H-D

NG

bor

der

- KH

A-N

TN b

orde

r2

2,74

65.

308.

6123

.68

170.

443

62

1TT

H-D

NG

bor

der

- KH

A-N

TN b

orde

r3

6,41

49.

9015

.62

35.7

811

3.90

261

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

155

0.15

0.23

6.50

13.6

828

60

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

21,

080

2.10

3.40

11.0

215

9.09

357

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

24,

055

7.84

12.7

231

.62

134.

682

55

1N

BH-T

HA

bor

der

to T

TH-D

NG

bor

der

21,

052

2.05

3.32

14.1

411

0.81

455

Tota

l95

.30

153.

7265

1.76

3,65

6.85

Page|104

Province-scaleroadnetworkadaptationcostsandbenefits

Thestudyalsoperformedanadaptationanalysisforeachofthethreeprovince-scaleroadnetworks.

Inallmapfiguresthatfollow,anadaptationanalysiswasperformedforeachofthelinkshighlightedin black or shades of red—signifying an exposure to hazard. As indicated in the maps, blackhighlighting represents a failed linkwith no alternative routes to nearby commune centers,which

impairs economic activity. Red highlighting indicates failed links with alternative routes, whichmaintainaccesstothenearestcommunecenters.

Ontheprovince-scale,identifyingsomespecificassetswithhighBCRswouldhelpprovideasenseof

which kinds of assets—national roads, provincial roads, local (commune or district) roads, orbridges—are key to the accessibility province-scale economic activities. The following sectionspresentlistsspecificassets—showingtheassettypes,thecommuneanddistrictstheyarelocatedin,

theirmaximum lengths of hazard exposures, the initial and total investments required to achieveclimateresilience,andtheminimumandmaximumbenefitsandBCRsfrominvestinginthoseassets.

LaoCaiprovince-scaleroadsnetwork

Figures 4.4A through 4.4D show, respectively, the maximum initial investment, the maximum

investmentsover35years,maximumvaluesoftheestimatedbenefitsover35years,andmaximumBCRsofadaptation forall identifiedroad linksexposedtoextremehazard.Thekeyhighlights fromthese results identify several road links in the province—prominent candidates for adaptation

investments—whose failures cut off access to commune centers and hence economic activities.Figure4.4DshowsseveralsuchlinksinthesouthoftheprovincehaveBCR>1,andincludesignificantpointsinthenetwork(possiblybridges)wheretheBCRsarehighest.

Table4.5givesthelistofthespecificroadnetworkassetsidentifiedbytheiradaptationofBCRs.Thetableranksthetop20assetsbymaximumBCRvalues,whilealsoreportingtheirminimumBCRs.Theresults indicate that some local (communeordistrict) roadsandbridges in theprovinceshave the

highest BCRs,making themmost significant to the provision of economic activities; consequently,building their climate resilience should be prioritized. In addition, several road links in Lao Caiimportant for gaining access to economic activities also emerge as priority assets for climate

adaptationinvestments.Theanalysisshowsthatforthese20assetsacumulativeclimateadaptationinvestmentamountstoapproximatelyUS$9millioninitially,withatotalinvestmentover35yearsofapproximatelyUS$12.9million.Thecumulativebenefitsofsuchinvestmentsover35years,estimated

byaddingthebenefitsfromindividuallinks,rangebetweenUS$16.4andUS$22.5million.

Page|105

Figure4.4.Investments,Benefits,andAdaptationBCROptionsforIdentifiedLinksintheLaoCaiRoadNetwork

A.Maximuminitialinvestment B.Maximuminvestmentovertime

C.Maximumbenefitovertime D.MaximumBCR

Page|106

Table4.5.Top20LaoCaiRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs

Ass

et t

ype

Com

mun

e na

me

Dis

tric

t na

me

Max

imum

haz

ard

expo

sure

leng

th

(met

ers)

Initi

al in

vest

men

t (U

S$)

Tota

l inv

estm

ent

(US$

)

Ben

efit

(US$

)B

CR

Min

.M

ax.

Min

.M

ax.

Bri

dge

Nam

Pun

gB

at X

at15

50,1

2259

,824

524,

009

565,

302

8.8

9.4

Loca

l roa

dG

ia P

huB

ao T

hang

9316

1,24

122

2,33

739

2,40

61,

758,

052

1.8

7.9

Bri

dge

Tan

An

Van

Ban

2890

,263

110,

696

543,

361

712,

690

4.9

6.4

Loca

l roa

dD

an T

hang

Van

Ban

3260

,139

81,3

8887

,584

431,

644

1.1

5.3

Expr

essw

ayTa

n A

nVa

n B

an10

036

5,27

757

0,97

21,

156,

642

1,34

2,00

82.

02.

4

Loca

l roa

dG

ia P

huB

ao T

hang

146

249,

353

345,

176

231,

463

766,

128

0.7

2.2

Loca

l roa

dVa

n H

oaLa

o Ca

i59

399

9,20

31,

388,

596

654,

402

2,82

7,63

00.

52.

0

Nati

onal

roa

dSo

n Th

uyVa

n B

an65

395

7,33

91,

387,

381

1,81

5,88

12,

668,

361

1.3

1.9

Loca

l roa

dKh

anh

Yen

Ha

Van

Ban

9015

5,69

721

4,60

913

9,48

937

5,26

80.

61.

7

Bri

dge

Ban

Lie

nB

ac H

a24

77,2

3592

,928

48,9

0514

0,40

30.

51.

5

Bri

dge

Nam

Pun

gB

at X

at15

50,8

4760

,709

55,8

7180

,054

0.9

1.3

Nati

onal

roa

dH

oa M

acVa

n B

an48

371

0,31

21,

028,

267

1,32

6,06

71,

337,

024

1.3

1.3

Nati

onal

roa

dM

inh

Luon

gVa

n B

an56

682

7,09

11,

199,

299

1,55

1,40

11,

555,

555

1.3

1.3

Nati

onal

roa

dTh

uong

Ha

Bao

Yen

553

809,

385

1,17

3,36

71,

516,

086

1,51

8,54

81.

31.

3

Nati

onal

roa

dPh

o R

ang

Bao

Yen

530

776,

177

1,12

4,73

11,

451,

507

1,45

1,82

51.

31.

3

Nati

onal

roa

dM

uong

Khu

ong

Muo

ng K

huon

g15

122

8,83

232

8,45

342

1,72

242

3,48

31.

31.

3

Nati

onal

roa

dXu

an H

oaB

ao Y

en39

457

8,96

683

8,57

61,

081,

134

1,08

1,18

51.

31.

3

Nati

onal

roa

dN

am X

eVa

n B

an39

257

5,96

083

4,17

41,

075,

350

1,07

5,37

61.

31.

3

Nati

onal

roa

dTh

anh

Bin

hM

uong

Khu

ong

493

724,

721

1,04

9,37

01,

352,

037

1,35

2,10

31.

31.

3

Nati

onal

roa

dM

uong

Khu

ong

Muo

ng K

huon

g36

854

1,06

578

3,06

71,

007,

730

1,00

7,73

51.

31.

3

Tota

l8,

989,

225

12,8

93,9

2116

,433

,046

22,4

70,3

73

Tabl

e 4.

5. T

op 2

0 La

o Ca

i Roa

d A

sset

s R

anke

d in

Des

cend

ing

Ord

er o

f Max

imum

Ada

ptati

on B

CRs

Page|107

BinhDinhprovinceroads

Theanalysisshowsthatforthese20assetsacumulativeclimateadaptationinvestmentamountstoapproximatelyUS$1.2millioninitially,andover35yearsreachesapproximatelyUS$1.6million(figure

4.5).Thecumulativebenefitsover35years,estimatedbyaddingthebenefitsfromindividuallinks,ofsuchinvestmentsrangebetweenUS$14.2andUS$31.4million(table4.6).

Figure4.5.Investments,Benefits,andBCRsofAdaptationOptionsforIdentifiedLinksintheBinhDinhRoadNetwork


C.Maximumbenefitovertime D.Benefit-costratio

Page|108

Table4.6.Top20BinhDinhRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs

Tabl

e 4.

6. T

op 2

0 Bi

nh D

inh

Road

Ass

ets

Ran

ked

in D

esce

ndin

g O

rder

of M

axim

um A

dapt

ation

BCR

s

Ass

et t

ype

Com

mun

e na

me

Dis

tric

t na

me

Max

imum

haz

ard

expo

sure

leng

th

(met

ers)

Initi

al in

vest

men

t (U

S$)

Tota

l inv

estm

ent

(US$

)

Bene

fit (U

S$)

BCR

Min

.M

ax.

Min

.M

ax.

Loca

l roa

dPh

uoc

My

Quy

Nho

n10

811

4,59

417

9,47

02,

670,

602

9,41

3,24

315

52

Nati

onal

road

Ghe

nh R

ang

Quy

Nho

n8

8,91

710

,436

124,

418

428,

047

1241

Brid

geA

n Ta

nA

n La

o8

29,1

6334

,232

683,

481

1,20

3,71

720

35

Loca

l roa

dTu

y Ph

uoc

Tuy

Phuo

c63

69,5

4210

7,44

83,

496,

164

3,62

9,34

233

34

Loca

l roa

dA

n N

ghia

Hoa

i An

122

128,

485

201,

678

1,27

1,25

34,

114,

864

620

Loca

l roa

dN

hon

Hau

An

Nho

n7

13,5

5517

,944

95,9

9332

5,04

15

18

Brid

geH

oai S

onH

oai N

hon

1139

,739

47,1

4523

2,84

777

1,42

25

16

Brid

geH

oai T

anH

oai N

hon

4513

9,97

116

9,52

82,

487,

608

2,75

3,17

715

16

Brid

gePh

uoc

My

Quy

Nho

n20

66,1

6579

,412

368,

444

1,26

0,27

85

16

Brid

geTa

y Xu

anTa

y So

n11

38,4

5645

,579

203,

829

640,

231

414

Brid

geA

n H

ao T

ayH

oai A

n13

45,4

8054

,155

383,

299

708,

861

713

Brid

geN

hon

Hau

An

Nho

n5

22,4

8326

,076

98,7

7232

7,81

94

13

Loca

l roa

dTa

y Xu

anTa

y So

n31

37,5

1656

,250

204,

282

640,

684

411

Brid

gePh

uoc

Qua

ngTu

y Ph

uoc

624

,850

28,9

6797

,273

324,

475

311

Nati

onal

road

Ghe

nh R

ang

Quy

Nho

n81

40,8

5056

,983

188,

705

523,

325

39

Loca

l roa

dTa

y A

nTa

y So

n16

817

3,69

727

3,95

591

8,27

22,

462,

332

39

Brid

geTa

y G

iang

Tay

Son

1860

,388

73,5

5728

8,15

960

9,25

24

8

Loca

l roa

dCa

t Cha

nhPh

u Ca

t56

62,0

4595

,463

222,

854

708,

369

27

Brid

geBi

nh N

ghi

Tay

Son

521

,950

25,4

2555

,223

168,

167

27

Loca

l roa

dPh

uoc

Thua

nTu

y Ph

uoc

3642

,295

63,8

9013

9,77

641

6,99

32

7

Tota

l1,

180,

139

1,64

7,59

414

,231

,253

31,4

29,6

40

Page|109

ThanhHoaprovinceroads

Figure 4.6 illustrates and table 4.7 lists the top 20 specific road network assets identified by theirBCRsofadaptation.AllassetshaveminimumandmaximumBCRs>1,whichmakesarobustcasefor

investingintheirclimateresilience.Forthese20assets,acumulativeclimateadaptationinvestmentamounts to approximatelyUS$1.5million initially,witha total ofUS$2.3millionover35 years. Thecumulative benefits of such investments over 35 years, estimated by adding the benefits from

individuallinks,rangebetweenUS$7.8andUS$23.4million.

Figure4.6.Investments,Benefits,andAdaptationBCRsOptionsforIdentifiedLinksintheThanhHoaRoadNetwork


C.Maximumbenefitovertime D.BCR

Page|110

Table4.7.Top20ThanhHoaRoadAssetsRankedinDescendingOrderofMaximumAdaptationBCRs

Tabl

e 4.

7. T

op 2

0 Th

anh

Hoa

Roa

d A

sset

s R

anke

d in

Des

cend

ing

Ord

er o

f Max

imum

Ada

ptati

on B

CRs

Ass

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Decision-MakingunderUncertainty:SensitivityofCostsofAdaptation

The global sensitivity analysis shown in figure 4.7 demonstrates that the variations in initial

investmentcostestimates fornational roadsaremostsensitivetothecostsofpavementupgradesand improvements in slope stability, accomplished by installing embankment-retaining gabions(figure 4.7A). The initial climate resilience investment costs in themountainous terrain of Lao Cai

(figure4.7B)aremostsensitivetothe investmentcostsofupgradingandprotectingembankments,whileBinhDinh(figure4.7C)andThanhHoa(figure4.7D)aremostsensitivetotheinvestmentcostsof improving slope stability through road embankments, building embankments, and upgrading

pavements. The numbers in figure 4.7 showwhat percentages of costs are driven by variations indifferenttypesofinvestments.

Figure4.7.ParameterSensitivityforNational-andProvince-ScaleRoads

A.Parametersensitivityfornationalroads

B.ParametersensitivityforLaoCai

C.ParametersensitivityforBinhDinh

D.ParametersensitivityforThanhHoa

Code Item Code Item Code Item

Gravel DT LineDrainL&R SS3 Concrete/blockFace

Sealed EW1 EmbankmentConstruction SS4 Riverbank

Concrete EW2 CutslopeFormation

P

ConcreteFW SS1 CutslopeToeRetainingGabions

SS2 EmbankmentRetainingGabions SS6 Bioengineering

Page|112

Rangesanduncertaintiesinadaptationestimates

National-scaleroads

Thissectiondiscussestherangesanduncertaintiesintheestimationofadaptationcostsandbenefits.Figures 4.8A through 4.8D show for individual national-scale road network links the ranges of: (a)

initial investmentcosts, (b) theNPVoftotal investmentsoverthe35-yearplanninghorizon; (c) theNPV of adaptation benefits due to losses avoided over the 35-year planning horizon, and (d) theBCRs.

The variations in cost estimates for links in figures 4.8A and 4.8B are evident because of theirexposure to multiple hazards; thus, the analysis estimates costs depending upon the extent ofexposuretoeachhazard.Asdiscussedintheprevioussection,thevariationsininitialinvestmentcost

estimatesaremostsensitivetothecostsofpavementupgradesandimprovementsinslopestabilitythrough the installation of embankment-retaining gabions. Total investments costs over time aremost sensitive to the increases in pavement maintenance costs; in the model, most pavement

maintenance costs are far more than other maintenance costs. The results show the highestestimated initial investments for building climate-resilient national-scale road network range fromUS$52toUS$55million,withtheNPVoftotalinvestmentovertimegoingupfromUS$87toUS$105

million.However,theseinvestmentsmightnotnecessarilybeforthesameroadlinks.

Variations in NPVs of the adaptation benefits shown in figure 4.8C are sensitive generally to theavoided economic losses, and to some extent the costs of rehabilitation. For links with very high

NPVs, thebenefits aredrivenmainlyby thevaluesof avoidedeconomic losses. The importanceofthese links to the wider network makes the case for investing in climate resilience. The analysisshows that the highest NPVs of adaptation benefits range from US$118 to US$334 million,

substantiallylargerthantheNPVsoftotalinvestments.Asshowninfigure4.8D,forseverallinksthisleadstogenerallyhighbenefit-costratios(BCR>>1).Significantly,theanalysisshowsthatfromthe60th percentile onwards, theminimum andmaximumBCRs of several links are both greater than

one,indicatingthatundereveryextremeclimatehazardscenario,investingintheclimateresilienceofasignificantproportionofnational-scaleroadswouldbebeneficial.

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Figure4.8.AdaptationAnalysisResultsfortheNational-ScaleRoadNetworkinVietnam

A.Rangeofinitialadaptationinvestments B.Rangeoftotaladaptationinvestments

C.Rangeofadaptationbenefits D.RangeofadaptationBCRs

Comparinghazard-specificadaptationBCRsacrossdifferentscenariosofthesamehazardtypeshowshowthehazarduncertaintiesinfluencetheadaptationanalysisoutcomes.Suchacomparisonismade

for thecaseofnational-scaleroadnetworkrisksduetoriver flooding incurrentconditionsandforthefutureunderRCP4.5andRCP8.5emissionscenarios,asshown infigure4.9.TheresultsshowasignificantupliftofBCRsofadaptationwhenclimateresilienceinvestmentsaremadetoavoidfuture

levels of extreme river flooding driven risks. The analysis mostly does not show any differencebetween BCRs of adaptationwhen evaluated for RCP4.5 and RCP8.5 climate scenario driven riverfloodingrisks,whichisreflectedthroughthecompleteoverlapofthecurvesforthesetwoscenarios.

Significantly, in comparison to the current scenarios, large percentiles of road links have BCRs > 1whenadaptingtofutureclimatescenarios.ThisresultprovidesaverycompellingcaseforaneedinVietnam to plan climate resilience investments in anticipation of future climate change driven

hazards,whentherisksandthereforebenefitsofavoidingthoseriskswillbemuchhigher.

Figure4.9.RangesofAdaptationBCRsfortheNational-ScaleRoadNetworkinVietnam,EvaluatedforCurrentandFutureExtremeRiverFloodingScenarios

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Province-scaleroads

Theprovince-scaleanalysesshow,incomparisontomostroadassets,arelativelysmallpercentileofroad assets have significantly higher adaptation costs. Initial climate-resilience investments for

individual road assets reach in excess of US$2 million for 1) approximately the top 10 percentileassetsinLaoCai;and2)approximatelythetop2to3percentileassetsinBinhDinhandThanhHoa.Someoftheseveryhighvalueassetsmightpotentiallyhavehighbenefits;hence,theircostsmightbe

justified.TheanalysesshowverylittlevariationsofinitialandtotalinvestmentcostsinbothLaoCaiandBinhDinh,whilethevariabilityismoreapparentinThanhHoa.ThelackofvariationsinLaoCaiandBinhDinhstemsfromtheroadnetworkassetsinbothprovinceshavingsimilarexposurelengths

tothevarioushazardsscenarios;therefore,sincethecosts,whichdependuponthelengthsofroadassets exposed to hazards, are also similar. As discussed in the previous section, the sensitivity ofinitialclimateresilienceinvestmentcostsineachprovinceareasfollows:a)LaoCaiismostsensitive

totheinvestmentcostsforupgradingandprotectingembankments,duetoitsmountainousterrain;and b) Both BinhDinh and ThanhHoa aremost sensitive to investment costs for improving slopestabilityofroadsembankments,buildingembankments,andupgradingpavements.

Therangesofadaptationbenefitsinthethreeprovince-scaleanalysesalsoshowthatforarelativelysmallpercentileofroadassets, thebenefitsofadaptationaremuchhigherthanfortherestof the

networkassets. Initialclimateresilience investments for individual roadassetsexceedUS$2millionfor:a)approximatelythetop5percentileassetsinLaoCai;andb)aroundthetop2to3percentileassetsinBinhDinhandThanhHoa.Thevariationsintherangesofbenefitsofadaptationinthethree

province-scaleanalysesarisearemainlysensitivetothechangesintheavoidedeconomicimpactsofdisruptions. For such assets the case for investing in climate resilience is compelling, because thebenefitsoutweighthecosts,asisevidentfromtheBCRplotforthethreeprovinces.

Figure4.10DshowsthatinLaoCaiapproximatelythetop15%percentileofassets(roughly190intheLaoCainetwork)atriskofhazardhavemaximumBCR>1.InBinhDinh(figure4.10C)againassetsin

thetop2to3percentilehavemaximumBCRs>1,whichamounttoroughly220to330assetsinBinhDinh and530 to 800 assets in ThanhHoa. Thougha relatively small proportion, thesenumbers ofassets are significantwhen it comes to prioritizing climate investments. The next section provides

more evidence of where and which assets could be further prioritized, based on identifying thelocationsandtypesofspecificassets.

TheresultsshowaclearcaseofincreasedBCRsinLaoCaiwhencomparingcurrentandfutureriverfloodingscenarios,asshowninfigure4.10B,wheremaximumBCRsbecomegreaterthanoneonlyfor

future extreme river flooding scenarios. In Binh Dinh and Thanh Hoa, the number of assets withmaximumBCRs>1inthefutureriverfloodingscenariosalsoshowanincrease.Aspresentedearlierinthereport,allincreasesarisefromincreasesinfutureriverfloodingrisks,whichavoidedeconomic

loss. Landslide susceptibility in LaoCai is includedhere as a prominenthazard in theprovince, forwhich theunderlyinghazarddataoffered some climate change scenario-based information. In theabsenceofanyprobabilistichazard informationon the landslide susceptibilitymuchof thecurrent

andfutureadaptationoptionsshowsimilaroutcomes.But,significantly,severalassetswithBCR>1createacompellingcaseforinvestinginclimateresilience.

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Figure4.10.AdaptationBCRsforProvince-ScaleRoadNetworks

A.RangeofBCRadaptationtolandsliderisks(LaoCai)

B.RangeofBCRadaptationtoriverfloodingrisks(LaoCai)

C.RangeofBCRadaptationtoriverfloodingrisks(BinhDinh)

D.RangeofBCRadaptationtoriverfloodingrisks(ThanhHoa)

Notes1.Dr.JasperCook,acivilengineer,servesasinfrastructureresearchmanagerfortheAfrica

CommunityAccessPartnership(AfCAP)andtheAsiaCommunityAccessParternship(ASCAP),researchprogramsundertheumbrellaoftheUKAid-fundedResearchforCommunityAccessPartnership(ReCAP).Formoreinformation,visit:http://research4cap.org.

2.Costsestimatesforbridgesarehighlyuncertainanddependheavilyontheparticularbridgebeingstudied.Hencenoclaimismadeherethatthebridgecostsconsideredinthisstudywillapplytoeach

andeverybridgeinVietnam.Thecostsaremoreanindicativeoftheordertoinvestmentthatmightberequiredintobridges.

3.TheGLOFRISmodelsestimatetheaveragemaximumflooddepthsfrom2010to2049,whichisusedasarepresentativeofa2030event.However,thesemodelsessentiallyestimatehazardscenariosto2049.

4.DataaccessedviatheInternationalMonetaryFundDataMapper:http://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD/VNM.

References

ADB(AsianDevelopmentBank).2011.GuidelinesforClimateProofingInvestmentintheTransportSector:RoadInfrastructureProjects.MandaluyongCity,Philippines:AsianDevelopmentBank.

https://www.adb.org/documents/guidelines-climate-proofing-investment-transport-sector-road-infrastructure-projects.

Page|116

CSCNDPC(CentralSteeringCommitteeforNaturalDisasterPreventionandControl).2017(unpublished).NaturalDisastersinVietnamin2017.InternalreportfundedbyUNDP.

CSIR(CouncilforScientificandIndustrialResearch),Paige-GreenConsulting(Pty)Ltd,andStHelensConsultingLtd.2018.ClimateAdaptation:RiskManagementandResilienceOptimisationfor

VulnerableRoadAccessinAfrica,ClimateAdaptationHandbook.AfricaCommunityAccessPartnership(AfCAP)ProjectGEN2014C.London:ResearchforCommunityAccessPartnership(ReCAP)forDepartmentforInternationalDevelopment(DFID).

http://research4cap.org/Library/CSIR-Consortium-2018-ClimateAdaptation-Handbook-AfCAP-GEN2014C-181130.pdf.

Ebinger,JaneOlgaandNancyVandycke.2015.“MovingTowardClimate-ResilientTransport:TheWorldBank’sExperiencefromBuildingAdaptationintoPrograms.”WorldBank,Washington,DC.https://openknowledge.worldbank.org/handle/10986/23685.

ICEM(InternationalCentreforEnvironmentalManagement).2017a.TechnicalReport14:DemonstrationEffectivenessAudit.PromotingClimateResilientRuralInfrastructureinNorthern

VietNamReportSeries.PreparedforMinistryofAgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-vietnam-report-series/

———.2017b.TechnicalReport18:SlopeProtectionDesignsandSpecifications.PromotingClimateResilientRuralInfrastructureinNorthernVietNamReportSeries.PreparedforMinistryof

AgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-vietnam-report-series/

———.2017c.TechnicalReport17:Bioengineering–FieldGuidelinesforSlopeProtection.Promoting

ClimateResilientRuralInfrastructureinNorthernVietnamReportSeries.PreparedforMinistryofAgricultureandRuralDevelopmentandAsianDevelopmentBank,Hanoi,Vietnam.http://icem.com.au/portfolio-items/promoting-climate-resilient-rural-infrastructure-in-northern-

vietnam-report-series/

MPWT(MinistryofPublicWorksandTransport,LaoPDR).2008.(unpublished).SlopeStability

Manual.Vientiane:GovernmentofLaoPDR.

GITEC.2018.ProjectPreparationStudy(PPS)forRuralInfrastructureProgrammePhaseVI(PPSRIP6):

GuidelineinClimateAdaptationforRIPRuralRoads,LaoPDR.https://gitec-consult.eu/index.php/en/projects?view=project&id=69.

Henning,TheunsFrederickPhillip,SusanTighe,IanDouglasGreenwood,andChristopherR.Bennett.2017.IntegratingClimateChangeintoRoadAssetManagement:TechnicalReport2017.Washington,DC:WorldBank.

http://documents.worldbank.org/curated/en/981831493278252684/Technical-report-2017.

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Chapter5:ExploringMultimodalOptionsforNational-ScaleTransport

Thischapterexplainshowthemultimodaltransportsystemwascreatedforthisstudy.Followingthis,thechapterevaluatestheeffectofmultimodalityonfailuresbyexploringmodalshiftsfromroadandrailnetworks.

In the analysis presented to this point, eachmode of transport has been assessed individually tounderstandtheimpactsoffailures.Inreality,failuresmayresultinexploringmultimodaloptionsformaintainingtransportservicecontinuity.Basedonthestudy’sunderstanding,multimodalswitching

optionshavenotbeenwellexploredinVietnam.Hence,thischapterexploresthepossiblechangesinnetwork failure effects if multimodal options were always available to accommodate for failures

alongindividualmodes.

Thechapterexploresthefollowingkeyquestions:

• What are themultimodal transport networksoptions available inproviding continuous

servicewhen individual transportmodesaredamagedordisruptedbyexternalnaturalhazardshocks?

• Arethereanygainsintermsofdecreasinglossesduetomultimodaloptions?

Switchingfromonemodetoanotherduringfailure,viaamultimodalconnection,presentsapotentialadaptationoption,andwheremodalswitchingisapreferredoption,itsbenefitsshouldbecompared

withthecostsinvolvedinfacilitatingandimprovingmultimodallinksandupgradingtransportmodes.Theanalysispresentedhereexploresthebenefitsofmultimodalityinmakinginformeddecisions.

Theanalysisquantifiesimpactsofmultimodalitythroughtwometrics:

• Totaleconomicimpactmetric:Theoveralleconomiccriticalityofnetworklinksmeasuredas the sumof theirmacroeconomic losses and the freight redistribution costs incurredduetotheirfailures

• TheAADFtonnageshift:Theoverallsystemicshiftoftonnagefromadisruptedmodetoothermodesduetomultimodaloptions

Thekeystepsinundertakingamultimodalanalysisinvolve:

• Identifying and creating links at all locations where multimodal transfers might beinferred

• Estimatingthecostsofmultimodalshifts

• Performingthefailureanalysis,aspreviouslyoutlinedinthecriticalityanalysis,withthe

change,nowthattheentiremultimodaltransportnetwork-of-networksisconsideredareroutingoption

• Estimatingthemultimodalmetrics

Themultimodalanalysispresentedhereconsideredonlythefailuresscenariosforthenational-scale

roadandtherailwaynetworks.

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IncludingMultimodalLinksandCosts

Thestudycreatedthephysicaltopologyofmultimodalnetworkforthenational-scaleanalysis,using

thenational-scaleroad,railway,inlandwaterway,andmaritimenetworkmodels.Thestudyassumedthefollowingwhilecreatingthemultimodalnetworklinks:

• Tocreatetheport-roadandport-rail links,all identifiedinlandandmaritimeportswereselectedasmultimodalpointsandjoinedtotheirnearestroadandrailnodes.

• To create the road-rail multimodal links, all rail stations were selected as multimodalpointsandjoinedtothenearestroadnodes.

• Tocreateamorerealisticspatialmapping,onlythosemultimodal linkswithlengths<3kilometerswereselected.

The multimodal network links represent a notional connectivity between modes, and at best

representthenearestpossibleaccessibilitypointfromonetransportmodetoanother.Asampleofthenetwork,zoomedinataselectedlocation,isshowninfigure5.1.

Figure5.1.MultimodalLinksCreatedintheNetworkModel

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ResultsofPartialMultimodalShiftsinRoadFailureAnalysis

The968casesof individual road failures identified in theroadcriticalityanalysiswereagain tested

with the multimodal networks. For each failure scenario, the analysis estimated the AADFdisruptions,themacroeconomiclosses,thereroutinglosses,andtheoveralleconomicimpacts.Theresultsshowedquiteevidentlythatbecauseofmultimodality,themaineffectwasseenintermsof

the changing redistribution costs, as most cases of single-point failure were eliminated. Hence,understandingtheneteconomicimpacts,duetochangingreroutingcosts,representsthemainresulthere.

Toassumeacompleteshiftofroadfreighttowardothermodesviamultimodallinkagesisunrealistic,because networks such as railways have no capacity to accommodate the large volumes of roadtonnages.Amore feasibleandrealisticanalysiswouldbe tounderstandcases thatexploreoptions

forreroutingasmallpercentageofthedisruptedflowsfromtheroadtomultimodalnetworks,whilererouting the remaining freight to undamaged roads. In discussing this option here, the studyassumesthatforeachroadfailurescenario,90percentoffreightisreroutedalongtheroadnetwork,

with the remaining 10 percent rerouted to the least-costmultimodal option—which could be theroadnetwork.

The result in figure 5.2 shows that even a 10 percent modal shift can help reduce the economic

impacts of road failure. For example, key sections of roads showgains, attributed tomodal shifts,ranging from US$0.18 to US$0.47 million per day. Also, across the minimum and maximum flowscenarios,thehighestlossesalongthehighestflowroutesgetreducedtoUS$0.53toUS$1.43million

perday, incomparisontonomultimodal reroutingoption lossvaluesofUS$0.67toUS$1.9millionper day. Notably, this 10 percent shift from road to other modes reduces economic losses byapproximately20to25percent.

Page|120

Figure5.2.TotalEconomicImpactsofNational-ScaleRoadLinksFailuresWhenConsidering90PercentFlowReroutingonRoadsand10PercentReroutingalongOtherNetworksviaMultimodalLinks

A.Minimumeconomicimpact B.Maximumeconomicimpact

Thechapternextpresentsthecausaleffectsoftheeconomicgainsandlossesduetomultimodalityindicate where the systemic modal shifts take place. Figure 5.3 shows the combined effects ofredistributionof10percentof flows in termsofAADF tonnagesshifts fromnational-scale roads to

othermodes—railways,inlandwaterways,andmaritime.Ineachfigure,panelAshowsresultsfortherailwaysandpanelBshowsthecombinedinlandwaterwaysandmaritime.Theresultsrevealthatapredominantnumberofdisruptedroadfailuresprefertobe,reroutedviarailwaysbecauseitisoften

the cheapest and closest option to high-flow road links. The original VITRANSS study estimatedrailwaytransportcostsinVietnamtobelowerthancostsforroadtransport(JICAetal2000),andthisfinding is reflected in this study. Consequently, if transportation assignments were based only on

transportation costs, railwayswould be preferred over roads. For theminimum tomaximum flow

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scenarios,therailwayswitnessanaddedmaximumofapproximately8,500to10,600tonsperdayinsystemic flowrerouting.Thiscan leadtoanalmostdoublingofrail tonnagevolumes,basedonthe

estimated tonnages in this study.According toexistinganalysis (Systra2018),mostof the railwayscurrentlyoperateundercapacity,forexample:

• AlongtheHanoi–HoChiMinhline,freightusesonly35to41percentofnetworkcapacity,

with an estimated leftover capacity between0 and 29 percent,with 0 percent aroundSaigon.

• Alongtheremaininglinesinthenorth(radiatingfromHanoi),freightusesonly11to30percent of network capacity, with an estimated leftover capacity between 48 and 85percent.

Basedontheaboveitwouldbequitefeasibletoredistributeroadfreighttowardrailways,especially

along the northern routes.Moreover, the figure 5.2 result indicates gains in transferring even 10

percentofdisruptedfreightsfromroadtorailalongtheHanoitoLaoCairoute.

Betweenrailwaysandwaterways,thepreferenceforrailwaysoutweighsthatofwaterways,exceptinasmallareainthenorthandapredominantpreferenceinthesouthernareasthathavenorailway

networks.Giventhehighvolumestransportedonsouthernwaterways,thetonnagesshownherecanbeaccommodatedbytheirpreferredmodeswithoutexceedingcapacities.

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Figure5.3.Redistributionof10PercentofMaximumRoadTonnageFlowsasAddedTonnagesontoRailandWaterwayNetworksfollowingRoadNetworkLinkDisruptions

A.Maximumtotaltonnageofroadtransfers

(Railways)

B.Maximumtotaltonnageofroadtransfers

(WaterwaysandMaritime)

RailFailureAnalysisResults

The164casesof individualrailfailuresidentifiedintherailcriticalityanalysiswerealsotested,andforeachfailurescenario,thestudyestimatedtheAADFdisruptions,themacroeconomic losses,thererouting losses, and the overall economic impacts. Again, multimodality quite evidently exerted

influenceintermsofthechangingredistributioncosts,asmostlycasesofsingle-pointsfailurewereeliminated.Themodeldidnotfindanysubstantialcasesofmacroeconomiclosses.Accordingly,theanalysisdemonstratestheneteconomicimpacts,mainlyduetochangingreroutingcosts.

Figure 5.4 shows the economic impacts of individual railway link failures corresponding to theminimum and maximum tonnages flows and generalized costs scenarios, where the economicimpacts fora linkarerepresentedonthatspecific link.Thebiggestchangehere—incomparisonto

theresultinfigure3.2—isthattheeliminationofallsinglefailurescenariossignificantlyreducestheeconomiclosses.Also,largesectionsoftherailwaynetworkclosertothesouthernendofHanoi–HoChi Minh line demonstrate several instances where multimodal options result in economic gains,

shownasnegativevaluesand in thecolorblueon themaps. Inparticular, theeconomicgainscan

Page|123

reach as high as US$0.02 to US$0.06 million per day, based on the minimum to maximum flowscenarios. In addition, the highest economic losses fall to US$0.05 to US$0.10 million per day,

contrastingwiththefigure3.2result,wheretheselinksshowedeconomiclossesrangingfromUS$2.3toUS$2.6millionperday for thesame failure scenarios.However, inagreementwith the result infigure5.2,asacheaperoptionthanroads,themultimodalshiftalongtheHanoi–LaoCairailwayline

showsaloss.

Figure5.4.TotalEconomicImpactsofRailLinkFailureswhenConsideringReroutingOptionsalongNational-ScaleRoads,InlandWaterwaysandMaritimeNetworksviaMultimodalLinks

A.Minimumeconomicimpact B.Maximumeconomicimpact

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Figure5.5showsthecombinedeffectsofflowredistributionintermsofAADFtonnagesshiftsfromrailwaysand roads toothermodes—national-scale roads, inlandwaterways,andmaritime. Ineach

figure,panelAshowstheresultsforthenational-scaleroadsandpanelBshowsthecombinedinlandwaterwaysandmaritime.Theresultsdemonstratethatapredominantnumberofdisruptedrailwayfailureswouldprefertobereroutedvianational-scaleroadsastheclosestoptionaroundhigh-flow

railway links.Thenational-scaleroadswitnessanaddedmaximumofapproximately8,300to9,900tonsperdayinsystemicflowreroutingfortheminimumtomaximumflowscenarios,largelyduetotransfers along the Hanoi–Lao Cai route. The waterway networks witness an added maximum of

approximately3,200to4,300tonsperdayinsystemicflowreroutingfortheminimumtomaximumflowscenarios,mainlyalongasmallnorthernsection.

Since railway flows aremuch lower when compared to the roads, these transfer volumes can beaccommodatedbytheothernetworks.Eventhoughthemodalshiftsfromrailwaystoothermodesreduce losses, the frequent traffic congestion along alternative road networks could cause costly

delays. The road network failure analysis presents a strong case for the benefits of investing in areliablerailwayandstrongermultimodaloptionsratherthanacontinuedrelianceonroadsalone.

Figure5.5.RedistributionofMaximumTonnageFlowsasAddedTonnagesontoNational-ScaleRoadsandWaterway(InlandandMaritime)NetworksfollowingRailNetworkLinkDisruptions

A.Maximumtotaltonnage(National-ScaleRoads)

B.Maximumtotaltonnage(InlandWaterwaysandMaritime)

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ReferencesJICA(JapanInternationalCooperationAgency),MoT(MinistryofTransport,SocialistRepublicof

Vietnam),TDSI(TransportDevelopmentandStrategyInstitute,Vietnam).2000.TheStudyontheNationalTransportDevelopmentStrategyIntheSocialistRepublicOfVietnam(VITRANSS)—TechnicalReportNo.3:TransportCostAndPricingInVietnam.Tokyo,Japan:JICA,ALMEC

Corporation,andPacificConsultantsInternational.http://open_jicareport.jica.go.jp/pdf/11596814.pdf.

Systra. 2018 (unpublished). Strengthening of the Railway Sector in Vietnam. Report 3.2: RailwayDemandAnalysis—Freight.ReportprovidedbySystraforthestudy.

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Chapter6:ConclusionsandPolicyRecommendations

Aspreviouslystated,thisreportaimstocreateamethodologicalandcomputational frameworkfortransportriskanalysisinVietnam.Throughthesuccessfuldemonstrationofthisframework,thestudy

deliveredanovelsystem-of-systemstoolforanalyzingandprioritizingtransportresiliencebasedonspatial criticalities, risks, and adaptation benefits. Hence, for Vietnam stakeholders, the report’sprimary recommendation is to recognize the investment value in a detailed spatial risk analysis

approachandbuildingontheexistingandnewknowledgecompiledandcreatedthroughthisproject.

Based on the analysis results and insights of this project, several other recommendations forstakeholdersemergefromthisstudy,asdiscussedbelow:

IncreasingVulnerabilitiesDuetoClimateChange

Recommendation:Vietnam’stransportsectorsneedtobepreparedfortheincreasingintensityandfrequencyofextremehazardsduetoclimatechange.

The analysis demonstrates that for all transport sectors in Vietnam, extreme hazard exposures tosimilar hazards over the same spatial scales increase under climate change scenarios. The overall

maximumkilometersofnational-scaleroads,railways,andprovince-scaleroadnetworksexposedtoextremehazardlevelsincreaseunderallclimatescenarios.Allmajorinland,coastal,andairportsarevulnerabletoriverfloodingwithincreasingfrequency,whenextremelevelsoffloodingseenin1,000-

yeareventscouldstartoccurringinfive-yearevents.

PrioritizingTransportNetworkResilienceforSystemicEconomicBenefit

Recommendation: The systemic understanding of locations whose failures create increasing

economic risks presents a strong economic case for investing in building climate resilience ofVietnam’stransportnetworks.

Themodelandanalysisresultsmostusefullyhighlightsystemiccriticalitiesandhazard-specific,high-risk network locations, which can be prioritized for further detailed investigations into climateresilience.Themodelresultsshowlocationsofroadnetworkswhosefailurescanresultinveryhigh

daily losses of up to US$1.9million per day, while railway failures can result in losses as high asUS$2.6millionperday.Thewiderimplicationsofmacroeconomiclossesduetotransportdisruptionsarequitecrucialwhenfactoringsuchlosses.Generally,transportanalysisignoressucheffects,which

resultinunderestimatingtheimpactsoftransportdisruptions.

ComparisonsofcurrentandfuturehazardrisksprominentlyhighlightthestrongcaseinVietnamtoinvest in building climate-resilient national-scale roads and railways. In addition, the comparisons

stronglyindicatethataccesstoeconomicopportunitiesinseveralareasofprovinceswillbeseverelyaffected without investment in building climate-resilient roads. The expected annual economiclosses—a measure of risks due to transport failures from future climate change-driven river

flooding—allsignificantlyincreasebyatleast100percentacrossnational-scaleroads,railways,andprovince-scaleroadnetworks.Allnetworklinkswithhighimpactsshowtheseincreases.

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MakingtheCaseforInvestmentinAdaptationtoBuildTransportNetworkResilience

Recommendation:Vietnam’s roadnetworks require investments tooverhaulexisting roadassets

to higher climate-resilient design standards. Though such investments could be costly, theirbenefits outweigh the costs for priority network assets, making adaptation investments viableoptionsforroads.

The analysis has demonstrated that the national-scale and province-scale road networks all needadaptation planning to protect against future climate-related hazards. In the Vietnam specific

context,therelevantadaptationinterventionsforroadsconsideredinthisstudyinclude:

• Pavementstrengthening;

• Improvingpavementdrainage;

• Earthworkprotection;

• Slopeprotection;and

• Improved cross drainage (culverts). To build a ‘climate proof’ road, a climate-resilient

roaddesignissuggestedinthisstudy,whichincludescombinationsofalloptions.

Prioritization of investments in transport network resilience should be based on evaluation of thecostsofinvestmentsinthetransportnetworkandthebenefitsyieldedbytheseinvestmentsthroughavoideddisruptions.Analysisofcostsandbenefitsof interventions inthetransportnetworkshould

berisk-based,i.e.,shouldconsiderboththeprobabilityandtheconsequencesoftransportnetworkfailure. This study has provided evidence of all these steps, providing essential evidence forprioritizinginterventions.

TheanalysisshowsthattheBenefit-CostRatios (BCR)ofadaptation investments intonational-scaleroads are mostly greater than one. The analysis suggests that, for some national-scale roads,

upgradingtoclimate-resilientdesignscouldcostuptoUS$3.4millionperkilometer,comparedtotheUS$1.0million per kilometer costs of building roads to existing standards. But the high economicbenefits of such investments justify the high costs. Similarly, for province-scale road networks a

relatively small—but significant—number of assets have BCRs > 1.Many of these are bridges andlocal roads,crucial foraccessing locationsofeconomicactivities.For roadnetworks, the increasingBCRsunderfutureclimate-hazardsscenariosstrengthenthecaseforinvestinginclimateresilienceto

protectagainfutureclimaterisks.

However, in some cases, the selected and analyzed adaptation optionsmight not be the best for

climatechangeadaptation, suggestinganeed to investigateseveralothersuitable technologies forincreasingtheresistanceoftransportinfrastructurestohazards.Tosomeextent,optionsarelocationspecific.Forexample,twootheradaptationoptionsworthconsideringcouldincludethefollowing:

1. Scourprotectionofbridgefoundationsandabutments,and

2. Forecasting and warning systems, which help to avoid vehicle accidents and train-

derailingincidentsthatexacerbatedisruptions.

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Giventhatthisreporthasdemonstratedhowadaptationoptionscanbeconsideredandhasshownindetailhowtoevaluatetheirbenefits,asimilarprocesscanbefollowedforothertypesofoptionsnot

assessedinthisstudy.Forexample,beyonddirectinvestmentinsuggestandnewclimateresiliencetechnologies,adoptionofappropriateoperationregimesiscentraltoensuringtheireffectivenessinthe long-term.Thecostsofoperationsshouldbe incorporatedwithinthe lifetimecostoftheasset,

ensuringrobustcost-benefitadaptationdecisionappraisal.

StrongCaseforImprovingMultimodalTransportLinkagesandEnhancingEfficiencyacrossModes

Recommendation: Vietnam’s transport networks need to function as integrated multimodal

systems,whichcanbeachievedbyimprovingexistingmultimodallinkagesandcreatingnewones.

Thisstudyhasshownthattheeconomicimpactsofdisruptionscanbereducedbybuildingadditional

transport multimodal connectivity. Often known as increasing “redundancy” in the network, thisphrase might be interpreted as being wasteful and inefficient, when in fact, providing extra

connectivityandcapacitywillassisttheeverydayflowofgoodsandpeopleaswellasprovidingnewreroutingopportunitieswhenmajordisruptionsoccur.

Thisstudyshowsaveryclearbenefitof redistributing flows fromtheroadnetworkto therailways

andinlandandmaritimenetworks.Evena10percentshiftofroadfreights,especiallytorailways,canreduceeconomic impactsby20to25percent. Improvements inenhancingrailwaysandwaterwaysefficiencycanreducerisksforalreadycongestedroadsandtakeadvantageofunusedcapacityinthe

railways to accommodate the additional modal shift from roads. The availability of multimodaloptions would significantly reduce potential losses on the railways, and such options should beconsideredinthefuture.

ImprovingExistingDataGapsforFurtherStudies

Recommendation: Under this study, Vietnam’s transport resilience planning benefits from asystem-of-systems transport risk analysis. The study recognizes the extreme importance ofincreasing capacity through cross-sector, cross-organization collaborations,whichwill enable the

pursuitoffurtherstudiesandcontinuedeffortstoimprovetheunderlyingdatasets.

Ashighlightedthroughoutthereport,thestudyhascopedwithseverelylimiteddata.Someofthese

limitationsexistinassemblingandstandardizingthefollowing:

• Topologicalrepresentationsofinfrastructurenetworkswithproperattributes

• Transportflowsthatrepresentthelatestconditionsandtrends

• Transportationcostassignmentsreflectiveofexistingconditions

• Economicflowsdatashowingcurrentstructureofthecountryandregionaleconomies

• Probabilistic hazards at similar spatial resolutions and with Vietnam-specific climatescenarios

• Seasonalityinformationofextremehazardslevelsandtransportnetworkflows

• Informationondisruptiondurationsandresponsebehaviors

• CostsofvariousadaptationoptionsmostrelevanttoVietnam

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Thestudyrecommendsthateffortisinvestedinimprovingthesedatagapsinordertoimprovefuturestudiesandmorerobustlymaketheeconomiccaseforinvestmentinthetransportnetwork.

For increasing capacity to standardize and share data, the study notes the extreme importance orcreatingacross-sectorandcross-organizationcollaboration,whichinvolvesMoT,DRVN,PDoT,CAAV,VINAMARINE,VIWA,TDSI,MARD,MoNRE,GeneralStatisticsOffice,amongotherdepartments.

Thestudyteamhassetouttheprocessofimprovinggapsbyprovidingthestudymodelasanopen-source resource (https://github.com/oi-analytics/vietnam-transport), and creating auserdocumentthat shows the standardized data requirements (https://vietnam-transport-risk-

analysis.readthedocs.io/en/latest/).GainingthemostbenefitfromtheseinnovativenewcapabilitieswillrequiretrainedpersonnelinVietnam.

Themodeldeveloped in thisstudycanbereadilyadaptedtoaccommodate further improvements.

Thestudyhasonlyaddressedsomeoftheeconomicandsocialfunctionsoftransportinfrastructure.Notably,thestudyhasnotexploredtheroleoftransportinfrastructureinenablingpassengertravelfor work or other purposes, or its role in labormarket participation. These should be included in

future studies, alongside other wider economic and social benefits of transport infrastructure. Intheserecommendationsthestudyhasalreadyidentifiedthattheinvestmentprioritizationneededtoimprove resilience of the transport network would require consideration of the costs and

effectivenessofalternativeinterventions,andrecommendthesestudiesserveasthenextstepafterthisstudyoftransportnetworkvulnerabilityandrisks.

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AppendixA:MethodologyforTransportRiskandAdaptationAssessment

MethodologicalFrameworkandImplementationStructure

FigureA.1providesagraphicaloverviewof thesystem-of-systemsmethodologicalapproach,which

consists of the following components explained below. The report chapters present details on thedataandmodelsforthefollowingcomponents:

A. Hazardassembly:(A-1)Assemblingcurrentandfuturehazarddatasetswiththespatialextent,

magnitudes, and return periods (or probabilities), and extracting those hazardswith values abovecertainthresholds.

B. Multimodal transport networks assembly: The aim is to assemble the multimodal transport

system-of-systems. The steps toward creating this system-of-systems include (B-1) CollectingGeospatial InformationSystems(GIS)dataandcreatingconnectednetworkmodelsfromsuchdata;(B-2)Identifyinglocationsonthenetworksandassigningthemattributes(e.g.,roadconditions,type

ofport,railstation,etc.);(B-3)Identifyingkeynetworknodeswherefreighttransportstarts(origins)andends(destination);(B-4)Collectingfreightdataandintegratingitwiththenetworklocations;(B-5)Assemblinginformationonmodalsplitoptionsandgeneralizedcostbasedperformancemeasures

forthemultimodalnetworks;(B-6)AssigningODflowsonthenetworksbasedonaleastgeneralizedcostcriteriatocreateaverageannualdailyfreight(AADF)estimates;and(B-7)Attheprovincialscalesassigningareasofpopulationconcentrationstolocationsofinterestviatheroadnetwork.

C. Failure and flow disruption analysis: Following from A and B, the flow disruption analysisinvolves:(C-1)Intersectingthehazardswiththenetworkstoinitiatenetworkedgefailureconditions;(C-2)Findingallexistingdisruptedorigin-destination(OD)routes;(C-3)Findingreroutingoptionsand

redirecting flows toward alternative routes; (C-4) Estimating flow disruptions in terms of freighttonnage lost or passenger counts lost when there are no rerouting options; and (C-5) Estimatingchanges in performance measures such as generalized cost changes for freight transport or for

changingaccessfortoimportantlocationinprovinces.

D. Macroeconomiclossanalysis:FollowingfromC,macroeconomiclossinvolves(D-1)Assembling

the regional input-output (IO) datasets to map the trading relationship between provinces; (D-2)ConvertingthefreighttonnagelossestoeconomicflowlossesasUS$perdaydirecteconomicsupplylosses anddirect economicdemand losses; and (D-3) Estimating the indirect economic lossesover

themulti-regionalsystem.

E. Adaptation options analysis: Estimation of Net Present Values (NPVs) of adaptation, whichfollowsD,involves(E-1)Selectingalistofinvestmentstrategiesforadaptation;(E-2)Assemblingdata

on the costs of damages of assets, the one-time costs and periodmaintenance costs of differentadaptation options; (E-3) Assembling estimates of the discount rates and timelines of adaptationplanning;and(E-4)CalculatingtheNPVs(BCRs)givenbyEquations(3)through(6).

F. Creation of future scenarios: To understand future transport failures and losses, the stepsinclude (F-1) Assembling statistics on future OD growth scenarios based on projected national orregional Gross Domestic Product (GDP) growth; (F-2) Incorporating structural changes to the

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networks(ifpossible)intermsofchangingconditionsoflinks(e.g.,increaseinpavedroads,upgradedrail lines); (F-3) Assembling statistics and estimating the changes in performance measures that

determine generalized cost functions in the future; (F-4) Creating modal options for new flowassignments.Tounderstand the systemicnatureofdisruptionsand losses, theanalysis is repeatedseveraltimesfordifferenthazardevents.Itcanalsobeupdatedforcurrentandfutureinfrastructure

network configurations and climate change driven hazard events to perform several vulnerability,risk,andadaptationassessments.

The above methodological framework is consistent with those previously applied to The United

Republic of Tanzania (Pant et al 2018b) and United Kingdom (Thacker et al 2017a), to informinfrastructure vulnerability and extreme hazard risk assessment at regional (Pant et al 2018b) andnational(Thackeretal2017b)scales.

FigureA.1.GraphicalRepresentationofInfrastructureSystem-of-SystemsRiskAssessmentFramework

Keydefinitions

The framework presents different types of system-of-systems assessments useful for decision-making:

• Criticality assessment: Criticality here is defined as a measure of the transport link’simportanceanddisruptive impacton the restof the transport infrastructure (Pantetal

2015). Criticality assessment results in ranking network assets based on their relativeimpactsontheserviceabilityofthetransportnetworks(ArgaJafino2017).

• Vulnerability assessment: Vulnerability is defined as the measure of the negativeconsequences due to failures of transport links from external shock events (Pant et al2016).Vulnerabilityassessment isdone in thecontextofnaturalhazardsandresults inunderstandingtherelativeimpactsofhazardsonthecontinuedtransportavailability.

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• Risk assessment:Risk is definedas the product of the probability of a hazard and theconsequences of transport link failures. Risk assessment results in understanding the

comparative impactsondifferenthazardfrequenciesandassigningcompositescorestomostdisruptedtransportlinks.

• Adaptationplanning:Plannedadaptationreferstomeasurestakentoreducerisks.Inthecontextofclimatechangetheplannedadaptationseekstocapitalizeontheopportunities

associatedwithclimatechange(Füssel2007).Fortransportsystems,adaptationplanningseeks to identify the assets and locations that could be prioritized for targetedinvestmentstoprovidemaximumbenefitsinreducingrisks.

Alltheaboveassessmentsaredonewiththeaimofunderstandingtheimpactsoftransportnetwork

service flow disruptions due to natural hazards. Network service flow is defined as the generalmeasureof volumesofmobilitybetweennetwork locationsovera suitably chosen time frame. The

locationsbetweenwhichserviceflowsaremeasuredarecalledorigin-destination(OD)pairs,withtheflowbeingdirectedfromanorigintowardadestination.Inthisstudynetworkflowsaremodeledandmeasuredintermsof:

• Averageannualdaily freight (AADF)volumes:Theestimated tonnageof freightmovingon an average day between different network locations. These measures are derivedfromamodel.

• Average annual vehicle traffic (AADT) counts: The estimated numbers of commercialvehiclesonanaveragedaybetweendifferent roadnetwork locations. Thesemeasuresarederivedfromobserveddata.1

• Net revenue measures: The estimated daily net revenue of goods and services withaccesstoan important locationviaatransportnetwork.Thesemeasuresareestimatedforunderstandinglocalroadaccesstoimportantlocationsattheprovincescale.

Theestimatesfornetworkflowsarebasedonperformancemeasuresthatquantifythecriteriaused

forassigningflowsonthenetworks.Inthisstudy,keynetworkperformancemeasuresforfreightandnetrevenueflowassignmentdependon:

• Identificationofkeylocationsandknowledgeofspatialpatternsoffreighttransport,forexample,mappingandtrackingoffreightroutesbetweenprovinces;

• Modalsplitsthatindicatehowfreighttransportmodechoicesaremade;and

• Generalizedcost,whichisacompositemeasureofthetransportcosts(inUS$),expressedasafunctionoftraveltime,transportprices,andvariabilityofservice.

Theestimationofnetworkflowsdisruptionsisdoneviafailureanalysis.Failurehereisdefinedasthe

conditionwhereanetworknodeorlinkhaslostcompletefunctionality.Thisstudydoesnotconsiderthecaseswherefailednetworknodesorlinksmightfunctionpartially.Therationaleforconsideringcomplete loss of functionality is that: a) The main goal is to understand how important the

uninterruptedfunctionalityofindividuallinksistotherestofthenetworks;andb)Theinteresthereisinsimulatingcatastrophiceventconditionswherethereareinstancesofcompletelossofnetworkfunctionalityalongaffectednodesorlinks.

Thisstudyperformedfailureanalysesbasedonspatiallyintersectinghazardswithnetworknodesandlinks,toinferhazardexposures.Ahazardsignifiesanexternalshockeventthatinitiatesfailureinthetransport networks. Inferring the asset failures criteria due to hazard exposure requires detailed

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physical process-based models of fragility curves2 of assets for given hazard magnitudes, whichgenerally is not possible. For example, the flood fragility of a road will depend upon its physical

dimensions, construction standards, material types, scour to flooding, etc. To select sets of failednodes and links, simplified failure criteria are chosen, where hazard levels above a threshold arecatastrophicenoughtocausefailures.

The characterization of failures within the transportation networks leads to characterizing thedisruptions to network flows. Disruptions refer to the change in network flows and performancemeasures due to the failures of network links. The disruption estimation consists of the following

steps:a)EachODroutethroughfailedlinksisassumedtobenolongeravailable;b)AflowreroutingoptionforthedisruptedODrouteischosenbasedonthenextbestrouteoptionthatgivestheleastgeneralizedcostestimate;c)Theredistributedflowsandperformancemeasuresarecomparedwith

thepre-disruptionvalues.

Insomeinstances,noflow-reroutingoptionsexistbecausephysicallytheODroutethroughthefailedlinksistheonlyavailableoption.Suchinstancesarecalledcompletefailurescenarios,andindividual

linkscausingsuchscenariosarecalledsinglepointsof failure.Thegeneral termapointof failure isdefinedtosignifya locationonthetransportrouteswhosefailuresignificantlythreatenstheflowofessentialservices. Inthisstudy,pointsoffailures’disruptiveimpactsaremeasuredas:a)AADFflow

losses;andb)dailylossesofnetrevenue.

Ininstanceswhereflow-reroutingoptionsareavailable,disruptiveimpactsaremeasuredintermsofthe freight redistribution costs, or the difference between the post-disruption and pre-disruption

generalizedcostestimatesofanODflow.Thestudyassumesthereroutingoptionsarefullyutilized,irrespectiveoftheincreaseintraveldistance,time,andcostofrerouting.

For thenational-scaleanalysis, thepointsof failure scenariosmay further result inmacroeconomicflowlosses,duetotheAADFflowlosses.Thesemacroeconomiclossesareestimatedthroughamulti-regionaleconomicinput-output(MRIO)datasetfordisasterlossestimations(KoksandThissen2016).

By tracking the flow typeand industry typeofeachdisruptedOD route, theeconomic losses fromAADFflowlossesresultin:

• Macroeconomic supply losses occur when the production capacities of industries arediminishedduetounavailabilityofcommoditiesrequiredforproduction.

• Macroeconomicdemandlossesoccurwhenthefinaldemandsforgoodsofthedisruptedindustriesarediminished,astheycannotreachthemarkets.

Theaboveeconomicflows lossesareconsidereddirect(output) losses thatcanbedescribedastheeconomicflowlosseswhichoccurtoindustrysectorsdirectlyaffectedbythedisruptionoftransport

flows.Intheeconomicinput-output(IO)systemtheseindustrysectorsaretradingwithseveralotherindustry sectors across regions,which togethermake up amulti-regional economic system. Thesetraderelationshipscreatebackward(supply)and/orforward(demand)linkages,whichgetdisturbed

duetodirectlosses.Thisresultsinindirect(output)lossesthatcanbedescribedasthesystem-wideeffects to other firms and industries via backward and/or forward linkages, proceeding tobecometherippleeffectoftheoriginaldirect loss(OkuyamaandSantos2014). Inthisstudy,thedirectand

indirecteconomiclossespointsoffailurescenariosareestimatedinUS$perdayvalues,toaccountfortheimpactofanAADFtonnageworthoffreightflowlosses.

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Estimationofadaptationoptions

Adaptationoptionsaredefinedasthearrayofstrategiesandmeasuresavailableandappropriatefor

addressingadaptationneeds(CSIRetal2016).Ideally,weshouldconsiderseveraltypesofadaptationmeasures, including structural, institutional, and social changes, among others. In this study,adaptationoptionsinvolvemakinginvestmentstoimprovethestructuralreliabilityoftransportlinks.

Theeffectivenessofdifferentadaptationoptions isevaluatedandcomparedthroughacost-benefitanalysis (CBA), which is a well-established technique to compare the costs of an option with itsbenefits (OECD2006).When the adaptationoptions involve investments to improve the structural

reliabilityofthetransportlinks,then:

1. ThebenefitsofadaptationarethesumoftheavoidedExpectedAnnualDamages(EAD)and

theExpectedAnnualEconomicLosses (EAEL).EADsignify thedirectdamagecosts incurreddue to physical collapse of transport links, while EAEL signify the costs incurred due totransportflowdisruptionsasdescribedintheprevioussection.

2. The costs of adaptation are the sum of the capital costs, CI, of an initial investment to

implementachosenoption,andfurthermaintenancecosts,CP,topreservethequalityoftheassetoveritslifetime.

3. Thevalueoftheadaptationoptionsisestimatedasthedifferencebetweenthebenefitsandthecosts.

4. Thebenefitsofadaptationareassumedtolastoveralongtimeperiod;thus,theactualvalueofanadaptationoptionisestimatedoveranannualtime-scalet0,…,tT,startingatthetimet0whentheoptionisimplementedandcontinuesoveritsplannedtimehorizonT.ThisleadstotheNetPresentValue(NPV)ofadaptationgivenas:

NPV=j=0j=TEADtj+EAELtj–CPtj1+rj–CIt0 (3)

WhereEADtj is the expected annual damages for year tj,EAELtj, is the expected annualeconomiclossforyeartj,CIt0,istheadaptationinvestmentmadeatthestartyeart0,CPtj,isthecostofperiodicinvestmentneeded,3risthediscountrate,andjisthecountfortheyearsoverwhichthevalueofadaptationisevaluated.

5. Themetricofbenefit-costratio(BCR)cansimilarlybeestimatedtoevaluatetheeffectivenessoftheadaptionoptions:

BCR=j=0j=TEADtj+EAELtj1+rjj=TCPtj1+rj+CIt0 (4)

ThepracticalstepsinestimatingtheNetPresentValue(NPV)ofadaptationinvolve:

• Havingasampleofprobabilistichazardevents;

• Gettingdataonthecostsofassetdamages,theone-timecostsoftheadaptationoption,andtheperiodiccostsofmaintenance;and

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• Estimating the economic losses due to transport link failures in two ways, dependingupon the scale of the analysis. In both cases the economic loss estimates are dailyestimates,whicharemultipliedbyanassumeddurationofdisruption:

a. Forthenational-scalenetworkanalysis,theeconomiclossesaregivenassumofthedailymacroeconomiclossesandthetotalcostsofrerouting.

b. Fortheprovince-scalenetworkanalysis,theeconomiclossesaregivenassumofthelossofdailynetrevenueandthetotalcostsofrerouting.

TheprocessofestimatingtheNPV(orBCR)ofanadaptationoption isundertakenforall the failed

transportlinks,followingwhichallNPV(andBCR)estimatescanberanked.ThelinkswiththehighestBCR values should be prioritized for investments, as they provide the highest benefits towardmaintainingthenetworkreliability.

A similar process can be repeated for a set of adaptation options, as there are potentially severaldifferentadaptationoptionsworthconsidering.Hence,theNPVscouldbecomparedacrossallassetsand all options to select the ones with high benefits for the system. When considering climate

change, theeffectivenessof thesamesetofadaptationoptionscanbe furthercomparedbetweencurrenthazardscenariosandfutureclimatechange-drivenscenarios.

Uncertaintyanalysis

Previous sections outline several sources of uncertainty throughout the whole process of the

criticality,vulnerability,riskandadaptationassessments.Someofthese,amongothers,include:

• Uncertainties associated with the estimation of the projected natural hazards under

differentclimatechangescenarios;

• Aleatory uncertainties (Bae et al 2004) in the networkmodel structures due to lack of

propertopologicalnetworkdata;

• Epistemic uncertainties4 in the transport flow estimation models, due to imprecise

informationonthenetworkspeeds,costs,andtonnages;and

• Uncertainties in the estimation of the costs of asset damages, costs of the adaptation

optionandtheperiodiccostsofmaintenance.

Toovercomethechallengesofaccountingforthedifferenttypesofuncertainties,thisstudyadoptsarobustdecision-makingapproachbysimulatinghundredstothousandsofdifferentsetsoftransport

link failures under different current and future hazards to describe how adaptation optionsmightperformundermanyplausiblefailurescenarios(Lempertetal2013).Ateverystepthesensitivityofthe model outputs to input parameters are analyzed to understand which parameters might

influence the estimates the most. This approach aligns with the Decision Making Under Deep-Uncertainty(DMDU)methods,whicharegainingpopularityamongdecision-makers(Hallegatteetal2012;Espinetetal2018).

TherobustestimatesoftheNPVs(orBCRs)ofdifferentadaptationoptionsfordifferenttransportlinkfailuresarecreatedbycalculatingtheminimumandmaximumNPVforeachtypeofoptionforeachtypeoffailurescenario.

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Toarriveattheseestimates,thestudyassumesthat:

• For every adaptation option, the costs of adaptation and maintenance are given orestimatedoverarange;

• Thedamagecostsofassetsaregivenorestimatedoverarange;and

• The economic losses are estimated by accounting for the range of possible tonnages,speeds,andcostsalongthenetworks.

The study concludes that transport links for which adaptation options give both NPVmin>0 (or

BCRmin>1𝐵𝐵𝐶𝑅m) and𝑁𝑃𝑉max>0NPVmax>0 (orBCRmax>1𝐵𝐶𝑅max>1), should be treated asno-regretoptionsandprioritizedforinvestments.Furtherprioritizationofsuchtransportlinkscanbe

donebyrankingBCRsandselectingthosewiththehighestvaluesasthesegivethehighestbenefitintermsofimprovingtransportreliability.

Notes

1.NoAADTmeasuresareavailableforothermodesoftransport.

2.Fragilitycurvesquantifytheconditionalprobabilityoffailureofanassetforagivenlevelofhazard.

Fragility curves that are derived from statistical analysis of historical failure data that record theconditionofanasset,itslevelofhazardexposure.Theycanalsobecreatedbysimulatingthephysicalproperties of a system based on its design standards and stress testing it under different hazard

levels.

3.Maintenancecouldbeconsideredtobepartofthebenefitsaswell,ifwewereconsideringpartial

failures. However, used here, maintenance represents a cost option undertaken to prevent thecatastrophicfailureofanasset.

4.Seenoteno.3.

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EconomicSystemsResearch26(1):1–12.doi:10.1080/09535314.2013.871505.

Pant,Raghav,JimW.Hall,Simon.P.Blainey,andJohnM.Preston.2015.“AssessingSinglePointCriticalityofMulti-ModalTransportNetworksattheNational-Scale.”Paperpresentedatthe25thEuropeanSafetyandReliabilityConference,Zurich,Switzerland,September2015.

https://eprints.soton.ac.uk/385456.

Pant,Raghav,JimW.Hall,andSimonP.Blainey.2016.“VulnerabilityAssessmentFrameworkfor

InterdependentCriticalInfrastructures:CaseStudyforGreatBritain’sRailNetwork.”EuropeanJournalofTransportandInfrastructureResearch16(1):174-194.https://eprints.soton.ac.uk/385442/.

Pant,Raghav,ElcoE.Koks,TomRussell,andJimW.Hall.2018a.TransportRisksAnalysisforTheUnitedRepublicofTanzania:SystemicVulnerabilityAssessmentofMulti-ModalTransport

Networks.FinalReportDraft.OxfordInfrastructureAnalyticsLtd.,Oxford,UK.doi:10.13140/RG.2.2.25497.26722

Pant,Raghav,ScottThacker,JimW.Hall,DavidAlderson,andStuartBarr.2018b.“CriticalInfrastructureImpactAssessmentDuetoFloodExposure.”JournalofFloodRiskManagement11(1):22–33doi:10.1111/jfr3.12288.

OECD(OrganisationforEconomicCo-operationandDevelopment).2006.Cost-BenefitAnalysisandtheEnvironment:RecentDevelopments.Paris:OECD.doi:10.1787/9789264010055-en

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Thacker,Scott,RaghavPant,andJimW.Hall.2017a.“System-of-SystemsFormulationandDisruptionAnalysisforMulti-ScaleCriticalNationalInfrastructures.”ReliabilityEngineeringandSystems

Safety167:30–41.doi:10.1016/j.ress.2017.04.023.

Thacker,Scott,StuartBarr,RaghavPant,JimW.Hall,andDavidAlderson.2017b.“Geographic

HotspotsofCriticalNationalInfrastructure.”RiskAnalysis37(12):2490–2505,doi:10.1111/risa.12840.

Page|141

AppendixB:OverviewofDatasets

Each transportmode in this study first represented as anetwork,which is the collection of nodes

joinedtogetherbyacollectionof links.Thephysicalnetworktopologydefinedasthestructurethatencodesthephysicalplacementofnodesandtheirconnectinglinksdescribesthenetworkstructure.Theexistenceofphysicallinkswithinformationabouttheirconnectingnodelocationsisanecessary

andessentialconditionforthecreationofthetransportnetworkmodels,becauseofthegeospatialnature of the transport systems. In the absence of such information the underlying infrastructuredatahastobeprocessedfurtherbyeitheraddingnewlinkgeometriestoconnectnodesorbyfilling

gapsinmissingphysicallinkgeometriestoconnectnodesandtherebycompletethephysicalnetworktopology.

Allnetworkmodelswerecreatedfromgeospatialdatathatneededpost-processingtofillgapsinthe

underlying rawdatasets (table B.1).Depending upon the quality of the received rawdatasets, thenetwork models represent the physical properties such as the lengths, conditions, and widths oftransportnetworkassetsasapproximationsoftheequivalentreal-worldsystems.

TableB.1.RawDatasetsCollectedfromDifferentDataSourcesfortheTransportRisksAnalysisModelinginVietnam

Dataset Datascope Source Year

Administrativeboundaryandstatistics

Commune,districtandprovincelevels

GSO,WHO 2016

Economicinput-outputdata Nationalscale GSO 2012

Nationalroads TDSI 2018

Provinceroads Vietbando 2018

Rail VITRANSSIIandMoT 2009–2016

Inlandwaterway VIWA 2018

Maritime Ports–VINAMARINERoutes–WHO

2018

Airports

National,province(roadsonly)

Airports–CAARoutes–OIAmodeled

2018

Inter-provinceODs VITRANSSII 2009ODflows

Cropproductionlocations IFPRI 2016

TransportCosts

NationalVITRANSSIIreports

(projectedto2016values)2009–2016

Adaptationcosts Roads JasperCook,VRAMS,PMU6 2018

Page|142

Naturalhazardsdataset

TableB.2provides the listofallassemblednaturalhazarddatasetsused in this study.From left to

right,thetablegivesthe:

• Namesofthehazardtypes;

• Shorthandmodelnames,eithergiveninthedatasourceorcreatedforthestudy;

• Assumedyearofhazardrepresentedbythemodel;

• Climatescenarios;

• Hazardprobabilities(ifany);

• Selectedbandedvaluesforbandedhazarddatasetsforsamplinghazardextremes;

• Selectedflood-depththresholdforallflooddatasetsforsamplingextremefloodingscenarios;

• Spatialresolutionintermsofthegridsizeunitareasinthemodel;

• Provincescoveredbythehazardmaps;and

• Sourceofthedatasets.

Ideally,foranaturalhazardriskassessmentthehazarddatasetsshouldcontainsatleastthefollowing

information for each type of natural hazard: a) spatial extent; b) magnitude of severity; and c)probability.Unfortunately,onlythefluvialfloodmapsprovidedforthisstudycontainedallthreeofthe above attributes. The typhoon-induced flood maps contained only the spatial extent and

magnitudes (flood depths) of flooding, and no probabilities. In the flashflood and landslidesusceptibility maps, likelihood banded-scores (representing high or very high susceptibility) overareaswereusedasproxiesforserveflashfloodandlandslideeventsrespectively,whiletherewasno

probabilisticinformation.

Some of the natural hazards further maps were also provided with future climate change model

outputs based on the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios(Meinshausenetal2011).RCP4.5scenariosassumethatglobalemissionspeakaround2040andthendecline, while RCP8.5 scenarios assume no decline in global emissions through the 21st century.

Flashfloodand landslidesusceptibilitymaps foreightnorthernprovincesgenerated forRCP4.5andRCP8.5 scenarios in 2025 and 2050were available for the study. Fluvial floodmaps for thewholecountry were also provided for current flooding and future flooding under RCP4.5 and RCP8.5

scenariosin2030,fromtheGLOFRIS(Winsemiusetal2013)model.Inaddition,thestudylookedatabaselinemodelpresentingcurrentfloodingconditions,calledEUWATCH.Also,thestudyconsideredflooding outcomes under different climate change scenarios, using bias-corrected future

meteorologicaldata foranensembleof fiveGlobalClimateModels (GCMs) in theCMIP5project–GFDL-ESM2M,1HadGEM2-ES,2IPSL-CM5A-LR,3MIROC-ESM-CHEM,4andNorESM1-M.5Allthecurrentand future floodmapswere derived by downscaling global distributed hydrologicalmodelswith a

resolutionof0.5°×0.5° (approximately50m×50kmattheequator)to1/120°×1/120°(900m×900maroundtheequator) togivecountryscalemaps.Using thesemodels, the future floodmapsshowed average maximum flood depths from 2010 to 2049, which in this study are assumed to

represent flood scenarios for the year 2030. For eachmodel output, nine different flood intensitymapswereproducedtorepresentfluvialfloodsthatcouldoccurevery:2,5,10,25,50,100,250,500,and1,000years.

Page|143

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Page|144

Notes

1.FromNOAAinUS:https://www.gfdl.noaa.gov/earth-system-model/.

2.FromUKMetOffice:https://portal.enes.org/models/earthsystem-models/metoffice-hadley-centre/hadgem2-es.

3.FromIPSLinFrance:http://icmc.ipsl.fr/index.php/icmc-projects/icmc-international-

projects/international-project-cmip5.

4.FromJapan:Watanabe,Mashahiro,TatsuoSuzuki,RyoutaO’ishi,YoshikiKomuro,ShingoWatanabe,SeitaEmori,ToshihikoTakemura,MinoruChikira,TomooOgura,MihoSekiguchi,Kumiko

Takata,DaiYamazaki,TokutaYokohata,ToruNozawa,HiroyasuHasumi,HiroakiTatebe,andMasahideKimoto.2010.“ImprovedClimateSimulationbyMIROC5:MeanStates,Variability,andClimateSensitivity.”JournalofClimate23:6312–35.doi:10.1175/2010JCLI3679.1.

5.FromNorway:Bentsen,M.,I.Bethke,J.B.Debernard,T.Iversen,A.Kirkevåg,Ø.Seland,H.Drange,C.Roelandt,I.A.Seierstad,C.Hoose,andJ.E.Kristjánsson.2013.“TheNorwegianEarthSystemModel,NorESM1-M—Part1:DescriptionandBasicEvaluationofthePhysicalClimate.”GeosciModel

Dev6(3):687–720.doi:10.5194/gmd-6-687-2013

References

Meinshausen,M.,S.J.Smith,K.Calvin,J.S.Daniel,M.L.T.Kainuma,J-F.Lamarque,K.Matsumoto,S.A.Montzka,S.C.B.Raper,K.Riahi,A.Thomson,G.J.M.Velders,D.P.P.vanVuuren.2011.“TheRCPGreenhouseGasConcentrationsandtheirExtensionsfrom1765to2300.Climatic

Change109(1-2):213–241.doi:10.1007/s10584-011-0156-z.

Winsemius,H.C.,L.P.H.VanBeek,B.Jongman,P.J.Ward,andA.Bouwman.2013.“AFramework

forGlobalRiverFloodRiskAssessments.”HydrologyandEarthSystemSciences17(5):1871–92.doi:10.5194/hess-17-1871-2013.

Page|145

AppendixC:VulnerabilityResultsforPorts

For the air transport, inland waterway, and maritime sectors, the report presents results of thecriticality andvulnerability assessmentperformedat themajorports scales, as theseare themostimportant assets on these networks. Here, the following section presents the summary tables for

eachsector showing thenameand locationofaport, theminimumandmaximumaverageannualdailyfreight(AADF)dailytonnageattheportthatcouldpotentiallybevulnerabletodisruptions,thetype of hazard that could cause said disruption, the climate scenario for the hazard, and the

minimumandmaximumprobabilitiesofhazardexposureoftheport.

Foreachsector,therisksofhazardexposuresduetoclimatechangewillincrease.Everytableshowsthat wherever the identified hazard has climate scenarios with probabilities, the maximum

probabilities of exposure under the climate scenario is greater than the current probability ofexposure.Forexample,inTableC.1themaximumhazardprobabilityoffloodinginHoChiMinhportincreasesto0.2(1infive-yearflooding)undertheRCP4.5andRCP8.5scenarios,incomparisontothe

0.04(1 in25-yearflooding)probabilityatpresent.ThismeansthattheHoChiMinhport isabout5timesmorepronetofrequentfloodinginthefuturethanatpresent.Similartrendsareobservedforallthemajorportsineverysector,includingmaritime(tableC.2)andinlandwaterways(tableC.3).

TableC.1.AirportswithAADFFlowsandHazardExposureScenarioResults

AADF

(tons/day)Probability

Name Commune District ProvinceMin Max

HazardtypeClimatescenario

Min Max

Landslide — — —

ĐàNẵng HoaThuanTay

HaiChau

DaNang 26.0 28.3 TyphoonFlooding

— — —

CátBi ThanhTo HaiAnHai

Phong5.6 5.8

TyphoonFlooding

— — —

— 0.001 0.002

RCP4.5 0.001 0.01River

Flooding

RCP8.5 0.001 0.01

Landslide — — —

PhúBài PhuBaiHuongThuy

ThuaThienHue

2.8 2.8

TyphoonFlooding

— — —

Page|146

TableC.2.MajorMaritimePortswithAADFFlowsandHazardExposureScenarioResults

AADF(tons/day) ProbabilityName Commune District Province

Min Max

Hazardtype

Climatescenario Min Max

— 0.001 0.04

RCP4.5 0.001 0.2TP.HồChíMinh

CatLai Quan2HoChiMinh

84,050.0 106,425.6River

Flooding

RCP8.5 0.001 0.2

HảiPhòng

MayToNgo

QuyenHaiPhong 80,688.2 101,739.6

TyphoonFlooding

— — —

— 0.001 0.04

RCP4.5 0.001 0.2CầnThơ BinhThuyBinhThuy

CanTho 68,863.8 86,686.3River

Flooding

RCP8.5 0.001 0.1

— 0.001 0.001ĐồngNai

LongBinhTan

BienHoa DongNai 27,534.1 29,102.7River

FloodingRCP4.5 0.1 0.1

NghiSơn NghiSon TinhGiaThanhHoa

18,140.3 18,822.4TyphoonFlooding

— — —

QuyNhơn

HaiCangQuyNhon

BinhDinh 4,917.8 5,376.7 Landslide — — —

Landslide — — —ThuậnAn

ThuanAnPhuVang

ThuaThienHue

1,105.6 2,893.8TyphoonFlooding

— — —

CửaLò NghiThuy CuaLo NgheAn 1,428.6 2,021.0TyphoonFlooding

— — —

BếnThủy NghiHoa CuaLo NgheAn 1,131.2 1,655.5TyphoonFlooding

— — —

— 0.001 0.04

RCP4.5 0.001 0.1CáiMépPhuocHoa

TanThanh

BaRia—VungTau

416.8 416.8River

Flooding

RCP8.5 0.001 0.1

Page|147

TableC.3.MajorInlandWaterwayPortswithAADFFlowsandHazardExposureScenarioResults

Name Commune District ProvinceAADF (tons per day) Hazard

typeClimate scenario

Probability

Min Max Min Max

Cảng Bình Long

Binh Long Chau Phu An Giang 24,681.4 55,018.2River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.1

Cảng Thủy nội địa Hà Hùng Anh

Gia DucThuy

NguyenHai Phong 32,265.7 40,662.7

Typhoon Flooding

— — —

Cảng Nhiệt Điện Thái Bình 2

My Loc Thai Thuy Thai Binh 36,441.7 37,206.7Typhoon Flooding

— — —

Cảng TNĐ Liên hiệp Khoa học Công nghệ Tài nguyên khoáng sản, môi trường và năng lượng

Dien Cong Uong Bi Quang Ninh 33,948.1 34,822.8Typhoon Flooding

— — —

CẢNG LONG BÌNH

Long Binh Quan 9Ho Chi Minh

25,214.8 31,927.5River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

Cảng Khuyến Lương

Yen So Hoang Mai Ha Noi 18,291.7 18,552.1River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng Bốc xếp hàng hóa An Giang

Vinh Thanh Trung

Chau Phu An Giang 6,174.6 14,637.2River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.1

Cảng Thủy nội địa Long Đức

Long Duc Tra Vinh Tra Vinh 3,257.6 14,515.1River

Flooding

— 0.001 0.004

RCP 4.5 0.001 0.04

RCP 8.5 0.001 0.04

An Hòa An Hong An Duong Hai Phong 8,064.4 10,160.4Typhoon Flooding

— — —

CẢNG TNĐ KHO VẬN MIỀN NAM

Truong Tho

Thu DucHo Chi Minh

7,553.0 9,560.9River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

Phuong 5 Vinh Long Vinh Long 8,051.3 9,080.0River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.04

RCP 8.5 0.001 0.04

CẢNG TNĐ TÍN NGHĨA

Long Tan Nhon Trach Dong Nai 8,320.2 9,022.3River

Flooding

— 0.001 0.02

RCP 4.5 0.001 0.04

RCP 8.5 0.001 0.1

Table continues on next page

Page|148

TableC.3continued

XÓA TÊN KHỎI DS (CẢNG TNĐ VẬN TẢI SONADEZI)

An Binh Bien Hoa Dong Nai 8,261.4 8,747.6River

Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng Phượng Hoàng

Cam Thuong

Hai Duong Hai Duong 6,186.8 6,226.3River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG HÙNG TÀI

Thien Tan Vinh Cuu Dong Nai 5,526.7 5,949.2River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

CẢNG TNĐ XĂNG DẦU LONG BÌNH TÂN

Long Binh Tan

Bien Hoa Dong Nai 5,516.1 5,879.8River

Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng Hồng Vân

Hong Van Thuong Tin Ha Noi 4,585.8 4,690.7River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG TNĐ AJINOMOTO


Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng thủy nội địa Vissai Gia Tân

Gia Tan Gia Vien Ninh Binh 3,392.6 3,762.9River

Flooding

NONE 0.001 0.001

RCP 4.5 0.001 0.002

RCP 8.5 0.001 0.004

Cảng thủy nội địa CuBi

Kim Xuyen Kim Thanh Hai Duong 3,705.2 3,715.1River

Flooding

— 0.001 0.001

RCP 8.5 0.001 0.001

CẢNG TNĐ HOÀNG LONG


Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

CẢNG TNĐ BÊ TÔNG LY TÂM LA PHƯƠNG NAM

Luong Binh

Ben Luc Long An 2,237.7 2,673.5River

Flooding

— 0.001 0.02

RCP 4.5 0.001 0.1

RCP 8.5 0.001 0.1

Cảng Binh Đoàn 11

Thanh Tri Hoang Mai Ha Noi 2,282.2 2,310.8River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng thủy nội địa xi măng Xuân Thành

Thanh Nghi

Thanh Liem Ha Nam 1,905.3 1,910.0River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng Thủy Nội Địa Cống Câu

Ngoc Son Tu Ky Hai Duong 1,855.1 1,870.9River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

XÓA TÊN KHỎI DS (CẢNG TNĐ VẬN TẢI SONADEZI)


Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng Phượng Hoàng

Cam Thuong

Hai Duong Hai Duong 6,186.8 6,226.3River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG HÙNG TÀI


Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

CẢNG TNĐ XĂNG DẦU LONG BÌNH TÂN

Long Binh Tan

Bien Hoa Dong Nai 5,516.1 5,879.8River

Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng Hồng Vân

Hong Van Thuong Tin Ha Noi 4,585.8 4,690.7River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG TNĐ AJINOMOTO


Flooding

— 0.001 0.001

RCP 4.5 0.1 0.1

Cảng thủy nội địa Vissai Gia Tân

Gia Tan Gia Vien Ninh Binh 3,392.6 3,762.9River

Flooding

NONE 0.001 0.001

RCP 4.5 0.001 0.002

RCP 8.5 0.001 0.004

Cảng thủy nội địa CuBi

Kim Xuyen Kim Thanh Hai Duong 3,705.2 3,715.1River

Flooding

— 0.001 0.001

RCP 8.5 0.001 0.001

CẢNG TNĐ HOÀNG LONG


Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

CẢNG TNĐ BÊ TÔNG LY TÂM LA PHƯƠNG NAM

Luong Binh

Ben Luc Long An 2,237.7 2,673.5River

Flooding

— 0.001 0.02

RCP 4.5 0.001 0.1

RCP 8.5 0.001 0.1

Cảng Binh Đoàn 11

Thanh Tri Hoang Mai Ha Noi 2,282.2 2,310.8River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng thủy nội địa xi măng Xuân Thành

Thanh Nghi


Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng Thủy Nội Địa Cống Câu

Ngoc Son Tu Ky Hai Duong 1,855.1 1,870.9River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02



Probability

Min Max Min Max

Page|149

TableC.3continued



Probability

Min Max Min Max

Cảng thủy nội địa Vissai Hà Nam

Thanh Thuy


Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng xi măng Vicem Bút Sơn

Chau Son Phu Ly Ha Nam 1,429.5 1,435.6River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG TNĐ VIỆT HÓA NÔNG

Phuoc Dong

Can Duoc Long An 978.3 1,214.8River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.1

RCP 8.5 0.001 0.1

Cảng hàng hóa Thành Hưng

My Khanh Phong Dien Can Tho 801.1 1,088.8River

Flooding

— 0.001 0.02

RCP 4.5 0.001 0.04

RCP 8.5 0.001 0.04

Cảng Trường Nguyên

Gia MinhThuy

NguyenHai Phong 797.0 1,000.3

Typhoon Flooding

— — —

Cảng thủy nội địa Nhà máy xi măng Thành Thắng

Thanh Nghi

Thanh Liem Ha Nam 859.0 866.0River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Cảng thủy nội địa xi măng Hoàng Long

Thanh Nghi

Thanh Liem Ha Nam 856.4 856.6River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

CẢNG TNĐ XI MĂNG SÀI GÒN

Long Binh Quan 9Ho Chi Minh

660.9 833.0River

Flooding

— 0.001 0.04

RCP 4.5 0.001 0.2

RCP 8.5 0.001 0.2

Cảng xuất sét xi măng Vicem Hải Phòng

Song Khoai

Quang Yen Quang Ninh 682.0 719.4Typhoon Flooding

— — —

Cảng Hoàng Gia

Kim Luong Kim Thanh Hai Duong 614.2 617.5Typhoon Flooding

— — —

Cảng Hải Nam

Luu KyThuy

NguyenHai Phong 416.0 552.3

Typhoon Flooding

— — —

Minh ĐứcDong Hai 1

Hai An Hai Phong 388.8 513.4Typhoon Flooding

— — —

Page|150

TableC.3continued



Probability

Min Max Min Max

Cảng thủy nội địa Châu Sơn

Chau Son Phu Ly Ha Nam 303.9 353.6River

Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

NGỪNG HOẠT ĐỘNG (CẢNG NHÀ MÁY KHÍ ĐIỆN NHƠN TRẠCH 2)

Phuoc Khanh

Nhon Trach Dong Nai 62.3 141.3River

Flooding

— 0.001 0.02

RCP 4.5 0.001 0.1

RCP 8.5 0.001 0.1

Cảng Bến Kiền

An Hong An Duong Hai Phong 10.9 38.6Typhoon Flooding

— — —

Cảng Kho trung chuyển xăng dầu Thái Bình

Hoa Binh Vu Thu Thai Binh 10.2 30.8

River Flooding

— 0.001 0.01

RCP 4.5 0.001 0.02

RCP 8.5 0.001 0.02

Typhoon Flooding

— — —

Page|151

AppendixD.CommunesandDistrictsbyExposureofRoadNetworkstoNaturalHazards

TableD.1.TwentyCommunesMostExposedtoFloodinginLaoCaiProvince

Flashfloodsusceptibility Riverflooding Landslidesusceptibility

Commune District Totalkm Current

(2016)RCP4.5(2025)

RCP8.5(2025)

Current(2016)

RCP4.5(2030)

RCP8.5(2030)

Current(2016)

RCP4.5(2025)

RCP8.5(2025)

TongSanh BatXat 9.2 46.0 46.0 20.4 41.6 41.6 41.6 7.8 7.8 19.7

TrungLengHo

BatXat 3.7 30.7 30.7 0.0 0.0 0.0 0.0 1.8 1.8 1.8

NamSai SaPa 6.6 21.2 21.2 21.2 0.0 0.0 0.0 9.3 9.3 16.3

MuongVi BatXat 13.8 19.0 19.0 19.0 19.5 19.5 19.5 1.6 1.6 9.2

NamKhanh BacHa 15.5 17.6 17.6 17.6 0.0 0.0 0.0 0.6 0.6 0.6

CocSan BatXat 16.2 17.0 17.0 2.4 10.2 10.2 10.2 7.2 7.2 16.2

SaPa SaPa 32.3 15.2 15.2 15.2 0.0 0.0 0.0 11.2 11.2 11.2

BanCai BacHa 23.5 14.2 14.2 14.2 0.0 0.0 0.0 3.2 3.2 3.2

NamXeVanBan

21.8 14.0 14.0 14.0 0.0 0.0 0.0 14.9 14.9 14.9

DenSang BatXat 20.8 12.4 12.4 12.4 0.0 0.0 0.0 4.2 4.2 3.3

BanLien BacHa 49.3 10.9 10.9 10.9 0.0 0.0 0.0 1.8 1.8 1.8

ThamDuongVanBan

18.9 10.4 10.4 10.4 0.0 0.0 0.0 4.7 4.6 6.1

YTy BatXat 23.4 9.6 9.6 11.2 0.0 0.0 0.0 3.4 3.4 3.4

NamRangVanBan

15.2 9.2 9.2 9.2 0.0 0.0 0.0 10.0 10.0 10.0

DuongQuyVanBan

30.8 9.1 9.1 9.1 0.0 0.0 0.0 2.1 2.1 2.1

TaPhoi LaoCai 35.7 8.4 8.4 7.0 16.5 16.5 16.5 11.1 8.6 8.7

NamCang SaPa 6.2 8.3 8.3 8.3 0.0 0.0 0.0 1.8 1.8 4.2

LaoChai SaPa 11.6 8.3 8.3 8.3 0.0 0.0 0.0 20.8 20.8 20.8

LangGiangVanBan

18.9 7.7 8.0 8.0 0.0 0.0 0.0 5.0 5.3 5.3

TaPhin SaPa 20.2 7.2 7.2 7.2 0.0 0.0 0.0 14.3 14.3 14.3

Page|152

TableD.2.TwentyCommunesMostExposedtoFloodinginBinhDinhProvince

RiverfloodingLandslide

susceptibilityTyphoonflooding


(2016)RCP4.5(2030)

RCP8.5(2030)

Current(2016)

Current(2016)

ThiNai QuyNhon 11.8 87.2 87.2 87.2 0.0 43.2

NhonHoa AnNhon 89.4 76.4 80.4 80.4 8.0 15.2

LyThuongKiet QuyNhon 15.7 78.9 78.9 78.9 0.0 0.0

LeHongPhong QuyNhon 16.1 71.6 71.6 71.6 0.0 0.0

TayXuan TaySon 34.7 67.2 67.2 67.2 9.7 0.0

PhuocNghia TuyPhuoc 33.5 55.0 55.0 55.0 9.8 14.2

TranPhu QuyNhon 6.5 60.4 60.4 60.4 0.0 0.0

PhuocHiep TuyPhuoc 92.3 45.1 45.1 45.1 5.6 14.3

NhonBinh QuyNhon 68.6 41.2 41.2 41.2 0.0 20.9

AnTuongDong HoaiAn 38.5 33.9 33.9 33.9 37.3 0.0

PhuocThanh TuyPhuoc 100.8 41.0 41.4 41.4 8.4 0.2

TayPhu TaySon 32.4 37.0 37.0 37.0 13.9 0.0

CanhLien VanCanh 6.3 24.2 24.2 24.2 45.8 0.0

NhonTan AnNhon 38.6 35.2 35.2 35.2 8.0 0.0

NhonTho AnNhon 41.8 32.0 32.0 32.0 10.3 2.8

PhuocAn TuyPhuoc 108.5 27.9 29.3 29.3 16.1 0.1

VinhAn TaySon 5.0 25.8 25.8 25.8 18.5 0.0

TranHungDao QuyNhon 6.4 27.6 27.6 27.6 0.0 3.3

BinhNghi TaySon 89.6 24.3 24.3 24.3 12.7 0.0

AnDung AnLao 8.6 0.0 0.0 0.0 81.8 0.0

Page|153

TableD.3.TwentyCommunesMostExposedtoFloodinginThanhHoaProvince

RiverfloodingLandslide

susceptibilityTyphoonflooding


(2016)RCP4.5(2030)

RCP8.5(2030)

Current(2016)

Current(2016)

DongSon BimSon 47.7 96.9 96.9 96.9 1.7 0.0

DongXuan DongSon 8.3 96.1 96.1 96.1 0.0 0.0

ThinhLoc HauLoc 12.5 94.3 94.3 94.3 0.0 0.0

HoangLong ThanhHoa 10.4 91.8 91.8 91.8 0.0 0.0

NgaThach NgaSon 37.5 87.0 87.0 87.0 0.0 3.1

HoaLoc HauLoc 34.7 87.7 87.7 87.7 0.0 0.0

DongTien TrieuSon 39.3 84.7 84.7 84.7 0.0 0.0

RungThong DongSon 5.7 82.5 82.5 82.5 0.0 0.0

DinhTang YenDinh 51.5 81.3 81.3 81.3 0.3 0.0

LamSon BimSon 22.3 79.7 79.7 79.7 2.2 0.0

DongLoi TrieuSon 37.4 78.7 78.7 78.7 2.8 0.0

HauLoc HauLoc 16.2 79.6 79.6 79.6 0.0 0.0

XuanLam ThoXuan 22.7 76.9 76.9 76.9 0.5 0.0

LienLoc HauLoc 28.4 76.5 76.5 76.5 0.0 0.0

HaVinh HaTrung 57.4 73.1 73.1 73.1 1.3 0.0

HungLoc HauLoc 36.6 71.5 71.5 71.5 0.0 3.3

LocTan HauLoc 35.5 72.1 72.1 72.1 0.0 0.0

NgaNhan NgaSon 19.3 72.0 72.0 72.0 0.0 0.0

XuanVinh ThoXuan 42.2 72.0 72.0 72.0 0.0 0.0

YenNinh YenDinh 17.1 70.8 70.8 70.8 0.0 0.0

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