IUFRO 2014 Salt Lake City: Session C-02 (193) From Understanding Drivers To Gaining Leverage At The Tropical Forest Margins: 20 Years of ASB Partnership

Identifying Policy Levers Of Deforestation and Recovery Of Tree CoverFrom The Driver Analyses: A Case Study From Indonesia

Sonya Dewi, Andree Ekadinata, Asri Joni

BackgroundProposed methodsResultsConclusions

Outline

Background

Descriptive: quantification of land use and cover changes and analysis of association of factors influencing and patterns of LUCC: what, how much, where, when – empirical studies

Explanatory: process of LUCC driven by proximate causes, underlying causes: who, how and why, interdependencies – conceptual framework

Predictive: future LUCC given proximate and underlying factors – spatial-quantitative up to proximate causes, econometric with underlying causes

So-what? Recommendation for intervention: policy levers to change the course toward promoting what should happen and avoiding what should not happen – should be specific and effective

Existing driver analysis/studies

Drivers

B1. Incentive structure through policy change (tax, subsidy etc)

A2. LU rights (e.g. community forest mngmnt)

B2. PES and conditional ES incentives

Response/ feedback options

Biodiversity, Watershed functions, GHG emissions,

Landscape beauty

Actors/ agents

Land use/coverchanges

Conse-quences &functions

Livelihoods, provisioning & profitability

A1. Land use policies, spatial development planning

Rights-based approaches

Economic incentives

Van Noordwijk, M., B. Lusiana, G. Villamor, H. Purnomo, and S. Dewi. 2011. Feedback loops added to four conceptual models linking land change with driving forces and actors. Ecology and Society 16(1): r1. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/resp1/

Deforestation is always bad and all has to be avoided

Deforestation is the only LUCC that matters There is no co-variation between

deforestation and other LUCC (completely independent processes of decision making)

There is no co-variation between causes/factors of LUCC

Implicit assumptions

reforestation and regrowth

Landuse and cover changes

Link between descriptive-quantitative pattern and causal processes and interdependencies among them

Be consistent and comparable with data and analysis Acknowledge legal and customary norms on top of

biophysical characteristics within zonation Capture, discuss and use knowledge and perception

of multiple stakeholders Synthesize local-specific contexts across

heterogeneous larger landscape to allow upscaling

In participatory processes to identify and negotiate policy levers from drivers at the planning stage

Needs

Proposed method

Local Knowledge on Land Use/Cover Change

• Historic and future land use change • Criteria and indicators for ‘legitimate’ and ‘illegitimate’

• Factors and causes; interdependencies

2

3

Knowledge• Identify policy levers at multiple levels • Formulate recommendation• Synthesis and comparative analysis

Action• Discussion and scenario development• Scenario simulation through what-if tools

such as LUMENS, LUWES• Negotiate way forward

5

Network Analysis of drivers of LUCC• •Quantify pattern of agent- and zone-specific changes, configuration,

•Structural network model of factors, causes, interdependencies• Policy Network Analysis or Analytic Network Process

• Dissemination, interpretation, iteration

4

Pattern Analysis of Land Use/Cover Change• Quantitative descriptive analysis of both side of

the curves: trajectory• Local variability/heterogeneity

1

Causes and factors (Nodes): Proximate causes Underlying causes Triggering events CapitalsInterdependencies of factors: Nature of relationships/interaction: direct

causes, stimulating, deferring, enabling, prohibiting

Strength: importance of relationships

Network Analysis

Results

0

400000

800000

1200000

Total deforLeg_deforIlleg_defor

77% from 10 provinces

RIAU

KALI

MANTA

N TEN

GAH

KALI

MANTA

N TIM

UR

PAPU

A

JAMBI

SUMATE

RA SEL

ATAN

KALI

MANTA

N BARAT

SULA

WES

I TEN

GGARA

NANGGROE ACEH

DAR...

SUMATE

RA UTA

RA0

200000

400000

600000

800000

1000000

1200000

Total deforLeg_deforIlleg_defor

2000-2005

2005-2010

Data issues

KALI

MANTA

N TEN

GAH

KALI

MANTA

N TIM

URRIA

U

SUMATE

RA SEL

ATAN

KALI

MANTA

N BARAT

SULA

WES

I SEL

ATAN

KALI

MANTA

N SEL

ATAN

SUMATE

RA UTA

RA

JAMBI

NUSA T

ENGGARA T

IMUR

0

500000

1000000

1500000

2000000

2500000

Total_regrowthAgroforestationReforestationTree cropping

72% from 10 provinces

SUMATE

RA SEL

ATAN

KALI

MANTA

N TEN

GAHRIA

U

KALI

MANTA

N BARAT

JAMBI

KALI

MANTA

N TIM

UR

SULA

WES

I TEN

GGARA

SUMATE

RA UTA

RA

BENGKU

LU

KALI

MANTA

N SEL

ATAN

0

500000

1000000

1500000

2000000

2500000

Total_regrowthAgroforestationReforestationTree cropping

2000-2005

2005-2010

SOUTH SUMATRA

South Sumatra

Total area of South Sumatra is 9.1 million hectare

Permanent in-migration for 2010 is estimated at 1,01 mil. people while the out-migration is 779,239 people

The population density in 2014 is estimated at 81 ppl/sq.km. Country average is 124 ppl/sq.km. Rate of population increase in South Sumatra is 1.85%/years.

HDI of South Sumatra has increased from 70.2 in 2005 to 74.3 in 2013 which is higher than average HDI in Indonesia (73.8) (source: http://www.bps.go.id/)

Papua

PAPUA

Papua is the largest province in Indonesia with total area of 31.9 million hectares.

The population density in 2010 is estimated at 8 ppl/sq.km. Rate of population increase in Papua is the highest in Indonesia, which reach 5.39%/years.

Permanent in-migration for 2010 is estimated at 435,773 people while the out-migration is 87,545 people

Human development index of Papua has increased from 62.08 in 2005 to 66.25 in 2013 although it is still below average HDI in Indonesia (73.8)

Land Use Changes

PAPUASOUTH SUMATRA

Land Use Trajectories Maps

Land use trajectories in Papua within the period of 1990-2010 were dominated by loss of tree cover/forest to logged over forest, while in South Sumatra, the most dominant land use trajectories were recovery to tree cropping

Land Use Trajectories across LU Zones

PAPUASOUTH SUMATRA

Recovery to tree cropping

Recovery to forest

Recovery to agroforest

Other

Loss to logged-over forest

Loss to infrastructure

Loss to cropland

Loss to bare land

Network Analysis

Drivers of Forest and Tree cover loss to Agriculture (Sumatra Selatan)

DRIVERS OF FOREST AND TREE COVER LOSS TO AGRICULTURE (PAPUA)

Drivers of Forest and Tree cover loss to Agriculture (Papua)

DRIVERS OF RECOVERY OF TREE COVER (PAPUA)

Drivers of Recovery of Tree Cover (Papua)

DRIVERS OF RECOVERY OF AGROFOREST (PAPUA)

Drivers of Recovery of Agroforest(Papua)

Cap-human

Cap-financial

Cap-natural

Prox-i

nfra

Und-cult

Und-demo

Und-eco

Und-pol0

0.10.20.30.40.5

Drivers of Loss to agriculture

SumSel Papua

People's environmental awarenessGovernment program for housing

Demand for land for housingLand availibility

Partnerships with investorsCultural changes

Lack of law enforcementGovernment program for community empowerment

High market price for commoditiesPopulation growthDemand for food

Infrastructure developmentDemand for local economic growth

Local customsNeed to increase local revenue

TransmigrationLand suitability for agricultureDemand for increasing incomeFood self sufficiency program

0.000 0.050 0.100 0.150 0.200

South Sumatra

Land suitability for agriculture

People's environmental awareness

People's skill

Population growth

Demand for jobs/employment

Demand for land for housing

Transmigration

Local migration

Improvement of local livelihood

Demand for food

0.000 0.050 0.100

Papua

Leverage points

Land suitability for agroforestForest degradation

TransmigrationFood self sufficiency program

PrivatizationMarket demand for tree crop commodity

Demand for improvement of local livelihoodDemand for NTFP

High market price of tree crop commoditiesLack of law enforcementLocal economic growth

Demand for woodLocal customs

Wood extractionInformation and technology

Population growthPeople's environmental awareness

Cultural changesLand grabbing

Land availibilityGovernment program for community empowerment

Easy access to marketPartnerships with investors

Demand for employmentLand suitability for tree crop

Rehabilitation programNeed to increase local revenueDemand for higher hh income

0.000 0.020 0.040 0.060 0.080 0.100

South Sumatra

Local custom

Simple cultivation technique

Demand for poverty alleviation

People's environmental awareness

Local migration

Demand for improvement of local livelihood

Market demand for coffee

Infrastructure development

Job shifting

Suitable soil condition

Need to increase local revenue

Market demand for tree crop commodity

Land procurement by investor

Land availibility

Demand for employment

Government program for tree-crop expansion

Land titling program

Land suitability for oilpalm

Conformity to land use plan/ land designation

0.000 0.010 0.020 0.030 0.040 0.050 0.060

Papua

Cap-financial Cap-natural Cap-physical Cap-social Event-pol Prox-infra Prox-wood Und-cult Und-demo Und-eco Und-pol Und-tech0

0.050.1

0.150.2

0.250.3

0.350.4

0.450.5

Drivers of Recovery to tree crop

SumSel Papua

Cap-fi-nancial

Cap-natural Cap-physical Prox-infra Und-cult Und-demo Und-eco Und-pol0

0.1

0.2

0.3

0.4

0.5

Drivers of Recovery to agroforestry

SumSel Papua

Land suitability for agroforest

Population growth

Demand for employment

Easy access to market

Need to increase local revenue

Partnerships with investors

Transmigration

Food self sufficiency program

Demand for poverty alleviation

Rehabilitation program

0.000 0.050 0.100 0.150 0.200

South Sumatra

Infrastructure development

Land suitability for agroforest

Climate suitability for coffee

Demand for improvement of local livelihood

Land availibility

Food self sufficiency program

0.000 0.050 0.100 0.150 0.200 0.250

Papua

CONCLUSIONS

Drivers are often connected in a graph-like structure rather than a list or tree or a fish bone

Locally specific leverage points can be identified through understanding interconnectedness and covariance across drivers

Comparisons between areas are possible; extrapolation domain can be found

The process is useful beyond its output; it stimulates multiple stakeholder to think and discuss the drivers and levers analytically and iteratively

Options of leverage points can then be further formulated into scenarios, taken Zoning into account, and simulated in a tool that allow ex-ante impacts to be analyzed, such as LUMENS (Land Use Planning for Environmental Services) tool

Conclusions

Thank you