STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH ...€¦ · STUDY OF CORRELATIONS OF...
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STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH
COLLECTED MUNICIPAL SOLID WASTE INCROATIA IN PERIOD 2009-2013
Authors:
Dr. Anamarija Grbeš
Ilijana Ljubić, Mag.Ing.
Doc. Želimir Veinović
Institution:
University of Zagreb
Faculty of Mining,
Geology and Petroleum Engineering
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1. Introduction & Motivation
• Increase the recycling rates (EU)
•Avoid the penalties (HR)
•Avoid an increase in the environmental costs
(Industrial ecologists)
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1. Introduction & Motivation
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CO2
=
CO2CO2 CO2 CO2
System1
System2
System3
System…
Systemn-1
Systemn
WMS
+ + + +
+
+ +
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1. Introduction & Motivation
4
CO2
=
CO2 CO2CO2CO2
Waste Collection/Transport Processing/
Recycling Degradation/Decomposition/Burning/Conversion
WMS
…
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1. Introduction & Motivation
Introduction of change into the system
higher/smaller/same emission
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CO2 efficiency
6
CO2
emissions of‘new’ system
CO2
emissions of‘old’ system
CO2
emissions of‘old’ system
-0+
efficiency:
increaseno changedecrease
CO2 intensity:
decreaseno changeincrease
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CO2 efficiency
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CO2
emissions of‘old’ system
PASTknown
CO2
emissions of‘new’ system
Collect data on fuel consumption/Trace the documentation
FUTUREunknown
Calculated guess
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County
#1 - #21
Diesel
consumption
(L)
MSW
Collected (t)
CO2 emission
(t)
CO2 emission
PER CAPITA
MIN 83 945 13 797 224 2
MAX 2 165 375 304 706 5 782 13
TOTAL 10 382 528 1 477 911 27 723 /
AVERAGE 494 406 70 377 1 320 6
CO2 emission from MSW collection in 2013
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2644
608960
514 495 526 608
3087
599297 224
799
21321768
910513
213115751276
275
5782
0
1000
2000
3000
4000
5000
6000
CO2 (t)
CO2 emission from MSW collection in 2013
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More info available at poster session
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CO2 efficiency of MSW collection afterintroduction of the change into the system
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Calculated guess / FORECAST
Technology/Methodology based calculationCeteris paribus assumptionEverything else remainssame
Relevant changes in MSW collection system due toother changes(population, tourism, urbanisation, …)
Technology/Methodology calculationin changed circumstances (forecast)
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CO2 REFERENCE LEVEL
CO2 technology/methodology related
CO2 due to other changes in system
past/real data (‘old’ system)
future/calculation
MSW generation mechanism
future/forecast
study of correlations
CO2 efficiency of MSW collection afterintroduction of the change into the system
In order to correctlyassess the collection Sand to target the realproblem
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‘new’ system
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2. Materials and methods
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Assumptions
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Potential for wasteavoidance and management:
Households with landHouseholds without land
Agr. Land used
Potential for increased waste(new consumers):
Nights spent at touristaccommodation
Place to live(town, municipality,
populated area)
Consumers:Households
Population (Census)Population registered
Buying power(employment, wages,
annual wages)
Other stat.facts(area of the county, pop.dens., roads) ?
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Indicators used in the study
• total number of inhabitants,
• total number of households,
• number of households with and without land,
• agricultural land used,
• average monthly wages, number of employees and total annual income per county
• tourist nights per county
• number of towns, municipalities, populated areas, length of roads, number of inhabitants per area
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Sources of data
• Croatian Environment Agency waste registry and reports: AZO (2009-2014)
• Croatian Bureau of Statistics website (census data and annual reports DZS 2010-2014)
• County Road Administration
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Data quality
The quality of the MSW data
• may vary due to the fact that the method of MSW quantity assessment varies between counties and companies.
• Some rely on weighting while others have no weighting devices and rely on estimates.
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Data quality
’’Nights spent at tourist accommodation’’ i.e. the number of registered guests
• difficult to estimate how close that number is to• the real number of guests and
• the time they spend at a certain destination;
• non registered guests?
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Statistical Analysis
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21 County
20 Counties (city of Zagreb excluded)
Continental Counties(city of Zagreb excluded)
Coastal Counties
2009-2013
Descriptive statistics
Correlations of indicatorswith MSW
Correlations betweenthe indicators
More info in completestudy soon availableonline!
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Statistical AnalysisData set (2009-2013) Descriptive statistics / Correlations
I21 counties (all)
Predictor variables
All predictor variables vs. MSW (2009-2013)
II20 counties(without Zagreb city)
Predictor variables
All predictor variables vs. MSW (2009-2013)
III
Group1: Continental(Zagreb cityexcluded)
Predictor variables
All predictor variables vs. MSW (2009-2013)
IVGroup 2(Coastal)
Predictor variables
All predictor variables vs. MSW (2009-2013)
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Correlations Explained
… meaning …
• At p<0.05 confidence level of 95%
• positive correlation coefficient ‘increase of this’ correlates with ‘increase of MSW’
• negative – II – ‘increase of this’ correlates with ‘decrease of MSW’
• strong correlation ABS(0.75-1.0)
• medium (high,low) -II- ABS(0.5-0.75; 0.25-0.5)
• low -II- ABS(0-0.25)
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Correlations Explained
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3. Results & Discussion
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Results0,94 0,94 0,94 0,940,9 0,88 0,86
0,77 0,75
0,430,4
0,31
-0,22
21 County
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Results0,9 0,89 0,88 0,87 0,86 0,85 0,85
0,7 0,680,63
0,550,52
0,47
0,34
20 Counties
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Results0,8 0,79
0,750,72 0,72 0,71
0,68 0,66
0,58
0,51 0,49
0,42 0,4
0,32
Continental
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Results0,99 0,99 0,98 0,97 0,96 0,95 0,94 0,93
0,89 0,89
0,69
0,57
0,470,43
Coastal
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-0,4
-0,2
0
0,2
0,4
0,6
0,8
121 County 20 Counties
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0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
20 Counties Continental
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0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
20 Counties Coastal
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0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
Continental Coastal
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Assumptions
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Potential for wasteavoidance and management:
Households with land +Households without land +
Agr. Land used (-)x
Potential for increased waste(new consumers):
Nights spent at touristaccommodation +
Place to live +(town+, municipality,
populated area)
Consumers:Households +
Population (Census) +Population registered +
Buying power +(employment+, wages,
annual wages+)
Other stat.facts(area of the county (/), pop.dens (/) ., roads+)
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Consumers
• Households• positively correlates in in all groups.
• Populaton• positively correlates in all groups
• Population registered• more precise than the population number taken from Census
2011.
• No significant changes in population due to proximity of Census, similar as for the population indicator.
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Buying power (Financial status of the consumers)
• Employees in legal entities and Annual income• Positively correlate with the MSW generation in all groups.
•Monthly Net Wages• Positively correlates with MSW except in Coastal
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Potential for waste avoidance and management
• Households without land• positively correlates with MSW in all groups (strong).
• Households with land• positively correlates with MSW in all groups (only in coastal part
strong)
• Used agricultural land variable • negative! in the 21 county group, in the 20C non existant
• In continental and coastal counties subgroups – positivecorrelation.
• Inconclusive!34
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Potential for increased waste production (newconsumers)
•Tourist nights •Positive in all groups except in the Continental
counties group where this activity is minor.
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Places to live
• Towns• MSW positively correlates with the number of towns in all
four analysis groups – more towns more MSW.
•Municipalities and Populated area • positively correlates in 20C, Continental, Coastal.
•positive correlation of the MSW generation with the number of places to live
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Other Stat.Facts
• Area of the county• positively correlates in the 20C and Continental
• Population density • positively correlates in 20C and Continental
• Roads• positively correlates in all!
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Waste Generation Mechanism Confirmed!
Municipal solid waste generation
Everyday consumption Consumer’s financial status Tourism
Annual income EmploymentTowns
Growing own food
Having animals to feed with food residue
Ability to convert the waste into compost with no especial efforts or incurred costs
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Equations and graphscan be found in the
study
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Implications and suggestions
Continental Croatia
• It would be worthwhile to study the collection of MSW free of organic matter from households with land.
• With proper preparatory action, new collection policy could be probably prepared.
• The waste freed of biodegradable matter is easy for further separation in recycling/collection centre.
• It would be useful to calculate whether the environmental costs and funds invested in the waste separation in recycling centres is more favourable than separate collection of MSW components in the context of large road infrastructure and low population density.
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Implications and suggestions
Coastal Croatia
• there could be a problem with waste management due to a large volume of MSW collected, which could result, in some circumstances, with pollution of the natural and human environment.
• Year to year increasing tourism activity accompanied with annual peaks in waste generation require appropriate response in waste management system.
• It would be worthy to consider some advanced technological solutions in terms of transport, but also in terms of waste recycling and treatment.
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Conclusion
• Waste generation mechanism confirmed as expected, however…BDP (industry) growth (?)
• Obvious differences• between the continent and coast,
• between the urban and rural area
• between the capital and rest of the country
• Composting practice from rural area should be recognized, valuated/rewarded and employed/guided towards…
• Tourism increase (!) could be chalenging for the WMS, could lead to pollution
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Thank you very much for the attention!Questions are welcome!
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How the industrial/BDP growth will affect theMSW generation?
Is there sufficient knowledge/will/need in the system to prevent polution and to make profit from waste?
Is the waste management sector ready for thecountry’s economic recovery from crisis and tourism that is growing above all expectations?
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Closure
• Growth good and desirable
• Not to be prepared for the growth bad and undesirable!
“Excellence is never an accident.
It is always the result
of high intention,
sincere effort,
and intelligent execution;
it represents the wise choice of many alternatives –
choice, not chance, determines your destiny.”
Aristotle
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