STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH ...€¦ · STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH COLLECTED MUNICIPAL SOLID WASTE IN CROATIA IN PERIOD 2009-2013

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

1. Introduction & Motivation

• Increase the recycling rates (EU)

•Avoid the penalties (HR)

•Avoid an increase in the environmental costs

(Industrial ecologists)

2


3

CO2

=

CO2CO2 CO2 CO2

System1

System2

System3

System…

Systemn-1

Systemn

WMS

+ + + +

+

+ +


4

CO2

=

CO2 CO2CO2CO2

Waste Collection/Transport Processing/

Recycling Degradation/Decomposition/Burning/Conversion

WMS

…


Introduction of change into the system

higher/smaller/same emission

5

CO2 efficiency

6

CO2

emissions of‘new’ system

CO2

emissions of‘old’ system

CO2


-0+

efficiency:

increaseno changedecrease

CO2 intensity:

decreaseno changeincrease

CO2 efficiency

7

CO2


PASTknown

CO2

emissions of‘new’ system

Collect data on fuel consumption/Trace the documentation

FUTUREunknown

Calculated guess

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

8

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

9

More info available at poster session

CO2 efficiency of MSW collection afterintroduction of the change into the system

10

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)

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

11

‘new’ system

2. Materials and methods

12

Assumptions

13

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) ?

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

14

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

15

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.

16

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?

17

Statistical Analysis

18

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!

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


III

Group1: Continental(Zagreb cityexcluded)

Predictor variables


IVGroup 2(Coastal)

Predictor variables


19

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)

20

Correlations Explained

21

3. Results & Discussion

22

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

23

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

24

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

25

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

26

-0,4

-0,2

0

0,2

0,4

0,6

0,8

121 County 20 Counties

27

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

20 Counties Continental

28

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

20 Counties Coastal

29

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Continental Coastal

30

Assumptions

31

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+)

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.

32

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

33

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

Potential for increased waste production (newconsumers)

•Tourist nights •Positive in all groups except in the Continental

counties group where this activity is minor.

35

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

36

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!

37

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

38

Equations and graphscan be found in the

study

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.

39

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.

40

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

41

42

Thank you very much for the attention!Questions are welcome!

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?

43

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

44

http://www.goodreads.com/author/show/2192.Aristotle

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STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH ...€¦ · STUDY OF CORRELATIONS OF STATISTICAL PARAMETERS WITH COLLECTED MUNICIPAL SOLID WASTE IN CROATIA IN PERIOD 2009-2013