PREDICTING MUNICIPAL SOLID WASTE GENERATION IN …

PREDICTING MUNICIPAL SOLID WASTE GENERATION IN ANDORRA WITH SYSTEM

DYNAMICS MODELLING

M. Pons, C. Pérez, J.J. de Felipe, E. Jover

OUTLINEPREDICTING MUNICIPAL SOLID WASTE GENERATION IN ANDORRA WITH SYSTEM DYNAMICS MODELLING

OUTLINE

1. INTRODUCTION

- Modelling MSW generation- State of Art- Study case context: Andorra

2. METHODOLOGY

3. RESULTS

Scenarios- Scenarios

4 CONCLUSIONS4. CONCLUSIONS

PREDICTING MUNICIPAL SOLID WASTE GENERATION IN ANDORRA WITH SYSTEM DYNAMICS MODELLING

1 MODELLING MSW GENERATION

Why? - Utility

1. MODELLING MSW GENERATION

• Waste management planning

• To achieve accurate and reliable waste information

-Municipal waste collection system

-Land demand for landfilling waste

Incinerators capacity evaluation- Incinerators capacity evaluation


1 MODELLING MSW GENERATION1. MODELLING MSW GENERATION

Traditional statistical forecasting methods

• Time analysis correlation

• Regression analysis

High quantity and quality data required

1

1. H. A. Abu Qdais, M. F. Hamoda, J. Newham. "Analysis of Residential Solid Waste at Generation Sites". Waste Management & Research. Vol. 15. pp. 395-405, (1997).



System Dynamics Modelling


Li it d d t

• Fuzzy, not-well defined environmentsQuality of• Limited data

• Large-scale complex systems

Quality of service

• Causal loops Tourist Offer

Infrastructures

Visitors Profits Investments

Offer

Visitors

Overnight

Profits Investments

Overnightstays



SD Modelling examples


g p

MSW generation in San Antonio 1

Waste generation, collectioncapacity and electricity in Dhaka 2

Based on economic and demographic information

1.Dyson, B., Chang, N.-B., “Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling.” Waste Management 25 (7), 669-679, (2005).2. Sufian, M.A., Bala B.K., “Modeling of urban solid waste management system: The case of Dhaka city.” Waste Management, 27(7), 858-868, (2007).

1. CASE STUDY CONTEXT - ANDORRA


I d d t P i i litIndependent Principality

Population: 84 484 inhabitants

Area: 468 km2

Population: 84.484 inhabitants

Pyrenees

Tourism main economic activityy

10.193.749 visitors in 2008

Case Study context - Andorra1. CASE STUDY CONTEXT - ANDORRA


300.000

Tourists 2008

200 000

250.000

150.000

200.000

TOURISTS

50.000

100.000

T

0

Data source: Tourism Department of Andorran Government

Case Study context - Andorra1. CASE STUDY CONTEXT - ANDORRA


1,35

1,40

MSW kg/person/day

1,25

1,30

1,35

1,15

1,20

kg

1,00

1,05

1,10

0,90

0,95

Data source: Environmental Department of Andorran Government

1. MSW GENERATION ON TOURISTIC AREAS


Balearic Islands:


MSW Generation /person /day

1,5 kgWinter

Summer 2,5 kg



TORREVIEJA URBANWASTE 2001 1


7

7,5

8

TORREVIEJA URBAN WASTE 2001 1

5

5,5

6

6,5

10 6kg

3

3,5

4

4,5

2,5

High impact of visitors flows on waste generation

Lack of models integrating visitors influence

g p g

1. J.F. Vera Rebollo and J.A. Ivars Baidal, “Measuring Sustainability in a Mass Tourist Destination: Pressures, Perceptions and Policy Responses in Torrevieja, Spain.” Journal of Sustainable Tourism, 11, 181-203, (2003).

2. METHODOLOGY


Model tackling with:

D t it- Data scarcity

- Importance of visitors flows

SD based on:

- Economic

- Visitors flows

- Demographic


2. METHODOLOGY: DYNAMICS SYSTEMS MODEL


2. METHODOLOGY: POPULATION

POPULATIONPOPULATION

TOTAL MSW GENERATION


2. METHODOLOGY: GDP

RESIDENT ECONOMICPOPULATION ECONOMIC



2. METHODOLOGY: TOURISTS

TOURISTS

RESIDENT ECONOMICVISITORSRESIDENT POPULATION ECONOMICVISITORS



2. METHODOLOGY: TOURISTS

TOURISTS ONE-DAY VISITORS

RESIDENT ECONOMICVISITORSRESIDENT POPULATION ECONOMICVISITORS



2. METHODOLOGY: POPULATION

POPULATIONPOPULATION



2. METHODOLOGY: DYNAMICS SYSTEMS MODEL

3. RESULTS: SCENARIOS


AS USUAL SCENARIOAS USUAL SCENARIO

HYPOTHESISHYPOTHESIS:2250 02300,02350,02400,0

GDP

Annual variation rate of variables

1950 02000,02050,02100,02150,02200,02250,0

Dolars

+ 1 %

Annual variation rate of variables

POPULATION 1900,01950,0

-1,2% +1 5% +3%GDP

1,2% +1,5% 3%

- 1,7 %TOURISTS

ONE DAY VISITORS

- 1,3 %

4. CONCLUSIONS


SD M d l i t ti d hi d i• SD Model integrating demographic and economicparameters as well as tourist flows

• Correlation between visitor flows and MSW generation

FURTHER WORK

• Compulsory when studying tourist destinations

FURTHER WORK• To validate the model

• To collaborate with local administrations

• To model the recovering energy capacity

• To collaborate with local administrations

ACKNOWLEDGEMENTS


f th i ll b tifor their collaboration

f f ll hifor a fellowship

and ALL OF YOU FOR YOUR ATTENTION!!and ALL OF YOU FOR YOUR ATTENTION!!

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PREDICTING MUNICIPAL SOLID WASTE GENERATION IN …