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Modeling Approach for Policy Making

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Research Paper on Urban Sustainability

Research Paper on Urban Sustainability

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  • 1. International Conference on Industrial Engineering & Operation Management Grand Hyatt Bali, Indonesia. January 7-9, 2014 Understanding Dynamics of Green House Gases Impacts on Urban Development: Jakarta Case Study Modeling Approach to Support Policy Making Akhmad Hidayatno, Irvanu Rahman, and Ricki Muliadi System Engineering, Modeling and Simulation Laboratory Industrial Engineering Department, Universitas Indonesia systems.ie.ui.ac.id IEOM 2014 - 272 1
  • 2. Outline This presentation is divided into four parts, 1 Background: Cities and Climate Change 2 Literature: Sustainable Development 3 Methodology: Modeling Approaches 4 Discussion IEOM 2014 - 272 2
  • 3. Cities Megatrend Megatrends imply significant challenges for city decision makers Megatrends Sustainable Urban Development Globalization & Urbanization  Global players / trade volume increase  2030: 60% of population in cities  High density living demands for new patterns in infrastructure Demographic Change  65+ generation will nearly double by 2030 (from 7% to 12%)  Need for adequate infrastructures as well as health- and elder care Climate Change  Cities responsible for ~80% GHG  Need for resource efficiency and environmental care  Cities are competing globally to make their urban areas attractive to live and to invest in Competitiveness Governance Environment Quality of Life  Challenge to balance between competitiveness, environment and quality of life, and to finance infrastructure solutions  Achieve committed CO2 targets ―Making Cities Work – Sustainable Urban Infrastructure‖ — Siemens (2012) IEOM 2014 - 272 3
  • 4. Jakarta’s Current Challenges and Concerns Major Forces: Growing Population, Land Use, and Climate Change 1970 1980 1990 2000 4.546.500 6.503.400 8.259.300 8.385.600 Policies Priorities Regional Government Plan 1 Build Good Urban Governance Spatial Plans (RTRW 2030) IEOM 2014 - 272 2 GHG Mitigation Plan (RAD-GRK 2030) Develop Resilience Eco-City Coastal Defense Strategy (JCDS) 3 Promote PublicPrivate Partnership Electronic Road Pricing (ERP) Low Carbon Transport (MRT, BRT) 4
  • 5. Conflicting Demand between Policy Targets Regional Government Targets (in 2030): • Sustaining 7 - 8 % economic growth per annum • 30 % emission reduction from BAU • 30 % green space expansion • Is it possible to reduce emission and expand the green space without slowing operation and economic growth ? • Can the green space satisfy emission reduction target ? • At what costs ? IEOM 2014 - 272 5
  • 6. Research Objectives ▪ Develop integrated development model of Jakarta to obtain understanding on how GHG emission affects Jakarta’s urban system structure. ▪ The model consist of three modules: economic; social; environmental, and it will run for twenty five years, from 2006 to 2030. ▪ The resulting model will be used as a tool for policy testing to help decision makers in tackling Jakarta’s future challenges and achieving policies targets (Future Research). IEOM 2014 - 272 6
  • 7. Outline This presentation is divided into four parts, 1 Background: Jakarta and Climate Change 2 Literature: Sustainable Urban Development 3 Methodology: Modeling Approaches 4 Discussion IEOM 2014 - 272 7
  • 8. Sustainable Urban Development Sustainable urban development focuses on balancing economic activities, social, and environment. (Chen, Ho, & Jan, 2006). Integration of these three dimensions allows the government and stakeholders to develop long-term and integrated visions for sustainable urban planning. (Rotmans, Asselt, & Vellinga, 2000). Workforce, Household Consumption Human and Social Capital Economic Capital Revenue, Employment Opportunities (+) investment (-) Emission Economic resources, absorb/ release pollution IEOM 2014 - 272 Environmental Capital Energy Infrastructure Health Effect Environmental awareness 8
  • 9. Outline This presentation is divided into four parts, 1 Background: Jakarta and Climate Change 2 Literature: Sustainable Urban Development 3 Methodology: Modeling Approaches 4 Discussion IEOM 2014 - 272 9
  • 10. Modeling Typology for Policy Making Process We use exploratory approach in developing our model to focus on dynamic insight of behaviors, not the numbers produced (predictive modeling). Model such as this one are useful, not because they predict the details of number, but because building and using them improves our insight (Bankes, 1993). Predictive Modeling Exploratory Modeling ― trying to predict the unpredictable ‖ ― the search for insight ‖ ―Exploratory Modeling and the Use of Simulation for Policy Analysis‖ — Bankes(1993) IEOM 2014 - 272 10
  • 11. Model Concept: Threshold 21 Threshold 21 model is a National Sustainability Development Model developed by Millennium Institute (USA) using system dynamics. The model consists of three dimensions of sustainable development, which has interconnected relationships among its endogenous structure. Our model is developed using this concept and translated into city level by using yearly statistical data officially published by the regional government and national statistics numbers. IEOM 2014 - 272 11
  • 12. Causal-Loop Diagram of Endogenous Variables GHG IEOM 2014 - 272 12
  • 13. Model Conceptualization: System’s Perspective Diagram System of Jakarta Sustainable Urban Model Regional Govenrmnet Policies Sustainable-City Indicators Exogenous Variables Input IEOM 2014 - 272 (Endogenous) Process Output Problem Owners 13
  • 14. Model Assumptions Endogenous Exogenous Excluded Population Migration Natural Disaster Life Expectancy Exchange Rate Corruption Labor Force Inflation Rate Crime Gross Regional Domestic Product Education Spending Portion Terrorism Technology Development War Investment Political Issues Consumption Other Regions’ Growth Fossil Fuel Emission Education Employment IEOM 2014 - 272 14
  • 15. Model: Modules and Structure Social Module Environmental Module industrial petroleum usage in mmbtu CO2 to NOx MOLECULAR W EIGHT RESIDENTIAL PETROLEUM DEMAND CH4 EMISSION FACTOR number of household Population birth total NOx emission from petroleum CH4 to CO2e SW ITCH GOS RTRW policy industrial natural gas usage in mmbtu ELECTRICITY PETROLEUM DEMAND CH4 EMISSION FACTOR NOx to CO2e total CO2e emission from petroleum 81.76 k m ² petroleum for electricity CH4 emission total CO2 emission from petroleum green open space grow th total petroleum usage in barrel PM10 concentration petroleum for electricity saving Population petroleum for residential saving effect of fossil fuel emission on mortality gross regional domestic product 0.05 k m ³/yr public service clean w ater demand CLEAN W ATER DEMAND PER CAPITA CLEAN W ATER SHARE Se conda ry School se conda ry school e nrollm e nt High School high school e nrollm e nt Gra dua te School gra dua te school e nrollm e nt STUDY DUR ATIO N SS STUDY DUR ATIO N P S STUDY DUR ATIO N HS P R IMAR Y SC HO O L ENTR ANC E R ATE P rim a ry School Age P op lite ra te pe ople ra te pe r ye a r stude nts P R IMAR Y SC HO O L AGE P O P P ER C ENTAGE Population production capacity gra dua te school drop out le ve l 0.10 k m ³/yr industrial clean w ater demand Population Clean Water Air Quality Lite ra te P e ople e duca tion inde x stude nts to popula tion ra tio SW ITCH environmental aw areness clean w ater saving SW ITCH environmental aw areness P rim a ry School prim a ry school e nrollm e nt Clean W ater Availability PAM clean w ater total clean w ater demand PAM clean w ater supply production gra dua te school drop out ra te high school drop out le ve l household clean w ater demand GRDP per capita SERVICE LEVEL high school drop out ra te se conda ry school drop out ra te se conda ry school drop out le ve l prim a ry school drop out le ve l 0.39 k m ³/yr 0.26 k m ³/yr CO2 emission reduction from low control air purification strategy SW ITCH low control prim a ry school drop out ra te 0.24 k m ³/yr clean w ater effect CO2 emission reduction from medium control air purification strategy RATIO INDUSTRIAL PETROLEUM USAGE PER UNIT RATIO TRANSPORTATION PETROLEUM USAGE PER UNIT petroleum for transportation saving Green Space SW ITCH medium control petroleum usage RATIO RESIDENTIAL PETROLEUM USAGE PER UNIT groundw ater extraction rate groundw ater extraction 0.08 k m ³/yr industrial petroleum usage industrial production government edu spend 0.49 k m ³/yr Groundw ater Level health index TOTAL LAND AREA total petroleum usage in kiloliter residential petroleum usagetransportation 662.33 k m ² TOTAL LAND AREA CO2 emission reduction from high control air purification strategy emission reduction KILOLITER TO BARREL petroleum usage for electricity RATIO PETROLEUM FOR ELECTRICITY USAGE PER UNIT TOTAL LAND AREA 0.08 k m ³ groundw ater recharge percentage of green space SW ITCH high control CO2e emission concentration volume CO2e transportation petroleum CH4 emission Populasi death rate population density Ground W ater Infiltration 0.04 k m ³/yr 0.12 emission absorbsion from GOS net CO2e emission in ton atmosphere height birth rate Surface W ater Level w ater discharge w ater evaporatio developed area green open space RTRW grow th SW ITCH GOS emission absorbsion CO2 EMISSION FACTOR FOR PETROLEUM DEMAND AVERAGE FAMILY SIZE 16.11 k m ³ Green Open Space Green Open Space NOx EMISSION FACTOR FOR INDUSTRIAL NATURAL GAS CH4 to CO2 equivalent TRANSPORTATION PETROLEUM DEMAND CH4 EMISSION FACTOR surface w ater recharge reclamation rate EMISSION ABSORBSION FROM GOS PER KM2 NOx to CO2 equivalent total CH4 emission from petrolium death SCF to BTU CH4 EMISSION FACTOR FOR INDUSTRIAL NATURAL GAS total CO2e emission from natural gas CO2 to CH4 MOLECULAR W EIGHT residential petroleum CH4 emission tranportation transportation petroleum usage in petroleum usage mmbtu migration MIGRATION RATE PRECIPITATION industrial petroleum CH4 emission petroleum usage for petroleum usage for electricity electricity in mmbtu industrial production industrial natural gas usage CO2 emission froam natural gas industrial petroleum usage residential residential petroleum usage petroleum usage in mmbtu RATIO INDUSTRIAL NATURAL GAS USAGE PER UNIT CO2 EMISSION FACTOR FOR NATURAL GAS tranportation residential petroleum usage in petroleum usage inpetroleum usage for mmbtu mmbtu electricity in mmbtu PETROLEUM DEMAND NOx EMISSION FACTOR industrial petroleum usage in mmbtu INDUSTRIAL PETROLEUM DEMAND CH4 EMISSION FACTOR KILOLITER TO MMBTU Education a dult lite ra cy inde x Total Labor Demand net labor demand LABOR ACCEPTANCE RATE labor acceptance labor cost of capital industrial labor demand service area labor demand agricultural labor demand Economic Module Jobs Opening Employment Level net labor demand Average Labor Cost labor cost inflation INFLATION RATE unemployment unemployment level Service Employed net service hiring AVERAGE SERVICE LABOR RATE Total W orkforce w orkforce supply rate w orkforce decrease rate service area labor demand SERVICE CAP RATIO birth rate Population EXTRAORDINARY SPENDING PORTION extraordinary expenditure and lending URBANIZATION RATE expenditure and net lending government healthcare expenditure agricultural investment government economics services expenditure INITIAL AGRI PRODUCTION Government Income Increase Rate Income Class government revenue EDU SPEND PORTION Inc Class Size government revenue Service Employed industrial labor demand Population INFLATION RATE Capital Service Reg. Gov. Expenditure agricultural labor demand depreciation service Investment Services Service Employed service capital intensity change Private Investment Private Investment Rate effect capital intensity service productivity Effectiveness of Public Investment Table INIT EFFECT CAPITAL INTENSITY SERV PRODUCTIVITY Public Investment INFLATION RATE Government Controlled Investment INITIAL LABOR SERVICE PRODUCTIVITY INDEX service production Public Consumption Investment Services Consumption Increase Rate relative consumer price elasticity of demand to relative prices investment on agriculture INFLATION RATE TIME TO PERCEIVED CHANGES IN RELATIVE PRICES per capita demand Population effect of relative prices on investment shares indicated investment shares demand Nominal GRDP gross regional domestic product real sectoral production real per capita regional income elasticity of price to demand supply balance relative sector GDP ratio INFLATION RATE gross regional income health cost per capita industrial production real sectoral production Population Service gross regional income Initial Tech Multiplier tech capital cost Producer Price INITIAL SECTOR Producer Price PRODUCER PRICE producer price change nominal sectoral production real per capita GRDP nominal GRDP per capita PP Index real GRDP at factor cost nominal GRDP at factor cost domestic produced domestic marketed goods and services taxes on goods and services nominal GRDP at factor cost revenue from non tax indirect taxes supply Initial sectoral taxes on goods and services nominal sectoral production Relative Price Taxes on Goods and Services Table nominal sectoral production Fraction of Indirect Tax on revenue Capital Service tech advance parameter Technology industrial production labor relative technology ELASTICITY ON INDUSTRIAL CAPITAL COST OF STAY PER DAY avg relative industrial labor productivity INITIAL NON TAX REVENUE INITIAL REGIONAL GOVERNMENT REVENUE special funds etc government revenue US-RP EXCHANGE RATE Technology education effect on industrial labor productivity Reg.Gov. Revenue Respiratory Hospital Admission Respiratory Hospital Admission Cost industrial employment COST OF ERV PM10 concentration healthy effect on labor productivity Emergency Room Visit emergency room visit cost industrial labor demand health cost Restricted Activity Days TIME FOR CHANGE FOR DEATH RATE TO AFFECT PRODUCTIVITY RAD cost effect health on industry productivity table Industry health cost per capita Population LOST DAY W AGE RATE Average Labor Cost IEOM 2014 - 272 AVERAGE STAY relative industrial employment INITIAL INDUSTRIAL EMPLOYMENT TotalRevenue taxes on goods and services education index sectoral taxes and goods and services CP index education index Technological Percentage over Investment Population budgetary revenue Revenue from Tax direct taxes relative consumer price sectoral taxes and goods and services GRDP deflator Investment property taxes property tax yoy other tax revenue property taxes table nominal GRDP at factor cost US-RP EXCHANGE RATE consumer price relative price Sectoral Production avg relative industrial capital productivity Time to collect taxes GRDP deflator real sectoral production domestic share other tax revenue table initial GDP deflator relative GDP deflator real sectoral production effective indirect tax domestic consumer rate price Technology tech advance total real investment INITIAL INDUSTRIAL PRODUCTION US-RP EXCHANGE RATE property taxes Life Expectancy AVG LIFE OF INDUSTRIAL CAPITAL Agricultural Capital GRDP deflator death rate Death Rates per age effect of fossil fuel emission on mortality Industrial Capital depreciation on industrial capital real per capita regional income demand supply indicated producer balance price TIME TO ADJUST PRICE Death Rates per age group INITIAL CAPITAL INDUSTRI relative industrial capital labor cost of capital sector GDP ratio service production GRDP deflator Restricted Activity Days Death Rate Table medium term average real per capita income Indicated Life in USD in PPP Expectancy normal life expectancy Initial Medium Term Average Per Capita Income Investment Industry Sectoral Production Producer Price Agriculture Production LOCAL CONDITION LE normal life expectancy ADJUSTMENT table PARAMETER education index Population INITIAL SECTOR PRODUCER PRICE supply nominal inflation relative price inital per capita demand initial real PC income INITIAL INVESTMENT SHARE perceived relative price INITIAL SECTOR GDP RATIO indicated per capita demand PPP PARAMETER real per capita income in USD in PPP health effect on service productivity W ORKING DAYS IN YEAR initial sectoral demand supply disequilibrium feasible share of per capita demand investment shares adjustment INITIAL RELATIVE PRICE elasticity of demand supply to income gross regional domestic product Industrial Capital Sector investment share INVESTMENT SHARE ADJUSTMENT TIME ELASTICITY OF INVESTMENT TO RELATIVE PRICES MALE-FEMALE LE DIFFERENCE Agricultural INIT AGRI CAPITAL tech effetc on serv prod Technology GRDP deflator real per capita GRDP Population real per capita gross national income INITIAL W ORKER VALUE ADDED IN 2006 TIME DELAY FOR INCOME TO AFFECT LIFE EXPECTANCY service labor productivity Investment Industry Real Investment GRDP deflator TECHNOLOGY EFFECTS ON GRAIN PRODUCTIVITY relative agri capital education effect on service productivity capital intensity service Mean household income Mean household income PERCENTAGE OF AGRICULTURAL INVESTMENT SHARE ON LABOR Technology real budgetary expenditure GRDP deflator AVERAGE FAMILY SIZE agricultural labor demand value added each w orker AVERAGE LIFE CAPITAL SERVICE budgetary expenditure government edu spend Agriculture Production agriculture production increasing rate government other expenditure Grow th Rate Income Level Unit Income Mean pc Income Labor agricultural depreciation investment on agriculture $112,322,964.44 INFLATION RATE Average Labor Cost Agricultural Capital government revenue Exchange Rate USRP government economics services expenditure Capital Service death rate INITIAL POPULATION W ORKFORCE PERCENTAGE AVERAGE W ORKING DAYS Health 15
  • 16. Model Validation Economic Module Real GDRP Per Capita $4.800,00 $4.600,00 $4.400,00 $4.200,00 $4.000,00 $3.800,00 $3.600,00 $3.400,00 2006 2007 2008 JDA 2009 2010 T21 Real GDRP Per Capita IEOM 2014 - 272 16
  • 17. Model Validation (2) Economic Module Service and Industrial Production $45.000.000.000,00 $40.000.000.000,00 $35.000.000.000,00 $30.000.000.000,00 $25.000.000.000,00 $20.000.000.000,00 $15.000.000.000,00 $10.000.000.000,00 $5.000.000.000,00 $2006 SERV - JDA 2007 SERV - T21 2008 IND - JDA 2009 2010 IND - T21 Service Industry IEOM 2014 - 272 17
  • 18. Model Validation (3) Social Module Population 16000000 14000000 12000000 10000000 8000000 6000000 4000000 2000000 JDA 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 0 T21 Population IEOM 2014 - 272 18
  • 19. Model Validation (4) Environmental Module Green Space 90 km² 80 km² 70 km² 60 km² 50 km² 40 km² 30 km² 20 km² 10 km² JDA 2030 2029 2028 2027 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 0 km² T21 Green Space IEOM 2014 - 272 19
  • 20. Outline This presentation is divided into four parts, 1 Background: Jakarta and Climate Change 2 Literature: Sustainable Urban Development 3 Methodology: Modeling Approaches 4 Discussion: Results and Insights IEOM 2014 - 272 20
  • 21. Framework of Analysis: Sustainable City Indicators Health Quality Socially Inclusive Job Openings Unemployment Environmental Friendly Air Quality Clean Water Balance Green Space Proportion Sustainable Development Regional GDP Per Capita Income Economically Competitive Emission Per GDRP IEOM 2014 - 272 21
  • 22. Results and Insights Emission Dynamics • Behavior Over Time (BOT) Graphs of Variables related to GHG Emission Industrial Production vs GHG Emission, Number of Sick days vs GHG Emission • Economic Development • Sectoral productions and proportion Model Insights • Social Indicators • Population growth, employment, and unemployment • Environmental Sustainability • Green space proportion and capabilities. IEOM 2014 - 272 22
  • 23. Industrial Production vs GHG Emission Industrial production is the major emitter of green house gases. The rapid growing of industrial production will significantly boost emission produced. Hence, the pattern of behavior both variables is similar. 12000000 18000000 16000000 10000000 14000000 12000000 10000000 6000000 80000000 4000000 2000000 60000000 Industrial Sektor Industri Produksi Production GHG Gas Rumah Kaca EmisiEmissions 0 IEOM 2014 - 272 40000000 20000000 0 23 tons Emision US Dollars 8000000
  • 24. GHG Emission vs Number of Sick Days Air pollution has strong impacts towars human health (respiratory illlness) and even mortality (Ostro, 1994). Our model captured this relationship and measured in terms of sick days. The increasing number of sick days will induce productivity loss and possibly slow economic growth. 0,035 da 18000000 16000000 0,03 da 14000000 Number of Sick Days 12000000 0,02 da 10000000 80000000 0,015 da 60000000 0,01 da 40000000 0,005 da Sick Days Sick Days 20000000 GHG Gas Rumah Kaca EmisiEmissions 0 da IEOM 2014 - 272 0 24 tons Emision 0,025 da
  • 25. Results and Insights GHG Emission Dynamics • Behavior Over Time (BOT) Graphs of Variables related to GHG Emission Industrial Production vs GHG Emission, Number of Sick days vs GHG Emission • Economic Development • Sectoral productions and proportion Model Insights • Social Indicators • Population growth, employment, and unemployment • Environmental Sustainability • Green space proportion and capabilities. IEOM 2014 - 272 25
  • 26. Economic Development As mentioned earlier, industrial emission will induce economic productivity. Without government intervention, Jakarta will face economic slow down. It is also suspected that Jakarta’s economy has reached a saturation point (limit of growth) 90.000.000.000 131 % “ limit to growth ‖ 80.000.000.000 70.000.000.000 USD 60.000.000.000 50.000.000.000 40.000.000.000 30.000.000.000 industrial production service production Agriculture production real GRDP at factor cost 20.000.000.000 10.000.000.000 0 2006 IEOM 2014 - 272 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 26
  • 27. Economic Development Sectoral Production – Service sector still dominate Jakarta’s economy until 2030 2006 0% Industrial Production 2030 15% 0% 12% 85% 88% Service Production IEOM 2014 - 272 27
  • 28. Social Indicators Stable Growth of Population and Workforce Trends 16.000.000 14.000.000 12.000.000 person 10.000.000 8.000.000 6.000.000 Population 4.000.000 Total Workforce Employment Level 2.000.000 unemployment 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 IEOM 2014 - 272 28
  • 29. Social Indicators Unemployment Proportion 2006 12% 2030 13% 88% 87% Employment Rate IEOM 2014 - 272 Unemployment Rate 29
  • 30. Social Indicators Per Capita Income – “Law of deminishing return” 6.000 5.000 USD 4.000 3.000 2.000 1.000 0 2006 2007 IEOM 2014 - 272 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 30
  • 31. Environmental Sustainability Green Space Proportion – Rapid Grow of Air Purification Capacity 4% 12% 96% 2006 88% 2030 Green Open Space IEOM 2014 - 272 Urbanized Area 31
  • 32. Environmental Sustainability In BAU scenario, the number of emission absorbed by the green space is within range 4 – 8 % per year or 5 percent per year in average. Emission Absorbed IEOM 2014 - 272 Total GHG Emission 32
  • 33. Overall Indicators Result These result serve as a baseline to support our future works. Variables 2006 2030 Change Economic Indicators Real PDRB USD 35.159.022.970 79.974.364.824 127% ↑ Service Production USD 29.847.528.520 70.705.365.349 137% ↑ Social Indicators Population person 8.961.680 13.363.175 49% ↑ Employment person 3.531.799 5.206.481 47% ↑ Unemployment person 473.176 774.906 64% ↑ 1% ↑ 36% ↑ 60% ↑ 61% ↑ 232% ↑ 232% ↑ Unemployment Rate % Real PDRB Per Capita USD/Person Number of Sick Days 12 % 3.834 days 13 % 5.212 20 32 Environmental Sustainability Indicators GHG Emission ton Green Open Space (GOS) km2 GOS Emission Absorption ton IEOM 2014 - 272 34.215.285 24,61 1.400.481,27 55.129.906 81,76 4.652.830,71 33
  • 34. Concluding Remarks Summary • This researh aim to build an integrated development model of Jakarta in order to obtain understanding on how GHG emission affects Jakarta’s urban system structure. • The developed model consist of three modules: economic; social; environmental, as the basic structure of sustainable urban development concept. • The result shows that GHG emission would harm all city’s sectors, especially health equity of people which play a main role as the backbone of Jakarta’s economic. Future Direction • Next step of this research will be focusing on developing policy model and integrate it within this current model. Future model will serve as a medium for policy testing tools and support the government in decision making. Acknowledgement This research is made possible through the support from Regional Government of Jakarta and Institute for Transportation and Development Policy (ITDP) Indonesia for the insights and data support, and University of Indonesia who provide financial support through their research grants programme. Contact System Engineering, Modeling, and Simulation Laboratory Industrial Engineering Department, University of Indonesia irvanu.rahman@yahoo.com | systems.ie.ui.ac.id IEOM 2014 - 272 34
  • 35. Readings Bankes, S. C. (1993). Exploratory Modeling and The Use of Simulation for Policy Analysis. RAND Note Bassi, A. M. (2008). Modelling US Energy Policy with Threshold 21: Understanding Energy Issues and Informing the US Energy Policy Debate with T21, an Integrated Dynamic Simulation Software VDM Verlag Dr. Muller Aktiengesellschaft Chen, M.-C., Ho, T.-P., & Jan, C.-G. (2006). A System Dynamics Model of Sustainable Urban Development: Assessing Air Purification Policies at Taipei City. Asian Pacific Planning Review Vol. 4, No.1 , 1. Cole, M. A., & Neumayer, E. (2006). The Impact of Poor Health on Total Factor Productivity. Routledge: Taylor & Francais - Journal of Development Studies , 918938. Dhakal, S. (2009). Urban energy use and carbon emissions from cities in China and policy implications. Elsevier - Energy Policy , 4208-4219. Feng, Y. Y., Chen, S. Q., & Zhang, L. X. (2012). System Dynamics modeling for urban energy consumption and CO2 emission: A case study of Beijing, China. Elsevier - Ecological Modeling , 1. Firman, T. (2011, Nove). Potential climate-change related vulnerabilities in Jakarta: Challenges and current status. Elsevier - Journal of Habitat International , 1. Fong, W.-K., Matsumoto, H., & Lun, Y.-F. (2009). Application of System Dynamics model as decision making tool in urban planning process toward stabilizing carbon dioxide emissions from cities. Elsevier - Building and Environment , 1528-1537. Guan, D., Gao, W., Su, W., Li, H., & Hokao, K. (2011). Modeling and dynamic assessment of urban economy–resource–environment system with a coupled system dynamics – geographic information system model. Elsevier - Journal of Ecological Indicators . Han, J., & Hayashi, Y. (2008). A system dynamics model of CO2 Mitigation in China's Intercity passenger transport. Elsevier - Transportation Resrarch Part D , 298305. Hidayatno, A., Rahman, I., & Muliadi, R. (2012). A System Dynamics Sustainability Model to Visualize the Interaction Between Economic, Social, and Environmental Aspects of Jakarta's Urban Development. International Seminar on Science and Technology Innovation , 179-183. Rotmans, J., Asselt, M. v., & Vellinga, P. (2000). An integrated planning tool for sustainable cities. Elsevier Science Inc. , 3. Siemens. (2010). Asian Green City Index. Germany: Siemens. Sterman, J. D. (2000). Business Dynamics: System Thinking and Modeling for A Complex World . Boston: The McGraw Hill Companies, Inc. Walker, W. E. (1978). A Reviews of Model in Policy Process. Santa Monica, California: The Rand Corporation. Widyanadiari (2012). Adequacy Analysis of Green Open Space as CO2 Emission Absorber in Urban Area by using Stella Program. Final Year Project. Sepuluh November Institute of Technology, Surabaya. World Bank. (2010). Jakarta: Tantangan Perkotaan Seiring Perubahan Iklim. Jakarta: World Bank. IEOM 2014 - 272 35