Policy Effects Assessment System Dynamics Model of Regional Low-carbon Development Prof. Tan Xin,Tianjin University February, 01 Adelaide
Contents 1 Significance of Constructing a SD Model of Regional Low-carbon Development Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Verification of the Model and Future Scenario Simulation 4 Conclusion and Prospect
1 Significance of Constructing a SD Model of Regional Low-carbon Development 1 A low-carbon economy has two basic goals, economic growth and low-carbon emission. These two goals should be achieved by the government at the same time by means of proper policy making with great resolution and necessary promotion. As for a country or a smaller region, it is important to make necessary evaluation before the implementation of any policy set. Using System Dynamic model as a "policy laboratory" of reality, we can effectively simulate the future scenario of regional development after the implementation of the policy set.
Contents 1 Significance of Constructing a SD Model of Regional Low-carbon Development Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Verification of the Model and Future Scenario Simulation 4 Conclusion and Prospect
Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development The structure of regional low-carbon economy system could refer to the basic structure of economy, environment and energy (E) system design. The setting of subsystems of low-carbon economic system was designed with reference to the sustainable development model with 5 subsystems as economy, environment, population, resources, and society. The basic structure of the regional low-carbon economic system refers to the sustainable development model, but with innovation and improvement, as shown in Fig. 1.
Resources outside Region Import & Export Boundary Resources Structure Storage Renew Load/Limit Experience Support vs. Consume Control Resources Supply & Alternative compensation Learning Manage/F eedback Money outside Region Exchange Economy Cost Structure Benefit Fond & Job Labor & Tech. Socio-government Public Admin Policy Control Edu-tech development Carbon Emission. space Environment Capability Pollution Self-purification Mutual Impact Environment outside Region Consume VS Produce Training& Supply Protect/Damage VS. load Quantity Quality Exchange outside Region Fig. 1 Basic Structure of Regional Lowcarbon Development System Model
Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Based on system dynamics theory, we identify the main causal factors of regional low-carbon economy systems, as shown in Fig.. Carbon Tax Policy - Carbon Emission Energy Consumption - Technological Progress Policy - Policy of Public Expenditure Labor - Quality of Labor Force GDP Tax Policy Fiscal Revenue Economy Policy Investment in Education and Technology Fig. Cause-and-effect graph of SD model
Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Generally, people will pursue an increase in GDP and minimize carbon emissions, which give the system different feedback mechanisms in operation. As we can see, the causal loop diagram contains the following main causal feedback chain. Positive feedback : 1 Economic policy GDP Public expenditure policy Investment in Education and Technology Quality of labor force population GDP; Public expenditure policy Investment in Education and Technology Technology development GDP 4 GDP Public expenditure Carbon emission 5 Labor force population GDP 6 Tax policy Public expenditure 7 Energy consumption Carbon emission Negative feedback : 1 Technology development Energy consumption Carbon emission Carbon tax policy Carbon emission policy Quality of labor force population
Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Fig. is the flowchart of a low-carbon economy SD model. Correlations between variables and state equations in the SD model are deduced from historical data using statistical methods. Vensim software is used to establish the SD model to make a systematic research on the regional low-carbon development.
<Technology Development> Carbon Policy Factor Carbon Intensity of <Energy Consumption> Reduction of Energy Consumption Carbon Intensity <GDP> Carbon Emission Carbon Emission of Living Carbon Emission Reduction Carbon Emission Generation <Time> <Time> <Policy Influence Factor> Growth Rate Growth Proportion of Investment in Education Fiscal Revenue Income Tax Policy Energy Emission Factor GDP per capita Fiscal Revenue Gas Consumption Elasticity Coefficie Policy Influence Factor <GDP Growth Rate> New Energy Consumption <GDP> Labor Force Investment in Education Gas Consumption Gas Consumption Growth Rate Quality of Labor Force Investment in Technology New Energy Development Growth Rate Energy Consumption <GDP Growth Rate> Proportion of Investment in Technology Traditional Energy Consumption Labor Capital Contribution Rate Technology Development Petroleum Consumption GDP Growth Rate Coal Consumption GDP Coal Consumption Growth Rate Fig. Flowchart of low-carbon economy system dynamic model <Policy Influence Factor> Petroleum Consumption Growth Rate Capital Contribution Rate GDP Growth Capital Stock Coal Consumption <GDP Growth Elasticity Coefficient Rate> <Time> Petroleum Consumption Elasticity Coefficient <Time>
Contents 1 Significance of Constructing a SD Model of Regional Low-carbon Development Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Verification of the Model and Future Scenario Simulation 4 Conclusion and Prospect
Verification of the Model and Future Scenario Simulation.1 Verification Although the SD model has broad applicability, the current research is using China as a case study, but. Figure and Table 1& show the comparison of simulated data and statistics data of China from the year 001 to 009. The statistical data is from the China Statistical Yearbook and the data of carbon emissions is based on literature data. By comparing the simulation results and the previous statistics, the error between them does not exceed 10%, so the model is effective. x 10 5 7 6 5 Bule-Simulation Numbers Red-Statistic Numbers Compare simulation and statistic data Carbon Emission Engergy Consumption GDP Revenue Carbon Emission-R Engergy Consumptio-R GDP-R -R Revenue-R 4 1 0 001 00 00 004 005 006 007 008 009 Fig. Verification of the model by comparing the simulation data and statistic data of China
Verification of the Model and Future Scenario Simulation Table 1 Simulation Results Year Carbon Emission (10 thousand tons) Energy Consumption (10 thousand tons) GDP (100 million yuan) (10 thousand ) Fiscal Revenue (100 million yuan) 001 54. 150406 108068 1767 1686 00 75489.7 16005.8 15089.8 10115.7 0151.76 00 4685.4 0640.6 14479.6 1196. 466.1 004 500770.4 449.9 167610.8 1869.6 995.57 005 59897.1 41147.8 19409 15140.5 6.6 006 557774. 57657.9 4650.4 16017.5 468 007 585495.8 74407.8 60110 1651.1 516.1 008 61977.6 9150.6 01189.1 1668.7 6501.44 009 640655 08716. 4878. 16414.1 7441. Year Carbon Emission (10 thousand tons) Table Data of China Statistical Yearbook Energy Consumption (10 thousand tons) GDP(100 million yuan) (10 thousand) 001 -- 150406 108068 1767 1686 00 1400 15941 119095 1845 1890 00 41000 1879 15174 197 1715 004 9800 1456 159586 19988 696 005 461100 5997 185808 10756 1649 006 511000 58676 175 11448 8760 007 559900 80508 6776 119 511 008 60700 91448 168 180 610 009 66000 06647 4464 1474 68518 Fiscal Revenue (100 million yuan)
Verification of the Model and Future Scenario Simulation. Simulation According to China s carbon emission reduction target (COP15, The United Nations Climate Change conference in Copenhagen), the emission intensity of 00 will be reduced by 40% to 45% compared to the emissions level of 005. The carbon emission intensity of 005 is.7 tons per 10 thousands RMB. This means the carbon intensity level of 00 should be lowered down to 1.5 ton per 10 thousands RMB. Continuing the policy setting used in the validation of the model, we can simulate the scenario of China s low-carbon development in 00. Fig. shows the simulation results of this scenario. Table Simulation data of China s low-carbon development of 00 Year 00 GDP (100 million yuan) 1757469 (10 thousand) 116679 Fiscal Revenue (100 million yuan) 44097 Energy Consumption(10 thousand tons) 56 Carbon Emission(10 thousand tons) 890107
According to the simulation results, China 01-00 year low carbon development trend of the indicator as shown in Fig. 4. In this scenario, the government retains China s family planning policy, keeps the GDP growth rate at 15% per year. The carbon intensity of 00 is 0.51 tons per 10 thousands RMB. x 10 5 18 16 人口能源消耗量碳排放量 GDP 财政收入 14 1 10 8 6 4 0 01 01 014 015 016 017 018 019 00 Fig. 4 China s low carbon development forecast figure of 01-00
Verification of the Model and Future Scenario Simulation. Simulation Now we tried another policy set as the input figures in the Vensim program model in order to get a new scenario to see what result would be. In this scenario the carbon emission intensity in 00 is 1.6 ton/per 10 thousands RMB, which is reduced by 41% compared to 005 s intensity level. This reduction achieves the reduction target. 18 x 10 5 Fiscal Revenue Energy Consumption 16 GDP Carbon Emission 14 1 10 8 6 4 0 01 01 014 015 016 017 018 019 00 Fig. 5 New Simulation results from 01-00
Contents 1 Significance of Constructing a SD Model of Regional Low-carbon Development Structure of Policy Effects Assessment SD Model of Regional Low-carbon Development Verification of the Model and Future Scenario Simulation 4 Conclusion and Prospect
4 Conclusion and Prospect 1. According to the simulation, both scenarios showed in this presentation are able to achieve the policy goal. The higher growth rate of GDP can be a main factor to achieve a lower carbon intensity, although the total carbon emission could be comparatively huge.. Building a SD model and simulate the future scenario is only the first step of analysis the policy decision making. The next step is to find a proper way to determine which policy set is the best or most proper one under same circumstances.. In order to select the best set of policies, decision making method needs to be introduced. There is a need to provide decision makers with a simple mechanism with less human interference (that is yet still transparent) that integrates all key criteria and allocates some in particular to be chosen as critical criteria.
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