Carbon emissions of power industry in China have accounted for more than half of the total emissions. How to decrease them is important for realizing carbon emission reduction. This paper proposes a carbon market feedback mechanism to power market, comprehensively considering the influence of generation structure, carbon intension, and technological progress on carbon emission reduction in power industry, and builds a potential model based on dynamic system. Operation system results show that the increasing trend of carbon emission can be controlled effectively but always with a lag. At the same time, sensitivity analysis results show that carbon emission reduction can be better realized by adjusting power structure and improving technological level; the former can reduce 32% and the latter can reduce 60% at most.
In the 21st century, climate change has been one of the most serious problems; carbon dioxide, discharged by fossil fuel combustion, plays the main role in causing global greenhouse effect, in which discharge by thermal power accounts for more than half of the total emissions. Facing increasing environmental costs, environmental pollution treatment simply depending on administrative control lacks persistence [
In the research of controlling carbon dioxide emissions through market mechanism, international and domestic academics have made some valuable achievements. Emissions trading is an effective measure to promote carbon emission reduction. The theory of emissions trading comes from the “Pigovian Tax.” Pigou, who is welfare economist, advocates punitive emissions or rewards for emission-controlled enterprises, which could control pollution and protect the environment more effectively [
China’s research on carbon emission reduction mechanism is slightly later than other countries. From the perspective of China’s carbon market, it is appropriate to adopt an allocation method at present, which is free allocation as the main and paid quotas for the auxiliary [
This paper puts forward a set of feedback mechanisms, which applies to the status quo of domestic power industry carbon emissions. Through the carbon market feedback to the electricity market, it restrains carbon emissions growth in power industry. With system dynamics as a platform, this paper builds carbon emission reduction potential model in power industry, sets related parameters according to different scenarios, takes power structure, carbon intensity, and technology progress for sensitivity analysis, and verifies the feasibility and effectiveness of the feedback mechanism according to the operation results.
China’s per unit electricity carbon emission is 1.3–1.4 times that of the United States; in the meantime, the power industry is one of the larger proportion industries in total carbon emissions. How to effectively control carbon emission is the key point for future time. Exploring carbon reduction potential and realization path in power industry is the critical path in the process of developing low carbon economy in our country [
System basic variable causal loop diagram.
According to the analysis of system structure and the modeling principle of system dynamics, the software Vensim was used to draw carbon reduction potential analysis model in power industry [
Modeling language adopts DYNAMO; in the equation,
The system dynamic model of power demand module was shown at the bottom-right part in Figure
Carbon reduction potential system dynamics model in power industry.
The system dynamic model of power generation structure module was shown at the upper-right part in Figure
The system dynamic model of carbon emissions trading module was shown at the upper-left part in Figure
Because of the delay when counting up the value added of carbon emissions, a delay link needs to be set; the delay time is the interval from moment
In order to better study the influence of carbon emissions on the carbon emissions trading market, this paper sets a desired value of carbon emissions and, according to the deviation rate between the carbon emission actual value and the carbon emission target value, calculates the carbon emissions initial quota weighted coefficient, the carbon emissions trading limit weighted coefficient, and the carbon emissions trading price weighted coefficient. Because of the deviation rate, delay link was introduced.
One thing to note is that these weighting coefficients refer in particular to the distributable initial quota of power industry, tradable credits of power industry, and trading price of power industry.
IF THEN ELSE (
The system dynamic model of carbon emissions intensity module was shown at the center part in Figure
Here, changes of power consumption growth rate are revised on the basis of power consumption growth rate calculated by per capita GDP, GDP growth rate, and power demand elasticity. When the difference between carbon emission intensity actual value and the desired value is less than zero, power consumption growth rate stays the same; if the difference is greater than zero, a weight, which is 0.95, is added to the power consumption growth rate.
The data of the China economy and the power industry in 2010 are taken as a benchmark in the model; the model researched effects of carbon emission reduction in electric power industry and key influencing factors in the systems from 2015 to 2050. Among them, the growth rate forecast of electricity consumption primary considered GDP per capita, GDP growth rate, and electricity demand elasticity of the three factors. Table
Some indicators statistics from the 2003 to 2011.
Years | Electricity consumption growth (%) | GDP per capita (ten thousand yuan/people) | GDP growth rate (%) | Electricity demand elasticity |
---|---|---|---|---|
2003 | 15.30 | 1.04 | 10.00 | 1.56 |
2004 | 15.46 | 1.23 | 10.10 | 1.52 |
2005 | 13.90 | 1.40 | 11.30 | 1.19 |
2006 | 14.16 | 1.64 | 12.70 | 1.15 |
2007 | 14.42 | 2.02 | 14.20 | 1.01 |
2008 | 5.49 | 2.38 | 9.60 | 0.58 |
2009 | 6.44 | 2.55 | 9.20 | 0.78 |
2010 | 14.77 | 2.98 | 10.40 | 1.27 |
2011 | 11.97 | 3.50 | 9.30 | 1.30 |
The results show that there are significant and stable positive correlations among GDP per capita, GDP growth rate, and electricity demand elasticity.
For the control measures of carbon market can be effectively fed back to the electricity market, this model considers the deviation between the actual carbon emission and the target value and develops some weighting coefficients, including power industry carbon emissions initial quota weighted coefficient, trade quota weighted coefficient, and carbon trading price weighted coefficient. By adjusting the weighting coefficient, the model adjusts the carbon emission target and then controls the power industry carbon emissions [
Then, the model takes into account the goal of reducing carbon emissions to formulate a carbon emissions target; combined with the actual carbon emissions to compare, the power industry carbon emissions quotas initial weighting coefficient is adjusted based on the deviation rate, detailed as follows:
Then, the model takes into account the goal of reducing carbon emissions to formulate a carbon emissions target; combined with the actual carbon emissions to compare, the power industry carbon emissions trading credits weighting factor is adjusted based on the deviation rate, detailed as follows:
Then, the model takes into account the goal of reducing carbon emissions to develop a carbon emissions target; combined with the actual carbon emissions to compare, power industry carbon emissions trading price weighting coefficient is adjusted based on the deviation rate, detailed as follows:
Multiple scenarios parameter design.
The impact factor of the power structure | Carbon intensity target factor | Impact factor of technological advances | |
---|---|---|---|
BASE | 1 | 1 | 1 |
CASE 1 | 0.9 | 1 | 1 |
CASE 2 | 0.8 | 1 | 1 |
CASE 3 | 0.7 | 1 | 1 |
CASE 4 | 0.6 | 1 | 1 |
CASE 5 | 1 | 0.9 | 1 |
CASE 6 | 1 | 0.8 | 1 |
CASE 7 | 1 | 0.7 | 1 |
CASE 8 | 1 | 0.6 | 1 |
CASE 9 | 1 | 1 | 0.9 |
CASE 10 | 1 | 1 | 0.8 |
CASE 11 | 1 | 1 | 0.7 |
CASE 12 | 1 | 1 | 0.6 |
Among them, the impact factors of the power structure adjust the whole system by affecting thermal power generation ratio; then carbon emissions and carbon intensity are changed; the impact factors of carbon intensity target adjust the whole system by affecting carbon emission target; then carbon emissions of power industry were affected; impact factors of technical progress adjust the whole system by reducing coal consumption rate and other parameters; then carbon emissions of power industry were affected.
Based on the simulation system constructed in this paper, parameters under the basic situation and data in 2010 are input; the operating system obtains relevant factors as shown in Table
Some parameters operating results under basic scenario.
Year | The proportion of thermal power generation |
Carbon emissions intensity (T/O) | Electricity consumption growth |
The proportion of the power industry carbon emission |
Power industry carbon emissions (ten thousand tons) | Deviation rate and the target of carbon emission intensity |
Cost ratio of carbon trading |
---|---|---|---|---|---|---|---|
2010 | 0.800 | 3.1983 | 0.0687 | 0.4677 | 224029 | 0.3153 | 0.0043 |
2015 | 0.710 | 3.4633 | 0.0717 | 0.2912 | 279351 | 0.3821 | 0.0080 |
2020 | 0.680 | 3.3796 | 0.0717 | 0.2550 | 378294 | 0.3816 | 0.0112 |
2025 | 0.661 | 3.1620 | 0.0705 | 0.2533 | 518953 | 0.3548 | 0.0149 |
2030 | 0.648 | 2.9223 | 0.0688 | 0.2671 | 712318 | 0.3190 | 0.0200 |
2035 | 0.637 | 2.7073 | 0.0672 | 0.2905 | 974315 | 0.2834 | 0.0275 |
2040 | 0.628 | 2.5312 | 0.0659 | 0.3211 | 1326818 | 0.2533 | 0.0389 |
2045 | 0.621 | 2.3943 | 0.0649 | 0.3579 | 1800051 | 0.2315 | 0.0562 |
2050 | 0.614 | 2.2912 | 0.0643 | 0.3999 | 2436133 | 0.2187 | 0.0828 |
(1) refers to the unit of each parameter.
Figure
The trend of thermal power plants electricity generated proportion under the baseline scenario.
The trend of carbon intensity under the baseline scenario.
Figure
The trend of electricity consumption growth rate under the baseline scenario.
The trend of power industry carbon emissions under the baseline scenario.
Figure
The trend of proportion of power industry carbon emissions under the baseline scenario.
In order to study the key elements of carbon emission reduction in power industry, this paper performs sensitivity analysis on impact factors-generation structure, carbon intension, and technological progress and gets relevant conclusions according to the analysis results.
According to the parameters in Table
The trend of carbon emission intensity under different scenarios.
Figure
The trend of electricity consumption growth rate under different scenarios
Figure
The trend of carbon emissions in power industry under different scenarios
The trend of proportion of carbon emissions in power industry
According to the parameters in Table
The trend of carbon emissions in power industry under different scenarios
The trend of proportion of carbon emissions in power industry under different scenarios
Figure
The trend of carbon emission intensity under different scenarios
The trend of electricity consumption growth rate under different scenarios
According to the parameters in Table
The trend of carbon emissions in power industry under different scenarios
The trend of proportion of carbon emissions in power industry under different scenarios
Figure
The trend of carbon emission intensity under different scenarios
The trend of electricity consumption growth rate under different scenarios
Through the sensitivity analysis for the key factors of carbon emission reduction in power industry, the carbon emission reduction effect of adjusting technological progress is the most obvious, the adjustment of generation structure takes second place, and the adjustment of carbon intension is the worst, which may even increase carbon emissions. The emission reduction effect of three key factors was shown in Figure
The trend of emission reduction factor sensitivity results.
Through building carbon reduction potential analysis model in power industry, this paper discusses the effectiveness of the carbon market feedback mechanism on power market, conducts the sensitivity analysis for the key factors of carbon emission reduction, and draws the following conclusions.
Total value
Birth or death rate
GDP growth rate
Electricity consumption growth rate
Power demand elasticity coefficient
Error coefficient of fitting function
Consumption
Proportion of thermal power
The proportion of each energy
Power generation structure impact factors
Proportion parameters of each energy
Weighted coefficient
Emission intensity
The desired value of thermal power generation proportion
The difference between actual carbon emission and desired value
Delay coefficient
Carbon emissions in power industry
Carbon emissions coefficient
Total carbon emissions
Target value.
Time period from
The amount of change
Delay function
Integral function.
The authors declare that there are no conflicts of interest.
This paper is supported by the Fundamental Research Funds for the Central Universities (2016XS82), the National Natural Science Foundation of China (Grants nos. 71273090 and 71573084), and the Beijing Municipal Social Science Foundation (16JDYJB044).