CO2 emissions from China’s power industry: Policy implications from both macro and micro perspectives

Cui, Herui; Zhao, Tian; Wu, Runlong

doi:10.1016/j.jclepro.2018.07.330

Cited by 38 publications

(14 citation statements)

References 19 publications

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“…Line loss rate is found to be a critical factor negatively influencing the growth of CO 2 emissions of the electricity sector, and such a conclusion is consistent with [42,52]. This indicates the loss rate of electricity during the transport process has been decreasing, leading to the amount of electricity reaching the terminal to rise.…”

Section: Discussionsupporting

confidence: 68%

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Cui

Zhao

2018

Energies

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China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy.

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Section: Discussionsupporting

confidence: 68%

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Cui

Zhao

2018

Energies

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“…According to the idea of biological evolution, when the fitness of a certain strategy is better than the average fitness of the ethnic groups, the strategy is gradually adopted by more individuals, and eventually developed, or it will gradually decay. Finally, the system reaches an evolutionary stable state [37][38][39].…”

Section: Evolutionary Game Modelmentioning

confidence: 99%

Study on China’s Renewable Energy Policy Reform and Improved Design of Renewable Portfolio Standard

et al. 2019

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China officially implemented the renewable portfolio standard (RPS) on 1 January 2019, and it remains uncertain as to whether this can effectively solve the problem of renewable energy consumption in China and ease the pressure of government subsidies. In order to study the impact of this policy on China's renewable energy power generation and explore RPS policy that is more suitable for the characteristics of China's renewable energy, we first develop a revenue function model based on the just released RPS policy to explore the effectiveness of the policy, the feasibility conditions for successful implementation, and the problems that may be encountered during the implementation process. Then, we propose policy recommendations based on the possible problems of the current policy and design an "incremental electricity price" supplementary policy to improve the possibility of successful implementation of the RPS policy. Finally, an evolutionary game model is established to simulate and verify the possibility of successful implementation of the supplementary policy. The main research results are: (1) the essence of the current RPS policy is the comprehensive implementation policy of the RPS and feed-in-tariff (FiT); (2) because of the characteristics of China's energy structure, the implementation of this policy reform is more resistant; (3) the quantitative research on the revenue function model shows that the current transaction price of the green certificate market is very low, which is not conducive to alleviating the state's subsidy pressure on renewable energy power generation; and (4) analysis of empirical data shows that the successful implementation of the "incremental electricity price" policy relies on the initial strategies of grid companies and users. , the feasibility of successful implementation, and the problems that may be encountered during implementation. Then, based on the above analysis, a policy more suitable for the development of China's renewable energy power generation is designed to improve the possibility of successful implementation of the RPS mechanism in China. Review of the Literatures Research on Renewable Portfolio Standard Policy ApplicationThe number of FiT adopters increased from 2 in 1990 to approximately 70 in 2018, while RPS adopters increased from 1 in 1999 to approximately 15 in 2018. Countries from every geographic region and income level have adopted these policies. Contrary to what rational self-interested actors would do based on costs, benefits, and utility maximization, some of the top oil exporters in the world have adopted RPS or FiT. These countries include Iran and the United Arab Emirates in the Middle East, Kazakhstan in Asia, Nigeria and Algeria in Africa, Norway and the United Kingdom in Europe, and Brazil in the Americas and so on [11][12][13].The analysis of an effective support policy is the basis for promoting the development of renewable energy. Like China, several other countries have also experienced policy reforms from RPS to FiT or from FiT to RPS. Ac...

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“…On the other hand, to formulate effective carbon mitigation policy, a number of carbon reduction studies related to electricity generation have been conducted at multiple levels. At the industry level, Cui et al (2018) explored the comprehensive policy implications of carbon reduction for the power industry in China from both macro and micro perspectives. Li et al (2018) used the dynamic computable general equilibrium model to quantitatively analyze the impact of carbon-emission trading on the power industry.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Interprovincial Cooperative Carbon Reduction in China: An Electricity Generation Perspective

Zeng

Zhao

et al. 2021

Front. Energy Res.

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As the world faces great challenges from climate change, carbon reduction has become China’s basic national policy. However, as the main model for carbon reduction in China, the non-cooperative carbon reduction model (NCCRM) is a simple regulation mode, which is not beneficial for optimization of electricity generation capacity structure and cannot effectively motivate carbon reduction at the provincial level. Therefore, we propose an interprovincial cooperative carbon reduction model (CCRM) from the perspective of electricity generation, which provides a mechanism integrating two fundamental principles of efficiency and fairness. The CCRM consists of two parts: (1) an optimal model of carbon reduction with the object of minimizing the carbon emission of the cooperation union to determine the optimal annual electricity generation for each participating province and (2) a model that distributes the economic benefits of the cooperation among the provinces in the cooperation based on the Shapley value method. We applied the CCRM to the case of an interprovincial union of Shanghai, Sichuan, Shanxi, and Gansu in China. The results, based on the data from 2014 to 2017, show that cooperation can significantly reduce the carbon emission of the union by 425.78 × 108 kg, 11.06%; meanwhile, Shanghai, Sichuan, Shanxi, and Gansu can, respectively, get 2.79 × 108, 11.11 × 108, 4.07 × 108, and 3.19 × 108 CNY of extra benefits from carbon reduction. To test the impact of different parameter values on the results of the CCRM, a sensitivity analysis was conducted. Some policy recommendations are proposed to promote the implementation of the CCRM.

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CO2 emissions from China’s power industry: Policy implications from both macro and micro perspectives

Cited by 38 publications

References 19 publications

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Study on China’s Renewable Energy Policy Reform and Improved Design of Renewable Portfolio Standard

Modeling Interprovincial Cooperative Carbon Reduction in China: An Electricity Generation Perspective

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