Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement
China’s electricity sector mainly relies on coal-fired thermal generation, thus resulting that nearly 50% of China’s total CO2 emissions coming from the electricity sector. This study focuses on the provincial CO2 emissions from China’s thermal electricity generation. M...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/5/1096 |
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| author | Qingyou Yan Yaxian Wang Tomas Baležentis Yikai Sun Dalia Streimikiene |
| author_facet | Qingyou Yan Yaxian Wang Tomas Baležentis Yikai Sun Dalia Streimikiene |
| author_sort | Qingyou Yan |
| collection | DOAJ |
| description | China’s electricity sector mainly relies on coal-fired thermal generation, thus resulting that nearly 50% of China’s total CO2 emissions coming from the electricity sector. This study focuses on the provincial CO2 emissions from China’s thermal electricity generation. Methodologically, Index Decomposition Analysis (IDA), facilitated by the Shapley Index, is applied to discover the driving factors behind CO2 emission changes at the provincial level. In addition, the Slack-based Model (SBM) is used to identify which provincial power grids should be allocated with a higher (lower) CO2 reduction burden. The IDA results indicate that economic activity pushed the CO2 emissions up in all 30 provincial power grids, excluding Beijing and Shanghai; the carbon factor contributed to a decrease in the CO2 emissions in all 30 provincial power grids, with the exception of Jilin, Guangdong, and Ningxia; though the effect of energy intensity varied across the 30 provinces, it played a significant role in the mitigation of CO2 emissions in Beijing, Heilongjiang, Liaoning, Jilin, Shanghai, Anhui, and Sichuan. According to the SBM results, the lowest carbon shadow prices are observed in Yunnan, Shanghai, Inner Mongolia, Jilin, Qinghai, Guizhou, Anhui, and Ningxia. These provincial power grids, thus, should face higher mitigation targets for CO2 emissions from thermal electricity generation. |
| first_indexed | 2024-04-14T06:39:28Z |
| format | Article |
| id | doaj.art-c3cc59e124bb4d36bd72c35f09c03166 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T06:39:28Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c3cc59e124bb4d36bd72c35f09c031662022-12-22T02:07:22ZengMDPI AGEnergies1996-10732018-04-01115109610.3390/en11051096en11051096Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for AbatementQingyou Yan0Yaxian Wang1Tomas Baležentis2Yikai Sun3Dalia Streimikiene4School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaLithuanian Institute of Agrarian Economics, Kudirkos Str. 18-2, Vilnius LT-03105, LithuaniaState Grid Zhejiang Economy Research Institute, Hangzhou 310008, ChinaLithuanian Institute of Agrarian Economics, Kudirkos Str. 18-2, Vilnius LT-03105, LithuaniaChina’s electricity sector mainly relies on coal-fired thermal generation, thus resulting that nearly 50% of China’s total CO2 emissions coming from the electricity sector. This study focuses on the provincial CO2 emissions from China’s thermal electricity generation. Methodologically, Index Decomposition Analysis (IDA), facilitated by the Shapley Index, is applied to discover the driving factors behind CO2 emission changes at the provincial level. In addition, the Slack-based Model (SBM) is used to identify which provincial power grids should be allocated with a higher (lower) CO2 reduction burden. The IDA results indicate that economic activity pushed the CO2 emissions up in all 30 provincial power grids, excluding Beijing and Shanghai; the carbon factor contributed to a decrease in the CO2 emissions in all 30 provincial power grids, with the exception of Jilin, Guangdong, and Ningxia; though the effect of energy intensity varied across the 30 provinces, it played a significant role in the mitigation of CO2 emissions in Beijing, Heilongjiang, Liaoning, Jilin, Shanghai, Anhui, and Sichuan. According to the SBM results, the lowest carbon shadow prices are observed in Yunnan, Shanghai, Inner Mongolia, Jilin, Qinghai, Guizhou, Anhui, and Ningxia. These provincial power grids, thus, should face higher mitigation targets for CO2 emissions from thermal electricity generation.http://www.mdpi.com/1996-1073/11/5/1096CO2 emissionsindex decomposition analysisshadow pricesenvironmental efficiency |
| spellingShingle | Qingyou Yan Yaxian Wang Tomas Baležentis Yikai Sun Dalia Streimikiene Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement Energies CO2 emissions index decomposition analysis shadow prices environmental efficiency |
| title | Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement |
| title_full | Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement |
| title_fullStr | Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement |
| title_full_unstemmed | Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement |
| title_short | Energy-Related CO2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement |
| title_sort | energy related co2 emission in china s provincial thermal electricity generation driving factors and possibilities for abatement |
| topic | CO2 emissions index decomposition analysis shadow prices environmental efficiency |
| url | http://www.mdpi.com/1996-1073/11/5/1096 |
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