Electricity generation and CO2 emissions in China using index decomposition and decoupling approach
Over the past three decades, energy and economic growth have been joined by enormous environmental pollution and growing global concerns. It is necessary to check the factors' effects impacting CO2 emissions and decouple CO2 from economic growth for the biggest emitter China. This study uses th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Energy Strategy Reviews |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X24000117 |
| _version_ | 1797320946800918528 |
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| author | Linying Li Muhammad Yousaf Raza Marco Cucculelli |
| author_facet | Linying Li Muhammad Yousaf Raza Marco Cucculelli |
| author_sort | Linying Li |
| collection | DOAJ |
| description | Over the past three decades, energy and economic growth have been joined by enormous environmental pollution and growing global concerns. It is necessary to check the factors' effects impacting CO2 emissions and decouple CO2 from economic growth for the biggest emitter China. This study uses the logarithmic mean Divisia index extended by introducing electricity substitution factors (i.e., activity, population, electricity intensity, electricity overall, generation structure, energy efficiency, and fuel emission factor effects) and is then combined with Tapio's decoupling method to analyze the CO2 emission drivers, states and sectorial emissions for the years 1991–2020. The findings show that: (1) population and activity effects are the main driving factors in increasing CO2 emissions by adding trade and electricity generation structure effects. (2) Decoupling states presented the two decoupling states through electricity CO2 emissions and economic growth effects, showing that expansive negative decoupling is dominant. This shows that both factors show an increasing return to scale. (3) Individual factors and sectorial decoupling indexes show long-run variations and relationships between them, which means that industrial structure adjustment will help mitigate CO2 emissions and sustain economic development. Finally, based on empirical findings, the results suggest more ambitious targets for emerging low-carbon technologies that could help the rapid decarbonization of China's electricity sector. |
| first_indexed | 2024-03-08T04:50:26Z |
| format | Article |
| id | doaj.art-2a30093bedf649bdb6548de97bf1cc22 |
| institution | Directory Open Access Journal |
| issn | 2211-467X |
| language | English |
| last_indexed | 2024-03-08T04:50:26Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj.art-2a30093bedf649bdb6548de97bf1cc222024-02-08T05:07:49ZengElsevierEnergy Strategy Reviews2211-467X2024-01-0151101304Electricity generation and CO2 emissions in China using index decomposition and decoupling approachLinying Li0Muhammad Yousaf Raza1Marco Cucculelli2School of Management, China University of Mining & Technology, Beijing, Beijing, 100083, ChinaSchool of Economics, Shandong Technology and Business University, Yantai, Shandong, 255000, China; Corresponding author at: School of Economics, Shandong Technology and Business University, Yantai, Shandong, 255000, China.Department of Economics and Social Sciences, Marche Polytechnic University, Piazzale Martelli 8, 60100 Ancona, ItalyOver the past three decades, energy and economic growth have been joined by enormous environmental pollution and growing global concerns. It is necessary to check the factors' effects impacting CO2 emissions and decouple CO2 from economic growth for the biggest emitter China. This study uses the logarithmic mean Divisia index extended by introducing electricity substitution factors (i.e., activity, population, electricity intensity, electricity overall, generation structure, energy efficiency, and fuel emission factor effects) and is then combined with Tapio's decoupling method to analyze the CO2 emission drivers, states and sectorial emissions for the years 1991–2020. The findings show that: (1) population and activity effects are the main driving factors in increasing CO2 emissions by adding trade and electricity generation structure effects. (2) Decoupling states presented the two decoupling states through electricity CO2 emissions and economic growth effects, showing that expansive negative decoupling is dominant. This shows that both factors show an increasing return to scale. (3) Individual factors and sectorial decoupling indexes show long-run variations and relationships between them, which means that industrial structure adjustment will help mitigate CO2 emissions and sustain economic development. Finally, based on empirical findings, the results suggest more ambitious targets for emerging low-carbon technologies that could help the rapid decarbonization of China's electricity sector.http://www.sciencedirect.com/science/article/pii/S2211467X24000117Electricity generationCO2 emissionsTapio's decouplingLMDIChina |
| spellingShingle | Linying Li Muhammad Yousaf Raza Marco Cucculelli Electricity generation and CO2 emissions in China using index decomposition and decoupling approach Energy Strategy Reviews Electricity generation CO2 emissions Tapio's decoupling LMDI China |
| title | Electricity generation and CO2 emissions in China using index decomposition and decoupling approach |
| title_full | Electricity generation and CO2 emissions in China using index decomposition and decoupling approach |
| title_fullStr | Electricity generation and CO2 emissions in China using index decomposition and decoupling approach |
| title_full_unstemmed | Electricity generation and CO2 emissions in China using index decomposition and decoupling approach |
| title_short | Electricity generation and CO2 emissions in China using index decomposition and decoupling approach |
| title_sort | electricity generation and co2 emissions in china using index decomposition and decoupling approach |
| topic | Electricity generation CO2 emissions Tapio's decoupling LMDI China |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X24000117 |
| work_keys_str_mv | AT linyingli electricitygenerationandco2emissionsinchinausingindexdecompositionanddecouplingapproach AT muhammadyousafraza electricitygenerationandco2emissionsinchinausingindexdecompositionanddecouplingapproach AT marcocucculelli electricitygenerationandco2emissionsinchinausingindexdecompositionanddecouplingapproach |