Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective
Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation r...
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| Autor*in: |
Wang, Ting [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 29(2022), 51 vom: 10. Juni, Seite 77908-77926 |
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| Übergeordnetes Werk: |
volume:29 ; year:2022 ; number:51 ; day:10 ; month:06 ; pages:77908-77926 |
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| DOI / URN: |
10.1007/s11356-022-21202-0 |
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SPR048412147 |
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| 520 | |a Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. | ||
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10.1007/s11356-022-21202-0 doi (DE-627)SPR048412147 (SPR)s11356-022-21202-0-e DE-627 ger DE-627 rakwb eng Wang, Ting verfasserin aut Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. Energy consumption structure (dpeaa)DE-He213 Income structure (dpeaa)DE-He213 Panel data (dpeaa)DE-He213 Primary energy structure (dpeaa)DE-He213 Coal-based energy structure (dpeaa)DE-He213 Decomposition methodology (dpeaa)DE-He213 Liu, Jianghua (orcid)0000-0002-0933-1485 aut Xu, Yongqiang aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 51 vom: 10. Juni, Seite 77908-77926 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:51 day:10 month:06 pages:77908-77926 https://dx.doi.org/10.1007/s11356-022-21202-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 51 10 06 77908-77926 |
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10.1007/s11356-022-21202-0 doi (DE-627)SPR048412147 (SPR)s11356-022-21202-0-e DE-627 ger DE-627 rakwb eng Wang, Ting verfasserin aut Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. Energy consumption structure (dpeaa)DE-He213 Income structure (dpeaa)DE-He213 Panel data (dpeaa)DE-He213 Primary energy structure (dpeaa)DE-He213 Coal-based energy structure (dpeaa)DE-He213 Decomposition methodology (dpeaa)DE-He213 Liu, Jianghua (orcid)0000-0002-0933-1485 aut Xu, Yongqiang aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 51 vom: 10. Juni, Seite 77908-77926 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:51 day:10 month:06 pages:77908-77926 https://dx.doi.org/10.1007/s11356-022-21202-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 51 10 06 77908-77926 |
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10.1007/s11356-022-21202-0 doi (DE-627)SPR048412147 (SPR)s11356-022-21202-0-e DE-627 ger DE-627 rakwb eng Wang, Ting verfasserin aut Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. Energy consumption structure (dpeaa)DE-He213 Income structure (dpeaa)DE-He213 Panel data (dpeaa)DE-He213 Primary energy structure (dpeaa)DE-He213 Coal-based energy structure (dpeaa)DE-He213 Decomposition methodology (dpeaa)DE-He213 Liu, Jianghua (orcid)0000-0002-0933-1485 aut Xu, Yongqiang aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 51 vom: 10. Juni, Seite 77908-77926 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:51 day:10 month:06 pages:77908-77926 https://dx.doi.org/10.1007/s11356-022-21202-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 51 10 06 77908-77926 |
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10.1007/s11356-022-21202-0 doi (DE-627)SPR048412147 (SPR)s11356-022-21202-0-e DE-627 ger DE-627 rakwb eng Wang, Ting verfasserin aut Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. Energy consumption structure (dpeaa)DE-He213 Income structure (dpeaa)DE-He213 Panel data (dpeaa)DE-He213 Primary energy structure (dpeaa)DE-He213 Coal-based energy structure (dpeaa)DE-He213 Decomposition methodology (dpeaa)DE-He213 Liu, Jianghua (orcid)0000-0002-0933-1485 aut Xu, Yongqiang aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 51 vom: 10. Juni, Seite 77908-77926 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:51 day:10 month:06 pages:77908-77926 https://dx.doi.org/10.1007/s11356-022-21202-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 51 10 06 77908-77926 |
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10.1007/s11356-022-21202-0 doi (DE-627)SPR048412147 (SPR)s11356-022-21202-0-e DE-627 ger DE-627 rakwb eng Wang, Ting verfasserin aut Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. Energy consumption structure (dpeaa)DE-He213 Income structure (dpeaa)DE-He213 Panel data (dpeaa)DE-He213 Primary energy structure (dpeaa)DE-He213 Coal-based energy structure (dpeaa)DE-He213 Decomposition methodology (dpeaa)DE-He213 Liu, Jianghua (orcid)0000-0002-0933-1485 aut Xu, Yongqiang aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 29(2022), 51 vom: 10. Juni, Seite 77908-77926 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:29 year:2022 number:51 day:10 month:06 pages:77908-77926 https://dx.doi.org/10.1007/s11356-022-21202-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 51 10 06 77908-77926 |
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Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective |
| abstract |
Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract China’s coal-based energy structure makes its carbon peak and neutrality goals very challenging. As a result, optimizing the energy structure has become an important means, and researching its influencing factors and trends has become the foundation and prerequisite for policy formulation related to energy structure optimization. Especially after the severe economic crisis, the economic structure has undergone profound changes, and the impact of related factors on the energy structure has also changed. This study adopts regression methods considering heteroskedasticity and cross-section correlation to study the panel data of 30 provinces in China and obtains the changes in the relationship between fossil and non-fossil fuel consumption in different regions and their influencing factors after two global financial crises. Research results show that China’s energy consumption tends to decouple from GDP. Income structure changes, especially trade changes, are important factors in influencing energy consumption and energy structure. The deviation between energy resource distribution and consumption distribution tends to increase. Regional heteroskedasticity is evident in the impact of urbanization development models, energy prices, and efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Primary energy consumption structure and the influencing factors in China: an income decomposition and post-economic crisis era perspective |
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Liu, Jianghua Xu, Yongqiang |
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| score |
7.4001484 |