The underlying drivers of energy efficiency: a spatial econometric analysis
Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment anal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xing [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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 30(2022), 5 vom: 19. Sept., Seite 13012-13022 |
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| Übergeordnetes Werk: |
volume:30 ; year:2022 ; number:5 ; day:19 ; month:09 ; pages:13012-13022 |
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| DOI / URN: |
10.1007/s11356-022-23037-1 |
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SPR049242520 |
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| 520 | |a Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. | ||
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10.1007/s11356-022-23037-1 doi (DE-627)SPR049242520 (SPR)s11356-022-23037-1-e DE-627 ger DE-627 rakwb eng Wang, Xing verfasserin aut The underlying drivers of energy efficiency: a spatial econometric analysis 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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. Energy efficiency (dpeaa)DE-He213 Spatial dependence (dpeaa)DE-He213 Spatial agglomeration (dpeaa)DE-He213 Direct effect (dpeaa)DE-He213 Indirect effect (dpeaa)DE-He213 Zhou, Dequn aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 5 vom: 19. Sept., Seite 13012-13022 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:5 day:19 month:09 pages:13012-13022 https://dx.doi.org/10.1007/s11356-022-23037-1 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 30 2022 5 19 09 13012-13022 |
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10.1007/s11356-022-23037-1 doi (DE-627)SPR049242520 (SPR)s11356-022-23037-1-e DE-627 ger DE-627 rakwb eng Wang, Xing verfasserin aut The underlying drivers of energy efficiency: a spatial econometric analysis 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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. Energy efficiency (dpeaa)DE-He213 Spatial dependence (dpeaa)DE-He213 Spatial agglomeration (dpeaa)DE-He213 Direct effect (dpeaa)DE-He213 Indirect effect (dpeaa)DE-He213 Zhou, Dequn aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 5 vom: 19. Sept., Seite 13012-13022 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:5 day:19 month:09 pages:13012-13022 https://dx.doi.org/10.1007/s11356-022-23037-1 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 30 2022 5 19 09 13012-13022 |
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10.1007/s11356-022-23037-1 doi (DE-627)SPR049242520 (SPR)s11356-022-23037-1-e DE-627 ger DE-627 rakwb eng Wang, Xing verfasserin aut The underlying drivers of energy efficiency: a spatial econometric analysis 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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. Energy efficiency (dpeaa)DE-He213 Spatial dependence (dpeaa)DE-He213 Spatial agglomeration (dpeaa)DE-He213 Direct effect (dpeaa)DE-He213 Indirect effect (dpeaa)DE-He213 Zhou, Dequn aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 5 vom: 19. Sept., Seite 13012-13022 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:5 day:19 month:09 pages:13012-13022 https://dx.doi.org/10.1007/s11356-022-23037-1 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 30 2022 5 19 09 13012-13022 |
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10.1007/s11356-022-23037-1 doi (DE-627)SPR049242520 (SPR)s11356-022-23037-1-e DE-627 ger DE-627 rakwb eng Wang, Xing verfasserin aut The underlying drivers of energy efficiency: a spatial econometric analysis 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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. Energy efficiency (dpeaa)DE-He213 Spatial dependence (dpeaa)DE-He213 Spatial agglomeration (dpeaa)DE-He213 Direct effect (dpeaa)DE-He213 Indirect effect (dpeaa)DE-He213 Zhou, Dequn aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 5 vom: 19. Sept., Seite 13012-13022 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:5 day:19 month:09 pages:13012-13022 https://dx.doi.org/10.1007/s11356-022-23037-1 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 30 2022 5 19 09 13012-13022 |
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10.1007/s11356-022-23037-1 doi (DE-627)SPR049242520 (SPR)s11356-022-23037-1-e DE-627 ger DE-627 rakwb eng Wang, Xing verfasserin aut The underlying drivers of energy efficiency: a spatial econometric analysis 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. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. Energy efficiency (dpeaa)DE-He213 Spatial dependence (dpeaa)DE-He213 Spatial agglomeration (dpeaa)DE-He213 Direct effect (dpeaa)DE-He213 Indirect effect (dpeaa)DE-He213 Zhou, Dequn aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 30(2022), 5 vom: 19. Sept., Seite 13012-13022 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:30 year:2022 number:5 day:19 month:09 pages:13012-13022 https://dx.doi.org/10.1007/s11356-022-23037-1 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 30 2022 5 19 09 13012-13022 |
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underlying drivers of energy efficiency: a spatial econometric analysis |
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The underlying drivers of energy efficiency: a spatial econometric analysis |
| abstract |
Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China’s energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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The underlying drivers of energy efficiency: a spatial econometric analysis |
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https://dx.doi.org/10.1007/s11356-022-23037-1 |
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Zhou, Dequn |
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10.1007/s11356-022-23037-1 |
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| score |
7.401143 |