A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions
The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a rea...
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Gespeichert in:
| Autor*in: |
Wu, Fei [verfasserIn] Ji, Dan-Jun [verfasserIn] Zha, Dong-Lan [verfasserIn] Zhou, De-Qun [verfasserIn] Zhou, Peng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Rechteinformationen: |
Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of management science and engineering - Amsterdam : Elsevier, 2016, 7(2022), 2 vom: Juni, Seite 330-345 |
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| Übergeordnetes Werk: |
volume:7 ; year:2022 ; number:2 ; month:06 ; pages:330-345 |
| Links: |
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| DOI / URN: |
10.1016/j.jmse.2021.12.001 |
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1809721083 |
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| 520 | |a The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. | ||
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10.1016/j.jmse.2021.12.001 doi (DE-627)1809721083 (DE-599)KXP1809721083 DE-627 ger DE-627 rda eng Wu, Fei verfasserin (DE-588)1196035679 (DE-627)1677909366 aut A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions Fei Wu, Dan-Jun Ji, Dong-Lan Zha, De-Qun Zhou, Peng Zhou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Marginal abatement cost (dpeaa)DE-206 Carbon dioxide emissions (dpeaa)DE-206 Directional distance function (dpeaa)DE-206 Weak disposability (dpeaa)DE-206 Data envelopment analysis (dpeaa)DE-206 Ji, Dan-Jun verfasserin aut Zha, Dong-Lan verfasserin aut Zhou, De-Qun verfasserin aut Zhou, Peng verfasserin (DE-588)1208216678 (DE-627)1694426548 aut Enthalten in Journal of management science and engineering Amsterdam : Elsevier, 2016 7(2022), 2 vom: Juni, Seite 330-345 Online-Ressource (DE-627)1665781963 (DE-600)2972364-4 2589-5532 nnns volume:7 year:2022 number:2 month:06 pages:330-345 https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.jmse.2021.12.001 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 2 6 330-345 26 01 0206 4163949291 x1z 11-07-22 2403 01 DE-LFER 4190587788 00 --%%-- --%%-- n --%%-- l01 21-09-22 2403 01 DE-LFER https://doi.org/10.1016/j.jmse.2021.12.001 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf |
| spelling |
10.1016/j.jmse.2021.12.001 doi (DE-627)1809721083 (DE-599)KXP1809721083 DE-627 ger DE-627 rda eng Wu, Fei verfasserin (DE-588)1196035679 (DE-627)1677909366 aut A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions Fei Wu, Dan-Jun Ji, Dong-Lan Zha, De-Qun Zhou, Peng Zhou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Marginal abatement cost (dpeaa)DE-206 Carbon dioxide emissions (dpeaa)DE-206 Directional distance function (dpeaa)DE-206 Weak disposability (dpeaa)DE-206 Data envelopment analysis (dpeaa)DE-206 Ji, Dan-Jun verfasserin aut Zha, Dong-Lan verfasserin aut Zhou, De-Qun verfasserin aut Zhou, Peng verfasserin (DE-588)1208216678 (DE-627)1694426548 aut Enthalten in Journal of management science and engineering Amsterdam : Elsevier, 2016 7(2022), 2 vom: Juni, Seite 330-345 Online-Ressource (DE-627)1665781963 (DE-600)2972364-4 2589-5532 nnns volume:7 year:2022 number:2 month:06 pages:330-345 https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.jmse.2021.12.001 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 2 6 330-345 26 01 0206 4163949291 x1z 11-07-22 2403 01 DE-LFER 4190587788 00 --%%-- --%%-- n --%%-- l01 21-09-22 2403 01 DE-LFER https://doi.org/10.1016/j.jmse.2021.12.001 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf |
| allfields_unstemmed |
10.1016/j.jmse.2021.12.001 doi (DE-627)1809721083 (DE-599)KXP1809721083 DE-627 ger DE-627 rda eng Wu, Fei verfasserin (DE-588)1196035679 (DE-627)1677909366 aut A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions Fei Wu, Dan-Jun Ji, Dong-Lan Zha, De-Qun Zhou, Peng Zhou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Marginal abatement cost (dpeaa)DE-206 Carbon dioxide emissions (dpeaa)DE-206 Directional distance function (dpeaa)DE-206 Weak disposability (dpeaa)DE-206 Data envelopment analysis (dpeaa)DE-206 Ji, Dan-Jun verfasserin aut Zha, Dong-Lan verfasserin aut Zhou, De-Qun verfasserin aut Zhou, Peng verfasserin (DE-588)1208216678 (DE-627)1694426548 aut Enthalten in Journal of management science and engineering Amsterdam : Elsevier, 2016 7(2022), 2 vom: Juni, Seite 330-345 Online-Ressource (DE-627)1665781963 (DE-600)2972364-4 2589-5532 nnns volume:7 year:2022 number:2 month:06 pages:330-345 https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.jmse.2021.12.001 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 2 6 330-345 26 01 0206 4163949291 x1z 11-07-22 2403 01 DE-LFER 4190587788 00 --%%-- --%%-- n --%%-- l01 21-09-22 2403 01 DE-LFER https://doi.org/10.1016/j.jmse.2021.12.001 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf |
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10.1016/j.jmse.2021.12.001 doi (DE-627)1809721083 (DE-599)KXP1809721083 DE-627 ger DE-627 rda eng Wu, Fei verfasserin (DE-588)1196035679 (DE-627)1677909366 aut A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions Fei Wu, Dan-Jun Ji, Dong-Lan Zha, De-Qun Zhou, Peng Zhou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Marginal abatement cost (dpeaa)DE-206 Carbon dioxide emissions (dpeaa)DE-206 Directional distance function (dpeaa)DE-206 Weak disposability (dpeaa)DE-206 Data envelopment analysis (dpeaa)DE-206 Ji, Dan-Jun verfasserin aut Zha, Dong-Lan verfasserin aut Zhou, De-Qun verfasserin aut Zhou, Peng verfasserin (DE-588)1208216678 (DE-627)1694426548 aut Enthalten in Journal of management science and engineering Amsterdam : Elsevier, 2016 7(2022), 2 vom: Juni, Seite 330-345 Online-Ressource (DE-627)1665781963 (DE-600)2972364-4 2589-5532 nnns volume:7 year:2022 number:2 month:06 pages:330-345 https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.jmse.2021.12.001 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 2 6 330-345 26 01 0206 4163949291 x1z 11-07-22 2403 01 DE-LFER 4190587788 00 --%%-- --%%-- n --%%-- l01 21-09-22 2403 01 DE-LFER https://doi.org/10.1016/j.jmse.2021.12.001 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf |
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10.1016/j.jmse.2021.12.001 doi (DE-627)1809721083 (DE-599)KXP1809721083 DE-627 ger DE-627 rda eng Wu, Fei verfasserin (DE-588)1196035679 (DE-627)1677909366 aut A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO2 emissions Fei Wu, Dan-Jun Ji, Dong-Lan Zha, De-Qun Zhou, Peng Zhou 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Marginal abatement cost (dpeaa)DE-206 Carbon dioxide emissions (dpeaa)DE-206 Directional distance function (dpeaa)DE-206 Weak disposability (dpeaa)DE-206 Data envelopment analysis (dpeaa)DE-206 Ji, Dan-Jun verfasserin aut Zha, Dong-Lan verfasserin aut Zhou, De-Qun verfasserin aut Zhou, Peng verfasserin (DE-588)1208216678 (DE-627)1694426548 aut Enthalten in Journal of management science and engineering Amsterdam : Elsevier, 2016 7(2022), 2 vom: Juni, Seite 330-345 Online-Ressource (DE-627)1665781963 (DE-600)2972364-4 2589-5532 nnns volume:7 year:2022 number:2 month:06 pages:330-345 https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.jmse.2021.12.001 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 7 2022 2 6 330-345 26 01 0206 4163949291 x1z 11-07-22 2403 01 DE-LFER 4190587788 00 --%%-- --%%-- n --%%-- l01 21-09-22 2403 01 DE-LFER https://doi.org/10.1016/j.jmse.2021.12.001 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2096232021000688/pdfft?md5=0a37b5073463012638902146d0f90697&pid=1-s2.0-S2096232021000688-main.pdf |
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The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. |
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The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. |
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The directional distance function (DDF) framework has been widely used to estimate the marginal abatement cost (MAC) of CO2 emissions to support decision-making in environmental sustainability and climate change issues. In the use of DDF, an important task is mapping evaluated entities towards a realistic production technology frontier. This study develops a new nonparametric approach for estimating the MAC of CO2 emissions. The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages. First, it avoids the arbitrariness in mapping directions. Second, it captures the heterogeneity in optimization paths across different decision-making units (DMUs). Third, it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle. We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level. The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO2 emissions when CO2 emissions are unregulated. Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs. |
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|
| score |
7.4000826 |