Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition

An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interac...

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Published in:	Energy policy Vol. 66; pp. 630 - 644
Main Authors:	Yu, Shiwei, Wei, Yi-Ming, Wang, Ke
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.03.2014 Elsevier Elsevier Science Ltd
Subjects:	Air pollution caused by fuel industries Algorithms Allocation Applied sciences Carbon Carbon dioxide Carbon dioxide emissions Carbon emission reduction Cardinality Case studies China China (People's Republic) Climatology. Bioclimatology. Climate change Cluster analysis Clustering Cost and standard of living Decomposition Earth, ocean, space Economic development Emission standards Emissions Emissions control emissions factor Endowments Energy Energy development Energy economics Energy. Thermal use of fuels Exact sciences and technology External geophysics Function words Fuzzy logic General, economic and professional studies General. Regulations. Norms. Economy greenhouse gas emissions Growth rate Industry Meteorology Methodology. Modelling Optimization Optimization algorithms Provinces Residential areas Shapley value decomposition Standard of living Studies Targets allocation Value value added China Targets allocation Shapley value decomposition Carbon emission reduction
ISSN:	0301-4215, 1873-6777
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Abstract	An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. •A PSO–FCM–Shapley approach for carbon emission reduction target allocation is proposed.•Provinces of China are clustered into four classes based on factors influencing carbon emissions.•Provinces with large total emissions and high emission intensity are required more burdens than others.•Fifteen provinces should exceed the national average decrease rates (30.8%) in coming 10 years.
AbstractList	An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO-FCM-Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO-FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. . [Copyright Elsevier Ltd.] An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO-FCM-Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO-FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. [PUBLICATION ABSTRACT] An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO-FCM-Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO-FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. [PUBLICATIONABSTRACT] An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020. •A PSO–FCM–Shapley approach for carbon emission reduction target allocation is proposed.•Provinces of China are clustered into four classes based on factors influencing carbon emissions.•Provinces with large total emissions and high emission intensity are required more burdens than others.•Fifteen provinces should exceed the national average decrease rates (30.8%) in coming 10 years.
Author	Yu, Shiwei Wei, Yi-Ming Wang, Ke
Author_xml	– sequence: 1 givenname: Shiwei surname: Yu fullname: Yu, Shiwei email: ysw81993@sina.com organization: School of Economics and Management, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: Yi-Ming orcidid: 0000-0002-8476-7334 surname: Wei fullname: Wei, Yi-Ming organization: Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China – sequence: 3 givenname: Ke orcidid: 0000-0002-2092-6340 surname: Wang fullname: Wang, Ke organization: Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100181, China
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SubjectTerms	Air pollution caused by fuel industries Algorithms Allocation Applied sciences Carbon Carbon dioxide Carbon dioxide emissions Carbon emission reduction Cardinality Case studies China China (People's Republic) Climatology. Bioclimatology. Climate change Cluster analysis Clustering Cost and standard of living Decomposition Earth, ocean, space Economic development Emission standards Emissions Emissions control emissions factor Endowments Energy Energy development Energy economics Energy. Thermal use of fuels Exact sciences and technology External geophysics Function words Fuzzy logic General, economic and professional studies General. Regulations. Norms. Economy greenhouse gas emissions Growth rate Industry Meteorology Methodology. Modelling Optimization Optimization algorithms Provinces Residential areas Shapley value decomposition Standard of living Studies Targets allocation Value value added
Title	Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition
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