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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Shiwei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan - Hering, Alexandra ELSEVIER, 2019, the international journal of the political, economic, planning, environmental and social aspects of energy, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:66 ; year:2014 ; pages:630-644 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.enpol.2013.11.025 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 650 | 7 | |a Targets allocation |2 Elsevier | |
| 650 | 7 | |a Shapley value decomposition |2 Elsevier | |
| 650 | 7 | |a Carbon emission reduction |2 Elsevier | |
| 700 | 1 | |a Wei, Yi-Ming |4 oth | |
| 700 | 1 | |a Wang, Ke |4 oth | |
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10.1016/j.enpol.2013.11.025 doi GBVA2014009000027.pica (DE-627)ELV017462487 (ELSEVIER)S0301-4215(13)01132-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 77.50 bkl Yu, Shiwei verfasserin aut Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Targets allocation Elsevier Shapley value decomposition Elsevier Carbon emission reduction Elsevier Wei, Yi-Ming oth Wang, Ke oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:66 year:2014 pages:630-644 extent:15 https://doi.org/10.1016/j.enpol.2013.11.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 66 2014 630-644 15 045F 620 |
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10.1016/j.enpol.2013.11.025 doi GBVA2014009000027.pica (DE-627)ELV017462487 (ELSEVIER)S0301-4215(13)01132-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 77.50 bkl Yu, Shiwei verfasserin aut Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Targets allocation Elsevier Shapley value decomposition Elsevier Carbon emission reduction Elsevier Wei, Yi-Ming oth Wang, Ke oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:66 year:2014 pages:630-644 extent:15 https://doi.org/10.1016/j.enpol.2013.11.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 66 2014 630-644 15 045F 620 |
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10.1016/j.enpol.2013.11.025 doi GBVA2014009000027.pica (DE-627)ELV017462487 (ELSEVIER)S0301-4215(13)01132-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 77.50 bkl Yu, Shiwei verfasserin aut Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Targets allocation Elsevier Shapley value decomposition Elsevier Carbon emission reduction Elsevier Wei, Yi-Ming oth Wang, Ke oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:66 year:2014 pages:630-644 extent:15 https://doi.org/10.1016/j.enpol.2013.11.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 66 2014 630-644 15 045F 620 |
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10.1016/j.enpol.2013.11.025 doi GBVA2014009000027.pica (DE-627)ELV017462487 (ELSEVIER)S0301-4215(13)01132-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 77.50 bkl Yu, Shiwei verfasserin aut Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Targets allocation Elsevier Shapley value decomposition Elsevier Carbon emission reduction Elsevier Wei, Yi-Ming oth Wang, Ke oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:66 year:2014 pages:630-644 extent:15 https://doi.org/10.1016/j.enpol.2013.11.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 66 2014 630-644 15 045F 620 |
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10.1016/j.enpol.2013.11.025 doi GBVA2014009000027.pica (DE-627)ELV017462487 (ELSEVIER)S0301-4215(13)01132-4 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 77.50 bkl Yu, Shiwei verfasserin aut Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. Targets allocation Elsevier Shapley value decomposition Elsevier Carbon emission reduction Elsevier Wei, Yi-Ming oth Wang, Ke oth Enthalten in Elsevier Science Hering, Alexandra ELSEVIER Beyond prospective memory retrieval: Encoding and remembering of intentions across the lifespan 2019 the international journal of the political, economic, planning, environmental and social aspects of energy Amsterdam [u.a.] (DE-627)ELV003447960 volume:66 year:2014 pages:630-644 extent:15 https://doi.org/10.1016/j.enpol.2013.11.025 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 66 2014 630-644 15 045F 620 |
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Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition |
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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. |
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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. |
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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. |
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