How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces
Thermal power plants are considered to be the culprit of various pollutants. In China, a country dominated by coal-fired power generation, the problem is more serious. Regulators must use capacity control of coal-fired power generation as a key policy tool for emission reduction. This paper was conc...
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description | Thermal power plants are considered to be the culprit of various pollutants. In China, a country dominated by coal-fired power generation, the problem is more serious. Regulators must use capacity control of coal-fired power generation as a key policy tool for emission reduction. This paper was concerned about the environmental effect of changes in thermal power capacity utilization on pollutants emissions in China. Under the econometric strategy of panel smoothing transformation regression (PSTR) model, the switching regimes and paths of seven pollutants emissions to thermal power capacity utilization were evaluated with the transition variables of power generation and electricity consumption, respectively. The three statistics, LM test, LMF and pseudo-LRT, unanimously verify the necessity of nonlinearity. In addition to CO2 emissions whose transition function presents U-shaped feature both in the models with power generation and electricity consumption as transition variable, the other pollutants such as SO2, NOX and so on generally exhibit quasi S-shaped change trend. The interaction term of installed capacity and average annual operating hours of thermal power plants introduced to reflect the interactive environmental effect of thermal power capacity utilization on pollutants emissions, appear similarity in most pollutants. However, the negative coefficients of the transition functions for interactive environmental effect imply that the policymakers reduce pollution by defusing capacity, which needs to be supplemented by setting reasonable warning line for operating hours of thermal power.
•The PSTR model is employed to capture unobserved heterogeneity.•The relationship between capacity utilization and pollutant emissions is nonlinearity.•The transition functions of seven pollutants exhibit U-shaped and quasi S-shaped features.•The environmental disruption warning lines of operating hours of thermal power plants is instructive for policymakers. |
doi_str_mv | 10.1016/j.enpol.2019.06.010 |
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•The PSTR model is employed to capture unobserved heterogeneity.•The relationship between capacity utilization and pollutant emissions is nonlinearity.•The transition functions of seven pollutants exhibit U-shaped and quasi S-shaped features.•The environmental disruption warning lines of operating hours of thermal power plants is instructive for policymakers.</description><identifier>ISSN: 0301-4215</identifier><identifier>EISSN: 1873-6777</identifier><identifier>DOI: 10.1016/j.enpol.2019.06.010</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Averages ; Carbon dioxide ; Carbon dioxide emissions ; Coal ; Coal-fired power plants ; Consumption ; Econometrics ; Electric power generation ; Electricity ; Electricity consumption ; Electricity generation ; Emissions ; Emissions control ; Energy policy ; Environmental effects ; Genetic transformation ; Industrial plant emissions ; Longitudinal studies ; Nonlinear systems ; Panel data ; Policy making ; Pollutant emissions ; Pollutants ; Pollution ; Pollution control ; Power consumption ; Power generating capacity ; Power plants ; Provinces ; PSTR model ; Regression models ; Regulators ; Statistical tests ; Statistics ; Sulfur dioxide ; Thermal power ; Thermal power plants ; Thermal utilization ; Thermoelectricity ; Transformation ; Utilization</subject><ispartof>Energy policy, 2019-09, Vol.132, p.440-451</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Sep 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-d3093c324d84b01a73504c2e21121d423954f3a95fca56f40868c12e7408ab0e3</citedby><cites>FETCH-LOGICAL-c364t-d3093c324d84b01a73504c2e21121d423954f3a95fca56f40868c12e7408ab0e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0301421519303805$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27843,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Wang, Yongpei</creatorcontrib><creatorcontrib>Yan, Weilong</creatorcontrib><creatorcontrib>Komonpipat, Supak</creatorcontrib><title>How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces</title><title>Energy policy</title><description>Thermal power plants are considered to be the culprit of various pollutants. In China, a country dominated by coal-fired power generation, the problem is more serious. Regulators must use capacity control of coal-fired power generation as a key policy tool for emission reduction. This paper was concerned about the environmental effect of changes in thermal power capacity utilization on pollutants emissions in China. Under the econometric strategy of panel smoothing transformation regression (PSTR) model, the switching regimes and paths of seven pollutants emissions to thermal power capacity utilization were evaluated with the transition variables of power generation and electricity consumption, respectively. The three statistics, LM test, LMF and pseudo-LRT, unanimously verify the necessity of nonlinearity. In addition to CO2 emissions whose transition function presents U-shaped feature both in the models with power generation and electricity consumption as transition variable, the other pollutants such as SO2, NOX and so on generally exhibit quasi S-shaped change trend. The interaction term of installed capacity and average annual operating hours of thermal power plants introduced to reflect the interactive environmental effect of thermal power capacity utilization on pollutants emissions, appear similarity in most pollutants. However, the negative coefficients of the transition functions for interactive environmental effect imply that the policymakers reduce pollution by defusing capacity, which needs to be supplemented by setting reasonable warning line for operating hours of thermal power.
•The PSTR model is employed to capture unobserved heterogeneity.•The relationship between capacity utilization and pollutant emissions is nonlinearity.•The transition functions of seven pollutants exhibit U-shaped and quasi S-shaped features.•The environmental disruption warning lines of operating hours of thermal power plants is instructive for policymakers.</description><subject>Averages</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Coal</subject><subject>Coal-fired power plants</subject><subject>Consumption</subject><subject>Econometrics</subject><subject>Electric power generation</subject><subject>Electricity</subject><subject>Electricity consumption</subject><subject>Electricity generation</subject><subject>Emissions</subject><subject>Emissions control</subject><subject>Energy policy</subject><subject>Environmental effects</subject><subject>Genetic transformation</subject><subject>Industrial plant emissions</subject><subject>Longitudinal studies</subject><subject>Nonlinear systems</subject><subject>Panel data</subject><subject>Policy making</subject><subject>Pollutant emissions</subject><subject>Pollutants</subject><subject>Pollution</subject><subject>Pollution control</subject><subject>Power consumption</subject><subject>Power generating capacity</subject><subject>Power plants</subject><subject>Provinces</subject><subject>PSTR model</subject><subject>Regression models</subject><subject>Regulators</subject><subject>Statistical tests</subject><subject>Statistics</subject><subject>Sulfur dioxide</subject><subject>Thermal power</subject><subject>Thermal power plants</subject><subject>Thermal utilization</subject><subject>Thermoelectricity</subject><subject>Transformation</subject><subject>Utilization</subject><issn>0301-4215</issn><issn>1873-6777</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNp9kMtO3DAUhi1UJKYDT8DGEouukh5fcltUVTWCDhJSN2VtGecYHGXi1PYMglfgpXGYrlkd6_yXI3-EXDIoGbD6-1DiNPux5MC6EuoSGJyQFWsbUdRN03whKxDACslZdUa-xjgAgGw7uSJvW_9Me4-RpiekRs_auPRC98mN7lUn5yfq7aKFnR7p7J8x0EecMBw1bS2alPfjuE96ShR3LsasxJ_0-uB6nAxSG_zuo37WE46010kvpZsnN-lvkc7BH1z2xXNyavUY8eL_XJP7m-u_m21x9-f37ebXXWFELVPRC-iEEVz2rXwAphtRgTQcOWOc9ZKLrpJW6K6yRle1ldDWrWEcm_zSD4BiTa6Ovfnyvz3GpAa_D1M-qTjveCehqZrsEkeXCT7GgFbNwe10eFEM1EJdDeqDulqoK6hVpp5TP44pzB84OAwqGrdQ6F3IpFTv3af5dwz2jaw</recordid><startdate>201909</startdate><enddate>201909</enddate><creator>Wang, Yongpei</creator><creator>Yan, Weilong</creator><creator>Komonpipat, Supak</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TA</scope><scope>7TB</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>DHY</scope><scope>DON</scope><scope>F28</scope><scope>FQK</scope><scope>FR3</scope><scope>H8D</scope><scope>JBE</scope><scope>JG9</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>201909</creationdate><title>How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces</title><author>Wang, Yongpei ; Yan, Weilong ; Komonpipat, Supak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-d3093c324d84b01a73504c2e21121d423954f3a95fca56f40868c12e7408ab0e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Averages</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>Coal</topic><topic>Coal-fired power plants</topic><topic>Consumption</topic><topic>Econometrics</topic><topic>Electric power generation</topic><topic>Electricity</topic><topic>Electricity consumption</topic><topic>Electricity generation</topic><topic>Emissions</topic><topic>Emissions control</topic><topic>Energy policy</topic><topic>Environmental effects</topic><topic>Genetic transformation</topic><topic>Industrial plant emissions</topic><topic>Longitudinal studies</topic><topic>Nonlinear systems</topic><topic>Panel data</topic><topic>Policy making</topic><topic>Pollutant emissions</topic><topic>Pollutants</topic><topic>Pollution</topic><topic>Pollution control</topic><topic>Power consumption</topic><topic>Power generating capacity</topic><topic>Power plants</topic><topic>Provinces</topic><topic>PSTR model</topic><topic>Regression models</topic><topic>Regulators</topic><topic>Statistical tests</topic><topic>Statistics</topic><topic>Sulfur dioxide</topic><topic>Thermal power</topic><topic>Thermal power plants</topic><topic>Thermal utilization</topic><topic>Thermoelectricity</topic><topic>Transformation</topic><topic>Utilization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yongpei</creatorcontrib><creatorcontrib>Yan, Weilong</creatorcontrib><creatorcontrib>Komonpipat, Supak</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>International Bibliography of the Social Sciences</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Energy policy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yongpei</au><au>Yan, Weilong</au><au>Komonpipat, Supak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces</atitle><jtitle>Energy policy</jtitle><date>2019-09</date><risdate>2019</risdate><volume>132</volume><spage>440</spage><epage>451</epage><pages>440-451</pages><issn>0301-4215</issn><eissn>1873-6777</eissn><abstract>Thermal power plants are considered to be the culprit of various pollutants. In China, a country dominated by coal-fired power generation, the problem is more serious. Regulators must use capacity control of coal-fired power generation as a key policy tool for emission reduction. This paper was concerned about the environmental effect of changes in thermal power capacity utilization on pollutants emissions in China. Under the econometric strategy of panel smoothing transformation regression (PSTR) model, the switching regimes and paths of seven pollutants emissions to thermal power capacity utilization were evaluated with the transition variables of power generation and electricity consumption, respectively. The three statistics, LM test, LMF and pseudo-LRT, unanimously verify the necessity of nonlinearity. In addition to CO2 emissions whose transition function presents U-shaped feature both in the models with power generation and electricity consumption as transition variable, the other pollutants such as SO2, NOX and so on generally exhibit quasi S-shaped change trend. The interaction term of installed capacity and average annual operating hours of thermal power plants introduced to reflect the interactive environmental effect of thermal power capacity utilization on pollutants emissions, appear similarity in most pollutants. However, the negative coefficients of the transition functions for interactive environmental effect imply that the policymakers reduce pollution by defusing capacity, which needs to be supplemented by setting reasonable warning line for operating hours of thermal power.
•The PSTR model is employed to capture unobserved heterogeneity.•The relationship between capacity utilization and pollutant emissions is nonlinearity.•The transition functions of seven pollutants exhibit U-shaped and quasi S-shaped features.•The environmental disruption warning lines of operating hours of thermal power plants is instructive for policymakers.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.enpol.2019.06.010</doi><tpages>12</tpages></addata></record> |
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subjects | Averages Carbon dioxide Carbon dioxide emissions Coal Coal-fired power plants Consumption Econometrics Electric power generation Electricity Electricity consumption Electricity generation Emissions Emissions control Energy policy Environmental effects Genetic transformation Industrial plant emissions Longitudinal studies Nonlinear systems Panel data Policy making Pollutant emissions Pollutants Pollution Pollution control Power consumption Power generating capacity Power plants Provinces PSTR model Regression models Regulators Statistical tests Statistics Sulfur dioxide Thermal power Thermal power plants Thermal utilization Thermoelectricity Transformation Utilization |
title | How does the capacity utilization of thermal power generation affect pollutant emissions? Evidence from the panel data of China's provinces |
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