High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach
As the most important emerging transportation technology, high-speed rail (HSR) can reshape regional economic development patterns and exert an important effect on the ecological environment. Using a panel data set of 275 Chinese cities at the prefecture level and above from 2003 to 2014, this study...
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description | As the most important emerging transportation technology, high-speed rail (HSR) can reshape regional economic development patterns and exert an important effect on the ecological environment. Using a panel data set of 275 Chinese cities at the prefecture level and above from 2003 to 2014, this study is the first to adopt a continuous spatial difference-in-differences (SDID) model to investigate the effect and its mechanism of HSR service intensity on CO2 emissions. A series of robustness tests are performed, including the placebo test and using the propensity score matching method combined with the SDID (PSM-SDID) model. We also conduct a heterogeneity analysis using a spatial difference-in-difference-in-differences (SDDD) model. The results show that an increase in HSR service intensity significantly reduces urban CO2 emissions, resulting from the effects of transportation substitution, market integration, industrial structure, and technological innovation. Meanwhile, such an increase inhibits CO2 emissions in neighboring cities with a spatial attenuation boundary of 1000 km. On average, for every addition of 100 HSR trains in a city, the total CO2 emissions can be reduced by 0.14%. Moreover, the CO2 emission reduction effect of HSR is more significant in eastern China, large cities, and resource-based cities. However, higher levels of HSR service intensity in large cities and resource-based cities are not conducive to reducing CO2 emissions in neighboring cities. These findings can help to accurately evaluate the social benefits of expanding HSR networks and provide an important decision-making reference for climate governance during the era of HSR.
•We explore the effect and its mechanism of high-speed rail (HSR) on CO2 emissions.•We use a continuous spatial difference-in-differences model and China's urban data.•An increase in HSR service intensity significantly reduces urban CO2 emissions.•HSR curbs neighboring CO2 emissions with an attenuation boundary of 1000 km.•Every addition of 100 HSR trains in a city can reduce total CO2 emissions by 0.14%. |
doi_str_mv | 10.1016/j.eneco.2021.105271 |
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•We explore the effect and its mechanism of high-speed rail (HSR) on CO2 emissions.•We use a continuous spatial difference-in-differences model and China's urban data.•An increase in HSR service intensity significantly reduces urban CO2 emissions.•HSR curbs neighboring CO2 emissions with an attenuation boundary of 1000 km.•Every addition of 100 HSR trains in a city can reduce total CO2 emissions by 0.14%.</description><identifier>ISSN: 0140-9883</identifier><identifier>EISSN: 1873-6181</identifier><identifier>DOI: 10.1016/j.eneco.2021.105271</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Attenuation ; Carbon dioxide ; Carbon dioxide emissions ; China ; Cities ; Climate action ; Decision making ; Ecological effects ; Economic development ; Economic integration ; Emissions ; Emissions control ; Energy economics ; Governance ; Heterogeneity ; High speed rail ; Industrial structure ; Innovations ; Panel data ; Propensity ; Regional development ; Regional economic development ; Robustness ; Service intensity ; Social networks ; Spatial analysis ; Spatial difference-in-differences model ; Spatial spillover effect ; Technological change ; Technology ; Transportation ; Urban CO2 emissions</subject><ispartof>Energy economics, 2021-07, Vol.99, p.105271, Article 105271</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Jul 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-1650efbeea339285d9bbff7174a24868e1c5a8eb60ffd491fa25f4edccf35c5a3</citedby><cites>FETCH-LOGICAL-c462t-1650efbeea339285d9bbff7174a24868e1c5a8eb60ffd491fa25f4edccf35c5a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eneco.2021.105271$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,778,782,3539,27849,27907,27908,45978</link.rule.ids></links><search><creatorcontrib>Jia, Ruining</creatorcontrib><creatorcontrib>Shao, Shuai</creatorcontrib><creatorcontrib>Yang, Lili</creatorcontrib><title>High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach</title><title>Energy economics</title><description>As the most important emerging transportation technology, high-speed rail (HSR) can reshape regional economic development patterns and exert an important effect on the ecological environment. Using a panel data set of 275 Chinese cities at the prefecture level and above from 2003 to 2014, this study is the first to adopt a continuous spatial difference-in-differences (SDID) model to investigate the effect and its mechanism of HSR service intensity on CO2 emissions. A series of robustness tests are performed, including the placebo test and using the propensity score matching method combined with the SDID (PSM-SDID) model. We also conduct a heterogeneity analysis using a spatial difference-in-difference-in-differences (SDDD) model. The results show that an increase in HSR service intensity significantly reduces urban CO2 emissions, resulting from the effects of transportation substitution, market integration, industrial structure, and technological innovation. Meanwhile, such an increase inhibits CO2 emissions in neighboring cities with a spatial attenuation boundary of 1000 km. On average, for every addition of 100 HSR trains in a city, the total CO2 emissions can be reduced by 0.14%. Moreover, the CO2 emission reduction effect of HSR is more significant in eastern China, large cities, and resource-based cities. However, higher levels of HSR service intensity in large cities and resource-based cities are not conducive to reducing CO2 emissions in neighboring cities. These findings can help to accurately evaluate the social benefits of expanding HSR networks and provide an important decision-making reference for climate governance during the era of HSR.
•We explore the effect and its mechanism of high-speed rail (HSR) on CO2 emissions.•We use a continuous spatial difference-in-differences model and China's urban data.•An increase in HSR service intensity significantly reduces urban CO2 emissions.•HSR curbs neighboring CO2 emissions with an attenuation boundary of 1000 km.•Every addition of 100 HSR trains in a city can reduce total CO2 emissions by 0.14%.</description><subject>Attenuation</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>China</subject><subject>Cities</subject><subject>Climate action</subject><subject>Decision making</subject><subject>Ecological effects</subject><subject>Economic development</subject><subject>Economic integration</subject><subject>Emissions</subject><subject>Emissions control</subject><subject>Energy economics</subject><subject>Governance</subject><subject>Heterogeneity</subject><subject>High speed rail</subject><subject>Industrial structure</subject><subject>Innovations</subject><subject>Panel data</subject><subject>Propensity</subject><subject>Regional development</subject><subject>Regional economic development</subject><subject>Robustness</subject><subject>Service intensity</subject><subject>Social networks</subject><subject>Spatial analysis</subject><subject>Spatial difference-in-differences model</subject><subject>Spatial spillover effect</subject><subject>Technological change</subject><subject>Technology</subject><subject>Transportation</subject><subject>Urban CO2 emissions</subject><issn>0140-9883</issn><issn>1873-6181</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNp9kEFLxDAQhYMouK7-Ai8Bz12TtElTwcNS1BUW9qJXQ5pM3JTdtCZdwX9v1wrePA0z894M70PompIFJVTctgsIYLoFI4yOE85KeoJmVJZ5Jqikp2hGaEGySsr8HF2k1BJCuOByht5W_n2bpR7A4qj9Dutgcb1hGPY-Jd-FhH3Ah9jogOutD_oOL3Hq9eD1DlvvHEQIBjIfsr8uYd33sdNme4nOnN4luPqtc_T6-PBSr7L15um5Xq4zUwg2ZFRwAq4B0HleMclt1TTOlbQsNCukkEAN1xIaQZyzRUWdZtwVYI1xOR9X-RzdTHfHtx8HSINqu0MM40vFuCCyLCgrR1U-qUzsUorgVB_9XscvRYk6glSt-gGpjiDVBHJ03U8uGAN8eogqGX-MaX0EMyjb-X_931HBfQs</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Jia, Ruining</creator><creator>Shao, Shuai</creator><creator>Yang, Lili</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TA</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>DHY</scope><scope>DON</scope><scope>FQK</scope><scope>JBE</scope><scope>JG9</scope><scope>SOI</scope></search><sort><creationdate>20210701</creationdate><title>High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach</title><author>Jia, Ruining ; Shao, Shuai ; Yang, Lili</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-1650efbeea339285d9bbff7174a24868e1c5a8eb60ffd491fa25f4edccf35c5a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Attenuation</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>China</topic><topic>Cities</topic><topic>Climate action</topic><topic>Decision making</topic><topic>Ecological effects</topic><topic>Economic development</topic><topic>Economic integration</topic><topic>Emissions</topic><topic>Emissions control</topic><topic>Energy economics</topic><topic>Governance</topic><topic>Heterogeneity</topic><topic>High speed rail</topic><topic>Industrial structure</topic><topic>Innovations</topic><topic>Panel data</topic><topic>Propensity</topic><topic>Regional development</topic><topic>Regional economic development</topic><topic>Robustness</topic><topic>Service intensity</topic><topic>Social networks</topic><topic>Spatial analysis</topic><topic>Spatial difference-in-differences model</topic><topic>Spatial spillover effect</topic><topic>Technological change</topic><topic>Technology</topic><topic>Transportation</topic><topic>Urban CO2 emissions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Ruining</creatorcontrib><creatorcontrib>Shao, Shuai</creatorcontrib><creatorcontrib>Yang, Lili</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Materials Business File</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Environment Abstracts</collection><jtitle>Energy economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Ruining</au><au>Shao, Shuai</au><au>Yang, Lili</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach</atitle><jtitle>Energy economics</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>99</volume><spage>105271</spage><pages>105271-</pages><artnum>105271</artnum><issn>0140-9883</issn><eissn>1873-6181</eissn><abstract>As the most important emerging transportation technology, high-speed rail (HSR) can reshape regional economic development patterns and exert an important effect on the ecological environment. Using a panel data set of 275 Chinese cities at the prefecture level and above from 2003 to 2014, this study is the first to adopt a continuous spatial difference-in-differences (SDID) model to investigate the effect and its mechanism of HSR service intensity on CO2 emissions. A series of robustness tests are performed, including the placebo test and using the propensity score matching method combined with the SDID (PSM-SDID) model. We also conduct a heterogeneity analysis using a spatial difference-in-difference-in-differences (SDDD) model. The results show that an increase in HSR service intensity significantly reduces urban CO2 emissions, resulting from the effects of transportation substitution, market integration, industrial structure, and technological innovation. Meanwhile, such an increase inhibits CO2 emissions in neighboring cities with a spatial attenuation boundary of 1000 km. On average, for every addition of 100 HSR trains in a city, the total CO2 emissions can be reduced by 0.14%. Moreover, the CO2 emission reduction effect of HSR is more significant in eastern China, large cities, and resource-based cities. However, higher levels of HSR service intensity in large cities and resource-based cities are not conducive to reducing CO2 emissions in neighboring cities. These findings can help to accurately evaluate the social benefits of expanding HSR networks and provide an important decision-making reference for climate governance during the era of HSR.
•We explore the effect and its mechanism of high-speed rail (HSR) on CO2 emissions.•We use a continuous spatial difference-in-differences model and China's urban data.•An increase in HSR service intensity significantly reduces urban CO2 emissions.•HSR curbs neighboring CO2 emissions with an attenuation boundary of 1000 km.•Every addition of 100 HSR trains in a city can reduce total CO2 emissions by 0.14%.</abstract><cop>Kidlington</cop><pub>Elsevier B.V</pub><doi>10.1016/j.eneco.2021.105271</doi></addata></record> |
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subjects | Attenuation Carbon dioxide Carbon dioxide emissions China Cities Climate action Decision making Ecological effects Economic development Economic integration Emissions Emissions control Energy economics Governance Heterogeneity High speed rail Industrial structure Innovations Panel data Propensity Regional development Regional economic development Robustness Service intensity Social networks Spatial analysis Spatial difference-in-differences model Spatial spillover effect Technological change Technology Transportation Urban CO2 emissions |
title | High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach |
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