Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries
The recent COVD-19 pandemic has been a major shock, affecting various macroeconomic indicators, including the environmental quality. The question of how the pandemics-related uncertainty will affect the environment is of paramount importance. The study analyzes the asymmetric impact of pandemic unce...
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Veröffentlicht in: | Stochastic environmental research and risk assessment 2022-12, Vol.36 (12), p.4103-4117 |
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description | The recent COVD-19 pandemic has been a major shock, affecting various macroeconomic indicators, including the environmental quality. The question of how the pandemics-related uncertainty will affect the environment is of paramount importance. The study analyzes the asymmetric impact of pandemic uncertainty on CO
2
emissions in top-10 polluted economies (China, USA, India, Russia, Germany, Japan, Iran, South Korea, Indonesia, and Saudi Arabia). Taking panel data from 1996 to 2018, a unique technique, 'Quantile-on-Quantile (QQ)', is employed. CO
2
emissions are used as an indicator of environmental quality. The outcomes define how the quantiles of pandemic uncertainty impact the quantiles of carbon emissions asymmetrically by providing an effective paradigm for comprehending the overall dependence framework. The outcomes reveal that pandemic uncertainty promotes environmental quality by lowering CO
2
emissions in our sample countries at various quantiles. However, Japan shows mixed findings. The effect of PUN on CO
2
is substantially larger in India, Germany, and South Korea and lower in Russia and Saudi Arabia. Furthermore, the magnitude of asymmetry in the pandemic uncertainty-CO
2
emissions association differs by economy, emphasizing that government must pay particular caution and prudence when adopting pandemics-related uncertainty and environmental quality policies. |
doi_str_mv | 10.1007/s00477-022-02248-5 |
format | Article |
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2
emissions in top-10 polluted economies (China, USA, India, Russia, Germany, Japan, Iran, South Korea, Indonesia, and Saudi Arabia). Taking panel data from 1996 to 2018, a unique technique, 'Quantile-on-Quantile (QQ)', is employed. CO
2
emissions are used as an indicator of environmental quality. The outcomes define how the quantiles of pandemic uncertainty impact the quantiles of carbon emissions asymmetrically by providing an effective paradigm for comprehending the overall dependence framework. The outcomes reveal that pandemic uncertainty promotes environmental quality by lowering CO
2
emissions in our sample countries at various quantiles. However, Japan shows mixed findings. The effect of PUN on CO
2
is substantially larger in India, Germany, and South Korea and lower in Russia and Saudi Arabia. Furthermore, the magnitude of asymmetry in the pandemic uncertainty-CO
2
emissions association differs by economy, emphasizing that government must pay particular caution and prudence when adopting pandemics-related uncertainty and environmental quality policies.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-022-02248-5</identifier><identifier>PMID: 35873500</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Asymmetry ; Carbon dioxide ; Carbon dioxide emissions ; Chemistry and Earth Sciences ; Computational Intelligence ; Computer Science ; Earth and Environmental Science ; Earth Sciences ; Emissions ; Environment ; Environmental indicators ; Environmental quality ; Impact analysis ; Math. Appl. in Environmental Science ; Original Paper ; Pandemics ; Physics ; Probability Theory and Stochastic Processes ; Quantiles ; Statistics for Engineering ; Uncertainty ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Stochastic environmental research and risk assessment, 2022-12, Vol.36 (12), p.4103-4117</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-2943bbd93deaaf172b10c7be07bb8b316b4df5d44db9bf32ca6bbb550df58993</citedby><cites>FETCH-LOGICAL-c517t-2943bbd93deaaf172b10c7be07bb8b316b4df5d44db9bf32ca6bbb550df58993</cites><orcidid>0000-0002-4744-5803</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-022-02248-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-022-02248-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Chang, Lei</creatorcontrib><creatorcontrib>Chen, Kaiming</creatorcontrib><creatorcontrib>Saydaliev, Hayot Berk</creatorcontrib><creatorcontrib>Faridi, Muhammad Zahir</creatorcontrib><title>Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>The recent COVD-19 pandemic has been a major shock, affecting various macroeconomic indicators, including the environmental quality. The question of how the pandemics-related uncertainty will affect the environment is of paramount importance. The study analyzes the asymmetric impact of pandemic uncertainty on CO
2
emissions in top-10 polluted economies (China, USA, India, Russia, Germany, Japan, Iran, South Korea, Indonesia, and Saudi Arabia). Taking panel data from 1996 to 2018, a unique technique, 'Quantile-on-Quantile (QQ)', is employed. CO
2
emissions are used as an indicator of environmental quality. The outcomes define how the quantiles of pandemic uncertainty impact the quantiles of carbon emissions asymmetrically by providing an effective paradigm for comprehending the overall dependence framework. The outcomes reveal that pandemic uncertainty promotes environmental quality by lowering CO
2
emissions in our sample countries at various quantiles. However, Japan shows mixed findings. The effect of PUN on CO
2
is substantially larger in India, Germany, and South Korea and lower in Russia and Saudi Arabia. Furthermore, the magnitude of asymmetry in the pandemic uncertainty-CO
2
emissions association differs by economy, emphasizing that government must pay particular caution and prudence when adopting pandemics-related uncertainty and environmental quality policies.</description><subject>Aquatic Pollution</subject><subject>Asymmetry</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Chemistry and Earth Sciences</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Emissions</subject><subject>Environment</subject><subject>Environmental indicators</subject><subject>Environmental quality</subject><subject>Impact analysis</subject><subject>Math. Appl. in Environmental Science</subject><subject>Original Paper</subject><subject>Pandemics</subject><subject>Physics</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Quantiles</subject><subject>Statistics for Engineering</subject><subject>Uncertainty</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUtr3TAQhUVpaUKaP9CVoJtu3OhpWV0UwqUvCGSTvdBjnKrYkivZgfvvK_eGlHaRhZCQvnNmRgeht5R8oISoq0qIUKojjO1LDJ18gc6p4H3HmdQvn86CnKHLWqNrIsm1puQ1OuNyUFwSco6m63qcZ1hL9DjOi_UrziNebAowR1-7ApNdIeAteSirjWk94pzw4ZbhBjTfnOpHDA8xQCPwWPKM17x0lOAlT9O2a33eUisA9Q16NdqpwuXjfoHuvny-O3zrbm6_fj9c33ReUrV2TAvuXNA8gLUjVcxR4pUDopwbHKe9E2GUQYjgtBs587Z3zklJ2u2gNb9An062y-ZmCB5adTuZpcTZlqPJNpp_X1L8Ye7zg9FsGBjpm8H7R4OSf21QV9NG9TBNNkHeqmG9FoIRwXb03X_oz7yV1KYzrH2xVL3s947YifIl11pgfGqGErPHaU5xmhal-ROnkU3ET6La4HQP5a_1M6rfSVSjsg</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Chang, Lei</creator><creator>Chen, Kaiming</creator><creator>Saydaliev, Hayot Berk</creator><creator>Faridi, Muhammad Zahir</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4744-5803</orcidid></search><sort><creationdate>20221201</creationdate><title>Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries</title><author>Chang, Lei ; Chen, Kaiming ; Saydaliev, Hayot Berk ; Faridi, Muhammad Zahir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c517t-2943bbd93deaaf172b10c7be07bb8b316b4df5d44db9bf32ca6bbb550df58993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aquatic Pollution</topic><topic>Asymmetry</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>Chemistry and Earth Sciences</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Emissions</topic><topic>Environment</topic><topic>Environmental indicators</topic><topic>Environmental quality</topic><topic>Impact analysis</topic><topic>Math. Appl. in Environmental Science</topic><topic>Original Paper</topic><topic>Pandemics</topic><topic>Physics</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Quantiles</topic><topic>Statistics for Engineering</topic><topic>Uncertainty</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chang, Lei</creatorcontrib><creatorcontrib>Chen, Kaiming</creatorcontrib><creatorcontrib>Saydaliev, Hayot Berk</creatorcontrib><creatorcontrib>Faridi, Muhammad Zahir</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chang, Lei</au><au>Chen, Kaiming</au><au>Saydaliev, Hayot Berk</au><au>Faridi, Muhammad Zahir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2022-12-01</date><risdate>2022</risdate><volume>36</volume><issue>12</issue><spage>4103</spage><epage>4117</epage><pages>4103-4117</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>The recent COVD-19 pandemic has been a major shock, affecting various macroeconomic indicators, including the environmental quality. The question of how the pandemics-related uncertainty will affect the environment is of paramount importance. The study analyzes the asymmetric impact of pandemic uncertainty on CO
2
emissions in top-10 polluted economies (China, USA, India, Russia, Germany, Japan, Iran, South Korea, Indonesia, and Saudi Arabia). Taking panel data from 1996 to 2018, a unique technique, 'Quantile-on-Quantile (QQ)', is employed. CO
2
emissions are used as an indicator of environmental quality. The outcomes define how the quantiles of pandemic uncertainty impact the quantiles of carbon emissions asymmetrically by providing an effective paradigm for comprehending the overall dependence framework. The outcomes reveal that pandemic uncertainty promotes environmental quality by lowering CO
2
emissions in our sample countries at various quantiles. However, Japan shows mixed findings. The effect of PUN on CO
2
is substantially larger in India, Germany, and South Korea and lower in Russia and Saudi Arabia. Furthermore, the magnitude of asymmetry in the pandemic uncertainty-CO
2
emissions association differs by economy, emphasizing that government must pay particular caution and prudence when adopting pandemics-related uncertainty and environmental quality policies.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35873500</pmid><doi>10.1007/s00477-022-02248-5</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-4744-5803</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aquatic Pollution Asymmetry Carbon dioxide Carbon dioxide emissions Chemistry and Earth Sciences Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Emissions Environment Environmental indicators Environmental quality Impact analysis Math. Appl. in Environmental Science Original Paper Pandemics Physics Probability Theory and Stochastic Processes Quantiles Statistics for Engineering Uncertainty Waste Water Technology Water Management Water Pollution Control |
title | Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries |
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