Loading…
High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources
A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China’s carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO...
Saved in:
Published in: | Environmental science & technology 2014-06, Vol.48 (12), p.7085-7093 |
---|---|
Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3 |
---|---|
cites | cdi_FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3 |
container_end_page | 7093 |
container_issue | 12 |
container_start_page | 7085 |
container_title | Environmental science & technology |
container_volume | 48 |
creator | Wang, Jinnan Cai, Bofeng Zhang, Lixiao Cao, Dong Liu, Lancui Zhou, Ying Zhang, Zhansheng Xue, Wenbo |
description | A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China’s carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO2 emission gridded data has been built up for China based on point emission sources and other supporting data. The predominance of emissions from industrial point sources (84% of total emissions) in China supports the use of bottom-up methodology. The resultant emission map is informative and proved to be more spatially accurate than the EDGAR data. Spatial distribution of CO2 emissions in China is highly unbalanced and has positive spatial autocorrelation. The spatial pattern is mainly influenced by key cities and key regions, i.e., the Jing-Jin-Ji region, the Yangtze River delta region, and the Pearl River delta region. The emission map indicated that the supervision of 1% of total land could enable the management of about 70% of emissions in China. |
doi_str_mv | 10.1021/es405369r |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1537177640</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3350191431</sourcerecordid><originalsourceid>FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3</originalsourceid><addsrcrecordid>eNpl0NtKxDAQBuAgiq6HC19AAiLoRTVpmjS5lF1PICgeQK9K2kw00m3WpBV9eyOuB_RqYPiY-fkR2qRkn5KcHkAsCGdChQU0ojwnGZecLqIRIZRliom7FbQa4xMhJGdELqOVvJAFoaIYoftT9_CIryD6duid7_BYhzqNifOvzgA-mroYP_YnwRkDBk90r7H1AY8fXafxBIJ7SWsb_BRfetf1-NoPoYG4jpasbiNszOcauj0-uhmfZucXJ2fjw_NMs5L1mbUGdF5TJaTl1pQmF0qAZbVk1OZW1MrWrKxNCpzWUAglqaINl1BQUBrYGtr9vDsL_nmA2FcpcgNtqzvwQ6woZyUtS1GQRLf_0KeUtUvpkkoVSsmJTGrvUzXBxxjAVrPgpjq8VZRUH31X330nuzW_ONRTMN_yq-AEduZAx0a3NuiucfHHSa4UI-WP0038lerfw3fwZZLx</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1540588508</pqid></control><display><type>article</type><title>High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources</title><source>Access via American Chemical Society</source><creator>Wang, Jinnan ; Cai, Bofeng ; Zhang, Lixiao ; Cao, Dong ; Liu, Lancui ; Zhou, Ying ; Zhang, Zhansheng ; Xue, Wenbo</creator><creatorcontrib>Wang, Jinnan ; Cai, Bofeng ; Zhang, Lixiao ; Cao, Dong ; Liu, Lancui ; Zhou, Ying ; Zhang, Zhansheng ; Xue, Wenbo</creatorcontrib><description>A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China’s carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO2 emission gridded data has been built up for China based on point emission sources and other supporting data. The predominance of emissions from industrial point sources (84% of total emissions) in China supports the use of bottom-up methodology. The resultant emission map is informative and proved to be more spatially accurate than the EDGAR data. Spatial distribution of CO2 emissions in China is highly unbalanced and has positive spatial autocorrelation. The spatial pattern is mainly influenced by key cities and key regions, i.e., the Jing-Jin-Ji region, the Yangtze River delta region, and the Pearl River delta region. The emission map indicated that the supervision of 1% of total land could enable the management of about 70% of emissions in China.</description><identifier>ISSN: 0013-936X</identifier><identifier>EISSN: 1520-5851</identifier><identifier>DOI: 10.1021/es405369r</identifier><identifier>PMID: 24840164</identifier><identifier>CODEN: ESTHAG</identifier><language>eng</language><publisher>Washington, DC: American Chemical Society</publisher><subject>Air Pollutants - analysis ; Carbon ; Carbon cycle ; Carbon dioxide ; Carbon Dioxide - analysis ; China ; Cities ; Climatology. Bioclimatology. Climate change ; Desert Climate ; Earth, ocean, space ; Emissions ; Energy-Generating Resources ; Exact sciences and technology ; External geophysics ; Geography ; Industry ; Meteorology</subject><ispartof>Environmental science & technology, 2014-06, Vol.48 (12), p.7085-7093</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright American Chemical Society Jun 17, 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3</citedby><cites>FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28599307$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24840164$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Jinnan</creatorcontrib><creatorcontrib>Cai, Bofeng</creatorcontrib><creatorcontrib>Zhang, Lixiao</creatorcontrib><creatorcontrib>Cao, Dong</creatorcontrib><creatorcontrib>Liu, Lancui</creatorcontrib><creatorcontrib>Zhou, Ying</creatorcontrib><creatorcontrib>Zhang, Zhansheng</creatorcontrib><creatorcontrib>Xue, Wenbo</creatorcontrib><title>High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources</title><title>Environmental science & technology</title><addtitle>Environ. Sci. Technol</addtitle><description>A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China’s carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO2 emission gridded data has been built up for China based on point emission sources and other supporting data. The predominance of emissions from industrial point sources (84% of total emissions) in China supports the use of bottom-up methodology. The resultant emission map is informative and proved to be more spatially accurate than the EDGAR data. Spatial distribution of CO2 emissions in China is highly unbalanced and has positive spatial autocorrelation. The spatial pattern is mainly influenced by key cities and key regions, i.e., the Jing-Jin-Ji region, the Yangtze River delta region, and the Pearl River delta region. The emission map indicated that the supervision of 1% of total land could enable the management of about 70% of emissions in China.</description><subject>Air Pollutants - analysis</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Carbon dioxide</subject><subject>Carbon Dioxide - analysis</subject><subject>China</subject><subject>Cities</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Desert Climate</subject><subject>Earth, ocean, space</subject><subject>Emissions</subject><subject>Energy-Generating Resources</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Geography</subject><subject>Industry</subject><subject>Meteorology</subject><issn>0013-936X</issn><issn>1520-5851</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNpl0NtKxDAQBuAgiq6HC19AAiLoRTVpmjS5lF1PICgeQK9K2kw00m3WpBV9eyOuB_RqYPiY-fkR2qRkn5KcHkAsCGdChQU0ojwnGZecLqIRIZRliom7FbQa4xMhJGdELqOVvJAFoaIYoftT9_CIryD6duid7_BYhzqNifOvzgA-mroYP_YnwRkDBk90r7H1AY8fXafxBIJ7SWsb_BRfetf1-NoPoYG4jpasbiNszOcauj0-uhmfZucXJ2fjw_NMs5L1mbUGdF5TJaTl1pQmF0qAZbVk1OZW1MrWrKxNCpzWUAglqaINl1BQUBrYGtr9vDsL_nmA2FcpcgNtqzvwQ6woZyUtS1GQRLf_0KeUtUvpkkoVSsmJTGrvUzXBxxjAVrPgpjq8VZRUH31X330nuzW_ONRTMN_yq-AEduZAx0a3NuiucfHHSa4UI-WP0038lerfw3fwZZLx</recordid><startdate>20140617</startdate><enddate>20140617</enddate><creator>Wang, Jinnan</creator><creator>Cai, Bofeng</creator><creator>Zhang, Lixiao</creator><creator>Cao, Dong</creator><creator>Liu, Lancui</creator><creator>Zhou, Ying</creator><creator>Zhang, Zhansheng</creator><creator>Xue, Wenbo</creator><general>American Chemical Society</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7ST</scope><scope>7T7</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>20140617</creationdate><title>High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources</title><author>Wang, Jinnan ; Cai, Bofeng ; Zhang, Lixiao ; Cao, Dong ; Liu, Lancui ; Zhou, Ying ; Zhang, Zhansheng ; Xue, Wenbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Air Pollutants - analysis</topic><topic>Carbon</topic><topic>Carbon cycle</topic><topic>Carbon dioxide</topic><topic>Carbon Dioxide - analysis</topic><topic>China</topic><topic>Cities</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>Desert Climate</topic><topic>Earth, ocean, space</topic><topic>Emissions</topic><topic>Energy-Generating Resources</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Geography</topic><topic>Industry</topic><topic>Meteorology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jinnan</creatorcontrib><creatorcontrib>Cai, Bofeng</creatorcontrib><creatorcontrib>Zhang, Lixiao</creatorcontrib><creatorcontrib>Cao, Dong</creatorcontrib><creatorcontrib>Liu, Lancui</creatorcontrib><creatorcontrib>Zhou, Ying</creatorcontrib><creatorcontrib>Zhang, Zhansheng</creatorcontrib><creatorcontrib>Xue, Wenbo</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Environmental science & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Jinnan</au><au>Cai, Bofeng</au><au>Zhang, Lixiao</au><au>Cao, Dong</au><au>Liu, Lancui</au><au>Zhou, Ying</au><au>Zhang, Zhansheng</au><au>Xue, Wenbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources</atitle><jtitle>Environmental science & technology</jtitle><addtitle>Environ. Sci. Technol</addtitle><date>2014-06-17</date><risdate>2014</risdate><volume>48</volume><issue>12</issue><spage>7085</spage><epage>7093</epage><pages>7085-7093</pages><issn>0013-936X</issn><eissn>1520-5851</eissn><coden>ESTHAG</coden><abstract>A high spatial resolution carbon dioxide (CO2) emission map of China is proving to be essential for China’s carbon cycle research and carbon reduction strategies given the current low quality of CO2 emission data and the inconsistencies in data quality between different regions. Ten km resolution CO2 emission gridded data has been built up for China based on point emission sources and other supporting data. The predominance of emissions from industrial point sources (84% of total emissions) in China supports the use of bottom-up methodology. The resultant emission map is informative and proved to be more spatially accurate than the EDGAR data. Spatial distribution of CO2 emissions in China is highly unbalanced and has positive spatial autocorrelation. The spatial pattern is mainly influenced by key cities and key regions, i.e., the Jing-Jin-Ji region, the Yangtze River delta region, and the Pearl River delta region. The emission map indicated that the supervision of 1% of total land could enable the management of about 70% of emissions in China.</abstract><cop>Washington, DC</cop><pub>American Chemical Society</pub><pmid>24840164</pmid><doi>10.1021/es405369r</doi><tpages>9</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0013-936X |
ispartof | Environmental science & technology, 2014-06, Vol.48 (12), p.7085-7093 |
issn | 0013-936X 1520-5851 |
language | eng |
recordid | cdi_proquest_miscellaneous_1537177640 |
source | Access via American Chemical Society |
subjects | Air Pollutants - analysis Carbon Carbon cycle Carbon dioxide Carbon Dioxide - analysis China Cities Climatology. Bioclimatology. Climate change Desert Climate Earth, ocean, space Emissions Energy-Generating Resources Exact sciences and technology External geophysics Geography Industry Meteorology |
title | High Resolution Carbon Dioxide Emission Gridded Data for China Derived from Point Sources |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T23%3A36%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=High%20Resolution%20Carbon%20Dioxide%20Emission%20Gridded%20Data%20for%20China%20Derived%20from%20Point%20Sources&rft.jtitle=Environmental%20science%20&%20technology&rft.au=Wang,%20Jinnan&rft.date=2014-06-17&rft.volume=48&rft.issue=12&rft.spage=7085&rft.epage=7093&rft.pages=7085-7093&rft.issn=0013-936X&rft.eissn=1520-5851&rft.coden=ESTHAG&rft_id=info:doi/10.1021/es405369r&rft_dat=%3Cproquest_cross%3E3350191431%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a373t-ffdea2b1968f5fd7d2696ef3b831f2f6b9fb37bd8406efe4698191c58e41e9ae3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1540588508&rft_id=info:pmid/24840164&rfr_iscdi=true |