Article(id=1242137607073768182, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242137589919068720, articleNumber=null, orderNo=13, doi=10.3981/j.issn.1000-7857.2019.11.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1541952000000, receivedDateStr=2018-11-12, revisedDate=1551283200000, revisedDateStr=2019-02-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1561025569627, onlineDateStr=2019-06-20, pubDate=1560355200000, pubDateStr=2019-06-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1561025569627, onlineIssueDateStr=2019-06-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078283324, creator=sys-migrate, updateTime=1774078283324, updator=sys-migrate, issue=Issue{id=1242137589919068720, tenantId=1146029695717560320, journalId=1146031591421210625, year='2019', volume='37', issue='11', pageStart='1', pageEnd='108', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=null, issueType=-1, specialIssue=null, createTime=1774078279233, creator=sys-migrate, updateTime=1774078279233, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=92, endPage=100, ext={EN=ArticleExt(id=1242137610924143218, articleId=1242137607073768182, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Spatial distribution analysis on water withdrawal of main energy production in China, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=In this paper, we try to explore the relevance between energy production and water withdrawal by means of spatial autocorrelation analysis of water pressure index based on energy production in order to provide a reference for the optimal adjustment of energy structure and the sustainable development of energy and water in China. The results are as follows. At present, the high-productive areas of main energy production in China are mainly distributed in the north and east-central regions, with the energy production of 5 provinces such as Inner Mongolia and Shandong accounting for 67% of the total China's main energy production; low-productive areas appear in some provinces in the south and southwestern parts. There are differences between the spatial distributions of water withdrawal for the 5 kinds of main energy production, and basically the spatial distribution of water withdrawal for each kind is consistent with the regional distribution of energy itself. The high value areas of water pressure index based on energy production are mainly distributed in the central and northern parts of China. The spatial agglomeration analysis shows that the water pressure index based on energy production has obvious spatial concentration characteristics and the H-H concentration feature is significant., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=YHg/PN0WghTISCx48TTHLw==, pdfFileSize=1700297, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China;
2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China, fund=null, authors=DONG Donglin¹, ZHANG Ruomeng¹, FU Jingying^2,3, LIN Gang¹, authorsList=DONG Donglin, ZHANG Ruomeng, FU Jingying, LIN Gang), CN=ArticleExt(id=1242137609862984296, articleId=1242137607073768182, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=中国能源生产用水的空间特征分析, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=根据能源生产用水这一概念，通过能源生产用水压力指数的空间自相关分析，探究了能源生产与用水之间的关系。结果表明：现状年（2016）下，中国能源的高产区主要分布在北部、中东部地区，其中内蒙古、山东等5省（区）的主要能源总产量占中国主要能源生产总量的67%，低值区出现在南部及西南部部分省（区）；5类主要能源生产用水的空间分布特征有所不同，但与能源资源本身的地区分布基本保持一致；主要能源的生产用水压力指数高值区主要分布在中国中部及北部省（区），空间集聚分析表明能源生产用水压力指数具有显著的空间集聚特征，H-H集聚特征显著。, correspAuthors=null, authorNote=董东林,教授,研究方向为水资源与生态环境研究,电子信箱:ddl9266@163.com, correspAuthorsNote=付晶莹(通信作者),副研究员,研究方向为资源环境遥感应,电子信箱:fujy@igsnrr.ac.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=yiP2qsmgPudQfKa3uHG8GA==, pdfFileSize=1700297, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 中国矿业大学(北京)地球科学与测绘工程学院, 北京 100083;
2. 中国科学院地理科学与资源研究所, 北京 100101;
3. 中国科学院大学资源与环境学院, 北京 100049, fund=中国科学院战略性先导科技专项（XDA19040305）；中国科学院地理科学与资源研究所优秀青年人才基金项目（2016RC203）；中国工程院院士咨询项目（2017-ZD-03-05-01）；中央高校基本科研业务费专项（2019QD01）, authors=董东林¹, 张若萌¹, 付晶莹^2,3, 林刚¹, authorsList=董东林, 张若萌, 付晶莹, 林刚)}, authors=null, keywords=[Keyword(id=1242137609435165280, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137607073768182, language=CN, orderNo=1, keyword=能源结构), Keyword(id=1242137609514857057, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137607073768182, language=CN, orderNo=1, keyword=空间分布差异性), Keyword(id=1242137609598743139, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137607073768182, language=CN, orderNo=1, keyword=能源生产用水压力指数), Keyword(id=1242137609686823525, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242137607073768182, language=CN, orderNo=1, keyword=空间自相关分析), Keyword(id=1242137610492129900, tenantId=1146029695717560320, 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