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Article(id=1241408719758610571, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725811200000, receivedDateStr=2024-09-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773904503049, onlineDateStr=2026-03-19, pubDate=1745078400000, pubDateStr=2025-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773904503049, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773904503049, creator=13701087609, updateTime=1773904503049, updator=13701087609, issue=Issue{id=1241408710791189399, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='4', pageStart='1777', pageEnd='2368', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773904500911, creator=13701087609, updateTime=1773904624658, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241409229878259747, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241409229878259748, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2333, endPage=2345, ext={EN=ArticleExt(id=1241408721188868385, articleId=1241408719758610571, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Research on three-dimensional full footprint stereoscopic correlation of water energy carbon in Chinese industries, columnId=1234106417825772207, journalTitle=China Environmental Science, columnName=Environmental Impact Assessment and Management, runingTitle=null, highlight=null, articleAbstract=

Against the backdrop of China entering a new stage of development, implementing new development concepts, and building a new development pattern, how to pursue healthy industrial development while controlling water energy consumption and reducing carbon emissions has become an important and urgent practical task. On the base of exploring deep level industrial economic connections, the theoretical framework of the three-dimensional full footprint stereoscopic correlation of industry water energy carbon was constructed. Then the article designed an input-output calculation model for industrial factor footprints and created a function correction input-output table to solidify the data foundation. Selecting Chinese industries from 2002 to 2022 as the research object, the three-dimensional full footprint of industrial water, energy, and carbon were calculated. Based on the three dimensional full footprint stereoscopic correlation network of water energy carbon in Chinese industries, the evolution characteristics of network attributes and relationship structures have been compared and analyzed by combining dynamic and static methods. The results showed that:(1) During the research period, the average annual growth rate of China's industrial water, energy, and carbon total carbon footprint had decreased, but the total amount had increased significantly. The increase in water footprint was mainly due to the increase in industrial direct footprint, while the increase in energy footprint and carbon footprint were due to the increase in industrial indirect footprint. (2) There were significant differences in the three-dimensional footprint of water energy carbon and dual factor among various industries, and it was necessary to integrate the characteristics of industries and footprint characteristics to improve the efficiency of factor utilization. (3) During the research period, various indicators and coupled performance of the industrial water energy carbon network had improved, but the circular sustainability, symbiosis and mutual benefit, and correlation had not reached the ideal state.

, correspAuthors=Chuan-ming YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, 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=null, fund=null, authors=null, authorsList=Chuan-ming YANG), CN=ArticleExt(id=1241408732865810894, articleId=1241408719758610571, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=中国产业水-能-碳三维全足迹立体关联研究, columnId=1234106419604157142, journalTitle=中国环境科学, columnName=环境影响评价与管理, runingTitle=null, highlight=null, articleAbstract=

在中国“进入新发展阶段、贯彻新发展理念、构建新发展格局”的大背景下，如何追求产业良性发展的同时控制水能消耗并减少碳排放，已成为一个重要而紧迫的现实任务.为此挖掘深层次产业经济联系，架构产业水-能-碳三维全足迹立体关联理论框架，设计产业要素足迹投入产出计算模型，创建函数修正投入产出表夯实数据基础.选择2002年至2022年中国产业为研究对象，计算产业水、能、碳三维全足迹，构建中国产业水-能-碳三维全足迹立体关联网络，动静态对比解析网络属性及关系结构演变特征.结果表明:(1)研究期中国产业水、能、碳全碳足迹年均增长率有所下降但总量大幅增加，其中水足迹增加主要源于产业直接足迹增加，而能足迹和碳足迹则是归于产业间接足迹增加.(2)各产业水-能-碳及双要素的三维足迹存在巨大差异，需要综合考虑产业特点和足迹特征提升要素使用效率.(3)研究期产业水-能-碳各种网络各项指标及耦合后性能均有所改善，但循环可持续性、共生互利及关联状况均未达到理想状态..

, correspAuthors=杨传明, authorNote=null, correspAuthorsNote=
* 责任作者，教授，
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杨传明(1979-)，男，山东莱阳人，教授，博士，研究方向为区域可持续发展.发表论文60余篇..

, authorsList=杨传明)}, authors=[Author(id=1241408733448819199, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=cmlucky@163.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1241408733591425547, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, authorId=1241408733448819199, language=EN, stringName=Chuan-ming YANG, firstName=Chuan-ming, middleName=null, lastName=YANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=Urban Development Think Tank, Suzhou University of Science and Technology, Suzhou 215009, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1241408733717254673, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, authorId=1241408733448819199, language=CN, stringName=杨传明, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=苏州科技大学城市发展智库，江苏 苏州 215009, bio={"content":"

杨传明(1979-)，男，山东莱阳人，教授，博士，研究方向为区域可持续发展.发表论文60余篇..

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杨传明(1979-)，男，山东莱阳人，教授，博士，研究方向为区域可持续发展.发表论文60余篇..

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Speech Communication，2022，137:44-51., articleTitle=Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach, refAbstract=null)], funds=[Fund(id=1241408739656389567, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, awardId=22BJY130, language=CN, fundingSource=国家社会科学基金(22BJY130), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241408733260075500, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, xref=null, ext=[AuthorCompanyExt(id=1241408733268464109, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, companyId=1241408733260075500, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Urban Development Think Tank, Suzhou University of Science and Technology, Suzhou 215009, China), AuthorCompanyExt(id=1241408733276852718, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, companyId=1241408733260075500, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=苏州科技大学城市发展智库，江苏 苏州 215009)])], figs=[ArticleFig(id=1241408735931847320, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Fig.1, caption=Theoretical framework for the three-dimensional full footprint stereoscopic correlation of industry water energy carbon, figureFileSmall=WwDlO/fYqLpZisyLJpKfgQ==, figureFileBig=HiaVlOpo0QJDNvGgAzSNOQ==, tableContent=null), ArticleFig(id=1241408736045093541, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=图1, caption=产业水-能-碳三维全足迹立体关联理论框架, figureFileSmall=WwDlO/fYqLpZisyLJpKfgQ==, figureFileBig=HiaVlOpo0QJDNvGgAzSNOQ==, tableContent=null), ArticleFig(id=1241408736380637878, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Fig.2, caption=FCI values of associated networks for each year, figureFileSmall=+17TyGfGZeyiKI4vqSThuA==, figureFileBig=wQjRvYVuC/keF1/E/hn5Qw==, tableContent=null), ArticleFig(id=1241408736527438532, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=图2, caption=各年份关联网络FCI值, figureFileSmall=+17TyGfGZeyiKI4vqSThuA==, figureFileBig=wQjRvYVuC/keF1/E/hn5Qw==, tableContent=null), ArticleFig(id=1241408736695210702, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Fig.3, caption=Robust curves of correlation networks of each phase, figureFileSmall=vwJFsXiD23edtY/FuktnIQ==, figureFileBig=0Asoqx+ak905m7nFe46c/Q==, tableContent=null), ArticleFig(id=1241408736850399959, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=图3, caption=各阶段关联网络鲁棒曲线图, figureFileSmall=vwJFsXiD23edtY/FuktnIQ==, figureFileBig=0Asoqx+ak905m7nFe46c/Q==, tableContent=null), ArticleFig(id=1241408736976229090, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Fig.4, caption=Utility relationships among various network departments throughout the entire period, figureFileSmall=tWv/dJ5IO7IOdWJ7t37B9g==, figureFileBig=YhFDSuSuCEtlrnNZughxcQ==, tableContent=null), ArticleFig(id=1241408737106252525, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=图4, caption=全时期各网络部门效用关系, figureFileSmall=tWv/dJ5IO7IOdWJ7t37B9g==, figureFileBig=YhFDSuSuCEtlrnNZughxcQ==, tableContent=null), ArticleFig(id=1241408737227887353, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Fig.5, caption=Change rate of interdepartmental control dependency relationship throughout the entire period, figureFileSmall=RoaJj0msExj+5nEHr2fY8A==, figureFileBig=GM3uEAfGMj/mrRwwc6Gr2Q==, tableContent=null), ArticleFig(id=1241408737387270918, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=图5, caption=全时期部门间控制依赖关系变化率, figureFileSmall=RoaJj0msExj+5nEHr2fY8A==, figureFileBig=GM3uEAfGMj/mrRwwc6Gr2Q==, tableContent=null), ArticleFig(id=1241408737559237394, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Table 1, caption=

The connotation of main conceptual nouns

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概念名词内涵
直接水、能、碳足迹本产业边界内产品生产服务活动中直接产生的水、能、碳等要素足迹
间接水、能、碳足迹由于本产业产品生产服务活动中使用的中间产品服务，而导致其他产业产生的水、能、碳要素足迹
全水、能、碳足迹直接水、能、碳足迹与间接水、能、碳足迹之和
能相关基础水消费量能源在开采、加工、转化等过程中消耗的水要素
水相关基础能消费量生产供应水要素以及处理污水等过程耗费的能源
水相关基础碳排放量生产供应水要素以及处理污水等过程产生的碳
能相关全水足迹由能相关基础水消费量结合投入产出表计算所得
水相关全能足迹由水相关基础能消费量结合投入产出表计算所得
水相关全碳足迹由水相关基础碳排放量结合投入产出表计算所得
全水能足迹全水足迹+能相关全水足迹
全能水足迹全能足迹+水相关全能足迹
全碳水足迹全碳足迹+水相关全碳足迹
), ArticleFig(id=1241408737706038050, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表1, caption=

主要概念名词内涵

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概念名词内涵
直接水、能、碳足迹本产业边界内产品生产服务活动中直接产生的水、能、碳等要素足迹
间接水、能、碳足迹由于本产业产品生产服务活动中使用的中间产品服务，而导致其他产业产生的水、能、碳要素足迹
全水、能、碳足迹直接水、能、碳足迹与间接水、能、碳足迹之和
能相关基础水消费量能源在开采、加工、转化等过程中消耗的水要素
水相关基础能消费量生产供应水要素以及处理污水等过程耗费的能源
水相关基础碳排放量生产供应水要素以及处理污水等过程产生的碳
能相关全水足迹由能相关基础水消费量结合投入产出表计算所得
水相关全能足迹由水相关基础能消费量结合投入产出表计算所得
水相关全碳足迹由水相关基础碳排放量结合投入产出表计算所得
全水能足迹全水足迹+能相关全水足迹
全能水足迹全能足迹+水相关全能足迹
全碳水足迹全碳足迹+水相关全碳足迹
), ArticleFig(id=1241408737802507051, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Table 2, caption=

Classification of industrial sectors

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序号产业部门序号产业部门序号产业部门
D1农林牧渔业D10造纸印刷及文教体育用品制造业D19通信设备计算机及其他电子设备制造业
D2煤炭开采和洗选业D11炼焦煤气及石油加工业D20仪器仪表及文化办公机械制造业
D3石油和天然气开采业D12化学工业D21其他制造业
D4金属矿采选业D13非金属矿物制品业D22电热力生产及供应业
D5非金属矿和其他矿采选D14金属冶炼及压延加工业D23水生产及供应业
D6食品制造及烟草加工业D15金属制品业D24建筑业
D7纺织业D16通用专业设备制造业D25交通运输仓储和邮电通信业
D8纺织服装鞋帽皮革羽绒及制品业D17交通运输设备制造业D26批发及零售贸易餐饮业
D9木材加工及家具制造业D18电气机械及器材制造业D27其他服务业
), ArticleFig(id=1241408737919947572, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表2, caption=

产业部门分类

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序号产业部门序号产业部门序号产业部门
D1农林牧渔业D10造纸印刷及文教体育用品制造业D19通信设备计算机及其他电子设备制造业
D2煤炭开采和洗选业D11炼焦煤气及石油加工业D20仪器仪表及文化办公机械制造业
D3石油和天然气开采业D12化学工业D21其他制造业
D4金属矿采选业D13非金属矿物制品业D22电热力生产及供应业
D5非金属矿和其他矿采选D14金属冶炼及压延加工业D23水生产及供应业
D6食品制造及烟草加工业D15金属制品业D24建筑业
D7纺织业D16通用专业设备制造业D25交通运输仓储和邮电通信业
D8纺织服装鞋帽皮革羽绒及制品业D17交通运输设备制造业D26批发及零售贸易餐饮业
D9木材加工及家具制造业D18电气机械及器材制造业D27其他服务业
), ArticleFig(id=1241408738049971008, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Table 3, caption=

Comparison of update function precision

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函数STPERMSEWADMAETheil USIM
GRAS6.3660.017348.7410.0470.1490.875
TLS6.6750.020342.3620.0420.1470.798
MCE6.2720.019329.0140.0450.1430.861
STAD6.8710.021335.8770.0440.1510.824
WLC6.0480.016316.2600.0400.1370.913
), ArticleFig(id=1241408738159022924, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表3, caption=

更新函数精度比较

, figureFileSmall=null, figureFileBig=null, tableContent=
函数STPERMSEWADMAETheil USIM
GRAS6.3660.017348.7410.0470.1490.875
TLS6.6750.020342.3620.0420.1470.798
MCE6.2720.019329.0140.0450.1430.861
STAD6.8710.021335.8770.0440.1510.824
WLC6.0480.016316.2600.0400.1370.913
), ArticleFig(id=1241408738301629275, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Table 4, caption=

Full footprint of water, energy, and carbon in industrial sectors

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
全水足迹(亿m3)全能足迹(×107t)全碳足迹(×107t)全水足迹(亿m3)全能足迹(×107t)全碳足迹(×107t)
D140578137421554745517482567
D23701119119544416161209
D3346872102629812821442
D4521514429600724507
D5298197223578357330
D69219171931601343524704138
D7242613161872168014701763
D8272110471897299414042146
D98264187871048571990
D1010678311178142612181666
D113832274191641737533106
D123189664955173780101036378
D13648337710607485455414963
D14137883401623810981265329968
D156281003178662916462598
D16247530387308244147029187
D17171320894382225735877160
D18154119604276151025926531
D19212228355986249032986498
D20458225800421331548
D21562346407891461277
D22804427383511311413449360613
D23229120151243238272
D249804847320800116091612041125
D251292326363431880648413031
D26394115382642415324723396
D2791894791921012546884614915
), ArticleFig(id=1241408738452624233, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表4, caption=

产业部门水、能、碳全足迹

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
全水足迹(亿m3)全能足迹(×107t)全碳足迹(×107t)全水足迹(亿m3)全能足迹(×107t)全碳足迹(×107t)
D140578137421554745517482567
D23701119119544416161209
D3346872102629812821442
D4521514429600724507
D5298197223578357330
D69219171931601343524704138
D7242613161872168014701763
D8272110471897299414042146
D98264187871048571990
D1010678311178142612181666
D113832274191641737533106
D123189664955173780101036378
D13648337710607485455414963
D14137883401623810981265329968
D156281003178662916462598
D16247530387308244147029187
D17171320894382225735877160
D18154119604276151025926531
D19212228355986249032986498
D20458225800421331548
D21562346407891461277
D22804427383511311413449360613
D23229120151243238272
D249804847320800116091612041125
D251292326363431880648413031
D26394115382642415324723396
D2791894791921012546884614915
), ArticleFig(id=1241408738599424886, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=EN, label=Table 5, caption=

Water energy footprint of industrial sectors (Billion cubic meters)

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
全水能直接水能间接水能全水能直接水能间接水能
D140760401675934764947203446
D24764651161959821
D34694511846444816
D4579543367457404
D5323301226436368
D6942163987821387278313089
D72920564235619244891435
D82863177268633291933137
D98818180011571011057
D1011647004631534689845
D11114399614618701654216
D12391224641448499531971798
D13891640250940685255
D142090174734326982417281
D15773203569949333616
D162892317257533623752988
D171985222176228183102509
D181688186150219422551687
D193209410279930505352515
D202174117630767240
D2123412011423914891
D2210797106101871020110033169
D232552015430222874
D24108061712909414264190812356
D251653611104227667891977
D26414614122733463718422795
D279853460052531411666797436
), ArticleFig(id=1241408738691699583, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表5, caption=

产业部门水能足迹(亿m3)

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
全水能直接水能间接水能全水能直接水能间接水能
D140760401675934764947203446
D24764651161959821
D34694511846444816
D4579543367457404
D5323301226436368
D6942163987821387278313089
D72920564235619244891435
D82863177268633291933137
D98818180011571011057
D1011647004631534689845
D11114399614618701654216
D12391224641448499531971798
D13891640250940685255
D142090174734326982417281
D15773203569949333616
D162892317257533623752988
D171985222176228183102509
D181688186150219422551687
D193209410279930505352515
D202174117630767240
D2123412011423914891
D2210797106101871020110033169
D232552015430222874
D24108061712909414264190812356
D251653611104227667891977
D26414614122733463718422795
D279853460052531411666797436
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Energy and water footprint of industrial sectors(Million tons)

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产业部门第一阶段(2002-2011年)第二阶段(2012-2022年)
全能水直接能水间接能水全能水直接能水间接能水
D11928118774124961635862
D211961161351723169329
D3948933151435141718
D4618600179959915
D5261242194814729
D620788351243288310991783
D712158613541625970656
D81249156109415742121362
D9480159321630203427
D1010127292821432777655
D112311220111038243565259
D12720364267771069596391057
D133505329321246064359247
D14889286712211403213754278
D15115340974417587401018
D163450671277946848373847
D172346357198937596073152
D182269266200227964082388
D193254421283336087342874
D202705421736783285
D21389213176462338125
D225066486020677487436312
D231651372829022366
D249649653899617506116016345
D2533912670722683150731758
D261895934960283717151121
D275985196540201029437666527
), ArticleFig(id=1241408738897220495, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408719758610571, language=CN, label=表6, caption=

产业部门能水足迹(×107t)

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产业部门第一阶段(2002-2011年)第二阶段(2012-2022年)
全能水直接能水间接能水全能水直接能水间接能水
D11928118774124961635862
D211961161351723169329
D3948933151435141718
D4618600179959915
D5261242194814729
D620788351243288310991783
D712158613541625970656
D81249156109415742121362
D9480159321630203427
D1010127292821432777655
D112311220111038243565259
D12720364267771069596391057
D133505329321246064359247
D14889286712211403213754278
D15115340974417587401018
D163450671277946848373847
D172346357198937596073152
D182269266200227964082388
D193254421283336087342874
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Carbon and water footprint of industrial sectors(Million tons)

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
全碳水直接碳水间接碳水全碳水直接碳水间接碳水
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产业部门碳水足迹(×107t)

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产业部门第一阶段(2002~2011年)第二阶段(2012~2022年)
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Network SR values of each phase

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阶段水网络水能网络能网络能水网络碳网络碳水网络平均值
第一阶段0.22750.22900.20160.20790.15820.17070.1992
第二阶段0.24070.24680.22710.23230.17260.18440.2173
全时期0.22790.23170.20940.21510.16030.17220.2028
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各阶段网络SR值

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阶段水网络水能网络能网络能水网络碳网络碳水网络平均值
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第二阶段0.24070.24680.22710.23230.17260.18440.2173
全时期0.22790.23170.20940.21510.16030.17220.2028
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中国产业水-能-碳三维全足迹立体关联研究
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杨传明 *
中国环境科学 | 环境影响评价与管理 2025,45(4): 2333-2345
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中国环境科学 | 环境影响评价与管理 2025, 45(4): 2333-2345
中国产业水-能-碳三维全足迹立体关联研究
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杨传明*
作者信息
  • 苏州科技大学城市发展智库，江苏 苏州 215009

    • 杨传明(1979-)，男，山东莱阳人，教授，博士，研究方向为区域可持续发展.发表论文60余篇..

通讯作者:

* 责任作者，教授，
Research on three-dimensional full footprint stereoscopic correlation of water energy carbon in Chinese industries
Chuan-ming YANG*
Affiliations
  • Urban Development Think Tank, Suzhou University of Science and Technology, Suzhou 215009, China
出版时间: 2025-04-20
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摘要
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在中国“进入新发展阶段、贯彻新发展理念、构建新发展格局”的大背景下，如何追求产业良性发展的同时控制水能消耗并减少碳排放，已成为一个重要而紧迫的现实任务.为此挖掘深层次产业经济联系，架构产业水-能-碳三维全足迹立体关联理论框架，设计产业要素足迹投入产出计算模型，创建函数修正投入产出表夯实数据基础.选择2002年至2022年中国产业为研究对象，计算产业水、能、碳三维全足迹，构建中国产业水-能-碳三维全足迹立体关联网络，动静态对比解析网络属性及关系结构演变特征.结果表明:(1)研究期中国产业水、能、碳全碳足迹年均增长率有所下降但总量大幅增加，其中水足迹增加主要源于产业直接足迹增加，而能足迹和碳足迹则是归于产业间接足迹增加.(2)各产业水-能-碳及双要素的三维足迹存在巨大差异，需要综合考虑产业特点和足迹特征提升要素使用效率.(3)研究期产业水-能-碳各种网络各项指标及耦合后性能均有所改善，但循环可持续性、共生互利及关联状况均未达到理想状态..

水-能-碳  /  三维全足迹  /  投入产出分析  /  关联网络  /  中国产业

Against the backdrop of China entering a new stage of development, implementing new development concepts, and building a new development pattern, how to pursue healthy industrial development while controlling water energy consumption and reducing carbon emissions has become an important and urgent practical task. On the base of exploring deep level industrial economic connections, the theoretical framework of the three-dimensional full footprint stereoscopic correlation of industry water energy carbon was constructed. Then the article designed an input-output calculation model for industrial factor footprints and created a function correction input-output table to solidify the data foundation. Selecting Chinese industries from 2002 to 2022 as the research object, the three-dimensional full footprint of industrial water, energy, and carbon were calculated. Based on the three dimensional full footprint stereoscopic correlation network of water energy carbon in Chinese industries, the evolution characteristics of network attributes and relationship structures have been compared and analyzed by combining dynamic and static methods. The results showed that:(1) During the research period, the average annual growth rate of China's industrial water, energy, and carbon total carbon footprint had decreased, but the total amount had increased significantly. The increase in water footprint was mainly due to the increase in industrial direct footprint, while the increase in energy footprint and carbon footprint were due to the increase in industrial indirect footprint. (2) There were significant differences in the three-dimensional footprint of water energy carbon and dual factor among various industries, and it was necessary to integrate the characteristics of industries and footprint characteristics to improve the efficiency of factor utilization. (3) During the research period, various indicators and coupled performance of the industrial water energy carbon network had improved, but the circular sustainability, symbiosis and mutual benefit, and correlation had not reached the ideal state.

water energy carbon  /  three-dimensional full footprint  /  input-output analysis  /  associative network  /  Chinese industries
杨传明. 中国产业水-能-碳三维全足迹立体关联研究. 中国环境科学, 2025 , 45 (4) : 2333 -2345 .
Chuan-ming YANG. Research on three-dimensional full footprint stereoscopic correlation of water energy carbon in Chinese industries[J]. China Environmental Science, 2025 , 45 (4) : 2333 -2345 .
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水、能、碳是社会经济发展的三大关键环境要素，联合国人口行动组织年度报告表明，2022年中国人均水资源量仅为世界平均水平的23.16%，能源消费量和碳排放量则分占全球的26.23%和29.21%，业已成为最大的水要素消耗国（2004年至今）、能源消费国（2008年至今）和碳排放国（2005年至今）.为此，降低用水消耗、提升能源效率、减少碳排放量成为中国产业绿色转型升级的核心目标，而水-能-碳三个要素全足迹相互依赖深度纠缠，任何产业任何足迹的波动均会影响关联产业.因此，在要素环境约束趋紧、节能减排压力巨大的背景下，如何比较发掘中国产业水-能-碳三维全足迹时空演变特征，清晰梳理立体关联关系，科学解析综合作用机理，提出切实可行调控建议，成为深入践行新时代高质量发展理论与实践双重要的紧迫课题.
在借助扎根理论法梳理收集的568篇中文及272篇英文文献后，从两个主要方面分解当前对于产业层面水、能、碳的研究.第一、产业水、能、碳关系.多数研究分别单独解析了产业的水、能、碳要素[1-3].要素两两关系研究主要包括三个方面，一是从用水能耗、能源水耗角度梳理了区域产业水-能交互关系[4-5]，二是剖析了农业、电力等产业水-碳联动关系[6-7]，三是关注了产业能-碳因果关系[8-9].当前仅有极少数学者注意到了农业、钢铁等单一产业或区域的水-能-碳关联关系[10-12].第二、产业水、能、碳衡量指标.主流研究分别以总量、强度、消耗为主要指标衡量产业的水、能、碳[13-15]，而这些指标主要从机理、经济或要素关系等单方面切入，存在一定局限性.自Galli综合提出足迹家族概念后[16]，足迹从要素利用源头及过程分析另辟蹊径，并能与全要素绩效无缝对接，以其有效性和通用性引起了各界重点关注.目前学者分别利用水足迹、碳足迹及能源足迹展开了产业层面的研究[17-19].
由上分析可见，当前产业水、能、碳相关研究已经取了一定成绩，但针对产业水-能-碳三维全足迹立体关联研究非常薄弱.第一、当前研究主要针对特定产业水、能、碳的单一要素或两两关系展开分析，极少数研究又仅关注了单一产业或较小区域的三者关系，未能系统梳理全体产业水-能-碳三维要素的立体关联关系.第二、绝大多数现有研究仅采用水、能、碳显性模块衡量产业责任，漠视了产业间存在的隐性模块，尚未见综合使用全足迹方法针对产业水-能-碳关联展开的有效研究.第三、研究数据来源多采用静态投入产出表，未充分考虑产业水-能-碳关联时滞动态随机扰动项，缺乏基于长时间维度对产业关联网络静动态特性内在关系的综合分析.
为此，本文构建产业水-能-碳三维全足迹立体关联理论框架，设计产业要素全足迹投入产出分析模型（CTF），改进WLC函数修正投入产出表夯实数据基础.计算2002年至2022年中国产业水、能、碳三维全足迹，构建中国产业水-能-碳三维全足迹立体关联网络，从循环率、鲁棒性、网络效用、控制度及依赖度等多角度，基于不同时间段对比解析网络属性及关系结构，以期为新时代中国产业协同节水、降能、减碳提供科学决策依据.
1　理论框架及研究模型
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1.1　理论框架
基于产业水、能、碳三要素属性解析相关依赖控制耦合关系，构建立体关联分析理论框架（图1），其中主要概念见表1.
具体而言，首先综合计算获得各产业单一要素基础数量（基础水消费量、基础能消费量及基础碳排放量），再结合单位系数（单位系数1为单位能源生产耗水量、2为单位水要素能耗量、3为单位水要素碳排放量）深入挖掘相关要素基础数量，分别为能相关基础水消费量、水相关基础能消费量及水相关基础碳排放量.继而构建CTF，设置改进型WLC函数增强投入产出表精准性，结合单一及相关要素基础数量，从直接、间接及全足迹角度分别计算水、能、碳及能相关水、水相关能、水相关碳的三维足迹，通过单一及相关要素对应三维足迹加总即得到水能、能水和碳水等双要素的三维足迹，以此定量描述水、能、碳在产业部门内部及部门间的流动数值.在此基础上，运用生态网络分析法（ENA）测度循环率、鲁棒性、网络效用、控制度及依赖度，系统对比解析中国产业水-能-碳三维足迹立体关联关系.
1.2　研究模型
1.2.1　数据来源及部门分类
研究数据包括两个基础来源，一是对应年份的《中国统计年鉴》《中国能源统计年鉴》《中国水资源公报》《中国环境年鉴》，二是研究期内已公布的9份投入产出表及延长表，以及基于改进函数推算出的其他年份投入产出表.为了方便比较，结合已有分类标准和行业特征将产业部门整合为27个，见表2.
确定产业部门后，结合公开数据源挖掘基础模型数据，首先计算单一要素基础数量.鉴于历年水要素公报仅统计了农林牧渔业、工业、生活及生态用水量，为此基于《中国经济普查年鉴》提供工业及服务业的分行业水要素消耗量，依据各年部门增加值占比推算各细分产业部门基础水消费量（其中服务业相关部门结合生活和生态用水量加以调整）；基础能消费量设置为各产业部门煤炭、焦炭、原油、汽油、煤油、柴油、燃料油、天然气、电力9种能源消费量的总和；基础碳排放量则参考《IPCC国家温室气体清单指南》提供9种能源消费量的碳排放系数及碳氧化率进行换算加总.
而后计算三个单位系数.单位能源生产耗水量系数综合已有研究[20-21]，结合9种能源特征分别推算，其中煤炭和焦炭耗水量系数主要依据产出量及井上井下生产及生活、洗煤、熄焦等环节用水总量加以计算，分别为0.74及1.7m3/t；油类及天然气按照生产过程中钻井、洗井、压裂、注水、炼制、加工等环节，根据实际各种油类生产量、炼油量及用水定额推算单位产量用水系数，计算得原油4.21m3/t、汽油5.33m3/t、煤油4.78m3/t、柴油4.72m3/t、燃料油5.12m3/t及天然气0.002m3/m3；鉴于水力、风力、太阳能发电过程几乎无水耗，故重点考虑火力发电中冷却系统、化学自用、除灰除渣、锅炉补给用水环节以及核电反应堆冷却用水量，而后通过全国各类型发电占比及总用水量推算电力耗水量系数为2.53m3/（MW·h）.单位水要素能耗量系数依照取水、供水、分配水、用水及废水处理的全水生命环节耗能系数[22-24]，结合各产业部门各环节用水总量计算单位水要素能耗量系数，再利用能源消费量的碳排放系数及碳氧化率推算单位水要素碳排放量系数.在此基础上，将单位系数与单一要素基础数量相乘即得到各产业部门的三个相关要素基础数量.
1.2.2　创建CTF测算模型
纵观现有要素测算模型，经典投入产出生命周期评价模型相对有效[25-26]，模型见公式（1）.
式中:X为各产业要素足迹，R和y分别是资源消耗系数对角矩阵和最终需求对角矩阵，单位矩阵I和直接消耗系数矩阵A组成了列昂惕夫逆矩阵,可以描述产业间相互依赖和消耗关系.经典投入产出模型虽能厘清单个产业部门要素足迹静态分布状况，但仍无法有效解析多产业部门要素足迹动态联动情况，且尚存在着直接消耗系数矩阵更新度不足、单位消耗系数对角矩阵等诸多问题[25-26].为此基于经典投入产出模型，设计产业要素全足迹投入产出分析模型CTF测算各要素三维足迹，计算公式如下:
式中:T为各产业部门全要素足迹矩阵，矩阵元素tij代表第i部门为第j部门提供生产服务而产生的直接足迹，tij之和为i部门直接足迹，行向量ti反映了i部门生产服务过程中要素足迹与各部门消费需求间的关系，列向量tj描述了j部门消费需求与其他部门直接足迹间的关系；当i≠j时，tij为j部门的间接足迹，tj为满足j部门消费需求产生的间接足迹.利用∑t i及∑tj则可分别从生产端及消费端视角描绘部门足迹的分布情况.令aij=xij/Xj为A中元素，xij为i部门为j部门提供的生产服务，Xj是j部门的总产出；设置Q为区域要素足迹消耗系数的对角矩阵，通过i部门的要素消耗量除以总产出可得到矩阵元素qi；Y修正为投入产出表的最终使用需求对角矩阵.
CTF模型数据基础为投入产出表，但该表非每年公布且存在明显时滞.为了解决该问题，学界公认较为有效的方法为最小二乘函数（LS）及交叉熵函数（CE）[27-28]，两者均通过计算基准及目标年份系数差值推算新的投入产出表，不同点在于两者差值计算的方式有所区别且各有优缺点.经典LS函数能有效提升数据流量精度，但对系数矩阵控制度不足[27-28]；经典CE函数通过接近度检验、保号分析严密控制系数矩阵，但会导致离差期望小而方差大的数据不均衡问题[29-30].鉴于两种方法均基于差值测算原理，使得联合成为可能，为了更好综合运用两种方法的优点，设置改进型设计权重型最小二乘函数WLS及权重型交叉熵函数WCE函数，并综合构建为WLC修正函数:
式中:WLS函数基于经典LS函数构建信息权重矩阵Wn×n ，并依据数据变化动态设置矩阵元素wij.面对确定性数据时，将设置为正无穷或极大值以保证数据不变；对于不确定数据，依据更新后数据的合理取值区间设置，使与的取值区间长度成反比；当数据完全未知时，则令为1以保证不影响数据更新.由此，借助权重矩阵WLS函数可有效降低先验信息损耗，确保交易流量精度，并更好维持了系数矩阵的平稳性.WCE函数则通过引入信息权重矩阵细化信息熵使用范围，并设置及为标准条件，利用拉格朗日乘数法分别阐释数据，从而有效减少了矩阵系数剧烈波动，脱离了机械式强制均衡并提升了信息利用效率.
WLC函数中权重ω′取值区间为[0，1]，实际运算过程可结合WLS及WCE函数特点，根据系数矩阵控制度或数据流量精度不同需求进行设置，本文取值为0.5以综合保障投入产出表的平衡性和精准性.此外，经典LS及CE函数拥有相同的三项约束条件，WLC函数直接引入前两项，分别为及以控制数据流量平衡性，其中及分别是目标表中间使用及投入的列及行之和和分别为目标表的总产出和总投入.针对第三项非负约束，鉴于产业结构、技术进步等影响系数均具有长期演化特征，使得单位时间内投入产出表系数波动总体平稳，导致实际求解过程最优解极易被驱至非负边界，造成大量近零值堆砌的无意义状况.为此，WLC函数该约束改进为，从而确保初始系数与更新系数同步保持为零值，更有效继承基准投入产出表的零值结构.
为了全面验证WLC函数效用，以泛化双比例算法（GRAS）、二次最小二乘法（TLS）、标准交叉熵法（MCE）以及标准化绝对差值法（STAD）作为比较函数，基于GAMS27.0编写函数，选取2018年投入产出表为基础表，分别推算2020年投入产出表最优解.而后拣选标准误差百分比（STPE）、均方根误差（RMSE）、加权绝对偏差（WAD）、平均绝对值误差（MAE）、泰尔U值（Theil U）、伊罗相似性指数（SIM）指标，分别从更新数值整体精度、离散程度、与权重数据偏差、与真实数据平均绝对值误差、与最小真实数据误差以及系统数据误差6个维度，将推算最优解与实际值展开对比，其中前五项指标数值越小越好，SIM则越接近1越好，结果如表3所示.
上表显示，WLC函数前五项统计指标数值分别为6.048，0.016，316.260，0.040，0.137，均为最小值，且SIM指标数值为0.913，与标准值最为接近.比较证明WLC函数具备了最佳的数据更新精准度，可有效满足基础数据测算要求.
1.3　设置立体关联网络模型
鉴于产业水-能-碳全足迹关系极为复杂，为此构建生态型立体关联网络模型，借助芬恩循环指数（FCI）及系统鲁棒性（SR）辨识网络属性，结合网络效用分析法（NUA）及控制分配系数（CA）和依赖分配系数（DA）解析部门关系结构.
（1）FCI
利用FCI可以考察关联网络中要素流动循环效率，指数越大说明该要素有效利用率越高，计算公式如下:
式中:Hi及HSH分为关联网络中进出i部门及所有部门的要素流通量，nii为从i部门出发并再次循环流入i部门的要素量，nii-1为从i部门出发经过两次以上循环再流回i部门的要素量.
（2）SR
虽然采用FCI可有效评价网络循环效率，但鉴于网络中存在适当系统冗余可以增强弹性并提升抗干扰能力，所以FCI指数并非越大越好，为此引入SR指数评估网络循环效率及冗余度间的平衡性.计算公式如下:
上式中α=A/C ，该值越接近1显示网络效率越高，越接近0说明该网络拥有更好的弹性.A定义为上升性指数，用于表征促进网络要素流通的有效行为；C为网络发展能力，用于综合反映网络中有效行为及剩余无组织行为；tij为中间部门消费的从i到j流通的要素量.
（3）NUA
采用NUA法可以量化网络中不同部门间相互关系，进而明确各部门的网络定位和功能作用.该方法利用要素流通量分析可得直接效用矩阵D和无量纲总效用矩阵U:
式中:D0,1...k反映了网络中两部门之间经过k步的流量效用（D0为单个部门的初始流量，D1反映了两部门的直接流量效用，Dk显示了两部门经过k步的间接流量效用），I为单位矩阵，U则描述了网络中任意两个部门之间的整体关系.借助D及U元素正负号组合可有效梳理部门间的效用关系，并分别归为互惠（+，+）、掠夺（+，-）、控制（-，+）和竞争（-，-）四种类型.在此基础上，进一步构建共生指数M=S+/S−定量分析网络整体共生状况，S+和S-分别为效用矩阵中正负效用无量纲数量.
（4）CA及DA
通过CA指数可从投入角度量化i部门对j部门的控制程度，DA指数则从消费角度反映了j部门对i部门的依赖程度.当时候，CA、DA数值均为零；当时，两者计算公式如下:
2　中国产业水-能-碳三维全足迹立体关联解析
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为了全面分析中国产业水能碳全足迹立体耦合特征，选取2002年至2022年中国产业为研究对象，并以中国特色社会主义进入新时代的2012年为分界，将研究期分为两个阶段，以便更好比较分析网络立体关联特征.基础数据结合各年《中国统计年鉴》《中国环境年鉴》等资源，选择研究期公布的9份投入产出表及延长表，利用WLC函数推算出未公布年份的投入产出表.为了消除价格因素影响，各部门增加值均按照2001年不变价格进行调整，而后利用CTF模型测算各要素三维全足迹.
2.1　计算中国产业水-能-碳三维全足迹
表4显示了计算所得各时期产业部门单一要素全足迹，其中水足迹单位为亿立方米，能足迹及碳足迹单位为百万吨.
由上可见，第一阶段全水足迹占比前五位的部门依次为D1、D22、D24、D6、D27，第二阶段调整为D1、D6、D27、D24、D22，其中D6和D27排名上升原因在于第二阶段产业规模增长较快.全时期排名前五部门占整体部门全水足迹比为74.01%，其中D1占比为37.59%，大幅领先于第二名D6的9.14%，为水足迹最主要产生部门，提高D1用水效率是降低全水足迹的关键所在.第一阶段全能足迹占比前五位的部门依次为D14、D24、D12、D27、D13，第二阶段随着以房地产为代表的建筑业和交通运输行业的迅猛发展，全能足迹排序调整为D24、D14、D12、D27、D25，全时期排名前五部门占整体部门比高达53.03%.第一阶段及第二阶段全碳足迹占比前五位的部门一致，依次为D22、D24、D14、D13、D27，全时期前五部门占整体部门比为65.91%，由于中国火力发电比重一直高于60%，致使D22全碳足迹占比排名第一（24.88%）.第二阶段整体产业水、能、碳全足迹较第一阶段分别增长了17.37%、51.22%及53.79%，其中全水足迹增长主要归因于直接水足迹的增长，而全能足迹及全碳足迹增长主要源于各产业相关间接足迹的增加.而后基于各单一要素及相关要素足迹计算各阶段产业部门双要素足迹，见下诸表.
如表5所示，第一阶段产业全水能足迹总量为116397亿m3，排名前五的部门分别是D1、D24、D22、D27和D6，5个部门总占比高达70.14%.第二阶段全水能足迹总量为141396亿m3，较第一阶段增了21.48%，排名前五的部门仅位次发生了一定变化，总占比则上升至70.80%.从第一阶段到第二阶段，受益于部门资源利用效率和生产工艺水平有所提高，D3、D7、D19、D22四个部门全水能足迹有所下降，而其他部门全水能足迹总量均有所增加，但总体占比变化都不大.全水能足迹在全时段和第一阶段年均增长率分为1.05%和3.10%，第二阶段则大幅下降至-0.63%，说明该阶段水生产过程能效得到明显提升.
表6显示第一阶段产业全能水足迹总量为721.77亿t，排名前五的部门分别是D24、D14、D12、D27和D22，5个部门总量占比为50.98%.第二阶段全能水足迹总量为1113.69亿t，为第一阶段的154.30%，排名前五部门全能水足迹均大幅增长且均超过了70亿t.全能水足迹全时段、第一阶段及第二阶段年均增长率分别为4.34%、8.09%、1.14%，增长原因在于能源需求随着经济规模的扩大而提升，也相应消耗了更多的水资源.
如表7所示，第一阶段产业全碳水足迹总量为1544.01亿t，排名前五的部门分别是D22、D24、D14、D13、D27，5个部门总量占比为60.96%.第二阶段全碳水足迹总量为2404.46亿t，较第一阶段大幅增长55.73%，排名前五的部门仅D13及D27位次发生对调，总占比则上升至66.45%.从第一阶段到第二阶段，除D7、D20因较为成功的数字化转型降低了全碳水足迹外，其他部门总量均有所增加.全碳水足迹在全时段年均增长率分为4.84%，其中第一阶段高达10.08%，第二阶段则下调至0.47%，客观显示第二阶段产业碳减排工作取得了一定成效.
2.2　构建中国产业水-能-碳三维全足迹关联网络
2.2.1　网络属性解析
基于所得的要素全足迹数值，运用ENA法构建中国产业水-能-碳三维全足迹关联网络.首先计算各年FCI数值反映产业部门间水-能-碳网络要素循环流动率，量化水-能-碳耦合对于网络循环的影响程度，详见图2.
上图显示，各网络要素循环率总体呈现波动上升趋势，说明研究期产业水流、能流及碳流返回关联网络的效率在逐步提高，但循环率均未超过0.7，尚存较大提升空间.就各网络年均值及标准差而言，水及水能网络相对最大（分为0.486，0.473和0.080，0.081），能及能水网络第二（分为0.328，0.342和0.028，0.034），碳及碳水网络最小（分为0.240，0.252和0.020，0.024）.由此可见，水要素在部门间循环流动率及波动性最大，能要素两项指标次之，碳要素均位居第三，其原因在于相对较多的能源种类拉低了能要素在各部门间的流速，而碳要素又是能源消费衍生产物，流速进一步降低.此外，耦合后水要素对能及碳单一网络循环效率都产生了一定的提升作用，而能要素则拉低了单水网络效率.就各部门而言，全时期D1和D27在水网络FCI占比分别为63.73%和13.65%，其余产业总和仅为22.62%.需要特别指出的是，水网络中资源能源转换部门（电热力生产及供应业、水生产及供应业）全水足迹占整体部门比重为14.7%，远超其FCI占比的5.44%，显示该类型部门水利用效率亟待提升.水能耦合后，在水能网络中D1和D27的FCI占比较水网络分别下降了2.54%及1.40%，其余产业变化则均不到0.3%.在能网络中，D12、D25、D27和D14的FCI占比分别为18.66%、16.17%、12.18%和8.91%，其余部门占比均未超过5%，耦合后能水网络中除D25及D27排名进行了互换外，格局未发生重大变化.碳网络中排名前三的D22、D25和D14的FCI占比分别为38.89%、17.88%和11.11%，碳水网络中这三个部门FCI占比均小幅下降，D1的FCI占比则显著增加了5.27%，较碳网络循环效率有所增长.
表8及图3显示了各阶段网络鲁棒性SR数值及曲线图，以此评估各关联网络的可持续发展性.
由上可见，6个网络各个时期SR均处于0.15~0.25之间，第二阶段较第一阶段呈现递增趋势，显示出各个部门要素利用效率得到了一定优化，但网络数值仍都位于均衡点的左侧，呈现低效率高冗余的不可持续发展状态，网络要素利用效率尚待提升.各个时期SR值由高到低均为水能网络、水网络、能水网络、能网络、碳水网络及碳网络，水主导网络和能主导网络表现相对较好，碳水网络及碳网络SR数值则均低于各阶段网络均值，呈现了较高冗余性和脆弱性，网络性能亟待优化.此外，各个双要素网络SR值均大于对应的单一要素网络，客观原因在于双要素网络为产业部门间要素流动构建了更多关联路径.
2.2.2　关系结构分析
通过NUA法分析全时期网络中四种效用关系，见图4.继而计算共生指数M，其值大于1代表着网络中积极关系大于消极关系，数值越大说明共生互利性越好，数值越小表示协作性越差.
计算显示，全时期水网络拥有35对互惠关系、94对控制关系、51对掠夺关系和198对竞争关系，M数值为0.3475；水能耦合后，水能网络较水网络互惠关系增加1对、掠夺和竞争关系各增加4对，而控制关系则减少了9对，M上升至0.3500.能网络包括了33对互惠关系、80对控制关系、72对掠夺关系和193对竞争关系，M为0.3550，能水网络互惠关系增加1对，竞争关系减少2对，M上调至0.3626.碳网络包括了34对互惠关系、66对控制关系、48对掠夺关系和230对竞争关系，M为0.2700；碳水网络互惠关系不变，而竞争关系减少了6对，M上调至0.2835.由上可见，各个网络消极关系均显著多于积极关系，但要素耦合后均对各自单要素网络的部门生态关系产生了正向影响，其原因在于要素耦合带来了新的流动路径，产生更多良性关联.此外，计算显示各网络第二阶段的积极关系数量及M值较第一阶段均有所上升，部门间协作性有所增强，一定程度上显示了向好趋势.
鉴于在单一及对应双要素的关联网络中，同一要素从部门i对j的流动路径方向不会发生变化，为此基于CA及DA指数特性，分别计算单一及对应双要素网络两指数变化率，以从投入及消费角度反映各部门自身单一要素及相关要素足迹间的差异性影响.具体而言，若CA或DA变化率为正，则说明考虑水-能-碳要素耦合关系后，部门i对j的控制力或部门j对i的依赖度有所增强，其原因在于相较于i部门单一要素足迹占整体部门单一要素足迹占比，i部门相关要素足迹在整体部门的相关要素足迹占比更大；若CA或DA变化率为负，说明考虑要素耦合关系后，部门i对j的控制力或部门j对i的依赖度有所减弱，其原因在于相较于i部门相关要素足迹在整体部门的相关要素足迹占比，i部门单一要素足迹占整体部门单一要素足迹占比更大.
图5（a）显示，考虑水能耦合后，11个部门控制力有所提升，其中D11、D14和D3对其他部门的平均控制力分别大幅提升100%、95.89%和92.46%，这三个产业均是以高水耗和高能耗为特点的重工业，水能耦合进一步增强了系统控制力；D2、D4、D21和D12平均控制力也均超过了30%；仅有D10和D1对其他部门的控制力所有下降，幅度分为15.55%和4.06%，其余14个部门没有变化.如图5（d）所示，其他部门对D14、D11、D22的平均依赖度分别增加了9.36%、5.99%及0.21%，显示这几个部门用能产生的相关全水足迹在整体产业占比较大，对其他部门产生了较大影响；D23、D10、D5和D12的平均受依赖度则分别下降了16.11%、11.77%、7.67%和4.68%，另有6个部门平均受依赖度微幅下调.
图5（b）显示，考虑能水耦合后，7个部门对其他部门控制力有所提升，排名前三的D22、D1、D5平均控制力分别上升了48.25%、34.62%、23.95%，说明这些部门用水产生的相关全能足迹在整体产业中占比相对较大；13个部门控制力有所下降，其中D25、D14和D11的平均控制力分别下降了14.86%、11.57%和10.98%，显示这些部门自身所用的全能足迹占比相对较大.图5（e）显示，10个部门平均受依赖度有所提升，其中D1、D22、D26、D23、D5均增加超过了5%；7个部门平均受依赖度有所下降，D25、D13、D14分别减少了5.51%、4.66%、4.15%.
图5（c）显示，考虑碳水耦合后，鉴于用水产生的全碳足迹在整体产业中占比相对较大，D1、D5和D4平均控制力分别增加了33.33%、9.08%及4.54%.D1对9个部门的控制力增加了100%，显示了很强控制力；8个部门平均控制力有所减弱，除D25下降8.68%外，其余部门下降幅度均小于5%.图5（f）显示，6个部门平均受依赖度有所提升，其中D1、 D25、D12平均受依赖度上升了7.10%、1.91%及1.35%，另有3个部门微幅上调；D13、D14、D4、D25平均受依赖度分别下降了-2.39%、-2.21%、-1.43%、-0.85%，其他部门没有变化.
综合图5可见，耦合后水、能、碳单一要素网络的部门控制力变化区间分别为[-15.55%，100%]、[-14.86%，48.25%]、[-8.68%，33.33%]；依赖性变化区间分为[-16.11%，9.36%]、[-5.51%，14.61%]、[-2.39%，7.10%]；耦合变化幅度由高到低分别为水网络、能网络及碳网络.
3　结论及建议
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3.1　结论
产业水-能-碳综合利用是一个典型环境相关的非确定性复杂博弈问题，对国家社会经济可持续发展有着重大影响.本文构建理论框架，设计产业要素全足迹投入产出分析模型，核算2002~2022年中国产业水-能-碳三维全足迹，建立单要素及耦合要素全足迹立体关联网络，洞察网络属性结构及产业关联关系，并形成以下结论.
（1）鉴于碳排放衡量标准基本一致、各类水资源使用替代性较强而各种能源消费替代性较弱的原因，各产业部门水足迹与水能足迹间、碳足迹与碳水足迹间的要素数值占比呈现了较大的趋同性，而能足迹与能水足迹要素数值占比则呈现了一定的差异性，且各部门生产特点及整体产业结构是影响水使用、能消费及碳排放的关键因素.研究期第二阶段水、能、碳三维全足迹年均增长率较第一阶段有所下降而总体数值均大幅增加，说明虽然当前中国产业转型升级已经取得了一定成效.
（2）产业部门受到水-能-碳要素耦合影响程度迥然不同，其中农林牧渔业和电热力生产及供应业分别是水能和碳水足迹最大流通部门，但两个部门对整体产业产值贡献率不高，且耦合要素间接足迹远小于直接足迹；建筑业是能水足迹占比最大的部门，其他服务业的水能、能水及碳水足迹占比均较大，且这两个部门都拥有较高的产值贡献度，但耦合要素间接足迹远大于直接足迹.
（3）研究期水、能、碳各种网络循环效率及鲁棒性总体均有所改善，耦合后要素网络较耦合前网络性能均有所提升；其中水足迹相关网络两项指标表现较能、碳足迹网络更为优秀，其原因在于水资源种类较少且供给能力较强，在部门间流动更为顺畅并提升了网络稳定性；但所有网络的水、能、碳要素利用率仍需提升，各网络循环可持续性均未达到理想状态.
（4）研究期第二阶段较第一阶段各网络共生性及积极关系均有所增长，但各网络中消极关系均显著多于积极关系，均未达到共生互利的合理状态.考虑要素耦合后，网络积极关系有所增长；各网络控制力较依赖度变化更为明显，对产业间控制依赖关系的影响程度由高到低依次为水能耦合、能水耦合及碳水耦合，但总体影响程度均不高，一定程度上说明研究期产业间水-能-碳网络关联性仍不强，控制与依赖关系部分处于偶发状态.
3.2　建议
（1）关注各部门生产特点，运用绿色技术提升要素代谢效率，结合区域特征科学调整经济结构，合理施策促进产业高质量可持续发展. 此外，寻求产业水-能-碳的协同优化，需要在厘清耦合流通性和产值贡献率的基础上，综合考虑要素直接及间接足迹，辨析重点部门及关键路径，充分挖掘跨部门耦合效应.
（2）需要进一步加强对产业部门间水-能-碳流动的协同管控及约束，适度增强要素流动关联度，提升要素（特别是能及碳）流通路径的利用效率，缓解要素使用压力，增强网络稳定程度，促进网络可续发展.同时进一步提升产业水-能-碳要素综合利用管理水平，尽快形成有效的协同效应.
（3）研究期中国产业水-能-碳足迹关联关系错综复杂，客观要求必须立足整体，统筹兼顾各产业部门要素直接及间接足迹需求，考虑资源要素代谢特征及各产业部门要素需求情况，结合单一及耦合网络结构及关联关系，摸清各产业部门在要素关联网络中的不同地位及作用.而后按照由节点及链条、由局部至整体、由单要素到多要素的思路，制定符合各产业特点的节水、降能、减碳综合协同型措施，杜绝“一刀切”或“运动式”的简单化政策，切实避免措施效用抵消甚至效益背反情况.
基金
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  • 国家社会科学基金(22BJY130)
文献
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参考文献 引证文献
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2025年第45卷第4期
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  • 接收时间：2024-09-09
  • 首发时间：2026-03-19
  • 出版时间：2025-04-20
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  • 收稿日期：2024-09-09
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    苏州科技大学城市发展智库，江苏 苏州 215009

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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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