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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SE</journal-id><journal-title-group><journal-title>Society and Economy</journal-title></journal-title-group><issn>2995-4959</issn><eissn>2995-4975</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SE.11788</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>有效还是失灵：政策对中国省域碳排放强度影响机制研究</title><url>https://artdesignp.com/journal/SE/2/10/10.61369/SE.11788</url><author>牛奔</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>2</volume><issue>10</issue><history><date date-type="pub"><published-time>2024-10-20</published-time></date></history><abstract>探究政策实施效果对碳中和碳达峰目标的实现具有积极意义。研究选取《能源生产与消费革命战略》，基于2008&amp;mdash;2021年中国30省市数据，运用合成控制法对《战略》政策实施效果及驱动因素进行分析。结果显示：（1）《战略》对响应省份发挥了正向的调节作用，在碳排放强度数值方面，河北下降10%，江苏下降15%，山东下降4%。（2）从政策效应角度，响应省份具有差异性。（3）从调节效应角度，第三产业占比、城镇化率、技术创新能力对碳排放强度起到正向的调节作用。</abstract><keywords>碳排放强度,合成控制法,政策效应,碳减排</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 杜祥琬. 对我国《能源生产和消费革命战略(2016&amp;mdash;2030)》的解读和思考[J]. 中国科技奖励,2017,(07):6-7.[2]CHEN F, CHEN Q, HOU J, et al. Effects of China's carbon generalized system of preferences on low-carbon action: A synthetic control analysis based on text mining[J].Energy Economics, 2023, 124: 106867.[3]WANG Z, LIU T. Scenario analysis of regional carbon reduction targets in China: A case study of Beijing[J]. JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY,2015, 7(4):043125[4]HAN X, JIAO J, LIU L, et al. China's energy demand and carbon dioxide emissions: do carbon emission reduction paths matter?[J]. NATURAL HAZARDS, 2017, 86(3):1333-1345.[5] 蒋水全, 谭蕴林, 孙芳城, 等. 低碳城市建设、环境审计与企业碳排放&amp;mdash;&amp;mdash; 基于低碳城市试点政策的准自然实验[J]. 审计与经济研究,2024,39(03):20-32.[6] 魏亿钢, 石佳伟, 许冠南. 中国低碳政策演进、阶段特征与治理模式变革[J]. 中国科学院院刊,2024,39(04):761-770.DOI:10.16418/j.issn.1000-3045.20230112003.[7] 周成.&amp;ldquo;双碳&amp;rdquo;政策的知识图谱、研究热点与理论框架[J]. 北京理工大学学报( 社会科学版),2023,25(04):94-112.DOI:10.15918/j.jbitss1009-3370.2023.1966.[8]KAUL A, KLOESSNER S, PFEIFER G, et al. Standard Synthetic Control Methods: The Case Of Using All Preintervention Outcomes Together With Covariates[J].JOURNAL OF BUSINESS &amp;amp; ECONOMIC STATISTICS, 2022, 40(3): 1362-1376.[9]QI X, HAN Y. How Carbon Trading Reduces China's Pilot Emissions: An Exploration Combining LMDI Decomposition and Synthetic Control Methods[J]. POLISH JOURNALOF ENVIRONMENTAL STUDIES, 2020, 29(5): 3273-3284.[10] 张毅, 李学军, 汪觉恒, 等. 能源领域双层多段式碳排放驱动因素分析[J]. 洁净煤技术,2024,30(S1):149-155.DOI:10.13226/j.issn.1006-6772.CN24022801.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
