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Article(id=1241116647784763855, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, 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=1723132800000, receivedDateStr=2024-08-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773834867664, onlineDateStr=2026-03-18, pubDate=1742400000000, pubDateStr=2025-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773834867664, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773834867664, creator=13701087609, updateTime=1773834867664, updator=13701087609, issue=Issue{id=1241116641321350143, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773834866123, creator=13701087609, updateTime=1773881366030, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241311676130193619, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241311676130193620, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1745, endPage=1753, ext={EN=ArticleExt(id=1241116651702244008, articleId=1241116647784763855, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=The environmental governance effect of the centralized environmental enforcement system reformA quasi-experiment based on 192 cities., columnId=1234106417825772207, journalTitle=China Environmental Science, columnName=Environmental Impact Assessment and Management, runingTitle=null, highlight=null, articleAbstract=

The traditional environmental enforcement framework is fraught with conflicts stemming from the dichotomy between fragmented enforcement and integrated governance, the tension between local protectionism and stringent environmental regulation, and the disparity between imbalanced information structures and the need for precise governance. These conflicts have led to weakened environmental regulation and a concomitant decline in environmental quality. In response, the Chinese central government has initiated a centralized environmental enforcement system reform（EESR）. The theoretical underpinnings of this reform are rooted in the integration of functions to mitigate the fragmentation, the reconfiguration of power to prevent local interference, and the leveraging of technology to facilitate comprehensive and transparent oversight. Based on this, we have constructed annual panel data for 192 prefecture-level cities spanning the period from 2013 to 2022. Utilizing a Difference-in-Differences（DID）approach, we found that EESR has significantly led to stricter environmental enforcement, with a post-EESR increase of 49.8% in environmental administrative penalties levied by cities. However, economic factors would modulate the efficacy of the EESR. Cities closer to the western regions exhibit a less pronounced positive impact of the reform on environmental enforcement. Further analysis indicated that the EESR has had a significant positive effect on air quality, with substantial reductions in concentrations of PM2.5, PM10, and SO2 following its implementation. This study offers guidance for the further development and improvement of the EESR in later stages, as well as insights into understanding the positive effects of a centralized environmental control system.

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传统的环境执法体制存在执法碎片化与整体性治理的张力，地方保护主义与严格环境执法的张力，信息结构不对称与精准治理的张力，导致了环境规制宽松和环境质量恶化.为此，中央政府实施了集中式环境执法体制改革.从职能、权力和信息结构出发，发现改革的理论逻辑在于职能整合破解碎片化张力，权力重塑遏制地方干预，技术赋能实现全景敞视监督.基于此，构建了包含192个地级市的2013～2022年年度面板数据，通过倍差法研究发现，集中式环境执法体制改革显著促进严格了环境执法，改革后样本城市环境行政处罚数量增加了49.8%.但是，改革的效果受经济因素的影响，样本城市越临近西部地区，改革对环境执法的积极影响越被抑制.进一步分析发现，改革产生了显著的空气质量改善效应，随着改革的实施，PM2.5、PM10和SO2浓度显著下降.本文为做好环境执法体制改革的“后半篇”文章，理解集中式环境规制体制的积极效果提供了有益启示.

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* 责任作者，教授，
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汤瑜(1995-)，男，江苏淮安人，西安交通大学博士研究生，主要从事环境经济学研究.发表论文10余篇..

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汤瑜(1995-)，男，江苏淮安人，西安交通大学博士研究生，主要从事环境经济学研究.发表论文10余篇..

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汤瑜(1995-)，男，江苏淮安人，西安交通大学博士研究生，主要从事环境经济学研究.发表论文10余篇..

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Trade liberalization and industrial upgrading in China: Facts and mechanisms [J]. The Journal of World Economy，2016，39(10):78-102., articleTitle=Trade liberalization and industrial upgrading in China: Facts and mechanisms, refAbstract=null)], funds=[Fund(id=1241116665530864434, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, awardId=11304222010705, language=CN, fundingSource=西安交通大学青年拔尖人才项目(11304222010705), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241116658966777985, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, xref=null, ext=[AuthorCompanyExt(id=1241116658979360900, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, companyId=1241116658966777985, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, China), AuthorCompanyExt(id=1241116658983555205, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, companyId=1241116658966777985, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=西安交通大学公共政策与管理学院，陕西 西安 710049)])], figs=[ArticleFig(id=1241116662989115895, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Fig.1, caption=Schematic diagram of EESR, figureFileSmall=gBGHI8Btya23uZM6q+j+AA==, figureFileBig=jq8VFqTKc+sZnRJ8k85qJg==, tableContent=null), ArticleFig(id=1241116663102362117, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=图1, caption=集中式环境执法体制改革示意, figureFileSmall=gBGHI8Btya23uZM6q+j+AA==, figureFileBig=jq8VFqTKc+sZnRJ8k85qJg==, tableContent=null), ArticleFig(id=1241116663400157743, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Fig.2, caption=Theoretical logic of EESR, figureFileSmall=+pwjPwDa5ddxl0L8UtzEnQ==, figureFileBig=Juk0z84EQ2inrNr/NC1Qgw==, tableContent=null), ArticleFig(id=1241116663567929917, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=图2, caption=集中式环境执法体制改革的理论逻辑, figureFileSmall=+pwjPwDa5ddxl0L8UtzEnQ==, figureFileBig=Juk0z84EQ2inrNr/NC1Qgw==, tableContent=null), ArticleFig(id=1241116663685370442, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Fig.3, caption=Temporal trends of the number of environmental administrative penalty, figureFileSmall=76fLl/vJlQOaqIG+UR8bMQ==, figureFileBig=8N29B7OL8Ef1u5U5RrSvWg==, tableContent=null), ArticleFig(id=1241116663786033749, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=图3, caption=环境行政处罚案件数量的时间趋势, figureFileSmall=76fLl/vJlQOaqIG+UR8bMQ==, figureFileBig=8N29B7OL8Ef1u5U5RrSvWg==, tableContent=null), ArticleFig(id=1241116663882502760, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Fig.4, caption=Parallel trend test, figureFileSmall=yfhxz8bP7GzciUWt2x11uA==, figureFileBig=BfvyYZ9yv14xDzYgcwnJ8A==, tableContent=null), ArticleFig(id=1241116663995748983, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=图4, caption=平行趋势检验, figureFileSmall=yfhxz8bP7GzciUWt2x11uA==, figureFileBig=BfvyYZ9yv14xDzYgcwnJ8A==, tableContent=null), ArticleFig(id=1241116664129966724, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Fig.5, caption=Placebo test, figureFileSmall=LBqjS/Is2P58E4n/PZsJmw==, figureFileBig=ha8Jd5nimSK/tf4XpA5WJw==, tableContent=null), ArticleFig(id=1241116664222241422, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=图5, caption=安慰剂检验, figureFileSmall=LBqjS/Is2P58E4n/PZsJmw==, figureFileBig=ha8Jd5nimSK/tf4XpA5WJw==, tableContent=null), ArticleFig(id=1241116664348070561, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Table 1, caption=

Descriptive statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
变量样本数均值标准差最小值最大值
EESR16850.3690.48301
Enforcement1337163.619372.81015241
Urban16851.6371.094-2.0414.060
Pop168515.0820.62512.74016.587
Ind168544.09410.43312.08076.990
Fiscal16850.4390.2050.0701.541
GDP168525.9100.83123.93528.806
Tech16855.1572.044-9.21010.262
FDI16852.8061.43308.099
), ArticleFig(id=1241116664478094004, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=表1, caption=

描述性统计

, figureFileSmall=null, figureFileBig=null, tableContent=
变量样本数均值标准差最小值最大值
EESR16850.3690.48301
Enforcement1337163.619372.81015241
Urban16851.6371.094-2.0414.060
Pop168515.0820.62512.74016.587
Ind168544.09410.43312.08076.990
Fiscal16850.4390.2050.0701.541
GDP168525.9100.83123.93528.806
Tech16855.1572.044-9.21010.262
FDI16852.8061.43308.099
), ArticleFig(id=1241116664570368707, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Table 2, caption=

Benchmark regression results

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Benchmark
(1)(2)(3)
EESR1.056***
(0.069)		0.477***
(0.102)		0.498***
(0.101)
Urban-0.037
(0.173)
Pop-8.782
(12.414)
Ind-0.011
(0.010)
Fiscal-1.037
(0.640)
GDP0.313
(0.327)
Tach0.051**
(0.026)
FDI-0.052
(0.113)
城市固定效应是是是
时间固定效应否是是
R20.1730.5570.561
样本数133713371337
), ArticleFig(id=1241116664721363666, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=表2, caption=

基准回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Benchmark
(1)(2)(3)
EESR1.056***
(0.069)		0.477***
(0.102)		0.498***
(0.101)
Urban-0.037
(0.173)
Pop-8.782
(12.414)
Ind-0.011
(0.010)
Fiscal-1.037
(0.640)
GDP0.313
(0.327)
Tach0.051**
(0.026)
FDI-0.052
(0.113)
城市固定效应是是是
时间固定效应否是是
R20.1730.5570.561
样本数133713371337
), ArticleFig(id=1241116664901718759, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Table 3, caption=

Regression results of excluding policy disturbances, adjusting cluster level and heterogeneity analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Enforcement
(1)(2)(3)
EESR0.473***
(0.098)		0.498***
(0.148)		0.968***
(0.224)
CEPI-0125*
(0.070)
Zero0.168
(0.154)
Covid3.181***
(0.7741)
Blue0.589***
(0.193)
Vertical-0.339**
(0.153)
Irao-0.266
(0.162)
EESR×Location-0.233**
(0.097)
控制变量是是是
城市固定效应是是是
时间固定效应是是是
R20.5670.5610.565
样本数133713371337
), ArticleFig(id=1241116665082073854, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=表3, caption=

剔除政策干扰、调整聚类层级与异质性分析的回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量Enforcement
(1)(2)(3)
EESR0.473***
(0.098)		0.498***
(0.148)		0.968***
(0.224)
CEPI-0125*
(0.070)
Zero0.168
(0.154)
Covid3.181***
(0.7741)
Blue0.589***
(0.193)
Vertical-0.339**
(0.153)
Irao-0.266
(0.162)
EESR×Location-0.233**
(0.097)
控制变量是是是
城市固定效应是是是
时间固定效应是是是
R20.5670.5610.565
样本数133713371337
), ArticleFig(id=1241116665262428947, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=EN, label=Table 4, caption=

Regression results of the impact of EESR on air quality

, figureFileSmall=null, figureFileBig=null, tableContent=
变量PM2.5PM10SO2
(1)(2)(3)
EESR-3.030**
(1.330)		-3.287***
(1.240)		-2.640***
(0.790)
控制变量是是是
城市固定效应是是是
时间固定效应是是是
R20.0890.7050.669
样本数144114411441
), ArticleFig(id=1241116665405035299, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647784763855, language=CN, label=表4, caption=

改革对空气质量影响的回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量PM2.5PM10SO2
(1)(2)(3)
EESR-3.030**
(1.330)		-3.287***
(1.240)		-2.640***
(0.790)
控制变量是是是
城市固定效应是是是
时间固定效应是是是
R20.0890.7050.669
样本数144114411441
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集中式环境执法体制改革的环境治理效应——基于192个城市的“准实验”
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汤瑜 , 刘哲 * , 陶小垒
中国环境科学 | 环境影响评价与管理 2025,45(3): 1745-1753
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中国环境科学 | 环境影响评价与管理 2025, 45(3): 1745-1753
集中式环境执法体制改革的环境治理效应——基于192个城市的“准实验”
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汤瑜 , 刘哲* , 陶小垒
作者信息
  • 西安交通大学公共政策与管理学院，陕西 西安 710049

    • 汤瑜(1995-)，男，江苏淮安人，西安交通大学博士研究生，主要从事环境经济学研究.发表论文10余篇..

通讯作者:

* 责任作者，教授，
The environmental governance effect of the centralized environmental enforcement system reformA quasi-experiment based on 192 cities.
Yu TANG , Zhe LIU* , Xiao-lei TAO
Affiliations
  • School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, China
出版时间: 2025-03-20
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摘要
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传统的环境执法体制存在执法碎片化与整体性治理的张力，地方保护主义与严格环境执法的张力，信息结构不对称与精准治理的张力，导致了环境规制宽松和环境质量恶化.为此，中央政府实施了集中式环境执法体制改革.从职能、权力和信息结构出发，发现改革的理论逻辑在于职能整合破解碎片化张力，权力重塑遏制地方干预，技术赋能实现全景敞视监督.基于此，构建了包含192个地级市的2013～2022年年度面板数据，通过倍差法研究发现，集中式环境执法体制改革显著促进严格了环境执法，改革后样本城市环境行政处罚数量增加了49.8%.但是，改革的效果受经济因素的影响，样本城市越临近西部地区，改革对环境执法的积极影响越被抑制.进一步分析发现，改革产生了显著的空气质量改善效应，随着改革的实施，PM2.5、PM10和SO2浓度显著下降.本文为做好环境执法体制改革的“后半篇”文章，理解集中式环境规制体制的积极效果提供了有益启示.

环境执法体制改革  /  环境集权  /  倍差法  /  环境执法  /  空气质量

The traditional environmental enforcement framework is fraught with conflicts stemming from the dichotomy between fragmented enforcement and integrated governance, the tension between local protectionism and stringent environmental regulation, and the disparity between imbalanced information structures and the need for precise governance. These conflicts have led to weakened environmental regulation and a concomitant decline in environmental quality. In response, the Chinese central government has initiated a centralized environmental enforcement system reform（EESR）. The theoretical underpinnings of this reform are rooted in the integration of functions to mitigate the fragmentation, the reconfiguration of power to prevent local interference, and the leveraging of technology to facilitate comprehensive and transparent oversight. Based on this, we have constructed annual panel data for 192 prefecture-level cities spanning the period from 2013 to 2022. Utilizing a Difference-in-Differences（DID）approach, we found that EESR has significantly led to stricter environmental enforcement, with a post-EESR increase of 49.8% in environmental administrative penalties levied by cities. However, economic factors would modulate the efficacy of the EESR. Cities closer to the western regions exhibit a less pronounced positive impact of the reform on environmental enforcement. Further analysis indicated that the EESR has had a significant positive effect on air quality, with substantial reductions in concentrations of PM2.5, PM10, and SO2 following its implementation. This study offers guidance for the further development and improvement of the EESR in later stages, as well as insights into understanding the positive effects of a centralized environmental control system.

environmental enforcement system reform  /  environmental centralization  /  Difference-in-Differences（DID）approach  /  environmental enforcement  /  air quality
汤瑜, 刘哲, 陶小垒. 集中式环境执法体制改革的环境治理效应——基于192个城市的“准实验”. 中国环境科学, 2025 , 45 (3) : 1745 -1753 .
Yu TANG, Zhe LIU, Xiao-lei TAO. The environmental governance effect of the centralized environmental enforcement system reformA quasi-experiment based on 192 cities.[J]. China Environmental Science, 2025 , 45 (3) : 1745 -1753 .
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天下之事，不难于立法，而难于法之必行.单纯的环境立法并不能显著抑制污染排放，环境立法的有效性有赖于环境执法的严格度[1].法贵在行，也难在行，基层环境执法中的政企合谋、变通式应对和选择性执行屡禁不止[2].推动中国环境执法体制改革，建立统一、独立、权威、有效的环境执法体制有利于遏制地方环境保护主义，严格环境执法.
2018年以前，我国的环境执法体制是分散的.环境污染执法权分散在农业、水利、国土、海洋、环境等职能部门.同时，基层环境执法部门的管理权限属于同级政府，上级环保部门缺少对其的控制权.2018年12月，中共中央、国务院办公厅印发《关于深化生态环境保护综合行政执法改革的指导意见》，正式实施集中式环境执法体制改革.改革后，分散的环境执法职责和执法队伍整合，由环境部门统一负责环境保护执法任务.同时，县环境执法大队调整为市环境执法支队的派出机构.目前，集中式的环境执法体制改革已经实施了一段时间，但它能否会促进严格环境执法仍然是悬而未决的问题.
纵观已有研究，学界围绕环保约谈、环境信息公开和中央环保督察展开了丰富的理论研究，但关于环境执法体制改革的学术研究仍十分有限[3].仅有一些实证研究探讨了环境监测监察执法垂直管理改革对环境质量[4]、污染减排[5]和环境治理[6]的影响，而未将视野聚焦于环境执法体制改革和环境执法行为.鉴于此，本研究将中央政府推行的环境综合行政执法改革视作一项准实验，利用2013~2022年间192个地级市的环境执法体制改革、环保处罚以及空气质量数据，通过构建双重差分模型，实证检验集中式环境执法体制改革对环境执法行为和空气质量的影响.
相关主题的研究目前主要有三类：第一类是关于何种环境管理体制为优的研究.从条块关系出发，围绕环境管理体制设计存在两种不同的政策主张：属地管理与垂直管理.一般认为，实行“条块结合、以块为主、分级管理”的行政管理体制即为属地管理[4].在属地管理体制下，环境职能部门受到同级地方政府和上级环境部门的双重管理，同级地方政府控制环境职能部门的“人权、物权和财权”，上级环境部门对环境保护工作和环境执法工作进行业务指导[6].这种制度设计有利于落实地方政府的环境主体责任，立足地区现状统筹经济发展与环境保护.但是，批评者认为属地管理会削弱中央政府和上级政府的监督和控制，助长地方环境保护主义，最终破坏区域生态环境[7].因此，他们主张实行环境垂直管理体制，将环境部门的管理权限从地方政府转移至上级部门，由省级或中央环境部门统筹管理基层环境部门的“人权、事权、物权和财权”，从而减少地方政府干预，严格环境执法[8].但垂直管理体制在强化上级目标控制的同时也会削弱地方政府的职能完整性，使得地方政府缺少执法主体履行环境保护责任[9].此外，地方环境执法部门的灵活性和自主性也会受到不同程度的限制.因此，对于促进严格环境执法和改善环境质量而言，究竟是实行分权的属地管理还是集权的垂直管理，亟待逻辑推演和实证检验.
第二类是关于综合行政执法改革的研究.所谓综合行政执法改革，就是将职能相近的不同部门的行政执法权整合，建立综合行政执法队伍.从理论上来说，这是一种追求整体性治理的组织模式，以政府内部结构的整体性运作为出发点，强调跨组织的协调耦合.研究认为，整合是整体性治理的重要概念，要把一些功能相近的机构重新整合，建立一个大部门的体制，从而解决极度碎片化产生的低效和无效[10].对于综合行政执法改革，目前学界围绕乡镇、市场监管、城市管理以及环境保护等场景领域展开了丰富讨论，并聚焦于三个层面.第一，从整体上对综合行政执法改革进行学理阐释，回答为什么要推动综合行政执法改革，背后有什么理论渊源，改革的法治功能是什么，改革应该遵循何种法律原则的问题[11].第二，从中观、微观层面对综合行政执法改革进行解构，讨论改革为什么发生的社会情境[12]，分析改革过程中多元行动者的互动策略以及结构制度的变革逻辑[13].第三，对综合行政执法改革的内容、趋势和模式展开讨论，分析改革过程中的实践困境和可能优化路径[14].可以发现，既有研究多采用案例分析和理论分析的定性研究方法对综合行政执法改革展开讨论，较少以定量分析的方法对改革的政策效果展开实证分析.
第三类是关于环境执法体制改革的研究.目前直接围绕集中式环境执法体制改革的研究相对较少，少有的环境执法体制改革研究侧重于理论分析和实践总结：一是对环境执法体制改革的法律分析与总结，如综合环境执法的法理逻辑与法治构造[15]、环保垂改与环境执法改革的法律衔接路径[16]、环境执法体制改革的法制困境[17]、环境执法体制的历史演变[2]等；二是环境执法体制改革的制度分析与政策建议，如改革的权力学解释[7]、改革的结构学解释[8]、改革的现实困境与完善路径[18]等，对环境执法体制改革效果的实证分析相对不足.如前所述，环境立法以制度的形式对区域污染减排以硬性约束，但立法效果的好坏在多数情况下取决于环境执法，严格的环境执法有利于立法效果的发挥[1].集中式环境执法体制改革是党中央、国务院围绕“严格基层环境执法，落实地方政府主体责任”做出的最直接、最重要的战略部署，厘清改革的理论逻辑与政策效果对于理解我国环境执法体制设计以及改善环境质量具有重要意义.
总体而言，本文在三个方面补充了既有研究：(1)从职能、权力和信息结构三个维度对集中式环境执法体制改革的理论逻辑进行推导，完成对环境执法体制改革“过程黑箱”的解构；(2)选择衡量环境执法行为的直接指标——环境行政处罚数量，以准实验的方式厘清集中式环境执法体制改革对环境执法的影响，为遏制基层环境规制偏差提供数据和思路支撑；(3)进一步从经济因素出发，讨论了制约环境执法权集中改革效果的影响因素，同时还厘清了改革对空气质量的积极作用.
1　理论分析与研究假设
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在人文社会科学研究中，结构是一个非常重要的分析维度.结构反映了组成事物的元素以及不同元素之间的关系，结构直接影响着事物的功能和“生命”.研究表明，环境执法体制是中央与地方政府、地方政府和环境执法部门、不同环境执法部门围绕环境执法相关的机构设置、职能配置、隶属关系、权力划分的具体体系和组织制度[2].因此，集中式环境执法体制改革的实施，必然会引起政府和环境执法部门的结构变动，进而引起执法行为和执法结果的变动.
横向的职能结构上，集中式环境执法体制改革实现职能整合和环境执法权归并.生态环境系统的整体性决定了环境执法的综合性，污染防治和环境监管应该打破区域界限，打破部门界限，统筹河湖、陆地与天空，统筹地表和地下，统筹城市和农村.面对环境执法主体碎片化问题，环境综合行政执法改革整合相关部门的环境执法职能，由环境部门统一行使环境执法权(图1).职能整合后，生态环境保护综合执法队伍以生态环境部门的名义统一行使行政处罚权以及和行政处罚相关的行政检查、行政强制权等执法职能，原部门保留监督管理职能.从这个意义出发，集中式环境执法体制改革通过环境执法职责的整合以及环境执法权力的横向归并，实现了环境执法的综合性和整体性要求，有利于构建系统综合的污染防治模式.
纵向的权力结构上，集中式环境执法体制改革实现权力上移与力量下沉.如图1所示，环境执法主体由原来的省市县三级环境主管部门调整为市县两级，其中省级环境部门主要负责监督指导和组织协调，市级部门负责属地环境执法，县级执法部门更改为市级部门的派出机构.管理上，改革后市环境执法支队属于市生态环境局下属事业单位，整体上仍属于市政府的工作部门，实行市政府和省环保厅双重管理，但由省环保厅负主要管理职责.人事上，市生态环境局负责人由省厅提名，提交市级党委、人大同意后按程序任免，地方政府对环境执法的绝对控制权逐渐减弱.在县一级，改革剥夺了县级政府对环境执法部门的人财物管理权，县环境执法大队授权执法.另外，执法权上收的同时伴随执法力量下沉.改革方案中明确要求充实基层执法组织的人财物资源配置，增强基层环境执法力量，目的是夯实基层政府的环境主体责任.也就是说，环境执法体制改革一方面提高了环境执法权配置层级，使得环境执法主体拥有更高的执法势能；另一方面将执法力量下沉至区县、乡镇，使得基层执法队伍拥有更多的执法资源，这对遏制地方保护主义，严格环境执法，推动协同治理具有重要作用.
整体的信息结构上，集中式环境执法体制改革的过程是信息技术手段嵌入环境执法的过程.为了解决环境执法系统中的信息不对称问题，环境执法体制改革大力推动非现场执法，通过污染源监控、用电监控、用能监控、噪音监控等大数据在线监测实时掌握重点污染企业的生产活动和排放信息；通过无人机、无人船、走航车和卫星遥感等手段在不进入企业，不干扰正常生产的情况下即可了解所需环境信息.在技术嵌入环境执法后，环境执法部门既可以高效掌握工业企业的环境信息，并依此进行环境执法和环境处罚.同时中央和省级环境部门的“感官”和“触觉”也借信息技术而延伸，可以快速掌握环境污染和基层政府的一手信息，大大消除了环境管理活动中的信息不对称问题.这实现了福柯所谓的“全景敞视监督”——即使中心的瞭望塔中没有监视者，广场四周的被监视者也因对瞭望塔具体情况的未知而自觉地接受规训.面对全景敞视监督，无论是基层政府抑或是污染排放主体，剩余信息生产权都将受到极大压制，闪转腾挪的物理空间和信息空间将大幅压缩，这将对地方环境保护主义和企业环境违法行为产生巨大伤害.
长期以来，环境执法分权存在三个问题：一是同级环境执法部门横向碎片化，不同环境执法职能部门权力分散、冲突，环境执法出现“真空区域”；二是上下级环境执法部门纵向分离，上级环境执法部门对下级执法部门有指导权无控制权，地方保护主义盛行；三是上级与下级、环境执法部门与污染企业的信息不对称，监管者难以快速、便捷了解被监管对象的真实情况，并依此决策、执法.导致的结果便是地方环境保护主义、基层环境执法宽松和环境质量恶化.集中式环境执法体制改革在横向上实现职责整合和执法权归并，在纵向上实现环境执法权上移和力量下沉，在整体上实现技术嵌入和全景敞视监督，从而实现对职责真空的填补、政府干预的遏制以及信息不对称的再平衡，最终将促进严格环境执法和空气质量改善(图2).基于此，本文提出如下研究假说：
假说1：集中式环境执法体制改革会促进严格环境执法.
假说2：集中式环境执法体制改革会促进空气质量改善.
2　研究方法
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由于生态环境保护综合行政执法改革的实施具有“准实验”属性，本文采用倍差法识别集中式环境执法体制改革对环境执法和空气质量的影响.如果实施改革的城市和未实施改革的城市间具有相似的发展特征，改革前后实验组的变化减去对照组的变化便是集中式环境执法体制改革的政策效果.
2.1　模型设计
为了探究集中式环境执法体制改革前后各地区环境执法行为的变化情况，参考既有研究[19]，本文设置了如下的倍差法计量模型：
式中：Enforcementit表示城市i在t时的环境执法情况；EESRit为集中式环境执法体制改革的虚拟变量，如果城市i在t时实施了环境执法体制改革，则该虚拟变量赋值为1，反之为0；Xit是样本城市i在t时的控制变量合集，包括社会特征和经济因素；ϕi是个体固定效应，用于捕捉城市特征的影响；ωt表示时间固定效应，用于捕捉年度因素的影响；εit为误差项；α是常数项；β表示集中式环境执法体制改革对环境执法的影响；γ表示控制变量的回归系数.
在使用倍差法对集中式环境执法体制改革的政策效果进行评估时，需要判定集中式环境执法体制改革的对照组是否是处理组合适的“反事实”，即实施环境执法体制改革的城市(处理组)与尚未实施环境执法体制改革的城市(对照组)在改革前的环境执法行为不存在明显差异或者具有共同发展趋势.为此，本文借鉴既有研究的做法[20]，采用事件研究法对平行趋势假设进行检验.具体的模型如下：
式中：Ti,t0+n是一系列虚拟变量，如果城市i在t0+n期实施了集中式环境执法体制改革，则取值为1，否则赋值为0；t0表示城市i实施集中式环境执法体制改革的当年；n表示开始环境执法体制改革之后的第n年；其他变量含义同式(1).本文以政策实施前的第1a为基准组，时间窗口为集中式环境执法体制改革实施前6a和实施后3a.
2.2　变量选择
2.2.1　因变量
环境执法(Enforcement).与采用排污费入库额、政府工作报告中环保词频、工业污染治理投资额等间接方法衡量环境执法不同，本文选择环境执法部门直接实施的环境行政处罚衡量环境执法.具体而言，本文选择样本城市环境行政处罚案件的数量衡量环境执法，进而判断改革前后各地区环境执法是否更加严格.需要说明的是，该数量为环境执法是否严格的最低估计，因为环境执法部门面对“轻微不罚”“无过错不罚”、和“首违不罚”时下达了大量的“整改通知”，这部分信息环境部门通常不会进行公开公示，而这些行为亦属于环境执法的范畴.此外，有观点认为可根据处罚类型设置环境执法力度变量，但对于不同处罚对象来说“罚款”和“停产整顿”的力度不一(大企业对罚款的忍受程度高于停产整顿，小微企业则相反).同时，如何界定“法律、行政法规规定的其他行政处罚种类”的力度亦存在很大挑战.因此，本文选择使用环境行政处罚数量衡量环境执法.
2.2.2　自变量
集中式环境执法体制改革(EESR).当样本城市处于改革状态时，EESR等于1，反之赋值为0.样本城市是否处于改革状态的判断标准为该城市该年是否成立“XX市(生态)环境(保护)综合行政执法支队”.市级环境综合行政执法支队的成立，标志着各地区集中式环境执法体制改革已经取得初步进展.与名称大相径庭的改革方案和改革意见相比，市级环境综合行政执法支队的成立这一标准更加清晰、准确.
2.2.3　控制变量
参考现有文献[6，21-22]，本文的控制变量(X)包括：(1)城市化水平(Urban)，使用夜间灯光亮度的年度均值衡量；(2)人口数量(Pop)，使用样本城市年均常住人口数量衡量；(3)产业结构(Ind)，使用第二产业增加值占GDP比值衡量；(4)地区生产总值(GDP)，使用地区生产总值(亿元)衡量；(5)科技发展水平(Tech)，使用授权的发明型专利数量(个)衡量；(6)财政自主率(Fiscal)，使用财政收入与支出的比值衡量；(7)外商投资(FDI)，使用规模以上工业企业中外商投资企业数量(个)衡量.
2.3　数据来源
环境执法数据来源于北大法宝(https://www.pkulaw.com)，按照“行政处罚—环保”检索条件从北大法宝中收集各城市各年度公开的环境行政处罚数据.集中式环境执法体制改革数据来自各城市的公开新闻报道和政府公告.夜间灯光数据来自Wu等矫正后的类DMSP-OLS数据[23].常住人口数据来自美国橡树岭国家实验室的LandScan全球人口数据(https://landscan.ornl.gov).其余经济数据来自《中国城市统计年鉴》和各地区统计年鉴.为了缩小数据间绝对差异，避免个别极端值的影响，并尽可能满足经典线性模型假定，对环境行政处罚数量、夜间灯光亮度、人口数量、地区生产总值、发明型专利授权数、外商投资企业数等做对数化处理.基于数据完整性和可得性的考虑，本文数据类型为2013～2022年地级市年度非平衡面板.
3　结果与分析
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3.1　描述性统计
表1报告了所有变量描述性统计的结果.可以发现，样本城市年环境执法案件数量最小为1件，最大为5241件，192个城市年平均执法案件163.6件，约每2d一件.图3展示了2013~2022环境行政处罚案件数量的时间趋势图.可以发现，2013~2021年间全国环境行政处罚案件呈上升趋势，并于2021年达到峰值，然后开始下降；中、西部地区环境行政处罚案件数量远小于东部地区，其中西部地区略小于中部地区.
3.2　基准回归结果
表2报告了基准回归的估计结果.列(1)未添加控制变量和时间固定效应，列(2)未添加控制变量，列(3)控制了个体、时间双重固定效应以及经济、社会因素的影响.可以发现，集中式环境执法体制改革显著促进了环境行政处罚数量的增加.在控制了一系列因素的影响后，集中式环境执法体制改革(EESR)的估计系数为0.498，在1%的水平下显著为正.这意味着相较于未实施改革的城市，实施改革的城市环境行政处罚案件数量增加了49.8%，约占改革前环境执法案件均值的12.8%，具有明显的经济显著性，假说1得到验证.集中式环境执法体制改革通过职能结构、权力结构和信息结构的重组和优化，的确遏制了地方环境保护主义，促进了严格环境执法.
3.3　稳健性检验
3.3.1　平行趋势检验
图4报告了方程(2)中的估计系数β2及其90%置信区间.可以发现，集中式环境执法体制改革实施前的估计系数为负且都不显著；改革实施后，集中式环境执法体制改革的估计系数开始显著为正.由此可知，平行趋势假设成立，集中式环境执法体制改革实施前实验组与对照组之间的确不存在显著差异.同时，在改革发生之后，严格执法效应开始出现，这也证明了集中式环境执法体制改革确实提高了样本城市的环境执法严格程度.并且从长期趋势来看，这一政策效应具有一定的持续性.
3.3.2　安慰剂检验
在基准回归模型中，某些不可观测因素可能会导致显著的估计结果.为了检验基准回归结果是随机产生的还是具有统计意义，参考周茂等[24]的研究，本文采用间接性的安慰剂检验方法.首先，根据式(1)，可以得出系数的表达式：
式中：M包括所有控制变量和固定效应；θ为不可观测因素对环境执法(因变量)的影响.如果θ=0，则不可观测因素不会影响估计结果，此时是无偏的.由于无法直接验证θ是否等于0，本文采用间接安慰剂检验.其逻辑是找到一个理论上对因变量没有影响的虚假变量(EESR′)来代替真实的EESR.由于EESR′是随机生成的，这意味着β=0.在此前提下，如果估计出的=0，则可以反推θ=0，即估计值是无偏的.因此，本文随机生成一份实施环境综合行政执法改革的地级市名单，从而产生了虚拟的估计值.本文重复此过程500次，并绘制出500个的分布图.
图5为虚拟估计系数、对应P值及核密度估计的分布.其中竖虚线为真实估计系数，横虚线代表P值为0.1.可以发现，集中在0值附近且服从正态分布.此外，真实估计系数明显大于虚拟估计系数，结果符合安慰剂检验预期.
3.3.3　排除政策干扰
本文在评估集中式环境执法体制改革的对环境执法的影响时可能会受到其它政策或事件的干扰，从而影响估计结果的稳健性.为了识别和解决这一问题，本文搜索了2013~2022年间发生的可能影响政府环境执法的政策和事件：中央环保督察(CEPI)、无废城市建设(Zero)、新冠肺炎疫情(Covid)、蓝天保卫战(Blue)、环保垂改(Vertical)、入河排污口排查整治行动(Irao).为了更有力地证明基准回归结果不受其它政策或事件的影响，参考既有研究[19]的做法，在基准回归方程(1)中加入上述政策和事件的虚拟变量作为控制变量，当样本城市发生上述政策或事件时，CEPI、Zero、Covid、Blue、Vertical和Irao赋值为1，否则为0.表3(1)列报告了回归结果，可以发现，在剔除其他政策的影响后，基准回归结果仍然稳健.
3.3.4　调整聚类层级
如果样本城市不同时期之间的误差项存在自相关问题，可以使用聚类稳健标准误进行估计，从而在异方差问题的处理上进行稳健性检验.为避免同一省域范围内的城市之间存在相互影响，本部分将聚类层次调整到省级尺度，回归结果见表3的第(2)列.结果表明，集中式环境执法体制改革仍然显著促进了环境执法，集中式环境行政执法体制改革每增加一个单位，环境行政处罚数量增加49.8%，再次说明了基准回归结果的稳健性.
3.4　异质性分析
研究表明，经济因素常影响区域环境执法与环境质量，如环境库兹涅茨曲线讨论了经济发展水平与环境污染的非线性关系，污染天堂假说讨论了经济发展、环境执法与环境质量的关系.那么集中式环境执法体制改革对环境执法行为的影响是否受经济因素的影响？如果存在影响，不同经济因素影响下有何种差异？在中国，东部地区的经济发展水平通常被认为优于中西部地区.因此，本文设置了区位虚拟变量(Location)，并生成其与自变量(EESR)的交互项，重点观察交互项系数的变化.当样本城市处于东部地区时，Location赋值为1；当样本城市处于中部地区时，Location赋值为2；当样本城市处于西部地区时，Location赋值为3.表3(3)列报告了对应的估计结果，可以发现，交互项系数显著为负.这表明，集中式环境执法体制改革对环境行政处罚的因果效应在西部地区和东部地区存在异质性.样本城市越靠近西部地区，集中式环境执法体制改革的积极效果被抑制的越明显.此外，本文还将样本城市分为东、中、西部三组分别进行回归.结果显示改革在东部和中部地区的严格执法效应显著，在西部地区城市的政策效果不显著.对于异质性分析的结果，本文认为它反映了经济因素及其背后财政压力对改革和环境执法的影响，这在理论和实践中已被充分证明[3,5].
3.5　进一步讨论
既然集中式环境执法体制改革可以促进严格环境执法，合理的预期是改革对空气污染治理具有同样的积极效果.为此，本文从“中国空气质量在线监测分析平台”中收集了样本城市的细颗粒物(PM2.5)、可吸入颗粒物(PM10)和二氧化硫(SO2)的污染浓度数据.回归结果如表4所示.可以发现，集中式环境执法体制改革显著促进了样本城市空气质量的改善，PM2.5、PM10和SO2等主要空气污染指标的估计系数均为负，且分别在5%，1%和1%的水平下高度显著.假说2得到验证，集中式环境执法体制改革会促进空气质量改善.这表明改革通过环境执法权的集中在环境规制和空气污染治理方面均产生了积极的政策效果.另外，为了控制气象条件对上述结果的影响，本文在公式(1)的基础上新增了年平均风速、气温和降水量3个控制变量，结果表明，集中式环境执法体制改革对PM2.5、PM10和SO2的估计系数仍为负且高度显著.
4　结论
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4.1　集中式环境执法体制改革显著促进了严格环境执法
在控制了经济、社会因素以及个体、时间固定效应后，集中式环境执法体制改革每增加一个单位，环境行政处罚案件数量增加49.8%，约占改革前环境执法案件均值的12.8%，这一发现经过系列稳健性检验后依然成立.
4.2　集中式环境执法体制改革对环境行政处罚的影响受经济因素的约束
样本城市越靠近西部地区，集中式环境执法体制改革对环境行政处罚的积极效果被抑制的越明显.
4.3　集中式环境执法体制改革产生了显著的空气质量改善效应
随着改革的实施，PM2.5、PM10和SO2等主要空气污染物浓度显著下降.
5　建议
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5.1　基层环境执法事项多、任务繁重，在进行环境执法权集中的同时应同步进行执法力量下沉，保证基层环境执法单位拥有充足的人力、物力和财力开展环境执法工作.
5.2　环境执法改革涉及多部门环境执法职能的整合，鉴于土壤污染防治、地下水污染防治以及核污染防治的高度专业性，需要进一步加强环境执法队伍建设，全面提高执法人员专业技能.
5.3　环境执法职能整合并非单纯的职责合并，配套人员划拨与编制调整的滞后加重了环境执法人员的工作负担，为了更好的发挥基层环境执法的“查企”作用，应配套实施环境执法人事关系改革，保障基层环境执法人力资源充足与人才保障充分.
5.4　空气污染具有外溢性，基层环境执法主体很难协调跨界污染治理，因此要进一步发挥省市环保部门的统筹协调职能，通过对话协商对跨界污染协同治理.
5.5　考虑到改革效果的异质性，经济发展水平短时间难以发生重大转变，因此需要进一步强化地方政府的“国之大者”意识，树立地方保护主义现象的反面典型，提高环境信息公开的及时性与准确性，以内外合力的方式促进基层严格环境执法和空气污染治理.
5.6　本研究还存在诸多不足之处，如环境处罚金额、不同环境处罚类型比重、环境处罚对象的细节(区位、企业登记注册类型差异、纳税额等)等数据尚未整理.未来应尝试从各地区信息公开网站收集环境行政处罚完整数据，以提高结论的可靠性和研究深度.
基金
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  • 西安交通大学青年拔尖人才项目(11304222010705)
文献
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  • 接收时间：2024-08-09
  • 首发时间：2026-03-18
  • 出版时间：2025-03-20
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  • 收稿日期：2024-08-09
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西安交通大学青年拔尖人才项目(11304222010705)
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    西安交通大学公共政策与管理学院，陕西 西安 710049

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