产业绿色转型对边界环境绩效的影响研究

Research on the Influence of Industrial Green Transformation on Boundary Environment Performance

以2004~2018年国家主要流域河流水质监测站,以及七大水系监测断面数据,采用渐进式三重差分(DDD)的方法,探讨在经济管理权限下放后,产业绿色转型与边界环境优化之间的内在逻辑。研究发现:产业绿色转型减少了省边界县主要水质污染指标,其影响路径为:①现有晋升考核机制同时包含了强制减排和GDP指标,产业的绿色转型能够同时实现经济发展与环境优化,更加符合考核要求;②在分权体制下,省级政府直接对接县级政府,同时加强了对后者的环境问责机制,以限制由于"逐底竞争"策略造成的边界污染问题;③省边界县多为经济落后且生态脆弱的农耕地区,为发展经济,地方政府利用扩大的自主权制定适合当地实际情况的利好政策。据此,不管从实现政府职能还是官员个人晋升的角度,推动产业绿色转型均为最佳发展路径,可最终实现经济发展和环境优化的双重目标。

Based on the monitoring data of the national river basin water quality monitoring stations and the seven major river systems from 2004 to 2018, the progressive triple difference(DDD) method was used to explore the internal logic between industrial green transformation and boundary environment optimization. The study found that industrial green transformation reduces the main water quality pollution indicators of provincial border counties, and its impact path is: ① The existing promotion assessment mechanism also includes mandatory emission reduction and GDP indicators, and promoting industrial green transformation can achieve economic development and environmental optimization at the same time. More in line with the assessment requirements;② Under the decentralization system, the provincial government directly connects with the county government, and at the same time strengthens the environmental accountability mechanism for the latter to limit the boundary pollution problem caused by the "bottom-by-bottom competition" strategy;③ boundary Most of the counties are farming areas with economic backwardness and ecological fragility. In order to change the status quo, local governments use expanded autonomy to formulate favorable policies that are suitable for local conditions. According to this, no matter from the perspective of realizing government functions or personal promotion of officials, promoting industrial green transformation is the best development path, which can ultimately achieve the dual goals of economic development and environmental optimization.