数据要素聚集的减污降碳效应及空间溢出效应研究  

Pollution Reduction and Carbon Reduction Effect and Spatial Spillover Effect of Data Factor Aggregation

作　　者：石虹[1] 余少龙 SHI Hong;YU Shao-long(School of Economics,Guizhou University,Guiyang 550025)

机构地区：[1]贵州大学经济学院,贵阳550025

出　　处：《软科学》2025年第1期108-115,共8页Soft Science

基　　金：贵州省理论创新课题专项项目(GZLCZB-2022-3-10);生态环境部课题项目(2018A162);贵州省高校人文社会科学研究基地项目(23GZGXRWJD021)。

摘　　要：基于设立国家级大数据综合试验区引发的数据要素聚集,构建多期双重差分模型实证研究了数据要素聚集的减污降碳效应。研究发现:数据要素聚集具有显著的减污降碳效应,该结论在PSM-DID等稳健性检验后仍成立;数据要素聚集的减污降碳效应在数字基础设施建设较完善以及环境规制较强的地区更显著。机制分析表明,宏观层面,数据要素聚集的技术效应、结构效应有助于减少污染和碳排放;微观层面,数据要素聚集通过推动企业承担环境、社会责任实现减污降碳。空间效应检验表明,数据要素聚集的减污降碳存在显著的空间溢出效应。Based on the establishment of a national-level comprehensive pilot big data zone,a multi-period difference-in-differences model was constructed to study the pollution-reducing and carbon-reducing effects of data factor aggregation.It is found that data factor aggregation has a significant pollution and carbon reduction effect,which is still valid after PSM-DID and other robustness tests,the pollution and carbon reduction effect of data factor aggregation is more significant in areas with better digital infrastructure construction and stronger environmental regulations.Mechanism analysis shows that at the macro level,the technological and structural effects of data factor aggregation help reduce pollution and carbon emissions,at the micro level,data factor aggregation realizes pollution and carbon reduction by promoting enterprises to undertake environmental and social responsibilities.The spatial effect test shows that there is a significant spatial spillover effect of data factor aggregation in reducing pollution and carbon emissions.

关 键 词：数据要素 国家级大数据综合试验区 准自然实验 多期DID 

分 类 号：X32[环境科学与工程—环境工程]