{"title":"Research on Green Process Innovation Enabling the Synergistic Effect of Pollution Reduction and Carbon Reduction","authors":"Hongshuang Wu, Wenyi Liu","doi":"10.22158/ibes.v6n3p1","DOIUrl":null,"url":null,"abstract":"Achieving the objective of \"peak carbon dioxide emissions and carbon neutrality\" in the new stage of development will require the synergistic effect of pollution reduction and carbon reduction. In the new stage of development, achieving the goal of \"peak carbon dioxide emissions and carbon neutrality\" will necessitate the synergistic effect of pollution reduction and carbon reduction. This study assesses the degree to which pollution reduction and carbon reduction have a synergistic effect using the coupling coordination model, and uses the fixed effect model, the dynamic panel GMM model, the OLS model and the threshold effect model to examine how green process innovation affects pollutant reduction and carbon reduction synergistically, including direct, heterogeneous, and nonlinear effects. The results show that: firstly, green process innovation has a direct promoting effect on synergistic effect of pollution reduction and carbon reduction; Secondly, due to the differences in the economic development of different regions in China, the impact of green process innovation on the synergistic effect of pollution reduction and carbon reduction is heterogeneous in different regions, and presents a gradient distribution of \"east > west > middle\". Finally, there is a threshold effect in the impact of green process innovation on regional synergistic effect of pollution reduction and carbon reduction. This paper provides some reference value for promoting green process innovation and enhancing the collaborative impact of mitigating pollution and decreasing carbon emissions.","PeriodicalId":504548,"journal":{"name":"International Business & Economics Studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Business & Economics Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22158/ibes.v6n3p1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving the objective of "peak carbon dioxide emissions and carbon neutrality" in the new stage of development will require the synergistic effect of pollution reduction and carbon reduction. In the new stage of development, achieving the goal of "peak carbon dioxide emissions and carbon neutrality" will necessitate the synergistic effect of pollution reduction and carbon reduction. This study assesses the degree to which pollution reduction and carbon reduction have a synergistic effect using the coupling coordination model, and uses the fixed effect model, the dynamic panel GMM model, the OLS model and the threshold effect model to examine how green process innovation affects pollutant reduction and carbon reduction synergistically, including direct, heterogeneous, and nonlinear effects. The results show that: firstly, green process innovation has a direct promoting effect on synergistic effect of pollution reduction and carbon reduction; Secondly, due to the differences in the economic development of different regions in China, the impact of green process innovation on the synergistic effect of pollution reduction and carbon reduction is heterogeneous in different regions, and presents a gradient distribution of "east > west > middle". Finally, there is a threshold effect in the impact of green process innovation on regional synergistic effect of pollution reduction and carbon reduction. This paper provides some reference value for promoting green process innovation and enhancing the collaborative impact of mitigating pollution and decreasing carbon emissions.