Xiaoyu Duan , Qingxu Huang , Ziwen Liu , Ling Zhang , Penghui Li , Chunyang He , Delin Fang , Zhenci Xu , Yihao Li
{"title":"The differentiated impacts of interprovincial trade on achieving nine environment-related SDGs in China","authors":"Xiaoyu Duan , Qingxu Huang , Ziwen Liu , Ling Zhang , Penghui Li , Chunyang He , Delin Fang , Zhenci Xu , Yihao Li","doi":"10.1016/j.indic.2025.100589","DOIUrl":null,"url":null,"abstract":"<div><div>In the wave of globalization, trade has had profound and unique impacts on resource allocation and the Sustainable Development Goals (SDGs) among different regions. However, we still have a limited understanding of the impacts of interprovincial trade on environment-related SDGs, especially compared with the counterfactual no-trade scenario. Therefore, via an environmentally extended multiregional input‒output model and scenario analysis, we explored the impacts of interprovincial trade on the achievement of nine environmental-related SDG targets across 31 provinces in China. The results revealed that interprovincial trade has improved environmental sustainability across the country, with the total score of the nine SDGs increasing by 4.3%. Specifically, trade positively contributed to the achievement of SDG6.4 (sustainable water use) and SDG9.4 (clean industrialization) in eight sectors and slightly inhibited the achievement of SDG7.3 (primary energy efficiency). In addition, interprovincial trade has exerted greater environmental pressure on developing provinces (e.g., Nei Mongol, Jilin and Heilongjiang) with better natural resource endowments than developed provinces (e.g., Beijing, Tianjin, Shanghai). Importantly, we found that the impacts on the total SDG score was significantly associated with GDP per capita (R = 0.65, <em>p</em> < 0.01). Therefore, regions and sectors that are vulnerable to negative impacts need increasing technical and policy support.</div></div>","PeriodicalId":36171,"journal":{"name":"Environmental and Sustainability Indicators","volume":"25 ","pages":"Article 100589"},"PeriodicalIF":5.4000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental and Sustainability Indicators","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665972725000108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
The differentiated impacts of interprovincial trade on achieving nine environment-related SDGs in China
In the wave of globalization, trade has had profound and unique impacts on resource allocation and the Sustainable Development Goals (SDGs) among different regions. However, we still have a limited understanding of the impacts of interprovincial trade on environment-related SDGs, especially compared with the counterfactual no-trade scenario. Therefore, via an environmentally extended multiregional input‒output model and scenario analysis, we explored the impacts of interprovincial trade on the achievement of nine environmental-related SDG targets across 31 provinces in China. The results revealed that interprovincial trade has improved environmental sustainability across the country, with the total score of the nine SDGs increasing by 4.3%. Specifically, trade positively contributed to the achievement of SDG6.4 (sustainable water use) and SDG9.4 (clean industrialization) in eight sectors and slightly inhibited the achievement of SDG7.3 (primary energy efficiency). In addition, interprovincial trade has exerted greater environmental pressure on developing provinces (e.g., Nei Mongol, Jilin and Heilongjiang) with better natural resource endowments than developed provinces (e.g., Beijing, Tianjin, Shanghai). Importantly, we found that the impacts on the total SDG score was significantly associated with GDP per capita (R = 0.65, p < 0.01). Therefore, regions and sectors that are vulnerable to negative impacts need increasing technical and policy support.