Beidi Diao

In the era of economic globalization, international trade has been growing at an accelerating pace. However, the issue of pollution transfer associated with trade presents a significant challenge to pollution management. To explore the factors influencing trade-embodied pollution, we take China as a case study. First, the MRIO model is chosen for quantitative analysis, and the generalized DID and reweighted semiparametric DID methods are used to explore the relationship between industrial structural adjustment and trade-embodied air pollution. We then employ mediation and moderation effect models to analyze the pathways of industrial transfer and environmental regulation. The main conclusions are as follows: (1) Over time, the volume of embodied pollution transfer has gradually decreased. Developed regions serve primarily as exporters of pollution, whereas developing regions are inclined to be importers of pollutants. (2) The adjustment of industrial structures and the level of economic development will lead to a temporary increase in trade-embodied air pollution. (3) Industrial transfer partially mediates the link between industrial structural adjustments and trade-embodied air pollution, and environmental regulation serves as a positive moderating factor. The present findings yield policy recommendations for addressing trade-driven transboundary air pollution in three ways: promoting joint prevention and control, improving the quality of industrial structural adjustments, and strengthening regional environmental regulation. This study not only helps us examine the relationship between industrial structure and trade-embodied pollution but also enables us to explore pathways toward sustainable development.

Transboundary air pollution (TAP) imposes high health and economic burdens on neighboring regions, further causing the issue of environmental injustice. In this study, changes in PM_2.5 concentrations resulting from TAP across various provinces in China were quantitatively analyzed, and the quadratic assignment procedure (QAP) method was employed to explore influencing factors from a regional heterogeneity perspective, with the aim of reducing the impact of transboundary pollution. The results reveal the following: (1) The impact of TAP continued to decrease in all provinces. Compared with those in other regions, inland central cities were more significantly affected. (2) Hebei and Jiangsu were identified as the primary pollution sources, and they exported significant amounts of pollutants to neighboring provinces. In contrast, Shaanxi and Chongqing were the main recipients. The reason for this finding is that TAP is influenced by both geographical proximity and regional development differences. (3) Disparities in the total factor energy efficiency played a crucial role in determining pollution spillover in the short term. In the long term, reducing the gaps in environmental regulations between regions constituted the core element for mitigating TAP. Additionally, the subregional regression analysis results indicated that differences in the industrial structure positively affected the broker and net spillover sectors. On the basis of these findings, targeted policy recommendations for regional collaboration, balanced spatial development, and differentiated governance were formulated.

The reduction of CO₂ emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO₂ emissions. Spatial autocorrelation of provincial industrial CO₂ emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO₂ emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO₂ emission reduction, while the potential CO₂ emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO₂ emission reduction are suggested accordingly.