Examining industrial air pollution embodied in trade: implications of a hypothetical China-UK FTA

Zhang, Yuquan W.; Geng, Yong; Zhang, Bin; Shen, Yang; Izikowitz, David V.; Yin, Hujun; Wu, Fei; Hao, Yu; Liu, Huiwen; Zhou, Weiduo

doi:10.1007/s10668-022-02612-z

Cited by 1 publication

(1 citation statement)

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“…For China, on the one hand, the world's largest developing country, in 2012, the proportion of inter-provincial trade to GDP was as high as 72%. On the other hand, while inter-provincial trade plays an increasingly important role in connecting production and consumption, alleviating regional resource shortages, and stimulating consumption and economic growth [7,8], changes in production and consumption patterns have made it challenging to address the "emission transfer" and "emission leakage" caused by interprovincial trade in terms of reducing carbon dioxide and atmospheric pollutants [9,10]. At the same time, the impact of trade on the environment is complex and diverse, posing greater challenges to the coordinated control of both [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China

Qi,

Lang,

Wang

et al. 2024

Sustainability

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Inter-provincial trade leads to changes in CO2 and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO2 and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO2 and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO2 and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade.

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