Tracing the Uncertain Chinese Mercury Footprint within the Global Supply Chain Using a Stochastic, Nested Input–Output Model

Abstract

A detailed understanding of the mercury footprint at subnational entity levels can facilitate the implementation of the “Minamata Convention on Mercury”, especially for China, the largest mercury emitter worldwide. Some provinces of China have more than 100 million people, with economic activities and energy consumption levels comparable to those of smaller G7 countries. We constructed a stochastic, nested multiregion input–output (MRIO) model, which regionalized the China block in the EXIOBASE global-scale MRIO table, to model the mercury footprint associated with global supply chains spanning China’s regions and other countries. The results show that Tianjin, Shanghai, and Ningxia had the highest per capita mercury footprint in China, which was comparable to the footprint of Australia and Norway and exceeded the footprint of most other countries. Some developed regions in China (e.g., Guangdong, Jiangsu) had higher mercury final product-based inventories (FBI) and consumption-based inventories (CBI) than production-based inventories (PBI), emphasizing the role of these regions as centers of both consumption and economic control. Uncertainties of Chinese provincial mercury footprint varied from 8% to 34%. Our research also revealed that international and inter-regional final product and intermediate product trades reshape the mercury emissions of Chinese provinces and other countries to a certain extent.

Improved sustainability assessment of the G20’s supply chains of materials, fuels, and food

“…Due to the limited quality and availability of mining-related water and land use data, future work should also improve mining-related water and land impacts. Moreover, future research is needed to analyze this study's results for uncertainty, which could be addressed by extending the approach of Lenzen al [81] and Zhang et al [82] to this study's methodology and database.…”

Improving Subnational Input–Output Analyses Using Regional Trade Data: A Case-Study and Comparison

“…28−30 These studies highlight three main issues: (1) treating a very large nation (e.g., China, which drives ∼30% of global material flow) as a homogeneous entity within a global multiregional input−output table (GMRIO) may mean researchers are unable to analyze important dynamics in the trade; 5,20,31 (2) it may bring bias to national results in total, 26,32−34 and (3) policymakers may struggle to convert messages from global and national analyses to regional strategies and targets. 18,22,24,25,31,32 Early EE-IO studies often used a single-region input−output (SRIO) table. However, to be able to account for trade, researchers had to make certain assumptions, e.g., that imports were produced with the same technology as the importing country (i.e., that an electronic product imported from Japan into the U.S.A. is assumed to have been produced with U.S. technologies rather than Japanese).…”

“…Previous studies have highlighted that the use of the TSA approach requires further analysis and investigation. 16,24,41 Overcoming this TSA simplification presents a key data and knowledge gap. Previous economic studies showed that the trade structure among domestic regions and foreign countries are essential to subnational modeling.…”

Critical transmission sectors in embodied atmospheric mercury emission network in China

“… is a vector of environmental pressure intensities of all the sectors accounted for, or PBA as denoted in other research (Z.‐M. Chen et al., 2018; Lindner et al., 2013; Södersten et al., 2018; H. Zhang et al., 2019). is an identity matrix.…”