Quantitative Global Carbon Inequality Network

TL;DR

The paper introduces the Ecological Economic Equality Index ($EEEI$), a quantitative metric integrated with environmentally extended multi-regional input-output analysis to assess carbon inequality within the global trading network from 1995 to 2022. By combining $EEEI$ with complex-network techniques, it constructs a carbon inequality network that highlights how emissions burdens and economic benefits diverge across regions, revealing widening inequality and a concentration of impact in a few regions. Key findings show persistent advantages for the EU27/UK, rising centrality for China, and increasing disparities for developing regions, with a notable shift around 2009 in network structure. The work provides a scalable, open toolkit (ExioNet) and a framework for policy-oriented analysis of trade-climate inequities, with potential extensions to sectoral and social dimensions for broader sustainability assessments.

Abstract

International trading networks significantly influence global economic conditions and environmental outcomes. A notable imbalance between economic gains and emissions transfers persists, manifesting as carbon inequality. This study introduces a novel metric, the Ecological Economic Equality Index, integrated with complex network dynamics analysis, to quantitatively evaluate the evolving roles within the global trading network and to pinpoint inequities in trade relationships from 1995 to 2022. Utilising high spatiotemporal resolution data from the Environmentally Extended Multi-regional Input-output model, our findings reveal a widening disparity in carbon inequality and dynamic patterns. This analysis emphasises the gap in regional carbon inequality and identifies unequal trade. The study underscores that carbon inequality is a critical challenge affecting both developing and developed regions, demanding widespread attention and action.

Figures (6)

• Figure 1: Regional carbon inequality is illustrated through an analysis of net emission burdens (unit in million ton, Mt) on the x-axis and net economic benefits (unit in million Euro, M.EUR) on the y-axis over four distinct periods: 1) 1995 to 2001, 2) 2002 to 2008, 3) 2009 to 2015, and 4) 2016 to 2022. The black box magnifies the regions in the middle to provide clearer visualisation. Regions such as China and Russia are positioned in the first quadrant as emission exporters with positive trade surpluses but also bear substantial emission burdens. In contrast, regions in the second quadrant, including the EU27/UK and Japan, are emission importers with lower emission burdens but substantial economic benefits.
• Figure 2: The dynamic trajectory of quantitative carbon inequality, encapsulated by the Ecological Economic Equality Index (EEEI), is summarised across 13 regions from 1995 to 2022. Regions with higher values indicate a favourable position within global trade dynamics. The EEEI highlights the uneven distribution of carbon inequality and observes an increasing trend, signalling a widening disparity in carbon equity across both developed and developing regions.
• Figure 3: The dynamic networks of emission footprints (unit in million ton, Mt) and value-added footprints (unit in million Euro, M.EUR) across four distinct periods: 1) 1995 to 2001, 2) 2002 to 2008, 3) 2009 to 2015, and 4) 2016 to 2022. Nodes represent domestic emissions and value-added, while edges indicate the net flow of international footprints. These networks highlight a concerning trend: developing regions exhibit a significant increase in emissions over time but only a modest rise in value-added. This disparity in growth rates underscores a widening gap in carbon inequality, where the economic gains do not correspond proportionately with the environmental costs incurred.
• Figure 4: By integrating the EEEI and complex network analysis, we construct the carbon inequality network. Nodes are defined by regional EEEI values, and edges represent trades impacted by carbon inequality. The carbon inequality network highlights uneven international trades across four periods: 1) 1995 to 2001, 2) 2002 to 2008, 3) 2009 to 2015 and 4) 2016 to 2022. The direction of the network indicates the regions that benefit from bilateral trade.
• Figure 5: The pipeline of proposed ExioNet Toolkit. We calculate the footprint matrix based on the EE-MRIO ExioBase 3.8.2 framework and model the net emissions, net value-added networks, and EEEI-based carbon inequality network leverages NetworkX hagberg2008exploring incorporating measures such as the clustering coefficient and centrality. We visualised dynamic networks in Gephi bastian2009gephi illustrating the network's temporal structural changes.