Carbon Emission Transfers in the Global Energy Trade Network: An Analysis Based on Multi-Regional Input-Output Model

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Relevant for trade-embodied emissions research and MRIO methodology refinement.

🏢実務担当者:Helps supply chain managers anticipate CBAM-related compliance requirements and identify high-emission trade routes.

🏛政策担当者:Informs carbon border adjustment design and consumption-based emission allocation policies.

As the global energy transition gains momentum, fossil fuels are slowly being replaced by renewable sources such as solar photovoltaic panels, wind turbines, and green hydrogen carriers.Despite this shift, international energy trade remains a major channel for transferring embodied carbon dioxide emissions across borders.This transfer distorts traditional territorial emission accounts, which are central to climate policy frameworks.To address this, our study develops an enhanced multi-regional input-output model using the Eora Global v199.82 database, projected forward to 2025 with data from the International Energy Agency's World Energy Outlook 2025 and the Global Carbon Project's preliminary 2025 report.The model traces embodied emissions through global supply chains, with a focus on key players: China, the United States, the European Union-27, Russia, and the Rest of the World.We incorporate detailed sectoral linkages, bilateral trade flows, and evolving emission intensities to separate direct emissions from extraction and transport from indirect ones from upstream activities like steel fabrication for drilling rigs and digital systems for supply chain oversight.Our 2025 results indicate that net exporters, primarily China with 3.0 GtCO and Russia with 1.3 GtCO, bear about 32% of the world's embodied emissions in energy trade, amounting to 12.2 GtCO overall.In contrast, net importers like the European Union with 2.5 GtCO net inflow and the United States with 1.1 GtCO net inflow displace more than 22% of their consumption-related emissions via imports.Robust sensitivity tests, accounting for variations in trade elasticities and decarbonization trajectories, expose the network's sensitivity to external shocks such as the persistent impacts of the 2022 Ukraine conflict and to emerging low-carbon innovations, including photoelectrochemical hydrogen technologies.These insights reveal deep imbalances in how emission responsibilities are shared globally and point to actionable policies, such as carbon border adjustment mechanisms enhanced by blockchain traceability.For logistics, we propose AI-driven route optimizations that could cut maritime emissions by 14-20%.In services, distributed ledger technologies could enable instant carbon verification for trade transactions.Drawing on the IEA's 2025 projection of $3.3 trillion in total energy investments with $2.2 trillion directed to clean energy for the first time exceeding fossil fuels, this work merges advanced economic modeling with practical strategies to build resilient, low-carbon global supply chains.

• openalex https://doi.org/10.33168/jliss.2025.0902first seen 2026-08-02 18:25:31