Identifying the leverage points for biodiversity loss mitigation in global supply chains under different future scenarios

Biodiversity loss is part of the triple planetary crises and is strongly driven by global patterns of consumption, production, and international trade. Environmentally extended multi-regional input–output (EE-MRIO) models are widely used to trace environmental pressures along global supply chains, yet most biodiversity footprint studies are limited to historical snapshots or marginal changes after shocks. Forward-looking assessments that capture long-term structural transformations in socioeconomic development, technology, and trade remain scarce.

In this study, we develop a framework for constructing prospective EE-MRIOs by systematically integrating scenario outputs from the integrated assessment model IMAGE into the global MRIO database EXIOBASE. The economic scenario information includes projections of macroeconomic development, industrial production, energy and crop system transitions, prices, and trade patterns. The environmental stressors include greenhouse gas emissions and land use, two main drivers of biodiversity loss. Prospective EE-MRIOs are generated for a business-as-usual baseline (SSP2) and three policy-oriented scenarios of reduced demand, protected areas, and climate mitigation.

As an initial application, we compare CO₂ emission footprints in 2019 with projections in 2035 under SSP2. The results show that rapidly growing regions such as rest of Asia, India, and rest of Africa experience strong emissions increases due to fast economic growth combined with relatively slow decarbonization, whereas emissions decline in regions such as the USA, Western Europe, and China as a result of stronger decarbonization and slower population-driven GDP growth. This highlights a growing divergence in regional emission trajectories driven by unequal structural transformation.

Building on this baseline, the prospective EE-MRIOs enable a consistent forward-looking analysis of the responsibility allocation of biodiversity loss along global supply chains, the evolution of consumption-based drivers of biodiversity loss under different development pathways, and the assessment of synergies, trade-offs, and leakage effects under climate mitigation, conservation, and demand-side strategies. By providing a structurally consistent representation of future global supply chains, this work offers a new quantitative basis for evaluating long-term biodiversity risks and supporting long-term policy design.