Study introduces a novel country classification for sustainability assessment, highlighting ecological and economic factors.
Biodiversity loss, driven by human activities such as land-use change, climate change, and pollution, is a critical global challenge. Effective sustainability performance assessments, especially at the national level, require tools that account for biophysical characteristics alongside economic and social factors. Current methods often overlook the ecological constraints and opportunities shaped by biomes—large-scale regions defined by climatic and ecological attributes that influence ecosystem services. This study introduces Econobiome classifications , a novel country categorization system based on terrestrial ecosystem composition and the spatial distribution of economic activities, including agriculture, forestry, and night light data. Using partitioning clustering techniques, we classify countries into four distinct groupings: cropland (11 clusters), forestry (5 clusters), night light (7 clusters), and all activities (7 clusters). The classifications highlight how dominant biomes and economic activity locations shape ecological conditions and sustainability constraints. We apply the Econobiome framework to benchmark the environmental efficiency of national agricultural systems (NAS) using data envelopment analysis (DEA). Results reveal significant variation in eco-efficiency across clusters, underscoring the importance of context-sensitive comparisons in sustainability diagnostics. While the Econobiome framework improves ecological comparability across countries, it relies on national-scale data and does not capture subnational ecological and socio-economic variation. Yet, the Econobiome classification provides policymakers and researchers with a robust tool to align environmental assessments with ecological realities, improving the accuracy of sustainability benchmarks and supporting evidence-based decision-making in the face of the biodiversity crisis.
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Matthieu et al. (2026) studied this question.
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