Article

Mapping fine-resolution nested social-ecological system archetypes to reveal archetypical human-environmental interactions

Author

Yang Yuanyuan, Associate Professor, School of Public Administration and Policy, RUC

Abstract

Scaling up case-based knowledge to understand human-environmental interactions is key to addressing the sustainability challenges we face in the Anthropocene. Mapping social-ecological system (SES) archetypes has emerged as a promising tool in identifying these interactions in a spatially explicit manner and developing transferrable sustainability solutions based on context-specific challenges. However, state-of-the-art SES archetype mapping is mainly bound to administrative borders. Lack of spatial details in SES maps does not always capture the panorama of SES configurations and hidden mechanisms. Empowered by multiple open-access geospatial big data streams, this study mapped two-tier nested SES archetypes at 1 km fine resolution in the Beijing-Tianjin-Hebei (BTH) urban agglomeration with high validation accuracy. Global SES archetypes derived from top-down rule-based classification revealed broad social-ecological configurations and social-ecological gradients across SESs. Meanwhile, regional SES sub-archetypes generated by bottom-up hierarchical clustering presented detailed SES characteristics that helped to identify location-specific sustainability challenges. Additionally, by scrutinizing the relationships among SES variables, we identified empirical evidence supporting the existence of four archetypical human-environmental interactions in an urban agglomeration area, manifesting as telecoupling between human and environment, human dependence on the environment, human adaption to the environment, and human modification and conservation of the environment. Generally, fine-resolution SES archetypes demonstrate great potential in supporting the recognition of local needs and gaps in existing policies, and further promoting sustainable landscape management in the BTH region and similar regions.

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