Unveiling the dynamics of multi-scale carbon emissions in urban agglomerations: A hierarchical causal framework for China's strategic economic corridor

Abstract

Understanding multi-scale spatiotemporal dynamics of urban carbon emissions is critical for crafting targeted decarbonization strategies. However, existing studies predominantly examine emissions at singular scales, overlooking cross-scale interactions and causal spatial dependencies. This study proposes a hierarchical analytical framework integrating urban agglomeration, county, and 500m grid levels to dissect carbon emission patterns across China's Yangtze River economic belt (YREB) from 2010 to 2020. Leveraging convergent advances in satellite-derived NPP-VIIRS-like nighttime light data and provincial energy inventories, we develop an ensemble approach combining geographical convergent cross mapping (GCCM) with multi-scale geographically weighted regression (MGWR) to unravel causal mechanisms and scale-dependent drivers. Our findings reveal three insights: (1) Emission trajectories exhibit strong path dependency, with the Yangtze River delta agglomeration contributing 60.4 % of total YREB emissions through 2020, while emerging hotspots demonstrate spatial decoupling from traditional economic cores; (2) Causal analysis identifies technology-intensity and tertiary sector growth as dominant mitigation factors, contrasting with persistent carbon lock-in effects from legacy infrastructure; (3) MGWR exposes paradoxical regional dynamics where urbanization drives emission reductions in advanced economies yet accelerates emissions in developing regions. The framework advances spatial econometrics by reconciling Simpson's paradox in cross-scale analysis while providing actionable intelligence for tiered carbon governance. This contribution establishes a replicable paradigm for transboundary emission management in mega-economic corridors globally.