Street Vitality–Low-Carbon Coordination: Spatial Heterogeneity and Nonlinear Mechanisms from Interpretable Machine Learning

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

🔬研究者:Offers a novel integrated framework and nonlinear analysis for studying vitality-carbon coordination at street level using interpretable ML.

🏢実務担当者:Provides urban planners with diagnostic tools to identify vitality-carbon mismatches and guide context-specific interventions.

🏛政策担当者:Suggests that policies should aim for balanced built-environment conditions rather than maximizing single indicators like density or green space.

This study reframes street-level sustainable urban renewal as a coordination problem between street vitality and relative low-carbon performance, rather than treating vibrant activity and carbon-pressure reduction as separate planning objectives. Its main contribution is an integrated street-level diagnostic framework that combines multidimensional vitality measurement, township-constrained carbon-emission reference estimation, vitality–carbon mismatch identification, and interpretable nonlinear mechanism analysis within unified street analytical units. Although previous studies have substantially advanced the measurement of street vitality and urban carbon emissions, these two strands of research have often developed separately. As a result, limited evidence is available on whether high-vitality streets also perform well in low-carbon terms, where vitality–carbon mismatches emerge, and which built-environment conditions are associated with more coordinated outcomes. Taking the five central districts of Chengdu, China, as a case, this study integrates multi-source activity, mobility, built-environment, and emission-related data. Street vitality is measured through activity agglomeration, temporal continuity, functional support, and external connectivity, while relative low-carbon performance is derived from the reverse normalization of length-normalized carbon-emission intensity based on a township-constrained street-level emission reference estimate. The results show that street vitality and low-carbon performance are spatially uneven and frequently mismatched, as high activity does not automatically translate into stronger low-carbon performance, and lower-carbon pressure does not necessarily indicate a vibrant urban environment. More coordinated streets are associated with context-specific combinations of functional organization, transport operation, built form, street-interface quality, and ecological background. Nonlinear diagnostic results further suggest that coordination is favored by moderate, balanced, and locally adapted built-environment conditions rather than by the simple maximization of individual indicators. These findings shift the discussion from whether vitality and low-carbon performance are desirable in isolation to how they can be jointly diagnosed and improved in street-level urban renewal.

• openalex https://doi.org/10.3390/su18125965first seen 2026-06-12 05:12:44