In recent years, the strategic development and utilization of historical and cultural resources in historic urban areas to foster advanced cultural industries has become a pivotal approach for promoting urban renewal. However, there are significant differences between the urban development models of Western countries and the unique context of China, rendering it challenging to directly apply Western theories to explain China’s urban development. As one of the leading cities in both cultural industry growth and urban renewal in China, Guangzhou offers a valuable case for examining how cultural industries can effectively empower urban renewal. This paper aims to explore and analyze the mechanisms through which cultural industry development influences urban renewal in China, using Guangzhou as a case to illustrate the dynamic interactions between cultural industries and urban transformation. Furthermore, it proposes a contextualized model and strategy for the role of cultural industries in driving urban renewal, thereby providing evidence-based insights and practical recommendations for enhancing the contribution of cultural industries to urban development. This study suggests that Guangzhou should further advance this goal by strengthening policy support, promoting the digital cultural industry, strengthening infrastructure for cultural industry spaces, and developing distinctive cultural industry projects, thus facilitating sustainable and high-quality urban development.

Urban areas concentrate buildings, mobility and energy-related activities, making them key arenas for carbon mitigation. Yet fine scale low carbon zoning still lacks a systematic approach for translating nonlinear morphology carbon relationships into spatially actionable units. This study develops such a framework for Guangzhou by delineating Local Carbon Emission Zones (LCEZs) from nonlinear responses of residential and transport CO2 emissions (RTCE) to urban morphology. RTCE from the 2024 EDGAR inventory was analysed across 1 km grid cells using nine indicators representing two-dimensional built form, three-dimensional built intensity and street–landscape configuration. Four tree-based models were evaluated using independent testing and spatial cross-validation. XGBoost achieved the strongest spatial cross-validation performance (R2 = 0.433) while maintaining an independent-test R2 of 0.658. SHAP identified building volume ratio (BVR) as the leading morphology predictor and revealed reproducible nonlinear response transitions for BVR, building density (BD) and road-network density (RND). Bootstrap-supported transitions at BVR = 3.572 m3 m−2, BD = 20.236% and RND = 16.701 km km−2 defined binary response states. Multidimensional response-state encoding combined these states to delineate eight LCEZ types. The types were further grouped into four planning coordination classes according to the number of simultaneously active morphology dimensions, accounting for 48.89%, 25.13%, 18.06% and 7.92% of grid cells. By encoding nonlinear fitted contributions as multidimensional response states, the framework extends LCEZ delineation beyond morphology-level classification and provides an interpretable basis for differentiated low-carbon planning.