Dynamic urban decarbonization pathways revealed in rapidly urbanizing Chinese cities
- 1University of Cambridge
- 2University of Sussex Business School
- 3Potsdam Institute for Climate Impact Research (PIK)
Abstract
Urban economies and their associated CO 2 emissions depend on a specific urban structure and change dynamically. However, few studies have captured the co-evolution of carbon and broader urban development, or identified the governance-related factors associated with such change across different types of cities. This study addresses this gap by analysing data from 232 Chinese cities between 1999 and 2019. As a key contribution, we investigate urban types as dynamic states by applying K -Means++ clustering to pooled city-year observations and identify six archetypes of urban carbon evolution, together with three typical transition pathways. We further apply a low carbon city indicator framework and develop a pooled CatBoost model selected from five candidate algorithms, using SHAP values to interpret archetype-stratified feature contributions to carbon emission predictions. Our identified pathways indicate that carbon control can be compatible with rapid economic development. Cities with similar per capita emissions can nevertheless differ in transition maturity, highlighting distinct capacities and priorities for further decarbonization. SHAP analysis shows that, in earlier industry-led phases, predicted emissions are most strongly associated with industrial-activity and energy-mix indicators; the broader predictor profiles in more mature phases suggest a shift in decarbonization priorities from sector-specific improvements towards systemic action within and across sectors. In particular, Archetype 5 low-carbon leading transition cooling-intensive megacities account for 45% of total emissions in 2019, suggesting that cross-system coordination and the translation of innovation inputs into energy-efficiency improvements and low-carbon infrastructure warrant further assessment as potential mitigation directions. This study develops an integrated framework linking dynamic urban carbon archetypes, development-stage-specific transition pathways, and differentiated policy priorities, providing a diagnostic basis for understanding heterogeneous urban decarbonization transitions.
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BibTeX
@article{chen2026dynamic,
title = {{Dynamic urban decarbonization pathways revealed in rapidly urbanizing Chinese cities}},
author = {Tong Chen and Felix Creutzig and Ramit Debnath},
journal = {Environmental Research Letters},
year = {2026},
month = oct,
doi = {10.1088/1748-9326/aeb14a},
url = {https://iopscience.iop.org/article/10.1088/1748-9326/aeb14a},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dynamic urban decarbonization pathways revealed in rapidly urbanizing Chinese cities
AU - Tong Chen
AU - Felix Creutzig
AU - Ramit Debnath
PY - 2026
DA - 2026/10/14
JO - Environmental Research Letters
DO - 10.1088/1748-9326/aeb14a
UR - https://iopscience.iop.org/article/10.1088/1748-9326/aeb14a
AB - Urban economies and their associated CO 2 emissions depend on a specific urban structure and change dynamically. However, few studies have captured the co-evolution of carbon and broader urban development, or identified the governance-related factors associated with such change across different types of cities. This study addresses this gap by analysing data from 232 Chinese cities between 1999 and 2019. As a key contribution, we investigate urban types as dynamic states by applying K -Means++ clustering to pooled city-year observations and identify six archetypes of urban carbon evolution, together with three typical transition pathways. We further apply a low carbon city indicator framework and develop a pooled CatBoost model selected from five candidate algorithms, using SHAP values to interpret archetype-stratified feature contributions to carbon emission predictions. Our identified pathways indicate that carbon control can be compatible with rapid economic development. Cities with similar per capita emissions can nevertheless differ in transition maturity, highlighting distinct capacities and priorities for further decarbonization. SHAP analysis shows that, in earlier industry-led phases, predicted emissions are most strongly associated with industrial-activity and energy-mix indicators; the broader predictor profiles in more mature phases suggest a shift in decarbonization priorities from sector-specific improvements towards systemic action within and across sectors. In particular, Archetype 5 low-carbon leading transition cooling-intensive megacities account for 45% of total emissions in 2019, suggesting that cross-system coordination and the translation of innovation inputs into energy-efficiency improvements and low-carbon infrastructure warrant further assessment as potential mitigation directions. This study develops an integrated framework linking dynamic urban carbon archetypes, development-stage-specific transition pathways, and differentiated policy priorities, providing a diagnostic basis for understanding heterogeneous urban decarbonization transitions.
SN - 1748-9326
ER -