Unveiling the carbon neutrality pathways of compact cities: a simulation-based scenario analysis from China

In the global effort to address climate change, achieving carbon neutrality has become a central objective in urban planning worldwide. Compact cities, due to their potential for land conservation, resource efficiency, and carbon emissions reduction, have garnered significant attention. This study examines the core mechanisms of carbon reduction in compact cities, using Chinese cities as case studies. By employing machine learning models, we analyze how key features of compact cities—such as population density, land-use mix, and public transportation development—contribute to the achievement of carbon neutrality. Additionally, we simulate future carbon emission levels under various urban development scenarios. The results indicate that an optimal population density (2000 to 2500 persons per square kilometer) and an appropriate land-use mix (entropy value between 0.8 and 0.9) can significantly reduce carbon emission intensity and accelerate the attainment of carbon neutrality. However, excessive density or overly mixed land use may lead to negative effects. Furthermore, the carbon reduction potential of public transportation depends on energy transitions and the adoption of new energy vehicles (NEVs); merely expanding public transit does not automatically lead to a reduction in carbon emission intensity. Scenario simulations further reveal that, due to the implementation of compact development strategies, many Chinese cities have already experienced a deceleration in carbon emission growth. Some economically advanced and industrialized cities are even showing a decoupling trend between economic growth and carbon emissions. This research not only identifies the key pathways for carbon reduction in compact cities but also provides theoretical foundations and empirical evidence to support the formulation of low-carbon urban policies.