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March 12, 2026Journal of Urban Planning and Development0 citations

Revealing the Driving Influence Mechanism of Urban Spatial Environment on Carbon Emission Based on Multisource Data and Multidimensional Evaluation: Taking Hefei, China, as an Example

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WXWei XuanYLYanfei LuJTJunhan Tang

Key Points

  • The aim is to explore how urban spatial elements influence carbon emissions, using a multisource data approach.
  • Created a multisource dataset for Hefei Municipal District
  • Estimated carbon emissions through direct and indirect methods
  • Applied nighttime lighting data for correction
  • Established a carbon emission indicator system based on the 5D spatial indicator framework
  • Utilized spatial autocorrelation models to analyze carbon emissions and indicators
  • Functional composite strength, public transport station density, and road network density were found to be major drivers of carbon emissions
  • Driving forces for these factors were 70.2%, 63.3%, and 63.3%, respectively
  • Improving these urban factors can significantly aid in carbon emission reduction efforts

Abstract

As cities are the main carriers of carbon emissions, researchers have been continuously exploring the relationship between various elements in cities and carbon emissions. In this study, a multisource data set was produced for the Hefei Municipal District in Anhui Province, China, and the carbon emission data estimated by both direct and indirect methods were fitted and corrected by the function of nighttime lighting data. Carbon emission drivers were established in accordance with the current 5D spatial indicator system. Based on the current 5D spatial indicator system, the carbon emission driver indicators were established, taking into account the complex process of carbon sources and sinks, and incorporating natural spatial factors other than man-made space. The spatial autocorrelation model was used to analyze the distribution of carbon emissions and each indicator separately and spatially, and the driving force of each factor in the driving system was measured and verified by combining the characteristics of multiple models. The results showed that among the five dimensions of urban space, the driving forces of functional composite strength, public transport station density, and road network density are higher, which are 70.2%, 63.3%, and 63.3%, respectively, which are important factors affecting carbon emission intensity. By prioritizing the improvement of these factors and optimizing public transport and land use, valuable lessons can be provided for spatial planning for carbon reduction and the construction of low-carbon cities.

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Cite This Study

Xuan et al. (2026) studied this question.

synapsesocial.com/papers/69b2577096eeacc4fcec616ehttps://doi.org/10.1061/jupddm.upeng-5634
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