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Impact of Urban Green Space Patterns on Carbon Emissions: A Gray BP Neural Network and Geo-Detector Analysis

Author

Listed:
  • Yao Xiong

    (College of Art and Design, Nanjing Forestry University, Nanjing 210037, China)

  • Yiyan Sun

    (College of Art and Design, Nanjing Forestry University, Nanjing 210037, China)

  • Yunfeng Yang

    (College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China)

Abstract

Rapid urbanization has altered the land use pattern, reducing urban green space and increasing carbon emissions, and it is critical to scientifically examine the interaction mechanism between green space and carbon emissions in order to drive low-carbon urban development. Using Nanjing as an example, this study examined the spatiotemporal evolution characteristics of urban green space patterns and carbon emissions between 2000 and 2020. Carbon emissions at the city and county levels were estimated with great precision using a gray BP neural network model and a downscaling decomposition method. Using urban green space landscape pattern indices and geographic detectors, significant driving factors were discovered and their impact on carbon emissions examined. The results show the following: (1) Carbon emissions are mostly influenced by socioeconomic factors, and the gray BP neural network model (R 2 = 0.9619, MAPE = 1.68%) can predict outcomes accurately. (2) Between 2000 and 2020, Nanjing’s overall carbon emissions increased by 118.9%, demonstrating a “core–periphery” pattern of spatial divergence, with significant emissions from industrial districts and emission reductions in the central urban region. (3) The urban green space exhibits “quantity decreasing and quality increasing” characteristics, with the total area falling by 4.84% but the structure optimized to form a networked pattern with huge ecological patches as the backbone. (4) The primary drivers are the LPI, COHESION, and AI. This study reveals the complex relationship mechanism between the spatial configuration of urban green space and carbon emissions and, based on the results, proposes a green space optimization framework with three dimensions, protection of core ecological patches, enhancement of connectivity through ecological corridors, and implementation of low-carbon maintenance measures, which will provide a scientific basis for the planning of urban green space and the construction of low-carbon cities in the Yangtze River Delta region.

Suggested Citation

  • Yao Xiong & Yiyan Sun & Yunfeng Yang, 2025. "Impact of Urban Green Space Patterns on Carbon Emissions: A Gray BP Neural Network and Geo-Detector Analysis," Sustainability, MDPI, vol. 17(16), pages 1-34, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7245-:d:1721895
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    References listed on IDEAS

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    1. Shurui Gao & Peiyuan Tao & Zhiming Zhao & Xinyue Dong & Jiayan Li & Peng Yao, 2024. "Estimation and Differential Analysis of the Carbon Sink Service Radius of Urban Green Spaces in the Beijing Plain Area," Sustainability, MDPI, vol. 16(4), pages 1-15, February.
    2. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    3. Yuxin Liu & Chenjing Fan & Dongdong Xue, 2024. "A Review of the Effects of Urban and Green Space Forms on the Carbon Budget Using a Landscape Sustainability Framework," Sustainability, MDPI, vol. 16(5), pages 1-29, February.
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