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Exploring the Spatiotemporal Impact of Landscape Patterns on Carbon Emissions Based on the Geographically and Temporally Weighted Regression Model: A Case Study of the Yellow River Basin in China

Author

Listed:
  • Junhui Hu

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

  • Yang Du

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

  • Yueshan Ma

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

  • Danfeng Liu

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

  • Jingwei Yu

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

  • Zefu Miao

    (School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471000, China)

Abstract

In promoting the “dual-carbon goals” and sustainable development strategy, analyzing the spatio-temporal response mechanism of landscape patterns to carbon emissions is a critical foundation for achieving carbon emission reductions. However, existing research primarily targets urbanized zones or individual ecosystem types, often overlooking how landscape pattern affects carbon emissions across entire watersheds. This research examines spatial–temporal characteristics of carbon emissions and landscape patterns in China’s Yellow River Basin, utilizing Kernel Density Estimation, Moran’s I, and landscape indices. The Geographically and Temporally Weighted Regression model is used to analyze the impact of landscape patterns and their spatial–temporal changes, and recommendations for sustainable low-carbon development planning are made accordingly. The findings indicate the following: (1) The overall carbon emissions show a spatial pattern of “low upstream, high midstream and medium downstream”, with obvious spatial clustering characteristics. (2) The degree of fragmentation in the upstream area decreases, and the aggregation and heterogeneity increase; the landscape fragmentation in the midstream area increases, the aggregation decreases, and the diversity increases; the landscape pattern in the downstream area is generally stable, and the diversity increases. (3) The number of patches, staggered adjacency index, separation index, connectivity index and modified Simpson’s evenness index are positively correlated with carbon emissions; landscape area, patch density, maximum number of patches, and average shape index are negatively correlated with carbon emissions; the distribution of areas positively or negatively correlated with average patch area is more balanced, while the spread index shows a nonlinear relationship. (4) The effects of landscape pattern indices on carbon emissions exhibit substantial spatial heterogeneity. For example, the negative impact of landscape area expands upstream, patch density maintains a strengthened negative effect downstream, and the diversity index shifts from negative to positive in the upper reaches but remains stable downstream. This study offers scientific foundation and data support for optimizing landscape patterns and promoting low-carbon sustainable development in the basin, aiding in the establishment of carbon reduction strategies.

Suggested Citation

  • Junhui Hu & Yang Du & Yueshan Ma & Danfeng Liu & Jingwei Yu & Zefu Miao, 2025. "Exploring the Spatiotemporal Impact of Landscape Patterns on Carbon Emissions Based on the Geographically and Temporally Weighted Regression Model: A Case Study of the Yellow River Basin in China," Sustainability, MDPI, vol. 17(20), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:20:p:9140-:d:1771966
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    References listed on IDEAS

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