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Optimization of Spatial Land Use Patterns with Low Carbon Target: A Case Study of Sanmenxia, China

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  • Li Li

    (School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China)

  • Zhichao Chen

    (School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China)

  • Shidong Wang

    (School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China)

Abstract

Land use change is an important factor in atmospheric carbon emissions. Most of the existing studies focus on modeling the land use pattern for a certain period of time in the future and calculating and analyzing carbon emissions. However, few studies have optimized the spatial pattern of land use from the perspective of the impact of carbon emission constraints on land use structure. Therefore, in this study, the effects of land use change on carbon emissions from 1990 to 2020 were modeled using a carbon flow model for Sanmenxia, Henan, China, as an example. Then, the land use carbon emission function under the low carbon target was constructed, and the differential evolution (DE) algorithm was used to obtain the optimized land use quantity structure. Finally, the PLUS model was used to predict the optimal spatial configuration of land use patterns to minimize carbon emissions. The study produced three major results. (1) From 1990 to 2020, the structural change of land use in Sanmenxia mainly occurred between cultivated land, forest land, grassland and construction land. During this period of land use change, the carbon emissions from construction land first increased and then decreased, but despite the decrease, carbon emissions still exceeded carbon sinks, and the carbon metabolism of land use was still far from equilibrium. (2) Between 2010 and 2020, the area of cultivated land began to decrease, and the area of forest land rapidly increased, and land-use-related carbon emissions showed negative growth. This showed that the structural adjustment of energy consumption in Sanmenxia during the period decreased carbon emissions in comparison with the previous period. (3) A comparison of predicted optimized land use patterns with land use patterns in an as-is development scenario showed a decrease in construction land area of 23.05 km 2 in 2030 with a steady increase in forest land area and a decrease in total carbon emission of 20.43 t. The newly converted construction land in the optimized land use pattern was concentrated in the ribbon-clustered towns built during urban expansion along the Shaanling basin of the Yellow River and the Mianchi–Yima industrial development area.

Suggested Citation

  • Li Li & Zhichao Chen & Shidong Wang, 2022. "Optimization of Spatial Land Use Patterns with Low Carbon Target: A Case Study of Sanmenxia, China," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14178-:d:957911
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    References listed on IDEAS

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    Cited by:

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    2. Wenxing Du & Yuxia Wang & Dingyi Qian & Xiao Lyu, 2022. "Process and Eco-Environment Impact of Land Use Function Transition under the Perspective of “Production-Living-Ecological” Spaces—Case of Haikou City, China," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    3. Xiangxue Han & Meichen Fu & Jingheng Wang & Sijia Li, 2024. "Optimizing Territorial Spatial Structures within the Framework of Carbon Neutrality: A Case Study of Wuan," Land, MDPI, vol. 13(8), pages 1-24, July.
    4. Lusheng Che & Shuyan Yin & Junfang Jin & Weijian Wu, 2024. "Assessment and Simulation of Urban Ecological Environment Quality Based on Geographic Information System Ecological Index," Land, MDPI, vol. 13(5), pages 1-20, May.
    5. Song Xu & Huichen Gao, 2025. "Navigating Research Frontiers in China’s Rural Planning: A Bibliometric Analysis of Sustainable Development," Sustainability, MDPI, vol. 17(1), pages 1-23, January.
    6. Jie He & Jun Yang, 2023. "Spatial–Temporal Characteristics and Influencing Factors of Land-Use Carbon Emissions: An Empirical Analysis Based on the GTWR Model," Land, MDPI, vol. 12(8), pages 1-23, July.
    7. Luo, Haizhi & Li, Yingyue & Gao, Xinyu & Meng, Xiangzhao & Yang, Xiaohu & Yan, Jinyue, 2023. "Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China," Applied Energy, Elsevier, vol. 348(C).

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