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Land Use Structure Optimization and Ecological Benefit Evaluation in Chengdu-Chongqing Urban Agglomeration Based on Carbon Neutrality

Author

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  • Zhi Wang

    (Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fengwan Zhang

    (College of Management, Sichuan Agricultural University, Chengdu 611130, China)

  • Shaoquan Liu

    (Institute of Mountain Hazards and Environment, Chinese Academy of Sciences and Ministry of Water Resources, Chengdu 610041, China)

  • Dingde Xu

    (College of Management, Sichuan Agricultural University, Chengdu 611130, China
    Sichuan Center for Rural Development Research, College of Management, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

Optimizing land use structure in urban agglomerations is essential to mitigating climate change and achieving carbon neutrality. However, the studies on low-carbon (LC) land use in the urban agglomeration based on carbon neutrality are still limited and lack the consideration of the optimized land ecological benefits. To reduce land use carbon emissions (LUCEs) and improve the ecological benefits of urban agglomerations, we constructed the framework of land use structure optimization (LUSO) under carbon neutrality. Then, in view of land use quantity structure and spatial distribution, we compared the results of LUCEs and the ecological benefits of the Chengdu–Chongqing urban agglomeration (the CCUA) in 2030 under different scenarios. The results showed that in 2030, the LUCEs of the CCUA is 3481.6632 × 10 4 t under the carbon neutral scenario (CN_Scenario), which is significantly lower than the baseline scenario (BL_Scenario) and 2020. In the CN_Scenario, the land use/cover change (LUCC) in the CCUA is more moderate, the aggregation degree of the forestland (FL), grassland (GL), wetland (WL), and water (WTR) patch area deepens, and the overall landscape spreading degree is increased, which is more conducive to play the ecological benefit of carbon sink land. The results can provide a reference for the more efficient use of land resource areas and the formulation of land use and spatial planning.

Suggested Citation

  • Zhi Wang & Fengwan Zhang & Shaoquan Liu & Dingde Xu, 2023. "Land Use Structure Optimization and Ecological Benefit Evaluation in Chengdu-Chongqing Urban Agglomeration Based on Carbon Neutrality," Land, MDPI, vol. 12(5), pages 1-22, May.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:5:p:1016-:d:1140029
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    References listed on IDEAS

    as
    1. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Zhou, Yan & Huang, Xianjin & Zhong, Taiyang & Chen, Yi & Yang, Hong & Chen, Zhigang & Xu, Guoliang & Niu, Lede & Li, Hehui, 2020. "Can annual land use plan control and regulate construction land growth in China?," Land Use Policy, Elsevier, vol. 99(C).
    3. Shizhen Cao & Zaoli Yang, 2022. "Development Potential Evaluation for Land Resources of Forest Tourism Based on Fuzzy AHP Method," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, August.
    4. Zhang, Honghui & Zeng, Yongnian & Jin, Xiaobin & Shu, Bangrong & Zhou, Yinkang & Yang, Xuhong, 2016. "Simulating multi-objective land use optimization allocation using Multi-agent system—A case study in Changsha, China," Ecological Modelling, Elsevier, vol. 320(C), pages 334-347.
    5. repec:dau:papers:123456789/12867 is not listed on IDEAS
    6. Liu, Jing & Jin, Xiaobin & Xu, Weiyi & Sun, Rui & Han, Bo & Yang, Xuhong & Gu, Zhengming & Xu, Cuilan & Sui, Xueyan & Zhou, Yinkang, 2019. "Influential factors and classification of cultivated land fragmentation, and implications for future land consolidation: A case study of Jiangsu Province in eastern China," Land Use Policy, Elsevier, vol. 88(C).
    7. Adib Ahmad Kurnia & Ernan Rustiadi & Akhmad Fauzi & Andrea Emma Pravitasari & Izuru Saizen & Jan Ženka, 2022. "Understanding Industrial Land Development on Rural-Urban Land Transformation of Jakarta Megacity’s Outer Suburb," Land, MDPI, vol. 11(5), pages 1-19, April.
    8. Alexiadis, Alessio, 2007. "Global warming and human activity: A model for studying the potential instability of the carbon dioxide/temperature feedback mechanism," Ecological Modelling, Elsevier, vol. 203(3), pages 243-256.
    9. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    10. Han, Yu & Jia, Haifeng, 2017. "Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China," Ecological Modelling, Elsevier, vol. 353(C), pages 107-116.
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