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Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China

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  • Yonghua Li

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China
    Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou 310058, China)

  • Song Yao

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Hezhou Jiang

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Huarong Wang

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Qinchuan Ran

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Xinyun Gao

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Xinyi Ding

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China)

  • Dandong Ge

    (Department of Regional and Urban Planning, Zhejiang University, Hangzhou 310058, China
    Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou 310058, China)

Abstract

Land-use/land-cover change (LUCC) is an important factor affecting carbon storage. It is of great practical significance to quantify the relationship between LUCC and carbon storage for regional ecological protection and sustainable socio-economic development. In this study, we proposed an integrated framework based on multiobjective programming (MOP), the patch-level land-use simulation (PLUS) model, and the integrated valuation of ecosystem service and trade-offs (InVEST) model. First, we used the InVEST model to explore the spatial and temporal evolution characteristics of carbon storage in Hangzhou from 2000 to 2020 using land-cover data. Second, we constructed four scenarios of natural development (ND), economic development (ED), ecological protection (EP), and balanced development (BD) using the Markov chain model and MOP, and then simulated the spatial distribution of land cover in 2030 with the PLUS model. Third, the InVEST model was used to predict carbon storage in 2030. Finally, we conducted a spatial correlation of Hangzhou’s carbon storage and delineated carbon storage zoning in Hangzhou. The results showed that: (1) The artificial surfaces grew significantly, while the cultivated land decreased significantly from 2000 to 2020. The overall trend was a decrease in carbon storage, and the changing areas of carbon storage were characterized by local aggregation and sporadic distribution. (2) The areas of artificial surfaces, water bodies, and shrubland will continue to increase up to 2030, while the areas of cultivated land and grassland will continue to decrease. The BD scenario can effectively achieve the multiple objectives of ecological protection and economic development. (3) The carbon storage will continue to decline up to 2030, and the EP scenario will have the highest carbon storage, which will effectively mitigate the carbon storage loss. (4) The spatial distribution of carbon storage in Hangzhou was inextricably linked to the land cover, which was characterized by a high–high concentration and a low–low concentration. The results of the study can provide decision support for the sustainable development of Hangzhou and other cities in the Yangtze River Delta region.

Suggested Citation

  • Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2213-:d:994436
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    References listed on IDEAS

    as
    1. Nuno Carvalhais & Matthias Forkel & Myroslava Khomik & Jessica Bellarby & Martin Jung & Mirco Migliavacca & Mingquan Μu & Sassan Saatchi & Maurizio Santoro & Martin Thurner & Ulrich Weber & Bernhard A, 2014. "Global covariation of carbon turnover times with climate in terrestrial ecosystems," Nature, Nature, vol. 514(7521), pages 213-217, October.
    2. Peter M. Cox & Richard A. Betts & Chris D. Jones & Steven A. Spall & Ian J. Totterdell, 2000. "Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model," Nature, Nature, vol. 408(6809), pages 184-187, November.
    3. Wenbo Cai & Wanting Peng, 2021. "Exploring Spatiotemporal Variation of Carbon Storage Driven by Land Use Policy in the Yangtze River Delta Region," Land, MDPI, vol. 10(11), pages 1-12, October.
    4. Stephen Polasky & Erik Nelson & Derric Pennington & Kris Johnson, 2011. "The Impact of Land-Use Change on Ecosystem Services, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(2), pages 219-242, February.
    5. Yingxue Li & Zhaoshun Liu & Shujie Li & Xiang Li, 2022. "Multi-Scenario Simulation Analysis of Land Use and Carbon Storage Changes in Changchun City Based on FLUS and InVEST Model," Land, MDPI, vol. 11(5), pages 1-17, April.
    6. T. Gasser & C. Guivarch & K. Tachiiri & C. D. Jones & P. Ciais, 2015. "Negative emissions physically needed to keep global warming below 2 °C," Nature Communications, Nature, vol. 6(1), pages 1-7, November.
    7. Wang, Guangjie & Peng, Wenfu, 2021. "Detecting influences of factors on GDP density differentiation of rural poverty changes," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 141-151.
    8. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    9. Jiang, Weiguo & Deng, Yue & Tang, Zhenghong & Lei, Xuan & Chen, Zheng, 2017. "Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linking the CLUE-S and the InVEST models," Ecological Modelling, Elsevier, vol. 345(C), pages 30-40.
    10. Li Gao & Xin Wen & Yuntong Guo & Tianming Gao & Yi Wang & Lei Shen, 2014. "Spatiotemporal Variability of Carbon Flux from Different Land Use and Land Cover Changes: A Case Study in Hubei Province, China," Energies, MDPI, vol. 7(4), pages 1-19, April.
    11. A. Baccini & S. J. Goetz & W. S. Walker & N. T. Laporte & M. Sun & D. Sulla-Menashe & J. Hackler & P. S. A. Beck & R. Dubayah & M. A. Friedl & S. Samanta & R. A. Houghton, 2012. "Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps," Nature Climate Change, Nature, vol. 2(3), pages 182-185, March.
    12. Qing Liu & Dongdong Yang & Lei Cao & Bruce Anderson, 2022. "Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Tropics: A Case Study of Hainan Island, China," Land, MDPI, vol. 11(2), pages 1-24, February.
    13. Lyndré Nel & Ana Flávia Boeni & Viola Judit Prohászka & Alfréd Szilágyi & Eszter Tormáné Kovács & László Pásztor & Csaba Centeri, 2022. "InVEST Soil Carbon Stock Modelling of Agricultural Landscapes as an Ecosystem Service Indicator," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    14. Li, Sinan & Zhao, Xiaoqing & Pu, Junwei & Miao, Peipei & Wang, Qian & Tan, Kun, 2021. "Optimize and control territorial spatial functional areas to improve the ecological stability and total environment in karst areas of Southwest China," Land Use Policy, Elsevier, vol. 100(C).
    15. Yingting He & Chuyu Xia & Zhuang Shao & Jing Zhao, 2022. "The Spatiotemporal Evolution and Prediction of Carbon Storage: A Case Study of Urban Agglomeration in China’s Beijing-Tianjin-Hebei Region," Land, MDPI, vol. 11(6), pages 1-25, June.
    16. Peter M. Cox & Richard A. Betts & Chris D. Jones & Steven A. Spall & Ian J. Totterdell, 2000. "Erratum: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model," Nature, Nature, vol. 408(6813), pages 750-750, December.
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    Cited by:

    1. Jiaji Zhu & Xijun Hu & Wenzhuo Xu & Jianyu Shi & Yihe Huang & Bingwen Yan, 2023. "Regional Carbon Stock Response to Land Use Structure Change and Multi-Scenario Prediction: A Case Study of Hunan Province, China," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    2. Jian Chen & Xiaoxiao Zhang & Kai Wang & Zhenguo Yan & Wei Zhang & Lixin Niu & Yanlong Zhang, 2023. "Spatial-Temporal Evolution and Prediction of Carbon Storage in Areas Rich in Ancient Remains: A Case Study of the Zhouyuan Region, China," Land, MDPI, vol. 12(6), pages 1-17, June.

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