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National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Author

Listed:
  • Yuhan Yu

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Mengmeng Yu

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Lu Lin

    (School of Economics and Management, Academy of Chinese Energy Strategy, China University of Petroleum, Beijing 102249, China)

  • Jiaxin Chen

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Dongjie Li

    (Tourism College, Hunan Normal University, Changsha 410006, China)

  • Wenting Zhang

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
    Laboratory of urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518000, China)

  • Kai Cao

    (Department of Geography, National University of Singapore, Singapore 117570, Singapore)

Abstract

Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R 2 ) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194,978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

Suggested Citation

  • Yuhan Yu & Mengmeng Yu & Lu Lin & Jiaxin Chen & Dongjie Li & Wenting Zhang & Kai Cao, 2019. "National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:576-:d:199998
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    Cited by:

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    2. Jendrzejewski, Błażej, 2020. "Bioeconomic modelling – An application of environmentally adjusted economic accounts and the computable general equilibrium model," Land Use Policy, Elsevier, vol. 92(C).
    3. Norouzi, Nima & Fani, Maryam & Talebi, Saeed, 2022. "Green tax as a path to greener economy: A game theory approach on energy and final goods in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Chen Lin & Hao Cheng & Chi Zhang & Yang Zhang & Liangjie Wang, 2020. "Using High-resolution Remote Sensing Images to Detect Freshwater Ecosystem Changes – a New Perspective of Different Ecosystem Types and Shapes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3565-3584, September.
    5. Shiyin Chen & Qingxu Huang & Ziwen Liu & Shiting Meng & Dan Yin & Lei Zhu & Chunyang He, 2019. "Assessing the Regional Sustainability of the Beijing-Tianjin-Hebei Urban Agglomeration from 2000 to 2015 Using the Human Sustainable Development Index," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    6. Lin, Boqiang & Zhou, Yicheng, 2022. "Measuring the green economic growth in China: Influencing factors and policy perspectives," Energy, Elsevier, vol. 241(C).
    7. Elena Cigu & Mihai-Bogdan Petrișor & Alina-Cristina Nuță & Florian-Marcel Nuță & Ionel Bostan, 2020. "The Nexus between Financial Regulation and Green Sustainable Economy," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    8. Yingyu Lu & Bo Cao & Yidi Hua & Lei Ding, 2020. "Efficiency Measurement of Green Regional Development and Its Influencing Factors: An Improved Data Envelopment Analysis Framework," Sustainability, MDPI, vol. 12(11), pages 1-23, May.
    9. Saša Stjepanoviæ & Daniel Tomiæ & Marinko Škare, 2022. "A new database on Green GDP; 1970–2019: a framework for assessing the green economy," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 949-975, December.
    10. Yohanes Boni & Wa Ode Rachmasari Ariani & Hasddin Hasddin, 2023. "Study of Environmental Economic Performance According to Energy Use and CO2 Emissions, Air Quality, and Government Policies to Achieve SDGs in Baubau City," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 452-462, November.

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