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

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 (R2) 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 194978.29 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.

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

“…Compared with existing research, some of our results are controversial or contrary, such as the function of relationships between ESV and the GDP, light index, and population values. Yu found that the ESVs of China was decreased slightly while the GDP value was increased substantially [43]. However, Xie found that there is no linear relationship between the total ESV and GDP [5].…”

Green Economy: A Necessary Decision to be Taken

“…They lead to low carbon emissions, use of natural resources efficiently, and be socially inclusive. The economic sectors of production that directly impact the environment should be encouraged through viable and reasonable alternatives by the public sector in general [52,53], as expressed in Figure 2.…”

Untitled

“…There are insufficient approaches for land use land cover change prediction which include Cellular and Hybrid models, Markov Chain and Cellular Automata Analysis (Ca-Markov) Mode (Katana et al, 2013). The CA-Markov Model is suitable for land use land cover simulations (Hyandye and Martz, 2017;Yu et al, 2019;Yulianto et al, 2019). This is because they consider the spatiotemporal dynamics of land use land cover changes (Ghurah et al, 2018).…”