Predicting long-term urban growth in Beijing (China) with new factors and constraints of environmental change under integrated stochastic and fuzzy uncertainties

Abstract

Numerous studies related to the simulation and prediction of urban growth to address land-use and land-cover (LULC) changes have been conducted in recent years, but very few have considered the impact of climate change, flooding impact, government relocation, corridor cities, and long-term rainfall variations simultaneously. To bridge the gap, this study predicts possible future LULC changes for 2030 and 2050 in Beijing (China), since Beijing is one of the fastest-growing megacities in the world. The proposed integrated modeling analysis covers four key scenarios to reflect the influences of different factors and constraints on LULC changes, in which cellular automata, Markov chain, and multi-criteria evaluation are fully coupled. While fuzzy membership function was used to address the uncertainty associated with the decision analysis, Markov chain, which is regarded as a stochastic process, was applied to predict future urban growth pathways. In addition, a statistical downscaling model driven by possible climate change scenarios was employed to address long-term rainfall variations in Beijing, China. This study differs from previous ones for Beijing in terms of not only the effects of climate change and flooding impact but also the newly-developed economic free trade zone in Xiong'an and the central government's plan to relocate to the Tongzhou district. Findings indicate that there is no marked difference in LULC over the four key scenarios. Compared to the baseline LULC in 2010, the predicted results indicate that urban expansion is expected to increase more than 6 and 11% in 2030 and 2050, respectively.

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