Regional industrial growth and environmental impacts in the Bohai Sea rim region of China: uncertainty in location choice
详细信息   
  • 作者:Chaoran Wang ; Dan Xie ; Yi Liu
  • 刊名:Regional Environmental Change
  • 年:2016
  • 出版者:Springer Berlin / Heidelberg
  • 期:5
  • DOI:10.1007/s10113-015-0863-5
  • 来源:SpringerLink
  • 类型:期刊
摘要
Regional industrial growth is facing the problems of no control and disorder in rapidly transitioning China, especially in mega-regional areas. These problems have significantly intensified the use of regional resources and the level of environmental stress. The integration of industrial development and the environmental pollution pressure simulation at the mega-regional level must be supported at the planning stage. In this study, a Computational System for Regional Industrial Distribution Simulation and Environmental Impact Assessment (RESEA) that combines a multi-nominal logit model and uncertainty analysis was developed. This system aimed to explore efficient industrial spatial distribution simulations and potential environmental pressures at the mega-regional level. This study also developed an uncertainty analysis framework to identify and apply a bottom-up system with aggregate and sparse data following the basic processes of an HSY algorithm and Global-Formed Regional Sensitivity Analysis, which is capable of considering both input uncertainty and parameter uncertainty. By applying the RESEA system, a process of model estimation and sensitivity analysis was implemented based on historic data from 2002 to 2008 for the Bohai Sea rim region in China. The future industry distribution for the year 2015 was later aggregated based on the chosen sizes and locations of newly added industrial plants. Finally, the pollution loads of surface water into every sub-region were calculated, and the potential environmental impacts of different strategies were discussed.KeywordsIndustrial spatial distributionMulti-nominal logit modelInput uncertaintyRegional sensitivity analysisEnvironmental impact assessment