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- W2030168968 abstract "Urban planning has been widely applied as a regulatory measure to guide a city’s construction and management. It represents official expectations on future population and economic growth and land use over the urban area. No doubt, significant variations often occur between planning schemes and actual development; in particular in China, the world’s largest developing country experiencing rapid urbanization and industrialization. This in turn leads to difficulty in estimating the environmental consequences of the urban plan. Aiming to quantitatively analyze the uncertain environmental impacts of the urban plan’s implementation, this article developed an integrated methodology combining a scenario analysis approach and a stochastic simulation technique for strategic environmental assessment (SEA). Based on industrial development scenarios, Monte Carlo sampling is applied to generate all possibilities of the spatial distribution of newly emerged industries. All related environmental consequences can be further estimated given the industrial distributions as input to environmental quality models. By applying a HSY algorithm, environmentally unacceptable urban growth, regarding both economic development and land use spatial layout, can be systematically identified, providing valuable information to urban planners and decision makers. A case study in Dalian Municipality, Northeast China, is used to illustrate applicability of this methodology. The impacts of Urban Development Plan for Dalian Municipality (2003−2020) (UDP) on atmospheric environment are also discussed in this article." @default.
- W2030168968 created "2016-06-24" @default.
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- W2030168968 date "2010-03-03" @default.
- W2030168968 modified "2023-09-25" @default.
- W2030168968 title "Application of an Uncertainty Analysis Approach to Strategic Environmental Assessment for Urban Planning" @default.
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- W2030168968 doi "https://doi.org/10.1021/es902850q" @default.
- W2030168968 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20199065" @default.
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