Back to Search
Start Over
Urban growth boundaries optimization under low-carbon development: Combining multi-objective programming and patch cellular automata models.
- Source :
-
Journal of Environmental Management . Aug2023, Vol. 340, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Urban Growth Boundaries (UGBs) are a tool to control urban sprawl. However, the way to optimize future urban land uses and fix their boundaries is not clear. This paper presents a new framework to delimit UGBs while accounting for ecological, economic, and carbon storage benefits. Aggregate land-use constraints are included in a multi-objective optimization algorithm to capture non-inferior solutions on the Pareto Surface (PS) under different objective scenarios. A patch-level cellular automata simulation model is then used to spatially allocate these land uses, followed by a new two-step adjustment method to delineate the UGBs. This modeling is applied to Wuhan, China. The results show that: (1) One district (Caidian) will have a strong economic growth under low-carbon development. (2) The maximization of carbon storage reduces losses in ecological benefits, suggesting that carbon storage be considered in urban growth planning. (3) The combined model framework and two-step boundary adjustment method can help urban planners define different UGB scenarios and make science-based policy decisions. [Display omitted] • Modeling methodology to delineate urban growth boundaries (UGBs). • Combining Multi-objective Programming (MOP) and land-use simulation. • Objectives include economic, ecological and carbon storage benefits. • MOP captures land allocations on the Pareto Surface. • Low-carbon urban development maximizes land-use carbon storage. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03014797
- Volume :
- 340
- Database :
- Academic Search Index
- Journal :
- Journal of Environmental Management
- Publication Type :
- Academic Journal
- Accession number :
- 163587212
- Full Text :
- https://doi.org/10.1016/j.jenvman.2023.117934