Urban land-use planning under multi-uncertainty and multiobjective considering ecosystem service value and economic benefit - A case study of Guangzhou, China

Abstract

Effective land-use planning considering ecosystem service value (ESV) is indispensable in facilitating economic development and eco-environment sustainability. In this study, a Monte-Carlo-based interval fuzzy De Novo programming (MC-IFDP) method is developed for land-use planning under uncertainty. MC-IFDP can achieve optimal system design to maximize multiple conflicting objectives simultaneously, and provide a number of alternatives for decision-makers. It can also address both stipulation uncertainty and parameter uncertainty in constraint and objective function. MC-IFDP is then applied to a real case for land-use planning of Guangzhou (China), where six scenarios related to different decision-makers’ preferences are examined. Results reveal that (i) different decision-makers’ preferences (denoted as S1 to S6) result in different land-use planning schemes, leading to varied satisfactory degrees, system benefits, ESVs and pollutant emissions; (ii) system benefit would reduce as satisfactory degree λ decreases, which would be RMB¥ [4287.5, 11025.7] × 109 with the degree of λ being [0.43, 0.76]; (iii) under advantage condition (S1), the area of ecological land would increase from [455.1, 494.1] × 103 ha to [503.9, 537.8] × 103 ha from period 1 to period 2; the total ESV would expect to have an average annual growth rate of [1.3, 1.5] %; (iv) pollutant emissions (e.g., solid waste, sulfur dioxide, dust, COD, and NH3-N) would be mitigated over the planning horizon under all scenarios. The findings are rewarding for decision-makers to identify desired land-use planning strategies and coordinate the conflicts among satisfactory degree, economic benefit, ESV, and pollutant mitigation under multi-uncertainty.