Monte Carlo simulation based interval chance-constrained programming for regional ecosystem management - A case study of Zhuhai, China

A Monte Carlo simulation based interval chance-constrained programming (MC-ICCP) model is developed for planning regional ecosystem under uncertainty. MC-ICCP cannot only deal with uncertainties described as discrete intervals and probability distributions, but also examine the risk of violating system constraints. MC-ICCP is then applied to planning regional ecosystem sustainable development in the City of Zhuhai, where different ecosystem service value (ESV) assessment measures, risk levels and eco-environmental indicators are investigated. Solutions of land-use pattern, system benefit, and ecosystem service value under 192 scenarios have been obtained. Results reveal that (i) different ESV assessment measures result in different grass and forest areas; (ii) ESV assessment measure can affect land-use pattern and system benefit; (iii) risk level (p) is the most impact factor. Decisions at a lower risk level (a lower p level) would lead to an increased reliability in fulfilling ecosystem requirements but with a lower system benefit. Results also disclose that forest and grass coverage has more significant effect on land-use pattern than reclamation intension and soil erosion. More attention should be paid on ecosystem land-use that could facilitate regional ecological sustainability. The findings could help decision makers balance system benefit and constraint-violation risk, adjust land-use pattern with consideration of ecosystem sustainability and protect regional ecological environment.