Land Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system 2. Scenario-based application for impact assessment of land-use policies
Land Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system 2. Scenario-based application for impact assessment of land-use policies
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DOI：
10.1016/j.ecoinf.2010.02.001
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发表时间：
2010-05-01
影响因子：
5.1
通讯作者：
Vlek, Paul L. G.
中科院分区：
文献类型：
--
作者：
Le, Quang Bao;Park, Soo Jin;Vlek, Paul L. G.
土地利用政策的未来社会生态后果的评估是有用的，以支持决定什么和在哪里投资的最佳整体环境和发展的结果，但是，任务面临着巨大的挑战，由于耦合的人类景观系统的内在复杂性和可持续性评估所需的长期视角。多主体系统模型已被公认为非常适合表达的共同演变的人类和景观系统的政策干预。本文应用Le等人[Ecological Informatics 3（2008）135]提出的土地利用动态模拟器（LUDAS）框架，支持越南中部山区流域的土地利用政策设计，以提高长期的环境和社会经济效益。我们的目的是通过测量长期景观和社区差异来评估政策干预的相对影响（与基线相比）从给定政策的最广泛的合理选择范围驱动。模型，合理评估耦合模型的结构，以及使用敏感性/不确定性分析的行为测试。我们设计了研究区域相关政策因素的重复模拟实验，包括（i）森林保护区划，（ii）农业推广和（iii）农用化学品补贴。正如预期的那样，绩效指标涉及的人与环境的相互作用越强，指标的不确定性越大。与Liu等人[Science 317（2007）1513]在全球范围内总结的研究结果相似，在我们的案例中观察到土地使用政策的实施与社会生态后果的出现之间存在时滞。在总种植面积、农场规模和收入分配对森林保护区划变化的反应中发现了长期遗产，这意味着自然保护政策对农村生计的影响评估必须在几十年内考虑。生态情景表明，通过直接扩大目前的森林保护，研究结果还表明，加强流域关键地区森林保护的实施，同时为不那么关键的地区的农业生产创造激励和机会的政策干预，从长远来看可能会促进森林恢复和社区收入。为模型开发提出未来方向。(C)2010爱思唯尔B V版权所有
Assessment of future socio-ecological consequences of land-use policies is useful for supporting decisions about what and where to invest for the best overall environmental and developmental outcomes However, the task faces a great challenge due to the inherent complexity of coupled human-landscape systems and the long-term perspective required for sustainability assessment. Multi-agent system models have been recognized to be well suited to express the co-evolutions of the human and landscape systems in response to policy interventions. This paper applies the Land Use Dynamics Simulator (LUDAS) framework presented by Le et al [Ecological Informatics 3 (2008) 135] a mountain watershed in central Vietnam for supporting the design of land-use policies that enhance environmental and socio-economical benefits in long term With an exploratory modelling strategy for complex integrated systems, our purpose is to assess relative impacts of policy interventions by measuring the long-term landscape and community divergences (compared with a baseline) driven from the widest plausible range of options for a given policy Model's tests include empirical verification and validation of sub-models, rational evaluation of coupled model's structure, and behaviour tests using sensitivity/uncertainty analyses. We design experiments of replicated simulations for relevant policy factors in the study region that include (i) forest protection zoning, (ii) agricultural extension and (iii) agrochemical subsidies As expected, the stronger human-environment interactions the performance indicators involve, the more uncertain the indicators are. Similar to the findings globally summarised by Liu et al [Science 317 (2007) 1513], time lags between the implementation of land-use policies and the appearance of socio-ecological consequences are observed in our case. Long-term legacies are found in the responses of the total cropping area, farm size and income distribution to changes in forest protection zoning, implying that impact assessment of nature conservation policies on rural livelihoods must be considered in multiple decades Our comparative assessment of alternative future socio-ecological scenarios shows that it is challenging to attain better either household income or forest conservation by straightforward expanding the current agricultural extensions and subsidy schemes without improving the qualities of the services The results also suggest that the policy intervention that strengthens the enforcement of forest protection in the critical areas of the watershed and simultaneously create incentives and opportunities for agricultural production in the less critical areas will likely promote forest restoration and community income in long run We also discuss limitations of the simulation model and recommend future directions for model development. (C) 2010 Elsevier B V All rights reserved