Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage
Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage
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DOI：
10.3808/jeil.201900003
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
M. Kozlova;J. Yeomans
中科院分区：
文献类型：
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作者：
M. Kozlova;J. Yeomans
环境决策通常涉及多方面的问题，表现出相当大的不确定性。蒙特卡洛模拟方法已被用于各种环境规划场所，以解决这些不确定的方面。基于仿真的输出通常以概率分布的形式呈现。最近，一种被称为模拟分解（SD）的方法已被引入，该方法通过增强输入和模拟输出的多变量组合之间的因果关系的解释力来扩展蒙特卡洛结果的分析。SD通过将一些不确定的输入变量预分类为状态，将这些不同状态的各种组合聚类为场景，然后收集归因于每个多变量输入场景的模拟输出，来构建模拟输出的子分布。由于细分情景对总体输出的贡献很容易直观地描绘出来，可持续发展可以突出和揭示以前未识别的输入和输出的多变量组合之间的联系。SD的方法是一般化的任何蒙特卡洛模型，可以忽略不计的额外的计算开销，因此，可以很容易地用于环境分析，采用模拟模型。这项研究说明了SD在环境分析中的有效性，从中国的碳捕获和封存项目。
Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.