Discovering plausible energy and economic futures under global change using multidimensional scenario discovery
Discovering plausible energy and economic futures under global change using multidimensional scenario discovery
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DOI：
10.1016/j.envsoft.2012.09.001
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发表时间：
2013-06-01
影响因子：
4.9
通讯作者：
Borsuk, M. E.
中科院分区：
文献类型：
--
作者：
Gerst, M. D.;Wang, P.;Borsuk, M. E.
使用假设情景作为组织和交流关于不确定的未来社会经济条件的信息的一种手段，对全球变化分析很有价值。一小组定义明确的情景可以提供一组标准的参考案例，用于评估候选政策在替代未来的表现。然而，定义场景的传统方法可能会产生与下游模型的功能不一致的故事情节。也很难评估所构建的情景的数量和范围是否最有效地涵盖了可能的结果。最近提出了基于模型的方法的“场景发现”，应用统计数据挖掘算法，大量的模型模拟，以确定区域的随机参数空间，导致不可接受的政策性能。这些地区，然后划定实际相关的和内部一致的“发现的障碍”。为了区分“可接受的”和“不可接受的”政策结果，现有的方法需要预先指定一个阈值上的一个单一的性能指标。我们认为，当决策者对多个政策目标的相对重要性持有不同观点时，这一要求可能会构成障碍。因此，我们描述了一种场景发现方法，是多维的结果空间，从而排除了需要用户同意一个单一的性能阈值或权衡权重集。我们展示了我们的方法在ENGAGE结果中的应用，ENGAGE是一种基于代理人的经济增长、能源技术和碳排放模型（ABM）。我们相信，场景发现可以为基于代理的建模增加特定的价值，因为ABM通常比聚合规模模型生成更广泛的可能未来。因此，必须有一种系统的方法，对许多随机模型模拟进行分类，以确定政策的脆弱性和机会。最后，我们讨论了我们的方法可能适用于发展的社会经济情景下的“代表性浓度途径”（RCP）框架。(C)2012爱思唯尔有限公司保留所有权利。
The use of scenarios has proven valuable for global change analysis as a means for organizing and communicating information about uncertain future socioeconomic conditions. A small group of well-defined scenarios can provide a set of standard reference cases for assessing the performance of candidate policies under alternative futures. However, traditional methods of defining scenarios may yield storylines that do not align well with the capabilities of downstream models. It can also be difficult to assess whether the number and scope of constructed scenarios most effectively cover the space of possible outcomes. Model-based methods of 'scenario discovery' have recently been proposed that apply statistical data-mining algorithms to a large number of model simulations to identify regions of the stochastic parameter space that lead to unacceptable policy performance. These regions then delineate practically relevant and internally consistent 'discovered scenarios'. To distinguish 'acceptable' from 'unacceptable' policy outcomes, existing methods require pre-specification of a threshold value on a single performance metric. We believe this requirement may present a barrier when decision-makers hold differing views on the relative importance of multiple policy objectives. Therefore, we describe a scenario discovery method that is multidimensional in the outcome space, thus precluding the need for users to agree on a single performance threshold or set of tradeoff weights. We demonstrate application of our approach to the results of ENGAGE, an agent-based model (ABM) of economic growth, energy technology, and carbon emissions. We believe that scenario discovery can add particular value to agent-based modeling, as ABMs typically generate a wider array of possible futures than aggregate-scale models. For this reason, a systematic method for sorting through the many stochastic model simulations to identify policy vulnerabilities and opportunities is essential. We conclude by discussing how our methodology might be applicable to the development of socioeconomic scenarios under the 'representative concentration pathways' (RCP) framework. (C) 2012 Elsevier Ltd. All rights reserved.