Mathematical Sciences: Global Change Research Program

数学科学：全球变化研究计划

• 批准号: 9634300
• 负责人: Robert Lempert
• 金额: $ 10万
• 依托单位: Rand Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9634300&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Global; Change; Research

9634300 Lempert Although technology issues appear vital to climate-change policy, the Integrated Assessment community currently lacks a suitable framework for considering the implications of new technology for policy choices. The fundamental problem is the unpredictability of the evolution of technology over the time spans of relevance. Currently available decision-analysis tools are predicated on the ability to craft some credible best- estimate prediction of the future. However, policy problems such as climate change face extreme uncertainties for which it is not possible to construct well-characterized best estimates. In such cases, prediction-based policy analysis is often unable to resolve acrimonious debate among stakeholders who believe in different best estimates, and often results in constrained thinking about possible policy options. In their recent work the investigators have demonstrated an alternative framework for policy analysis that does not depend on making best-estimate predictions of the future. The approach is based on two concepts called adaptive strategies and exploratory modeling. Exploratory modeling treats problems having massive uncertainty by conducting a large number of computer simulation- experiments on many plausible formulations of the problem, rather than using computer resources to increase the resolution of a single best-estimate model. Adaptive-decision strategies focus on modeling environmental policies where decision-makers can make midcourse corrections based on observations of the relevant environmental and economic systems. In this research project the investigators will use the exploratory modeling/adaptive strategies framework to examine how the potential of new technology should affect near- term climate change policy choices. The research has two specific goals: 1) demonstrate and advance the capabilities of the exploratory modeling/ adaptive strategies framework, and 2) examine a central climate-change po licy question -- under what conditions should society pursue technology-specific policies (e.g., tax credits, subsidies, or government procurements) in addition to carbon taxes? Policy-makers must often make decisions whose ultimate success depends on factors that cannot be predicted at the time the decision is made. However, currently available decision- analysis tools used to help policy-makers compare alternative policies are predicated on the ability to craft some credible best-estimate prediction of the future. Under conditions of extreme uncertainty, prediction-based policy analysis is often unable to resolve acrimonious debate among stakeholders who believe in different best estimates, and often results in constrained thinking about possible policy options. Climate change provides an important example. Future technology developments could have profound impacts on climate-change policy, but no one can accurately predict the impacts of technology on the future costs of reducing greenhouse-gas emissions, nor the effects that various policies will have on these costs. In recent work, the investigators have demonstrated a new framework for policy analysis that does not depend on making best-estimate predictions of the future. The approach is based on two concepts, called adaptive strategies and exploratory modeling, that take advantage of the new analytic capabilities provided by recent computer technology. Exploratory modeling treats problems having extreme uncertainty by conducting computer simulation-experiments on a very large number of plausible estimates of the future and using sampling strategies and data visualization to find important patterns in the results. Adaptive-decision focus on modeling environmental policies where decision- makers can make midcourse corrections based on observations of the relevant environmental and economic systems. In this research project, the investigators will use the exploratory modeling/adaptive strategies framework to examine how the potential of new technology should affect near-term climate change policy choices. In particular, the project will: 1) demonstrate and advance the capabilities of the exploratory modeling/adaptive strategies framework, and 2) examine a central climate-change policy question -- under what conditions should society pursue technology-specific policies (e.g., tax credits, subsidies, or government procurements) in addition to carbon taxes?

小行星9634300 尽管技术问题似乎对气候变化政策至关重要， 评估界目前缺乏一个适当的框架来考虑 新技术对政策选择的影响。最根本的问题是 在相关的时间跨度内技术发展的不可预测性。目前 现有的决策分析工具是以能够对未来作出可靠的最佳估计预测为前提的。然而，气候变化等政策问题 极端的不确定性，不可能构建特征良好的最佳 估算在这种情况下，基于预测的政策分析往往无法解决 利益相关者之间的激烈辩论，他们相信不同的最佳估计， 导致对可能的政策选择的思考受到限制。在他们最近的工作中， 研究人员已经展示了一种替代的政策分析框架， 取决于对未来做出最佳估计的预测。该方法基于两个 这些概念被称为适应性策略和探索性建模。探索性建模治疗 通过进行大量计算机模拟来解决具有巨大不确定性的问题-对问题的许多合理公式进行实验，而不是使用计算机 资源来提高单个最佳估计模型的分辨率。自适应决策 战略的重点是对决策者可以制定的环境政策进行建模， 根据对有关环境和经济状况的观察作出的中途修正 系统.在本研究项目中，研究人员将使用探索性建模/自适应 研究新技术的潜力如何影响短期气候变化政策选择的战略框架。本研究有两个具体目标：1）展示和 提高探索性建模/自适应策略框架的能力，以及2） 研究一个核心的气候变化政策问题--在什么条件下社会应该 执行特定技术政策（例如，税收抵免、补贴或政府 除此之外，碳税？ 决策者必须经常做出决定，其最终成功取决于以下因素： 在做出决定时无法预测。然而，目前用于帮助决策者比较替代政策的决策分析工具是基于 对未来做出可信的最佳估计的能力。 条件下 极端不确定性，基于预测的政策分析往往无法解决激烈的问题 利益相关者之间的辩论，他们相信不同的最佳估计， 限制了对可能的政策选择的思考。气候变化提供了一个重要的 example.未来的技术发展可能对气候变化产生深远影响 政策，但没有人能准确预测技术对未来成本的影响， 减少温室气体排放，也不考虑各种政策对这些排放的影响 成本在最近的工作中，研究人员展示了一个新的政策框架， 不依赖于对未来做出最佳估计的分析。的 方法基于两个概念，称为自适应策略和探索性建模， 它利用了现代计算机提供的新的分析能力， 技术.探索性建模处理具有极端不确定性的问题， 对大量可信的估计进行计算机模拟实验 并使用抽样策略和数据可视化来发现重要的模式 在结果中。适应性决策侧重于对环境政策进行建模，决策者可以根据对相关环境的观察进行中期修正。 环境和经济系统。在这个研究项目中，研究人员将使用 探索性建模/适应性战略框架，以研究新的 技术应该影响短期气候变化政策选择。特别是，该项目 将：1）展示和推进探索性建模/自适应 战略框架，以及2）审查一个核心气候变化政策问题-根据 社会应该在什么条件下推行技术特定政策（例如，税收抵免， 补贴，或政府采购）除了碳税？