Modeling Investment Uncertainty in the Costs of Global CO2 Emission Policy

最近对全球二氧化碳排放量上升的担忧 大气层和随之而来的“温室效应”促使 要求对总产量设置上限，甚至减少总产量， 燃烧化石燃料产生的二氧化碳， 世界经济 本项目将研究 全球能源经济中的投资不确定性建模 与不同国家碳排放限制的相互作用 世界各地。 该项目将开发一个模型， 假设长期经济增长最大化的决策。 这一目标与当前经济决策的一致性 将对旨在鼓励新的 技术投资，未发现资源的勘探， 和节能替代品。 总体目标将是 确定结果不确定的投资 可能会在限制碳排放的预期经济成本方面发挥作用 施政纲要而 该模型将作为Manne的扩展构建， Richels的全球2100模型已经考虑了经济 没有明确随机投资的碳限制效应 方面的影响.这些影响将纳入拟议的模型 这将采取随机非线性规划的形式。 的 解决方法将是一个分布式分解方案 运行在强大的计算机工作站网络上。新 有限信息期望的定界技术 将开发绑定的信息和价值的价值 解决复杂随机模型的方法。

Recent concern over rising quantities of carbon dioxide in the atmosphere and the accompanying "greenhouse effect" has prompted calls for caps on, or even reductions in, the overall output of carbon dioxide from fossil fuels burned to produce energy for the world economy. This project will investigate the effects of modeling investment uncertainty in a global energy-economic interaction with varying limits on carbon emissions in different regions of the world. The project will develop a model that assumes long run economic growth maximizing decisions are taken. The consistency of this objective with current economic decisions will have implications for policies aimed at encouraging new technology investment, exploration for undiscovered resources, and energy saving substitutions. The overall goal will be to determine the effect that investment with uncertain outcomes might play in limiting expected economic costs of carbon emission policies. The model will be built as an extension of Manne and Richels' Global 2100 model that has already considered economic effects of carbon limits without explicit random investment effects. These effects will be incorporated in the proposed model that will take the form of a stochastic nonlinear program. The solution methodology will be a distributed decomposition scheme that runs on a network of powerful computer workstations. New bounding techniques for expectations with limited information will be developed to bound the value of information and the value of solving the complex stochastic model.