Modeling Investment Uncertainty in the Costs of Global CO2 Emission Policy

最近，人们对大气中二氧化碳含量不断上升以及随之而来的“温室效应”感到担忧，这促使人们呼吁限制甚至减少化石燃料的二氧化碳总排放量，这些燃料是为世界经济提供能源的。本项目将研究在全球不同地区碳排放限制不同的能源-经济相互作用中，投资不确定性建模的影响。该项目将开发一个模型，该模型假设采取了长期经济增长最大化决策。这一目标与当前经济决策的一致性将对旨在鼓励新技术投资、勘探未发现资源和节能替代的政策产生影响。总体目标将是确定具有不确定结果的投资在限制碳排放政策的预期经济成本方面可能发挥的作用。该模型将作为Manne和Richels的全球2100模型的延伸，该模型已经考虑了碳限制的经济影响，但没有明确的随机投资效应。这些影响将被纳入提议的模型，该模型将采取随机非线性程序的形式。解决方法将是一个分布式的分解方案，运行在一个强大的计算机工作站网络上。对于有限信息的期望，将发展新的边界技术来约束信息的价值和求解复杂随机模型的价值。

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.