DOI: 10.1007/s10584-014-1300-3
Scopus记录号: 2-s2.0-84916884311
论文题名: Adaptive stochastic integrated assessment modeling of optimal greenhouse gas emission reductions
作者: Shayegh S. ; Thomas V.M.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2015
卷: 128, 期: 2018-01-02 起始页码: 1
结束页码: 15
语种: 英语
英文关键词: approximate dynamic programing
; Bayesian inference
; climate sensitivity
; stochastic dynamic programing
; tipping point
Scopus关键词: Bayesian networks
; Climate change
; Dynamic programming
; Emission control
; Gas emissions
; Greenhouse gases
; Inference engines
; Risk assessment
; Stochastic models
; Stochastic systems
; Uncertainty analysis
; Bayesian inference
; Climate sensitivity
; Dynamic programing
; Stochastic dynamics
; Tipping point
; Climate models
; algorithm
; Bayesian analysis
; climate change
; cost-benefit analysis
; greenhouse gas
; integrated approach
; pollution control
; stochasticity
; uncertainty analysis
英文摘要: We develop a method for finding optimal greenhouse gas reduction rates under ongoing uncertainty and re-evaluation of climate parameters over future decades. Uncertainty about climate change includes both overall climate sensitivity and the risk of extreme tipping point events. We incorporate both types of uncertainty into a stochastic model of climate and the economy that has the objective of reducing global greenhouse gas emissions at lowest overall cost over time. Solving this problem is computationally challenging; we introduce a two-step-ahead approximate dynamic programming algorithm to solve the finite time horizon stochastic problem. The uncertainty in climate sensitivity may narrow in the future as the behavior of the climate continues to be observed and as climate science progresses. To incorporate this future knowledge, we use a Bayesian framework to update the two correlated uncertainties over time. The method is illustrated with the DICE integrated assessment model, adding in current estimates of climate sensitivity uncertainty and tipping point risk with an endogenous updating of climate sensitivity based on the occurrence of tipping point events; the method could also be applied to other integrated assessment models with different characterizations of uncertainties and risks. © 2014, Springer Science+Business Media Dordrecht. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84686
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA, United States; H. Milton Stewart School of Industrial and Systems Engineering and School of Public Policy, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA, United States
Recommended Citation:
Shayegh S.,Thomas V.M.. Adaptive stochastic integrated assessment modeling of optimal greenhouse gas emission reductions[J]. Climatic Change,2015-01-01,128(2018-01-02)