Deterministic and stochastic analysis of alternative climate targets under differentiated cooperation regimes

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• 作者：Richard Loulou ; Maryse Labriet ; Amit Kanudia
• 关键词：Climate policies ; Imperfect cooperation ; Energy model ; Partial equilibrium ; Stochastic programming
• 刊名：Energy Economics
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：31
• 期：Supplement 2
• 页码：S131-S143
• 全文大小：1748 K

文摘

This article analyzes the feasibility of attaining a variety of climate targets during the 21st century, under alternative cooperation regimes by groups of countries. Five climate targets of increasing severity are analyzed, following the EMF-22 experiment. Each target is attempted under two cooperation regimes, a First Best scenario where all countries fully cooperate from 2012 on, and a Second Best scenario where the World is partitioned into three groups, and each group of countries enters the cooperation at a different date, and implement emission abatement actions in a progressive manner, once in the coalition. The resulting ten combinations are simulated via the ETSAP-TIAM technology based, integrated assessment model. In addition to the 10 separate case analyses, the article proposes a probabilistic treatment of three targets under the First Best scenario, and shows that the three forcing targets may in fact be interpreted as a single target on global temperature change, while assuming that the climate sensitivity C_s is uncertain. It is shown that such an interpretation is possible only if the probability distribution of C_s is carefully chosen.

The analysis of the results shows that the lowest forcing level is unattainable unless immediate coordinated action is undertaken by all countries, and even so only at a high global cost. The middle and the high forcing levels are feasible at affordable global costs, even under the Second Best scenario. Another original contribution of this article is to explain why certain combinations of technological choices are made by the model, and in particular why the climate target clearly supersedes the usually accepted objective of improving energy efficiency. The analysis shows that under some climate targets, it is not optimal to improve energy efficiency, but rather to take advantage of certain technologies that help to reach the climate objective, but that happen to be less energy efficient than even the technologies in the reference scenario. This is particularly observable in the power generation sector and in some end-use sectors. Finally, the article discusses the pros and cons of the stochastic programming treatment of forcing targets, and compares it with the separate simulations of the various deterministic cases.