不确定性在控制气候变化中的作用
The Role of Uncertainty in Controlling Climate Change
摘要：
气候和经济综合评估模型（IAM）旨在分析旨在控制气候变化的政策的影响和有效性，如碳税和补贴。IAM的一个主要特征是，其地球物理部门决定了工业化前水平上的平均表面温度升高，这反过来又决定了损害函数。大多数现有的IAM都是完美预见的前瞻性模型，假设我们知道所有的未来信息。然而，气候与经济系统中存在显著的不确定性，包括参数不确定性、模型不确定性、气候倾斜风险、经济风险和模糊性。例如，气候损害是不确定的：一些研究人员假设气候损害与瞬时产出成正比，而另一些研究人员则假设气候损害对经济增长有更持久的影响。气候倾覆风险代表（几乎）不可逆转的气候事件，可能导致气候系统发生重大变化，如格陵兰冰盖崩溃，而条件，倾覆概率，持续时间和相关损害也是不确定的。碳捕获和储存、适应、可再生能源和能源效率方面的技术进步也不确定。面对这些不确定性，政策制定者必须做出决策，考虑风险规避、不平等规避以及经济和生态系统的可持续性等重要因素。解决这个问题可能需要比标准IAM更丰富和更真实的模型，以及先进的计算方法。最近的文献表明，这些不确定性可以被纳入到IAM，并可能改变最佳的气候政策显着。
英文摘要：
Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the damage function. Most of the existing IAMs are perfect-foresight forward-looking models, assuming that we know all of the future information. However, there are significant uncertainties in the climate and economic system, including parameter uncertainty, model uncertainty, climate tipping risks, economic risks, and ambiguity. For example, climate damages are uncertain: some researchers assume that climate damages are proportional to instantaneous output, while others assume that climate damages have a more persistent impact on economic growth. Climate tipping risks represent (nearly) irreversible climate events that may lead to significant changes in the climate system, such as the Greenland ice sheet collapse, while the conditions, probability of tipping, duration, and associated damage are also uncertain. Technological progress in carbon capture and storage, adaptation, renewable energy, and energy efficiency are uncertain too. In the face of these uncertainties, policymakers have to provide a decision that considers important factors such as risk aversion, inequality aversion, and sustainability of the economy and ecosystem. Solving this problem may require richer and more realistic models than standard IAMs, and advanced computational methods. The recent literature has shown that these uncertainties can be incorporated into IAMs and may change optimal climate policies significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
