摘要翻译：
气候和经济综合评估模型旨在分析旨在控制气候变化的政策的影响和功效，如碳税和补贴。IAMs的一个主要特点是，它们的地球物理部门决定了比工业化前水平平均表面温度的增加，这反过来又决定了损害函数。大多数现有的IAMs都是完美的前瞻性模型，假设我们知道所有的未来信息。然而，气候和经济系统中存在着重大的不确定性，包括参数不确定性、模型不确定性、气候倾覆风险、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，而另一些研究人员假设气候损害对经济增长有更持久的影响。气候倾覆风险是指（几乎）不可逆转的气候事件，可能导致气候系统的重大变化，如格陵兰冰盖崩塌，而倾覆的条件、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展也不确定。面对这些不确定性，政策制定者必须做出考虑到风险规避、不平等规避以及经济和生态系统可持续性等重要因素的决定。解决这个问题可能需要比标准IAMs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IAMs，并可能显著改变最佳气候政策。
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英文标题：
《The Role of Uncertainty in Controlling Climate Change》
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作者：
Yongyang Cai
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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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.
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PDF链接：
https://arxiv.org/pdf/2003.01615