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The value of knowledge accumulation on climate sensitivity uncertainty: comparison between perfect information, single stage and act then learn decisions

机译:知识积累对气候敏感性不确定性的价值:完美信息,单一阶段和行动之间的比较,然后学习决策

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In COP21 followed by the Paris Agreement, the world is now seriously planning actions to mitigate greenhouse gas emissions toward a "below 2 A degrees C above preindustrial levels" future. Currently, we are still far from identifying the emission pathways to achieve this target because of the various uncertainties in both climate science and the human behavior. As a part of the ICA-RUS project, conducted by Dr. Seita Emori of the National Institute for Environmental Studies we have studied how these uncertainties are eliminated by the accumulation of scientific knowledge and the decision-making processes. We consider the following three questions: first, when and how will the uncertainty range on the global temperature rise be eliminated, second which global emission pathway should be chosen before we get the perfect information, and third how much expenditure is justified in reducing the climate uncertainties. The first question has been investigated by one of the authors. Shiogama et al. (Sci Rep 6:18903, 2016) developed the Allen-Stott-Kettleborough (ASK) method further to estimate how quickly and in what way the uncertainties in future global mean temperature changes can decline when the current observation network of surface air temperature is maintained. Fourteen global climate model results in CMIP5 (CMIP , 2017) are used as virtual observations of surface air temperature. The purpose of this study is to answer the remaining two questions. Based on the ASK research outcomes, we apply the multi stage decision-making known as Act Then Learn (ATL) process to an integrated assessment model MARIA which includes energy technologies, economic activities, land use changes and a simple climate model block. We reveal how accumulating observations helps to mitigate economic losses by expanding the existing ATL method to deal with the uncertainty eliminating process by ASK. The primary findings are as follows. First, the value of information largely increases as the climate target policy is more stringent. Second, even if the uncertainties in the equilibrium climate sensitivity are not fully resolved, scientific knowledge is still valuable. In other words, the expenditure for scientific researches is rationalized when we really concern the global climate changes.
机译:在紧随其后的《巴黎协定》第二十一届联合国气候变化大会(COP21)上,世界正认真计划采取行动,以减少温室气体的排放,以实现“比工业化前水平低2摄氏度以下”的未来。目前,由于气候科学和人类行为的各种不确定性,我们仍然无法确定实现该目标的排放途径。作为国家环境研究所的Seita Emori博士进行的ICA-RUS项目的一部分,我们研究了如何通过科学知识的积累和决策过程来消除这些不确定性。我们考虑以下三个问题:首先,何时以及如何消除全球温度上升的不确定性范围,其次,在获得完美信息之前应该选择哪种全球排放途径,以及第三,减少气候变化的合理支出是多少不确定性。第一个问题已由一位作者调查。 Shiogama等。 (Sci Rep 6:18903,2016)开发了Allen-Stott-Kettleborough(ASK)方法,进一步估算了当维持目前的地面空气温度观测网络时,未来全球平均温度变化的不确定性可以多快和以何种方式下降。 CMIP5(CMIP,2017)中的14个全球气候模型结果被用作地面气温的虚拟观测。本研究的目的是回答其余两个问题。基于ASK的研究成果,我们将称为先学习后行动(ATL)过程的多阶段决策应用于综合评估模型MARIA,该模型包括能源技术,经济活动,土地利用变化和简单的气候模型块。我们揭示了累积的观测值如何通过扩展现有的ATL方法来应对ASK消除不确定性的过程来减轻经济损失。主要发现如下。首先,随着气候目标政策更加严格,信息的价值大大增加。其次,即使平衡气候敏感性的不确定性没有得到完全解决,科学知识仍然是有价值的。换句话说,当我们真正关注全球气候变化时,用于科学研究的支出就被合理化了。

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