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Charting pathways to climate change mitigation in a coupled socio-climate model

机译:在社会-气候耦合模型中绘制减缓气候变化的途径

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摘要

Geophysical models of climate change are becoming increasingly sophisticated, yet less effort is devoted to modelling the human systems causing climate change and how the two systems are coupled. Here, we develop a simple socio-climate model by coupling an Earth system model to a social dynamics model. We treat social processes endogenously—emerging from rules governing how individuals learn socially and how social norms develop—as well as being influenced by climate change and mitigation costs. Our goal is to gain qualitative insights into scenarios of potential socio-climate dynamics and to illustrate how such models can generate new research questions. We find that the social learning rate is strongly influential, to the point that variation of its value within empirically plausible ranges changes the peak global temperature anomaly by more than 1°C. Conversely, social norms reinforce majority behaviour and therefore may not provide help when we most need it because they suppress the early spread of mitigative behaviour. Finally, exploring the model’s parameter space for mitigation cost and social learning suggests optimal intervention pathways for climate change mitigation. We find that prioritising an increase in social learning as a first step, followed by a reduction in mitigation costs provides the most efficient route to a reduced peak temperature anomaly. We conclude that socio-climate models should be included in the ensemble of models used to project climate change.
机译:气候变化的地球物理模型正变得越来越复杂,但是人们花费了更少的精力来对造成气候变化的人类系统以及这两个系统如何耦合进行建模。在这里,我们通过将地球系统模型与社会动力学模型耦合来开发一个简单的社会气候模型。我们内在地对待社会过程(源于管理个人如何学习社会以及社会规范如何发展的规则),并且受到气候变化和减缓成本的影响。我们的目标是对潜在的社会气候动态场景进行定性分析,并说明这些模型如何产生新的研究问题。我们发现社会学习率具有很大的影响力,以至于其值在经验上合理的范围内变化会使全球温度峰值异常改变超过1°C。相反,社会规范会加强多数人的行为,因此在我们最需要的时候可能无法提供帮助,因为它们抑制了缓解行为的早期传播。最后,探索模型的缓解成本和社会学习的参数空间,为缓解气候变化提供了最佳的干预途径。我们发现,优先考虑增加社会学习作为第一步,然后降低缓解成本,这是减少峰值温度异常的最有效途径。我们得出结论,应将社会气候模型纳入用于预测气候变化的模型集合中。

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