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Decisions from experience reduce misconceptions about climate change

机译:经验决定减少对气候变化的误解

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Research has shown widespread misconceptions in public understanding of the dynamics of climate change: A majority of people incorrectly infer that carbon-dioxide (CO_2) concentrations can be controlled by stabilizing emissions at or above current rates (correlation heuristic), and while emissions continuously exceed absorptions (violation of mass balance). Such misconceptions are likely to delay actions that mitigate climate change. This paper tests a way to reduce these misconceptions through experience in a dynamic simulation. In a laboratory experiment, participants were randomly assigned to one of two conditions: description, where participants performed a CO_2 stabilization (CS) task that provided them with a CO_2 concentration trajectory over a 100 year period and asked them to sketch the corresponding CO_2 emissions and absorptions over the same period; and experience, where participants performed the same task in a dynamic climate change simulator (DCCS), followed by the CS task. In both conditions, half of the participants were science and technology (STEM) majors, and the other half were non-STEM. Results revealed a significant reduction in people's misconceptions in the experience condition compared to the description condition. Furthermore, STEMs demonstrated better performance than non-STEMs. These results highlight the potential for using experience-based simulation tools like DCCS to improve understanding about the dynamics of climate change.
机译:研究表明,公众对气候变化动态的理解存在广泛的误解:大多数人错误地推断出,可以通过将排放稳定在当前或更高水平(相关启发式)来控制二氧化碳(CO_2)的浓度,而排放持续超过吸收(违反质量平衡)。这种误解很可能会拖延减轻气候变化的行动。本文测试了一种通过动态仿真的经验来减少这些误解的方法。在实验室实验中,参与者被随机分配到以下两个条件之一:描述,参与者执行了CO_2稳定(CS)任务,为他们提供了100年的CO_2浓度轨迹,并要求他们绘制相应的CO_2排放量,以及同期吸收和经验,参与者在动态气候变化模拟器(DCCS)中执行相同的任务,然后执行CS任务。在这两种情况下,一半的参与者是科学与技术(STEM)专业,另一半是非STEM。结果表明,与描述条件相比,人们对体验条件的误解明显减少。此外,STEM表现出比非STEM更好的性能。这些结果凸显了使用基于经验的模拟工具(如DCCS)来增进对气候变化动态的理解的潜力。

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