是人类活动导致了气候变化吗?澳大利亚土地所有者的观点

Stephanie Hernandez, Hanabeth Luke, Mathew Stephen Alexanderson
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摘要

澳大利亚农业和依赖农业的农村社区预计将受到气候变化的严重影响。认识到人类在气候变化中的作用是设计和实施有效战略以减轻和适应气候变化影响的核心。鉴于私人土地所有者作为大片自然资源的监护人,了解公众(如私人土地所有者)对人为气候变化的认识程度至关重要。农村社会基准研究是了解土地所有者价值观和信念的有用工具。在此,我们利用农村社会基准调查来研究澳大利亚四个农业地区的土地所有者对人类造成气候变化的程度的一致看法。我们进行了分层聚类分析,以确定具有相似调查回答模式的土地所有者子群。然后,我们利用贝叶斯序数回归法对 "人类活动正在影响气候变化 "这一说法的认同程度进行了评估,以确定群组成员和人口特征的影响。我们的研究结果表明,根据对调查问卷中有关参与者价值观信念和规范问题的回答模式,我们发现了三个不同的群组。群组成员身份对同意度的正面影响最大(0.52,95% CI:0.37 至 0.67)。其次是较高的教育水平(0.32,95% CI:0.22 至 0.41)。性别显示出中等程度的不确定性,但具有积极影响。房产居住年限、参与者年龄和房产面积的影响很小,而降雨区的负面影响为-0.29(95% CI:-0.47 至-0.12)。我们的研究结果表明,推广计划需要考虑基于生活经验和人口统计学相结合的土地所有者类型。
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Is human activity driving climate change? Perspectives from Australian landholders
Australian agriculture and the rural communities that depend upon it are expected to experience significant impacts from climate change. The recognition of the human role in climate change is central in the design and implementation of effective strategies to mitigate and adapt to its impacts. Understanding the extent to which members of the public, such as private landholders, acknowledge human-caused climate change is critical, given their role as custodians of large tracts of natural resources. Rural social benchmarking studies are a useful tool for understanding landholder values and beliefs. Here, we use a rural social benchmarking survey to examine landholder agreement regarding the extent to which humans contribute to climate change across four Australian agricultural regions. We perform hierarchical clustering analysis to determine subgroups of landholders with similar patterns of survey responses. We then evaluate this effect of cluster membership and demographic characteristics using Bayesian ordinal regression on levels of agreement with the statement “that human activities are influencing climate change.” Our findings reveal three distinct clusters based on patterns of responses to survey questions eliciting participants values beliefs and norms. Cluster membership exhibits the strongest positive influence on agreement (0.52, 95% CI: 0.37 to 0.67). This was followed by higher education levels (0.32, 95% CI: 0.22 to 0.41). Gender showed a moderately uncertain but positive influence. Years residing on the property, participant age, and property size showed very little influence, while rainfall zones showed a negative influence of-0.29 (95% CI: −0.47 to-0.12). Our results underscore the need for extension programs to consider landholder typologies based on a combination of lived experience and demographics.
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