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本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

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分类依据与纠错

AI 根据所提供的论文材料归类:摘要以情景调查中的有序响应和潜在效用刻画公众成本承担意愿,直接涉及离散选择与偏好建模。

反馈分类问题

How Much Does the Far Future Matter? A Hierarchical Bayesian Analysis of the Public's Willingness to Mitigate Ecological Impacts of Climate Change

遥远未来有多重要?公众减缓气候生态影响意愿的层级贝叶斯分析

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
David F Layton
Richard A Levine

遥远未来有多重要?公众减缓气候生态影响意愿的层级贝叶斯分析

研究问题
公众愿意现在承担多少额外成本以减缓未来的气候生态损失,这种意愿如何随森林损失程度和影响发生时间变化?
方法与思路
作者利用变化损失、减缓方式、成本和时间的情景调查,以潜在效用和复杂有序响应建立层级贝叶斯模型并用 Gibbs 抽样拟合。
材料陈述的贡献
摘要称,模型可处理非矩形概率区域并改善抽样混合;估计意愿随生态影响程度非线性上升,且分析了未来影响的折现。
阅读关注点
应核对调查情景设计、有序效用结构、参数识别处理及抽样诊断,并查阅原文确认折现率的数值、单位和不确定性。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要;折现率写作“somewhat less than 1%% per year”,符号有疑点,需查原文,不能自行改写。

原文摘要

How much does the far future matter? This question lies at the heart of many important environmental policy issues, such as global climate change, biodiversity loss, and the disposal of radioactive waste. Although philosophers, experts, and others offer their viewpoints on this deep question, the solution to many environmental problems lies in the willingness of the public to bear significant costs now to make the far future a better place. Short of national plebiscites, the only way to assess the public's willingness to mitigate impacts in the far future is to ask them. Using a unique set of survey data in which respondents were provided with sets of scenarios describing different amounts of forest loss due to climate change, along with associated mitigation methods and costs, we can infer the respondents' willingness to bear additional costs to mitigate future ecological impacts of climate change. The survey also varied the timing of the impacts, which allows us to assess how the willingness to mitigate depends on the timing of the impacts. The responses to the survey questions are a consequence of latent utilities with complex ordinal structures that result in nonrectangular probabilities. Whereas the nonrectangular probabilities complicate standard maximum likelihood–based approaches, we show how the nonrectangular probabilities fit neatly into a hierarchical Bayesian model. We show how to fit these models using the Gibbs sampler, overcoming problems in parameter identification to improve mixing of the induced Markov chain. The results indicate that the public's willingness to incur additional costs to mitigate ecological impacts of climate change is an increasing nonlinear function of the magnitude of the impact, and that they discount future impacts at somewhat less than 1%% per year.

笔记与分组