Computational development economics Applying ML to the multiple avoidance mechanism problem: willingness to pay to avoid pollution in the Global South

我提议的研究议程将提供一种新的量化方法，以估计人们在多大程度上重视以牺牲经济增长为代价来改善环境结果的政策。这项研究议程很重要，因为它告诉各国政府在设计环境政策时应该如何在环境和经济结果之间进行权衡。全球南方关于这一主题的现有研究严重低估了人们对环境监管的重视程度，因为它只考虑了人们避免环境破坏的单一途径。例如，Ito&Zhang(2018)和Zhang&Mu(2018)最近的研究在估计中国个人会重视限制空气污染的法规时，只分别考虑了HEPA空气净化器和颗粒物过滤口罩的支出。我的研究将涉及生产一种基于机器学习的估计器，使我们能够识别个人为避免环境破坏而进行的购买类型。然后，我会使用这种新方法重新评估伊藤和张(2018)对中国空气污染的研究。我将提供关于人们用来避免空气污染的适应行为的新证据。首先，我将就适应行为的相对重要性提供新的证据。为了避免空气污染的影响，人们是否在预防(例如购买带有HEPA过滤器的口罩)或减轻损害(例如购买药物以管理与呼吸空气污染相关的健康状况)上花费更多？我预计，我还将大幅提高对个人对环境监管重视程度的估计。这项研究的政策影响是立竿见影的。如果估值确实比现有研究显示的要大得多，这将为中华人民共和国政府支持更积极的环境监管提供证据。看似合理的是，这项研究的更大影响在于提供了一种新的环境结果评估方法，适用于广泛的环境结果。虽然我将展示空气污染监管的应用，但在估计政府应该如何在经济增长和其他环境结果之间进行权衡时，这种方法似乎是相关的，为监管的价值提供了证据，以避免环境破坏的其他原因。例如，这项工作可能适用于评估与气候变化相关的降水(降雨量)和温度的变化。反过来，它将为最优的气候变化政策提供证据。

My proposed research agenda would provide a new quantitative method to estimate how much people value policy that improves environmental outcomes at the cost of economic growth. This research agenda is important because it informs how governments should make trade-offs between environmental and economic outcomes when designing environmental policy. Existing research on this topic in the Global South has severely underestimated how much people value environmental regulation because it only considers a single avenue through which people avoid environmental damage. For example, the recent research of Ito & Zhang (2018) and Zhang & Mu (2018) only consider expenditure on HEPA air purifiers and particulate-filtering face masks, respectively, when estimating how much individuals in China would value regulation that limits air pollution. My research would involve producing a machine learning-based estimator that allows us to identify what kind of purchases individuals make to avoid environmental damage. I would then reestimate the study of Ito & Zhang (2018) on air pollution in China using this new approach. I will provide new evidence on people's adaptive behaviours that they employ to avoid air pollution. Firstly, I will provide novel evidence on the relative importance of adaptive behaviours. To avoid the impacts of air pollution, do people spend more on prevention (e.g. buying face masks with HEPA filters) or damage mitigation (e.g. purchasing medication to manage health conditions associated with breathing air pollution)? I expect that I will also materially increase the estimate of how much individuals value environmental regulation. The policy implications of this research are immediate. If the valuation is indeed materially larger than existing studies have shown, this would provide evidence to the Government of the People's Republic of China in favour of more aggressive environmental regulation. Plausibly, the larger impact of the research, though, is in providing a novel method for valuation of environmental outcomes that is applicable across a broad range of environmental outcomes. While I will show application to air pollution regulation, this approach is plausibly relevant in estimating how governments should make trade-offs between economic growth and other environmental outcomes, providing evidence on the value of regulation to avoid other causes of environmental damage. For example, this work might find application to valuing changes in precipitation (rainfall) and temperature associated with climate change. In turn, it would provide evidence on optimal climate change policy.