[论文解读] Modelling Distributional Impacts of Carbon Taxation: a Systematic Review and Meta-Analysis

Jules Linden, Cathal O'Donoghue+1 more|arXiv (Cornell University)|Jan 12, 2026

Climate Change Policy and Economics被引用 0

一句话总结

对碳税的微观仿真研究进行系统综述与荟萃分析，展示建模选择如何影响估计的分配影响以及跨国的回归性与进步性结果。

ABSTRACT

Carbon taxes are increasingly popular among policymakers but remain politically contentious. A key challenge relates to their distributional impacts; the extent to which tax burdens differ across population groups. As a response, a growing number of studies analyse their distributional impact ex-ante, commonly relying on microsimulation models. However, distributional impact estimates differ across models due to differences in simulated tax designs, assumptions, modelled components, data sources, and outcome metrics. This study comprehensively reviews methodological choices made in constructing microsimulation models designed to simulate the impacts of carbon taxation and discusses how these choices affect the interpretation of results. It conducts a meta-analysis to assess the influence of modelling choices on distributional impact estimates by estimating a probit model on a sample of 217 estimates across 71 countries. The literature review highlights substantial diversity in modelling choices, with no standard practice emerging. The meta-analysis shows that studies modelling carbon taxes on imported emissions are significantly less likely to find regressive results, while indirect emission coverage has ambiguous effects on regressivity, suggesting that a carbon border adjustment mechanism may reduce carbon tax regressivity. Further, we find that estimates using older datasets, using explicit tax progressivity or income inequality measures, and accounting for household behaviour are associated with a lower likelihood of finding regressive estimates, while the inclusion of general equilibrium effects increases this likelihood.

研究动机与目标

• 评估微观仿真模型如何被构建以对家庭和个人进行碳税模拟。
• 识别驱动分配影响估计差异的关键建模选择。
• 量化实施决策如何影响在跨国比较中发现回归性 versus 进步性影响的可能性。
• 讨论结果解释的含义以及在具有分配关注的碳税政策设计中的启示。

提出的方法

• 遵循PRISMA的系统综述，以挑选基于自下而上的碳税微观仿真研究。
• 将建模选择分类为数据、税覆盖范围以及行为/一般均衡整合。
• 使用对数似然回归（probit）在217个估计、来自71个国家的数据上进行荟萃分析，以评估建模选择对回归性结果的影响。
• 在概念性建模选择（如间接排放）与实施性选择（如IO数据库、多区域与单区域）之间进行区分。
• 与现有文献的比较，以分离特定建模决策对分配结果的影响。

Figure 1: Modelling steps and choices.

实验结果

研究问题

• RQ1 不同的微观仿真建模选择如何影响碳税的分配性影响估计？
• RQ2 实施选择（如间接排放建模、IO数据、区域范围）是否系统性地改变碳税呈现为回归性或进步性的可能性？
• RQ3 税覆盖设计和收入回收在分配性结果中起到何种作用？
• RQ4 在考虑数据来源和进口排放覆盖范围的情况下，是否包含一般均衡效应会改变对回归性的估计？
• RQ5 关于建模方法的不同，跨国在回归性与进步性发现方面有哪些模式？

主要发现

• 建模选择存在显著多样性，尚无标准做法出现。
• 对进口排放和更广义的间接排放的建模可能影响回归性结果的概率，聚焦进口排放往往与较少的回归性发现相关。
• 较旧的数据集、明确的税制进步性或收入不平等度量，以及包含家庭行为，与更高的进步性估计相关。
• 在考虑IO数据库和进口排放覆盖范围的前提下，纳入一般均衡效应会增加回归性结果的可能性。
• 使用MRIO/EE-IO方法和更广的排放覆盖往往对分配性结果的影响与单一区域或仅直接排放的模型不同。
• 荟萃分析表明，实施相关的预测变量能显著提高解释力，关于回归性vs进步性的发现的解释力提升可达约55%（在Probit模型中）。

Figure 2: Comparison of carbon tax burdens by welfare concept and ranking variable for Hungary.