Biancalani Francesco, Gnecco Giorgio, Metulini Rodolfo, Riccaboni Massimo
AXES - IMT School for Advanced Studies, Lucca, Italy.
Department of Economics, University of Bergamo, Bergamo, Italy.
Sci Rep. 2024 Aug 24;14(1):19676. doi: 10.1038/s41598-024-70260-6.
Despite the negative externalities on the environment and human health, today's economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005-2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions.
尽管对环境和人类健康存在负面外部性，但当今的经济体仍产生过多的二氧化碳排放。因此，各国政府正试图将生产和消费转向更可持续的模式，以减少二氧化碳排放对环境的影响。特别是欧盟，通过创建排放权市场（排放交易体系）实施了一项创新政策来减少二氧化碳排放。本文的目的是进行一项反事实分析，以衡量排放交易体系对减少二氧化碳排放的影响。为此，使用了一种最近开发的统计机器学习方法，即带固定效应估计的矩阵补全，并与传统计量经济学技术进行比较。我们将带固定效应估计的矩阵补全应用于预测二氧化碳排放矩阵中缺失的反事实条目，该矩阵的元素（按国家逐行、按年份逐列索引）表示没有排放交易体系时国家 - 年份对的排放量。通过稳健的诊断测试得到的结果表明，排放交易体系对减少二氧化碳排放有显著影响：在我们的分析中，大多数欧盟国家在2005 - 2020年排放交易体系处理期间，其（与选定行业相关的）总二氧化碳排放量比在没有排放交易体系政策情况下本可实现的（与相同行业相关的）总二氧化碳排放量减少了约(15.4%)。最后，讨论了该研究的一些管理/实际意义以及可能的扩展。
