This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change.

中文翻译：

---

使用面板数据和机器学习分析从全球角度探索甲烷排放的决定因素

本文为探讨甲烷排放决定因素的文献做出了贡献。人们对二氧化碳排放进行了很多探索，但很少有研究集中在另一种最长寿的温室气体（GHG）——甲烷上，它对气候变化有很大影响。在实证分析中，使用了一个大型数据集，考虑了 192 个国家的 1960 年至 2022 年数据，并考虑了一系列广泛的决定因素（中央政府债务总额、私营部门的国内信贷、商品和服务出口、人均 GDP 、总失业率、可再生能源消耗、城市人口、基尼指数以及声音和责任）。面板分位数回归 (PQR) 估计显示所有选定变量（基尼指数除外）对分布分位数的不可忽略的统计影响。此外，简单回归树（SRT）模型使我们能够观察到，失败的国家位于世界最贫困地区，自然资源丰富，是那些有望遏制甲烷排放的国家。为此，数字化、绿色教育、绿色融资、确保增加话语权和问责制以及绿色就业等公共干预措施将使失败者跻身胜利者之列，并确保有效应对气候变化。