In this study, we employ a Moran's based Lasso (Mi-Lasso) methodology to address the spatial dependence of an unspecified functional form, investigating the association between a country's economic growth and the rate of deforestation. Our aim is to explore the existence of a forestry environmental Kuznets curve (EKC). Our approach to handling spatial dependence overcomes limitations identified in existing EKC literature. We estimate a series of cross-sectional data models spanning the period from 1990 to 2020 for 146 countries. Our findings indicate a non-linear relationship, revealing a change peak rate of deforestation over time. Additionally, we observe that the income threshold at which the deforestation rate begins to decrease changes over time with differences observed between model specifications. Crucially, our results highlight that failing to account for spatial dependence leads to a significant absolute upward bias in ordinary least squares (OLS) estimates of income and worse model fit.

中文翻译：

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全球森林的环境库兹涅茨曲线：mi-lasso 估计器的应用


在本研究中，我们采用基于莫兰的套索（Mi-Lasso）方法来解决未指定功能形式的空间依赖性，调查一个国家的经济增长与森林砍伐率之间的关联。我们的目的是探索林业环境库兹涅茨曲线（EKC）的存在性。我们处理空间依赖性的方法克服了现有 EKC 文献中发现的局限性。我们估算了 1990 年至 2020 年期间 146 个国家的一系列横截面数据模型。我们的研究结果表明了一种非线性关系，揭示了森林砍伐峰值率随时间的变化。此外，我们观察到，森林砍伐率开始下降的收入阈值随着时间的推移而变化，模型规格之间存在差异。至关重要的是，我们的结果强调，未能考虑空间依赖性会导致收入的普通最小二乘 (OLS) 估计值出现显着的绝对向上偏差，并且模型拟合更差。