Environmental impacts of economic growth: A STIRPAT analysis using machine learning algorithms

被引:1
作者
Krishnendu, J. [1 ]
Patra, Biswajit [1 ]
机构
[1] Indian Inst Sci Educ & Res IISER Bhopal, Dept Econ Sci, Bhopal, India
关键词
Carbon emissions; Freshwater; Forests; Biodiversity; Machine learning; STIRPAT model; Environmental Kuznets Curve; KUZNETS CURVE; WATER-USE; BIODIVERSITY LOSS; REGRESSION; SELECTION; CHINA; MODEL;
D O I
10.1016/j.sftr.2024.100404
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study examines the environmental consequences of economic growth using four key dimensions - carbon dioxide emissions, freshwater availability, forest area and biodiversity, and introduces a novel methodology to verify the environmental Kuznets Curve (EKC) using pointwise derivatives. Built upon an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we expand the original model to include sectoral growth, trade openness and quality of government institutions. Given the high dimensionality of the data and potential nonlinearities in relationships, we employ advanced machine learning regression techniques like ridge, LASSO, elastic net and kernel-regularised least squares (KRLS) to perform segmented analyses across low, lower-middle, upper-middle and high-income countries. Our findings reveal complex, income-dependent environmental impacts, with growth in lower-income groups typically worsening environmental quality and resource depletion, while wealthier countries suffer reduced environmental strain. While the inverted U-shaped EKC is observed in most cases, more complex patterns, such as an M-shaped curve, emerge as countries progress economically. This study provides valuable insights to inform targeted, regionspecific sustainability strategies.
引用
收藏
页数:12
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