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基于机器学习的环境退化、制度质量与经济增长建模

Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth.

作者信息

Jabeur Sami Ben, Ballouk Houssein, Arfi Wissal Ben, Khalfaoui Rabeh

机构信息

Institute of Sustainable Business and Organizations, Confluence: Sciences Et Humanités - UCLY, ESDES, 10 place des archives, 69002 Lyon, France.

CEREFIGE Laboratory, University of Lorraine, Nancy, France.

出版信息

Environ Model Assess (Dordr). 2022;27(6):953-966. doi: 10.1007/s10666-021-09807-0. Epub 2021 Nov 24.

DOI:10.1007/s10666-021-09807-0
PMID:34840524
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8611244/
Abstract

This study was aimed at investigating the determinants of environmental sustainability in 86 countries from 2007 to 2018. The natural gradient boosting (NGBoost) algorithm was implemented along with five machine learning models to forecast the trends of CO emissions. In addition, the SHapley Additive exPlanation (SHAP) technique was used to interpret the findings and analyze the contribution of the individual factors. The empirical results indicated that the predictions obtained using NGBoost were more accurate than those obtained using other models. The SHAP value exhibited a positive correlation among the amount of CO emissions, economic growth, and opportunity entrepreneurship. A negative correlation was observed among the governance, personnel freedom, education, and pollution.

摘要

本研究旨在调查2007年至2018年期间86个国家环境可持续性的决定因素。实施了自然梯度提升（NGBoost）算法以及五个机器学习模型来预测一氧化碳排放趋势。此外，使用夏普利值附加解释（SHAP）技术来解释研究结果并分析各个因素的贡献。实证结果表明，使用NGBoost获得的预测比使用其他模型获得的预测更准确。SHAP值在一氧化碳排放量、经济增长和机会创业之间呈现正相关。在治理、人员自由、教育和污染之间观察到负相关。

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