Scholarlink

<p>This study employs Bayesian regression and Bayesian Model Averaging (BMA) to analyze the determinants of the ecological footprint (EF) in Finland from 1990 to 2023. Using Hamiltonian Monte Carlo (HMC) sampling and model averaging through the Bayesian Adaptive Sampling (BAS) algorithm, the paper accounts for parameter uncertainty, nonlinearity, and multicollinearity in environmental data. The analysis includes key predictors such as GDP, renewable energy consumption (REN), foreign direct investment (FDI), urbanization (URB), and innovation (measured by PA), alongside interaction term FDI⋅URB. Posterior estimates reveal a positive association between GDP and EF, while REN is robustly linked to a reduction in EF. The squared GDP term shows only weak support, leaving the existence of an Environmental Kuznets Curve (EKC) pattern in Finland uncertain. The interaction FDI×URB is negative, suggesting that foreign investment may exert a less harmful ecological impact in highly urbanized settings, though this effect is not robust. Bayesian diagnostics confirm model convergence and predictive reliability, supported by low LOOIC and WAIC values. Model comparison using Bayes Factors shows no substantial evidence favoring complex specifications over simpler ones. The BMA results identify REN as the most robust determinant, whereas GDP and its squared term display only moderate inclusion probabilities, with all other predictors showing weak empirical support. This study provides methodological and policy-relevant contributions by integrating advanced Bayesian modeling with environmental macroeconomics, offering robust insights into sustainability transitions in Finland.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bayesian assessment of ecological footprint drivers in Finland: a model averaging approach under structural and model uncertainty

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

查看全文
  • Irina Georgescu,
  • Jani Kinnunen

摘要

This study employs Bayesian regression and Bayesian Model Averaging (BMA) to analyze the determinants of the ecological footprint (EF) in Finland from 1990 to 2023. Using Hamiltonian Monte Carlo (HMC) sampling and model averaging through the Bayesian Adaptive Sampling (BAS) algorithm, the paper accounts for parameter uncertainty, nonlinearity, and multicollinearity in environmental data. The analysis includes key predictors such as GDP, renewable energy consumption (REN), foreign direct investment (FDI), urbanization (URB), and innovation (measured by PA), alongside interaction term FDI⋅URB. Posterior estimates reveal a positive association between GDP and EF, while REN is robustly linked to a reduction in EF. The squared GDP term shows only weak support, leaving the existence of an Environmental Kuznets Curve (EKC) pattern in Finland uncertain. The interaction FDI×URB is negative, suggesting that foreign investment may exert a less harmful ecological impact in highly urbanized settings, though this effect is not robust. Bayesian diagnostics confirm model convergence and predictive reliability, supported by low LOOIC and WAIC values. Model comparison using Bayes Factors shows no substantial evidence favoring complex specifications over simpler ones. The BMA results identify REN as the most robust determinant, whereas GDP and its squared term display only moderate inclusion probabilities, with all other predictors showing weak empirical support. This study provides methodological and policy-relevant contributions by integrating advanced Bayesian modeling with environmental macroeconomics, offering robust insights into sustainability transitions in Finland.