School of Finance and Economics, Jiangsu University, China.
Sunway Business School, Sunway University, Malaysia.
J Environ Manage. 2024 Jul;364:121440. doi: 10.1016/j.jenvman.2024.121440. Epub 2024 Jun 13.
Amid the urgent global imperatives concerning climate change and resource preservation, our research delves into the critical domains of waste management and environmental sustainability within the European Union (EU), collecting data from 1990 to 2022. The Method of Moments Quantile Regression (MMQR) results reveal a resounding commitment among EU member states to diminish their reliance on incineration, which is evident through adopting green technologies and environmentally conscious taxation policies, aligning with the European Union's sustainability objectives. However, this transition presents the intricate task of harmonizing industrial emissions management with efficient waste disposal. Tailoring waste management strategies to accommodate diverse consumption patterns and unique circumstances within individual member states becomes imperative. Cointegrating regressions highlighted the long-run relationship among the selected variables, while Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Errors (PCSE) estimates roughly confirmed MMQR results. ML analyses, conducted through two ensemble methods (Gradient Boosting, GB, and Extreme Gradient Boosting, XGBoost) shed light on the relative importance of the predictors: in particular, environmental taxation, consumption-based emissions, and production-based emissions greatly contribute to determining the variation of combustible renewables and waste. This study recommends that EU countries establish monitoring mechanisms to advance waste management and environmental sustainability through green technology adoption, enhance environmental taxation policies, and accelerate the renewable energy transition.
在应对气候变化和资源保护的全球紧迫需求中,我们的研究深入探讨了欧盟内部废物管理和环境可持续性的关键领域,从 1990 年到 2022 年收集数据。矩量分位数回归(MMQR)的结果表明,欧盟成员国强烈承诺减少对焚烧的依赖,这通过采用绿色技术和具有环保意识的税收政策得以体现,符合欧盟的可持续性目标。然而,这种转变带来了协调工业排放管理和高效废物处理的复杂任务。制定适合不同消费模式和各个成员国独特情况的废物管理策略变得至关重要。协整回归突出了所选变量之间的长期关系,而可行广义最小二乘法(FGLS)和面板校正标准误差(PCSE)的估计大致证实了 MMQR 的结果。通过两种集成方法(梯度提升,GB 和极端梯度提升,XGBoost)进行的 ML 分析,阐明了预测因子的相对重要性:特别是环境税收、基于消费的排放和基于生产的排放对确定可燃烧的可再生能源和废物的变化有很大贡献。本研究建议欧盟国家建立监测机制,通过采用绿色技术、加强环境税收政策以及加速可再生能源转型,推进废物管理和环境可持续性。