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预测建模作为一种工具,可用于在不同的领土层级制定有效的城市废物管理政策。

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels.

机构信息

Institute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic.

Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69, Brno, Czech Republic.

出版信息

J Environ Manage. 2021 Aug 1;291:112584. doi: 10.1016/j.jenvman.2021.112584. Epub 2021 Apr 27.

Abstract

Nowadays, the European municipal waste management policy based on the circular economy paradigm demands the closing of material and financial loops at all territorial levels of public administration. The effective planning of treatment capacities (especially sorting plants, recycling, and energy recovery facilities) and municipal waste management policy requires an accurate prognosis of municipal waste generation, and therefore, the knowledge of behavioral, socio-economic, and demographic factors influencing the waste management (and recycling) behavior of households, and other municipal waste producers. To enable public bodies at different territorial levels to undertake an effective action resulting in circular economy we evaluated various factors influencing the generation of municipal waste fractions at regional, micro-regional and municipal level in the Czech Republic. Principal components were used as input for traditional models (multivariable linear regression, generalized linear model) as well as tree-based machine learning models (regression trees, random forest, gradient boosted regression trees). Study results suggest that the linear regression model usually offers a good trade-off between model accuracy and interpretability. When the most important goal of the prediction is supposed to be accuracy, the random forest is generally the best choice. The quality of developed models depends mostly on the chosen territorial level and municipal waste fraction. The performance of these models deteriorates significantly for lower territorial levels because of worse data quality and bigger variability. Only the age structure seems to be important across territorial levels and municipal waste fractions. Nevertheless, also other factors are of high significance in explaining the generation of municipal waste fractions at different territorial levels (e.g. number of economic subjects, expenditures, population density and the level of education). Therefore, there is not one single effective public policy dealing with circular economy strategy that fits all territorial levels. Public representatives should focus on policies effective at specific territorial level. However, performance of the models is poor for lower territorial levels (municipality and micro-regions). Thus, results for municipalities and micro-regions are weak and should be treated as such.

摘要

如今,基于循环经济范式的欧洲城市废物管理政策要求在公共行政的所有领土层次上封闭物质和财务循环。处理能力(特别是分类厂、回收和能源回收设施)的有效规划和城市废物管理政策需要准确预测城市废物的产生,因此,需要了解影响家庭和其他城市废物生产者的废物管理(和回收)行为的行为、社会经济和人口因素。为了使不同层次的公共机构能够采取有效的行动,实现循环经济,我们评估了捷克共和国在区域、微观区域和市级层面上影响城市废物产生的各种因素。主成分被用作输入,用于传统模型(多元线性回归、广义线性模型)以及基于树的机器学习模型(回归树、随机森林、梯度提升回归树)。研究结果表明,线性回归模型通常在模型准确性和可解释性之间提供了良好的折衷。当预测的最重要目标是准确性时,随机森林通常是最佳选择。开发模型的质量主要取决于所选的领土层次和城市废物分数。由于数据质量较差和变异性较大,这些模型在较低的领土层次上的性能明显恶化。只有年龄结构似乎在不同的领土层次和城市废物分数之间很重要。然而,在解释不同领土层次上的城市废物分数时,其他因素也具有很高的重要性(例如,经济主体数量、支出、人口密度和教育水平)。因此,没有一个单一的有效的公共政策能够应对适合所有领土层次的循环经济战略。公共代表应该专注于在特定领土层次上有效的政策。然而,对于较低的领土层次(市和微观区域),模型的性能很差。因此,对于市和微观区域的结果较弱,应予以处理。

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