欧盟国家城市固体废物管理效率的决定因素

Monika Osińska
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摘要

本文的主要目的是评估欧盟国家的城市固体废物管理(MSWM)效率,并确定在引入(欧盟)第 2018/851 号指令前后该效率的决定因素。研究针对 23 个欧盟成员国,以分析两种最优先的废物处理方法(材料回收和能源回收)以及废物管理部门排放的温室气体水平。2015-2020 年的数据摘自欧盟统计局数据库。数据期间分为两个子期间:2015-2017 年(引入该指令之前的期间)和 2018-2020 年。MSWM 效率得分采用 DEA 方法计算。随后,应用托比特回归模型(TRM)来确定决定因素。效率分析表明,与 2015-2017 年相比,2000 年之前加入欧盟的国家在 2018-2020 年的 MSWM 效率有所提高。另一方面,2000 年后加入欧盟的国家的平均效率得分有所下降。在 2015-2017 年,以下澳门金沙线上领彩金网废物管理效率的决定因素在统计上具有显著性:人口密度、失业率、专利数量和旅游强度指数;而在 2018-2020 年,以下决定因素在统计上具有显著性:人口密度、失业率、研发(R&D)支出、高等教育比例和产生的澳门金沙线上领彩金网废物。对这些变量的详细分析表明,2000 年后加入欧盟的国家应首先增加研发支出,并支持本国居民提高教育水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The determinants of municipal solid waste management efficiency in EU countries
The main purpose of this paper is to assess the municipal solid waste management (MSWM) efficiency of European Union countries and to identify the determinants of this efficiency before and after introducing Directive (EU) 2018/851. The research was conducted for 23 EU Member States in order to analyse the two highest-priority waste treatment methods (material recycling and energy recovery) and the level of greenhouse gases emitted by the waste management sector. The data for 2015-2020 were extracted from the Eurostat database. The period of data was divided into two sub-periods: 2015-2017 (the period before introducing the Directive) and 2018-2020. MSWM efficiency scores were calculated using the DEA method. Later, the Tobit Regression Model (TRM) was applied to identify the determinants. The efficiency analysis showed that the countries which joined the EU before 2000 improved their MSWM efficiency in 2018-2020 compared with 2015-2017. On the other hand, the average efficiency scores of the countries that joined the EU after 2000 decreased. In 2015-2017, the following determinants of MSWM efficiency occurred to be statistically significant: population density, unemployment rate, the number of patents and the tourism intensity index, while in 2018-2020: population density, unemployment rate, Research & Development (R&D) expenditure, higher education proportion and MSW generated. A detailed analysis of these variables showed that the countries that joined the EU after 2000 should first increase their R&D expenditure and support their inhabitants in increasing their educational level.
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