Boosting Separate Collection of Dry Recyclables With Door-to-Door Bio-Waste Collection in EU Capitals
Description
In this study we investigate whether door-to-door bio-waste collection contributes to boosting the collection of dry recyclables such as glass, metal, paper and plastic in 28 European Union capitals (pre-Brexit). Employing Multiple Linear Regression (MLR), we sequentially test for 13 control variables including six, related to different waste management system and seven controls related to urban, economic and political aspects. We find evidence that door-to-door bio-waste collection is associated with greater amounts of separately collected dry recyclables. Cities with door-to-door bio-waste collection, on average, sort 60 kilograms per capita per year more of dry recyclables. Although the causal mechanisms behind such a relationship need further investigation, this finding indicates that European Union waste management could benefit from a stronger promotion of door-to-door bio-waste collection. The MLR models were built using R programming language while controlling for six waste management system related variables (PAYT system, glass bring points, metal bring points, door-to-door paper collection, door-to-door plastic collection and number of other collection systems) as well as seven additional control variables: two urban indicators (population and population density), two economic indicators (GDP per capita, material and social deprivation ratio) and three political indicators (the ratio of environmentally aware citizens, governing party’s position on the environment and level of trust in local government). The dataset is assembled of data from various sources which are referenced in the excel file with extended data. The R code and R data file are also provided.
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A dataset for an innovative approach using Multiple Linear Regression models requires various types of data from multiple sources. In addition to waste statistics, we collected data on appropriate socio-economic and political factors. The resulting complex dataset was used to build Multiple Linear Regression models and establish relationships that are discussed in the methods section. It is important to note, that our research design is shaped by (a lack of) data availability. Put differently, we choose a research design that makes the most of the little data available. A dataset for an innovative approach using Multiple Linear Regression models requires various types of data from multiple sources. In addition to waste statistics, we collected data on appropriate socio-economic and political factors. The resulting complex dataset was used to build Multiple Linear Regression models and establish relationships that are discussed in the methods section. It is important to note, that our research design is shaped by (a lack of) data availability. Put differently, we choose a research design that makes the most of the little data available. The most recent reliable city level data with the necessary level of detail on waste collection systems and captured waste fractions was from a 2015 study on separate collection in EU capitals commissioned by the European Commission (BiPRO/CRI, 2015). The amount of collected dry recyclables was derived from data in this report, which comprised of six main categories of waste. Five of them are – bio-waste and four types of dry recyclables – glass, metals, paper and plastics. In the absence of more recent data sources and the above discussed problem with data availability, this waste dataset was and remains the best – and only – option to date. Other control variables were found based on the relevance to our research and were obtained from EUROSTAT and other sources, which are referenced in the excel file “Extended-data-manuscript-2022” under tabs called “Waste Data + Controls T1” and “Controls T2”.