Multiple Linear Regression - Effectiveness of Door-to-Door Bio-Waste Collection in Driving Sorting of Dry Recyclables

We hypothesise that implementing door-to-door bio-waste collection system is positively associated with increasing the amounts of dry recyclables collected, and that this effect is more significant compared to alternative waste collection systems. Our research shows that this hypothesis holds when running Multiple Linear Regression (MLR) analysis using R programming language, when 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 socio-economic and political factors (such as population, population density, ratio of well-informed citizens, governing party’s position on the environment, trust in local government, material and social deprivation ratio and GDP per capita). 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 used to construct MLR models in Table 1 and Table 2 are also provided.本研究提出如下假设：推行生物废弃物上门收集系统与提升可回收干垃圾收集量呈正相关，且相较于其他垃圾收集系统，该举措的促进效应更为显著。本研究借助R编程语言开展多元线性回归（Multiple Linear Regression, MLR）分析，在控制六项垃圾管理系统相关变量（PAYT（Pay As You Throw）系统、玻璃投放点、金属投放点、纸张上门收集服务、塑料上门收集服务及其他收集系统数量）以及七项额外社会经济与政治因素（包括人口规模、人口密度、知情公民占比、执政党环保立场、对地方政府的信任度、物质与社会剥夺率及人均国内生产总值）后，该假设得以成立。本数据集整合自多源数据，相关引用信息详见附随的扩展Excel文件。本文还提供了用于构建表1与表2中多元线性回归模型的R代码及R数据文件。