Prioritizing action plans to save resources and better achieve municipal solid waste management KPIs: An urban case study.

The management of municipal solid waste (MSW) in cities is one of the most complex tasks facing local administrations. For this reason, waste management performance measurement structures are increasingly implemented at local and national levels. These performance structures usually contain strategic objectives and associated action plans, as well as key performance indicators (KPIs) for organizations investing their resources in action plans. This study presents the results of applying a methodology to find a quantitative-based prioritization of MSW action plans for the City Council of Castelló de la Plana in Spain. In doing so, cause-effect relationships between the KPIs have been identified by applying the principal component analysis technique, and from these relationships it was possible to identify those action plans which should be addressed first to manage public services more efficiently. This study can be useful as a tool for local administrations when addressing the actions included in their local waste plans as it can lead to financial savings.Implications: This paper introduces and implements a methodology that uses principal component analysis to analyze real data from waste management KPIs and provide municipal solid waste managers with a decision-making tool for prioritizing action plans. The methodology saves financial resources and time, as well as reinforcing the probability of reaching the meta values of the main performance system KPIs.

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method.

Municipal solid waste (MSW) management is vital in achieving sustainable development goals. It is a complex activity embracing collection, transport, recycling, and disposal; and whose management depends on proper strategic decision-making. The use of decision support methods such as multi-criteria decision-making (MCDM) is widespread in MSW management. However, their application mainly focuses on selecting plant locations and the best technologies for waste treatment. Despite the critical role played by transport in promoting sustainability, MCDM has seldom been applied for the selection of sustainable transport alternatives in the field of MSW management. There are a few MCDM studies about choosing waste collection vehicles, but none that include the most recent green vehicles among the options or consider feasible future scenarios. In this article, different engine technologies for collection trucks (diesel, compressed natural gas (CNG), hybrid CNG-electric, electric, and hydrogen) are evaluated under sustainability criteria in a Spanish city by applying the stratified best and worst method (SBWM). This method enables considering the uncertainty associated with future events to establish various feasible scenarios. The results show that the best-valued options are electric and diesel trucks, in that order, followed by CNG and hybrid CNG-electric, and with hydrogen-powered trucks coming last. The SBWM has proven helpful in defining a comprehensive framework for selecting the most suitable engine technology to support long-term MSW collection. Considering sustainability among the criteria and feasible future scenarios in waste management collection decision-making provides more comprehensive and conclusive results that help managers and policymakers make better informed and more reliable decisions.

Statistical Analysis of the Long-Term Influence of COVID-19 on Waste Generation-A Case Study of Castellón in Spain.

Existing research recognizes the COVID-19 impact on waste generation. However, the preliminary studies were made at an early pandemic stage, focused on the household waste fraction, and employed descriptive statistics that lacked statistical support. This study tries to fill this gap by providing a reliable statistical analysis setting inferential confidence in the waste generation differences found in Castellón. Repeated measures ANOVA were carried out for all the waste fractions collected and recorded in the city landfill database from 2017 to 2020. Additionally, Bonferroni's multiple comparison test (p < 0.05) was used to assure confidence level correction and identify which pairs of years' differences appeared. The longitudinal study identified trends for each waste fraction before the pandemic and showed how they changed with the advent of the crisis. Compared to 2019, waste collection in 2020 significantly grew for glass and packaging; remained unchanged for beaches, paper and cardboard, and dropped substantially for households, streets, markets, bulky waste, hospitals, and recycling centres. Total waste showed no differences between 2017 and 2019 but dropped significantly in 2020. These findings may help us better understand the long-term implications of COVID-19 and improve municipal solid waste management in a similar crisis.