Héctor Moreno Solaz, Miguel-Ángel Artacho-Ramírez, Víctor-Andrés Cloquell-Ballester, Cristóbal Badenes Catalán
{"title":"Prioritizing action plans to save resources and better achieve municipal solid waste management KPIs: An urban case study.","authors":"Héctor Moreno Solaz, Miguel-Ángel Artacho-Ramírez, Víctor-Andrés Cloquell-Ballester, Cristóbal Badenes Catalán","doi":"10.1080/10962247.2023.2244461","DOIUrl":null,"url":null,"abstract":"<p><p>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.<i>Implications</i>: 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.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/10962247.2023.2244461","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/7 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
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.