Claudio Marchesi, Michele Francesco Arrighini, Laura Zecchi, Marialuisa Volta
{"title":"A top-down approach for climate change mitigation strategies","authors":"Claudio Marchesi, Michele Francesco Arrighini, Laura Zecchi, Marialuisa Volta","doi":"10.1016/j.ifacsc.2025.100297","DOIUrl":null,"url":null,"abstract":"<div><div>This research examined the effects of various GHG reduction policies on climate change via optimization techniques using a top-down approach. The aim was to examine how different aspects of policies to reduce CO<sub>2</sub> and CH<sub>4</sub> emissions would affect changes in temperature compared to pre-industrial levels from 2025 to 2100. The proposed top-down approach allows for the investigation of several factors that may influence the results: (i) the objective function, (ii) the reduction pathway, and (iii) the starting point of the optimization. Two different objective functions were minimized: the overall sum of the temperature between 2025–2100 and the value at 2100. The results were also compared in terms of the reduction trajectories: two different emission trends were assumed: a gradual (gaussian) fall in emissions or a fast (exponential) decline, starting in 2025, in 2030, and in 2035. The mitigation of greenhouse gas (GHG) emissions was limited to a certain range of scenarios outlined by the Intergovernmental Panel on Climate Change (IPCC). These scenarios were determined by analyzing economic, social, and technical developments expected to occur in the next few decades. The analysis also included the interaction in global warming of air pollutant emission variations due to climate policies. The results revealed that exponential trajectories, depending on the initial year, can facilitate the stabilization of global temperatures below 1.5 °C. In contrast, gaussian trajectories were more likely to overtake this threshold if implementation is delayed beyond 2025.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"31 ","pages":"Article 100297"},"PeriodicalIF":1.8000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFAC Journal of Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468601825000033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This research examined the effects of various GHG reduction policies on climate change via optimization techniques using a top-down approach. The aim was to examine how different aspects of policies to reduce CO2 and CH4 emissions would affect changes in temperature compared to pre-industrial levels from 2025 to 2100. The proposed top-down approach allows for the investigation of several factors that may influence the results: (i) the objective function, (ii) the reduction pathway, and (iii) the starting point of the optimization. Two different objective functions were minimized: the overall sum of the temperature between 2025–2100 and the value at 2100. The results were also compared in terms of the reduction trajectories: two different emission trends were assumed: a gradual (gaussian) fall in emissions or a fast (exponential) decline, starting in 2025, in 2030, and in 2035. The mitigation of greenhouse gas (GHG) emissions was limited to a certain range of scenarios outlined by the Intergovernmental Panel on Climate Change (IPCC). These scenarios were determined by analyzing economic, social, and technical developments expected to occur in the next few decades. The analysis also included the interaction in global warming of air pollutant emission variations due to climate policies. The results revealed that exponential trajectories, depending on the initial year, can facilitate the stabilization of global temperatures below 1.5 °C. In contrast, gaussian trajectories were more likely to overtake this threshold if implementation is delayed beyond 2025.