000162148 001__ 162148
000162148 005__ 20251017144650.0
000162148 0247_ $$2doi$$a10.1002/ese3.70187
000162148 0248_ $$2sideral$$a144721
000162148 037__ $$aART-2025-144721
000162148 041__ $$aeng
000162148 100__ $$aRivera-Niquepa, Juan David
000162148 245__ $$aAssessing Computational Complexity in Selecting Periods for LMDI Techniques in Energy‐Related Carbon Dioxide Emissions: An Alternative Approach
000162148 260__ $$c2025
000162148 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162148 5203_ $$aThe Logarithmic Mean Divisia Index (LMDI) decomposition analysis is widely employed to examine the drivers behind changes in carbon dioxide emissions related to energy consumption. This analysis has been applied using single‐period, year‐by‐year, and multi‐period time frames worldwide. However, these time frames often overlook trend changes in carbon emission time series, which may lead to inaccurate and biased identification of driving factors. This study replicates previous findings and proposes a novel multi‐period methodology for defining time frames in decomposition analysis. The proposed approach addresses the limitations of traditional methods by accounting for trend changes in the time series and performing an exhaustive search to optimally identify the most suitable time frames for LMDI‐based decomposition. The methodology comprises two stages: the first generates an exhaustive list of possible time series partitions, and the second determines the optimal partition by minimizing the total mean square error (TMSE) using sequential linear models. The results, supported by computational performance tests, demonstrate that the proposed method effectively identifies optimal time frame definitions, making it particularly suitable for annualized case studies on carbon dioxide emissions decomposition in the context of the energy transition.
000162148 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162148 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162148 700__ $$0(orcid)0000-0003-3174-9703$$aYusta, Jose M.$$uUniversidad de Zaragoza
000162148 700__ $$aDe Oliveira-De Jesus, Paulo M.
000162148 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000162148 773__ $$g13, 7 (2025), 3464-3472$$pEnergy sci. eng.$$tEnergy Science & Engineering$$x2050-0505
000162148 8564_ $$s890336$$uhttps://zaguan.unizar.es/record/162148/files/texto_completo.pdf$$yVersión publicada
000162148 8564_ $$s2232531$$uhttps://zaguan.unizar.es/record/162148/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162148 909CO $$ooai:zaguan.unizar.es:162148$$particulos$$pdriver
000162148 951__ $$a2025-10-17-14:35:56
000162148 980__ $$aARTICLE