000150495 001__ 150495
000150495 005__ 20251017144639.0
000150495 0247_ $$2doi$$a10.1016/j.seta.2024.104156
000150495 0248_ $$2sideral$$a142618
000150495 037__ $$aART-2025-142618
000150495 041__ $$aeng
000150495 100__ $$aRivera-Niquepa, Juan David
000150495 245__ $$aKaya factor decomposition assessment of energy-related carbon dioxide emissions in Spain: A multi-period and multi-sector approach
000150495 260__ $$c2025
000150495 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150495 5203_ $$aUnderstanding the underlying factors causing changes in energy-related carbon dioxide (CO2) emissions is crucial for informed policymaking, particularly at the sectoral level. The research background has employed divisia index methods to analyze CO2 from fossil fuel combustion emissions and identify their constituent components associated with specific drivers within defined time frames. Although these analyses have accounted single-period, multi-period, and cumulative year-by-year frames, none considered the changes in emission trends to determine suitable decomposition periods for sectoral level analysis. Incorporating shifts in emission trends is essential for precise driver identification. This study introduced a comprehensive methodology for detailed and disaggregated decomposition at the sectoral level. Our approach selected decomposition periods based on aggregate energy-related CO2 emission trends. To achieve this, we employed an algorithm that minimizes the total mean square error for period selection. For the decomposition process, we applied the logarithmic mean divisia index method (LMDI) to the Kaya factors governing energy-related CO2 emissions of the Spanish economy. Additionally, we explored various levels of disaggregation within seven sectors from economy related to energy consumption. Through this analysis, we identified and scrutinized six decomposition periods from 1995 to 2020. Our findings highlight the substantial effects of electricity and heat, transportation, and industry sectors. We identified opportunities for reducing energy intensity, carbon intensity and, in some cases, structural factors associated with economic activities contributing to emissions. This methodology offers a more straightforward interpretation of results and establishes a basic time frame for decomposition analysis at a granular level of disaggregation.
000150495 540__ $$9info:eu-repo/semantics/embargoedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000150495 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000150495 700__ $$0(orcid)0000-0003-3174-9703$$aYusta, Jose M.$$uUniversidad de Zaragoza
000150495 700__ $$aDe Oliveira-De Jesus, Paulo M.
000150495 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000150495 773__ $$g74 (2025), 104156 [11 pp.]$$tSustainable Energy Technologies and Assessments$$x2213-1388
000150495 8564_ $$s764755$$uhttps://zaguan.unizar.es/record/150495/files/texto_completo.pdf$$yPreprint$$zinfo:eu-repo/date/embargoEnd/2027-01-04
000150495 8564_ $$s414041$$uhttps://zaguan.unizar.es/record/150495/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint$$zinfo:eu-repo/date/embargoEnd/2027-01-04
000150495 909CO $$ooai:zaguan.unizar.es:150495$$particulos$$pdriver
000150495 951__ $$a2025-10-17-14:31:17
000150495 980__ $$aARTICLE