Data-Driven Probabilistic MACCs for Smart Cities: Monte Carlo Simulation and Bayesian Inference of Rebound Effects

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:42Z
Fecha de publicación2026-01-01
ResumenThe shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect (η=0.5) causes a 10.09% decrease in total net abatement, dropping from 24.86 to 22.35 tCO2e, and significantly raises costs. Notably, the number of strictly cost-effective strategies (MAC<0) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty.es_ES
Doihttps://doi.org/10.3390/data11040087es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1692
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónDataes_ES
URL relacionadohttps://doi.org/10.3390/data11040087es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteData
TítuloData-Driven Probabilistic MACCs for Smart Cities: Monte Carlo Simulation and Bayesian Inference of Rebound Effectses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorRodriguez-Sanchez, Arnoldo Eluzaim
AutorTello-Leal, Edgar
AutorMacías-Hernández, Bárbara A.
AutorHernandez-Resendiz, Jaciel David
AutorRodriguez-Sanchez, Arnoldo Eluzaimes_ES
AutorTello-Leal, Edgares_ES
AutorMacías-Hernández, Bárbara A.es_ES
AutorHernandez-Resendiz, Jaciel Davides_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número4es_ES
Rango de páginas87es_ES
URL relacionadahttps://doi.org/10.3390/data11040087
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen11es_ES

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