A Synthetic Dataset for Decarbonization Policy: Integrating Monte Carlo Simulations and Rebound Effects

We argue that a data-driven, probabilistic framework that combines Monte Carlo (MC) simulations and Bayesian Networks (BN) is crucial for assessing the vulnerability of "win-win" measures and for establishing a more robust basis for climate policy in Smart Cities.

This dataset includes a comprehensive set of technical, economic, and environmental variables for nine urban mitigation strategies across the residential, commercial, and transportation sectors. The data were collected using a bottom-up approach that combined documented scientific literature with real-world urban case studies. These inputs were analyzed with a probabilistic engine to produce empirical distributions of the Marginal Abatement Cost (MAC) and other performance indicators, replacing static point estimates with a multivariate probabilistic knowledge structure.