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

This research hypothesizes that traditional, deterministic Marginal Abatement Cost Curves (MACC) consistently overestimate the mitigation potential of urban decarbonization strategies by ignoring parametric uncertainty and behavioral feedback, particularly the rebound effect. 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.