Using Synthetic Controls to Evaluate Wildfire Policy Impacts: Evidence from Madia’s Law in Italy

Wildfire policies commonly define agency responsibility for wildfire management, but policy effectiveness is difficult to evaluate because of multiple interacting factors. Our research aims to determine (1) if synthetic control estimations can serve as a data-driven approach to assess effects of wildfire policy interventions, and (2) if the wildfire regime in Italy has been altered in response to a policy intervention (Madia’s law) that in 2017 imposed changes in the wildfire management system in most regions. Using a control pool of European countries, and with and without consideration of fire weather, we demonstrate that synthetic control estimations can be a suitable approach to model counterfactual trends in fire activity following a policy intervention. In Italy, models suggest the attribution of higher burned area and average fire size in the first year after Madia’s Law policy intervention was effective, though the effect appears to a varying degree across regions. We conclude that synthetic control estimations can form a valuable complement to expert-based assessments of wildfire policies in a range of flammable landscapes, although challenges remain due to complex interacting factors.