Evaluating wildfire policy impacts using synthetic controls: a data-driven assessment in Italy

Background: Wildfire policy effectiveness is difficult to evaluate, because fire activity depends on multiple interacting factors. Aims: We conduct a data analysis study to determine (1) if synthetic control estimations are a suitable methodology for detecting the effect of wildfire policy interventions, and (2) if shifts in the wildfire regime in Italy can be attributed to the ‘Madia’s law’ policy intervention that in 2017 changed agency responsibility for firefighting in most regions. Methods: We use synthetic controls with and without consideration of fire weather to model fire activity in aggregated and individual regions in Italy that were subject to the policy intervention, using a control pool of European countries. Key results: Synthetic control is demonstrated as a suitable approach to model counterfactual trends in fire activity after a policy intervention. In the case of Madia’s law, models support the attribution of higher burned area and average fire size to the policy intervention in the first year after its implementation, though this effect appears to a varying extent across regions. Conclusions: Data-driven approaches offer insights into policy effectiveness, but challenges remain owing to complex interacting factors. Implications: Synthetic controls can complement expert-based assessments of wildfire policies in a range of flammable landscapes.