There is a broad consensus in the academic and policy communities over the need to shift the focus from fire suppression to fire prevention. To inform policies that effectively promote this shift, we distinguish between prevention actions aimed at more fire-resilient landscapes and those focused on the protection of people, i.e., wildfire mitigation and adaptation (WM&A), respectively. With the goal of discussing the usefulness of this distinction and identifying local factors and external resources that promote each of those preventive actions, we developed an analysis of collective WM&A actions across 116 parishes in a wildfire-prone region in Portugal, using primary and secondary data. Two principal component analyses were used to explore relationships between variables expressing collective WM&A actions. Random forest, a machine learning technique based on multiple decision trees, was used to model how those actions are related to local factors (land use/land cover, population, institutions) and access to policy funding for wildfire prevention. Our results showed that collective mitigation and adaptation responses to wildfire are locally independent, in coherence with their distinct goals, actors involved, and institutional and policy framing. Mitigation through owners’ collaboration proved to be strongly related to policy funding (notably that exclusively addressed at mitigation), local socioeconomic dynamism, and ownership structure, whereas adaptation responses are related with leadership by local governments. Considering these differences, the incipiency of adaptation actions, and the difficulties in expanding owner’s collaboratives beyond the most favourable local conditions, we conclude that mitigation and adaptation actions are currently supported by two distinct policy domains with unequal consolidation but equally underfunded.

学术界和政策界就需要将重点从灭火转移到防火上达成广泛共识。为了为有效促进这种转变的政策提供信息，我们区分了旨在提高防火景观的预防行动和侧重于保护人员的行动，即野火缓解和适应 (WM&A)，分别。为了讨论这种区别的有用性并确定促进这些预防措施的当地因素和外部资源，我们使用主要和次要数据对葡萄牙野火易发地区 116 个教区的集体 WM&A 行动进行了分析. 两个主成分分析被用来探索表达集体 WM&A 行动的变量之间的关系。随机森林，一种基于多决策树的机器学习技术被用来模拟这些行动如何与当地因素（土地利用/土地覆盖、人口、机构）以及获得野火预防政策资金的关系。我们的结果表明，对野火的集体缓解和适应反应在当地是独立的，与其独特的目标、参与的参与者以及制度和政策框架一致。事实证明，通过所有者协作实现的减排与政策资金（特别是专门针对减排的资金）、当地社会经济活力和所有权结构密切相关，而适应响应则与地方政府的领导力相关。考虑到这些差异，适应行动的初期，