The Decision Trees Method to Support the Choice of Economic Evaluation Procedure: The Case of Protection Forests

Abstract

The adoption of nature-based solutions, such as forests, is playing an increasingly important role in risk analysis and related decision-making. However, decision-makers struggle to put a value on the services provided by these solutions, as there is no reference market, and are thus faced with several challenges, which relate to the choice of the best forest management program or the interventions needed to make a forest resistant and resilient to the expected negative impacts of ongoing climate change. In this article, we started with an exploratory analysis to identify the key factors in the choice of an economic method to build predictive models to support the choice in an evaluation of the forest protection service against natural hazards. The exploratory analysis showed that non-demand-based methods have a good degree of replicability and reliability and are cheaper, whereas stated preference methods can estimate the intangible component. Concerning predictive models, almost all methods showed a high level of correct classification (95%), apart from the avoided damages method (90%) and, more generally, there is no method that is valid for all operational contexts but rather the choice changes depend on the demands made by the stakeholders and their availability in economic, human, and technological terms. In conclusion, it should be remembered that the methodological framework chosen should not be seen as a substitute for the human ability to analyze complex situations but rather as an aid to this process. Study Implications: The adoption of decision support systems and methodological frameworks and guidelines can help decision-makers to make the most effective and efficient choices, in terms of time needed, resources used, and intervention costs. The combination of this decision support system with other tools, such as frameworks and guidelines, provides a flexible support system aimed at improving the design and implementation of future ecosystem service assessments and management as well as related decision-making.