A multilevel analysis of the perception and behavior of Europeans regarding climate change

Abstract

This study aims to identify the influence of management plans, management boards and tourism management on the relationship between performance indicators in the management of protected areas and degradation processes. To understand these relationships, 283 protected areas (PAs) in Brazil were analyzed. The first stage of the research used classification models based on machine learning algorithms, which revealed that predictive variables were a promising way to assess PAs vulnerability to deforestation and wildfire, giving decision-makers an 87.5% and 72.8% chance, respectively, of correctly identifying the PAs more susceptible to these threats. The predictive variables more relevant to deforestation were biome, area, and tourism management, while for wildfire, governance and PA type were the most relevant. Predictive variables were also a promising way to assess PAs management, giving decision-makers a 79.7% and 78.1% chance, respectively, to correctly identify the PAs with higher levels of effectiveness and governance. In addition, in the second stage, to empirically reinforce the models, multivariate analyses were performed, through which it was possible to confirm that deforestation levels are significantly higher in areas of sustainable use than in fully protected areas and determine how the positive interaction with tourism management contributes to the reduction in deforestation records and improves effectiveness. Therefore, it is understood that tourism management can strongly influence the sustainability of natural resources, and it is of utmost importance to generate tourism management policies with potential value generation for natural spaces.