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Abstract Private lands are important for conservation worldwide, but knowledge about their effectiveness is still insufficient. To help fill this important knowledge gap, we analyzed the impacts of a national policy for conservation on private lands in Brazil, a global biodiversity hotspot with high potential for nature-based climate solutions. Through the evaluation of over 4 million private rural properties from the Rural Environmental Cadastre, we found that the last policy review in 2012 mainly affected the Amazon Forest. The amnesty granted to 80% of landowners of small properties prevented the restoration of 14.6 million hectares of agricultural land with a carbon sequestration potential of 2.4 gigatonnes. We found that private lands exist within the limits of public conservation areas and that between 2003 and 2020 deforestation rates in these private lands were higher than those across all conservation areas. The Rural Environmental Cadastre can be an effective tool for managing forests within private lands, with potential to integrate governance approaches to control deforestation and mitigate climate change.

The world has entered the United Nations Decade on Ecosystem Restoration (2021–2030), yet many regions of the world still face environmental degradation. In this context a question arises: under what conditions may a given region shift from a trajectory of environmental degradation to environmental recovery? Answering this question constitutes an important endeavor for the scientific community, policymakers, and organizations leading the planning and implementation of restoration projects. This study examines some of the human-environment conditions underpinning the net gains in natural forest cover in a region that has experienced environmental degradation: the Atlantic Forest biome, Brazil. Using land-use/cover (LULC) data, we assessed the loss and successive gain in forest cover during the 1987–2001 and 2001–2015 periods. Municipality-level statistics on agriculture and economy, together with LULC and biophysical data, were used to develop models for assessing forest cover trajectories. Of the 1,972 municipalities experiencing net forest loss during the 1987–2001 period, 59% shifted their trajectory to a net gain during the 2001–2015 period. This shift, known as forest transition, followed a complex socio-economic pathway characterized by (i) the stagnation of traditional agricultural activities favoring the replacement of pasturelands by mechanized agriculture, and (ii) the emergence of non-agricultural rural activities together with multifunctional landscapes. Furthermore, a trend of decrease in precipitation seems to be associated with land abandonment, thus, favoring the return of natural vegetation. Our findings suggest that forest transition at the biome level is possible if environmental and land regulations take advantage of specific context-dependent situations that foster net forest gains.

Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.