Illustration of a forest ecosystem within a network of data-driven technologies, created with Canva’s AI image generator. Using AI to generate this illustration demonstrates that digital innovations are not merely technical tools but active forces in shaping the ways of visualising and understanding socio-environmental realities.

By Danilo Urzedo, Nikki P. Dumbrell, and Catherine J. Robinson.

Read the full paper here.

Data-driven technologies are being applied to tackle global environmental degradation and biodiversity loss. While big data and models offer new solutions to inform conservation decisions, actions, and evaluations, these techniques also present limitations like biases, errors, and uncertainties. In this research, we assess how data influence the establishment and measurement of conservation success.

We focused on two major carbon programs: Australia’s Land Restoration Fund and Brazil’s Amazon Fund. These funds seek to reduce carbon emissions through different land and vegetation management strategies that also deliver biodiversity and social benefits. In this study, we show how data-driven approaches adopted for both carbon programs determine what aspects of social and environmental impacts are valued, how they are measured, and whose interests are considered.

In Australia, the Land Restoration Fund uses ecological and socio-economic data to invest in projects that restore land and benefit local communities. In Brazil, the Amazon Fund employs social and environmental indicators to evaluate the outcomes of sustainable forest management projects at the local level. Based on these experiences, we introduce the concept of “conservation data infrastructures” to illustrate how data-driven solutions involve not just technical issues but also complex interactions between policies, people, and cultures that influence decisions.

Through this study, we identified that simply relying on technological innovation is not enough to bring about transformative conservation decisions. While technologies are changing carbon accounting methods by including biodiversity and social benefits, these changes do not automatically lead to inclusive and participatory results. These data innovations should be accompanied by the consideration of diverse viewpoints and local knowledge, based on dialogue and collaboration between conservation researchers, technology specialists, policymakers, and communities.

By reimagining how to plan, collect, and use conservation data, administrators of conservation programs can rethink how their strategies direct investments and assess projects to address the complexity of environmental and social challenges.