Monitoring socioecological impacts of policy interventions aimed at changing land-use practices is a major challenge in sustainable development and conservation. Reducing emissions from deforestation and forest degradation (REDD+) intends to compensate local stakeholders for demonstrated carbon emission reduction and increased removals accounted for internationally, while promoting social and environmental benefits locally. Thus, monitoring REDD+ inherently requires the use of interdisciplinary data at different scales. Forest carbon monitoring, central to REDD+, is considerably advanced, yet the progress on social and environmental monitoring systems is uneven. We argue that scalar and interdisciplinary integration of REDD+ monitoring is crucial to uncover and understand trade-offs and synergies on which effectiveness, efficiency and equity of REDD+ may depend.
We review previous efforts in integrating environmental and social monitoring, as well as efforts specific to REDD+, and discuss how old and new knowledge can contribute towards integrated monitoring. We observe that there are many challenges, but strong advantages, in an integrated monitoring approach. The current emergence of diverging standards and methodologies with narrow focus can inform future integrative efforts but could, in the long run, hinder coherence in national processes. We conclude that recent technological advances open new opportunities to integrate information across scale and disciplines, leveraging and combining existing data with targeted additional measures. The application of mixed methods in data collection can foster integration, in particular from the local level upwards. However, this requires greater coordination at higher levels to efficiently upscale multiple data streams. The unequal standpoint of carbon, social and environmental monitoring efforts provide a timely opportunity to promote integration, learn from advances in carbon monitoring, and build on existing and emerging platforms and tools that are locally to globally relevant.
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