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Co-production is an increasingly popular approach for environmental and sustainability research, but what is actually produced through its practice remains understudied. This paper reviews recent examples of co-produced research alongside current theorization on the topic. Focusing on the area of climate change adaptation, we find that co-produced climate change adaptation research appears to be improving knowledge use, among other positive outcomes, but a difference emerges between the range of outcomes reported in practice and the scope of ambition conceived through theory. This raises important questions about how the practice of knowledge co-production should be evaluated and, fundamentally, what we should expect to produce through co-production. We argue that understanding and reconciling the transformative potential of science-practice collaborations within the context of the incremental progress achieved through its current practice will catalyze a more integrated and actionable scholarship and practice.

Researchers, stakeholders and funding organizations have embraced co-production of knowledge to solve sustainability problems. Research focusing on the practice of co-production can help us understand what works in what contexts and how to avoid potentially undesirable outcomes.

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution. [ABSTRACT FROM AUTHOR]