## 4 Discussion  
### R2C Model for Co-Production of Sustainable Solutions in Environmental Resilience Technology  
Environmental resilience technology offers great promise for shifting science from observing environmental change toward providing solutions that serve society. An important step is creating _analytics of value_ using, for example, machine learning and artificial intelligence [59]. Moreover, cutting-edge artificial intelligence and data fusion can lead to important and robust predictions about our possible environmental future [60; 61]. There is a growing call to leverage science to better inform market solutions [10; 62]. The R2C model, demonstrated in this paper, reflects the integration of public-private capabilities [48; 63]. The R2C model integrates the WKID Innovation framework [25] with HCD, maps the information gaps across multiple user types and identifies the greatest gain while developing market-driven solutions. WKID Innovation provides an in-depth systematic analysis of the decision space to inform _solutions-oriented science_. Research on its own, however, does not precipitate sustainable solutions. Sustainable solutions consider a funding model beyond preliminary research or development [12]. Designing and clearly stating the requirements for how research fits into the decision space at the outset ensures an off-ramp from research to application [64] but does not mitigate risks for sustaining operations [65]. By integrating WKID Innovation with HCD and market research, we leverage the strengths of each framework to mitigate risks to technology adoption. Arguably, R2C offers a model to scale use-inspired translational research by environmental science domain experts [66] and drive tomorrow's technologies and solutions. R2C sits in the research-to-commercial taxonomy of \"contract research and consultancy\". R2C integrates traditional agency knowledge of translational research and development (e.g., WKID Innovation) with resource-based methods motivated by understanding organizational needs (e.g., HCD) [67; 68]. By leveraging HCD, R2C is similar to other models proposed for co-production [69] that rely on iterative feedback from users, but it does provide more top-down complex systems analysis to overcome HCD limitations [19; 20; 21]. While translational ecology [70] links ecological knowledge to decision making for use-inspired research [66] and real world outcomes [71], R2C uses solutions-oriented science to co-develop technologies providing analytics of value. While co-production models [72; 73] and translational ecology [70; 71; 74] offer mechanisms to build cross-sector partnerships with technology users, they focus on an end-user uptake of information rather than the potential markets to sustain solutions. Translational ecology and co-production models do, however, acknowledge the importance of building workforce with skills beyond academia [74]. While there is a long history of research-to-market pathways for engineering [75], medicine and pharmaceuticals [76], computer science [77], and biology [78], there is a gap for ecology and environmental sciences. The incredible wealth of environmental data from satellite sensors, social media platforms, government records, and other data sources offer remarkable opportunities for market-driven solutions to complex environmental challenges. For example, carbon markets offer a means for offsetting fossil fuel emissions by sequestering carbon [79] or reparative finance for water security [80]. Some companies are starting to use data to verify and validate credits that support improved natural resource management, but there is a need for such analytics to be trustworthy--i.e., transparent, consistent, and secure (Table 2). Co-production between industry and scientific research in academia offers a foundation for developing trustworthy information. Co-production that merges academia with industry requires navigating differences in culture and institutional practices.