## 1 Introduction
In modern society, multiple organizations often tackle wicked problems, presenting differences in values across entities that complicate decision making. Decision making uses two elements [24]: (1) data, which can be quantitative or qualitative, and (2) values, which are represented as the subjective weighting of costs and benefits of the outcome of that decision. While data may provide guidance for potential outcomes, often, the solution depends on different users' values, which often results in disagreement about the cause of and solution to the problem. WKID Innovation [25] uses the Knowledge Hierarchy [26], the PEST (Policy, Economics, Sociocultural Factors, and Technology) model [27], NASA system engineering [28], Theory of Change [29], and to direct scientific discovery that informs how people and communities act, which is henceforth defined as _solutions-oriented science_. Transitioning research to commercialization can be achieved through academic-private partnerships. Establishing effective academic-private partnerships, however, is challenging. In this paper, we provide a research-to-commercialization (R2C) model to support academic-private partnerships that develop environmental resilience technology. The R2C model aims to achieve the following: (1) systematically identify information gaps in the decision space, (2) define algorithm requirements, and (3) develop sustainable solutions for resilience to global environmental change through market viability. The model is based on a pilot project of the Deloitte Consulting and the University of Colorado Boulder (CU Boulder) Climate Innovation Collaboratory (CIC). This paper describes the outcomes achieved from the wildfire analytics project to support community wildfire preparedness, resilience, and  
Figure 1: Environmental Resilience Technology addresses societal challenges through solution development that involves cross-sector collaborations for scaling and adoptions. Specific to this manuscript, we develop a model for solutions-oriented science that provides value-added analytics. response. In the following sections, we apply our R2C model to a case study to address the wicked wildfire problem in the United States. Case Study Background: The Wicked Wildfire Problem in the United States  
For the scope of this work, we explored how data analytics could support megafire mitigation, resilience, and response. The wicked wildfire problem refers to the fires that matter or the fires that have negative social impact [30]; these are commonly referred to by the public as \"megafires\". Megafire is a sociopolitical term and therefore has no standard unit, but in the western United States, a megafire generally refers to fires that meet some or all of the following criteria: (1) they burn more than 50,000 acres or \(\sim\)20,234 hectares [26], (2) they produce smoke affecting millions of people over regional scales [31], (3) they threaten residential homes or community structures [32; 33], and (4) they may be fast moving, creating entrapment scenarios for local residence and making suppression efforts even more difficult. Megafires result from the interactions between flammable fuels, climate, and at least one source of ignition [34]. These interactions do not share a common mechanism, so managing for megafires requires a complex understanding of multiple systems and the ability to execute complex decision making across those systems. Climate change from greenhouse gasses emitted from the burning of fossil fuels [4] results in warmer, dryer, and windier conditions that are rip for increased fire danger [35] and the likelihood of large fires in the continental United States [36; 37; 38]. Despite fuel constraints from increased fire in the future, the fire area is expected to increase [39]. Ignition rates for fires, including from lightning and people (e.g., prescribed fire, arson, overheated cars, power lines, bonfires, fireworks, etc. ), are variable.