The economic exploitation of ecosystem services now threatens our food security, the stability of natural and managed ecosystems, and global biodiversity. Environmental Research Infrastructures (ERI) have emerged to fill important gaps in our understanding of ecological processes, in response to the drivers of change the likes of climate, extreme climate events, invasive species, and land use change (Table 1, Heinz Foundation, 2006, 2008; Peters et al., 2014). While ERIs are designed to fill such gaps, their potential to transform other sectors of the economy and decision-making patterns remains largely untapped worldwide. Moreover, meeting societal needs in a changing environmental landscape provides a new context for the private sector to explore emerging business opportunities. Academicians (who utilize ERIs) and the private sector increasingly recognize they may need to move toward less conventional partnerships to address societal needs, and to justify ERIs operational budgets (Buhmann, 2016). Because these are still nascent ERIs, emerging economies and the corresponding opportunity are also still novel, and the means to establish public/private partnerships likewise requires creative models of engagement.

ERIs were established to address unprecedented ecological research questions, such as, the deep-rooted causes and equally far-reaching consequences of the rising threats to global ecosystems (Soranno and Schimel, 2014). They now have a unique opportunity to contribute to new economies, natural resource management, and meet the needs of private enterprises. ERIs, however, are structurally more akin to university and large research structures. Likewise, the private sector and corporate structures function quite differently from ERIs, universities, and public enterprises. For instance, the ability for ERIs to collect data, challenge our current knowledge and provide (model) analytics would allow them to effectively forecast ecological states, i.e., decision spaces (Schimel and Keller, 2015; Chabbi et al., 2017; Loescher et al., 2017). The financial and intellectual capital brought to bear in this example typically cannot be afforded by the private sector. Likewise, the internal science scope and fiduciary responsibilities of ERIs do not necessarily allow for the development of market-driven endeavors. The accuracy, precision, and timescales to deliver research data (seasons to years) to academicians differ from the near real-time need for actionable data capable of yielding a competitive advantage to the private sector. Hence, ERIs, private enterprises, and other decision-makers must embrace a creative frontier in the variety of innovative partnerships models, identify and overcome barriers to work together and achieve sustainable new economies. Here, we explore these frontiers, barriers, and present actionable solutions to link ERIs, private enterprises and other decision-makers toward developing new economies, e.g., integrated information services for real-time environmental management across a wide market-sector of research, State and Local governments, natural resource managers, and other novel businesses.

To date, the scientific knowledge derived from ERIs typically makes its way into scientific publications by researchers exploring basic and applied science. This flow of knowledge-based systems is a common paradigm for funding agencies (Stocker, 2017), where the justification of ERIs operations directly supports scientific research and indirectly, the economics of research institutions, e.g., universities, agencies, and ministries. In today's current political environment, there are implicit and explicit mandates to enhance the public resources spent on science for societal relevance, value-added efforts and innovative types of engagement with the private sector to create new economies and jobs (PCSAT, 2011; Chabbi et al., 2017). It may stand to reason that new value created by leveraging scientific expertise to co-develop new knowledge and services may be beneficial to the private sector. This results in a fundamental difference in funding mechanisms (public vs. corporate finance) and motivation (advancement of knowledge vs. competitive advantage and profit). In other words, few within the private sector would consider a direct investment in research solely based on publication performance (Lin and Bozeman, 2006). A business model that is likely to bring these two disparate communities together has yet to be established and with it, the necessary common language to bridge these communities. Indeed, capturing the value added of scientific research through metrics traditionally found in business models and adjacent marketing strategies is difficult (Ehret et al., 2013).

The timelines that correspond to funding and project lifecycles are different for scientists and entrepreneurs. Public funding mechanisms typically invest in research projects within a 1–5 year timeframe, whereas entrepreneurial projects often develop over much longer periods of time. There are cultural barriers in the different ways these partners think about their roles and responsibilities in reaching innovation milestones, e.g., researchers forgo their academic calling to advance frontier science, or be unclear as to their advisory/leadership role in relations to their corporate counterparts. On the other hand, entrepreneurs may struggle with the notion that “better” is often the enemy of “good” (Rodrigo et al., 2013). The lack of a formalized structure or process to bring these communities together around innovational ideas creates a disjunct relation at best and a disincentive at worse.

In an effort to bridge this gap, governments have encouraged universities to become innovation incubators in association with industries, through public incentives for cross-sector technological transfer and public/private partnerships (Perkmann and Walsh, 2004). Moreover, capital investments return to the universities in a business model which has proven fruitful (Youtie and Shapira, 2008). Universities provide the environment, the structure and functions needed to foster innovation and start-up companies. Innovations typically come from within the university faculty or student body. The core functions they support include legal support; management of intellectual property rights (IPR); physical development space; access to engineering, design, manufacturing and marketing expertise, market analyses, access to venture capital (VC), and ongoing mentorship in the development process (Figure 1). In some instances, the private sector can also partner with universities to tap into promising scientific advances and universities' developmental concepts, which cannot be generated in-house. Through these types of business partnerships, universities can also access other means of capital that would otherwise not be available, e.g., seed funds, other VC and access to external “private” datasets (Bozeman and Gaughan, 2007). This “non-zero sum game” continues to provide unexpected advantages and opportunities to all parties involved, e.g., new innovations/products capable of assisting decision-makers, anticipating regulatory pressures, and identify additional public funds (Boardman and Corley, 2008). A key strength of this university model is that it can support a large range of innovation projects with needs and markets yet to be determined.

Examples of university-based investments in such a model are numerous. As such, the capabilities of their core functions have limits. Universities cannot incorporate all ideas and innovations through their processes and often rely on an application process by way to prioritize partners/ideas most likely to succeed, and make periodic evaluations to determine the ongoing project validity and manage the corresponding risk portfolio. While this proves a sustainable model, it also results in key limitation in scaling such functions beyond what is fiscally (cost-return structure) and physically feasible in such a setting (Figure 1).

An alternative model is to develop a cyber infrastructure, analytical tools, and adaptive capacity, to augment new capabilities based on the ongoing needs of the client base (private sector; Figure 2). This value-added model is more service-oriented, targeted at decision-making and utilizes open data sources. Key to this models' success is the use of synthesis centers to bring together diverse stakeholder communities (e.g., researchers, educators, decision-makers, managers, agencies/ministries, corporations, and entrepreneurs) and distill the needs and capabilities of a novel project. This business model is also based on the notion that analytics for decision-making are most effective and critical within a 2–3-year planning window. This is true for governments and municipalities charged with managing public economics and growth models, but also for private enterprises seeking to maintain their competitive advantage. This helps develop a clear, concise, and constrained project definition (use case) that can be managed and articulated into contractual form. It also identifies the needs, skillsets, and level-of-effort needed to complete the deliverable(s) required by the client. The needs for each (private) client will be different and a tailored approach is required.

Developing a competitive advantage in natural resource management (supply chain) is only effective within a 2–3-year planning window (Christopher and Peck, 2004; Tietenberg and Lewis, 2012). Moreover, the nuance with public environmental data (and ERIs) is that they provide data that is free and openly available to research and educational communities. While advanced applied analytics for private enterprises are not included in this model, there is a social imperative in making these products available to the public at some point in time, when competitive advantage is no longer a contractual requirement (after the above-mentioned 2–3-year window). A 2–3-year competitive advantage, if successful, provides the opportunity for private and public enterprises to return to the partnership and develop the next analytic that can generate their next 2–3-year window of competitive advantage. This model has been proven successful by public/private partnerships in the National Center for Atmospheric Research; Extreme Climate Engineering Partnerships (www.ecep.ucar.edu; Figure 2).

Generally, environmental open-data represents an untapped resource for innovation worldwide. The need to better integrate large-scale “Big Data” science into the private sector is articulated as a “must do” by stakeholders, governments, and the public at large (Pulwarty and Maia, 2015). Yet, opportunities to do so are limited and successes even less common. Often, success occurs by happenstance rather than targeted consideration of the joint interests of public and private entities (see Figure 1.3 in Chabbi et al., 2017). In this instance, changing the current (low success) paradigm of scientist-initiated innovation lies in developing the stakeholder-based needs first, then engaging in development pathways. One of the roles of ERIs is therefore to create the forum to change the current paradigm of how public and private enterprises interact, in order to facilitate innovation (Chabbi et al., 2017; Specht, 2017).

There are clear strengths and weaknesses with both the university-based and value-added models for academia and private sector to partner. A potential middle-ground model might be a more holistic partnership with a public-private enterprise that incorporates all the functional attributes of both models and is directed toward key environmental markets. The scalability issue in the university model can be addressed by federating existing physical capabilities from ERIs, universities, private enterprises, and synthesis centers. Providing a sustainable structure to bring together innovators and academicians will be instrumental in generating successful outcomes and effectively modifying the current paradigm (see above). Pilot projects are needed to provide proof-of-concept and lessons learned in an adaptive structure. Training, joint strategic planning, and stakeholder engagement efforts will play an essential role in bridging gaps in cultures, languages and approaches—and ultimately foster innovation.

In all models, there is a persistent issue in managing the IPR and data sovereignty (DS) challenges that inevitably arise from working across geopolitical borders and other public and private protection frameworks. The mandate for open-source data is required of all governmentally-funded projects, i.e., the European Commission, US National Science Foundation, US Department of Energy, etc. While managing IPR and DS are not novel, new specific challenges arise in relation to environmental sciences. For example, DS issues may arise when developing new ventures in crop futures, food security and or in incentives for shifting agronomic economics that have competitive implications across borders and governments (Teece, 2010). There are nevertheless encouraging approaches to address IPR and DS and open-data access mandates, grounded in the delivery of added-value analytics, decision-space tools and federated web services tailored to the private client needs—though this will have to be further developed through use cases and prototype activities.

It is difficult for traditional grant structures based on the research capacity of principle investigators (PI) to provide consistent, long-term, quality-controlled, multi-dimensional data designed to provide data for services, and decision-making. This is particularly true as most PI-based research is targeted for their specific hypotheses, and funding horizons are typically 1–5 years. Nevertheless, ERIs stand to provide novel solutions to foster long-term sustainability in designing deliverables for commercialization. Because of the large public expenditures for ERIs (often > $200 M US investments), they are subject to stringent governmental oversight. This in turn requires the use of formal project management, system engineering, and corporate planning tools (Loescher et al., 2017).

These corporate planning tools help ERIs define science scope, budget, identify and mitigate risk, resource load, define internal roles and responsibilities, integrated resource loaded schedules and ultimately, provide reporting metrics to their sponsors (Lozano et al., 2016). Moving from PI-based hypothesis testing to a requirements-based framework is novel for the academic environmental community (Loescher et al., 2017). This approach paves the way in to justify and delineate the scope of large governmental projects. The potential of large-scale, integrated science requirements are however, only now being acknowledged outside the ERI framework, which has large implications for commercialization. For example, ERI's can clearly provide the physical and cyber-infrastructure interfaces to ingest real-time data and its associated metadata.

Other planning tools currently used by ERIs include classic SWOT analyses (Hill and Westbrook, 1997; Humphrey, 2005), stakeholder engagement (Herremans et al., 2016), sigma six (Tennat, 2001), PmBok (PMI, 2013), verification and validation engineering, commissioning processes, and the like (Bartocci and Picciaia, 2013). There are often heated debates on the types of software and specific reporting analytics used in project management, but the corresponding variance is approximatively ~10% among these tools (T. Beasley, pers. commun., 2009). These tools accomplish a “linearization” of the inherently “non-linear” and time-variant problems that face all project development at such a large scale. These planning tools provide the scope, the institutional health metrics, human resource planning, resources prioritization, market viability (Calabrese et al., 2016), key performance indicators, and organizational goals (Maas et al., 2016) necessary for ERIs to make the case to private entities, for them to integrate their relevance and eventually engage and develop collaborative planning horizons (Braama et al., 2016).

Research quality data relies on mean trend estimates relative to the processes in question (the signal) and variance measures (potential noise) in order to perform more advanced analytics. The general public often looks toward science to provide answers to environmental problems (Miller et al., 2014). Yet providing uncertainty estimates intended for decision-makers differs in approach to the classic production of statistics to test scientific hypotheses (JCGM, 2008). ERIs provide statistically and “International Standards Organization (ISO)” defensible measures of uncertainty (Taylor and Loescher, 2013; rf. Csavina et al., 2017). But the lack of statistical approaches capable of underpinning a range of probabilities given a specific decision about natural resource management that has to be made today. Important to note, that it is also imperative to attribute to causality, not just trend and variance estimates, and not to confuse a cause and a consequence of a particular process of interest. This is a particularly salient for any service-oriented collaboration that utilizes ERI data. The crux of this issue lies with the ability of private entities to articulate their constraints to address a specific problem—and the level of uncertainty needed to design a valuable commercial product, and in terms that scientists can understand. On the other hand, scientists struggle in communicating “what uncertainty means” in layman terms and are challenged to provide new statistical approaches to meet the growing societal demand for actionable results.

The question of bringing together ERIs and the private sector in an entrepreneurial venture is not novel. However, both ERIs and individual private entities must be mature enough in their approach toward engagement with the other partner, and have the appropriate skillsets and vocabulary (Chabbi et al., 2017; for a list of ERIs, see Table 1, and Tables 1–3 in Peters et al., 2014). Road mapping activities have identified early adopters presenting the skillsets likely to facilitate joint ventures (Table 2). Additional unforeseen and serendipitous opportunities may well arise as a result.

Overcoming the current challenges to bridge science and private enterprises are underpinned by the strong sense of social responsibility of all parties to co-develop strategic products and services. The frontier to tackle future environmental problems as well as the needed structure between public and private enterprise is unknown, and requires creative solutions. At the crux of this issue lies the shared responsibilities in demonstrating how advanced environmental analytics identify and structure priorities in organizational changes toward long-term, sustainable value to society at large. Secondary to the strong societal imperative to tackle these issues, are also the cultural barriers that have to be overcome to link public and private enterprises together. Successful programmatic tools and organizational models do exist to help overcome these barriers that should be part of the explicit planning for any specific project. Each innovation project is different, and as such, require unique solutions. Yet we highlight some common issues external and internal, that have to be addressed and cognizant of in the development of public/private enterprises. Here, we have provided the current scientific, commercial, and political landscape by which these solutions will develop, and we have outlined some, creative, and successful models for such endeavors.

We would also contend that knowledge-based economies should bring together all the actors involved (broad stakeholder involvement) are more likely to harness the diversity of such wide-ranging challenges. At the same time, such collaborations cannot be everything to everyone, and each specific collaborative project should be well constrained and scoped. The benefit for ERI lies principally in building upon publically-funded, bottom-up science, whereas the added valued of the use of public funds in developing market-driven solutions also builds economic resilience (rather than the detrimental impacts of environmental change alone).

AC initiated the writing of the paper and led the work. HL contributed substantially with the overall concept, ideas, and writing of the paper. MD participated in the writing of the paper.