Innovation Capacity, New Market Opportunities, and Strengthen Competitiveness through Distributed Experimental Infrastructure

Generally, environmental RIs represent 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 (Pulwarty and Maia 2015). Yet opportunities to do so are limited and successes even less common. Often, success occurs by happenstance (see Figure 1.3a) rather than targeted consideration of the joint interests of public and private entities. Here, we change the current (low-success) paradigm of scientist-centric and initiated innovation by developing stakeholder-based needs first, then engaging scientist skillsets (Figure 1.3b). This model has been used for other public/private enterprises and knowledge transfer (e.g., medical industry, cancer research, agrochemical), but has not yet been applied for environmental/ecosystem science. Yet to advance the state of the science, more research is needed to ensure codevelopment of such integration, including lessons learned from previously successful and unsuccessful public/private collaborations; scalability in size, scope, and diversity of partners; how to plan for the extensibility of public/private integrations; how to overcome legal, political, cultural, and institutional barriers; and codevelopment of dynamic business models that can accommodate change.

The innovative merits of experimentation RI are as deep as they are broad. But the utility of integrating environmental RI with the private sector has yet to be fully realized. Academicians, modelers, data scientists, etc., recognize the importance of "exporting" their science to private industries but lack a venue to do so that is fair, equitable, and objective. Further, the opportunities to advertise advances in the science and engage the attention of private industries are limited. That is, it is not clear to either party how experimental environmental RI can be used to benefit and meet the needs of the private sector. To date, the need for this integrative imperative has been acknowledged, but the vision and opportunity to integrate science, academia, and the private sector have not occurred within the experimental environmental realm. Such integration could benefit traditional sectors such

FIGURE 1.3

Conceptual diagram of how the dynamic to engage public/private partnerships will change from the current paradigm. Academicians and researchers already often work closely (shown in dark gray), while the private sector (e.g., planners, government agencies, small-to-medium- size enterprises, and decision-makers) does not have formal established interactions (in light gray). (a) Depicts the current paradigm where much of the interaction stems from academia outward to engage private interests (represented by arrows). Efforts vary in scale and focus, with correspondence to private interest often unplanned and serendipitous. (b) Are current efforts to shift the current paradigm by researching and codeveloping the strategic dynamic that emanates from the private sector, bringing academic partners closer, and specifically targeting core functions and products desired by the private sector and deliverable by academia. We note that (c) represents a desirable sustainable model from longer-term public/private partnerships, which includes formalized interconnectivity, and joint collaborations occur within an overlapping and trusted structure.

as the agronomic economy, rural and urban planners, high-impact weather mitigation natural resource managers, environment regulation, and supporting innovative technologies. In a time of limited public funding, the factors contributing to managing these sectors are just a subset of the plethora of competing needs being weighed by planners who need to prioritize the use of scarce resources (funding and otherwise). While decision-makers may be aware of environmental/ecosystem science advances and likely benefits of inclusion in their policies or projects, they often lack the information to be able to quantify those benefits and make informed choices.

Emergent economies, for example, risk and resilience management, federated data services ("Big Data," Future Earth), and food security, also provide novel areas for innovative research and collaboration. For instance, traditional flood-risk mitigation policies tend to favor solutions that require lower capital investments. While this approach is more affordable initially, the resultant solutions—for example, resistive barriers, such as levees—often prove to be not only unsustainable but also exacerbate the consequences to society and ecosystems when failure does occur (Adger et al. 2012, Tye et al. 2015). Enhancing decision-makers' access to integrated knowledge of changing hazards, exposure, and vulnerability as well as the benefits of ecosystem

services to regional biodiversity, improved habitat protection and conservation, and potential mental and physical health benefits is an obvious step toward enhanced resilience. We define resilience as systems that recover to a stable state after a disruptive event that led to systematic failure (Tye 2015). Thus, a resilient system balances ecological, economic, and societal factors and facilitates a "graceful failure" and subsequent recovery.

There are countless position papers that call for the integration of public/ private innovation partnerships for policy and decision-making (e.g., Dilling and Lemos 2011, The Royal Society 2014, Tye et al. 2015). And while this is an imperative, we choose to take smaller, more intentional steps toward building this new economy. As such, we have identified there are a few early adopters who seem to be natural partners in the initiation and propagation of these types of public/private partnerships.

Insurance and reinsurance companies are increasingly exposed to economic impacts from weather and climate extremes (Munich Re 2015). There are a variety of reasons for these increases, including societal changes and climate variability and change. Increasingly urbanized societies in vulnerable locations and an associated loss of resiliency have contributed substantially to the trends (Donner and Rodriguez 2008, Flood and Cahoon 2011, Sweet and Marra 2014), and this will continue. Population increases also bring stresses on food and water supplies, which are more likely to collapse under climate extremes such as droughts (Vorosmarty et al. 2000, Rosegrant and Cline 2003), and the built nature of cities amplifies climate variations such as heat waves and intense rain events (Coutts et al. 2007, Rosenzweig et al. 2011).

To date, the analyses used by re/insurance companies to evaluate their exposure rely on the integration of theory-model-observations to advance prognostic capability and evaluation of exposure. Underlying assumptions are based on recent observations, current climate, and other known drivers of changes in ecological processes. These assumptions are not expected to hold true for future conditions given chronic and long-term changes in the environment (e.g., nitrogen deposition, increases in population, temperature, and CO2) and increases in the frequency and severity of weather and climatic events. The only way to glean insight into the impacts from, and likely future evolution of, these changes is through Experimental RI and integration of results with climate model output. In addition, changing the status quo from one of risk response to risk mitigation requires an integrated approach that balances the expertise of financial, social, environmental, and academic partners. Hence, codesigned and codeveloped experimental data are of benefit to re/insurance institutions to reduce portfolio exposure and advance socioecological-socioeconomic resilience through integrated land-use planning.

  • 1. Agronomy and Agro-Business: Food security is becoming more paramount with each passing day (Whitacre et al. 2010, FAO 2011, FAO et al. 2012, Chavez et al. 2015). Traditional agronomic experimental designs to test crop yield, water-use efficiency, genetics, and management techniques are a natural fit with the experimental approaches found within the RI. Traditional process-based models to manage natural resources, for example, crops, land erosion, and water use, also rely on theoretical and observation-based models to generate estimates of risk. However, the assumptions guiding these models do not always match reality and have limited opportunity to evaluate feedbacks between the agronomic and simulated output. With a focus on a different factor, the need for integrated planning to manage evolving responses to anthropogenic changes is similar to that of the re/insurance industry.
  • 2. Sensor/instrumentation companies have a history of working with academicians to advance innovation, and hence are natural partners. There is an identified and immediate need to automate the linkage between observations and data flow activities (e.g., flow of data from sensor to quality assurance and quality control algorithms) to an ensemble of high-level data products and modeling activities. This integrated workflow for environmental RIs (novel sensor design, sensor-to-final data product) is a frontier and a large opportunity for industry partnerships. But there is also a longer-term vision of likely new discoveries and future types of sensing technologies required to manage and balance natural resources, societal well-being, and science. Partnerships with Experimental RI potentially offer explicit climate change scenarios, introduction of (invasive) species, broader comparative capability, diverse modeling platforms, effect data flow, common standards, informatics, and the like, to enhance the production of results and competitive advantage.

We have also identified legal barriers to developing public/private partnerships. For instance, intellectual property rights (IPRs) and data sovereignty issues arise when working across geopolitical borders. Legal frameworks have been developed separately by the public and private communities, respectively, to manage some of these issues. There are active community forums where these discussions and frameworks are developed, for example, RDA, DataOne, OGC, Earth System Integration Partnership, and others. However, there are only a few examples where these frameworks have been codeveloped, for example, National Center for Atmospheric Research's Engineering for Extreme Climate Partnerships (ECEP, Tye et al. 2015) and some relevant University Technical and Innovation parks. The novelty lies with public/private partnerships where competitive advantage and periods of data propriety become negotiable. On one hand, public funding mandates open access to the data. On the other hand, private enterprises wish to maintain a competitive advantage, which implies data propriety. A proven compromise entails "value-added" analytics applied to the openly available

data. But the community using the research infrastructures determine the shared goals, culture, and common vision for the multidisciplinary data integration. But the advantage is really only competitive for a few years at a time, for example, 2-3 years, after which the analytics can also become public. This approach has been adopted within ECEP, whereby collaborative research is carried out between academia and the private entity and a moratorium placed on data sharing until after journal articles have been published. The result is a net win for both research and industry, facilitating scientific advances and short-term competitive advantage, and demonstrating that the private entity is at the forefront of technology. Hence, to maintain the corporate advantage and to maintain at the competitive forefront, development of new analytics becomes a long-term, sustainable partnership. This becomes a manageable task where research activities can be codeveloped and roadmapped a priori and also implies the need for new business models for both public and private enterprises to take advantage of such partnerships (Figure 1.4).

This is not to say that there are not both institutional and cultural barriers that still exist. Public/private partnerships are still nascent in the environmental sciences and require a cultural shift for implementation. Building

FIGURE 1.4

Here, we change the current (low-success) paradigm of scientist-centric and initiated innovation by developing stakeholder-based needs first, then engaging scientist skillsets (i.e., Figure 1.3). Moreover, we identify barriers to implementation, required architecture, and prototype integration into a sustainable development pathway. This model has been used for other public/ private enterprises and knowledge transfer (e.g., medical industry, cancer research, agrochemical), but has not yet been applied for environmental/ecosystem science.

cultural capital determines and disseminates the shared goals and common vision for the multidisciplinary data integration. It is about changing discipline cultures and working toward community building, which generates trust, sharing and providing data, and constructing bridges between experts in different fields. Multidisciplinary community building is a long-term effort as cultures, languages, and approaches are quite different among public and private enterprises, as well as across disciplines. Developing a shared culture does not happen overnight and requires the codevelopment of joint long-term goals and the activities to foster a cultural change, for example, trainings, joint strategic planned efforts, and building new cohorts of stakeholders and early career users. However, the end goal is one of a collaborative community that can work independently, yet balance the needs and expertise of other sectors.

 
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