Science and Technology Networks: A Helping Hand to Boost Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030?

The Sendai Framework for Disaster Risk Reduction 2015–2030 calls for a holistic approach that generates and utilizes knowledge across sectors and disciplines (UNISDR 2015). It is evident that in times of increased uncertainty caused by climatic, environmental, socioeconomic, demographic, and political pressures, disaster risk reduction (DRR) at all levels is imperative. Science and Technology (S&T) availability is rapidly expanding and its use in DRR is an increasing trend (for example, the use of geoinformatics for participatory hazard and vulnerability mapping). But much work is needed to transform this technology and scientific information into societal contexts. Under the term of science, this article uses the Organisation for Economic Co-operation and Development (OECD) (2007) classification of science that includes natural sciences, engineering and technology, medical and health sciences, agricultural sciences, social sciences, and humanities. S&T is increasingly recognized as a means of providing evidence for effective DRR strategies, policy development, and decision making (Carabine 2015; UNISDR 2015; Dickinson et al. 2016). The Sendai Framework refers to the term technology 19 times, whereas its predecessor, the Hyogo Framework for Action 2005–2015, mentioned the term three times (UNITAR 2015). The Sendai Framework also recommends that S&T communities should be mobilized through the coordination of existing networks and scientific research institutions at all levels and in all regions, with the support of the UNISDR Science and Technology Advisory Group (UNISDR 2015, Paragraph 25g). Ultimately, such partnerships and networks help create an evidence base and facilitate practical implementation of measures that can result in reducing disaster risks and losses to lives, and as such contribute to the implementation of the Sendai Framework (UNISDR 2015).

A further stepping stone for the role of S&T networks in DRR was the UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. This assembly was held in January 2016 and resulted in the UNISDR S&T Roadmap to 2030 as well as the UNISDR S&T Partnership (UNISDR 2016). The conference underlined the value of partnerships and networks for initiating multidisciplinary research agendas and fostering the linkage of science, policy, and practice (Aitsi-Selmi et al. 2016; Dickinson et al. 2016). One of the outcomes of the conference was the creation of the United Nations Major Group for Children and Youth (UNMGCY) Young Scientists Platform (YSP) on DRR. The YSP provides a space for professional development, knowledge sharing, and research dissemination among young scientists, as well as S&T and policy actors (UNMGCY 2015).

It is evident that both the Sendai Framework and the post-Sendai era value S&T networks. The knowledge and evaluation of existing networks and their role in DRR, however, remains limited (Aitsi-Selmi et al. 2016). We support the principle that networks are a powerful means of facilitating the implementation of the Sendai Framework, and hypothesize that the lack of literature on the role of S&T networks in DRR is not owing to a lack of networks, but is a consequence of the challenge of capturing and documenting the effectiveness of networks and associated learning. The effectiveness of networks remains emphasized and encouraged in rhetoric, unofficial scientific communication, and post-2015 agendas. But existing knowledge does not offer an evidence base of how S&T networks for DRR can contribute to implementation of the Sendai Framework (Carabine 2015). Yet a vast amount of literature from other fields (for example, governance, global policy networks, international science partnerships) discusses benefits and challenges of network approaches for solving pressing issues of modern society. We maintain that learning from other fields can benefit existing and emerging networks, partnerships, and platforms in the field of DRR, and can enhance development and sharing of the best S&T practices. Our opinion piece hopes to initiate discussion and calls for further research on the topic. It is motivated by the authors’ direct or indirect involvement in S&T networks including the YSP. The goal of this short opinion article is to serve as a foundation for increased debate and research into the role of S&T networks, which would in turn provide much needed guidance for existing and emerging networks.

The terms partnerships, networks, and platforms are often ambiguous and used interchangeably (Assens and Courie Lemeur 2016). A common ground between networks is the desire to foster different levels of collaboration, cooperation, and coordination (Himmelman 1996). We use the term “networks” as the least formal entity with indistinct boundaries (Vilaplana 1998). Networks are defined as “an interconnected group of people linked to one another in a way that makes them capable of beneficial collaboration” (King 2011, p. 376). They “arise from and are sustained by the relationships between individuals over some shared concern, belief or value” (Lowndes et al. 1997 cited in Vilaplana 1998, p. 6). In relation to DRR, networks not only bring people together, but also support the organizations and infrastructure that enable actionable research (for example, the Clinical Research Network in the UK). The term network implies more than the sum of the actors and their links—networks are forms of social organization (Provan and Kenis 2008). Thus networks are holistic in nature.

There are many examples of established S&T networks, partnerships, and platforms for DRR. Examples of global partnerships include the Global Volcano Model Network,¹ the Global Flood Partnership,² the Global Alliance of Disaster Research Institutes,³ the International Network for Government Science Advice,⁴ the Young Hydrologic Society,⁵ and even national networks such as the UK Alliance for Disaster Research.⁶ It is beyond the scope of this opinion piece to map out the different types of current S&T networks for DRR. Our focus is instead upon understanding the value that networks may have and encouraging deeper insights into the role of S&T networks for DRR. This mapping exercise and the development of a comprehensive database of S&T networks for DRR needs to be done in order to evaluate existing S&T networks in DRR and guide their future development.

While examining existing international science “partnerships” in DRR (for example, the International Decade for Disaster Risk Reduction), Carabine (2015) concludes that it is crucial to improve the existing evidence base and use of S&T, especially when taking on board and coordinating a variety of stakeholders. The main contribution of the international science partnerships to DRR is through creating scientific evidence and promoting the use of S&T for creation of evidence-based policy. We propose that a parallel can be drawn with the potential role of S&T networks in DRR, especially at the science-policy interface. By acknowledging the complex nature of DRR, the Sendai Framework recognizes the need for collaborative action between key stakeholders (including policymakers, practitioners, and the S&T community) from all regions and levels. Their collaboration will “identify knowledge gaps, co-design and co-produce knowledge, and make science more available and accessible to support DRR decision making on the ground” (Aitsi-Selmi et al. 2016, p. 4). In this sense, effective S&T networks for DRR are inherently transdisciplinary.

The contribution of S&T networks for DRR can go beyond mere informing of evidence-based policy. For instance, the collaboration between different S&T networks can assist the implementation of the Sendai Framework, particularly by supporting monitoring and review of the Framework’s targets (United Nations General Assembly 2016). S&T networks can provide knowledge and resources to address the gaps identified in the assessment and monitoring of the Hyogo Framework 2005–2015, “which was generally admitted to be too weak and based on self-reporting or voluntary, self-initiated peer review” (Mysiak et al. 2015, p. 2192). With the power to integrate climate change policy, DRR, and poverty reduction, S&T networks also have the potential to work across the implementation of multisectoral international agendas such as the Sendai Framework, the Sustainable Development Goals,⁷ and the COP21 Agreement on Climate Change.⁸ Providing a shared platform of knowledge on disaster risk and mitigation can enhance the international commitment towards collaborative risk management plans.

To the best of our knowledge, there is a lack of published literature on the role, potential, and position of S&T networks in the field of DRR. We propose that transferring lessons and associated learning from other fields can offer invaluable lessons for S&T networks for DRR. Such cross-fertilization can ultimately enhance a needed holistic approach for the implementation of the Sendai Framework. In order to gain an initial insight into the benefits of a network approach and the common challenges experienced, we conducted a literature review. It is important to emphasize that due to the lack of available literature on the role of S&T networks in the context of DRR, the benefits and challenges of networks discussed in this review are based on the network and partnership literature in the wider context. However we acknowledge that these characteristics are potentially transferable to S&T networks for DRR.

Formulating, implementing, and managing any form of a network comes with challenges that can hinder that network’s effectiveness. Table 1 demonstrates that networks should be aware of endogenous challenges, such as members leaving or reducing their degree of participation (for several reasons: for example, lack of internal legitimacy of the network, or the network is not living up to their expectations), and exogenous challenges, such as political pressure, or losing flexibility and becoming institutionalized (Provan and Kenis 2008). The nuance absent from this overview, however, is that many challenges present themselves in a mix of internal and external factors that compound to decrease network performance (Provan and Kenis 2008). One example of such a complex mixed challenge is funding, because funding is heavily dependent on its sources (whether internal or external) and the wider context of social, political, and economic disruption of the organization and its surrounding system.