Part 1: Adaptation science has a big data, disparate theory problem

For background on this 3-part series, see the introductory post “Many Cool Heads for a Hot and Unequal World: Reflections on adaptation research and the social sciences“ which reflects on a Global Environmental Change editorial by Arun Agrawal and others. Here is Part 2; Part 3; Conclusion.

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“we need to build databases, produce case studies, design robust quantitative and qualitative analytical approaches, and compare across the rich library of studies available in the literature focusing on local adaptation so as to build theoretical generalizations that are useful across geographies, cultures, and political systems and also relevant to more specific studies interested in individual contexts.” (Agrawal et al. 2012, p. 329)

As three recent papers show (Nalau & Verrall 2021, Vincent & Cundill 2021, Sietsma et al. 2021), the adaptation community really took this call (to produce research) to heart, and how. Working on the IPCC’s 1.5C Special Report we found that the adaptation literature has burgeoned, more than doubling from 2008-2011 (Bassett and Fogelman 2013) and growing by 150% from 2011-2014 (Webber 2016). This is the ‘big literature’ challenge in adaptation where numerous place-based case studies have proliferated (as they should) but synthesis and identifying entries for enabling effective adaptation implementation remain relatively underreported.

While collectively, these cases build a formidable database of “specific studies interested in individual contexts”, I believe we have somewhat failed to make sense of them to “build theoretical generalizations that are useful across geographies, cultures, and political systems”. For example, we continue debating the links between adaptation and development (as an indication, follow Wilbanks et al. 2004 to Yohe et al. 2007 in the IPCC’s AR4 to McGray et al. 2007 to Schipper et al. 2021). We continue to ask how we should delineate adaptation as beyond business-as-usual development? Or do we mainstream adaptation at the cost of diluting it? Or do we look for ‘triple wins’ and ‘co-benefits’? Does climate-compatible development capture the multiple trade-offs and decisions people, households, cities and countries face as they try to cope and adapt or do climate-resilient development pathways better capture the inequities and path dependencies we must negotiate?

Building on Maria Lemos’s framing of generic vs. specific capacities, I have argued, with colleagues, that in places where development delivery remains a gap, building a bedrock of development is essential to nurture adaptive capacity but insufficient to deliver adaptation to climatic risks. But we all are partial to particular frames and indicators, and in the multiplicity of cases and ‘recommendations’, I find adaptation implementers, funders, and researchers find themselves at sea. Theoretical plurality is a sign of debate, of a growing field but it can paralyse and confuse; and “the apparent lack of critical reflection upon the robustness of these (adaptation) heuristics for diverse contexts may contribute to potential cognitive bias with respect to the framing of adaptation by both researchers and practitioners” Preston et al. (2017, p. 467).

Combing through Agrawal et al.’s call further, one begins to ask, who should build these (adaptation) databases (and towards what end), what processes and funding mechanisms are needed to store, manage and update them, and how do we share this burden especially in countries facing multiple, compounding risks (of which climate impacts may be one of many)? The current UNFCCC reporting system which focuses more on climate ambition, has proved inadequate and uneven in reporting adaptation action. Research teams have tried to develop databases (e.g. Olazabal et al. 2021 on urban adaptation across Europe; the unfunded, organic Global Adaptation Mapping Initiative (GAMI) led by Lea Berrang-Ford and others capturing the breadth of adaptation research across continents, sectors, and scales) but these remain early, incomplete exercises, often curtailed by language, focus on peer-reviewed research, and particular search criteria.

More critically, as we think of developing adaptation databases, underlying resource and financial inequities come to the fore. We only have to look at successive IPCC Assessment Reports to see that the sections on ‘knowledge gaps’ repeatedly highlight particular geographies: within Asia, West and Central Asia are the usual suspects, in Europe, it is Eastern European countries. How are we plugging these geographical gaps as we move towards building (adaptation) datasets and producing case studies? More critically, as we move ahead with savvier tools of machine learning and AI-dependent data mining, we need to carefully consider what knowledge these adaptation databases are including/excluding, who is holding and using this data, and how information is/can be weaponised to fund and perpetuate unequal research and practice relationships.