Systematic review approaches for climate change adaptation research - Berrang-Ford, Lea; Pearce, Tristan; Ford, James D. (Regional Environmental Change, 2015)
Recent controversy has led to calls for increased standardization and transparency in the methods used to synthesize climate change research. Though these debates have focused largely on the biophysical dimensions of climate change, human dimensions research is equally in need of improved methodological approaches for research synthesis. Systematic review approaches, and more recently realist review methods, have been used within the health sciences for decades to guide research synthesis. Despite this, penetration of these approaches into the social and environmental sciences has been limited. Here, we present an analysis of approaches for systematic review and research synthesis and examine their applicability in an adaptation context. Customized review frameworks informed by systematic approaches to research synthesis provide a conceptually appropriate and practical opportunity for increasing methodological transparency and rigor in synthesizing and tracking adaptation research. This review highlights innovative applications of systematic approaches, with a focus on the unique challenges of integrating multiple data sources and formats in reviewing climate change adaptation policy and practice. We present guidelines, key considerations, and recommendations for systematic review in the social sciences in general and adaptation research in particular. We conclude by calling for increased conceptual and methodological development of systematic review approaches to address the methodological challenges of synthesizing and tracking adaptation to climate change.
Keywords: evidence_review; openalex:cites; openalex:import; selected_for:effects of climate ch...on learning...LMICs 2024; tagged

The next generation of machine learning for tracking adaptation texts - Sietsma, Anne J.; Ford, James D.; Minx, Jan C. (Nature Climate Change, 2023)
Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses, fostering collaboration and promoting ‘machine learning literacy’, including understanding bias. More fundamentally, machine learning enables a paradigmatic shift towards automating repetitive tasks and makes interactive ‘living evidence’ platforms possible. Broadly, the adaptation community is failing to prepare for this shift. Flagship projects of organizations such as the IPCC could help to lead the way.
Keywords: Climate-change impacts; Interdisciplinary studies; Research data; _z:class:countries; _z:no_pdf; openalex:citedBy; openalex:import; selected_for:effects of climate ch...on learning...LMICs 2024; tagged

Progress in climate change adaptation research - Sietsma, Anne J.; Ford, James D.; Callaghan, Max; Minx, Jan C. (Environmental Research Letters, 2021)
Abstract The scientific literature on climate change adaptation has become too large to assess manually. Beyond standard scientometrics, questions about if and how the field is progressing thus remain largely unanswered. Here we provide a novel, inquisitive, computer-assisted evidence mapping methodology that combines expert interviews ( n = 26) and structural topic modelling to evaluate open-ended research questions on progress in the field. We apply this to 62 191 adaptation-relevant scientific publications (1988–2020), selected through supervised machine learning from a comprehensive climate change query. Comparing the literature to key benchmarks of mature adaptation research, our findings align with trends in the adaptation literature observed by most experts: the field is maturing, growing rapidly, and diversifying, with social science and implementation topics arising next to the still-dominant natural sciences and impacts-focused research. Formally assessing the representativeness of IPCC citations, we find evidence of a delay effect for fast-growing areas of research like adaptation strategies and governance. Similarly, we show significant topic biases by geographic location: especially disaster and development-related topics are often studied in Southern countries by authors from the North, while Northern countries dominate governance topics. Moreover, there is a general paucity of research in some highly vulnerable countries. Experts lastly signal a need for meaningful stakeholder involvement. Expanding on the methods presented here would aid the comprehensive and transparent monitoring of adaptation research. For the evidence synthesis community, our methodology provides an example of how to move beyond the descriptive towards the inquisitive and formally evaluating research questions.
Keywords: _z:class:countries; openalex:cites; openalex:import; selected_for:effects of climate ch...on learning...LMICs 2024; tagged

Indigenous knowledge on climate change adaptation: a global evidence map of academic literature - Petzold, Jan; Andrews, Nadine; Ford, James D.; Hedemann, Christopher; Postigo, Julio C. (Environmental Research Letters, 2020)
There is emerging evidence of the important role of indigenous knowledge for climate change adaptation. The necessity to consider different knowledge systems in climate change research has been established in the fifth assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). However, gaps in author expertise and inconsistent assessment by the IPCC lead to a regionally heterogeneous and thematically generic coverage of the topic. We conducted a scoping review of peer-reviewed academic literature to support better integration of the existing and emerging research on indigenous knowledge in IPCC assessments. The research question underpinning this scoping review is: How is evidence of indigenous knowledge on climate change adaptation geographically and thematically distributed in the peer-reviewed academic literature? As the first systematic global evidence map of indigenous knowledge in the climate adaptation literature, the study provides an overview of the evidence of indigenous knowledge for adaptation across regions and categorises relevant concepts related to indigenous knowledge and their contexts in the climate change literature across disciplines. The results show knowledge clusters around tropical rural areas, subtropics, drylands, and adaptation through planning and practice and behavioural measures. Knowledge gaps include research in northern and central Africa, northern Asia, South America, Australia, urban areas, and adaptation through capacity building, as well as institutional and psychological adaptation. This review supports the assessment of indigenous knowledge in the IPCC AR6 and also provides a basis for follow-up research, e.g. bibliometric analysis, primary research of underrepresented regions, and review of grey literature.
Keywords: _z:class:countries

Frontiers in data analytics for adaptation research: Topic modeling - Lesnikowski, Alexandra; Belfer, Ella; Rodman, Emma; Smith, Julie; Biesbroek, Robbert; Wilkerson, John; Ford, James D.; Berrang‐Ford, Lea (WIREs Climate Change, 2019)
Rapid growth over the past two decades in digitized textual information represents untapped potential for methodological innovations in the adaptation governance literature that draw on machine learning approaches already being applied in other areas of computational social sciences. This Focus Article explores the potential for text mining techniques, specifically topic modeling, to leverage this data for large‐scale analysis of the content of adaptation policy documents. We provide an overview of the assumptions and procedures that underlie the use of topic modeling, and discuss key areas in the adaptation governance literature where topic modeling could provide valuable insights. We demonstrate the diversity of potential applications for topic modeling with two examples that examine: (a) how adaptation is being talked about by political leaders in United Nations Framework Convention on Climate Change; and (b) how adaptation is being discussed by decision‐makers and public administrators in Canadian municipalities using documents collected from 25 city council archives. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation
Keywords: openalex:cites; openalex:import; selected_for:effects of climate ch...on learning...LMICs 2024; tagged

Machine learning evidence map reveals global differences in adaptation action - Sietsma, Anne J.; Theokritoff, Emily; Biesbroek, Robbert; Canosa, Iván Villaverde; Thomas, Adelle; Callaghan, Max; Minx, Jan C.; Ford, James D. (One Earth, 2024)
Keywords: _z:class:countries

Global Tracking of Climate Change Adaptation Policy Using Machine Learning: a Systematic Map Protocol - Sietsma, Anne J.; Callaghan, Max; Biesbroek, Robbert; Theokritoff, Emily; Thomas, Adelle; Canosa, Iván Villaverde; Schleussner, Carl-Friedrich; Ford, James D.; Minx, Jan C. (2022)
Keywords: _z:class:countries