Search

Background — Countries around the globe have started implementing policies to respond to the current and future risks of climate change. The scientific literature on these adaptation policies is fragmented and no central typology is generally accepted, making tracking of global adaptation policy progress difficult.Methods — In this protocol, we describe how we use machine learning methods to classify scientific literature on adaptation policies following the ROSES guidelines. We use a broad search query in Scopus, MEDLINE and Web of Science (up to November 2021). We manually classify a subset of the documents and use this to train multiple supervised machine learning algorithms, including a state-of-the-art algorithm based on BERT. The classification scheme is aimed at providing a multi-functional database: we classify first based on a newly created typology, which is based around the well-established NATO categories of policy instruments; this is supplemented with categories on the types of impacts, evidence on maladaptation, constraints, evidence type, governance level and geographic location.Expected results – Using the typology and categories, as well as topic modelling, we create an overview of scientific literature on adaptation policies. This describes the breath of policy options, their geographic distribution, developments over time, and under-explored areas. If successful, this would result in the most comprehensive evidence map of adaptation policies to date; building on this, the machine learning algorithms and underlying data can serve as a basis for a living evidence map, moving towards the real-time tracking of adaptation progress.

Summary Background The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health. Methods We used supervised machine learning and other natural language processing methods (topic modelling and geoparsing) to systematically identify and map the scientific literature on climate change and health published between Jan 1, 2013, and April 9, 2020. Only literature indexed in English were included. We searched Web of Science Core Collection, Scopus, and PubMed using title, abstract, and keywords only. We searched for papers including both a health component and an explicit mention of either climate change, climate variability, or climate change-relevant weather phenomena. We classified relevant publications according to the fields of climate research, climate drivers, health impact, date, and geography. We used supervised and unsupervised machine learning to identify and classify relevant articles in the field of climate and health, with outputs including evidence heat maps, geographical maps, and narrative synthesis of trends in climate health-related publications. We included empirical literature of any study design that reported on health pathways associated with climate impacts, mitigation, or adaptation. Findings We predict that there are 15 963 studies in the field of climate and health published between 2013 and 2019. Climate health literature is dominated by impact studies, with mitigation and adaptation responses and their co-benefits and co-risks remaining niche topics. Air quality and heat stress are the most frequently studied exposures, with all-cause mortality and infectious disease incidence being the most frequently studied health outcomes. Seasonality, extreme weather events, heat, and weather variability are the most frequently studied climate-related hazards. We found major gaps in evidence on climate health research for mental health, undernutrition, and maternal and child health. Geographically, the evidence base is dominated by studies from high-income countries and China, with scant evidence from low-income counties, which often suffer most from the health consequences of climate change. Interpretation Our findings show the importance and feasibility of using automated machine learning to comprehensively map the science on climate change and human health in the age of big literature. These can provide key inputs into global climate and health assessments. The scant evidence on climate change response options is concerning and could significantly hamper the design of evidence-based pathways to reduce the effects on health of climate change. In the post-2015 Paris Agreement era of climate solutions, we believe much more attention should be given to climate adaptation and mitigation options and their effects on human health. Funding Foreign, Commonwealth & Development Office.

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.

Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that conventional evidence synthesis methods are no longer sufficient or feasible. Here, we outline a protocol using machine learning approaches to systematically synthesize global evidence on the relationship between climate change, climate variability, and weather (CCVW) and human health. We will use supervised machine learning to screen over 300,000 scientific articles, combining terms related to CCVW and human health. Our inclusion criteria comprise articles published between 2013 and 2020 that focus on empirical assessment of: CCVW impacts on human health or health-related outcomes or health systems; relate to the health impacts of mitigation strategies; or focus on adaptation strategies to the health impacts of climate change. We will use supervised machine learning (topic modeling) to categorize included articles as relevant to impacts, mitigation, and/or adaptation, and extract geographical location of studies. Unsupervised machine learning using topic modeling will be used to identify and map key topics in the literature on climate and health, with outputs including evidence heat maps, geographic maps, and narrative synthesis of trends in climate-health publishing. To our knowledge, this will represent the first comprehensive, semi-automated, systematic evidence synthesis of the scientific literature on climate and health.

Background: The global literature on the links between climate change and human health is large, increasing exponentially, and no longer feasible to collate and synthesize using traditional systematic review approaches. Methods: We use machine learning and other data science methods to systematically identify and map the scientific literature on climate change and health published since 2013. We classify >16,000 relevant publications by field of climate research, climate drivers, health impact, date and geography. Findings: We show that climate-health literature is dominated by impact studies, with mitigation and adaptation responses and their co-benefits and co-risks remaining niche topics. Air quality and heat stress are the most frequently studied exposures, with all-cause mortality and infectious disease incidence being the most frequently studied health outcomes. Seasonality, extreme weather events, heat, and weather variability are the most frequently studied climate-related hazards. Geographically, the evidence base is dominated by studies from high-income countries and China, with very limited evidence from the majority of countries that suffer most from the health consequences of climate change. Interpretation: We demonstrate the importance and feasibility of using automated machine-learning pipelines for mapping the science on climate change and human health and generating valuable inputs into global climate and health assessments. We find major gaps in evidence on climate-health research for mental health, undernutrition, and maternal-child health, and very limited evidence overall from low income regions. The scant evidence on climate change response options is concerning and will significantly hamper the design of evidence-based pathways to reduce the effects on health of climate change. We argue that in the post-2015 Paris Agreement era of climate solutions much more attention should be given to climate adaptation and mitigation options and their effects on human health. Funding Statement: None to declare. Declaration of Interests: Dr. Berrang-Ford reports grants from Dfid-FCDO UK (Gov't), from null, during the conduct of the study; all other authors have nothing to declare.