Scientific literature on carbon dioxide removal revealed as much larger through AI-enhanced systematic mapping.

Carbon dioxide removal plays an important role in any strategy to limit global warming to well below 2 °C. Keeping abreast with the scientific evidence using rigorous evidence synthesis methods is an important prerequisite for sustainably scaling these methods. Here, we use artificial intelligence to provide a comprehensive systematic map of carbon dioxide removal research.

PROTOCOL: Behavioral, information and monetary interventions to reduce energy consumption in households: A "living" systematic review.

This is the protocol for a Campbell systematic review. The objectives are as follows: Our proposed systematic review and meta-analysis will integrate the evidence available from all sources to answer the following questions: (1) to what extent can information, behavioral and monetary interventions reduce energy consumption of households in residential buildings? (average treatment effect of interventions) (2) what is the relative effectiveness of interventions? (account for heterogeneity in treatment effects across and within studies) (3) how effective are combinations of different interventions?

The 2023 Latin America report of the Countdown on health and climate change: the imperative for health-centred climate-resilient development.

In 2023, a series of climatological and political events unfolded, partly driving forward the global climate and health agenda while simultaneously exposing important disparities and vulnerabilities to climate-related events. On the policy front, a significant step forward was marked by the inaugural Health Day at COP28, acknowledging the profound impacts of climate change on health. However, the first-ever Global Stocktake showed an important gap between the current progress and the targets outlined in the Paris Agreement, underscoring the urgent need for further and decisive action.

Systematic review and meta-analysis of ex-post evaluations on the effectiveness of carbon pricing.

Today, more than 70 carbon pricing schemes have been implemented around the globe, but their contributions to emissions reductions remains a subject of heated debate in science and policy. Here we assess the effectiveness of carbon pricing in reducing emissions using a rigorous, machine-learning assisted systematic review and meta-analysis. Based on 483 effect sizes extracted from 80 causal ex-post evaluations across 21 carbon pricing schemes, we find that introducing a carbon price has yielded immediate and substantial emission reductions for at least 17 of these policies, despite the low level of prices in most instances.

Attention, sentiments and emotions towards emerging climate technologies on Twitter.

Public perception of emerging climate technologies, such as greenhouse gas removal (GGR) and solar radiation management (SRM), will strongly influence their future development and deployment. Studying perceptions of these technologies with traditional survey methods is challenging, because they are largely unknown to the public. Social media data provides a complementary line of evidence by allowing for retrospective analysis of how individuals share their unsolicited opinions.

The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms.

Unlabelled: The Countdown is an international research collaboration that independently monitors the evolving impacts of climate change on health, and the emerging health opportunities of climate action. In its eighth iteration, this 2023 report draws on the expertise of 114 scientists and health practitioners from 52 research institutions and UN agencies worldwide to provide its most comprehensive assessment yet. In 2022, the Countdown warned that people’s health is at the mercy of fossil fuels and stressed the transformative opportunity of jointly tackling the concurrent climate change, energy, cost-of-living, and health crises for human health and wellbeing.

Systematic mapping of global research on climate and health: a machine learning review.

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.

The effects on public health of climate change adaptation responses: a systematic review of evidence from low- and middle-income countries.

Climate change adaptation responses are being developed and delivered in many parts of the world in the absence of detailed knowledge of their effects on public health. Here we present the results of a systematic review of peer-reviewed literature reporting the effects on health of climate change adaptation responses in low- and middle-income countries (LMICs). The review used the 'Global Adaptation Mapping Initiative' database (comprising 1682 publications related to climate change adaptation responses) that was constructed through systematic literature searches in Scopus, Web of Science and Google Scholar (2013-2020).

Climate action for health and wellbeing in cities: a protocol for the systematic development of a database of peer-reviewed studies using machine learning methods.

Cities produce more than 70% of global greenhouse gas emissions. Action by cities is therefore crucial for climate change mitigation as well as for safeguarding the health and wellbeing of their populations under climate change. Many city governments have made ambitious commitments to climate change mitigation and adaptation and implemented a range of actions to address them.

Mapping global research on climate and health using machine learning (a systematic evidence map).

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.

Statistical stopping criteria for automated screening in systematic reviews.

Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents.

On the use of computer-assistance to facilitate systematic mapping.

The volume of published academic research is growing rapidly and this new era of "big literature" poses new challenges to evidence synthesis, pushing traditional, manual methods of evidence synthesis to their limits. New technology developments, including machine learning, are likely to provide solutions to the problem of information overload and allow scaling of systematic maps to large and even vast literatures. In this paper, we outline how systematic maps lend themselves well to automation and computer-assistance.