• Energy Research

Despite the importance of evaluating all mitigation options to inform policy decisions addressing climate change, a comprehensive analysis of household-scale interventions and their emissions reduction potential is missing. Here, we address this gap for interventions aimed at changing individual households’ use of existing equipment, such as monetary incentives or feedback. We have performed a machine learning-assisted systematic review and meta-analysis to comparatively assess the effectiveness of these interventions in reducing energy demand in residential buildings. We extracted 360 individual effect sizes from 122 studies representing trials in 25 countries. Our meta-regression confirms that both monetary and non-monetary interventions reduce the energy consumption of households, but monetary incentives, of the sizes reported in the literature, tend to show on average a more pronounced effect. Deploying the right combinations of interventions increases the overall effectiveness. We have estimated a global carbon emissions reduction potential of 0.35 GtCO2 yr−1, although deploying the most effective packages of interventions could result in greater reduction. While modest, this potential should be viewed in conjunction with the need for de-risking mitigation pathways with energy-demand reductions. Behavioural interventions can reduce energy consumption and hence carbon emissions among households. Khanna et al. compare the effectiveness of different types of monetary and non-monetary household interventions using a machine learning-assisted meta-analysis, and examine the situations where each is most useful.

Demand response has been recognised as a critical element in the transition to low carbon energy systems. It is hoped that shifting demand through price incentives can reduce energy consumption and facilitate the electrification of heat and transport. However, a rigorous assessment of the existing evidence from field trials using time-of-use pricing, critical peak pricing, real-time pricing or rebates to influence household energy use is missing. Previous reviews have also relied on evidence from utility-reports in the United States and Canada and evidence from pricing experiments in Europe, East Asia and developing Asia has not been accounted. Here, we address this gap by employing a machine learning-assisted systematic review and meta-analysis of experimental and quasi-experimental pilots aimed at shifting or reducing the energy demand of households during peak and off-peak hours.

Abstract This paper is the first to analyse the role of women authors in fostering justice-relevant topics in climate adaptation research. As representation, citation and payment patterns remain gender-biased across scientific disciplines, we explore the case of climate science, particularly adaptation, as its most human-oriented facet. In climate research and policy, there has been a recent surge of interest in climate justice topics: mentions of justice have increased almost tenfold in Intergovernmental Panel on Climate Change Working Group 2 reports between the latest assessment cycles (AR5 and AR6). We conduct a systematic examination of the topic space in the adaptation policy scholarship. As it is a vast and rapidly growing field, we use topic modelling, an unsupervised machine learning method, to identify the literature on climate justice and related fields, as well as to examine the relationship between topic prevalence and the gender of the authors. We find climate change adaptation policy research to be male dominated, with women holding 38.8% of first and 28.8% of last authorships. However, we observe topic-specific variability, whereby the share of female authors is higher among publications on justice-relevant topics. Female authorship is highly linked to topics such as Community, Local Knowledge, and Governance, but less to Food Security and Climate Finance. Our findings corroborate the evidence that female authors play a significant role in advancing the research and dialogue on the relationship between climate change and areas that have meaningful impact on lives of women and other marginalised groups.

This is a complementary dataset associated with the following publication: Berrang-Ford, Lea, et al. "Systematic Mapping of Global Research on Climate and Health Using Machine Learning." The Lancet Planetary Health. Meta-data are included.

Record-breaking temperatures were recorded across the globe in 2023. Without climate action, adverse climate-related health impacts are expected to worsen worldwide, affecting billions of people. Temperatures in Europe are warming at twice the rate of the global average, threatening the health of populations across the continent and leading to unnecessary loss of life. The Lancet Countdown in Europe was established in 2021, to assess the health profile of climate change aiming to stimulate European social and political will to implement rapid health-responsive climate mitigation and adaptation actions. In 2022, the collaboration published its indicator report, tracking progress on health and climate change via 33 indicators and across five domains.

Phasing out coal is a prerequisite to achieving the Paris climate mitigation targets. In 2018, the German government established a multi-stakeholder commission with the mandate to negotiate a plan for the national coal phase-out, fueling a continued public debate over the future of coal. This study analyzes the German coal debate on Twitter before, during, and after the session of the so-called Coal Commission, over a period of three years. In particular, we investigate whether and how the work of the commission translated into shared perceptions and sentiments in the public debate on Twitter. We find that the sentiment of the German coal debate on Twitter becomes increasingly negative over time. In addition, the sentiment becomes more polarized over time due to an increase in the use of more negative and positive language. The analysis of retweet networks shows no increase in interactions between communities over time. These findings suggest that the Coal Commission did not further consensus in the coal debate on Twitter. While the debate on social media only represents a section of the national debate, it provides insights for policy-makers to evaluate the interaction of multi-stakeholder commissions and public debates.

Augmenting traditional social science methods with computational analysis is crucial if we are to exploit the vast digital archives of text data that have become available over the past two decades. In this journal, Benites-Lazaro et al. (2018) showcase this in an application of topic modeling and other computational methods to an actor-specific examination of changes in policy discourse on ethanol in Brazil and point out methodological promises and challenges. However, their contribution also highlights the need for establishing codes of practice for computational text analysis. In this perspective, we discuss five areas for improvement when treating text as big data in light of guiding principles from computational research – transparency, reproducibility and validation – to facilitate rigorous research practice: (1) full transparency over data collection and corpus construction, (2) comprehensive method descriptions that enable reproducibility by other researchers, (3) application of rigorous model validation procedures, (4) results interpretation based on primary text and clear research design and (5) critical discussion and contextualization of main findings. We conclude that the energy social science community needs to develop codes of practice to build on the promising research within the field of computational text analysis and suggest first steps into this direction.