ABSTRACT
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?
ABSTRACT
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. Statistically significant emissions reductions range between -5% to -21% across the schemes (-4% to -15% after correcting for publication bias). Our study highlights critical evidence gaps with regard to dozens of unevaluated carbon pricing schemes and the price elasticity of emissions reductions. More rigorous synthesis of carbon pricing and other climate policies is required across a range of outcomes to advance our understanding of "what works" and accelerate learning on climate solutions in science and policy.
ABSTRACT
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. Our large-scale, comparative study of 1.5 million tweets covers 16 GGR and SRM technologies and uses state-of-the-art deep learning models to show how attention, and expressions of sentiment and emotion developed between 2006 and 2021. We find that in recent years, attention has shifted from general geoengineering themes to specific GGR methods. On the other hand, there is little attention to specific SRM technologies and they often coincide with conspiracy narratives. Sentiments and emotions in GGR tweets tend to be more positive, particularly for methods perceived to be natural, but are more negative when framed in the geoengineering context.
ABSTRACT
Climate change is an ongoing topic in nearly all areas of society since many years. A discussion of climate change without referring to scientific results is not imaginable. This is especially the case for policies since action on the macro scale is required to avoid costly consequences for society. In this study, we deal with the question of how research on climate change and policy are connected. In 2019, the new Overton database of policy documents was released including links to research papers that are cited by policy documents. The use of results and recommendations from research on climate change might be reflected in citations of scientific papers in policy documents. Although we suspect a lot of uncertainty related to the coverage of policy documents in Overton, there seems to be an impact of international climate policy cycles on policy document publication. We observe local peaks in climate policy documents around major decisions in international climate diplomacy. Our results point out that IGOs and think tanks-with a focus on climate change-have published more climate change policy documents than expected. We found that climate change papers that are cited in climate change policy documents received significantly more citations on average than climate change papers that are not cited in these documents. Both areas of society (science and policy) focus on similar climate change research fields: biology, earth sciences, engineering, and disease sciences. Based on these and other empirical results in this study, we propose a simple model of policy impact considering a chain of different document types: The chain starts with scientific assessment reports (systematic reviews) that lead via science communication documents (policy briefs, policy reports or plain language summaries) and government reports to legislative documents.
Subject(s)
Climate Change , Health PolicyABSTRACT
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). For this study, further screening was performed to identify studies from LMICs reporting the effects on human health of climate change adaptation responses. Studies were categorised by study design and data were extracted on geographic region, population under investigation, type of adaptation response and reported health effects. The review identified 99 studies (1117 reported outcomes), reporting evidence from 66 LMICs. Only two studies were ex ante formal evaluations of climate change adaptation responses. Papers reported adaptation responses related to flooding, rainfall, drought and extreme heat, predominantly through behaviour change, and infrastructural and technological improvements. Reported (direct and intermediate) health outcomes included reduction in infectious disease incidence, improved access to water/sanitation and improved food security. All-cause mortality was rarely reported, and no papers were identified reporting on maternal and child health. Reported maladaptations were predominantly related to widening of inequalities and unforeseen co-harms. Reporting and publication-bias seems likely with only 3.5% of all 1117 health outcomes reported to be negative. Our review identified some evidence that climate change adaptation responses may have benefits for human health but the overall paucity of evidence is concerning and represents a major missed opportunity for learning. There is an urgent need for greater focus on the funding, design, evaluation and standardised reporting of the effects on health of climate change adaptation responses to enable evidence-based policy action.
ABSTRACT
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.
Subject(s)
Air Pollution , Communicable Diseases , Adaptation, Physiological , Child , Climate Change , Humans , Machine LearningABSTRACT
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. However, a systematic record and synthesis of the findings of evaluations of the effect of such actions on human health and wellbeing is currently lacking. This, in turn, impedes the development of robust knowledge on what constitutes high-impact climate actions of benefit to human health and wellbeing, which can inform future action plans, their implementation and scale-up. The development of a systematic record of studies reporting climate and health actions in cities is made challenging by the broad landscape of relevant literature scattered across many disciplines and sectors, which is challenging to effectively consolidate using traditional literature review methods. This protocol reports an innovative approach for the systematic development of a database of studies of climate change mitigation and adaptation actions implemented in cities, and their benefits (or disbenefits) for human health and wellbeing, derived from peer-reviewed academic literature. Our approach draws on extensive tailored search strategies and machine learning methods for article classification and tagging to generate a database for subsequent systematic reviews addressing questions of importance to urban decision-makers on climate actions in cities for human health and wellbeing.
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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. We believe that it is a major priority to consolidate efforts to develop and validate efficient, rigorous and robust applications of these novel technologies, ensuring the challenges of big literature do not prevent the future production of systematic maps.
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The capture and use of carbon dioxide to create valuable products might lower the net costs of reducing emissions or removing carbon dioxide from the atmosphere. Here we review ten pathways for the utilization of carbon dioxide. Pathways that involve chemicals, fuels and microalgae might reduce emissions of carbon dioxide but have limited potential for its removal, whereas pathways that involve construction materials can both utilize and remove carbon dioxide. Land-based pathways can increase agricultural output and remove carbon dioxide. Our assessment suggests that each pathway could scale to over 0.5 gigatonnes of carbon dioxide utilization annually. However, barriers to implementation remain substantial and resource constraints prevent the simultaneous deployment of all pathways.
Subject(s)
Carbon Dioxide/economics , Carbon Dioxide/isolation & purification , Carbon Sequestration , Technology/economics , Technology/trends , Carbon Dioxide/metabolism , Charcoal/metabolism , Forests , Microalgae/metabolism , Photosynthesis , Soil/chemistrySubject(s)
Carbon Dioxide/isolation & purification , Carbon Sequestration , Environmental Policy/legislation & jurisprudence , Ethical Analysis , Global Warming/legislation & jurisprudence , Global Warming/prevention & control , Risk Management/ethics , Risk Management/methods , Animals , Biodiversity , Biomass , Carbon Dioxide/analysis , Feedback , Forestry , Human Rights , Humans , Models, Theoretical , Sustainable DevelopmentABSTRACT
China's annual CO(2) emissions grew by around 4 billion tonnes between 1992 and 2007. More than 70% of this increase occurred between 2002 and 2007. While growing export demand contributed more than 50% to the CO(2) emission growth between 2002 and 2005, capital investments have been responsible for 61% of emission growth in China between 2005 and 2007. We use structural decomposition analysis to identify the drivers for China's emission growth between 1992 and 2007, with special focus on the period 2002 to 2007 when growth was most rapid. In contrast to previous analysis, we find that efficiency improvements have largely offset additional CO(2) emissions from increased final consumption between 2002 and 2007. The strong increases in emissions growth between 2002 and 2007 are instead explained by structural change in China's economy, which has newly emerged as the third major emission driver. This structural change is mainly the result of capital investments, in particular, the growing prominence of construction services and their carbon intensive supply chain. By closing the model for capital investment, we can now show that the majority of emissions embodied in capital investment are utilized for domestic household and government consumption (35-49% and 19-36%, respectively) with smaller amounts for the production of exports (21-31%). Urbanization and the associated changes in lifestyle are shown to be more important than other socio-demographic drivers like the decreasing household size or growing population. We argue that mitigation efforts will depend on the future development of these key drivers, particularly capital investments which dictate future mitigation costs.
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Carbon Dioxide , China , Environmental MonitoringABSTRACT
Despite the emergence of regional climate policies, growth in global CO(2) emissions has remained strong. From 1990 to 2008 CO(2) emissions in developed countries (defined as countries with emission-reduction commitments in the Kyoto Protocol, Annex B) have stabilized, but emissions in developing countries (non-Annex B) have doubled. Some studies suggest that the stabilization of emissions in developed countries was partially because of growing imports from developing countries. To quantify the growth in emission transfers via international trade, we developed a trade-linked global database for CO(2) emissions covering 113 countries and 57 economic sectors from 1990 to 2008. We find that the emissions from the production of traded goods and services have increased from 4.3 Gt CO(2) in 1990 (20% of global emissions) to 7.8 Gt CO(2) in 2008 (26%). Most developed countries have increased their consumption-based emissions faster than their territorial emissions, and non-energy-intensive manufacturing had a key role in the emission transfers. The net emission transfers via international trade from developing to developed countries increased from 0.4 Gt CO(2) in 1990 to 1.6 Gt CO(2) in 2008, which exceeds the Kyoto Protocol emission reductions. Our results indicate that international trade is a significant factor in explaining the change in emissions in many countries, from both a production and consumption perspective. We suggest that countries monitor emission transfers via international trade, in addition to territorial emissions, to ensure progress toward stabilization of global greenhouse gas emissions.
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Carbon Dioxide/analysis , Commerce/trends , Greenhouse Effect , Vehicle Emissions/analysis , Climate , Developed Countries , Developing CountriesABSTRACT
The UK appears to be a leading country in curbing greenhouse gas (GHG) emissions. Unlike many other developed countries, it has already met its Kyoto obligations and defined ambitious, legally binding targets for the future. Recently this achievement has been called into question as it ignores rapidly changing patterns of production and international trade. We use structural decomposition analysis (SDA) to investigate the drivers behind annual changes in CO(2) emission from consumption in the UK between 1992 and 2004. In contrast with previous SDA-based studies, we apply the decomposition to a global, multiregional input-output model (MRIO), which accounts for UK imports from all regions and uses region-specific production structures and CO(2) intensities. We find that improvements from "domestic" changes in efficiency and production structure led to a 148 Mt reduction in CO(2) emissions, which only partially offsets emission increases of 217 Mt from changes in the global supply chain and from growing consumer demand. Recent emission reductions achieved in the UK are not merely a reflection of a greening of the domestic supply chain, but also of a change in the international division of labor in the global production of goods and services.