• Energy Research

Steady growth in global greenhouse gas emissions from transport is driven by growing demand for car travel. A sizable body of research investigates influences of urban form on travel behavior, but few European studies illustrate variation of these influences across multiple cities and countries using disaggregated data. Here, we compare car ownership and mobility patterns, and we use gradient boosting decision tree and regression models to investigate urban form influences on travel distances, vehicle ownership, and mode choice across nineteen diverse European cities. Residential proximity to the city center is the urban form feature with greatest predictive importance for trip distances, car ownership and car mode choice. The exponential reduction of car use with higher population density is clearly demonstrated with aggregate city data. We detect nonlinear relationships between urban form and modelled outcomes, identify urban form thresholds for sustainable mobility, and suggest targeted policy interventions.

Abstract New Energy Vehicles (NEVs), and especially electric cars, are rapidly changing the outlook of the car industry in China, the largest vehicle market in the world. However, an adjustment in subsidy schemes and breakout of COVID-19 appear to slow down the uptake of NEVs in the Chinese market. This raises the question of whether, other less costly, policy instruments can help the transformation towards cleaner vehicles. With the understanding that public support and user acceptance play a key role, we ask whether NEVs as part of increasingly popular car-sharing mode can gain further support, the increased uptake of new energy vehicles. To investigate this question, we perform an online survey, retrieving 1583 questionnaires, and scrutinize the perception of NEVs at the nexus with car-sharing. Relying on the Theory of Planned Behavior and ordered logistic regression model, we demonstrate that attitudes towards environmental protection and perceived benefits (economic and safety) play a key role in accelerating the adoption of shared electric cars. NEVs promotion policies need to specifically target groups by regions. Municipal agencies can substantially support NEV uptake by providing on-street parking exclusively for shared NEVs in cities, and information on the economic and social benefits of NEVs in rural areas.

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.

Abstract As current action remains insufficient to meet the goals of the Paris agreement let alone to stabilize the climate, there is increasing hope that solutions related to demand, services and social aspects of climate change mitigation can close the gap. However, given these topics are not investigated by a single epistemic community, the literature base underpinning the associated research continues to be undefined. Here, we aim to delineate a plausible body of literature capturing a comprehensive spectrum of demand, services and social aspects of climate change mitigation. As method we use a novel double-stacked expert—machine learning research architecture and expert evaluation to develop a typology and map key messages relevant for climate change mitigation within this body of literature. First, relying on the official key words provided to the Intergovernmental Panel on Climate Change by governments (across 17 queries), and on specific investigations of domain experts (27 queries), we identify 121 165 non-unique and 99 065 unique academic publications covering issues relevant for demand-side mitigation. Second, we identify a literature typology with four key clusters: policy, housing, mobility, and food/consumption. Third, we systematically extract key content-based insights finding that the housing literature emphasizes social and collective action, whereas the food/consumption literatures highlight behavioral change, but insights also demonstrate the dynamic relationship between behavioral change and social norms. All clusters point to the possibility of improved public health as a result of demand-side solutions. The centrality of the policy cluster suggests that political actions are what bring the different specific approaches together. Fourth, by mapping the underlying epistemic communities we find that researchers are already highly interconnected, glued together by common interests in sustainability and energy demand. We conclude by outlining avenues for interdisciplinary collaboration, synthetic analysis, community building, and by suggesting next steps for evaluating this body of literature.

Abstract To achieve the temperature goal of the Paris Agreement, transformative actions are needed. The circular economy (CE) is one concept that gained popularity in recent years, with its proclaimed selling point to combine economic development with benefits to businesses, society, and the environment. However, definitions of CE diverge, applications appear across vastly different settings, and overall there is a lack of understanding of how much CE strategies can contribute to climate change mitigation (mitigation). We systematically screened 3244 records in Web of Science and Scopus, restricted to papers in English. We then selected studies against pre-determined eligibility criteria that, had to (1) refer explicitly to CE or closely related concepts (e.g. performance economy, cradle-to-cradle, material or product efficiency); and (2) refer to a climate change mitigation potential. We identified 341 studies, summarized, and grouped into six sectors (industry, waste, energy, buildings, transport, and agriculture). These sectors are not completely mutually exclusive, but partially overlapping. Nonetheless, sectoral classifications relate to existing categorizations and map well with international assessments of climate change mitigations, such as those of the Intergovernmental Panel on Climate Change (IPCC). Our review sets out to summarize the results of the scientific literature on the extent to which CE strategies can contribute to mitigation. Even though our query explicitly required a consideration of climate change, only 10% of all studies contributed insights on how the CE can support mitigation. We find that the highest saving potential is evidenced in the industry, energy, and transport sector; mid-range savings in the waste and building sector; and lowest gains are to be expected in agriculture. The majority of studies investigate incremental measures claiming but not demonstrating climate change mitigation. Most studies indicate potential but implementation remains weak. Assessments should move from attributional to consequential analysis to avoid misleading policy makers.