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Ambitious climate mitigation policies face social and political resistance. One reason is that existing policies insufficiently capture the diversity of relevant insights from the social sciences about potential policy outcomes. We argue that agent-based models can serve as a powerful tool for integration of elements from different disciplines. Having such a common platform will enable a more complete assessment of climate policies, in terms of criteria like effectiveness, equity and public support. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Carbon Economy and Climate Mitigation > Policies, Instruments, Lifestyles, Behavior Policy and Governance > Multilevel and Transnational Climate Change Governance.

The Summary for Urban Policymakers (SUP) initiative provides a distillation of the IPCC reports into accessible and targeted summaries that can help inform action at city and regional scales. Volume I in the series, What the Latest Physical Science of Climate Change Means for Cities, identified the ways in which human-induced climate change is affecting every region of the world, and the cities and urban areas therein. Volume II, Climate Change in Cities and Urban Areas: Impacts, Adaptation and Vulnerability, assessed the feasibility and effectiveness of different adaptation options. To achieve climate resilient development, synergies between policies and actions for climate change adaptation, mitigation and other development goals are needed. This third volume in the series, What the Latest Science on Climate Change Mitigation Means For Cities and Urban Areas offers a concise and accessible distillation of the IPCC Working Group III Report for urban policymakers. The 21st century is characterized by a rapidly growing urban population, urban land expansion and associated rise in demand for resources, infrastructure and services. These trends are expected to drive the growth in emissions from urban consumption and production through 2100, although the rate of urban emissions growth will depend on the type of urbanisation and the speed and scale of mitigation action implemented. Aggressive and ambitious policies for transition towards net zero greenhouse gas emissions can be implemented in cities and urban areas, while contributing to sustainable development. Ultimately, mitigation action and adaptation are interdependent processes, and pursuing these actions together can promote sustainable development.

Urban street space is increasingly contested. However, it is unclear what a fair street space allocation would look like. We develop a framework of ten ethical principles and three normative perspectives on street space – streets for transport, streets for sustainability, and streets as place – and discuss 14 derived street space allocation mechanisms. We contrast these ethically grounded allocation mechanisms with real-world allocation in 18 streets in Berlin. We find that car users, on average, had 3.5 times more space available than non-car users. While some allocation mechanisms are more plausible than others, none is without disputed normative implications. All of the ethical principles, however, suggest that on-street parking for cars is difficult to justify, and that cycling deserves more space. We argue that ethical principles should be systematically integrated into urban and transport planning.

Mitigation solutions are often evaluated in terms of costs and greenhouse gas reduction potentials, missing out on the consideration of direct effects on human well-being. Here, we systematically assess the mitigation potential of demand-side options categorized into avoid, shift and improve, and their human well-being links. We show that these options, bridging socio-behavioural, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 40–80% in end-use sectors. Based on expert judgement and an extensive literature database, we evaluate 306 combinations of well-being outcomes and demand-side options, finding largely beneficial effects in improvement in well-being (79% positive, 18% neutral and 3% negative), even though we find low confidence on the social dimensions of well-being. Implementing such nuanced solutions is based axiomatically on an understanding of malleable rather than fixed preferences, and procedurally on changing infrastructures and choice architectures. Results demonstrate the high mitigation potential of demand-side mitigation options that are synergistic with well-being. Evaluation of mitigation actions often focuses on cost and overlooks the direct effects on well-being. This work shows demand-side measures have large mitigation potential and beneficial effects on well-being outcomes.

The Summary for Urban Policymakers (SUP) initiative provides a distillation of the IPCC reports into accessible and targeted summaries that can help inform action at city and regional scales. This is a collection of the three volumes of the SUP series combined together.

Scientific collaborations among nations to address common problems and to build international partnerships as part of science diplomacy is a well-established notion. The international flow of people and ideas has played an important role in the advancement of the 'Sciences' and the current pandemic scenario has drawn attention towards the genuine need for a stronger role of science diplomacy, science advice and science communication. In dealing with the COVID-19 pandemic, visible interactions across science, policy, science communication to the public and diplomacy worldwide have promptly emerged. These interactions have benefited primarily the disciplines of knowledge that are directly informing the pandemic response, while other scientific fields have been relegated. The effects of the COVID-19 pandemic on scientists of all disciplines and from all world regions are discussed here, with a focus on early-career researchers (ECRs), as a vulnerable population in the research system. Young academies and ECR-driven organisations could suggest ECR-powered solutions and actions that could have the potential to mitigate these effects on ECRs working on disciplines not related to the pandemic response. In relation with governments and other scientific organisations, they can have an impact on strengthening and creating fairer scientific systems for ECRs at the national, regional, and global level.

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

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.

Climate mitigation solutions are often evaluated in terms of their costs and potentials. This accounting, however, shortcuts a comprehensive evaluation of how climate solutions affect human well-being, which, at best, may only be crudely related to cost considerations. Here, we systematically list key sectoral mitigation options on the demand side, and categorize them into avoid, shift and improve categories. We show that these options, bridging socio-behavioral, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 50-80% in end use sectors. Based on expert judgement and literature survey, we then evaluate 324 combinations of wellbeing outcomes and demand side options. We find that these are largely beneficial in improving wellbeing across all measures combined (76% have positive, 22% neutral, and 2.4% have negative effects), even though confidence level is low in the social dimensions of wellbeing. Implementing demand-side solution requires i) an understanding of malleable not fixed preferences, ii) consistently measuring and evaluating constituents of wellbeing, and iii) addressing concerns of incumbents in supply-side industries. Our results shift the emphasis in the climate mitigation solution space from supply-side technologies to demand-side service provision.

The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the appropriate models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users. © 2019 by the authors. This research was funded by COMPLEX—Knowledge Based Climate Mitigation Systems for a Low Carbon Economy Project, EU-FP7-308601. I.A. was supported by the Spanish Government through María de Maeztu excellence accreditation MDM-2017-0714 and the grant RTI2018-099858-A-100, and the Czech Science Foundation under the project VEENEX GA Cˇ R 16-17978S.

© 2020 The Author(s) Households are responsible for a significant share of global greenhouse emissions. Hence, academic and policy discourses highlight behavioral changes among households as an essential strategy for combating climate change. However, formal models used to assess economic impacts of energy policies face limitations in tracing cumulative impacts of adaptive behavior of diverse households. The past decade has witnessed a proliferation of agent-based simulation models that quantify behavioral climate change mitigation relying on social science theories and micro-level survey data. Yet, these behaviorally-rich models usually operate on a small scale of neighborhoods, towns, and small regions, ignoring macro-scale social institutions such as international markets and rarely covering large areas relevant for climate change mitigation policy. This paper presents a methodology to scale up behavioral changes among heterogeneous individuals regarding energy choices while tracing their macroeconomic and cross-sectoral impacts. To achieve this goal, we combine the strengths of top-down computable general equilibrium models and bottom-up agent-based models. We illustrate the integration process of these two alien modeling approaches by linking data-rich macroeconomic with micro-behavioral models. Following a three-step approach, we investigate the dynamics of cumulative impacts of changes in individual energy use under three behavioral scenarios. Our findings demonstrate that the regional dimension is important in a low-carbon economy transition. Heterogeneity in individual socio-demographics (e.g. education and age), structural characteristics (e.g. type and size of dwellings), behavioral and social traits (e.g. awareness and personal norms), and social interactions amplify these differences, causing nonlinearities in diffusion of green investments among households and macro-economic dynamics.

© 2019 The Authors Households are responsible for 70% of CO2 emissions (directly and indirectly). While households as agents of change increasingly become a crucial element in energy transitions, bottom-up mechanisms facilitating behavioral change are not fully understood. A scientific understanding of individual energy use, requires eliciting factors that trigger or inhibit changes in energy behavior. This paper explores individual energy consumption practices and behavioral aspects that affect them. We quantitatively study the determinants of three energy actions: (1) investments in house insulation, solar panels and/or energy-efficient appliances, (2) conservation of energy by changing energy-use habits like switching off unused devices or adjusting house temperature, and (3) switching to green(er) electricity sources. To address this goal, we conduct a comprehensive survey among households (N = 1790) in two EU regions: Overijssel, the Netherlands and Navarre, Spain. We use probit regression to estimate how behavioral factors, households’ socioeconomic characteristics and structural attributes of dwellings influence energy related actions. Our analysis demonstrates that awareness and personal and social norms are as important as monetary factors. Moreover, education and structural dwelling factors significantly affect households’ actions. These results have implications for governmental policies aimed at reducing residential CO2 footprints and facilitating demand-side solutions in a transition to low-carbon economy.

Climate change is one of the major global environmental challenges faced by humanity in the 21st century. Global carbon emissions from fossil fuels stand at almost 37 GtCO2 per year and have grown by an average of 2.4% per year so far this century. Among these, households – directly and indirectly – are responsible for more than 70% of carbon emissions. Hence, decarbonization of the economy requires massive worldwide efforts and a strong involvement of regions, cities, businesses, and individuals in addition to commitments at national levels. In the last few years, the discussions about mitigation strategies stress the importance of demand-side solutions and shifts to transdisciplinary and bottom-up approaches in assisting climate mitigation efforts worldwide. The IPCC Special Report on 1.5 degrees names ‘behavioural and lifestyle changes’ as a vital climate change mitigation strategy complimentary to technological measures. Yet, despite behavioural change being emphasized as a crucial component of mitigation strategies worldwide, empirical studies on individual energy-related choices and behavioural factors impacting them are scarce. Individual energy behaviour, especially when amplified through social context, shapes energy demand and, consequently, carbon emissions. By changing their behaviours, individuals can play an essential role in the transformation process towards a low-carbon society and global emissions reduction. However, explaining and affecting human behaviour is a difficult task since human nature is complex and heterogeneous. As a result, quantitative tools to assess cumulative household emissions, given the diversity of behaviour and a variety of psychological and social factors influencing it beyond purely economic considerations, are scarce. This dissertation highlights the potential of behavioural changes among heterogeneous households regarding energy use and their role in mitigating climate change. To do so, (a) a comprehensive household survey is designed and conducted to explore how individuals choose to change their energy behaviour and what factors trigger or inhibit these choices; (b) simulation tools are designed and developed to aggregate these insights and quantitatively assess regional and national impacts of individual choices on carbon emissions; and (c) a novel method to upscale individual energy behaviour for climate change mitigation strategies is presented.

© 2018 The Authors Changing residential energy demand can play an essential role in transitioning to a green economy. Environmental psychology suggests that behavioral changes regarding energy use are affected by knowledge, awareness, motivation and social learning. Data on various behavioral drivers of change can explain energy use at the individual level, but it provides little information about implications for macro energy demand on regional or national levels. We address this challenge by presenting a theoretically-based and empirically-driven agent-based model to track aggregated impacts of behavioral changes among heterogeneous households. We focus on the representation of the multi-step changes in individual energy use behavior and on a quantitative assessment of their aggregated impacts on the regional level. We understand the behavioral complexity of household energy use as a dynamic process unfolding in stages, and explore the barriers for utilizing the full potential of a region for emissions reduction. We suggest a policy mix that facilitates mutual learning among consumers.

Coupled climate-economy systems are complex adaptive systems. While changes and out-of-equilibrium dynamics are in the essence of such systems, this dynamics can be of a very different nature. Specifically, it can take a form of either gradual marginal developments along a particular trend or exhibit abrupt nonmarginal shifts [1]. Nonlinearities, thresholds, and irreversibility are of particular importance when studying coupled climate-economy systems. Strong feedbacks between climate and economy are realized through energy: economy requires energy for literary every sector, for development in literary any sector, while emissions need to stabilize and be even reduced to avoid catastrophic climate change [2]. Possibilities of passing some thresholds that may drive these climate-energy-economy (CEE) systems in a completely different regime need to be explored. However, currently available models are not always suitable to study nonlinearities, paths involving critical thresholds and irreversibility [3]. To be able to formulate an appropriate energy policy for this complex adaptive CEE system, policymakers should ideally have decision support tools that are able to foresee changes in energy market over the coming decades to plan ahead accordingly. Many macro models, that assume rational representative agent with static behavior, are designed to study marginal changes only. So there is a need for models that are able to capture nonlinear changes and their emergence.

Understanding and predicting how climate will change, and whether and how a transition to low-carbon economies will develop over the next century is of vital importance. Nowadays there is high competition between countries to achieve a low-carbon economy. They are examining different ways e.g. different energy efficient technologies and low-carbon energy sources, however they believe that human choices and behavioural change has a crucial impact, which is many times discussed in the literature as well. In this paper, we aim to discuss challenges related to modelling behavioural changes on the demand side and show effect of the demand side activation (households behavioural change) in decreasing regional energy use and emissions. In following we will discuss (a) the challenges of studying households energy choices in the context of transition to low-carbon economy, and (b) the importance of a thorough underpinnings of modelling choices using inputs of behavioural sciences on climate and environment knowledge and awareness in households decisions. We further present an agent-based model rooted in established psychology theories of behavioural change and supported by empirical survey data.