Psychological inoculation strategies to fight climate disinformation across 12 countries.

Decades after the scientific debate about the anthropogenic causes of climate change was settled, climate disinformation still challenges the scientific evidence in public discourse. Here we present a comprehensive theoretical framework of (anti)science belief formation and updating to account for the psychological factors that influence the acceptance or rejection of scientific messages. We experimentally investigated, across 12 countries (N = 6,816), the effectiveness of six inoculation strategies targeting these factors-scientific consensus, trust in scientists, transparent communication, moralization of climate action, accuracy and positive emotions-to fight real-world disinformation about climate science and mitigation actions. While exposure to disinformation had strong detrimental effects on participants' climate change beliefs (δ = -0.16), affect towards climate mitigation action (δ = -0.33), ability to detect disinformation (δ = -0.14) and pro-environmental behaviour (δ = -0.24), we found almost no evidence for protective effects of the inoculations (all δ < 0.20). We discuss the implications of these findings and propose ways forward to fight climate disinformation.

Probabilistic projections of granular energy technology diffusion at subnational level.

Projections of granular energy technology diffusion can support decision-making on climate mitigation policies and infrastructure investments. However, such projections often do not account for uncertainties and have low spatial resolution. S-curve models of technology diffusion are widely used to project future installations, but the results of the different models can vary significantly. We propose a method to create probabilistic projections of granular energy technology diffusion at subnational level based on historical time series data and testing how various projection models perform in terms of accuracy and uncertainty to inform the choice of models. As a case study, we investigate the growth of solar photovoltaics, heat pumps, and battery electric vehicles at municipality level throughout Switzerland in 2000-2021 (testing) and until 2050 (projections). Consistently for all S-curve models and technologies, we find that the medians of the probabilistic projections anticipate the diffusion of the technologies more accurately than the respective deterministic projections. While accuracy and probabilistic density intervals of the models vary across technologies, municipalities, and years, Bertalanffy and two versions of the generalized Richards model estimate the future diffusion with higher accuracy and sharpness than logistic, Gompertz, and Bass models. The results also highlight that all models come with trade-offs and eventually a combination of models with weights is needed. Based on these weighted probabilistic projections, we show that, given the current dynamics of diffusion in solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland, the net-zero emissions target would be missed by 2050 with high certainty.

A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities.

Improving equity is an emerging priority in climate and energy strategies, but little is known how these strategies would alter inequalities. Regional inequalities such as price, employment and land use are especially relevant in the electricity sector, which must decarbonize first to allow other sectors to decarbonize. Here, we show that a European low-carbon electricity sector in 2035 can reduce but also sustain associated regional inequalities. Using spatially-explicit modeling for 296 sub-national regions, we demonstrate that emission cuts consistent with net-zero greenhouse gas emissions in 2050 result in continent-wide benefits by 2035 regarding electricity sector investments, employment gains, and decreased greenhouse gas and particulate matter emissions. However, the benefits risk being concentrated in affluent regions of Northern Europe, while regions of Southern and Southeastern Europe risk high vulnerabilities due to high adverse impacts and sensitivities, and low adaptive capacities. Future analysis should investigate policy mechanisms for reducing and compensating inequalities.

Historic data of the national electricity system transitions in Europe in 1990-2019 for retrospective evaluation of models.

This data package enables empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990-2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide 1359 processed data files in a format that is suitable as input to electricity system models. Four types of data files are included for each country: (i) a country file documenting national electricity demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing prices and CO2 emissions for each generation fuel or input resource, and (iv) load profiles describing 24 h national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions.

Regional impacts of electricity system transition in Central Europe until 2035.

Achieving current electricity sector targets in Central Europe (Austria, Denmark, France, Germany, Poland and Switzerland) will redistribute regional benefits and burdens at sub-national level. Limiting emerging regional inequalities would foster the implementation success. We model one hundred scenarios of electricity generation, storage and transmission for 2035 in these countries for 650 regions and quantify associated regional impacts on system costs, employment, greenhouse gas and particulate matter emissions, and land use. We highlight tradeoffs among the scenarios that minimize system costs, maximize regional equality, and maximize renewable electricity generation. Here, we show that these three aims have vastly different implementation pathways as well as associated regional impacts and cannot be optimized simultaneously. Minimizing system costs leads to spatially-concentrated impacts. Maximizing regional equality of system costs has higher, but more evenly distributed impacts. Maximizing renewable electricity generation contributes to minimizing regional inequalities, although comes at higher costs and land use impacts.

Scenario processes for socio-environmental systems analysis of futures: A review of recent efforts and a salient research agenda for supporting decision making.

This paper reviews the latest research on scenarios including the processes and products for socio-environmental systems (SES) analysis, modeling and decision making. A group of scenario researchers and practitioners participated in a workshop to discuss consolidation of existing research on the development and use of scenario analysis in exploring and understanding the interplay between human and environmental systems. This paper presents an extended overview of the workshop discussions and follow-up review work. It is structured around the essential challenges that are crucial to progress support of decision making and learning with respect to our highly uncertain socio-environmental futures. It identifies a practical research agenda where challenges are grouped according to the process stage at which they are most significant: before, during, and after the creation of the scenarios as products. These challenges for SES include: enhancing the role of stakeholder and public engagement in the co-development of scenarios, linking scenarios across multiple geographical, sectoral and temporal scales, improving the links between the qualitative and quantitative aspects of scenario analysis, addressing uncertainties especially surprise, addressing scenario diversity and their consistency together, communicating scenarios including visualization methods, and linking scenarios to decision making.

Perspectives of Informed Citizen Panel on Low-Carbon Electricity Portfolios in Switzerland and Longer-Term Evaluation of Informational Materials.

Low-carbon transition is gaining momentum, but relatively little is known about the public preferences for low- and zero-carbon electricity portfolios given their environmental, health, and economic impacts. Decision science literature argues that conventional opinion surveys are limited for making strategic decisions because the elicited opinions may be distorted by misconceptions and awareness gaps that prevail in the public. We created an informed citizen panel ( N = 46) in Switzerland using technology factsheets, interactive web-tool Riskmeter, and group discussions. We measured the evolution of the panel's knowledge and preferences from initial (uninformed) to informed and longer-term views 4 weeks after. In terms of energy transition, our elicited technology and portfolio preferences show strong support for the low-carbon electricity sector transition, especially relying on hydropower, solar power, electricity savings and efficiency, and other renewable sources. Since these informed preferences are structurally different from the futures considered by many energy experts, we argue that these preferences should also inform the Swiss Energy Strategy 2050s implementation. In terms of methodologies in decision science, our factsheets, Riskmeter, and group discussions all proved effective in forming the preferences and improving knowledge. But we also intriguingly found that in a longer run the participants tended to revert back to their initial opinions. The latter finding opens up multiple new research questions on the longer-term effectiveness of informational tools and stability of informed preferences.

Communicating Low-Probability High-Consequence Risk, Uncertainty and Expert Confidence: Induced Seismicity of Deep Geothermal Energy and Shale Gas.

Subsurface energy activities entail the risk of induced seismicity including low-probability high-consequence (LPHC) events. For designing respective risk communication, the scientific literature lacks empirical evidence of how the public reacts to different written risk communication formats about such LPHC events and to related uncertainty or expert confidence. This study presents findings from an online experiment (N = 590) that empirically tested the public's responses to risk communication about induced seismicity and to different technology frames, namely deep geothermal energy (DGE) and shale gas (between-subject design). Three incrementally different formats of written risk communication were tested: (i) qualitative, (ii) qualitative and quantitative, and (iii) qualitative and quantitative with risk comparison. Respondents found the latter two the easiest to understand, the most exact, and liked them the most. Adding uncertainty and expert confidence statements made the risk communication less clear, less easy to understand and increased concern. Above all, the technology for which risks are communicated and its acceptance mattered strongly: respondents in the shale gas condition found the identical risk communication less trustworthy and more concerning than in the DGE conditions. They also liked the risk communication overall less. For practitioners in DGE or shale gas projects, the study shows that the public would appreciate efforts in describing LPHC risks with numbers and optionally risk comparisons. However, there seems to be a trade-off between aiming for transparency by disclosing uncertainty and limited expert confidence, and thereby decreasing clarity and increasing concern in the view of the public.

Integrated decision-making about housing, energy and wellbeing: a qualitative system dynamics model.

BACKGROUND: The UK government has an ambitious goal to reduce carbon emissions from the housing stock through energy efficiency improvements. This single policy goal is a strong driver for change in the housing system, but comes with positive and negative "unintended consequences" across a broad range of outcomes for health, equity and environmental sustainability. The resulting policies are also already experiencing under-performance through a failure to consider housing as a complex system. This research aimed to move from considering disparate objectives of housing policies in isolation to mapping the links between environmental, economic, social and health outcomes as a complex system. We aimed to support a broad range of housing policy stakeholders to improve their understanding of housing as a complex system through a collaborative learning process. METHODS: We used participatory system dynamics modelling to develop a qualitative causal theory linking housing, energy and wellbeing. Qualitative interviews were followed by two interactive workshops to develop the model, involving representatives from national and local government, housing industries, non-government organisations, communities and academia. RESULTS: More than 50 stakeholders from 37 organisations participated. The process resulted in a shared understanding of wellbeing as it relates to housing; an agreed set of criteria against which to assess to future policy options; and a comprehensive set of causal loop diagrams describing the housing, energy and wellbeing system. The causal loop diagrams cover seven interconnected themes: community connection and quality of neighbourhoods; energy efficiency and climate change; fuel poverty and indoor temperature; household crowding; housing affordability; land ownership, value and development patterns; and ventilation and indoor air pollution. CONCLUSIONS: The collaborative learning process and the model have been useful for shifting the thinking of a wide range of housing stakeholders towards a more integrated approach to housing. The qualitative model has begun to improve the assessment of future policy options across a broad range of outcomes. Future work is needed to validate the model and increase its utility through computer simulation incorporating best quality data and evidence. Combining system dynamics modelling with other methods for weighing up policy options, as well as methods to support shifts in the conceptual frameworks underpinning policy, will be necessary to achieve shared housing goals across physical, mental, environmental, economic and social wellbeing.

Context-specific energy strategies: coupling energy system visions with feasible implementation scenarios.

Conventional energy strategy defines an energy system vision (the goal), energy scenarios with technical choices and an implementation mechanism (such as economic incentives). Due to the lead of a generic vision, when applied in a specific regional context, such a strategy can deviate from the optimal one with, for instance, the lowest environmental impacts. This paper proposes an approach for developing energy strategies by simultaneously, rather than sequentially, combining multiple energy system visions and technically feasible, cost-effective energy scenarios that meet environmental constraints at a given place. The approach is illustrated by developing a residential heat supply strategy for a Swiss region. In the analyzed case, urban municipalities should focus on reducing heat demand, and rural municipalities should focus on harvesting local energy sources, primarily wood. Solar thermal units are cost-competitive in all municipalities, and their deployment should be fostered by information campaigns. Heat pumps and building refurbishment are not competitive; thus, economic incentives are essential, especially for urban municipalities. In rural municipalities, wood is cost-competitive, and community-based initiatives are likely to be most successful. Thus, the paper shows that energy strategies should be spatially differentiated. The suggested approach can be transferred to other regions and spatial scales.