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This study presents a comparative accident risk assessment for different energy technologies, e.g., fossil fuels (incl. CCUS), Hydropower, H2, Nuclear, and new renewables, in the Organization for Economic Co-operation and Development (OECD) countries. The quantitative analysis is based on the historical observations collected in the Paul Scherrer Institute (PSI)'s ENergy-related Severe Accident Database (ENSAD) and updated using different sources, for the period 1970-2020, whereas for Nuclear a simplified level-3 Probabilistic Safety Assessment (PSA) is applied. Furthermore, for each energy technology, risk indicators, e.g., fatality rate and maximum consequences, are estimated to allow for comparison. Generally, fatality rates for Nuclear, Hydrogen, Hydropower and new renewables perform better than the fossil energy chains. In contrast, maximum consequences can be far highest for Nuclear, intermediate for fossil and Hydropower, and lowest for new renewables, which are less prone to severe accidents. Overall, no technology performs best or worst in all respects, thus trade-offs and priorities are needed to balance the conflicting objectives such as energy security, sustainability, and risk aversion to support rationale decision making.

Wind power plays a pivotal role in decarbonizing the world's electricity mix, and the current annual installed capacity growth is expected to continue, supported by ambitious targets, policies and cost reductions. However, wind power faces several social acceptance challenges that affect peoples' perception and can lead to opposition and protests. The main concerns are aesthetic impact, environmental effects on wildlife and biodiversity and health hazards. Risks and consequences due to accidents often lack an adequate analysis and discussion. Therefore, the aim of this study is a comparative risk assessment of onshore and offshore wind power accidents at a global level, under a societal perspective. First, a comprehensive data set is compiled that exclusively relies on open-source and publicly available information. The final data set comprises 2708 accidents for the period 2000-2022. Second, descriptive statistics and visualizations are used to identify temporal and geographic trends, and to relate accidents to different attributes (e.g., accident types). Third, selected indicators for fatality risk are calculated to compare different country groups and onshore vs. offshore activities. In summary, this study provides useful insights and a better understanding of accident risks with a focus on health impacts, thus complementing the industry's focus on occupational risk. Ultimately, it can help to smoothen controversies and achieve compromises in such complex decision-making processes.

In the broader context of the energy transition and the goal to achieve net-zero greenhouse gas emissions by 2050, it is of major interest to have a comparative perspective on risks related to accidents for a broad range of energy technologies. This is an essential contribution to support stakeholders in complex decision-making processes to plan, design and establish supply chains that are economic, efficient, reliable, safe, secure, and sustainable. Among renewable technologies, solar photovoltaic (PV) is expected to be a major contributor. Therefore, this study presents a first step on the assessment of accident risk considering a full-chain perspective for current and future PV technologies to be included in a comparative assessment for energy technologies. In particular, it focused on the comparative accident risk assessment for PV manufacturing. Designated hazardous substances involved in PV manufacturing chains are selected from life cycle inventories to characterize the risk of PV production processes. The assessment quantitatively estimated the accident risk of hazardous substances with risk indicators, e.g., fatality rate, using global historical data collected from multiple industrial accident databases. The hazardous substances risk indicators are allocated to the PV technologies to estimate manufacturing accident risk, and to compare their relative contributions to overall PV indicators. Results indicate that hydrochloric acid, hydrofluoric acid, and sodium hydroxide are the most significant hazardous substances. Furthermore, among the considered PV technologies, results reveal that copper-indium-gallium-diselenide (CIGS) panels have the worst risk performance compared to the other technologies, while cadmium telluride (CdTe) panels performed best.

The Brumadinho tailings dam collapse in 2019 killed hundreds and caused extensive damage to the surrounding area, including long-lasting environmental damage. Yet, this catastrophic event also triggered numerous proposals and activities to increase transparency related to environmental, social and governance (ESG) risks and sustainability in a broader context. Recently, several studies have proposed indicator-based approaches, but they generally lack a coherent aggregation and analysis of trade-offs and synergies between different sustainability criteria. Against this background, the current study seeks to create a global sustainability comparison of tailings dams at a country level, by combining harmonized data from multiple input sources through an iterative, multi-stage process. First, a comprehensive set of criteria and indicators is established that includes, among others, the impact on the environment, accident risks, and socio-political and governance aspects. Second, a dedicated Multi-Criteria Decision Analysis (MCDA) framework based on an outranking sorting approach (i.e., ELECTRE-TRI) is developed. Third, the evaluation system is applied to 43 countries that experienced at least one tailings dam failure since 1970, providing fact-based and transparent decision support to stakeholders and policy makers.

Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information to the CI. A measure of information transfer from each indicator is proposed along with two weightoptimization methods, which allow the weights to be adjusted to achieve either a targeted or maximized information transfer. The tools presented in this paper are applied to a case study for resilience assessment of energy systems, demonstrating how they can support the tailored development of CIs. These findings enable analysts bridging the statistical properties of the index with the weighting preferences from the stakeholders. They can thus choose a weighting scheme and possibly modify the index while achieving a more consistent (by correlation) index., QC 20211026