Risk Analysis最新文献

Many believe that enhanced geothermal systems (EGS) can greatly increase the extraction of geothermal energy worldwide, helping to decarbonize heat and electricity production. Effective communication is key to realizing the potential of EGS, yet we currently know little about how the public perceives this emerging technology. This exploratory study contributes to the literature with a cross-national survey in the United States (n = 1003) and Switzerland (n = 1028), two countries with active EGS projects. Specifically, we explore how EGS support relates to beliefs about the deep underground and perceptions of EGS as tampering with nature. The results show that respondents tend to perceive the deep underground as part of nature, dangerous, and unpredictable. The majority are positive about using the deep underground as a resource, although there were variations regarding specific underground activities. In both countries, EGS support is greater for respondents who perceive the underground as something for human use, perceive more benefits than risks from EGS, and support their country's transition to renewable energy. In Switzerland, EGS support is positively related to trust in industry developers and negatively related to perceptions that EGS is tampering with nature. The results offer novel theoretical insights into perceptions of the deep underground in relation to energy development. From a practical standpoint, the results suggest that those seeking to develop EGS may want to consider how to familiarize individuals with current subsurface energy activities, including efforts to protect the underground from unwanted consequences of "tampering," alongside engaging in discussions about the risks and benefits of EGS.

Recent events, including COVID-19, extreme floods, and explosion accidents, commonly induced localized closures and disruptions of urban road networks (URNs), resulting in significant impacts on human mobility and socio-economic activities. Existing studies on URN resilience to those events mainly took few cases for empirical studies, limiting our understanding on the URN resilience patterns across different cities. By conducting a large-scale nationwide resilience analysis of URNs in 363 cities in mainland China, this study attempts to uncover the resilience patterns of URNs against the worst-case single (SLDs) and multiple localized disruptions (MLDs). Results show that the distance from the worst-case SLD to the city center would be less than 5 km in 62.3% cities, as opposed to more than 15 km in 14.3% cities. Moreover, the average road network resilience of cities in western China could be 7% and 13% smaller than that of the eastern cities under the worst-case SLDs and MLDs, respectively. This inequality in the worst-case resilience is partly attributable to variations in urban socio-economic, infrastructure-related, and topographic factors. These findings could inspire nationwide pre-disaster mitigation strategies to cope with localized disruptions and help transfer insights for mitigation strategies against disruptive events across cities.

The study explores the multifaceted role of safety goals in fostering a risk-informed culture, reflecting the global experience within the nuclear safety domain. Analysis of the phases of transition evoked by establishing and applying safety goals sheds light on the need for epistemic, reflexive, and practical transitions for better management of nuclear safety. This pivotal role of safety goals underscores the importance of recognizing them not as ready-to-use turnkey products but as catalysts for stakeholder dialog, reassessment of existing safety paradigms, and regulatory framework refinement. Finally, this study explores the challenges associated with standardizing safety goals globally and navigating the transition process within the framework of transition management.

Three decades ago, several articles on the subjectivity in chemical risk judgments (i.e., labeled "intuitive toxicology") measured the divide between the public and toxicologists with different backgrounds regarding the validity of predicting health effects based on in vivo studies. Similar divides with impacts on societal discourse and chemical risk assessment practices might exist concerning alternative toxicity testing methods (i.e., in vitro and in silico). However, studies to date have focused either on the public's views of in vivo or stem cell testing or on experts' views of in vivo testing and potential alternatives (i.e., toxicologists and medical students), which do not allow for a direct investigation of potential divides. To fill this knowledge gap, we conducted two online surveys, involving members of the German-speaking public in Switzerland and European human health risk assessors, respectively. This article presents the results of these two surveys regarding the divide in the public's and risk assessors' perspectives on risk assessment based on in vivo, in vitro, and in silico testing. Particularly, the survey with the risk assessors highlights that, beyond scientific and regulatory barriers, alternatives to in vivo testing may encounter individual hurdles, such as higher uncertainty associated with them. Understanding and addressing these hurdles will be crucial to facilitate the integration of new approach methodologies into chemical risk assessment practices as well as a successful transition toward next-generation risk assessment, bringing us closer to a fit-for-purpose and more efficient regulatory landscape.

A major global catastrophe would likely disrupt trade in liquid fuels. Countries dependent on imported oil products might struggle to sustain industrial agriculture. Island nations importing 100% of refined fuels are particularly vulnerable. Our case study aimed to estimate the agricultural land area and biofuel volumes needed to feed the population of New Zealand in the absence of trade. Results showed that stored diesel would quickly be exhausted with ordinary use (weeks) and even with strict rationing (months). To preserve fuel, we found that farming wheat (requiring as little as 5.4 million liters [L] of diesel per annum) was more fuel-efficient than potatoes (12.3) or dairy (38.7) to feed the national population under a climate-as-usual scenario. In a nuclear winter scenario, with reduced agricultural yields, proportionately greater diesel is needed. The wheat would require 24% of current grain-cropped land, and the canola crop used as feedstock for the required biofuel would occupy a further 1%-7%. Investment in canola biodiesel or renewable diesel refineries could ensure supply for the bare minimum agricultural liquid fuel needs. Were subsequent analysis to favor this option as part of a fuels resilience response and as a tradeoff for routine food use, expansion in refining and canola cropping before a catastrophe could be encouraged through market mechanisms, direct government investment, or a combination of these. Logistics of biofuel refining scale-up, post-catastrophe, should also be analyzed. Further, biodiesel produced in normal times would help the nation meet its emissions reduction targets. Other countries should conduct similar analyses.

Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.

Based on cross-country data from 2002 to 2019, we explore the impact of climate change risk (CCR) on energy poverty (EP), and the moderating role in the CCR-EP nexus is also discussed. The empirical results suggest that CCR can exacerbate EP, especially for rural areas. Moderating effect analysis shows that financial development, technological innovation, and adaptation readiness can modify the negative impacts of CCR on EP to some extent. Moreover, the impact of CCR on EP is heterogeneous, demonstrating that CCR is more likely to exacerbate EP in countries with low economic development, low economic freedom, high carbon intensity, and the Africa region. Our findings emphasize the challenge of balancing EP alleviation with climate change response and provide the policy guidance to promote coordinated development of CCR management and energy supply security.

Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a prescriptive AI algorithm, some problems may occur, first and clearly, if the AI information is wrong or incomplete. But the main point of this article is that under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences. That risk attitude implemented in the software is chosen by the analysts, the organization that they serve, the experts who inform them, and more generally by the process of identifying possible options. The problem is that it may or may not represent, as it should, the preferences of the actual decision maker (the risk manager) and of the people subjected to his/her decisions. This article briefly describes the sometimes-serious problem of that discrepancy between the preferences of the risk managers who use an AI output, and the risk attitude embedded in the AI system. The recommendation is to make these AI factors as accessible and transparent as possible and to allow for preference adjustments in the model if needed. The formulation of two simplified examples is described, that of a medical doctor and his/her patient when using an AI system to decide of a treatment option, and that of a skipper in a sailing race such as the America's Cup, receiving AI-processed sensor signals about the sailing conditions on different possible courses.

People typically use verbal probability phrases when discussing risks ("It is likely that this treatment will work"), both in written and spoken communication. When speakers are uncertain about risks, they can nonverbally signal this uncertainty by using prosodic cues, such as a rising, question-like intonation or a filled pause ("uh"). We experimentally studied the effects of these two prosodic cues on the listener's perceived speaker certainty and numerical interpretation of spoken verbal probability phrases. Participants (N = 115) listened to various verbal probability phrases that were uttered with a rising or falling global intonation and with or without a filled pause before the probability phrase. For each phrase, they gave a point estimate of their numerical interpretation in percentages and indicated how certain they thought the speaker was about the correctness of the probability phrase. Speakers were perceived as least certain when the verbal probability phrases were spoken with both prosodic uncertainty cues. Interpretation of verbal probability phrases varied widely across participants, especially when rising intonation was produced by the speaker. Overall, high probability phrases (e.g., "very likely") were estimated as lower (and low probability phrases, such as "unlikely," as higher) when they were uttered with a rising intonation. The effects of filled pauses were less pronounced, as were the uncertainty effects for medium probability phrases (e.g., "probable"). These results stress the importance of nonverbal communication when verbally communicating risks and probabilities to people, for example, in the context of doctor-patient communication.

This study analyzed the acceptance of solar energy in terms of energy justice. The critical issue of energy supply, demand, and transition is a process of social redistribution of risks from old to new energy systems. The question of the appropriate distribution of risks for the energy system is closely related to energy justice. Previous studies are limited in empirically testing whether or not energy justice can contribute to the acceptance of new energy system. In addition, previous studies have heavily depended on energy justice in terms of anthropocentric type. Anthropocentric definitions of energy justice have focused primarily on the benefits and costs allocated only to humans. Such an anthropocentric view of justice lacks consideration of the value of various ecological beings. Therefore, this study aims to shed light on the role of not only four anthropocentric types of energy justices but also on for four ecological ones in the acceptance of solar energy. The analysis reveals that recognitive justice, generational justice, deep ecological justice, social ecological justice, and distributional justice positively influence the acceptance of solar energy, whereas procedural justice, restorative justice, and eco-socialist justice have no effect on it. In particular, this study found that recognitive justice moderates the effect of personal norms on acceptance of solar energy, whereas restorative justice moderates the effect of knowledge on it.