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A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

In the aftermath of the 2017 Manchester Arena bombing, the ensuing debate in the press and on social media underscored terrorism's potential to intensify divisions. This study delves into the social media and press dynamics of rumors following the attack, and into the subsequent discourse on migration policies. We collected a dataset consisting of 3,184 press articles and 89,148 tweets about the Manchester Arena bombing. This research aims to identify prevalent rumors, explore the short- and long-term impacts on user engagement, analyze the sentiment in tweets related to each rumor, and examine perceptions of terrorism threats and migration policies among both the press and X (previously Twitter) users. The study found that X acted as an echo chamber for misinformation, amplifying specific rumors related to the attack, while the press exhibited fact-checking practices and provided nuanced perspectives. Notably, one rumor suggesting the attacker was a refugee gained traction over the years, reflecting an increase in anti-immigrant sentiments. Emotional responses on X ranged from neutral to heightened distress and anger, highlighting the significant impact of social media narratives on public sentiment. The research underscores the polarization of views on social media, influenced by the condensed format of tweets and the rapid production cycle, with X users expressing predominantly very negative attitudes toward immigration. This study emphasizes the critical role of the media in dispelling misinformation and fostering nuanced public understanding in complex sociopolitical contexts.

In the digital era, social media platforms have become the focal point for public discourse, with a significant impact on shaping societal narratives. However, they are also rife with mis- and disinformation, which can rapidly disseminate and influence public opinion. This paper investigates the propagation of mis- and disinformation on X, a social media platform formerly known as Twitter. We employ a multidimensional analytical approach, integrating sentiment analysis, wavelet analysis, and network analysis to discern the patterns and intensity of misleading information waves. Sentiment analysis elucidates the emotional tone and subjective context within which information is framed. Wavelet analysis reveals the temporal dynamics and persistence of disinformation trends over time. Network analysis maps the intricate web of information flow, identifying key nodes and vectors of virality. The results offer a granular understanding of how false narratives are constructed and sustained within the digital ecosystem. This study contributes to the broader field of digital media literacy by highlighting the urgent need for robust analytical tools to navigate and neutralize the infodemic in the age of social media.

Social media platforms have a key role in spreading narratives about climate change, and therefore it is crucial to understand the discussion about climate change in social media. The discussion on anthropogenic climate change in general, and social media specifically, has multiple different narratives. Understanding the discourses can assist efforts of mitigation, adaptation, and policy measures development. In this work, we collected 333,635 tweets in English about anthropogenic climate change. We used Natural Language Processing (NLP) and machine learning methods to embed the semantic meaning of the tweets into vectors, cluster the tweets, and analyze the results. We clustered the tweets into four clusters that correspond to four narratives in the discussion. Analyzing the behavioral dynamics of each cluster revealed that the clusters focus on the discussion of whether climate change is caused by humans or not, scientific arguments, policy, and conspiracy. The research results can serve as input for media policy and awareness-raising measures on climate change mitigation and adaptation policies, and facilitating future communications related to climate change.

The aim of the chapter is to introduce and discuss novel systems’ analysis models and methodologies being developed within a joint project between National Academy of Science, Ukraine, and International Institute for Applied Systems Analysis—“Integrated modeling for robust management of food-energy-water-social-environmental (FEWSE) nexus security and sustainable development”. These approaches enable science-based support of policies providing coherent strategic coordination and regulations among food, energy, water, social sectors, accounting for complex linkages and differences in spatial and temporal scales between agriculture, energy and water security, potential systemic risks, and new feasible policies at various levels. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk exposures and decisions of various agents. The paper discusses the need for the two-stage stochastic optimization models enabling to design robust portfolios of precautionary ex-ante strategic and adaptive ex-post operational decisions making the interdependent systems flexible and robust with respect to risks of all kinds. Thus often, detailed sectoral and regional models have been used to independently plan the developments of respective sectors and regions. However, solutions that are optimal for a sub-system (a sector, a region, selected sectors/regions) may turn out to be infeasible for the entire system. The paper presents new approaches based on the linkage of detailed distributed models of subsystems (e.g., sectoral and regional models) under joint resource constraints thus allowing for truly integrative decision support through optimal and robust solutions across sectors and regions. The linkage solution procedures are based on the parallel solving of equivalent nonsmooth optimization models following a simple iterative stochastic quasigradients (subgradient) algorithm. The procedures can be considered

Systemic multi-sectoral and multi-hazard risk assessment is an important issue for stakeholders to mitigate the impacts of cascading and compounding events. The EU Horizon Europe ``PARATUS''---project deals in one of its work packages with this topic and aims to define a generic methodology for systemic risk assessment. The developed methodology will be applied and tested in four study sites: (i) Alps (Austria-Italy) focusing on extreme wind, floods, landslides, heat, droughts, snow avalanches and anthropogenic hazards, (ii) the Bucharest region and hazard chains associated with large earthquakes, (iii) Caribbean Islands considering tropical storms, tsunamis, volcanic eruptions and earthquake, and (iv) Istanbul, which is affected by earth-quakes-related hazards (e.g. soil liquefaction, landslides or tsunamis) and by hydrometeorological hazards (extreme temperatures, fires, and flooding). The multi-hazard assessment is performed in the four sites in current conditions and for future scenarios that incorporate climate changes as well as changes of exposure and vulnerability. On one side, the multi-hazard aspect is studied by including the occurrence of simultaneous or closely timed events (compounding), as well as the cascading hazards that may arise from their interactions. On the other side, the physical, social, environmental and systemic vulnerability is analyzed and a capacity assessment performed. Special attention is given to the uncertainty analysis of each component in the generic methodology and the future predictions to ensure the robustness and reliability of the assessment.

The urgency of addressing the impacts of climate change has underscored the critical need for effective adaptation policies. In this context, the incorporation of participatory elements in climate change adaptation policies plays a pivotal role in ensuring inclusivity and collaboration. This study presents a comprehensive analysis of participatory elements in Spanish climate change adaptation policies, with a focus on identifying the extent to which these policies incorporate participatory elements and highlighting potential gaps. Against this background, we conducted an analysis of participatory elements in Spanish climate change adaptation policies with Spain being one of the most vulnerable countries in the European Union (Rodrigo et al. 2023). We addressed the research questions of how well Spanish national climate change adaptation policies incorporate participatory elements and where we identify policy gaps. By scrutinizing participation mechanisms, policymakers can tailor policies to better address the needs and concerns of various stakeholders. The analysis facilitates the identification of best practices and case studies.

The paper considers the issues of creating systems for monitoring and predictive modeling the state of hazardous phenomena and objects, discusses various options for their use for risk analysis. Using the example of the Covid-19 pandemic, it is shown how the discrepancy between forecast and reality leads (after a critical analysis) to the model modification or a revision of the accepted external impact scenario.

In this study, a multi-dimensional model was designed based on the technology adoption model (TAM), the digital infrastructural characteristics of social media, and source credibility (SC), with the aim of predicting the intention to use social media when a disaster strikes. This is the first study in which utilitarian values—perceived usefulness (PU) and perceived ease of use (PEOU)—have been considered along with hedonic values (enjoyment) as predictors of attitude and intention to use social media to reduce the impacts of a disaster. The sample design strategically divides European countries into six regions, each with a country chosen randomly through cluster sampling. The questionnaires were subsequently distributed through popular social networking platforms, utilizing convenience sampling. The results of structural equation modeling using SmartPLS software revealed three infrastructural features of social media—perceived adaptability, reflexivity, and perceived interconnectedness— impact perceived social media usefulness. Source credibility predicted both PU and PEOU. The results indicated the intention to use social media for three purposes-acquiring public information, confirming journalistic reporting, and sharing information related to disasters was not influenced by hedonistic values. The findings suggested that to increase the use of social media in relation to disaster risk reduction, robust social media infrastructural characteristics should be considered aligning with the anticipated disaster conditions.

Mitigating maladaptation and effectively managing climate risks are crucial components of strategic planning in agriculture amidst climate change. Evaluation serves as a pivotal element in this process, facilitating the identification of effective adaptation strategies tailored to local contexts. Consequently, it's imperative to thoroughly evaluate these strategies to ensure their success and resilience. The current study evaluated adaptation methods tailored to the local context in southwest Iran across three categories-crop, farm, and water management-employing Multi-Criteria Decision-Making (MCDM) and the PROMETHEE-GAIA. Sensitivity analysis was performed during the AHP (Analytical Hierarchy Process) stage to confirm the criteria weights and in the PROMETHEE to confirm the ranking. A set of eight criteria, including effectiveness/importance, affordability, institutional feasibility, technical feasibility, social feasibility, traditional acceptance, flexibility, and environment side effects (positive) were applied to evaluate the adaptation measures. Our results indicated the three highest rankings in each set of measures, as follows: i) crop management—relay intercropping, change of crop type, and mixed intercropping; ii) farm management—pest and disease management, weed control, and crop rotation; iii) water management—lining water canals or covering their earth floors with nylon, using pipes rather than open canals to transfer water to the field, and increasing the time intervals between irrigations to deal with water shortages. The outcomes underscore the urgency of formulating region-specific adaptation policies that align with local expertise and contextual needs. By prioritizing the identified effective strategies, policymakers can enhance resilience against water scarcity in southwest Iran. Moreover, the study highlights the importance of ongoing evaluation and adaptation, emphasizing the dynamic nature of climate challenges and the need for continuous ref

Climatic extremes have caused immense harm around the world. Its harm in terms of the proportion of people and regions affected continues to increase every single day. Due to people’s psychological distance from such climatic threats, active initiatives are not undertaken for mitigation of its source. Rather, localized short-term solutions are marking a new status-quo. This study examines if fear can be used as a motivator to nudge people away from the psychological distance and motivate them to adopt electric vehicles (EVs). While subsidies and tax rebates are popularly adopted means to boost demand and supply of EVs, monetary incentives are costly to sustain for developing nations, amidst their diverse priorities. Instead, use of motivators like ‘fear’ is cheap, yet not much explored. Using the protection motivation theory, the study interviews 1112 Indian individuals, to examine if fear can nudge EV adoption. Using structural equation modeling and mediation analysis, the study finds that the expectation of personal harm from climatic threats can nudge one to actively mitigate the source of threat. Various aspects of threat and the associated coping processes that need to be triggered sequentially to nudge the formation of a pro-environmental intention to adopt EVs are also outlined.

To promote environmentally sustainable water governance, this study emphasizes the necessity of aligning institutional structures with ecological scales. The research focused on the Urmia Lake Basin in Iran facing the serious problem of drying up. Beyond the political and economic determinants shaping the water governance system in the region, the study evaluated the effect of Urmia Lake Restoration Program (ULRP), an environmental movement, on the basin's water governance structure. Employing statistical mechanics methods to scrutinize Hamiltonian system costs related to administrative interactions for water supply-demand, the study assessed the structural fit of the water governance system to the basin across distinct stages: without- and with-including the ULRP. Results revealed diminished costs following ULRP involvement, notably in entities with higher water demands, head offices and the system overall, further improved by water-saving measures. These findings highlighted the efficacy of vertical (re)arrangements and structural reform through ULRP incorporation in enhancing system fit, stressing the significance of its water-saving policy. The methodology provides a fast and explicit scan of the system structure, demonstrating its ability to project the effect of institutional reforms on the system state. Serving as a constructive tool for policymakers, it facilitates rapid, efficient and informed decision-making in water governance. Furthermore, following the UN SDG 6, this framework supports integrated water resources management (IWRM) across sectors and regions, particularly targeting water-stressed contexts.

Natural disasters like earthquakes, and global crises like pandemics have historically captured the public’s imagination and prompted people to seek explanations. However, in times of limited information, these explanations can take the form of conspiracy theories, particularly regarding the origins or causes of such events. With the advent of social media conspiracy theories can spread quickly and easily, leaving little room for critical thinking. The focus of this study is the analysis of the so-called High-Frequency Active Auroral Research Program (HAARP) conspiracy, which explains earthquakes through the employment of secret weather control weapons. This study aims to answer the research question of how the discourse on the HAARP conspiracy theory changes over time, and what are the potential catalysts for heightened attention to this conspiracy theory. This study uses the Twitter API to collect tweet frequencies about this conspiracy from January 2022 through March 2023. The empirical data include over one million tweets on HAARP. The sentiment analysis of the HAARP conspiracy theory is applied to the tweets before, during, and after the 6th of February 2023 earthquake in Syria and Turkey. In addition, this study investigates possible triggers of the development of the HAARP tweet frequency. This study finds that the frequency of HAARP discussion increases following a high-impact earthquake. There is also a positive correlation between average tweet sentiment and the number of tweets, which could indicate that the discussion of HAARP reinforces people’s beliefs. This study makes a significant contribution to the field of social psychology and communication by providing insights into the dynamics of belief reinforcement within online communities amidst heightened attention to conspiracy theories triggered by significant events. This knowledge has broader implications for understanding the impact of social media on public perception during crises.

As social media can play an important role in creating awareness of the need to mitigate climate change and of the benefits of renewable energy (RE), we examined the influences of social media on attitudes to renewable energy, including intention to use it. This study is novel in two ways: it simultaneously considers message characteristics and message-receiver perceptions as factors influencing the acceptance of renewable energy; and it is also the first study to use an extended information adoption model to evaluate the impact of Instagram on information adoption and the intention to use renewable energy sources. Our questionnaire, based on the theory of information adoption, included items on attitude toward information and trust in Instagram, and was randomly distributed online among followers of renewable energy pages on Instagram. A total of 173 people completed the questionnaire. Structural equation modeling showed that quality of argument had a positive and direct effect on the perceived usefulness of information. Source credibility as a peripheral path also indirectly increased perceived usefulness by changing attitude toward information. Moreover, perceived usefulness had a direct impact on information adoption and increased information adoption through trust of the materials.

The daily dissemination of a substantial amount of information concerning to disasters and crises on social media platforms, including Facebook, Instagram and Twitter in one side, and the sensitivity of this information, on the other hand, underscores the importance of evaluating the credibility of online information in this area. Fact-checking tools employing artificial intelligence represent a novel approach to verifying the validity of online information across various fields, including disaster management. The inclination of individuals to utilize fact-checking tools in such circumstances is influenced by their perceptions. Although there is a limited studies on the impact of perceptions and information processing on the intention to employ fact-checking tools in disaster-related contexts, it is anticipated that factors like critical thinking, as a concept that involves meticulous assessment of unclear or requiring careful consideration, heuristic processing, a concept indicating acceptance of news content without filtering, and the new-source tracking a concept demonstrating openness and positivity towards social media information, play pivotal roles in predicting this intention. Consequently, a conceptual framework was formulated wherein critical thinking, aside from its direct impact on the intention to use fact-checking tools, also exerts influence through two mediators of information processing and the new source tracking variables. This study's framework was examined using data from 202 respondents across various European countries, collected through an online survey. The conceptual framework analysised utilizing AMOS software. Descriptive findings indicate a moderate level of familiarity with misinformation detection tools among respondents (M=2.65; sd=1.04). Respondents exhibited close knowledge levels regarding fact-checking tools such as Rbutr, Foller, me and Botometer, Fakespot, NewsGuard, and Greek Hoaxes Detector, ranging between approximately (1.57

The application of science, technology, and innovation (STI) solutions is crucial for increasing participation in climate change adaptation. This case study analyses Germany's best practices in utilizing STI to address climate change adaptation, including climate-resilient infrastructure development, climate information services, advanced modelling, public information campaigns, technology transfer, integration of climate change adaptation in regulations, research and development, urban climate modelling, and cross-cutting instruments. The internal validity of these best practices is assessed based on their effectiveness, reliability, and consistency, while the external validity considers their relevance and applicability in diverse contexts. By continuously evaluating their validity and sharing knowledge, countries and stakeholders can advance their climate change adaptation efforts for a more resilient and sustainable future.

Following the 2017 Manchester Arena bombing, the ensuing discussions in the media and on social platforms highlighted the potential of terrorism to deepen societal divisions. This study investigates the dynamics of rumors on social media and in the press after the attack, as well as the subsequent discourse on migration policies. We compiled a dataset comprising 3,184 press articles and 89,148 tweets pertaining to the Manchester Arena bombing. The research aims to identify prevalent rumors, assess their short- and long-term effects on user engagement, analyze the sentiment in tweets related to each rumor, and scrutinize perceptions of terrorism threats and migration policies among both the press and Twitter users. The findings reveal that Twitter acted as an echo chamber for misinformation, amplifying specific rumors related to the attack, while the press demonstrated fact-checking practices and offered nuanced perspectives. Notably, one rumor suggesting the attacker was a refugee gained traction over time, reflecting a surge in anti-immigrant sentiments. Emotional responses on Twitter varied from a neutral tone to heightened distress and anger, underscoring the significant impact of social media narratives on public sentiment. The research highlights the polarization of views on social media, influenced by the concise format of tweets and the rapid production cycle, with Twitter users predominantly expressing very negative attitudes toward immigration. This study emphasizes the crucial role of the media in dispelling misinformation and cultivating a nuanced public understanding in complex socio-political contexts.

Social media platforms have a key role in spreading narratives about climate change, and therefore it is crucial to understand the discussion about climate change in social media. The discussion on anthropogenic climate change in general, and on social media specifically, has multiple different narratives. Understanding of the discourses can assist efforts of mitigation, adaptation, and policy measures development. In this work, we collected 333,635 tweets in English about anthropogenic climate change. We used Natural Language Processing (NLP) and machine learning methods to embed the semantic meaning of the tweets into vectors, cluster the tweets, and analyze the results. We clustered the tweets into four clusters that correspond to four narratives in the discussion. Analyzing the behavioral dynamics of each cluster revealed that the clusters focus on the discussion of whether climate change is caused by humans or not, scientific arguments, policy, and conspiracy. The research results can serve as input for media policy and awareness-raising measures on climate change mitigation and adaptation policies, and facilitating future communications related to climate change.

This paper presents a comprehensive analysis of participatory elements in climate change adaptation policies at both the EU level and within national contexts, focusing on Germany and Spain. The study delves into the crucial role of co-production and citizen engagement in shaping effective climate adaptation strategies. The research methodology involves policy identification and selection, analysis of participatory elements, and the application of Arnstein's ladder of citizen participation to evaluate the level of citizen engagement in the identified policies. The analysis reveals the diverse mechanisms and approaches employed to foster inclusive and participatory processes in climate adaptation policies. The study highlights the significance of stakeholder involvement, consultation mechanisms, transparency, capacity building, and feedback mechanisms in shaping robust climate adaptation strategies. Furthermore, it underscores the importance of citizen participation in driving transformative climate adaptation initiatives, emphasizing the need for broad geographical representation, inclusive approaches, and the integration of diverse knowledge systems. The study identifies gaps and areas for improvement in the participatory elements of the analyzed policies, emphasizing the need for more comprehensive mechanisms to engage the general public and vulnerable communities in the adaptation planning process. It also underscores the importance of systematic studies of gaps and barriers to stakeholder participation and the representation of marginalized communities in adaptation planning and decision-making processes. The paper offers valuable insights into the participatory elements of climate change adaptation policies, providing a nuanced understanding of the approaches employed at both the EU and national levels. The findings contribute to the ongoing discourse on inclusive and effective climate adaptation strategies, emphasizing the need for continuous improvement and t

Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.

Every time a significant societal catastrophe occurs, the resulting trauma intensifies the sense of risk awareness, which often wanes in the public consciousness over time. Even the most widely covered and important events, though, can fade from people's memories over time or even become the topic of false information. A subjective reality regarding the event, its causes, and its effects may be created as a result of cognitive biases and the dependence on shortcuts that these characteristics of human cognition induce. These biases can cause erroneous judgments and other types of irrationality if they are not addressed. Information on these events can be spread through digital technologies, which are currently opening up new avenues for information exchange. The historical event which is a case study of our research took place on May 22, 2017, at the Manchester Arena concert venue, more than five years ago. This raises concerns about the way in which these cognitive biases are being addressed through information webs. What are the trends in how people use websites like Wikipedia to find information about catastrophic events like the Manchester bombing? Is there a connection between the purpose of individuals to use social media to look up more details about an event after it has been covered in the media? What are the temporal dynamics of the traffic on the Wikipedia page for the Manchester bombing? Our analysis of the Wikipedia traffic data shows persistent interest in this historical event with seasonal picks on Memorial Day.

This study empirically examines pathways for formation of intention to adopt electric vehicle (EV) among the middle-class people in India in the near future. With the increasing incomes, and the enhancement of lifestyles, personal vehicles on the road is increasing. To meet the net-zero target by 2070, it is crucial to convert the increasing demand for personal vehicles to a demand for EV. The state of Assam in the eastern region of India has been used as a case study. Using the Theory of Planned Behavior (TPB) and extending it further with ‘herd-behavior’ and ‘cost’, the role of soft gain motivators like norms and attitude is analyzed. Using Structural Equation Modeling and Mediation Analysis, subjective norms are found to be a direct and strongest pathway for formation of intention to adopt EV, followed by perceived behavioral control. Herd behavior is another strong indirect determinant of intention. Cost of an EV is not found to directly influence intention, nevertheless it acts as a mediator for attitude. This study recommends the redesigning of the Indian government EV promotion policies, as those are found to be susceptible to various behavioral bias and might not effectively lead to an intention to adopt EV.

The agricultural sector in general, and in Iran in particular, is a major consumer of water and now finds itself under significant pressure due to water deficiency. This study used the Protection Motivation Theory to detect reasons for the imprudent consumption of water in Iran and to further its conservation. The Theory was extended for particular application to a seriously affected water basin, the Urmia Lake Basin in Northwest Iran. The factors governing water-saving intention among farmers in the Basin were investigated. Three hundred farmers were selected through a multi-stage, clustered, random sampling method. The results of structural equation modeling illustrated that while the original model variables accounted for 58 of the variance in water-saving intention, this rate increased to 63 in the extended model when institutional trust was used as a variable. Whereas response efficacy showed itself to be the strongest determinant of water-saving intention, all factors except perceived severity were significant in both models. Furthermore, the results of a multi-group analysis revealed that the intention to adopt water conservation measures is commensurate with the distance from the water resource and proximity to the (drying) lake. The findings of the study are expected to provide important information for policymakers looking to tailor policies to work in extreme water deficiency cases like the Urmia Lake Basin.

This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These techniques furnish an enriched comprehension of social phenomena dynamics, bolstering decision-making processes. The study’s versatility extends across myriad domains, offering insights into public sentiment and the foresight for strategic approaches. The findings suggest immense potential in Social Intelligence Mining to influence strategies, foster innovation, and add value across diverse sectors.

This paper deals with the issue of ensuring the reliability of microsystems in the modern-day development of digital control technologies. The use of digital technologies to manage the reliability of microsystems stems from the development of various modern energy technologies with complex structures that require enhanced management systems to ensure their reliability. In this paper, we propose the use of digital platforms to manage the reliability of microsystems. The way digital platforms function makes it possible to automate the process of collecting, processing, and storing the necessary information about both power equipment and the operating modes of distribution networks and, in so doing, to assess and ensure the reliability of microsystems at all stages of the life cycle. In this paper, we present the main operational characteristics and principles of digital platforms. To analyze the reliability of microsystems within the framework of the functioning of digital platforms, we propose the use of machine learning methods. We suggest two algorithms for assessing the reliability of microsystems. In the first algorithm, the model is trained to analyze the regime indicators of the microsystem and, on the basis of these, to determine the reliability indicators. In the second algorithm, the model is trained to immediately determine the reliability indicators of the microsystem. Practical results have shown the effectiveness of the proposed algorithms in terms of the speed of assessing the reliability of microsystems while maintaining the required accuracy of calculations.

In this chapter, we present stochastic methodologies for energy-efficient technology investment planning, which can provide robust decisions against inherent uncertainties for optimal energy production and storage capacity expansion and operation policies involving stochastic renewable energy sources. The approach for robust decision support relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons. This allows us to analyze and model systemic impacts of potential extreme events and structural changes emerging from policy interventions and stakeholders’ dialogues, which may occur during the decision- making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The model allows the representation of all relevant energy subsystem components (e.g., traditional and renewable) and their interactions, dealing with both strategic and operational decisions and planning. Energy storage is represented and modeled in a rather general way. For example, the excess electricity can be used for hydrogen and fertilizer production. Unlike the static nature of deterministic models, the proposed stochastic model delivers solutions that are responsive to revealed information about systemic uncertainties and risks such as stochastic supply, demand, prices, weather variability, technological change, in order to adjust local or regional energy structure and management policies in a cost-effective and risk hedging manner. Integration of the operational and strategic models under the umbrella of the two-stage stochastic optimization provides an effective way to make real-time decisions consistent with the long-term strategic goals of energy system planners to guarantee secure energy provision in all uncertainty scenarios.

The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020–March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions.

This paper focuses on analyzing discussions related to Genetically Modified Organisms (GMOs) on Twitter, with a specific focus on the spread of misinformation and conspiracy theories. The authors collected and analyzed 1,048,274 English tweets related to GMOs between January 2020 and December 2022 using the Twitter API. The tweets were subjected to topical and sentiment analysis to identify the prevalent themes and attitudes toward GMOs. 30.92% of the tweets in the observed period were negative, 21.65% were neutral, and 47.43% were positive. The authors identified four clusters of tweets associated with misinformation or conspiracy theories: GMOs and vaccines, GMOs and COVID-19, GMOs and Monsanto, and GMOs and Bill Gates. The findings of this analysis can inform strategies for combating the spread of false information and conspiracies on social media and improve public understanding and trust in GMO technology.

Planning regional economic developments and social welfare without addressing issues related to mitigation and adaptation to low probability-high consequences catastrophe risks may lead to dangerous clustering of people, production facilities, and infrastructure in hazard-prone areas thereby critically increasing regional vulnerability. The endogeneity of the risks on production allocation and land use decisions represents new challenges for regional sustainable development planning. This chapter argues that catastrophe risk analysis and management have to be addressed with an Integrated Assessment and Management Model (IAMM) linking catastrophe risk modeling (CRM) with stochastic optimization (STO) techniques for the design of optimal and robust mitigation and adaptation strategies for dealing with catastrophe risks of all kinds. IAMM enables us to address the challenging characteristics on policies, mutually dependent losses, the lack of information, the need for long-term perspectives and geographically explicit models, the involvement of various agents (such as individuals, farmers, producers, consumers, governments, insurers, investors), safety and security standards, and the need for robust decisions. Safety and security criteria relate to Value-at-Risk and Conditional Value at Risk measures generalizing the well-known risk criteria and indicators used for regulating engineering, critical infrastructure, energy, water, agricultural safety and security requirements. These are key indicators for dealing with low probability-high consequences risks. The linkage between CRM and STO is established through an iterative stochastic quasigradient procedure (SQG) defining a sequential “searching” process, which resembles an adaptive learning environment and improvement of decisions from data and simulations, i.e., the so-called Adaptive Monte Carlo optimization. The SQG methods are applicable in cases when traditional stochastic approximation, gradient or stochastic gra

The overall objective of the PARATUS project and the platform is the co-development of a web-based simulation and information service for first and second responders and other stakeholders to evaluate the impact chains of multi-hazard events with particular emphasis on cross-border and cascading impacts. This deliverable provides a first impression of the platform and its components. A central theme in the PARATUS project is the co-development of the tools with stakeholders. The central stakeholders within the four applications case studies are therefore full project partners. They will be directly involved in the development of the platform. We foresee that the PARATUS Platform will have two major blocks: an information service that provides static information (or regularly updated information) and simulation service, which is a dynamic component where stakeholders can interactively work with the tools in the platform. The PARATUS will further make sure that documentation (e.g., software accompanying documentation) is also publicly available via the project website1 and other trusted repositories. The deliverable 4.1 was submitted to the European Commission on 31/07/2023 and is waiting for approval by the Research Executive Agency. Therefore, this current version may not represent the final version of the deliverable.

In this paper, we identified seven most widely spread conspiracy discourses about earthquakes. These conspiracy discourses link earthquakes to military activities like secret nuclear bomb testing, God's Providence like the punishment of humans for their sins, space activities like aliens visiting our planet, the US secret weather control program HAARP, tests of the Large Hadron Collider, fracking projects, and freemasonic plots. Following the major earthquake in Indonesia at the end of November 2022, we extracted data from Twitter by keywords using the Hoaxy tool for tracking the spread of information on Twitter. Applying the Bot Sentinel tool, we also got data on the sentiment of the users. The divine and military discourses dominated the conspiracy discussion, followed by the discussions about extraction and HAARP. Though there were more human-like accounts than bot-like accounts, we found a positive correlation between the frequency of tweets on the conspiracy discourses and the bot scores of the accounts, which suggests that bot-like accounts were tweeting more than human-like accounts. It was also found that normal accounts tweeted more than toxic accounts, and there was a positive relationship between the bot score and the toxicity level of an account. It suggests that bot-like accounts were involved more in disruptive activities than human-like accounts.

The monkeypox outbreak in 2022 raised uncertainty leading to misinformation and conspiracy narratives in social media. The belief in misinformation leads to poor judgment, decision making, and even to unnecessary loss of life. The ability of misinformation to spread through social media may worsen the harms of different emergencies, and fighting it is therefore critical. In this work, we analyzed the discussion of misinformation related to monkeypox on Twitter by training different classifiers that differentiate between tweets that spread and tweets that counter misinformation. We collected over 1.4M tweets related to the discussion of monkeypox on Twitter from over 500K users and calculated word and sentence embeddings using Natural Language Processing (NLP) methods. We trained multiple machine learning classification models and fine-tuned a Robustly Optimized BERT Pretraining Approach (RoBERTa) model on a set of 3K hand-labeled tweets. We found that the fine-tuned RoBERTa model provided superior results and used it to classify the complete dataset into three categories, namely misinformation, counter misinformation and neutral. We analyzed the behavioral patterns and domains that were used in misinformation and counter misinformation tweets. The findings provide insights into the scale of misinformation within the discussion on monkeypox and the behavior of tweets and users that spread and counter misinformation over time. In addition, the findings allow us to derive policy recommendations to address misinformation in social media.

In this presentation we discuss methodologies, modeling tools and case studies on linking distributed disciplinary food, water, energy, environmental (FWEE) systems’ models into multi-systems multi-disciplinary integrated models for truly integrated analysis and managing of FWEE security NEXUS. Models’ linkage approaches enable to operationalize the concept of modeling and data platforms for distributed independent models’ “integration” and integrated FWEE security NEXUS management. Local, national and global FWEE security in the presence of climate change and risks of various kinds depend on the consistent coordination between and within the interdependent FWEE systems regarding sustainable resource supply and utilization. Detailed independent sectoral and regional systems’ models are often used to address these challenges. However, the independent approaches overlook the close linkages and feedbacks between and within the systems and, therefore, possible cross-sectoral implications. Critical cross-sectoral FWEE systemic supply-demand imbalances can trigger a disruption in a FWEE systems network. Disruptions and failures can be induced by human decisions in combination with natural shocks. For example, overuse of water in one system, e.g., agricultural, can lead to drying up of wells, decrease of reservoir water level, shortage of water in other systems, e.g., for colling power plants or hydropower production; an extra load in a power grid triggered by a power plant or a transmission line failure can cause cascading failures with catastrophic systemic outages; a hurricane in combination with inappropriate land use management can result in a catastrophic flood and human and economic losses, similar to the induced by Hurricane Katrina. These are examples of systemic risks motivating the development of proper models’ linkage approaches and integrated systems analysis. The linkage algorithms are becoming widely demanded in connection with the need for decentralized pla

The Internet as a common good implies the absence of any restrictions, closures, and blockages with censorship being unacceptable in democratic societies. However, it can lead to the uncontrolled growth and spread of disinformation and misinformation, which can have negative effects on democratic processes, on emergency management, and on human rights. While part of society sees the Internet as the last free space and considers the restriction of the Internet an infringement of citizens’ rights to freedom of communication and information, another part of society advocates at least reasonable censorship of the Internet. Parallel to this is the question of who will be behind the censorship – will it be the government, private companies, platforms, or search engines, and what will be the rules and algorithms of censorship. As part of its participation in the CORE project (sCience&human factOr for Resilient sociEty), IIASA held an online consultation with project participants to discuss the topic of “Internet as a free space and methods for combating the spread of disinformation and misinformation” and to prepare key principles and commitments as a contribution to the Global Digital Compact. This report provides a comprehensive overview of the key points raised by the participants in the consultation process

Online learning systems have become an applied solution for delivering educational content, especially in developing countries, since the start of the COVID-19 pandemic. The present study is designed to identify the factors influencing the behavioral intention of agricultural students at universities in Iran to use online learning systems in the future. This research uses an extended model in which the constructs of Internet self-efficacy, Internet anxiety, and output quality are integrated into the technology acceptance model (TAM). Data analysis was performed using the SmartPLS technique. The analyses showed the proposed model to be strong in terms of predicting the attitude to online learning and the intention to use it. The extended TAM model fit the data well and predicted 74% of the intention variance. Our findings show attitude and perceived usefulness to have directly affected intention. Output quality and Internet self-efficacy indirectly affected attitude and intention. Research findings can help with the design of educational policies and programs to facilitate education and improve student academic performance.

IntroductionIn this article, we summarize our findings from an EU-supported project for policy analyses applied to pandemics such as Covid-19 (with the potential to be applied as well to other, similar hazards) while considering various mitigation levels and consequence sets under several criteria.ec>MethodsIt is based on our former development for handling imprecise information in risk trees and multi-criteria hierarchies using intervals and qualitative estimates. We shortly present the theoretical background and demonstrate how it can be used for systematic policy analyses. In our model, we use decision trees and multi-criteria hierarchies extended by belief distributions for weights, probabilities and values as well as combination rules to aggregate the background information in an extended expected value model, taking into criteria weights as well as probabilities and outcome values. We used the computer-supported tool DecideIT for the aggregate decision analysis under uncertainty.ec>ResultsThe framework has been applied in three countries: Botswana, Romania and Jordan, and extended for scenario-building during the third wave of the pandemic in Sweden, proving its feasibility in real-time policy-making for pandemic mitigation measures.ec>DiscussionThis work resulted in a more fine-grained model for policy decision that is much more aligned to the societal needs in the future, either if the Covid-19 pandemic prevails or for the next pandemic or other society-wide hazardous emergencies.ec>

Misinformation is not a new phenomenon but, through social media, has gained new dynamics such as the rapidity of spread around the world within a few seconds. Past events have shown that misinformation can worsen the response to an emergency by leading to inappropriate behaviours, triggering fear and anxiety, or reducing the credibility of the measures by official actors. To better understand the dynamics of misinformation on social media or in the press and its effects on people’s beliefs and behaviour, we defined six case studies addressing different hazards and time periods. This allowed us to derive recommendations to prevent and fight the spread of and belief in misinformation along the entire communication chain - source, message, channel, receiver, effect, and feedback. Three of our key results are that: i) official actors should provide information on a regular basis to build credibility and trust, which will allow them to effectively communicate and counter misinformation during emergencies when people are under stressful conditions; ii) fragmented information on social media should be counterbalanced through external links to richer sources, where people can inform themselves about the broader context and details; and iii) the cultural context and ideological debates must be considered to address anchored beliefs and biases when developing strategies to prevent and fight misinformation.