Potential Domino Effect Research Articles

Investigating the potential domino effect of a dazzling fiscal-period on CSR performance: a luxury brand scenario

A consensus and buying pattern regarding luxury brands has endured a paradigm shift from being envied to being questioned or entirely overlooked. The pandemic has led to a fair share of economic implications. Brands are forced to watch their product range fully, except for reserving a portion of merchandise optimistic for brand jingoism. The study aims to quantify the impacts of financial metrics utilized to gain goodwill amidst an average consumer’s mindset. A composite Corporate Social Responsibility (CSR) score represents the extent of a luxury brand’s efforts in contributing to social and environmental concerns. The CSR score is hypothesized against these brands’ financial and brand-value metrics. A Few Research questions are proposed on the same. A Panel-level analysis is undertaken to quantify the dependence and obtain insights. Relevant data is collected, with metrics identified from financial statements. The impacts of financial metrics and the firm’s age on the CSR score are determined. While the Profit Margin, firm size, Tobin’s Q, and Firm Age contribute positively to the CSR score, the firm’s Return on Assets has a surprising negative influence. The impact of net income accrued is negligible, as inferred.

Potential domino effect of the 2023 Kahramanmaraş earthquake on the centuries-long seismic quiescence of the Dead Sea fault: inferences from the North Anatolian fault

Field observations conducted immediately following the February 6, 2023, Mw 7.8 Kahramanmaraş earthquake documented the southern surface rupture termination in the Amik Basin. The termination occurred in an en-echelon pattern, extending across the 3.5 km width of the approximately 10-km-wide stepover. This extension reached towards the northern tip of the Hacıpaşa Fault, which constitutes the main northern segment of the Dead Sea Fault Zone (DSFZ). Archaeoseismologic and paleoseismologic data show that the approximately 800-km-long DSFZ has been seismically quiet for more than 600 years in the north and 900 years in the south. A similar fault connection geometry at the western end of the 1939 Ms 7.9 Erzincan earthquake in the easternmost part of the North Anatolian Fault Zone and the subsequently triggered successive large magnitude earthquakes migrating westward within a few decades highlights an increased seismic hazard for the entire DSFZ. This heightened seismic hazard potential along the DSFZ, combined with historical population centers experiencing wars and migrations, puts millions of people at an unparalleled risk.

Unmasking the Subconscious Fallacies Within Critical Infrastructure Protection

Cybersecurity, a vital challenge in today’s ever-changing digital world, it has gained prominence with the global shift towards cyber-enabled critical infrastructures. Critical infrastructure protection efforts are fundamental for the continuation of essential services. Traditionally constituted as separate sectors, these infrastructures are increasingly interconnected, leading to potential domino effects during security breaches. For instance, failures within the power grid could have cascading effects on multiple sectors that depend on electricity for their operations, creating large-scale failures that affect functions on which society depends. The multidimensional nature of the infrastructures presents complex challenges for solutions, given their status as long-established legacy systems needing further development and enhancements to withstand the digital world. The lack of a concerted and focused infrastructure enhancement strategy has led to incremental approaches versus a comprehensive revamp to ensure a holistic cyber protection program. A lack of national focus has created inconsistencies that can lead to potentially catastrophic consequences. Understanding the decision-making processes within a complex environment is critical to the mission success. One significant risk is the cognitive roadblocks that have the potential to influence one’s judgements as this often outweighs balanced decisions. This study aims to investigate the subconscious biases that arise from a perceived resolution of the problem which can lead to de-prioritization within the decision-making processes. The study employs a convergent parallel mixed methods design to collect and analyse the data. The study then will compare the results allowing for the exploration of various aspects of the research. This approach is aligned to provide a thorough understanding of the challenges associated with protecting the infrastructures and the underlying subconscious fallacies in the digital age, thereby devising effective mitigation strategies, and fostering a more sustainable and resilient critical infrastructure that is useful for a variety of stakeholders, including policymakers, infrastructure owners and operators, cybersecurity professionals, and researchers

Risk Management for the Petrodiesel-Biodiesel Fuel Blend Supply Chain in Indonesia

This research addresses the management of risks within inter-business unit networks, focusing on two key levels of risk mitigation: strategic and operational. Prior research primarily explored risk management within specific fuel industry units supplied by a single source. This study represents a novel approach, delving into dual-source fuel strategies and engaging various supply chain stakeholders to identify and mitigate operational risks accordingly. The research findings demonstrate that stakeholders can reduce risks through integrated planning and collaboration among business units, with government support further reducing inter-unit risks. This study’s main contribution is establishing a basis for future research in eco-friendly fuel supply chains, specifically examining the long-term viability of blending petrofuel with biofuel. This extends risk analysis to supply chain networks involving multiple business units. Moreover, this research emphasizes the significance of case studies covering end-to-end product flows in fuel blend supply, encompassing various supply chain functions. Additionally, it investigates stakeholder perceptions of operational risks associated with different business unit types, addressing potential domino effects. It highlights the importance of information technology in risk reduction through collaborative information exchange within the supply chain.

A graph‐based optimization methodology for identifying firefighting strategies aimed at domino effects

AbstractFirefighting strategies at process plants would include simultaneous extinguishment of burning units and cooling of exposed units if firefighting resources are sufficient. This way, the fire can be contained and its propagation to the exposed units can be prevented, which would otherwise cause fire escalation and result in domino effects. However, when the firefighting resources are not sufficient to handle all the critical units—either burning or exposed—at once, firefighters need to decide which burning units to suppress first and which exposed units to cool first to minimize the risks. Making effective decisions in such situations becomes critical knowing that, by spreading the fire to adjacent units, the number of critical units grows exponentially, making the available firefighting resources even more insufficient. In the present study, after modelling fire spread in a tank terminal as a directed graph, closeness centrality—a graph centrality metric—is used to identify the critical units from the viewpoint of their contribution to potential domino effects. Knowing the critical units and considering available firefighting resources for suppression and cooling of these units, mathematical programming is applied for optimal allocation of firefighting resources. A comparison between the results of the present work and previous studies shows the effectiveness of the developed methodology.

An agent-based resilience model of oil tank farms exposed to earthquakes

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Agent-based risk modeling of domino effects in urban LNG stations

As the transfer hub between natural gas and users, urban LNG stations (ULSs) are the important link of the whole LNG industry chain. Due to special geographical location and various process equipment, potential risks in ULSs, especially domino effect accidents, usually lead to catastrophic consequences and social impacts. In this study, a dynamic risk assessment model for analyzing domino effects in urban LNG stations (DRADL) based on agent modelling and simulation is proposed. Tanks and tank cars as well as vaporizers and boilers in ULSs are modeled. A set of physical process and effect models are employed as the simulation process in this paper. Considering the lightweight nature and narrow flammability limit of LNG vapor cloud, a cyclic process is applied to judge the consequences of vapor cloud dispersion, which could avoid overestimating the results. A real urban LNG station is taken as a case study for modeling its potential domino effect risk. The results demonstrate the effectiveness and the advantages of the proposed ABMS approach for LNG dynamic risk assessment. The proposed DRADL model takes into account the combined effects of explosion and fire and LNG characteristics, which can provide guidance for risk management and rescue operation of domino effect accidents in urban LNG stations.

Transportation of hazardous material via railroad: Incident investigation and a case study of derailment in 2023

AbstractRailway transportation of hazardous materials (HAZMAT) is common and is generally considered safe. However, transporting toxic, flammable, and explosive substances via railways carries significant risk due to their high volume, proximity to populated areas, low public awareness, and potential domino effect. Particularly, the practice has come into question after a Norfolk Southern train derailed in Ohio in February of 2023. This derailment continuously reminds the public and the industry that such incidents can profoundly affect a community, critical infrastructure, and the environment. To identify the root cause of the Ohio train derailment and discover the deficiency of the safety system applied, combining the cause mapping approach with the safety triad concept was employed in this study. Based on this approach and the preliminary incident investigation released by the National Transportation Safety Board, the incident sequence is established, and the causal events leading to this incident are identified in the three essential pillars of its safety system: prevention, mitigation, and response, respectively. The study subsequently develops recommendations to improve the safety system of HAZMAT freight trains. This is expected to lower further the probability and consequence of HAZMAT freight train incidents and ultimately result in long‐term changes in railroad transportation.

Exploring The Mechanism Of Vetiver System For Slope Reinforcement On Diverse Soil Types – A Review

Landslide, one of the important geomorphological processes, is essentially a natural phenomenon that is often exacerbated by human-induced activities. A stable sloping terrain will tend to collapse when it is subjected to forces that tend to destabilise it. Slope instability is one of the main factors leading to disasters that might be catastrophic to the environment and human life. Since the beginning of the year 2000 only, thousands of fatalities annually occurred globally due to landslides. With its predominantly sloping topography, the landslide is also a frequent natural disaster in Indonesia. BNPB data stated that between 2013 and 2022, there were 7,297 recorded cases of landslides in the country with more than 100 casualties on one occasion. To mitigate the impact of this disaster, measures such as reinforcing slopes or implementing retaining walls in vulnerable areas are urgently required. In recent decades, bio-engineering techniques gain more attention in slope reinforcement by combining the mechanical and hydrological abilities of vegetation in erosion control and slope stabilisation. Vetiver grass is one of the vegetation species used in bioengineering techniques due to its low cost and more sustainable solutions in many infrastructure projects. In this paper, a qualitative literature review is conducted and processed using descriptive-analytical methods to address the mitigation of landslides and their potential domino effects on the economy and people's welfare.

From forests to faucets to fuel: Investigating the domino effect of extreme risk in timber, water, and energy markets

This study explores the interdependence between timber, water, and energy markets and investigates the potential domino effect of extreme risk across these markets. Specifically, using CAViaR-TVP-VAR, we examine the role of water and timber investments as safe-havens and hedges for traditional energy markets, before and after the COVID-19 pandemic. Our results indicate that water and timber markets can serve as reliable safe-haven options for energy investors. Moreover, our findings suggest a weak link between natural gas and its counterparts, underscoring the need for energy portfolios to diversify. This study provides valuable insights for investors seeking to navigate extreme risk.

Dynamic analysis of fire induced domino effects to optimize emergency response policies in the chemical and process industry

A fire accident is one the most dangerous accidents that may lead to knock-on effects, especially in the plants dealing with large quantities of hazardous substances. Different from other accidents, e.g., explosions, fires can last certain period of time and the thermal radiations emitted by them have a synergistic effect. At the same time, the heat-up of target installations under the effect of thermal radiation is also a process that takes a certain amount of time. These characteristics make fire induced domino effects a dynamic process. During an emergency response due to a fire accident, emergency personnel may arrive at the fire scene at different times, so they may face different accident situations. In this work, an adaptive timed Petri-net (ATPN) based approach is proposed to model the propagation of fires and perform a dynamic analysis of potential domino effects. The definition of ATPN as well as the enabling and execution rules is provided. Through simulations, not only the probabilities of fires in different installations, but also the probabilities of the fire extension propagated over time can be obtained. An example of a tank farm fire illustrates the approach. Our developed approach for carrying out a dynamic analysis of domino effects is helpful for emergency preparation.

Towards limiting potential domino effects from single flammable substance release in chemical complexes by risk-based shut down of critical nearby process units

The explosion load is a significant escalation factor possibly influencing the potential occurrence of domino accidents in chemical plants. It is not economical to install explosion isolation systems (e.g., extinguishing barrier) for all equipment or process units across a chemical plant. Although shutting down all equipment or process unit can prevent an explosion, it may also cause further economic losses. To prevent domino accidents, the process unit that needs to be shut down accurately should be selected, and the normal operation of other units needs to be ensured. A method to select the process unit to be isolated based on the Dimensioning Accidental Load (DAL) is proposed. By calculating the occurrence probability and consequences of the accident scenarios, the DAL of the surrounding units is determined. DAL is used as the impact intensity of the accident unit on the surrounding units. The probit model is used to calculate the damage probability of surrounding units. The case analysis results show that the method of selecting the process unit to be isolated based on DAL quantifies the impact intensity of the exploded unit on surrounding units from probability and consequence. Under the premise of meeting the acceptable risk criteria, the method can determine which units should be shut down and which units can operate normally when a release accident occurs. While preventing domino accidents, economic losses caused by the shutdown of all process units are reduced and a theoretical basis for accident prevention and safe operation of the plant is provided.

Could pool fire alone cause a domino effect?

The chain of accidents, also known as the domino effect, is responsible for several severe accidents in the chemical and process industries. The pool fire is often blamed as one of the primary accidents triggering a domino event. The present study is devoted to analyzing whether the pool fire alone can cause a domino event in processing and storage facilities. Two models, including a solid flame model and computational fluid dynamic model, are applied to simulate the escalation vector caused by a pool fire. The escalation vector probability is calculated using a probit model for a potential domino effect. This study also investigates the possible factors that can cause a domino effect and determine credible accident scenarios. The proposed concept of escalation vector and numerical models are tested using two past accidents. The study estimates the possibility of pool fires alone causing a domino effect. The results of this study show that although the pool fire alone has the ability to cause a domino event, it is unlikely to occur if a safe distance separates the equipment and proper mitigation measures are employed.

Texas LPG fire: Domino effects triggered by natural hazards

Abstract On February 2007, a massive fire in a propane de-asphalting unit in an oil refinery in Texas, USA happened due to liquid propane release from a cracked pipe in a control station injuring four people, damaging extensive equipment, causing significant business interruption, and resulting in more than $50 million losses. The accident was triggered by a natural hazard: freezing of piping at a control station caused an inlet pipe elbow to crack, which in turn, led to the release of high-pressure liquid propane which was rapidly ignited. In addition, there were two near-miss events due to potential domino effects. In fact, the accident could reasonably have resulted in much more severe consequences due to the exposure of large butane storage spheres and chlorine containers, increasing the possibility of a catastrophic domino effect. This paper develops a Natech (natural hazard triggering technological disasters) risk assessment methodology that relies upon Bayesian network capabilities and takes into account the potential Natech domino effects. The methodology is implemented in the intended refinery and mathematically graphically represents the dynamic cause–effect relations between units involved in the scenario, and handles uncertainties among the interactions. In addition, the methodology can provide a risk value for the entire scenario that can be used further for risk-based decision making.

Application of Graph Theory to Cost-Effective Fire Protection of Chemical Plants During Domino Effects.

In the present study, we have introduced a methodology based on graph theory and multicriteria decision analysis for cost-effective fire protection of chemical plants subject to fire-induced domino effects. By modeling domino effects in chemical plants as a directed graph, the graph centrality measures such as out-closeness and betweenness scores can be used to identify the installations playing a key role in initiating and propagating potential domino effects. It is demonstrated that active fire protection of installations with the highest out-closeness score and passive fire protection of installations with the highest betweenness score are the most effective strategies for reducing the vulnerability of chemical plants to fire-induced domino effects. We have employed a dynamic graph analysis to investigate the impact of both the availability and the degradation of fire protection measures over time on the vulnerability of chemical plants. The results obtained from the graph analysis can further be prioritized using multicriteria decision analysis techniques such as the method of reference point to find the most cost-effective fire protection strategy.

Developments in modelling of thermal radiation from pool and jet fires

In the past decades, the standard approach in the modelling of consequences of pool and jet fires would be to describe these fires as tilted cylindrical shaped radiating flame surfaces, having a specific SEP (Surface Emissive Power). Some fine tuning on pool fires has been done by Rew and Hulbert in the late nineties to divide the flame in a clear and sooty part, and provide some typical-substance dependent-values for SEPclear and SEPsoot. However, this approach still describes the pool-fire as a tilted and cylindrical shaped radiator. Unfortunately, in the world, the typical pool fire dimensions, and consequently the flame shape, are being determined by local circumstances, such as the presence of a semi-rectangular bund around storage tanks. Other pool shape determining examples are ditches, drains or even elevated platforms that restrict the free spreading of a pool and lead to a specific pool shape. Of course the resulting pool surface will not only determine pool burning rate, but also the radiation behaviour. In order to predict the consequences of these real world situation, TNO extended the fire modelling in its consequence calculation software EFFECTS® with a more elaborated radiation calculation, additionally providing the possibility to determine heat load distribution on a receiving object. In case of a pool fire, the expected pool dimensions can be drawn (on top of a topographic map or aerial photo) and potential receiving objects can be defined, such as nearby installations, including typical vulnerability thresholds. This enables the possibility to evaluate potential domino effects of a fire. The same approach is also used for jet fires, now describing the jet as a truly cone shaped radiator, which can be pointed in any direction. The paper will provide a full description of the applied method, including some typical application examples. © Copyright 2016, AIDIC Servizi S.r.l.

Economics of tipping the climate dominoes

Global warming could trigger irreversible regime shifts—‘tipping points’—in the climate system. This study analyses climate policy in the presence of a potential domino effect resulting from the interaction of such tipping points.

Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant.

Zur juristischen Begründung des ‚Rettungsschirms‘

Summary The financial and guarantee aids within the European Financial Stabilisation Mechanism suspend the Stability and Growth Pact as the basis of the European Economic and Monetary Union. The actual ‘suspension’ from the ‘no bail-out’ restriction (Art. 125 TFEU) and the prohibition of funding national debts (Art. 123 TFEU) transfers risks and financial burdens from deeply indebted states to solvent EU member states. This is the beginning of an unauthorized ‘transfer union’. This analysis does not display any evidence of market failure with respect to a potential domino effect for other EU member states. In addition, a destabilizing speculation cannot be identified. The case ‘Greece’ is rather a special case which has become a common model for other member states, caused by the Council of the European Union and the aid packages. Speculations build a part of the observable market reactions apart from pure hedging, but they simply indicate political system errors. The interventions by the European Central Bank do not lead to functioning markets. These interventions rather lead to yield structures that are inadequate in terms of risks and scarcity. Therefore they will cause a misallocation of financial resources with high economic costs.

Checkpoint space reclamation for uncoordinated checkpointing in message-passing systems

Uncoordinated checkpointing allows process autonomy and general nondeterministic execution, but suffers from potential domino effects and the associated space overhead. Previous to this research, checkpoint space reclamation had been based on the notion of obsolete checkpoints; as a result, a potentially unbounded number of nonobsolete checkpoints may have to be retained on stable storage. In this paper, we derive a necessary and sufficient condition for identifying all garbage checkpoints. By using the approach of recovery line transformation and decomposition, we develop an optimal checkpoint space reclamation algorithm and show that the space overhead for uncoordinated checkpointing is in fact bounded by N(N+1)/2 checkpoints where N is the number of processes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>