Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Application of Multi-Criteria Decision Analysis in Enterprise Risk Management

Enterprise risk management has become increasingly crucial in today’s complex and volatile business environment. This study explores the application of Multi-Criteria Decision Analysis (MCDA) in enterprise risk management. MCDA provides a systematic approach to handling multidimensional risk assessment issues. The research begins by analyzing various types of risks faced by enterprises, including financial, operational, and strategic risks. It then examines the specific applications of major MCDA methods, such as the Analytic Hierarchy Process (AHP) and TOPSIS, in risk identification, assessment, and response. The study finds that MCDA can effectively integrate qualitative and quantitative risk information, enhancing the scientific nature of risk decision-making. However, MCDA also faces challenges in practice, such as the subjectivity in determining indicator weights. To address this issue, the research proposes improved methods combining fuzzy theory and group decision-making. Finally, case analyses illustrate the effectiveness of MCDA applications in risk management across different industries. This study provides theoretical guidance for enterprises to build more comprehensive and dynamic risk management systems.

Predictive Analytics for Project Risk Management Using Machine Learning

Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.

Nursing Risk Management in the Prevention of Post-ERCP Pancreatitis

Objective:To study the role of nursing risk management in the prevention of post-ERCP pancreatitis.Methods:80 patients who underwent ERCP in our hospital from December 2023 to April 2024 were selected and randomly divided into an observation group and a control group using the random number table method,with 40 cases in each group.The observation group was given nursing risk management interventions and patients in the control group were given routine nursing interventions,and the patients in the two groups were compared in the incidence rate of pancreatitis and the satisfaction of nursing care.Results:The incidence of postoperative pancreatitis in the observation group was significantly lower than in the control group(P<0.05).The patient satisfaction of the observation group was significantly higher than that of the control group(P<0.05).Conclusion:Nursing risk management after ERCP can reduce the incidence of postoperative pancreatitis and improve patient satisfaction.

The Effect of the 2007/2008 Financial Crisis on Enterprise Risk Management Disclosure of Top US Banks

We document the effect of the 2007/2008 financial crisis on the volume and the quality of enterprise risk management （ERM） disclosure in the annual reports of the largest US banks, and analyze its determinants. Using a content analysis approach of the annual reports form 10-K for the years 2006, 2007, 2008, and 2009, we find that the ERM disclosure is significantly and positively associated with the crisis, bank size, board independence, duality and significantly and negatively associated with profitability, leverage, and board size. This paper seeks to fill a gap in the literature by investigating the effect of the crisis on ERM disclosure in the US banking sector context, and gives an insight into the factors affecting risk disclosure practices during the financial crisis.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

Factors affecting farmers'choice to adopt risk management strategies:The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers'adoption of risk management strategies(RMS)in Pakistan during times of uncertainty.The study examines farmers'adoption of RMS using both multinomial probit(MNP)and multivariate probit(MVP).Data were collected from 382 farmers sampled from four districts in KhyberPakhtunkhwa(KP)province of Pakistan via a multistage sampling technique.This study utilizes the MNP model,considering the assumption of Independence of Irrelevant Alternatives(IIA)and incorporating correlated error terms.The objective is to understand farmers'behavior in risky situations and determine if there is heterogeneity.Results are compared with the MVP model to assess robustness and gain deeper understanding of farmers'decisionmaking processes.The research findings reveal that our results are robust,and farmers behave homogeneously in various RMS scenarios.Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood.The risk-averse farmers,who perceive weather-related risks as a threat,access credits and information,and have farms close to a river are more likely to adopt RMS,irrespective of the format of the strategies available.Moreover,the predicted probabilities and correlation of the RMS and RM categories have strengthened our model estimation.These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Electronic Commerce Security and Risk Management: An Important Part of College Curriculum

With the rapid expansion of e-commerce,its security and risk management problems become increasingly prominent.In the current business environment,the ability to understand and apply e-commerce security and risk management has become an important criterion to measure a good person.Therefore,the importance of e-commerce security and risk management courses in college education is self-evident.This course can not only help students master the basic knowledge of e-commerce,but also enable them to understand how to deal with various risks in practical work and ensure the safe operation of e-commerce.At the same time,through the study of e-commerce security and risk management,students can better understand the operation mode and law of e-commerce,and lay a solid foundation for their future career.In general,e-commerce security and risk management occupy an important position in the curriculum of colleges and universities,and play a crucial role in cultivating e-commerce professionals with practical operation ability and innovative thinking.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Decision-making model for risk management of cascade hydropower stations

In a medium-term electricity market,in order to reduce the risks of price and inflow uncertainties, the cascade hydropower stations may use the options contract with electricity supply companies. A profit-based model for risk management of cascade hydropower stations in the medium-term electricity market is presented. The objective function is profit maximization of cascade hydropower stations. In order to avoid the risks of price and inflow uncertainties, two different risk-aversion constraints: a minimum profit constraint and a minimum conditional value-at-risk, are introduced in the model. In addition, the model takes into account technology constraints of the generating units, which includes reservoir flow balance, reservoir capacity limits, water discharge constraints, etc. The model is formulated as a mixed integer nonlinear programming problem. Because the search space of the solution is very large, a genetic algorithm is used to deal with the problem.

Safety risk management of underground engineering in China：Progress, challenges and strategies

Underground construction in China is featured by large scale, high speed, long construction period,complex operation and frustrating situations regarding project safety. Various accidents have been reported from time to time, resulting in serious social impact and huge economic loss. This paper presents the main progress in the safety risk management of underground engineering in China over the last decade, i.e.（1） establishment of laws and regulations for safety risk management of underground engineering,（2） implementation of the safety risk management plan,（3） establishment of decision support system for risk management and early-warning based on information technology, and（4） strengthening the study on safety risk management, prediction and prevention. Based on the analysis of the typical accidents in China in the last decade, the new challenges in the safety risk management for underground engineering are identified as follows：（1） control of unsafe human behaviors;（2） technological innovation in safety risk management; and（3） design of safety risk management regulations. Finally, the strategies for safety risk management of underground engineering in China are proposed in six aspects, i.e. the safety risk management system and policy, law, administration, economy, education and technology.

The use of UAVs for landslide disaster risk research and disaster risk management: a literature review

On a global scale,from 2005 to 2019,there were 275 high-magnitude,low-frequency disasters that involved 14,172 fatalities and four million affected people.Similar patterns have taken place during longer periods of time in recent decades.This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles(UAVs)in a literature review.The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management.The second section highlights background information and current applications of drones in the field of hazards and risk.The methodology,which included a systematic peer review of journals in the ISI Web of Science and SCOPUS,was presented in the third section,where the results include analyses of the considered data.This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response.Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local,subnational,national,regional and global levels.

Evolution of seismic risk management for insurance over the past 30 years

During the past 30 years, there has been spectacular growth in the use of risk analysis and risk management tools developed by engineers in the financial and insurance sectors. The insurance, the reinsurance, and the investment banking sectors have enthusiastically adopted loss estimation tools developed by engineers in developing their business strategies and for managing their financial risks. As a result, insurance/reinsurance strategy has evolved as a major risk mitigation tool in managing catastrophe risk at the individual, corporate, and government level. This is particularly true in developed countries such as US, Western Europe, and Japan. Unfortunately, it has not received the needed attention in developing countries, where such a strategy for risk management is most needed. Fortunately, in the last five years, there has been excellent focus in developing ＂Insur Tech＂ tools to address the much needed ＂Insurance for the Masses＂, especially for the Asian Markets. In the earlier years of catastrophe model development, risk analysts were mainly concerned with risk reduction options through engineering strategies, and relatively little attention was given to financial and economic strategies. Such state-of-affairs still exists in many developing countries. The new developments in the science and technologies of loss estimation due to natural catastrophes have made it possible for financial sectors to model their business strategies such as peril and geographic diversification, premium calculations, reserve strategies, reinsurance contracts, and other underwriting tools. These developments have not only changed the way in which financial sectors assess and manage their risks, but have also changed the domain of opportunities for engineers and scientists.This paper will address the issues related to developing insurance/reinsurance strategies to mitigate catastrophe risks and describe the role catastrophe risk insurance and reinsurance has played in managing financial risk due to natural catastrophes. Historical losses and the share of those losses covered by insurance will be presented. How such risk sharing can help the nation share the burden of losses between tax paying public, the ＂at risk＂ property owners, the insurers and the reinsurers will be discussed. The paper will summarize the tools that are used by the insurance and reinsurance companies for estimating their future losses due to catastrophic natural events. The paper will also show how the results of loss estimation technologies developed by engineers are communicated to the business flow of insurance/reinsurance companies. Finally, to make it possible to grow ＂Insurance for the Masses - IFM＂, the role played by parametric insurance products and Insur Tech tools will be discussed.

From risk management to uncertainty management: a significant change in project management

Starting with the meanings of the terms “risk” and “uncertainty,””he paper compares uncertainty management with risk management in project management. We bring some doubt to the use of “risk” and “uncertainty” interchangeably in project management and deem their scope, methods, responses, monitoring and controlling should be different too. Illustrations are given covering terminology, description, and treatment from different perspectives of uncertainty management and risk management. Furthermore, the paper retains that project risk management (PRM) processes might be modified to facilitate an uncertainty management perspective, and we support that project uncertainty management (PUM) can enlarge its contribution to improving project management performance, which will result in a significant change in emphasis compared with most risk management.

The Utility of Artificial Intelligence for Mood Analysis,Depression Detection,and Suicide Risk Management

Mood disorders are often an indication or a sign of depression,and individuals suffering from mood swings may face higher probability and increased suicidal tendencies.Depression-also called“clinical depression”or a“depressive disorder”-is a mood disorder that adversely impacts how an individual feels,thinks,and handles daily activities,such as sleeping,eating,or working.To be diagnosed with depression,symptoms must be present most of the time,nearly every day for at least minimum of 2 to 3 weeks.Feeling sad or having low emotional energy may be common among people.For most,however,these feelings are transitory and can be managed by changing daily life routines.But for some,prolonged mood disorders can lead to depression and foster suicidal tendencies.Suicide is a major public health concern.Over 47,000 people died by suicide in the United States in 2017.It is the 10th leading cause of death overall according to NIMH(National Institute of Mental Health).Suicide is complicated and tragic,but it is often preventable.Identifying the warning signs for suicide and how to get help can be a major mitigating factor.In this short communication,we are reviewing the promise and limitations of AI(artificial intelligence)with its integrated tools such as ML(machine learning)and DL(deep learning)for mood analysis as a means for detecting early signs of depression and increased suicidal tendencies for possible suicide risk management.

Observations on the Application of Nursing Risk Management in the Care of Critically Ill Patients in the Respiratory Unit

Objechive:Investigate the effectiveness of mursing risk management in the care of cntically ill patients in the respiratory umit.Methods:Among the cntically ill respiratory patients admitted to our hospital between May 2019 and April 2020,78 patients were randomly selected and divided into an observation group and a control group,each consisting of 39 patients.In the observation group.a mursing nisk management model was implemented,i.e,patients'clinical symptoms were observed at any time to monitor their treatment satisfaction and the effectiveness of their care and routine care was implemented for the control group.Results:The heart rate,respiratory rate,and pH of patients in the observation group were more stable than those in the control group,and their respiratory status was better,with differences in data.There was also sigmifcant statistical significance(P<0.05).The incidence of patient-provider disputes,unplanned extubation,and uplammed events were lower in the observation group conpared to the control group,and their data difference was satistically siguificant(P-0.05).The treatment satisfaction as well as the total effective rate of patients in the observation group was also much higher than that of the contol group,and there was also a statistically sigmificant difference in the data(P<0.05).Conclusion:The musing nisk management model has a significant therapeutic effect in the care of cnitically ill respiratory patients.Therefore,it is worth popularizing to use in the clinical mursing of respiratory cnitical patients.

New Basle Capital Agreement and Risk Management of China＇s Banking Industry

Adjustment of Basle Capital Agreement will influence the risk management and capital arrangement demand of the banks with different scales, operation level and environment. It will have widespread and profound effect on the competition strength of every country＇s banks in the global market. Starting with illustration of the present cond（tion of risk management in China＇s banks, the paper analyzes the major problems existing in the risk management system of China＇s banking industry, then puts forward some clues and suggestions to improve and better the risk management system of China＇s banking industry.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Community Based Flood Risk Management: Local Knowledge and Actor’s Involvement Approach from Lower Karnali River Basin of Nepal

Connecting to the disaster risk reduction (DRR) studies, community-based initiatives are found to be more effective in both developed and developing countries, with a specific focus on the empowerment of local communities to build resilience. Building on social capital theory, the paper investigates on local knowledge (LK) practices experienced by the actors in an emerging economy using the community-based flood risk management (CB-FRM) approach. The qualitative research method was used by collecting data from focused group discussions, and interviews with the key informants including actors from local governments and non-government organizations. Additionally, informal discussions, field visits, and desk studies were undertaken to support the findings. The findings reveal that the local communities carry out various local knowledge experiences to respond during disaster management phases. They own a creative set of approaches based on the LK and that empowers them to live in the flood-prone areas, accepting the paradigm shift from fighting with floods to living with that. The local actor’s involvement is recognized as an essential component for CB-FRM activities. Yet, their program’s implementation is more oriented towards humanitarian assistance in emergency responses. Even, they often overlook the role of LK. Additionally, the results show a high level of presence of local communities during the preparedness and recovery phases, while NGOs and local governments have a medium role in preparedness and low in recovery phase. The lack of local ownership has also emerged as the major challenge. The research provides valuable insights for integrated CB-FRM policies by adopting to LK practices.

The Relationship Between Credit Risk Management and Profitability Among the Commercial Banks in Kenya

Banks operate in an environment of considerable risks and uncertainty. Credit risk has always been a vicinity of concern not only to bankers but to all in the business world because the risks of a trading partner not fulfilling his obligations in full on due date can seriously jeopardize the affairs of the other partner. Credit risk management in banks has become more important not only because of the series of financial crisis that the world has experienced in the recent past, but also the introduction of Basel II Accord. The objective of the study was to establish the relationship between credit risk management and profitability in commercial banks in Kenya, Both qualitative and quantitative methods were used in order to fulfill the main purpose of the study. A regression model was used to do the empirical analysis. The results obtained from the regression model show that there is an effect of credit risk management on profitability at a reasonable level. The findings and analysis reveal that credit risk management has an effect on profitability in all the commercial banks analyzed.