Assessment Of Economic Losses Research Articles

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

Health impacts of a cold wave and its economic loss assessment in China’s high-altitude city, Xining

ObjectiveAmidst climate change, extensive research has centered on the health impacts of heatwaves, yet the consequences of cold spells, particularly in cooler, higher-altitude regions, remain under-explored.MethodsAnalyzing climatic data and non-accidental mortality in Xining, China’s second-highest provincial capital, from 2016 to 2020, this study defines cold spells as daily mean temperatures below the 10th, 7.5th, or 5th percentiles for 2–4 consecutive days. A time-stratified case-crossover approach and distributional lag nonlinear modeling were used to assess the link between cold spells and mortality, calculating attributable fractions (AFs) and numbers (ANs) of deaths. The study also examined the impact of cold spells over different periods and analyzed the value of a statistical life (VSL) loss in 2018, a year with frequent cold spells. Stratified analyses by sex, age, and education level were conducted.ResultsA significant association was found between cold spells and non-accidental mortality, with a relative risk of 1.548 (95% CI: 1.300, 1.845). The AF was 33.48%, with an AN of 9,196 deaths during the study’s cold period. A declining trend in mortality risk was observed from 2019–2020. The 2018 VSL was approximately 2.875 billion CNY, about 1.75% of Xining’s GDP. Higher risks were noted among males, individuals aged ≥ 65, and those with lower education levels.ConclusionThe findings underscore the vulnerability and economic losses of high-altitude cities to cold spells. Implementing interventions such as improved heating, educational programs, and community support is vital for mitigating these adverse health effects.

Попередня оцінка втрат для рибного господарства Харківської області внаслідок воєнних дій

Purpose. Based on the author’s materials, to carry out a preliminary assessment of ecological and economic losses caused to the fisheries complex of the Kharkiv region as a result of military actions. Methodology. Field studies on water bodies of the Kharkiv Region, which were affected by military actions, were conducted. Public data of the State Agency of Land Reclamation and Fisheries of Ukraine in the Kharkiv Region (2021–2023) were used. The following research methods were used during the assessment of the ecological consequences and economic losses caused to the aquatic biological resources of water bodies of the Kharkiv region: analytical – collection of information from official sources of enterprises and institutions carrying out economic activities, control and monitoring of water bodies; geoinformation – assessment of the actual hydrological regime of water bodies; identification of the spatial location, measurement of quantitative and qualitative characteristics of water areas formed after the reservoirs drying-up; hydrobiological – determination of fish species and other hydrobiont composition; statistical – for qualitative and quantitative assessment of commercial ichthyofauna and the economic damage inflicted to the fishery; forecast –assessment of the potential transformation of the species biodiversity of the ecosystem in time under different conditions of further development of the water sector of Ukraine. Findings. The impact of military actions on the conditions of existence of aquatic biological resources in the Oskil and Pechenegi reservoirs, on the functioning of special commercial fish farms, the production of aquaculture products, and the development of industrial and recreational fishing in the region has been preliminarily assessed. The calculation of economic losses for aquatic bioresources of water bodies of the Kharkiv region was carried out. Originality. For the first time, a preliminary assessment of losses for the fisheries industry (including commercial, recreational fishing, aquaculture) of the Kharkiv region due to military actions was carried out. Practical value. The obtained results have important and multifaceted practical value, in particular they can be used for: assessment of environmental damage caused to Ukraine as a result of military actions; rational management of water and fisheries management due to restoration of water ecosystems exposed to military impact; development of recommendations on restoration of ecosystem services; spread of social awareness about the post-war environmental challenges of the future; making decisions about investments in natural resources and the use of ecosystem services.

Premature mortality risk and associated economic loss assessment due to PM2.5 exposure in Delhi, India during 2015–2019

Premature mortality risk and associated economic loss assessment due to PM2.5 exposure in Delhi, India during 2015–2019

Research on Voltage Sag Loss Assessment Based on a Two-Stage Taguchi Quality Perspective Method

Voltage sags resulting from symmetrical or asymmetrical faults pose a significant threat to power quality. In response to this challenge, a voltage sag loss assessment method based on a two-stage Taguchi quality perspective approach is proposed to address the quantitative analysis of voltage sag economic losses. Initially, using the Taguchi quality perspective method, single-index quality loss functions are separately established for voltage sag magnitude and fault duration. Subsequently, by introducing a comprehensive load tolerance curve, sensitivity parameters within the quality loss function are accurately calculated. This yields a deterministic model for voltage sag assessment. Building upon this, the relative impact of the two indices on voltage sag loss is evaluated using the quality loss function. Consequently, a comprehensive loss model under the influence of multiple indices is formed by integrating two single-index evaluation models. The simulation results indicate that this method can effectively assess the economic losses of voltage sags under the combined influence of multiple factors. Compared to the original economic loss assessment method, it improves quantitative accuracy by approximately 3.72%. Moreover, the method reduces the computational complexity of loss assessment through the consolidation of intervals with similar sensitivity parameters.

The Economic Burden of Epstein–Barr Virus Infection in the Russian Federation

Introduction: Epstein–Barr virus-related diseases are of great medical and social importance, which requires assessment of economic losses due to costs of medical care and sickness absenteeism. Objective: To propose a methodology for assessing economic damage and to use it for quantification of total damages caused by Epstein–Barr virus (EBV) infections in the Russian Federation in 2022. Materials and methods: The economic damage from EBV infections was determined using official statistics on cases of infectious mononucleosis (2022), upper respiratory tract infections, cardiac arrhythmias, atopic dermatitis, dorsopathy, chronic tonsillitis in the acute phase, cerebrovascular disease, psoriasis, and multisystem inflammatory syndrome associated with COVID-19. Results: Based on the methodology for assessing economic damages described in the article, we found that the total financial losses from EBV-associated diseases in the Russian Federation in 2022 amounted to 1,203.0 billion or 1.2 trillion rubles, which is significantly higher than similar values for all other registered infectious diseases with the exception of COVID-19. COVID-19-related multisystem inflammatory syndrome, upper respiratory tract infections, and cerebrovascular disease account for more than 90 % of all losses. The structure of total costs is dominated by indirect economic losses (1,096.75 billion rubles or 91.2 %) due to sickness absence from work. Conclusion: The findings allow us to outline directions of developing measures to reduce the economic damage from EBV-associated diseases.

Investigating the role of refining urban flood direct loss in enhancing the accuracy of indirect economic loss assessment

Indirect Economic Loss (IEL) constitutes a significant component of urban flood loss, particularly in disasters affecting densely populated cities with a substantial concentration of assets and industrial supply chains. Information on the scope of disaster-induced damage is vital for assessing IEL, and its accuracy largely depends on the precision of information regarding direct loss. However, existing studies usually rely solely on administrative statistical data such as sectoral GDP or employed population. Few studies have focused on the impact of differences in the accuracy of direct information on IEL. Here, we improve the AMIL model and conduct a comparative study on the “7.6 Wuhan flood disaster” in 2016, measuring the role of refining the following three dimensions in enhancing the accuracy of IEL assessment results - direct loss data, industry subdivision number, and ripple region number -. The results indicate that (1) Using multiple sources of data, such as geographic and spatial data, to refine Direct Economic Loss (DEL) and the affected labor population can increase IEL by up to 10.49 %; (2) Subdividing industries with more severe loss to detailed sectors can improve IEL accuracy by up to 12.50 % compared to non-subdivision; (3) Refining direct loss statistics for sectors with higher proportions of intermediate input and output can balance statistical costs and precision requirements. This study aims to enhance the accuracy of IEL assessments resulting from disasters by proposing a more efficient system for direct loss statistics and providing theoretical and empirical support for such improvement.

Failure Probability and Economic Loss Assessment of a High-Rise Frame Structure under Synthetic Multi-Dimensional Long-Period Ground Motions

Multiple research studies and seismic data analyses have shown that multi-directional long-period ground motion affects crucial and intricate large-scale structures like oil storage containers, long-span bridges, and high-rise buildings. Seismic damage data show a 3–55% chance of long-period ground motion. To clarify, the chance of occurrence is 3% in hard soil and 83% in soft soil. Due of the above characteristics, the aseismic engineering field requires a realistic stochastic model that accounts for long-period multi-directional ground motion. A weighted average seismic amplification coefficient selected NGA database multi-directional long-period ground motion recordings for this study. Due to the significant low-frequency component in the long-period ground motion, this research uses empirical mode decomposition (EMD) to efficiently decompose it into a composite structure with high- and low-frequency components. Given the above, further investigation is needed on the evolutionary power spectrum density (EPSD) functions of high- and low-frequency components. Analyzing the recorded data will reveal these functions and their corresponding parameters. Proper orthogonal decomposition (POD) is needed to simulate samples of high- and low-frequency components in different directions. These samples can be combined to illustrate multi-directional long-period ground motion. Representative samples exhibit the seismic characteristics of long-period multi-directional ground motion, as shown by numerical examples. This proves the method’s engineering accuracy and usefulness. Moreover, this study used incremental dynamic analysis (IDA) to apply seismic vulnerability theory. This study investigated whether long-period ground motions in both x and multi-directional directions could enhance the seismic response of a high-rise frame structure. By using this method, a comprehensive seismic economic loss rate curve was created, making economic loss assessment clearer. This study shows that multi-directional impacts should be included when studying seismic events and calculating structure economic damages.

Seismic Economic Loss Assessment of Highway Girder Bridges Using Wenchuan Earthquake as a Sample

Seismic Economic Loss Assessment of Highway Girder Bridges Using Wenchuan Earthquake as a Sample

Seismic economic loss assessment of highway girder bridges using Wenchuan earthquake as a sample

Seismic economic loss assessment of highway girder bridges using Wenchuan earthquake as a sample

Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

Assessment of Flood Economic Losses Under Climate Change: A Case Study in the Ngan Sau River Basin, Ha Tinh Province and Vietnam

The Ngan Sau River basin, which is situated in Ha Tinh Province of Vietnam, experiences flooding during the rainy season, resulting in significant loss of property and human life. This research aimed to investigate the impact of climate change and land-use variation on flood losses. The study began by simulating the heavy rainfall events in August 2007 using the Weather Research and Forecast model with an ensemble method. Future rainfall was examined through numerical simulation based on pseudo-global warming constructed using six CMIP5 models (MIROC-ESM, MRI-CGM3, GISS-E2-H, HadGEM2-ES, HadGEM2-ES, and CNRM-CM5), and the variation in land-use was obtained from local authorities. Inundations caused by rainfall in 2007 and rainfall in the future were determined by the rainfall-runoff-inundation model. Finally, based on flood maps, land-use, and flood depth-damage functions, the economic losses were computed. The results of the average flood economic loss were $380 million in CTL, whereas the local authorities report an estimated loss of over $300 million. Under the impact of climate change and land-use variation, economic losses ranged from $380 million to $526 million in six CMIP5 models. The result of INMCM4 showed the highest value of $526 million, the results of MRI-CGM3, GISS-E2-H, HadGEM2-ES, and CNRM-CM5 fluctuated around $500 million, and the MIROC-ESM recorded the lowest at $380 million. The damage maps showed that the losses would be highest in urban areas, followed by forest areas, and lowest in agricultural areas. This information is essential for decision-makers to improve solutions for preventing economic losses caused by floods.

Measurement of multidimensional reconstruction fund planning on the effectiveness of urban post-disaster recovery and reconstruction using indirect economic impact

An increasing number of record-breaking extreme precipitation events worldwide has had an incalculable socioeconomic impact, challenging the ability of cities to recover and reconstruct after disasters. The key to the effectiveness of post-disaster recovery and reconstruction lies in the rational investment and allocation of reconstruction funds. However, few studies have examined the effectiveness of reconstruction fund planning in improving urban recovery and reconstruction from a post-disaster indirect economic loss assessment perspective. This study uses an improved input-output model to evaluate the degree of change in indirect economic losses under multidimensional reconstruction fund planning (funds' amount, the time efficiency of investment, and funds allocation) to measure the effectiveness. We exemplify this through a case study: a record-breaking extreme urban flood and its reconstruction planning occurred in Henan Province, China, in July 2021. The results indicate that (1) under the premise of ensuring sufficient financial input, improving the input time efficiency is a decision with a higher benefit-cost ratio, which can improve the effectiveness by 140%. (2) If funds are guaranteed, improving time efficiency is better than adding extra funds, and when both reach 80%, additional funds may not further improve the effectiveness. (3) The funds need to meet at least the recovery production needs (about 45% of direct economic losses); otherwise, the funds cannot be used to improve fortifications. The study results will provide theoretical and practical support for urban decision-makers to develop efficient and rational recovery and reconstruction programs.

A Rapid Earthquake Disaster Assessment Platform Based on Cloud Computing

In the past few decades, earthquakes have been a major cause of casualties and economic losses. The rapid assessment system for earthquake disasters plays an important role in earthquake prevention, disaster reduction, and emergency command by calculating the economic losses and casualties caused by earthquakes in a relatively short time after the occurrence of an earthquake. Although there are now many rapid assessment systems for earthquake disasters, most of them adopt C/S and B/S architectures, which have limited computational efficiency during large-scale earthquakes. Based on these factors, this study aims to build a cloud computing-based rapid assessment system for earthquake disasters. The main modules of the system include intensity impact field calculation, building damage assessment, economic loss assessment, casualties assessment, and model parameter calibration. The results show that the system significantly speeds up the calculation process and shortens the assessment time. It can provide timely disaster assessment information to relevant departments in a short period and provide technical support for emergency decision-making.

Framework for risk assessment of economic loss from structures damaged by rainfall-induced landslides using machine learning

ABSTRACT Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy.

A multi-model probabilistic framework to evaluate seismic resilience of UHV converter stations

Ultra-high voltage converter stations are significant electric power infrastructures with a high electric energy load. To quantitatively evaluate the seismic and recovery capability of a UHV converter station, this paper proposes a multi-model probabilistic resilience assessment framework divided into four analysis modules for hierarchical evaluation. The conditional probability set of the functional state between network nodes is determined using a matrix-based approach. Taking connectivity and power transmission capacity into account, the functional reliability model was built through the improved Bayesian network. A multi-parameter quantification of the post-earthquake recovery process was carried out by constructing the stepped functional recovery function as well as the time-varying seismic resilience function. The economic loss assessment model was built from multiple aspects. Taking a typical ± 800 kV UHV converter station as a case study for seismic resilience analysis, the probabilistic resilience assessment was realized, and variation trends of the multi-parameter expected value were obtained by mathematical expectation. This article shows that taking measures to postpone the sensitive interval of the system helps to avoid the seismic active area. This avoidance of seismic activity is the objective of optimization. The proposed framework can serve as a reference for the operation and optimization of power systems.

Causes of Post-Mortem Carcass and Organ Condemnations and Economic Loss Assessment in a Cattle Slaughterhouse.

The study was undertaken to investigate the main causes of carcass and organ condemnations, as well as to estimate the financial losses suffered by a cattle slaughterhouse. In this regard, an active abattoir survey, based on standard post-mortem inspection procedures for meat, was conducted on 151,741 cattle, from January 2021 to December 2022. Overall, 13.27% (n = 20,125) of the carcasses expressed lesions or pathological conditions and, out of them, 1.15% (n = 1738) were totally confiscated, while another 12.12% (n = 18,387) were partially admitted for human consumption. In the case of organs, the general inspection data reveal that 12.28% (n = 18,630), 7.56% (n = 11,477), 1.89% (n = 2862), and 0.27% (n = 412) of the examined liver, lung, heart, and kidney specimens presented one or more types of abnormalities. In addition, regarding the types of specific pathological findings, dystrophies/anomalies (69.8%), circulatory disorders (40.6%), fecal contamination (60.9%), and suspected bacterial/viral infections showed a dominant occurrence in the liver, lung, heart, and kidneys, respectively. Consequently, the total direct financial losses resulting from edible part condemnation over the two years was estimated at EUR 4,021,717.3, which represents 1.17% of the total achievable net revenue without carcass and organ condemnation. Of this, EUR 3,661,400.4 (1.07%) and EUR 360,316.9 (8.73%) was related to carcass and organ condemnation, respectively. The study results demonstrate that the post-mortem inspection of meat at the slaughterhouse level plays a crucial role in identifying pathological lesions, in addition to some other issues, such as fecal contamination or non-compliant laboratory results, relevant to both public health and economic factors.

Weakened economic impacts with future intensifying drought in Chinese mainland

Economic loss assessment of drought disaster (ELDA) plays an important supporting role in government emergency management decision-making and the sustainable development of national economy. Previous studies lack projected ELDA for composite drought under the background of global warming, and the evaluation models used have problems such as low calculation efficiency and poor universality. In order to explore the overall economic impact of composite drought disaster in Chinese mainland, an economic loss assessment model of drought disaster (ELDAM) was constructed based on the occurrence process and loss mechanism of drought. Under different global warming levels (GWLs), economic loss of drought disaster (ELD) in Chinese mainland increases with the intensification of drought. When the temperature rise reaches 4 °C, the ELD under the static scenario and the dynamic scenario that considers the future national economic development level can reach 164.00 ± 67.58 and 86.84 ± 38.06 billion USD2015. They only account for 0.014 ± 0.006% and 0.007 ± 0.003% of the projected gross domestic product (GDP), which is much lower than the reference period (2003–2018, 0.12%). The future economic impact of drought in Chinese mainland shows a downward trend against the background of intensifying droughts, but the economic impact of drought cannot be ignored under GWLs. ELDAM can not only be used to estimate ELD from historical drought disasters, but can also be combined with climate predictions to provide advance estimates of ELD, providing a reliable theoretical basis for strengthening regional drought prevention and resistance capabilities.

Health impact and economic loss assessment of indoor PM2.5 pollution: A case during the COVID-19 lockdown period

Indoor PM2.5 exposure is vital to accurately assess the health impacts and economic loss of ambient PM2.5 exposure. However, most previous studies have overlooked indoor PM2.5 and directly used outdoor PM2.5 concentration data as exposure concentrations to evaluate health impacts and economic losses, which could potentially lead to significant inaccuracies. In particular, since the COVID-19 outbreak, the Chinese government implemented a lockdown strategy that led to an increase in the time spent indoors. Here, we reveal the spatiotemporal characteristics of the health impacts and economic losses due to indoor PM2.5 pollution during the COVID-19 lockdown period in 2020. The bias induced by neglecting indoor PM2.5 pollution in assessing total health impacts and economic losses was calculated. The results showed that the annual average indoor PM2.5 concentration in China in 2020 was 18.16 μg/m3. The total health impact of indoor PM2.5 exposure in 2020 reached 8.1255 million people, and the economic loss of indoor PM2.5 exposure was 34,996 million USD, accounting for 0.24% of the national GDP. Compared to the period before the pandemic, the COVID-19 lockdown resulted in a 7.73% increase in health impacts and a 7.9% increase in economic losses associated with indoor PM2.5. Indoor pollution is the primary cause of PM2.5 pollution. The health impact and economic loss associated with indoor PM2.5 pollution accounting for 80.99% and 80.93% of the total impact and loss (caused by both indoor and outdoor PM2.5 pollution), respectively. If the indoor PM2.5 exposure concentrations were substituted with the ambient concentrations, the results would be overestimated by 76.22% and 77.11% for health impacts and health economic losses, respectively.

Analytical advances in homeowner flood risk quantification considering insurance, building replacement value, and freeboard

An accurate economic loss assessment for natural hazards is vital for planning, mitigation, and actuarial purposes. The widespread and costly nature of flood hazards, with the economically disadvantaged disproportionately victimized population, makes flood risk assessment particularly important. Here, flood risk is assessed as incurred by the homeowner vs. the flood insurer for insured U.S. properties through the derivation of average annual loss (AAL). AAL is estimated and partitioned using Monte Carlo simulation at the individual home scale, considering insurance coverage and deductible, and the first-floor height (i.e., height of the first floor above the ground), to determine the AAL proportion of homeowners (i.e., apportionment factor) for building and contents, distinguished from that borne by the insurer. In general, AAL estimates suggest that a large portion of the U.S. property flood risk is borne by the flood insurer. The flood insurance policy deductible directly influences the apportionment factor, whereby higher deductibles leave homeowners with a higher annual risk; however, the apportionment factor remains relatively insensitive to coverage values, especially for higher coverage amounts. The homeowner’s flood risk is further reduced by freeboard, with AAL, following an exponential decay trend as freeboard increases. These results reveal new perspectives about how flood insurance protects homeowners from flood risk. In general, the results enhance the proactive decision-making process that allows homeowners to self-assess their degree of preparation and vulnerability to the devastating economic impacts of flood.