Economic Loss Estimation Research Articles

Estimation of economic loss by earthquakes in Taiwan Region

Earthquakes in the Taiwan region have caused significant economic losses. We develop a model to assess seismic economic losses in the Taiwan region using the records of economic losses caused by historical earthquakes. Unlike existing models, we introduce Gross National Income Per Capita (GNIPC) as a parameter that responds to the influence of socio-economic development. The results show that our model can accurately estimate the earthquake economic losses in Taiwan region. The difference between the results of this model and those of existing models is evident, indicating differences between the Taiwan region and mainland China regarding geological background, seismic tectonics, and social resistance to earthquakes. The prediction results also imply that the society significantly underestimates the seismic economic losses in the Taiwan region. Our model can help the Taiwan region in disaster prevention and preparedness, contingency planning, allocation of relief resources, and post-disaster socio-economic recovery.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

Urban flooding damage prediction in matrix scenarios of extreme rainfall using a convolutional neural network

The pivotal role of quantitative risk assessment in managing urban stormwater is underscored by the high spatial heterogeneity and complex non-stationarity complex of urban flooding. Conventional methods, reliant on measurable data, often fall short in accurately mapping spatial variations and gauging the full impacts of urban flooding. Addressing this gap, this study proposed a robust convolutional neural networks (CNN)-based tool, specifically designed for urban flooding risk and economic losses estimation under extreme design rainfall scenarios. Furthermore, the study validates the transferability of CNN models trained in data-abundant regions to similar but data-scarce regions, using Guangzhou as a case study for urban flooding damage prediction. The findings reveal that the most affected areas, particularly the old, densely built-up urban areas in the south-central Guangzhou, are susceptible to significant economic losses during extreme rainfall events. Notably, under the most severe scenario (Scenario 5), estimated economic losses amount to approximately $6359.91 million, with industrial and residential sectors bearing the brunt, accounting for 28.29 % and 39.94 % of the total losses, respectively. These insights are crucial for prioritizing mitigation efforts and formulating effective evacuation strategies in high-risk areas, ultimately aiding in the reduction of economic losses.

Probabilistic assessment of climate-related impacts and risks in ports

Port activities are crucial for sustained, long-term economic growth, serving as the primary nodes for importing and exporting goods within global supply chains. Given their coastal locations, ports are inherently exposed to climate hazards, such as waves and extreme sea levels, requiring large investments in resilient infrastructure. This study introduces an innovative methodology for assessing climate-related impacts and risks in ports, applicable to both existing and new constructions. This approach aims to facilitate climate-informed decision-making and enhance the management of coastal structures and ports under high uncertainty. The methodology’s novelty resides in: (1) the development of a port-specific risk framework capable of estimating impacts from both extreme events and daily conditions; (2) the integration of the latest advancements in nearshore climate hazard modeling; (3) the application of high-resolution tools for accurately simulating wave propagation towards harbor basins and the interaction between waves and structures; (4) the probabilistic determination of failure modes and operational shutdowns susceptible to climate conditions; and (5) the estimation of economic losses resulting from diminished operational capacity, in addition to the degradation of reliability and functionality in port infrastructures. Formulated within the Intergovernmental Panel on Climate Change (IPCC) risk framework and anchored in established Spanish Recommendations for Maritime Works (ROM Program), this methodology has been applied to a complex, state-owned, newly-built outer port in the Mediterranean Sea. Preliminary findings suggest that, over the course of a 50-year lifespan, climate-related risks could lead to cumulative losses nearing 10 million euros for such infrastructure. Nevertheless, in scenarios marked by extreme events, potential losses could escalate to as much as 100 million euros, despite their occurrence being relatively rare (with a probability of only 0.1%). It stresses the significant uncertainties encountered when evaluating climate-related risks for critical infrastructure, including ports, and highlights the critical need for advanced methodologies to accurately understand these risks.

Estimation of economic losses due to leptospirosis in dairy cattle

There are few studies that effectively quantify the economic losses resulting from problems caused by leptospirosis in naturally infected dairy cattle. Given this gap, the objective of this study was to propose and apply a method to quantify the economic losses resulting from productive and reproductive problems in a commercial dairy herd naturally infected by Leptospira spp. For this study, the zootechnical and economic indicators at a property with Jersey cattle were analyzed during the period from 2014 to 2017. The leptospirosis outbreak occurred in 2014, and the therapeutic approach was carried out between 2015 and 2017, with the latter considered the year of control of the outbreak. The adopted integrated control strategy consisted of dividing the herd according to the serological results obtained through the microscopic agglutination test, the treatment of reagents with streptomycin, and vaccination against leptospirosis of non-reagent heifers and cows. The method used to evaluate the economic indicators of the property was the calculation of the gross margin by taking into account the implicit and explicit cost parameters associated with the manifestation of leptospirosis. The prevalence rate of leptospirosis decreased from 49.4 % in 2015 to 21.6 % in 2017. There was a reduction in the abortion rate (from 40.00 % in 2014 to 9.00 % in 2017), in the stillborn rate (from 2.63 % in 2014 to 1.69 % in 2017) and an increase in the calving rate (from 65.00 % in 2014 to 86.00 % in 2017). In addition, there were increases in the number of lactating cows (from 38 in 2014–57 in 2017) and the mean times of lactation duration, which increased from 275 days in 2014–295 days in 2017. As a result, the average annual production of milk increased from 164,655 liters in 2014–248,521 liters in 2017. In 2014, when treatment hadn’t yet started, the gross margin per liter of milk sold, considering implicit and explicit costs, was US$0.00. In 2015 and 2016, US$0.27 and US$0.30 were obtained, respectively, for this variable. In 2017, with the disease under control on the property, the gross margin per liter of milk reached US$0.36. The gross margin per liter of milk sold was higher in the period when the disease was controlled, showing losses of up to 84 % of the gross margin during the outbreak. Immediate treatment of positive cows and preventive measures had a significant impact on improving the productive and economic efficiency of the property.

Estimation of Economic Losses in Trammel Nets Fisheries Using the Length–weight Relationship

Estimation of Economic Losses in Trammel Nets Fisheries Using the Length–weight Relationship

Multi-source driven estimation of earthquake economic losses: A comprehensive and interpretable ensemble machine learning model

Rapid and precise quantification of economic losses post-earthquake is critical for crafting informed disaster management strategies by governmental and insurance entities. This study introduces an ensemble model, constituted by Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost), tailored for quick and interpretative prediction of GDP-related seismic loss assessments, with Sichuan Province serving as the empirical backdrop. The ensemble approach, pre-trained for expediency, draws on a knowledge-driven selection of 30 features that span seismic risks, socio-economic variables, and exposure factors. Enhanced by feature scaling and Bayesian hyperparameter optimization, the model's efficacy is validated through 6 metrics such as R-Squared, MSE, and MAE etc. The incorporation of SHAP values further unravels the model's decision-making, providing transparency to the often opaque computations of machine learning. This methodology offers a scalable, interpretable framework that equips stakeholders with timely and accurate insights for risk mitigation, ultimately strengthening earthquake resilience.

Estimating sectoral COVID-19 economic losses in the Philippines using nighttime light and electricity consumption data.

Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies. To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss. This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation. This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

A study on the economic loss estimation of industrial wastewater pollution discharges in the Yangtze River Delta urban agglomeration

ABSTRACT This study focuses on the industrial wastewater discharge of 27 cities in the Yangtze River Delta urban agglomeration. It selects five water pollutant indicators from industrial wastewater discharge, including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), petroleum pollutants (PP), and major heavy metal content (HM). A shadow price model for industrial wastewater discharge pollutants in each city of the Yangtze River Delta urban agglomeration is constructed, and empirical research is conducted using government-provided statistical data. The study reveals that the economic losses associated with industrial wastewater discharge in the 27 cities of the Yangtze River Delta urban agglomeration range from 5.9296 billion to 6.448 billion yuan. Shanghai demonstrates the highest economic losses caused by industrial wastewater discharge, surpassing 0.45 billion yuan annually. In contrast, Anqing city experiences the lowest economic losses, approximately ranging from 93 million to 104 million yuan per year due to industrial wastewater discharge.

Assessment of meat preservation methods used by retailers and the estimation of direct economic losses associated with meat spoilage in Kenya

Meat spoilage reduces the meat quality and the revenue from the meat industry. This study assessed the direct impact of ineffective meat preservation methods on economic loss among meat retailers in the Dagoretti region of Nairobi, Kenya. A cross-sectional survey was conducted in 87 butcheries and 9 supermarkets from low, middle, and high-income areas using a systematic sampling technique. The meat losses were measured by kilograms of spoilt meat and associated financial cost was estimated. Descriptive and Inferential statistics were used to establish the significance of the association between preservation and demographic factors. A correlation analysis was used to estimate the strength of the relationship between the factors. The study revealed that most of meat handlers heavily relied on refrigeration methods (73%) for storing meat, and 27% hung meat at room temperature due to the high cost of electricity and deep freezers. Furthermore, it revealed that each meat retail business lost 2.3 kg (1.0-3.0 kg) on average per week, which indicates financial losses of USD 11.5 (Range 5.0-15.0 USD) per week or USD 598 (Range USD 260-790) per year. These losses were mainly associated with moisture loss (49%) and microbial spoilage (22%). Poor storage and meat handling practices were blamed for spoilt meat. Meat retailers reported fly menace disturbing their business. It’s recommended that the government implement policies to reduce electricity bills and enhance the adoption of refrigeration methods, while industry stakeholders should facilitate initiatives for public awareness on appropriate methods of meat preservation. Additionally, retail cut meat products on display should be wrapped with permeable film to reduce moisture loss. Further study is needed for a comprehensive analysis of direct economic losses segregated by meat types and preservation forms.

Quantifying Drug Wastage and Economic Loss of Chemotherapy Drugs at an Adult Oncology Care of a Tertiary Care Public Hospital in India.

Background and objective New drugs have revolutionized cancer care, but their high cost requires cost-effectiveness studies. However, these studies only consider optimal use, neglecting real-world wastage. We aim to assess chemotherapy drug wastage and financial loss in our adult oncology care. Methods A total of 100 adult patients attending daycare oncology were prospectively evaluated. The total dose of parenteral anticancer drug, the amount administered, and the amount of drug wasted were recorded for each patient. The economic loss estimation was done considering the unit cost for the drug. Results Our study evaluated 157 parenteral drug administrations of 10 different anticancer drugs in 100 enrolled patients. The most common diagnosis was breast cancer (39/100; 39%), and the most commonly prescribed drugs were paclitaxel (36/157; 23%) and cyclophosphamide (21/157; 13%). However, the wastage percentage varied from 6% to 35.06%, and the overall wastage estimated was 16,298 mg (20.06%) of the total drug procured. Notably, the highest proportion of drug wastage was observed for carboplatin (2,525/7200 mg; 35.06%), whereas oxaliplatin, gemcitabine, 5-FU, and cisplatin wastage were more than 20% of the ordered drug. The total cost of the chemotherapy drug procured was 7,26,005 INR (8,738.78 USD), and drug wastage amounted to 17.14% of the total drug cost, resulting in an economic loss of 1,24,485 INR (1,498.40 USD). Gemcitabine (542.86 USD), oxaliplatin (452.66 USD), and paclitaxel (286.15 USD) were responsible for the maximum cost of wastage. Conclusion Drug wastage and financial loss are significant for carboplatin, oxaliplatin, and gemcitabine, with small proportions of paclitaxel also contributing to economic loss. Possible solutions include planning pharmacy inventory for multiple vial sizes and drug-wise batching strategies to facilitate vial sharing. However, these approaches may present challenges. The pharmaceutical industry can consider initiatives such as providing varying packaging sizes to minimize drug wastage.

Food waste: causes and economic losses estimation at household level in Pakistan.

Food waste is one of the major sustainability issues that need to be addressed due to its negative impacts on the economy, environment, and food security. To develop food waste reduction policies on regional and global level, it is mandatory to have a clear understanding of the various factors prompting food waste at household level and the extent of the economic losses incurred by food waste. Reducing food waste can decrease household expenditure on food, freeing resources for health, education, and well-being. The current study was aimed to (1) examine the food waste behavior of the respondents and to (2) determine the level of monetary losses from food waste. To address these objectives, a questionnaire survey and sample of food waste generated during 24 h were collected from 51 households in Tehsil Kahror Pakka, District Lodhran, Punjab, Pakistan. The survey focus was on levels of food waste and respondents' knowledge and behaviors about food waste. Economic estimation of food waste was also done. In the survey, respondents from both high- and low-income households revealed that their fruit and vegetables (31%; 32%) and peel and scrap (53%; 48%) losses were higher while egg losses (4%; 4%) were lower among various food waste categories. Wanting to eat fresh food and having no time to save food were the reasons for food waste. Monetary losses from food waste (US$ 12.8/Rs. 3677.01 per capita per annum) were higher in high-income households compared to low-income households.

Spatial Scale Dependence of Tropical Cyclone Damage Function: Evidence From the Mainland of China

AbstractTropical cyclone (TC) damage function (DF) is widely used to model TC‐event level damage and thus assess the TC risk for a country or region. The scalability of these DFs at more localized scales, such as the province scale, has not been systematically explored. We use a unique Chinese data set to examine the damage at the TC‐event scale and province scale. Our results show that the parameters and performance of TC DF are spatially dependent. For a sigmoidal DF, the parameter dependence is manifested by a flatter curve calibrated on the TC‐event scale compared to the province scale. In the case of a power‐law DF, the dependence of its parameters is evident in the statistically more significant coefficients of the explanatory variables that are aggregated to the TC‐event scale, compared to the province scale. Performance comparison results further reveal that the scale dependence of performance is related to the type of DF. Integrating hazard, exposure, and vulnerability, the power‐law DF complements the typical sigmoidal DF, producing more accurate estimates of direct economic loss and annual average damage at both the TC‐event and province scales. However, its performance, compared to that of the sigmoidal DF, is more influenced by the scale at which it is calibrated. Our findings elucidate scale‐related research questions in TC risk assessment, offer insights into the selection of DFs, and inspire the future prospect of using multiple DFs to reduce the functional uncertainty.

Estimating the economic loss caused by earthquake in Mainland China

In order to estimate the post-earthquake losses in Mainland China, this paper develops a set of economic loss estimation models for Mainland China based on fixed asset data and the damaging information compiled in the Mainland China composite damaging earthquake catalog (MCCDE-CAT). Considering the differences in seismicity and economic development levels across China, these estimation models are developed separately for different sub-regions. Before performing regression analysis, the Human development index (HDI) is used to normalize the economic loss ratio from earthquake occurrence to the present. The robustness and effectiveness of the loss estimation models are verified by separating the loss information in MCCDE-CAT into sample and test data. According to the loss estimation models developed and the normal distribution characteristics of the residuals between modeled and recorded loss data, the probability of different ranges of predicted earthquake economic loss can be determined. Finally, the 2021 Ms 6.4 Yangbi earthquake is taken as an example to demonstrate the application of our developed loss estimation model. The good consistency between the economic loss estimated based on post-earthquake investigations and the predicted economic loss range by our model further indicates the applicability of the model in rapid post-earthquake loss estimation. In the future, the economic loss estimation method will provide scientific guidance in post-earthquake government reconstruction decision-making and international humanitarian assistance. This method is also applicable to disaster loss estimation in any country and region in the world.

An economic evaluation of cattle tick acaricide-resistances and the financial losses in subtropical dairy farms of Ecuador: A farm system approach.

Estimates of economic losses in cattle due to tick infestations in subtropical areas are limited, such as in Ecuador. Ticks affect animal production and health, but those direct effects are difficult to estimate since financial exercises carried out in farms consider both costs of the inputs and revenues. This study aims to quantify the costs of inputs involved in milk production and to know the role of acaricide treatment in the production costs on dairy farms in subtropical zones using a farming system approach. Regression and classification trees were used to study the relationship between tick control, acaricide resistance and the presence of high level of tick infestation in the farm system. Even though there was no significant direct association between high levels of tick infestation and the presence of acaricide resistance in ticks, a more complex structure for resistances operates in the manifestation of high tick infestation involving levels of farm technology and no acaricide resistance. Farms with higher levels of technology allocate a lower percentage of sanitary expenses to control ticks (13.41%) in comparison to semi-technified (23.97%) and non-technified farms (32.49%). Likewise, more technified and bigger herds have a lower annual expenditure on acaricide treatment (1.30% of the production budget equivalent to 8.46 USD per animal) compared to non-technified farms where it can represent more than 2.74% of the production budget and where the absence of cypermethrin resistance increases the treatment cost to 19.50 USS per animal annually. These results can motivate the development of information campaigns and control programmes targeted to the reality of small and medium farms that are the most affected in terms of the money they invest in controlling ticks.

The Global Macroeconomic Burden of Burn Injuries.

Standardized estimates of global economic losses from burn injuries are lacking. The primary objective of this study was to determine the global macroeconomic consequences of burn injuries and their geographic distribution. Using the Institute of Health Metrics and Evaluation database (2009 and 2019), mean and 95% uncertainty interval (UI) data on incidence, mortality, and disability-adjusted life-years (DALYs) from injuries caused by fire, heat, and hot substances were collected. Gross domestic product (GDP) data were analyzed together with DALYs to estimate macroeconomic losses globally using a value of lost welfare approach. There were 9 million global burn cases (95% UI, 6.8 to 11.2 million) and 111,000 deaths from burns (95% UI, 88,000 to 132,000 deaths) in 2019, representing a total of 7.5 million DALYs (95% UI, 5.8 to 9.5 million DALYs). This represented welfare losses of $112 billion (95% UI, $78 to $161 billion), or 0.09% of GDP (95% UI, 0.06% to 0.13%). Welfare losses as a share of GDP were highest in low- and middle-income countries (LMICs) of Oceania (0.24%; 95% UI, 0.09% to 0.42%) and Eastern Europe (0.24%; 95% UI, 0.19% to 0.30%) compared with high-income country regions such as Western Europe (0.06%; 95% UI, 0.04% to 0.09%). Mortality-incidence ratios were highest in LMIC regions, highlighting a lack of treatment access, with southern sub-Saharan Africa reporting a mortality-incidence ratio of 40.1 per 1000 people compared with 1.9 for Australasia. Burden of disease and resulting economic losses because of burn injuries are substantial worldwide and are disproportionately higher in LMICs. Possible effective solutions include targeted education, advocacy, and legislation to decrease incidence and investing in existing burn centers to improve treatment access.

Delayed harvest time affects strength and color parameters in cotton fibers

AbstractCotton (Gossypium hirsutum L.) is the most widely used natural fiber worldwide in the textile industry; thus, maintaining or even improving fiber quality is essential to produce the best quality yarn and uniform fabrics. Final fiber quality properties are determined not only by genotypes or environmental conditions during crop development but can also be affected by other postmaturity factors, such as harvesting and ginning methods, and harvest time (HT). The aim of this study is to associate the different environmental factors that affect specific lint quality traits produced by a delay in HT, also assessing cultivar effect. This field study included four cotton cultivars evaluated at seven different HTs (manual harvested) for different quality parameters. Results indicate that prolonged exposure of cotton fiber to the environment affects lint percentage (LP), strength (Str), reflectance (Rd), and yellowness (+b). Str showed a decrease of 0.02 g tex−1 day−1 from defoliant application, whereas LP, Rd, and +b presented different rates according to the season. No differences were obtained neither for length nor for fineness and maturity (micronaire) traits. In addition, the analysis of the results together with the climatic data recorded during the experiments indicated a negative correlation among Str, Rd, and +b with the number of rainy days, accumulated precipitation, and frost temperatures. Overall, this study establishes specific relationships between certain fiber characteristics and environmental conditions, being an important tool for crop management with regards to climate predictions that could help to make estimates of economic losses when the harvest is delayed.

A combined unsupervised learning approach for electricity theft detection and loss estimation

AbstractElectricity theft is a great trouble for power companies. As the means of tampering with smart meters continue to increase, the electricity theft behaviours become more diversified and covert, which are difficult to be identified using the existing electricity theft detection method. In addition, the existing methods usually cannot estimate the economic losses caused by electricity theft. To address these issues, a combined unsupervised learning approach for electricity theft detection and loss estimation is proposed in this study. First, three anomaly measurement indexes including the mean index, fluctuation index, and trend index are proposed to capture different anomalies respectively. Then, based on historical electricity consumption data, we develop two unsupervised learning techniques including the sample‐to‐subsamples decomposition algorithm and clustering algorithm to obtain the typical ranges of index values, and the load samples whose index values are not in the typical ranges will be considered fraudulent. Furthermore, three anomaly measurement indexes are combined to judge whether the load sample is fraudulent, and the user whose most load samples are judged fraudulent will be considered as an electricity thief. Finally, an economic loss estimation method is proposed, which quantifies the losses of electricity theft. Numerical experiments are carried out based on the Irish smart meter dataset, and the results demonstrate the effectiveness and the superior performance of the proposed method compared with a series of electricity theft detection methods.

Rapid Estimation of Direct Economic Losses Caused by Significant Earthquakes: An Evidence-Based Model and its Applications

This paper presents a rapid or real-time estimation method of the economic value of direct stock damages caused by significant earthquakes in Japan. The result will contribute to both the government and private sectors’ early decision-making, particularly for provisional budget allocation. First, we developed a simple but evidence-based model for estimating stock losses explained by a representative earthquake hazard factor and an exposure factor, i.e., seismic intensity and existing stock of physical assets. The key characteristic of our estimation model is that the dependent variable is prefectural damage amount. Still, the explanatory variables come from municipal sources: we overcome this data availability problem through our estimation process. Second, we carefully checked the model’s specification, estimation, and performance to be soundly applied to a real-time assessment of future earthquake events. We also explain the automated measuring of the prefectural direct loss value and its distribution to every 250 m mesh. Finally, we show two examples of the application of our model; one is the case of the 2018 Northern Osaka Earthquake, and the other is the anticipated Tokyo inland earthquake.

A Systemic Analysis of the Impact of the Pandemic on the Indian Tourism Economy

The COVID-19 pandemic had a severe impact on the tourism industry across the world. Be it aviation or hospitality, transportation, tour operators or eateries, every activity related to tourism was adversely affected by the pandemic in an unprecedented manner. India saw the first severe impact during the first quarter of 2020–2021 when the tourism industry was severely affected, in terms of loss in tourism demand due to a significant fall in tourist arrivals. The industry saw gradual signs of recovery post-October 2020 but was hit again by the second wave during April–June 2021 and then by the third wave during November 2021–January 2022. Given the contribution that tourism makes to the entire economy in terms of income and employment generation, it is important to do a systemic estimation of the losses caused by the pandemic so that resilient policies are put in place to address the challenges at all levels and put the tourism sector back on the path it was traversing before the pandemic. This article presents the estimates of economic losses resulting from the changes experienced during the most critical period of the pandemic, that is, the first quarter of 2020–2021, which witnessed a complete lockdown, and the subsequent two quarters, wherein the sector started showing gradual recovery following various relaxations in economic activities and travel movements. The estimates are based on the methodology that draws from the framework laid out in the Tourism Satellite Account of India, which, in turn, is based on the methodological framework recommended by the United Nations World Tourism Organization. JEL Codes: L830, Z320, F620