Sex-related disparities in vehicle crash injury and hemodynamics.

Objective: Multiple studies evaluate relative risk of female vs. male crash injury; clinical data may offer a more direct injury-specific evaluation of sex disparity in vehicle safety. This study sought to evaluate trauma injury patterns in a large trauma database to identify sex-related differences in crash injury victims. Methods: Data on lap and shoulder belt wearing patients age 16 and up with abdominal and pelvic injuries from 2018 to 2021 were extracted from the National Trauma Data Bank for descriptive analysis using injuries, vital signs, International Classification of Disease (ICD) coding, age, and injury severity using AIS (Abbreviated Injury Scale) and ISS (Injury Severity Score). Multiple linear regression was used to assess the relationship of shock index (SI) and ISS, sex, age, and sex*age interaction. Regression analysis was performed on multiple injury regions to assess patient characteristics related to increased shock index. Results: Sex, age, and ISS are strongly related to shock index for most injury regions. Women had greater overall SI than men, even in less severe injuries; women had greater numbers of pelvis and liver injuries across severity categories; men had greater numbers of injury in other abdominal/pelvis injury regions. Conclusions: Female crash injury victims' tendency for higher (AIS) severity of pelvis and liver injuries may relate to how their bodies interact with safety equipment. Females are entering shock states (SI > 1.0) with lesser injury severity (ISS) than male crash injury victims, which may suggest that female crash patients are somehow more susceptible to compromised hemodynamics than males. These findings indicate an urgent need to conduct vehicle crash injury research within a sex-equity framework; evaluating sex-related clinical data may hold the key to reducing disparities in vehicle crash injury.

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical areas, which subsequently support the future funding allocation strategies. In recent years, there is a burgeoning interest in applying artificial intelligence (AI)-based models to perform crash prediction tasks. Despite the remarkable accuracy of these AI-based crash prediction models, they have been observed to yield biased prediction outcomes across areas of different socioeconomic statuses. These biases are primarily attributed to the inherent measurement and representation biases of AI-based prediction models. More precisely, measurement bias arises from the selection of target variables to reflect crash hazard levels, while representation bias results from the issue of imbalanced number of samples representing areas with different socioeconomic statuses within the dataset. Consequently, these biased prediction outcomes have the potential to perpetuate an unfair allocation of funding resources, contributing to worsen social inequality over time. Drawing upon a real-world case study in North Carolina, this study designs an AI-based crash prediction model that utilizes previous sociodemographic and crash-related variables to predict future severe crash rate of each area to reflect the crash hazardous level. By incorporating a fair regression framework, this study endeavors to transform the crash prediction model to become both fair and accurate, aiming to support equitable and responsible safety improvement funding allocation strategies.

An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes.

OBJECTIVES: Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. METHODS: This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. RESULTS: Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. CONCLUSIONS: The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

Assessing the Negative Binomial-Lindley model for crash hotspot identification: Insights from Monte Carlo simulation analysis.

Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety management. The Negative Binomial (NB) model is the most common model used in safety analyses and evaluations - including hotspot identification. The NB model, however, is not without limitations. In fact, this model does not perform well when data are highly dispersed, include excess zero observations, or have a long tail. Recently, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB. The NB-L model overcomes several limitations related to the NB, such as addressing the issue of excess zero observations in highly dispersed data. However, it is not clear how the NB-L model performs regarding the hotspot identification. In this paper, an innovative Monte Carlo simulation protocol was designed to generate a wide range of simulated data characterized by different means, dispersions, and percentage of zeros. Next, the NB-L model was written as a Full-Bayes hierarchical model and compared with the Full-Bayes NB model for hotspot identification using extensive simulation scenarios. Most previous studies focused on statistical fit, and showed that the NB-L model fits the data better than the NB. In this research, however, we investigated the performance of the NB-L model in identifying the hazardous sites. We showed that there is a trade-off between the NB-L and NB when it comes to hotspot identification. Multiple performance metrics were used for the assessment. Among those, the results show that the NB-L model provides a better specificity in identifying hotspots, while the NB model provides a better sensitivity, especially for highly dispersed data. In other words, while the NB model performs better in identifying hazardous sites, the NB-L model performs better, when budget is limited, by not selecting non-hazardous sites as hazardous.

Investigating safety and cost-effectiveness of cable median barriers in Louisiana.

OBJECTIVES: Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage-only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana. METHODS: Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit-cost analysis was conducted to determine the cost-effectiveness of CMBs. RESULTS: The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit-cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit-cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety. CONCLUSION: This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.

Longitudinal Assessment of Hair Cortisol as a Predictor of Psychological Symptoms During COVID-19.

BACKGROUND: There is a lack of evidence regarding enduring psychoneuroendocrine changes following an initial traumatic event, particular in the presence of an ongoing stressor. The coronavirus pandemic presents an opportunity to explore this matter. Consequently, the purpose of the present study was to investigate the impact of the ongoing pandemic (2021) on individuals, who experienced a first-time motor vehicle crash (MVC) at least 6 years earlier. To this end, we hypothesized that hair cortisol concentrations (HCC) following a first-time traumatic event positively predict symptoms of depression. METHOD: We investigated N = 69 individuals (18 - 65 yrs.), who were victims of a MVC during 2010 - 2014. Hair strands were collected 10 days (t1) and 3 months after the MVC (t2), as well during the pandemic in 2021 (t3). To assess symptoms of depression, the participants filled out the Beck Depression Inventory at t1 - t3 and were additionally interviewed (Structured Clinical Interview for DSM-IV Axis I) at t1 and t2. Exclusion criteria conveyed a lifetime or acute mental disorder (incl. past trauma exposure). RESULTS: Elevated pre-pandemic HCC following adversity (i.e., MVC) significantly predicted symptoms of depression in adults during the coronavirus pandemic (BDI: ß =.44, p =.010, R2 =.20), even after controlling for confounders. HCC significantly decreased over time, while in average psychological symptoms remained consistent. CONCLUSION: Cortisol dysregulation in the past presents an enduring vulnerability to ongoing stress. In this regard, vulnerable groups may benefit from preventive measures. This finding validates the predictive power of HCC and extended past evidence in this regard, at the same time reinforcing the concept of the diathesis-stress model.

The catastrophic cost of motorcycle road traffic injuries: Experience from a major reference centre in a lower-middle income country.

BACKGROUND: Motorcycle crashes are an increasing public health problem in low- and middle-income countries (LMICs). An accurate estimation of the economic burden of these crashes could be complex owing to a prevalent system of out-of-pocket (OOP) payment for health care services in these countries. Our study aims to objectively evaluate the cost implication of motorcycle Road Traffic Injuries (RTIs) among road crash victims managed at a major trauma reference hospital in Nigeria. Two economic evaluation methods were used to accurately reflect the cost-of-care (C-o-C) of each victim as well as for cross-validation. METHOD: This is a prospective cohort study conducted between August 2020 and May 2021. All patients involved in motorcycle road traffic crashes presenting to the Emergency Department of the University College Hospital, Ibadan, Nigeria, were included in the study. For each patient, all medical expenses from the time of injury (T0) to 30 days after injury (T30) or Time to death (TD) - whichever occurred first, were valued in costs, and added (Activity-based costing or ABC), while also estimating overall cost-of-care (C-o-C) at T30 or TD, using the willingness-to-pay (WTP) method. Following the WHO definition, catastrophic expenditure was defined as expenditure > 25% of the patient's estimated annual household income. RESULTS: Of the 150 consecutively managed motorcycle crashes victims during the study period, 112 had complete data. The median monthly household income for the cohort was $121 with 75% of them earning less than $180. The median cost-of-care (C-o-C), by ABC, was $242 ($143 - 828). For individual care items, expenditure on surgical intervention(s) was the highest followed by prosthesis and implant procurement, and radiological investigations. On the other hand, the estimated medical cost was $2356 (IQR $938 - 6475) by WTP. Only 14% had health insurance coverage. The overall expenditure was catastrophic for 46% of the patients. Monthly household income of < $180 (AOR=9.2; 95% CI=2.6-32.8; p < 0.001), absence of health insurance coverage (AOR=10.7; 95% CI=1.1-101.6; p = 0.040), and prolonged hospital stay above 14 days (AOR=25.1; 95% CI=5.5 -115.1; p = 0.001) were predictors of catastrophic expenditure. There was a weak positive correlation between actual cost-of-care using the ABC method and WTP (r = 0.247; p = 0.102). CONCLUSION: The aggregate cost of motorcycle RTIs is catastrophic for nearly half of the victims attending the University College Hospital, Ibadan. The willingness-to-pay method, though less tedious is often less reliable in these settings owing to a prevalent OOP payment system. This study identified the need to implement effective financial protection mechanisms against the high OOP expenditure faced by motorcycle crash victims in LMICs.

A multi-fidelity approach for reliability-based risk assessment of single-vehicle crashes.

Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions.

Does the financial flexibility prevent stock price crash risk during COVID-19 crisis? Evidence from the Vietnamese stock market.

Stock price crash risk is of particular interest in developing countries as it poses a significant threat to investors and can have detrimental effects on the stability of emerging markets. This study investigates the role of financial flexibility in preventing stock price crash risk in the Vietnamese stock market, with a specific focus on the COVID-19 pandemic. Using the fixed-effect, system GMM, and quantile regression methods on a sample of 645 Vietnamese listed firms from 2011 to 2021, this study found that financial flexibility has a significant impact on preventing stock price crash risk. This effect was augmented during the COVID-19 crisis. Furthermore, this study found that financial flexibility mitigated the impact of the COVID-19 crisis on stock price crash risk. The findings provide important implications for firm regulators, shareholders, and investors to respond to similar future crises.

Applying a Health Equity Lens to Work-Related Motor Vehicle Safety in the United States.

Motor vehicle crashes (MVCs) are the leading cause of fatal work-related injuries in the United States. Research assessing sociodemographic risk disparities for work-related MVCs is limited, yet structural and systemic inequities at work and during commutes likely contribute to disproportionate MVC risk. This paper summarizes the literature on risk disparities for work-related MVCs by sociodemographic and employment characteristics and identifies worker populations that have been largely excluded from previous research. The social-ecological model is used as a framework to identify potential causes of disparities at five levels-individual, interpersonal, organizational, community, and public policy. Expanded data collection and analyses of work-related MVCs are needed to understand and reduce disparities for pedestrian workers, workers from historically marginalized communities, workers with overlapping vulnerabilities, and workers not adequately covered by employer policies and safety regulations. In addition, there is a need for more data on commuting-related MVCs in the United States. Inadequate access to transportation, which disproportionately affects marginalized populations, may make travel to and from work less safe and limit individuals' access to employment. Identifying and remedying inequities in work-related MVCs, whether during the day or while commuting, will require the efforts of industry and multiple public sectors, including public health, transportation, and labor.

An in-depth analysis of head-on crash severity and fatalities in Ghana.

Head-on collisions are often linked to more serious injuries compared to other types of crashes, due to the intense impact they cause. In low- and middle-income countries, these collisions frequently involve high occupancy public transportation vehicles, leading to higher fatality rates per crash. Given the high risk of injury and potential for multiple casualties, this study delves into the factors influencing the outcomes of head-on crashes and the number of fatalities in Ghana. The study analyzed six years of historical head-on collision data from Ghana and developed two models to address the issue. The injury-severity analysis was performed using a random parameter multinomial logit with heterogeneity in means and variances approach and aimed to identify the factors that have a significant impact on the severity of injuries sustained in head-on collisions, while the random parameters negative binomial fatality count model was designed to examine the factors that contribute to the number of fatalities in these crashes in the country. Results showed that head-on collisions with drivers over 65, buses, motorcycles, and those between 25 and 65 years of age were more likely to result in fatalities. Speeding and vehicle malfunctions were also found to be significant contributing factors to fatal head-on collisions. Head-on crashes involving minibuses and incidents where the driver was attempting to overtake another vehicle were found to be more likely to result in a higher number of fatalities. The results of this study uncover an intriguing interaction between human-related elements and socioeconomic factors, which pose obstacles to the Government's endeavor to upgrade the major highways in the country. Additionally, the increasing need for transportation has led to the presence of vehicles on the roads that may not meet safety standards. Consequently, it is no surprise that several of the study's findings align with expectations. Nevertheless, within the specific context of Ghana, these findings furnish compelling data-driven evidence supporting the adoption and implementation of the safe systems approach as a means to tackle fatal head-on collisions in the country.

Weathering the crash: Do customer-company relationships pay off during economic crises?

Do stronger relationships with customers (customer-company relationships [CCR]) help firms better weather economic crises? To answer this question, we examine firm performance during the stock market crashes associated with the two most severe economic crises of the last 15 years-the protracted Great Recession crisis (2008-2009) and the shorter but extreme COVID-19 pandemic crisis (2020). Juxtaposing the predominant expected utility theory perspective with observed deviations in investor behavior during crises, we find that both pre-crash firm-level customer satisfaction and customer loyalty are positively associated with abnormal stock returns and lower idiosyncratic risk during a market crash, while pre-crash firm-level customer complaint rate negatively affects abnormal stock returns and increases idiosyncratic risk. On average, we find that one standard deviation higher CCR is associated with between $0.9 billion and $2.4 billion in market capitalization on an annualized basis. Importantly, we find that these effects are weaker for firms with higher market share during the COVID-19 crash, but not during the Great Recession crash. These results are found to be robust to a variety of alternate model specifications, time periods, sub-samples, accounting for firm strategies during the crises, and endogeneity corrections. When compared to relevant non-crash periods, we also find that such effects are equally strong during the Great Recession crash and even stronger during the COVID-19 pandemic crash. Contributing to both the marketing-finance interface literature and the nascent literature on marketing during economic crises, implications from these findings are provided for researchers, marketing theory, and managers. Supplementary information: The online version contains supplementary material available at 10.1007/s11747-023-00947-1.

COVID-19, a blessing in disguise for the Tech sector: Evidence from stock price crash risk.

In this paper we document that although COVID-19 has brought uncertainties to the overall economy, the Technology (tech) sector is the systematic beneficiary of the pandemic. Using a quasi-natural setup, we find a significant notion that the Stock Price Crash Risk (SPCR) of firms within the Tech sector decreases during the COVID-19 pandemic compared to the recent past and firms belonging to other sectors. Our analyses further reveal that firms in the Tech sector with stronger external monitoring and better information environment receive an even greater advantage from the pandemic. Overall, our study suggests that the higher systemic dependency on the Tech sector during the COVID-19 outbreak results in an economic benefit for this sector.

Biofidelity Assessment of the WIAMan Thorax by a Comparative Study With Hybrid III, THOR, and PMHS in Frontal Sled Testing.

The Warrior Injury Assessment Manikin (WIAMan) anthropomorphic test device (ATD) has been originally developed to predict and prevent injuries for occupants in military vehicles, in an underbody blast environment. However, its crash performance and biofidelity of the thoracic region have not been explored. The aim of this study was to determine and evaluate the WIAMan thoracic responses in a typical frontal sled test. The 40 kph frontal sled tests were conducted to quantify the WIAMan thoracic kinematics, chest deflection, and belt loads. Comparative biofidelities of the WIAMan thorax and other surrogates, including postmortem human surrogates (PMHSs), Hybrid III, and test device for human occupant restraint (THOR) ATDs, were assessed under comparable testing conditions. The similarities and differences between WIAMan and the other surrogates were compared and analyzed, including the motion of bilateral shoulders and T1, time histories of chest deflections, and belt loads. The CORrelation and Analysis (CORA) ratings were used to evaluate the correlations of thoracic responses between the ATDs and PMHS. Compared to the PMHS and THOR, the WIAMan experienced a similar level of left shoulder forward excursions. Larger chest deflection was exhibited in WIAMan throughout the whole duration of belt compression. Differences were found in belt loads between subject types. Overall, WIAMan had slightly lower CORA scores but showed comparable overall performance. The overall thoracic responses of WIAMan under the frontal sled test were more compliant than HIII, but still reasonable compared with PMHS and THOR. Comprehensive systematic studies on comparative biofidelity of WIAMan and other surrogates under different impact conditions are expected in future research.

An Evaluation of the Safety Effectiveness and Cost of Autonomous Vehicles Based on Multivariable Coupling.

There is a need for in-depth studies of autonomous vehicle safety that evaluate the effectiveness of safety functions and different "atomic" technology combinations for vehicles and roads. In this paper, we provide a crash avoidance effectiveness evaluation model for autonomous vehicles enabled with different sensor combinations based on multiple variables of 14 different "atomic" sensing technologies on the vehicle side and road side, 52 safety functions, and 14 accident types. Meanwhile, a cost-sharing model is developed based on the traveled distance during the life cycle of vehicles and based on the traffic flow over the life cycle of roads to evaluate the unit cost per km of different "atomic" technology combinations. The results clearly show that the cost increases with the addition of "atomic" sensing technologies on the vehicle side, while an increase in crash avoidance effectiveness decreases. It is necessary to switch to V2X and to introduce roadside "atomic" technology combinations to realize better safety effectiveness at a lower cost for vehicles. In addition, a map that covers the safety effectiveness and cost per kilometer of all "atomic" technology combinations is calculated for decision-makers to select combinations under the preconditions of cost and safety.

Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities.

In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind.

Firms' exposures on COVID-19 and stock price crash risk: Evidence from China.

This study examines the impact of firms' exposures on COVID-19 sentiment on the stock price crash risk. We show the exposure on COVID-19 sentiment related to the medical, travelling and uncertain aspects significantly increases the stock price crash risk, while the exposure on COVID-19 sentiment related to vaccines significantly decreases the risk of stock price crash. Furthermore, our findings are stronger for non-state-owned firms and firms with low information transparency. Overall, we provide timely policy implication for economic impacts of the COVID-19 on the stock market.

Green credit policy and the net value crash risks of fund holding heavily polluting enterprise stocks: a quasi-natural experiment.

Based on the data of China's open-end stock funds and partial stock funds from 2009 to 2020, we take the implementation of green credit guidelines (GCG) as a quasi-natural experiment and investigate the impact of green credit policies on the net value crash risks of fund holding heavily polluting enterprise stocks. The results show that green credit policies will significantly increase the net value crash risks of fund holding heavily polluting enterprise stocks. Green credit policies increase the net value crash risks of fund holding heavily polluting enterprise stocks by increasing investor redemptions. Further tests show that better fund performance and higher portfolio concentration weaken the positive impact of green credit policies on the net value crash risks of fund holding heavily polluting enterprise stocks, and higher proportion of institutional investors strengthens the positive impact of green credit policies on the net value crash risks of fund holding heavily polluting enterprise stocks. This study supplements the literature on green credit policies and funds, and provides policy guidance for regulators.

Central environmental protection inspector and stock price crash risk-evidence from polluting industries firms in China.

In recent years, under the background of vigorously promoting environmental governance, the implementation effect of the central environmental protection inspection is an issue of great concern to the government and the public. This paper systematically investigates the impact of central environmental protection inspection on the risk of stock price crash using a sample of listed firms in polluting industries. The results show that compared with non-supervised areas, central environmental protection inspection can reduce the polluting industries' firms' stock price crash risk by reducing stock price bubbles. After a series of robustness tests, the results still held. The above transmission mechanism is more effective in the samples of private enterprises, low information transparency and disclosure quality enterprises, non-national civilized urban areas, and high promotion incentive areas. Furthermore, this paper found that there were differences in the effects of central environmental protection inspection in different batches. Among the effects of central environmental protection inspection in different batches, the effect of environmental regulation in the second, third, and fourth batches was better, and the effect of central environmental protection inspection in different batches gradually deepened. Finally, by analyzing the environmental governance of the central environmental protection inspection, it is found that the central environmental protection inspection has significant short-term and long-term control effect in air pollution governance, and it is still necessary to strengthen the law enforcement in water pollution governance.