Natural disasters and corporate innovation Le, Huong; Nguyen, Tung; Gregoriou, Andros ...
The European journal of finance,
01/2024, Letnik:
30, Številka:
2
Journal Article
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We examine how natural disasters affect corporate innovation. Using a comprehensive sample of U.S. firms and inventors, we find that natural disasters significantly drop innovation quantity and ...quality. The results are robust to include a broad set of regional characteristics, matching analysis, and alternative proxies for innovation. These effects persist for up to three years after the disaster. We also provide suggestive evidence that financial constraints due to natural disasters give firms less incentive to innovate. Further analysis shows that natural disasters have impacts on inventor relocation, innovation productivity, and innovation risk.
•Evaluation of Smart Disaster Response Systems (SDRS) under uncertainty.•Smart Continuous Glucose Monitoring is demonstrated the reliability of the study.•Five SDRS evaluated to improve the ...reliability of the proposed framework.•Comparison of the proposed framework with others current methods.•The framework has outperformed the other methods in terms of evaluation accuracy.
As a major priority to get better resource utilization and to ensure a high quality of life, research on smart disaster response systems (smart DRS) that based on information and communication technology (ICT) has been widespread. It’s imperative for smart cities to have smart disaster response systems so they can easily manage natural disasters efficiently such as tsunamis, earthquakes, and hurricanes. Lately, the Internet of Things (IoT) provided several solutions to confront the disaster concerns such as early cautions, remote controlling, data analysis and knowledge building. To evaluate the performance of the smart disaster response systems, there are a group of criteria that need to be measured. This study proposed an integrated framework to evaluate the performance of smart disaster response systems under uncertainty. Due to ensure a more accurate evaluation process, the proposed framework is based on plithogenic set theory that handles ambiguity and uncertainty in evaluation by considering the contradiction degree between the evaluation criteria. The problem of performance evaluation of the smart disaster response systems is formulated as a multi-criteria decision-making problem. The proposed framework is constructed using three common MCDM methods which is AHP, TOSIS, and VIKOR. Five smart disaster response systems will be evaluated in order to improve the reliability of the proposed framework.
Green innovation has been positioned as an effective way to balance economic development and environmental governance. However, the impact of green innovation (i.e., innovation relating to the ...environmentally sound technologies (ESTs)) on carbon emission performance in a large developing country, such as China, has been paid little attention. This paper investigates the impact of green innovation on carbon emission performance based on a panel data set covering 218 prefecture-level cities in China from 2007 to 2013. First, we examine whether heterogeneous green innovations have a synergistic effect on carbon emission performance using the two-way fixed effect model, instrumental variable method, and spatial econometric model. Moreover, using a causal mediation effect model, we identify four kinds of potential transmission channels of green innovation affecting carbon emission performance: energy consumption structure effect, industrial structure effect, urbanization effect, and foreign direct investment (FDI) effect. The results indicate a positive effect of green innovation and its sub-categories on carbon emission performance in China. However, a noteworthy phenomenon is that direct carbon emission-reduction innovation and green administrative innovation have a weaker effect on carbon emission performance than other kinds of green innovations. In addition, the positive effect has an evident heterogeneity in different kinds of cities. To be specific, green innovation has an evident positive impact on carbon emission performance in key cities for environmental protection, resource-based cities, non-resource-based cities, and central cities. Meanwhile, a “snowball” effect and a symbiotic effect of carbon emission performance exist in local cities and between cities, respectively. Finally, we find that green innovation significantly decreases and increases carbon emission performance through industrial structure effect and FDI effect, respectively.
•We investigate the impact of heterogeneous green innovations on carbon emission performance in China.•We use a panel data set covering China's 218 prefecture-level cities over 2007–2013.•Instrumental variable method and spatial econometric model are employed.•We find a positive effect of heterogeneous green innovations on carbon emission performance in China.•Green innovation improves carbon emission performance through some mediation effects.
Using rainfall, public relief, and election data from India, we examine how governments respond to adverse shocks and how voters react to these responses. The data show that voters punish the ...incumbent party for weather events beyond its control. However, fewer voters punish the ruling party when its government responds vigorously to the crisis, indicating that voters reward the government for responding to disasters. We also find evidence suggesting that voters only respond to rainfall and government relief efforts during the year immediately preceding the election. In accordance with these electoral incentives, governments appear to be more generous with disaster relief in election years. These results describe how failures in electoral accountability can lead to suboptimal policy outcomes.
Despite a general lack of political knowledge among the public, research demonstrates that individuals intuitively know which level of government should be, and sometimes is, responsible for policy ...problems. In this article, we look at public federalism preferences in the context of disaster management, particularly for managing the risks associated with three different types of hazards—specifically global warming, earthquakes, and wildfires—and examine if their preferences are aligned with the division of responsibility in disaster management. Using survey data from Oklahoma, we find that individuals appropriately match their preferences to the intergovernmental nature of disaster federalism in the United States. Additionally, knowing the causes of these hazards is strongly associated with a preference for the appropriate, to disaster scope and scale, level of government. Finally, using seemingly unrelated regression techniques, we find that many, but not all, relationships are hazard general while some are hazard specific.
Hurricanes are a type of natural disaster for which it is possible to plan for prepositioning of supplies to improve the efficiency of the post-disaster relief effort. This paper develops a model for ...prepositioning supplies in such a setting. Our model has a distinguishing feature the possible destruction of supply points during the disaster event. To gain insight into our model, we develop a series of theoretical properties. To test the applicability of our model a series of computational tests are performed. From such tests we conclude that it is possible to solve relatively large instances of the problem utilizing standard optimization software. A methodology based on creation of clusters of demand points is proposed for solving even larger problems. Finally we study sensitivity of the results with respect to key parameters. These investigations provide important policy implications.
► Disaster relief modeling. ► Planning for a Hurricane disaster. ► Modeling destruction of supply points. ► Clustering to solve large-scale problems.
Many real world complex systems such as critical infrastructure networks are embedded in space and their components may depend on one another to function. They are also susceptible to geographically ...localized damage caused by malicious attacks or natural disasters. Here, we study a general model of spatially embedded networks with dependencies under localized attacks. We develop a theoretical and numerical approach to describe and predict the effects of localized attacks on spatially embedded systems with dependencies. Surprisingly, we find that a localized attack can cause substantially more damage than an equivalent random attack. Furthermore, we find that for a broad range of parameters, systems which appear stable are in fact metastable. Though robust to random failures-even of finite fraction-if subjected to a localized attack larger than a critical size which is independent of the system size (i.e., a zero fraction), a cascading failure emerges which leads to complete system collapse. Our results demonstrate the potential high risk of localized attacks on spatially embedded network systems with dependencies and may be useful for designing more resilient systems.
•We analyze the size and timing of investment in climate change adaptation for ports.•We apply a two-period real options game approach to transportation investment.•The model accounts for information ...gain and multiple sources of uncertainty.•Information gain, disaster probability and competition affect the timing decisions.•Early adaptation leads to higher social welfare because of the spillover effects.
We investigate the size and timing of investment in adaptation to climate change effects for ports, when efficiency of investment is uncertain and the market is competitive. We develop a two-period real options game model with two “landlord” ports, each consisting of a port authority (PA) and a downstream terminal operator company (TOC). The two PAs compete with each other at the upstream level, and the two TOCs downstream. The model assumes an accumulation of information about the adaptation projects over time, allowing decision-makers to improve the investment efficiency. The results show that the optimal timing of investment is significantly influenced by the disaster occurrence probability, the potential information gain over time and the level of competition. When competition is intensified, it is optimal for ports to invest earlier than later. However, immediate investments are less preferred when competition is weak, even lesser in the presence of information accumulation. Waiting until the next period to invest is also a better option if the disaster occurrence probability is low or if the shippers’ expected disaster losses are negligible. Moreover, information accumulation reduces the ports’ investment size, while improving the discounted welfare associated with late investments. These results hold for both the private and public ports and for simultaneous investments by the PAs. In most cases, social welfare is much higher with immediate investments, mainly because of the associated positive spillover effects on the surrounding areas and other sectors of economy. The implications of the assumption of Knightian uncertainty for the disaster occurrence probability are discussed.
This paper presents a model developed in the Netherlands for the estimation of damage caused by floods. The model attempts to fill the gap in the international literature about integrated flood ...damage modelling and develop an integrated framework for the assessment of both direct hazard-induced damages and indirect economic damages such as the interruption of production flows outside the flood affected area, as well as loss of life due to flooding. The scale of damage assessment varies from a specified flood-prone area in a river basin or a coastal region to the country's entire economy. The integrative character of the presented model is featured by the combination of information on land use and economic data, and data on flood characteristics and stage-damage functions, where the geographical dimension is supported by modern GIS to obtain a damage estimate for various damage categories. The usefulness of the model is demonstrated in a case study estimating expected flood damage in the largest flood-prone area in the Netherlands.
Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular ...importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies.
This paper provides a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated, and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms. The paper also highlights solutions to two machine learning problems: (1) Binary classification of video frames based on the presence and absence of fire flames. An Artificial Neural Network (ANN) method is developed that achieved a 76% classification accuracy. (2) Fire detection using segmentation methods to precisely determine fire borders. A deep learning method is designed based on the U-Net up-sampling and down-sampling approach to extract a fire mask from the video frames. Our FLAME method approached a precision of 92%, and recall of 84%. Future research will expand the technique for free burning broadcast fire using thermal images.
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