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Unleashing the power of emergency response: controlling natural disasters by addressing environmental risk

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A Correction to this article was published on 09 November 2023

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Abstract

The primary focus of the analysis is to investigate the impact of emergency response management and environmental risk on natural disasters by controlling the variables of national income and financial development. To investigate the model empirically, we have employed the quantile autoregressive distributed lag model that estimates the short- and long-run estimates across various quantiles. The long-run estimates of emergency response management are negative and significant only at higher quantiles, i.e., from 60 to 95th quantiles. In the short run, emergency response management’s estimated coefficients are negative and significant from 70 to 95th quantiles. Environmental risk shows a significant positive correlation with natural disasters across quantiles, while national income and financial development decrease natural disasters in the long run. Furthermore, we observed the asymmetric impact of emergency response management on natural disasters in both the short and long run.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Notes

  1. Quantile ARDL is a statistical modeling approach combining quantile regression with autoregressive distributed lag models to better grasp the connection between variables. It helps us estimate the impacts of independent variables on different levels of dependent variables. It is particularly helpful when the data dispersal or distribution is not normal.

  2. Symmetric assumption implies that if a positive change in independent variable improve the dependent variable by X%, the negative change in the independent variable hurts the dependent variable by X%. However, the asymmetric assumption implies that both negative and positive changes in independent variables may boost the dependent variable by X% and Z%, respectively, and vice versa.

  3. The Wald test aids in determining the significance of a certain variable in your model by comparing the estimated parameter associated with a particular variable to a threshold value. We can confidently say that the variable is essential if it rises over the cutoff point. In our context, we calculated a value by employing a Wald test and then compared it with the cutoff value to check whether the estimates attached to positive (ERM+, ER+, GDP+, and FD+) or negative (ERM−, ER−, GDP−, and FD−) series exert significantly different effects on ND or not.

  4. ADF is a statistical technique for determining if a time series dataset has a unit root, which suggests that it is non-stationary. It determines if a dataset displays a trend or pattern that remains constant over time. If there is a trend in the dataset, it means the results are not genuine; therefore, to get genuine results, we need to make our dataset data constant overtime by taking the difference of the dataset. ZA test also does the same job with one additional feature that also accounts for structural break (when a time series suddenly take a turn at a point in time).

  5. Stationary property of a time series means that a time series exhibits consistent statistical characteristics (mean, variance, and autocorrelation) throughout the observation period.

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Authors and Affiliations

  1. China University of Geosciences, Wuhan, China

    Yue Li & Junfeng Zhang

  2. Lebanese American University, Beirut, Lebanon

    Sana Ullah

Authors
  1. Yue Li
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  2. Junfeng Zhang
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  3. Sana Ullah
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Contributions

Li Yue: idea, software, methodology, writing—original draft preparation; Junfeng Zhang: reviewing and editing, methodology; Sana Ullah: conceptualization, writing—original draft preparation, reviewing and editing.

Corresponding author

Correspondence to Junfeng Zhang.

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Highlights

• The study examines the influence of emergency response management and environmental risk on natural disasters.

• The QARDL model is employed for empirical analysis.

• In the long run, emergency response management exhibits negative and significant impacts on natural disasters, particularly at higher quantiles (60th to 95th).

• Environmental risk is positively correlated with natural disasters across quantiles, indicating a significant association in the long run.

The original online version of this article was revised: Junfeng Zhang is only affiliated to affiliation 1.

Appendix

Appendix

Please see Table 5.

Table 5 Definitions and data sources
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Li, Y., Zhang, J. & Ullah, S. Unleashing the power of emergency response: controlling natural disasters by addressing environmental risk. Environ Sci Pollut Res 30, 114901–114911 (2023). https://doi.org/10.1007/s11356-023-30332-y

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  • DOI: https://doi.org/10.1007/s11356-023-30332-y

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