Management of Natural Disaster and Its Influence on Economic–Environmental Performance: Fresh Evidence From BRICS

Previous literature documented the importance of natural disasters and their impact on economic performance, but it ignored the effects on the environment. This study examines the effect of natural disasters on the economic and environmental performance of BRICS economies over the period 1995–2019. This study applies panel autoregressive distributed lag (ARDL) and panel quantile regression approaches. The empirical findings show that natural disasters decrease economic growth but increase CO₂ emissions. The findings of panel quantile regression display a significant negative impact of natural disasters on economic growth from the middle (30th) to higher (80th) quantiles. However, natural disasters significantly increase carbon emissions from the middle (50th) to higher (95th) quantiles. This study suggests the importance of proper planning for the management of natural disasters.

Introduction

Over the last three decades, the world has witnessed some of the biggest natural catastrophes including the 2001 earthquake of 9.0 magnitude in Japan and the resulting tsunami, the 2010 earthquakes in Haiti and Chile, the 2005 earthquake in Pakistan and Hurricane Katrina, and the 2004 tsunami in the Indian Ocean, which caused massive economic losses alongside a large number of human deaths. In the meantime, the world has also experienced upward trends in economic growth at a fast pace, particularly in some of the emerging economies (Asif and Muneer, 2007; Ullah et al., 2020). However, the traditional concept of economic growth has overlooked the serious concern of environmental degradation due to the rise in economic activities worldwide. The recent trend in environmental protection is not good for economic development (Usman et al., 2021; Sohail et al., 2022). Hence, the balance should be maintained between the goals of environmental protection and long-term economic growth.

In this era, two major threats to attaining the target of sustainable economic development are natural calamities and global warming. In addition to sustainable development, natural disasters and climate change are the major cause of the depletion of natural capital stock. According to an estimate since 1970, the world has seen 13,386 natural disasters in which almost 3.6 million people died and 7.7 billion people were affected. Furthermore, the financial price that the world has to pay was about US$ 3.3 trillion (Fang et al., 2019). These calamities do not limit to one or two catastrophes but include floods, droughts, epidemics, storms, land sliding, and earthquakes, among others. The piles of natural capital are at higher risk in the emerging economies as compared to developed economies because they are less equipped to fight natural disasters and the effects are more long-lasting in the emerging economies. Similarly, climate change and global warming also are major hurdles in the way of sustainable economic growth and the environment in emerging economies. Tol (2009) highlighted that carbon emission due to deforestation has increased manifold and it has reached 100 tons of average carbon discharges per hectare, whereas the economic cost of these emissions has also soared to about $50 per ton of carbon emissions. In this regard, a widely recognized source of environmental degradation is carbon emission which negatively affects human health, food security, and ecological balance.

The United Nations has constructed an Inclusive Wealth Index which is used to measure economic sustainability. The Inclusive Wealth Index analyzed three types of capital assets including natural, human, and produced capitals (Cheng et al., 2022; Sohail et al., 2022a). Among these, natural capital is most susceptible to natural calamities and weather fluctuations. Nations that are heavily dependent on natural capital for long-term economic growth are more likely to fall into the trap of the “natural resource curse.” According to the Inclusive Wealth Report, 127 out of 140 selected countries are reported to be deficient in natural capital.

The theoretical and empirical literature suggests that natural disasters can have either positive or negative effects on sustainable economic development. According to Benson and Clay (2004) and Xiao (2011), natural disasters and low economic growth rates are linked to each other. Household consumption is badly affected by the natural disaster for a long period of time, without any hope of improvement, and it is a big chunk of a country’s national income (Dercon, 2004; McDermot et al. 2014; Huang et al, 2022). As a result, natural disasters negatively impact the per capita income in the long-run (Raddatz, 2009), which in turn leads to the reduction of the overall welfare of the nation (Guo et al., 2015), escalates socioeconomic unpredictability (Porfiriev, 2012), and causes poverty traps (Hallegatte and Dumas, 2009). It is also observed that natural disaster not only spurs poverty by 1.5%–3.6% in developing economies but also significantly and negatively affects the development of human capital (Rodriguez-Oreggia et al., 2013; Sohail et al., 2014).

Conversely, after natural disasters, the process of rebuilding and recovery starts which leads to an improvement in economic growth, agricultural outputs, real estate development, and the process of capital formation (Skidmore and Toya, 2002; Shabnam, 2014; Cavallo and Noy, 2011; Sohail et al., 2019). These positive effects are envisaged due to the fact that, after a natural disaster, the new technology is installed to initiate the recovery process and to speed the post-disaster rebuilding phase, thereby improving overall economic development. On the basis of Schumpeter’s view of creative destruction, Skidmore and Toya (2002), natural calamities provide the opportunities to reinvest in capital and also allow the society to adopt new disaster management techniques through the introduction of new technologies. They further suggested that people will start investing more in human capital in the post-disaster recovery period, a key to the long-run and sustainable economic development because natural disasters reduce the rate of return on physical capital which is an invitation for people to invest in human capital (Cappelli et al., 2021; Sohail et al., 2020).

A growing body of literature has assessed the economic effects of natural disasters by employing vast ranges of empirical and modeling approaches. Literature has found the direct and indirect impacts of natural disasters on economic and environmental performance. The direct effect of natural disasters on economic performance belongs to the loss of physical assets, while on environmental performance, these effects occur in the form of rising greenhouse gas emissions. Moreover, indirect economic damages contain the destruction of productive capital, businesses, residential areas, infrastructure, livestock, and crops and most importunately mental and physical health. Garcia et al. (2020) noted that natural disasters and economic–environmental performance are inextricably linked. Huang et al. (2020) reported that natural disasters have a detrimental influence on agricultural production as well as economic development in China, but the size of their impact is different in each province. Natural disasters adversely influence each sector of the economy. Similar results are reported by Kusano and Kemmelmeier (2018) for global-level analysis. Several studies have explored the driving forces of economic and environmental performance in BRICS (Pao and Tsai, 2010; Dingru et al., 2021; Zhao et al., 2021; Li and Ullah, 2022; Naseem et al., 2022; Wahab et al., 2022), but none of the studies incorporate natural disasters variable in their models in the case of BRICS.

The growing body of literature on disaster management has confirmed that extreme weather conditions and other natural calamities such as floods, droughts, storms, heatwaves, rising sea levels, and hurricanes are due to the increased emissions of greenhouse gases (Trinh et al., 2021). Extreme weather can lead to natural disasters, and developing economies are the most vulnerable to such disasters; therefore, the attention of the policymakers and empirics has shifted toward the question of how people and societies can equip them to fight against natural disasters (Baccini and Leemann, 2021). Furthermore, it is also pertinent to analyze the role of natural disasters in the context of sustainable development of emerging economies.

The main motivation for selecting BRICS nations is that the intensity and frequency of natural disasters have increased significantly in recent years. The BRICS economies have the most experience of natural disasters in 2020 (Emergency Events Database, 2021). It is expected that this trend will continue in the future due to the intensification of climate change. According to the estimates of the World Bank (2020), the collective GDP of BRICS economies is approximately US$19.6 trillion (Wahab et al., 2022). Moreover, BRICS economies consist of 23 percent of worldwide GDP, 42 percent of the global population, and 18 percent of world trade. BRICS economies are considered among highly polluted economies. More specifically, Russia, India, and China are among the top four highly polluted economies of the world (Dingru et al., 2021; Naseem et al., 2022).

Consistent with this view, the main aim of this study is to explore the role of disaster management in analyzing the economic and environmental performance of BRICS economies. Our research contributes to the existing body of literature in the following ways. This study assesses the economic impacts of natural disasters in the case of BRICS economies that have not been assessed previously. This study is added to environmental literature through its novel contribution as it explores the impact of natural disasters on environmental conditions as well. This study provides the short-run and long-run parameters considering the impact of natural disasters on economic and environmental performance. Our study delivers important policy directions for environmentalists and economists while formulating and implementing their policy implications. The findings of this study will help in developing natural disaster resilience plans and setting priorities and policies at the provincial, regional, and national levels.

Model and Methods

In several possible scenarios of the macroeconomic impacts of natural disasters, we begin with a basic economic growth model to clarify the empirical link between natural disasters and economic–environmental performance (Salai-Martin and Barro, 1995; Hallegatte et al., 2016, Dingru et al, 2021). Natural disasters may also significantly affect economic and environmental progress. Following previous literature (Klomp and Valckx, 2014; Sloggy et al., 2021), we begin with the following economic growth and CO₂ emission models:

$\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}= {\mathrm{\pi }}_0+ {\mathrm{\pi }}_1\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_2\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_3\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_4\mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_5\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}(1)$

$\mathrm{C}{\mathrm{O}}_{2, \mathrm{i}\mathrm{t}}= {\mathrm{\pi }}_0+ {\mathrm{\pi }}_1\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_2\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_3\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_4\mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_5\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}(2)$

where ${\text{GDP}}_{\text{it}}$ and ${\text{CO}}_{2,\text{ it}}$ are GDP growth and CO₂ emissions, which are assumed to depend on natural disasters (ND), technological progress (Technology), financial development (FD), foreign direct investment inflow (FDI), and trade openness (Trade). In both equations, i denotes the country, t denotes the time period, ${\pi }_0$ is the constant term, while ${\epsilon }_t$ shows the error term. Natural disasters reduce economic growth dramatically in the short-run and long-run, and an estimate of ${\text{π}}_1$ should be negative. Similarly, an increased natural disaster frequency is to increase climate change, and an estimate of ${\text{π}}_1$ should be negative. Eqs 1, 2 are a long-run model, and estimates of ${\pi }_1$–${\pi }_5$ reflect long-run effect of focused and control variables on CO₂ emissions and economic growth. The short-run dynamic effects are also important; thus, we follow Pesaran et al.’s (2001) ARDL approach to estimate the long- and short-run effects in one step. Thus, we can re-express both basic equations in an error correction format, as follows:

$\mathrm{\Delta }\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}}= {\mathrm{\pi }}_0+ {\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{1\mathrm{k}}\mathrm{\Delta }\mathrm{G}\mathrm{D}{\mathrm{P}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+ {\displaystyle \sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\beta }}_{2\mathrm{k}}\mathrm{\Delta }\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{3\mathrm{k}}\mathrm{\Delta }\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+ {\displaystyle \sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\beta }}_{4\mathrm{k}}\mathrm{\Delta }\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{5\mathrm{k}}\mathrm{\Delta }\mathrm{F}\mathrm{D}{\mathrm{I}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{6\mathrm{k}}\mathrm{\Delta }\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+ {\mathrm{\pi }}_1\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{i}\mathrm{t}-1}+ {\mathrm{\pi }}_2\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_3\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{\mathrm{i}\mathrm{t}-1}+ {\mathrm{\pi }}_4\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_5\mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_6\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{\mathrm{i}\mathrm{t}-1}+ \mathrm{\lambda }. \mathrm{E}\mathrm{C}{\mathrm{M}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}},(3)$

$\mathrm{\Delta }\mathrm{C}{\mathrm{O}}_{2\mathrm{i}\mathrm{t}}= {\mathrm{\pi }}_0+ {\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{1\mathrm{k}}\mathrm{\Delta }\mathrm{C}{\mathrm{O}}_{ 2,\mathrm{i}\mathrm{t}-\mathrm{k}}+ {\displaystyle \sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\beta }}_{2\mathrm{k}}\mathrm{\Delta }\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{3\mathrm{k}}\mathrm{\Delta }\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+ {\displaystyle \sum }_{\mathrm{k}=0}^{\mathrm{n}}{\mathrm{\beta }}_{4\mathrm{k}}\mathrm{\Delta }\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{5\mathrm{k}}\mathrm{\Delta }\mathrm{F}\mathrm{D}{\mathrm{I}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+{\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{n}}{\mathrm{\beta }}_{6\mathrm{k}}\mathrm{\Delta }\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{ \mathrm{i}\mathrm{t}-\mathrm{k}}+ {\mathrm{\pi }}_1\mathrm{C}{\mathrm{O}}_{2,\mathrm{i}\mathrm{t}-1}+ {\mathrm{\pi }}_2\mathrm{N}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_3\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{n}\mathrm{o}\log {\mathrm{y}}_{\mathrm{i}\mathrm{t}-1}+ {\mathrm{\pi }}_4\mathrm{F}{\mathrm{D}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_5\mathrm{F}\mathrm{D}{\mathrm{I}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\pi }}_6\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}{\mathrm{e}}_{\mathrm{i}\mathrm{t}-1}+ \mathrm{\lambda }. \mathrm{E}\mathrm{C}{\mathrm{M}}_{\mathrm{i}\mathrm{t}-1}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}.(4)$

The above two equations include short- and long-run coefficient estimates, as “Δ” operator variables are reflected short-run estimates and long-run effects are inferred by the estimates of ${\pi }_2$–${\pi }_6$ on ${\pi }_1$. Regarding meaningful estimates, Pesaran et al. (2001) recommend two economic tests for cointegration. The F-test is used for assessing the joint significance of the lagged level variables. The null hypothesis (H0 = ${\pi }_1$ = ${\pi }_2$ = ${\pi }_3$ = ${\pi }_4$ = ${\pi }_5$ = ${\pi }_6$ = 0) is to be verified against an alternative hypothesis (H0 ≠ ${\pi }_1$ ≠ ${\pi }_2$ ≠ ${\pi }_3$ ≠ ${\pi }_4$ ≠ ${\pi }_5$ ≠ ${\pi }_6$ ≠ 0) to determine the existence of cointegration. Besides, the t-test is used to establish the significance of $\text{λ}$, which must be significant and negative. Indeed, under the panel ARDL method, variables could be a blend of levels and first-difference. To check whether these macroeconomic variables are stationary or not, first- and second-generation unit root tests have been applied to the panel data. Next, the panel ARDL captures the data generation process by taking a sufficient and suitable number of lags (Bahmani-Oskooee et al., 2021). This approach assumed all the variables are endogenous. Finally, the panel ARDL model is the most suitable approach in small sample size, as in our empirical case.

Data

This study explores the influence of natural disasters on the economic and environmental performance of BRICS economies for the period 1990−2019. Table 1 displays the details about variable symbols, definitions, and descriptive statistics. Economic performance is measured through GDP per capita at constant 2015 US$. However, environmental performance is measured by CO₂ emissions. Natural disaster impact is captured through a number of deaths from disasters. This study examines the impact of natural disasters on economic and environmental performance by controlling for a patent, Internet, financial development, FDI, and trade. Patent applications are taken to capture the impact of technology. The use of Internet is captured through total numbers of Internet users as percent of population. Financial development impact is measured by domestic credit to private sector as percent of GDP. FDI is taken into net inflows as percent of GDP. Trade is measured in terms of GDP. The required data have been scrutinized by the World Bank and Emergency Events Database (EM-DAT).

TABLE 1

TABLE 1. Descriptive statistics and definitions.

Results and Discussion

To detect the stationary properties of variables, this study adopted CIPS and CADF unit root tests. The stationarity results are reported in Table 2. It can be observed that both CIPS and CADF tests have produced the same results. The findings show that ND, patent, FD, and FDI are integrated at a level. However, GDP, CO₂, Internet, and FDI are integrated at first-difference. The pre-requisite for adopting the panel ARDL approach for analysis is that the variables of the model should be integrated of I(0) or I(1) series. Hence, the results of unit root tests support the pre-requisite for using the ARDL approach for analysis. The ARDL-PMG results of the economic growth model and CO₂ emission model are presented in Table 3.

TABLE 2

TABLE 2. Results of panel unit root tests.

TABLE 3

TABLE 3. ZARDL-PMG results of economic growth and CO₂ emissions.

The long-run and short-run estimates of CO₂ model and economic growth model have been estimated. The long-run results display that the impact of natural disasters on economic growth is proved significant and negative inferring that an increase in natural disasters tends to reduce the level of economic growth in BRICS economies. It shows that 1 percent intensification in natural disasters reduces economic growth by 0.125 percent in the long-run. This finding is also backed by Rajapaksa et al. (2017), who noted that natural disaster reduces inclusive economic growth in global economies. Several empirical and theoretical studies report a negative effect of natural disasters on growth (Skidmore and Toya, 2002; Noy, 2009; Klomp and Valckx 2014). According to Klomp and Valckx (2014), economic growth drops immediately after a natural disaster, and it has permanent effects on the level of output. The frequency of natural disasters rose dramatically in BRICS, which decreased economic performance in the short-run as well as the long-run. This finding is also supported by Keerthiratne and Tol (2018), who noted that natural disasters increase income inequality and decrease economic performance in Sri Lanka. A similar result is also found by Rosselló et al. (2020) for global level, who noted that natural disasters have negative effects on all domains of life, including tourism and economic performance. This result is reliable to Boustan et al. (2020), who inferred that economic response to natural disasters is most consistent and robust in the United States.

However, the impact of natural disasters on CO₂ emissions is proved to be statistically significant in the long-run, and hence, the relationship is robust. The findings show that natural disasters have dramatic economic and environmental consequences. Our findings infer that natural disasters increase climate change by increasing environmental pollution. This finding is also backed by Sloggy et al. (2021), who noted that natural disasters increase climate change. The finding infers that natural disaster is closely linked to global warming. Theoretically, Israel and Briones (2012) argued that natural disaster has hurt natural resources and the environment. An important possible channel is that natural disasters cause environmental degradation by damaging agriculture, forestry, and rangelands. A similar finding is also reported by Israel and Briones (2012) in the case of Philippines. This also means that natural disasters have been increasing over time due to environmental change, which in turn increases CO₂ emissions. This finding is also backed by Botzen et al. (2019), who inferred that the direct and indirect economic effects of natural disasters are increasing. This means that natural disasters cause environmental degradation destroying man-made and natural capital. Thus, with higher levels of natural disasters, natural capital depletion increases by mitigating environmental quality.

The impact of patent on economic growth is significant and positive revealing that 1 percent rise in patent increases economic growth by 0.931 percent in the long-run. However, the impact of patent on CO₂ emissions is significant and negative revealing that 1 percent rise in patent decreases CO₂ emissions by 0.272 percent in the long-run. The impact of Internet on economic growth is found to be significant and positive displaying that 1 percent upsurge in the use of Internet improves economic growth by 0.003 percent in the long-run. However, the impact of Internet on CO₂ emissions is found to be significant and negative displaying that 1 percent escalation in the use of Internet mitigates CO₂ emissions by 0.005 percent in the long-run. The findings further reveal that the effect of financial development on economic growth is significant and positive displaying that 1 percent rise in financial development increases economic growth by 2.197 percent in the long-run. However, the effect of financial development on CO₂ emissions is significant and positive revealing that 1 percent rise in financial development escalates CO₂ emissions by 0.325 percent in the long-run. Furthermore, the impact of FDI on economic growth is proved significant and positive inferring that the increase in the inflows of FDI tends to increase the level of economic growth in BRICS economies. It shows that 1 percent increase in inflow of FDI enhances economic growth by 0.492 percent in the long-run. However, the impact of FDI on CO₂ emissions is proved to be statistically insignificant in the long-run, and hence, the association is rejected. Similarly, the impact of trade on economic growth is proved to be statistically insignificant in the long-run. However, the impact of trade on economic growth is proved significant and positive inferring that the increase in trade tends to intensify CO₂ emissions in BRICS economies. It shows that 1 percent increase in trade enhances CO₂ emissions by 0.016 percent in the long-run. Hence, ND, patent, Internet, FD, FDI, and trade are found to be long-term determinants of economic and environmental performance.

In the short-run, the findings display that natural disaster has a statistically significant impact on economic growth. It shows that natural disasters have also a short-term effect on economic growth. However, natural disasters show a significant and positive impact on CO₂ emissions in the short-run. The findings further reveal that patent and Internet are significantly and positively associated with economic growth in the short-run. In contrast, patent and Internet have a statistically insignificant impact on CO₂ emission, which shows that improvement in technology and increase in the use of Internet are not linked with CO₂ emissions in the short-run. The findings also confirm that financial development, FDI, and trade report no significant impact on economic growth and CO₂ emissions. It shows that financial development, FDI, and trade are not associated with economic performance and environmental performance in the short-run.

The results of ECM term and F-stat prove that there exists a long-term relationship among the variables of the models. The results of robust models shown in columns (2) and (4) are consistent in terms of sign and significance level in most cases. The results of panel quantile regression report that natural disasters produce significant and negative impacts on economic growth from the 4th quantile to the 9th quantile. The results of panel quantile regression report that natural disasters produce a significant and negative impact on economic growth from the 4th quantile to the 9th quantile, as shown in Table 4. However, natural disasters produce a significant and positive impact on CO₂ emissions from the 6th quantile to the 11th quantile, as shown in Table 5.

TABLE 4

TABLE 4. Panel quantile regression of economic growth.

TABLE 5

TABLE 5. Panel quantile regression of CO₂ emissions.

Conclusion and Implications

During the last three decades, the severity and frequency of natural disasters have increased dramatically. A bulk of empirical literature has made effort to explore the effect of this rising trend on various variables. However, the obtained results are still inconclusive. There are several studies discussing the impact of natural disasters on economic development, but ignoring environmental degradation. However, the current study investigates the consequences of natural disasters simultaneously on environmental and economic performance. This study explores the said nexus for BRICS economies by employing the ARDL-PMG approach. By using this approach, this study reported short-run and long-run coefficient estimates of the models. The findings of this study report the significant and negative impact of natural disasters on economic growth in the long-run, showing that happenings of natural calamities discourage economic growth in BRICS economies. However, natural disasters produce no effect on CO₂ emissions in the long-run. In the short-run, the findings of the study report that natural disasters produce no impact on economic growth. However, natural disasters tend to produce an increasing impact on CO₂ emissions in the short-run. The findings also show that technology, Internet use, financial development, FDI, and trade reports significantly increase impact on economic growth and CO₂ emissions in the long-run. Hence, the overall findings of this study confirm that natural disasters, technology, Internet, financial development, FDI, and trade are significant determinants of environmental performance and economic growth.

The findings of our study deliver various policy implications for researchers, policymakers, and governments of BRICS economies. It is suggested that the governments of BRICS economies should develop such research institutes that provide prior information regarding the occurrence of natural disasters. These governments should improve their technological setup that supports providing such kind of information. Moreover, the government should increase investment in research and development. The governments should take immediate initiatives to increase their capacity to tackle the consequences of natural disasters and disaster-related reconstructions, mitigation, and prevention. It is also suggested that governments of BRICS economies should emphasize the significance of suitable planning for the management of natural disasters. Most specifically, pre-disaster and post-disaster appropriate planning and management are required for controlling the frequency of consequences of disasters. The economies can control maximum consequences of disasters by improving their preparation to confront any disaster significantly and timely. Policymakers and governments of concerned economies should deliver enough attention to natural disasters in formulating and instigating policies to promote economic development and poverty reduction. Insurance, financial post-disaster compensation measures, government spending, and social safety arrangements could be used to control the economic and environmental impacts of natural disasters.

This study contains several limitations. The current study is mainly considering BRICS economies, while in future research a large sample can be used for analysis by considering a mixture of developing and developed economies to make a comparison. Meanwhile, this study was done by employing the NARDL approach which provides an asymmetric impact of natural disasters on economic and environmental performance. In future research, the energy consumption role can be added to the analysis along with some other macro variables.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Asif, M., and Muneer, T. (2007). Energy Supply, its Demand and Security Issues for Developed and Emerging Economies. Renew. Sustain. energy Rev. 11 (7), 1388–1413. doi:10.1016/j.rser.2005.12.004

CrossRef Full Text | Google Scholar

Baccini, L., and Leemann, L. (2021). Do natural Disasters Help the Environment? How Voters Respond and what that Means. Political Sci. Res. Method. 9 (3), 468–484. doi:10.1017/psrm.2020.25

CrossRef Full Text | Google Scholar

Bahmani-Oskooee, M., Usman, A., and Ullah, S. (2020). Asymmetric J-Curve in the Commodity Trade between Pakistan and United States: Evidence from 41 Industries. Eurasian Econ. Rev. 10 (2), 163–188. doi:10.1007/s40822-019-00137-x

CrossRef Full Text | Google Scholar

Benson, C., and Clay, E. J. (2004). Understanding The Economic and Financial Impacts of Natural Disasters (No. 4). Washington: World Bank Publications.

Google Scholar

Botzen, W. W., Deschenes, O., and Sanders, M. (2019). The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies. Rev. Environ. Econ. Policy 13 (2), 167–188. doi:10.1093/reep/rez004

CrossRef Full Text | Google Scholar

Boustan, L. P., Kahn, M. E., Rhode, P. W., and Yanguas, M. L. (2020). The Effect of Natural Disasters on Economic Activity in US Counties: A Century of Data. J. Urban Econ. 118, 103257. doi:10.1016/j.jue.2020.103257

CrossRef Full Text | Google Scholar

Cappelli, F., Costantini, V., and Consoli, D. (2021). The Trap of Climate Change-Induced "natural" Disasters and Inequality. Glob. Environ. Change 70, 102329. doi:10.1016/j.gloenvcha.2021.102329

CrossRef Full Text | Google Scholar

Cavallo, E., and Noy, I. (2011). Natural Disasters and the Economy - A Survey. Int. Rev. Environ. Resource. Economic. 5 (1), 63–102. doi:10.1561/101.00000039

CrossRef Full Text | Google Scholar

Cheng, D., Xue, Q., Hubacek, K., Fan, J., Shan, Y., Zhou, Y., and Zhang, X. (2022). Inclusive Wealth Index Measuring Sustainable Development Potentials for Chinese Cities. Glob. Environ. Change 72, 102417. doi:10.1016/j.gloenvcha.2021.102417

CrossRef Full Text | Google Scholar

Dercon, S. (2004). Growth and Shocks: Evidence from Rural Ethiopia. J. Dev. Econ. 74 (2), 309–329. doi:10.1016/j.jdeveco.2004.01.001

CrossRef Full Text | Google Scholar

Dingru, L., Ramzan, M., Irfan, M., Gülmez, Ö., Isik, H., Adebayo, T. S., et al. (2021). The Role of Renewable Energy Consumption towards Carbon Neutrality in BRICS Nations: Does Globalization Matter? Front. Environ. Sci. 9, 796083. doi:10.3389/fenvs.2021.796083

CrossRef Full Text | Google Scholar

Fang, J., Lau, C. K. M., Lu, Z., Wu, W., and Zhu, L. (2019). Natural Disasters, Climate Change, and Their Impact on Inclusive Wealth in G20 Countries. Environ. Sci. Pollut. Res. 26 (2), 1455–1463. doi:10.1007/s11356-018-3634-2

CrossRef Full Text | Google Scholar

Garcia, S. N., Osburn, B. I., and Jay-Russell, M. T. (2020). One Health for Food Safety, Food Security, and Sustainable Food Production. Front. Sustain. Food Syst. 4, 1. doi:10.3389/fsufs.2020.00001

CrossRef Full Text | Google Scholar

Guo, J., Liu, H., Wu, X., Gu, J., Song, S., and Tang, Y. (2015). Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data. Sustainability 7 (12), 16783–16800. doi:10.3390/su71215847

CrossRef Full Text | Google Scholar

Hallegatte, S., and Dumas, P. (2009). Can Natural Disasters Have Positive Consequences? Investigating the Role of Embodied Technical Change. Ecol. Econ. 68 (3), 777–786. doi:10.1016/j.ecolecon.2008.06.011

CrossRef Full Text | Google Scholar

Huang, S., Zhou, T., Xu, C., and Zheng, J. (2022). Does Public Health Influence Economic Performance? Investigating the Role of Governance and Greener Energies for the Case of China. Front. Public Health 565, 4736. doi:10.3389/fpubh.2022.864736

CrossRef Full Text | Google Scholar

Israel, D. C., and Briones, R. M. (2012). Impacts Of Natural Disasters on Agriculture, Food Security, and Natural Resources and Environment in the Philippines (No. 2012-36). Makati, Philippines: PIDS discussion paper series.

Google Scholar

Keerthiratne, S., and Tol, R. S. J. (2018). Impact of Natural Disasters on Income Inequality in Sri Lanka. World Dev. 105, 217–230. doi:10.1016/j.worlddev.2018.01.001

CrossRef Full Text | Google Scholar

Klomp, J., and Valckx, K. (2014). Natural Disasters and Economic Growth: A Meta-Analysis. Glob. Environ. Change 26, 183–195. doi:10.1016/j.gloenvcha.2014.02.006

CrossRef Full Text | Google Scholar

Kusano, K., and Kemmelmeier, M. (2018). Ecology of Freedom: Competitive Tests of the Role of Pathogens, Climate, and Natural Disasters in the Development of Socio-Political Freedom. Front. Psychol. 9, 954. doi:10.3389/fpsyg.2018.00954

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, X., and Ullah, S. (2022). Caring for the Environment: How CO2 Emissions Respond to Human Capital in BRICS Economies? Environ. Sci. Pollut. Res. 29 (12), 18036–18046. doi:10.1007/s11356-021-17025-0

CrossRef Full Text | Google Scholar

McDermott, T. K. J., Barry, F., and Tol, R. S. J. (2014). Disasters and Development: Natural Disasters, Credit Constraints, and Economic Growth. Oxf. Econ. Pap. 66 (3), 750–773. doi:10.1093/oep/gpt034

CrossRef Full Text | Google Scholar

Naseem, S., Mohsin, M., Zia-Ur-Rehman, M., Baig, S. A., and Sarfraz, M. (2022). The Influence of Energy Consumption and Economic Growth on Environmental Degradation in BRICS Countries: an Application of the ARDL Model and Decoupling Index. Environ. Sci. Pollut. Res. 29 (9), 13042–13055. doi:10.1007/s11356-021-16533-3

CrossRef Full Text | Google Scholar

Noy, I. (2009). The Macroeconomic Consequences of Disasters. J. Dev. Econ. 88 (2), 221–231. doi:10.1016/j.jdeveco.2008.02.005

CrossRef Full Text | Google Scholar

Pao, H.-T., and Tsai, C.-M. (2010). CO2 Emissions, Energy Consumption and Economic Growth in BRIC Countries. Energy policy 38 (12), 7850–7860. doi:10.1016/j.enpol.2010.08.045

CrossRef Full Text | Google Scholar

Pesaran, M. H., Shin, Y., and Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. J. Appl. Econ. 16 (3), 289–326. doi:10.1002/jae.616

CrossRef Full Text | Google Scholar

Porfiriev, B. (2012). Reflections: Environmental Hazards and Disasters: Research Perspectives. Environ. Hazards 11 (4), 324–327. doi:10.1080/17477891.2012.736216

CrossRef Full Text | Google Scholar

Raddatz, C. E. (2009). The Wrath of God: Macroeconomic Costs of Natural Disasters. Washington: World Bank policy research working paper, 5039.

Google Scholar

Rajapaksa, D., Islam, M., and Managi, S. (2017). Natural Capital Depletion: the Impact of Natural Disasters on Inclusive Growth. EconDisCliCha 1 (3), 233–244. doi:10.1007/s41885-017-0009-y

CrossRef Full Text | Google Scholar

Rodriguez-Oreggia, E., De La Fuente, A., De La Torre, R., and Moreno, H. A. (2013). Natural Disasters, Human Development and Poverty at the Municipal Level in Mexico. J. Dev. Stud. 49 (3), 442–455. doi:10.1080/00220388.2012.700398

CrossRef Full Text | Google Scholar

Rosselló, J., Becken, S., and Santana-Gallego, M. (2020). The Effects of Natural Disasters on International Tourism: A Global Analysis. Tour. Manag. 79, 104080.

PubMed Abstract | Google Scholar

Salai-Martin, X. X., and Barro, R. J. (1995). Technological Diffusion, Convergence, and Growth. New Haven: Center Discussion Paper. No. 735.

Google Scholar

Shabnam, N. (2014). Natural Disasters and Economic Growth: A Review. Int. J. Disaster Risk Sci. 5 (2), 157–163. doi:10.1007/s13753-014-0022-5

CrossRef Full Text | Google Scholar

Skidmore, M., and Toya, H. (2002). Do Natural Disasters Promote Long-Run Growth? Econ. Inq. 40 (4), 664–687. doi:10.1093/ei/40.4.664

CrossRef Full Text | Google Scholar

Sloggy, M. R., Suter, J. F., Rad, M. R., Manning, D. T., and Goemans, C. (2021). Changing Climate, Changing Minds? the Effects of Natural Disasters on Public Perceptions of Climate Change. Clim. Change 168 (3), 1–26. doi:10.1007/s10584-021-03242-6

CrossRef Full Text | Google Scholar

Sohail, M. T., Aftab, R., Mahfooz, Y., Yasar, A., Yen, Y., Shaikh, S. A., et al. (2019). Estimation of Water Quality, Management and Risk Assessment in Khyber-Pakhtunkhwa and Gilgit Baltistan Pakistan. Dwt 171, 105–114. doi:10.5004/dwt.2019.24925

CrossRef Full Text | Google Scholar

Sohail, M. T., Ehsan, M., Riaz, S., Elkaeed, E. B., Awwad, N. S., and Ibrahium, H. A. (2022). Investigating the Drinking Water Quality and Associated Health Risks in Metropolis Area of Pakistan. Front. Mat. 9, 864254. doi:10.3389/fmats10.3389/fmats.2022.864254

CrossRef Full Text | Google Scholar

Sohail, M. T., Elkaeed, E. B., Irfan, M., Acevedo-Duque, Á., and Mustafa, S. (2022a). Determining Farmers’ Awareness about Climate Change Mitigation and Wastewater Irrigation: A Pathway towards Green and Sustainable Development. Front. Environ. Sci. 10, 900193. doi:10.3389/fenvs.2022.900193

CrossRef Full Text | Google Scholar

Sohail, M. T., Mahfooz, Y., Aftab, R., Yen, Y., Talib, M. A., and Rasool, A. (2020). Water Quality and Health Risk of Public Drinking Water Sources: a Study of Filtration Plants Installed in Rawalpindi and Islamabad, Pakistan. Dwt 181, 239–250. doi:10.5004/dwt.2020.25119

CrossRef Full Text | Google Scholar

Tayyab Sohail, M., Delin, H., Afnan Talib, M., Xiaoqing, X., and Muhammad Akhtar, M. (2014). An Analysis of Environmental Law in Pakistan-Policy and Conditions of Implementation. Res. J. Applied Sci. Engineer. Tech. 8 (5), 644–653. doi:10.19026/rjaset.8.1017

CrossRef Full Text | Google Scholar

Tol, R. S. J. (2009). The Economic Effects of Climate Change. J. Econ. Perspect. 23 (2), 29–51. doi:10.1257/jep.23.2.29

CrossRef Full Text | Google Scholar

Trinh, T.-A., Feeny, S., and Posso, A. (2021). “The Impact of Natural Disasters and Climate Change on Agriculture: Findings from Vietnam,” in Economic Effects of Natural Disasters (London: Academic Press), 261–280. doi:10.1016/b978-0-12-817465-4.00017-0

CrossRef Full Text | Google Scholar

Usman, A., Ozturk, I., Hassan, A., Maria Zafar, S., and Ullah, S. (2021). The Effect of ICT on Energy Consumption and Economic Growth in South Asian Economies: an Empirical Analysis. Telematics Inf. 58, 101537. doi:10.1016/j.tele.2020.101537

CrossRef Full Text | Google Scholar

Wahab, S., Imran, M., Safi, A., Wahab, Z., and Kirikkaleli, D. (2022). Role of Financial Stability, Technological Innovation, and Renewable Energy in Achieving Sustainable Development Goals in BRICS Countries. Environ. Sci. Pollut. Res., 2022, 1–12. doi:10.1007/s11356-022-18810-1

CrossRef Full Text | Google Scholar

World, B. (2020). World Development Indicators 2020. Washington: World Bank Publications.

Google Scholar

Xiao, Y. (2011). Local Economic Impacts of Natural Disasters*. J. Regional Sci. 51 (4), 804–820. doi:10.1111/j.1467-9787.2011.00717.x

CrossRef Full Text | Google Scholar

Zhao, W., Zhong, R., Sohail, S., Majeed, M. T., and Ullah, S. (2021). Geopolitical Risks, Energy Consumption, and CO₂ Emissions in BRICS: an Asymmetric Analysis. Environ. Sci. Pollut. Res. 28 (29), 39668–39679. doi:10.1007/s11356-021-13505-5

CrossRef Full Text | Google Scholar