Economic and ecological complexity in the wake of COVID-19 pandemic: evidence from 60 countries

Figures & data

Figure 1. Different shapes of EKC and PKC hypothesis.

Source: adapted from Dinda (2004), and Aljadani et al. (2021).

Figure 2. PH and PHH hypothesis.

Source: adapted from Cole (2004) and Elliott et al., 2020).

Figure 3. IPAT and PPP hypothesis.

Source: adapted from Nassani et al. (2021) and authors self-extract.

Table 1. Current literature on environmental sustainability during the COVID-19 pandemic.

Authors	Factors	Results
Chakraborty and Maity (2020)	Migration, environmental safety, and prevention	COVID-19 negatively affects the globalized world that needs environmental safety and prevention strategies to reduce disease transmission from one person to another.
Zambrano-Monserrate et al. (2020)	Waste and recycling issues	COVID-19 indirectly impacts the natural environment that sabotaged the green development agenda due to the contamination of land and water, which causes the spread of contagious disease.
SanJuan-Reyes et al. (2021)	Air pollutants and health hazards	The possible correlation between air pollutants and spreading the coronavirus cases needs to reduce anthropogenic pollutants in the atmosphere.
Cheval et al. (2020)	Environmental pollution and COVID-19 cases	The disposal of sanitary consumables causes severe air pollution that negatively affects the individuals' health, possibly lead to infection with coronavirus disease.
Espejo et al. (2020)	Climatic factors	Biodegradable medical supplies should be considered for preventing the future pandemic.
Rizou et al. (2020)	Food supply chain	The food industry is affected mainly due to increase coronavirus cases, which negatively affect the food supply chain globally.
Elavarasan and Pugazhendhi (2020)	Technological advancement	Advancement in key technologies helps to restore the natural environment and control COVID-19 cases globally.
Eroğlu (2021)	Green energy sector	The renewable energy sector is primarily affected by the COVID-19 pandemic in supply chain disruption, tax imposition, and uncertainty in economic policies that lead investors reluctant to invest in the clean energy infrastructure.
Atalan (2020)	COVID-19 measures and healthcare & environmental issues	The lockdown measures significantly suppressing the COVID-19 cases while its negative effect on psychological and environmental disorders worldwide.
Kumar et al. (2020)	Symptomatic treatment and healthy immune system	Besides strict measures of COVID-19, a healthy immune system is helpful to suppress COVID-19 cases.

Source: Authors extracted from the earlier studies.

Table 2. List of sample countries.

“USA, India, Brazil, Russia, UK, France, Spain, Italy, Turkey, Germany, Colombia, Mexico, Poland, South Africa, Ukraine, Peru, Indonesia, Czechia, Canada, Portugal, Chile, Romania, Belgium, Sweden, Austria, Serbia, Japan, Hungary, UAE, Jordan, Panama, Slovakia, Belarus, Malaysia, Georgia, Croatia, Azerbaijan, Bulgaria, Tunisia, Costa Rica, Lithuania, Slovenia, Kuwait, Greece, Moldova, Armenia, Guatemala, Qatar, Paraguay, Oman, Bosnia and Herzegovina, North Macedonia, China, Kyrgyzstan, Latvia, Singapore, El Salvador, Estonia, Luxembourg, Finland”.

Source: Worldometer (2021).

Table 3. Descriptive statistics.

Methods	CARB_DAM	COVID19	GDP_PC	SQGDP_PC	FDI_INF	FDI_INF/ COVID19	GDP_PC/ COVID19	POP_DEN	POP_DEN/ COVID19
Mean	1.663	1595440	22389.09	9.52E + 08	3.039	1.62E − 05	0.122	242.790	2.04E + 08
Maximum	5.233	28381220	111062.3	1.23E + 10	28.346	0.00047	2.093	7952.998	4.98E + 09
Minimum	0.259	51047	1116.358	1246255	−16.061	−0.0003	0.00019	4.075	926428.9
Std. Dev.	1.213	4056142	21410.70	1.84E + 09	5.203	7.71E − 05	0.317	1016.700	6.65E + 08
Skewness	1.153	5.263	1.601	4.233	1.646	2.522	4.752	7.450	6.391
Kurtosis	3.574	33.586	6.171	25.630	14.784	26.778	27.441	57.009	45.893

Note: CARB_DAM shows carbon damages, COVID19 shows coronavirus cases, GDP_PC shows GDP per capita, SQGDP_PC shows a square of GDP per capita, FDI_INF shows inbound FDI, FDI_INF/COVID19 shows the share of FDI inflows in COVID19 cases, GDP_PC/COVID19 shows the share of GDP per capita in COVID19 cases, POP_DEN shows population density, and POP_DEN/COVID19 shows the share of population density in COVID-19 cases.

Source: Author’s estimation.

Table 4. Markov switching regression (BFGS/Marquardt steps) estimates.

Dependent Variable: CARB_DAM
Variable	Coefficient	Std. Error	z-Statistic	Prob.
Regime 1
CARB_DAMt-1	0.356500	0.075069	4.748939	0.0000
COVID19	2.02E − 07	7.57E − 08	2.665884	0.0077
GDP_PC	−0.000133	1.75E − 05	−7.619423	0.0000
FDI_INF	0.021649	0.054147	0.399822	0.6893
POP_DEN	−0.010843	0.001183	−9.162716	0.0000
Constant	4.311054	0.348939	12.35475	0.0000
log(SIGMA)	−1.351943	0.246683	−5.480481	0.0000
Regime 2
CARB_DAMt-1	−0.093377	0.106891	−0.873578	0.3823
COVID19	−4.93E − 08	7.03E − 08	−0.701534	0.4830
GDP_PC	−7.59E − 05	1.71E − 05	−4.424314	0.0000
FDI_INF	−0.031878	0.040134	−0.794300	0.4270
POP_DEN	−0.000558	0.000240	−2.322904	0.0202
Constant	2.339277	0.391072	5.981706	0.0000
log(SIGMA)	−0.543267	0.155919	−3.484303	0.0005
Common
SQGDP_PC	1.10E-09	2.99E-10	3.683176	0.0002
GDP_PC/COVID19	−1.535242	0.880168	−1.744259	0.0811
FDI_INF/COVID19	11574.22	4143.012	2.793673	0.0052
POP_DEN × COVID19	3.98E − 10	1.96E − 10	2.033003	0.0421
Transition Matrix Parameters
P11-C	−0.432129	0.730400	−0.591633	0.5541
P21-C	−1.095450	0.559236	−1.958834	0.0501
Mean dependent var	1.677084	S.D. dependent var		1.218994
S.E. of regression	3.411578	Sum squared resid		477.1936

Note: CARB_DAM shows carbon damages, COVID19 shows coronavirus cases, GDP_PC shows GDP per capita, SQGDP_PC shows a square of GDP per capita, FDI_INF shows inbound FDI, FDI_INF/COVID19 shows the share of FDI inflows in COVID19 cases, GDP_PC/COVID19 shows the share of GDP per capita in COVID19 cases, POP_DEN shows population density, and POP_DEN/COVID19 shows the share of population density in COVID-19 cases.

Source: Author’s estimation.

Table 5. VAR Granger causality estimates.

Variables	∑CARB_DAM	∑COVID19	∑GDP_PC	∑FDI_INF	∑POP_DEN	∑SQGDP_PC	∑GDP_PC/COVID19	∑FDI_INF/COVID19	∑POP_DEN × COVID19
∑CARB_DAM	–	₦	₦	16.049*	8.485**	₦	₦	9.543*	₦
∑COVID19	₦	–	₦	5.018***	₦	₦	₦	₦	105.782*
∑GDP_PC	₦	7.220**	–	5.056***	₦	₦	₦	4.824***	₦
∑FDI_INF	₦	₦	₦	–	₦	₦	9.818*	₦	₦
∑POP_DEN	₦	₦	12.533*	11.634*	–	40.784*	124.343*	13.518*	₦
∑SQGDP_PC	5.151***	₦	₦	4.819***	₦	–	₦	5.232***	₦
∑GDP_PC/COVID19	₦	9.754*	₦	₦	₦	₦	–	₦	₦
∑FDI_INF/COVID19	₦	₦	6.405**	7.452**	₦	27.495*	79.156*	–	₦
∑POP_DEN × COVID19	₦	185.426*	₦	₦	₦	₦	₦	₦	–

Note: ₦ shows no causality. *, *8, and *** shows 1%, 5%, and 10% level of significance, respectively. CARB_DAM shows carbon damages, COVID19 shows coronavirus cases, GDP_PC shows GDP per capita, SQGDP_PC shows a square of GDP per capita, FDI_INF shows inbound FDI, FDI_INF/COVID19 shows the share of FDI inflows in COVID19 cases, GDP_PC/COVID19 shows the share of GDP per capita in COVID19 cases, POP_DEN shows population density, and POP_DEN/COVID19 shows the share of population density in COVID-19 cases.

Source: Author’s estimation.

Table 6. VDA estimates of carbon damages.

Months	CARB_DAM	COVID19	GDP_PC	GDP_PC/COVID19	FDI_INF	FDI_INF/COVID19	POP_DEN	POP_DEN × COVID19
September 2021	100	0	0	0	0	0	0	0
October 2021	92.839	0.083	2.515	0.019	0.357	0.548	0.526	0.051
November 2021	67.453	0.068	4.070	0.234	0.313	6.024	0.385	0.078
December 2021	52.065	1.032	5.823	0.302	1.822	6.536	8.173	0.068
January 2022	45.805	1.085	6.075	0.984	1.715	7.911	9.482	0.059
February 2022	34.325	0.865	7.128	8.923	3.235	14.569	7.837	0.043
March 2022	34.547	0.776	6.555	12.544	3.143	13.233	7.168	0.053
April 2022	24.151	0.546	4.589	10.139	3.825	21.761	6.644	0.042
May 2022	19.533	1.220	4.776	8.433	3.141	20.899	7.569	0.035
June 2022	13.586	1.141	3.553	9.060	4.449	24.759	5.536	0.035

Note: CARB_DAM shows carbon damages, COVID19 shows coronavirus cases, GDP_PC shows GDP per capita, SQGDP_PC shows a square of GDP per capita, FDI_INF shows inbound FDI, FDI_INF/COVID19 shows the share of FDI inflows in COVID19 cases, GDP_PC/COVID19 shows the share of GDP per capita in COVID19 cases, POP_DEN shows population density, and POP_DEN/COVID19 shows the share of population density in COVID-19 cases.

Source: Author’s estimation.

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Data availability statement

The data is freely available at Worldometer (2021) at https://www.worldometers.info/coronavirus and World Development Indicators published by World Bank (2021) at https://databank.worldbank.org/source/world-development-indicators