We rely on the approach of the Ambiental Kuznets curve (Grossman & Krueger, 1993: Dasgupta et al., 2002) that specifies the relationship between contamination and economic growth. Yet we will modify the initial model by selecting the monthly average of carbon monoxide emissions measured in micrograms per cubic meter of air µg/m³ as the dependent variable. We proxy the pandemic effects by using the monthly count of reported infection cases of COVID-19 at the country level. The model is specified as follows:
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Where COit is the monthly average carbon monoxide emissions of country i at month t, the monthly economic activity index by country EAIit, it’s square form EAIit2 to capture marginal effects, and a set of x covariates available (see for detail the variables by country in Appendix A) for the same monthly periodicity, in which we selected the unemployment rate, the inflation (considering the consumer price index by country), and the inclusion of the COVID-19 variable of total cases Cov. The investigated period is between December of 2014 to November of 2020 given the availability of the data. From specification (1), different estimations will take place by cumulatively adding the set of covariates and examining the change in the statistical significance and the parameters.
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)
Preliminary unit-root analysis (Appendix B) confirms that the order of integration of the variables is I(1) for the carbon monoxide emissions, economic activity indexes, and the total cases of COVID-19, meanwhile the covariates of unemployment and inflation also follows an I(1) process, converting expression (1) in a static approximation for the long-run. To obtain robust statistical inferences for the model, and to avoid a spurious regression under the context of the imbalanced panel data, Kao’s (1999) residual-based tests are performed, where the result indicates the evidence of cointegration between the variables (all sets of covariates included). To account for serial correlation, cross-sectional dependency, and heteroskedasticity in the results, we use the approach of robust standard errors for panel data regression proposed by Driscoll & Kraay (1998). Error Correction Models using an extended Engle & Granger panel data approach are also estimated.
Our sample contains the largest world economies based on the World Bank ranking by GDP as shown in Table 2.
Table 2
World Bank’s ranking of Economies by GDP
Code
|
Country
|
US
|
United States
|
JP
|
Japan
|
DE
|
Germany
|
IN
|
India
|
GB
|
United Kingdom
|
FR
|
France
|
IT
|
Italy
|
BR
|
Brazil
|
CA
|
Canada
|
Note: Due to the lack of information on multiple variables, China is excluded from our sample. Source: World Bank (2019). |
Data Description
The principal sources of data are the Federal Reserve Bank of ST. Lous -FRED- (2021), Centre for Monitoring Indian Economy -CMIE- (2021), Air Quality Open Data Platform -AQOP- 2021), (the European Centre for Disease Prevention and Control -ECDPC- 2021), and the World Bank (2020) (see Appendix A for details).
Table 3
Global Statistics of the Sample
Variable
|
Type
|
Mean
|
Std. Dev.
|
Min
|
Max
|
Observations
|
Carbon Monoxide Emissions
|
overall
|
2.688421
|
2.518964
|
.1
|
15.5081
|
N = 350
|
between
|
|
2.507587
|
.2211501
|
7.696009
|
n = 8
|
within
|
|
1.142907
|
− .0210413
|
14.63398
|
T-bar = 43.75
|
EAI
|
overall
|
99.61086
|
2.615567
|
77.18473
|
101.6701
|
N = 370
|
between
|
|
.2556287
|
99.31522
|
100.0532
|
n = 8
|
within
|
|
2.604763
|
77.37624
|
101.7994
|
T-bar = 46.25
|
Total cases COVID-19
|
overall
|
1447869
|
2915884
|
0
|
1.37e + 07
|
N = 40
|
between
|
|
1557607
|
58588.5
|
4375949
|
n = 8
|
within
|
|
2457659
|
-2928054
|
1.07e + 07
|
T-bar = 5.0
|
UE
|
overall
|
7.076216
|
3.426713
|
2.2
|
23.5
|
N = 370
|
between
|
|
3.068415
|
2.828261
|
10.86122
|
n = 8
|
within
|
|
1.838434
|
.6851051
|
23.06622
|
T-bar = 46.25
|
CPI
|
overall
|
104.3565
|
6.641746
|
94.19368
|
132.101
|
N = 370
|
between
|
|
5.408438
|
100.6913
|
114.3719
|
n = 8
|
within
|
|
4.488136
|
86.50099
|
122.0857
|
T-bar = 46.25
|
Source: Own Elaboration (2021). |
The average carbon monoxide emissions are 2.69 micrograms per cubic meter of air µg/m³, while the Economic Activity Index on average has a value of 99.61, indicating that for the sample between 2014 and 2020 the overall trend is decreasing. Total cases of COVID-19, on the other hand, have been growing exponentially reflecting an average of 1’447,869 people infected monthly, the unemployment rate on average is 7.07% and the inflation measured by the consumer price index has been growing.
Table 4
ID
|
Statistic
|
CO
|
EAI
|
Total Cases of COVID-19
|
UE
|
CPI
|
Brazil
|
Obs
|
44
|
45
|
3
|
45
|
45
|
|
Mean
|
4.265455
|
99.48158
|
1474374
|
10.69111
|
112.0492
|
|
Min
|
1.9
|
92.65543
|
2
|
4.3
|
94.19368
|
|
Max
|
6.79
|
101.6701
|
3908272
|
13.7
|
124.3005
|
|
sd
|
1.176121
|
1.714598
|
2123478
|
2.764123
|
8.564535
|
Canada
|
Obs
|
45
|
48
|
4
|
48
|
48
|
|
Mean
|
1.473005
|
99.60543
|
151243.5
|
6.645833
|
103.0026
|
|
Min
|
0.8821429
|
87.0349
|
20
|
5.4
|
98.40528
|
|
Max
|
2.2
|
101.2002
|
381557
|
13.7
|
107.8535
|
|
sd
|
0.282125
|
2.22515
|
163073.9
|
1.337982
|
2.737043
|
Germany
|
Obs
|
45
|
47
|
5
|
47
|
47
|
|
Mean
|
0.2422295
|
99.58878
|
314002.2
|
3.887234
|
102.5219
|
|
Min
|
0.1
|
89.42888
|
79
|
3
|
98.51642
|
|
Max
|
1.081818
|
101.1058
|
1069912
|
4.9
|
106.2177
|
|
sd
|
0.2884813
|
2.051226
|
433142.1
|
0.5877803
|
2.377384
|
France
|
Obs
|
45
|
47
|
5
|
47
|
47
|
|
Mean
|
1.485436
|
99.41934
|
557822.8
|
9.331915
|
102.0274
|
|
Min
|
0.1
|
77.18473
|
5
|
6.8
|
98.86
|
|
Max
|
2.878947
|
101.0142
|
2276874
|
10.5
|
105.09
|
|
sd
|
0.9012723
|
3.630418
|
970559
|
0.8790079
|
1.964058
|
India
|
Obs
|
36
|
40
|
7
|
40
|
40
|
|
Mean
|
7.696009
|
99.31522
|
2813148
|
7.51
|
114.3719
|
|
Min
|
4.986547
|
80.22737
|
0
|
3.4
|
102.1358
|
|
Max
|
11.91111
|
101.2744
|
9462809
|
23.5
|
132.101
|
|
sd
|
1.654087
|
4.272225
|
3822284
|
3.836919
|
9.137763
|
Italy
|
Obs
|
45
|
49
|
6
|
49
|
49
|
|
Mean
|
0.2211501
|
99.45953
|
390912.2
|
10.86122
|
101.4179
|
|
Min
|
0.1
|
85.04956
|
2
|
8.7
|
99.34884
|
|
Max
|
0.8228682
|
101.0073
|
1601554
|
12.5
|
103.5
|
|
sd
|
0.2109211
|
2.927137
|
605432.8
|
1.031143
|
1.337516
|
Japan
|
Obs
|
45
|
46
|
4
|
46
|
46
|
|
Mean
|
3.598105
|
100.0532
|
58588.5
|
2.828261
|
100.6913
|
|
Min
|
1.969231
|
92.01619
|
245
|
2.2
|
99.4
|
|
Max
|
5.770513
|
101.1165
|
148962
|
3.6
|
102.3
|
|
sd
|
0.7244478
|
1.455526
|
66877.74
|
0.419345
|
0.8868987
|
United States
|
Obs
|
45
|
48
|
6
|
48
|
48
|
|
Mean
|
3.562537
|
99.92356
|
4375949
|
4.877083
|
100.7417
|
|
Min
|
2.25684
|
92.01619
|
25
|
3.5
|
99.4
|
|
Max
|
15.5081
|
101.1165
|
1.37E + 07
|
13.3
|
102.3
|
|
sd
|
2.299613
|
1.619505
|
5146680
|
1.740046
|
0.901496
|
Note: Sample from January of 2014 to November of 2020. Source: Own Elaboration (2021). |
The country-level statistics show that Japan has the largest Economic Activity Index and carbon monoxide emissions followed by the U.S. Italy and India have the highest unemployment and inflation rates respectively, while the US has the largest number of reported monthly COVID-19 cases relative to the rest of the countries.