Panel-ARDL requires that none of the variables in the model is I(2) and the panel unit root tests tell us about the stationarity of our variables To that end, we have used panel unit root tests namely Levin, Lin and Chu (LLC), I'm, Pesaran and Shin (IPS) and ADF-Fisher. The results of the LLC show that most of the variables are stationary at first difference except BC, Insurance, and POP. However, when we apply IPS and ADF tests all the variables are stationary at first difference except the variable of POP. Table 2 findings confirm that we can apply the panel-ARDL technique. As the frequency of our data is annual we have imposed a maximum of three lags and optimal lag selection is based on Akaike Information Criterion (AIC).
Table 2: Panel unit root testing
|
LLC
|
|
|
IPS
|
|
|
ADF
|
|
|
|
I(0)
|
I(1)
|
|
I(0)
|
I(1)
|
|
I(0)
|
I(1)
|
|
GDP
|
-0.634
|
-5.856***
|
I(1)
|
-0.072
|
-3.760***
|
I(1)
|
0.432
|
-5.918***
|
I(1)
|
CO2
|
-1.663
|
-6.749***
|
I(1)
|
-1.159
|
-4.233***
|
I(1)
|
1.023
|
-7.125***
|
I(1)
|
BB
|
-1.643
|
-4.004**
|
I(1)
|
-0.423
|
-2.114*
|
I(1)
|
-1.035
|
-1.582*
|
I(1)
|
BC
|
-3.744*
|
|
I(0)
|
-0.866
|
-2.831***
|
I(1)
|
0.787
|
-3.578*
|
I(1)
|
Insurance
|
-2.708*
|
|
I(0)
|
-1.119
|
-5.060***
|
I(1)
|
1.197
|
-9.123
|
I(1)
|
EC
|
-1.359
|
-8.253***
|
I(1)
|
-0.657
|
-5.015***
|
I(1)
|
0.374
|
-9.060***
|
I(1)
|
Trade
|
-1.006
|
-5.766***
|
I(1)
|
-1.597
|
-4.910***
|
I(1)
|
-0.110
|
-8.752***
|
I(1)
|
POP
|
-2.571**
|
|
I(0)
|
-2.490*
|
|
I(0)
|
-2.012**
|
|
I(0)
|
Note: ***p<0.01; **p<0.05; and *p<0.1
After confirming the preliminary condition of Panel-ARDL we are now in a position to start the discussion on the estimates of our variables. Our dependent variables are GDP and CO2 emissions and we have used three different proxies of financial inclusion; bank branches, bank credit, and insurance. For both GDP and CO2 models we have included all the proxies of financial inclusion one by one. Table 3, shows the results of both in the short and long run. Moreover, cointegration tests and other diagnostics are also reported in table 3. First of all, we want to confirm whether our long-run results are cointegrated or not. Two tests of cointegration i.e. ECMt-1 and Kao confirm that our long-run estimates of GDP and CO2 are cointegrated meaning they are genuine or valid. Hausman test results have supported the panel ARDL-PMG model. First, we discuss the long-run results of the GDP and CO2 models in detail, and then the short-run results in brief.
The long-run estimates of BB and BC, in the GDP model, are positively significant and in the case of Insurance, the estimate is insignificant. As the variables are taken in the log form we can explain them by saying that a 1% increase in the bank branches and bank credits facilities improve the GDP by 0.021% and 0.271%. The estimate of bank credit is large as compared to the estimate of bank branches suggesting that instead of the number of branches, improved credit facilities are more helpful in increasing the GDP of the economy. As the number of branches and credit facilities in an economy increases the production activities also increase due to the easy availability of loans and other financial services for investment in large projects that can help the economy to grow at a great pace. Moreover, financial inclusion connects a large number of people to the financial system of the country that brings them into the mainstream economy which also helps in the development of the economy (Sharma, 2016).
Table 3: Panel-ARDL estimates of GDP and CO2 emissions
|
GDP
|
|
|
|
|
|
CO2
|
|
|
|
|
|
|
(1)
|
|
(2)
|
|
(3)
|
|
(4)
|
|
(5)
|
|
(6)
|
|
Variable
|
Coefficient
|
t-Stat
|
Coefficient
|
t-Stat
|
Coefficient
|
t-Stat
|
Coefficient
|
t-Stat
|
Coefficient
|
t-Stat
|
Coefficient
|
t-Stat
|
Long-run
|
|
|
|
|
|
|
|
|
|
|
|
|
BB
|
0.021***
|
6.507
|
|
|
|
|
0.015***
|
4.686
|
|
|
|
|
BC
|
|
|
0.271**
|
2.159
|
|
|
|
|
1.417***
|
2.718
|
|
|
INSURANCE
|
|
|
|
0.088
|
0.506
|
|
|
|
|
-0.181***
|
3.433
|
EC
|
0.853***
|
5.726
|
1.739***
|
6.014
|
1.628***
|
6.481
|
1.259***
|
8.864
|
1.992***
|
4.118
|
1.635***
|
23.65
|
TRADE
|
0.032***
|
3.918
|
0.039***
|
2.026
|
0.012***
|
3.577
|
-0.001
|
0.539
|
0.006
|
1.251
|
-0.004***
|
3.678
|
POP
|
0.458***
|
5.766
|
0.624
|
0.810
|
0.384
|
1.563
|
-0.557***
|
6.158
|
0.678
|
1.070
|
0.002
|
0.022
|
Short-run
|
|
|
|
|
|
|
|
|
|
|
|
|
D(BB)
|
0.021
|
0.463
|
|
|
|
|
0.024**
|
2.060
|
|
|
|
|
D(BB(-1))
|
0.002
|
0.125
|
|
|
|
|
|
|
|
|
|
|
D(BB(-2))
|
0.060
|
1.144
|
|
|
|
|
|
|
|
|
|
|
D(BC(-1))
|
|
|
0.037
|
0.692
|
|
|
|
|
0.301
|
1.323
|
|
|
D(BC(-2))
|
|
|
|
|
|
|
|
|
0.004
|
0.021
|
|
|
D(BC(-2))
|
|
|
|
|
|
|
|
|
0.449
|
1.610
|
|
|
D(INSURANCE)
|
|
|
|
-0.028*
|
1.815
|
|
|
|
|
-0.016*
|
1.963
|
D(INSURANCE(-1))
|
|
|
|
|
|
|
|
|
|
-0.008
|
0.159
|
D(EC)
|
0.207
|
1.170
|
0.262**
|
2.313
|
0.214
|
1.538
|
0.206
|
0.704
|
0.565**
|
2.024
|
0.090
|
0.295
|
D(EC(-1))
|
0.080
|
0.551
|
|
|
|
|
|
|
-0.060
|
0.282
|
-0.070
|
0.558
|
D(EC(-2))
|
-0.064
|
0.620
|
|
|
|
|
|
|
0.241
|
1.240
|
|
|
D(TRADE)
|
-0.004
|
0.925
|
0.001
|
0.462
|
0.001*
|
1.769
|
0.001**
|
2.372
|
0.000
|
0.038
|
0.002***
|
2.969
|
D(TRADE(-1))
|
-0.003
|
1.525
|
|
|
|
|
|
|
0.000
|
0.257
|
0.000
|
0.277
|
D(TRADE(-2))
|
-0.003
|
1.470
|
|
|
|
|
|
|
-0.004**
|
2.283
|
|
|
D(POP)
|
-0.002
|
0.002
|
0.170
|
1.589
|
-0.003
|
0.059
|
0.084
|
0.323
|
-0.265
|
0.519
|
0.158
|
0.972
|
D(POP(-1))
|
0.759
|
0.401
|
|
|
|
|
|
|
0.812
|
0.532
|
-0.098
|
0.754
|
D(POP(-2))
|
-0.556
|
0.807
|
|
|
|
|
|
|
-0.561
|
0.515
|
|
|
C
|
0.202
|
1.086
|
0.060
|
0.561
|
-0.180
|
1.136
|
1.706***
|
2.805
|
0.783
|
0.716
|
0.472
|
2.366
|
Diagnostics
|
|
|
|
|
|
|
|
|
|
|
|
Log-likelihood
|
368.1
|
|
351.5
|
|
330.1
|
|
272.6
|
|
292.1
|
|
279.0
|
|
ECM (-1)
|
-0.151*
|
1.906
|
0.212**
|
2.327
|
-0.173*
|
1.738
|
-0.329***
|
2.791
|
0.294**
|
2.136
|
-0.292**
|
2.695
|
Kao- cointegration
|
-2.723***
|
3.000
|
-2.365***
|
|
-2.945***
|
|
-3.845***
|
|
-2.635***
|
|
-4.254***
|
|
Hausman
|
0.221
|
|
1.235
|
|
1.354
|
|
1.355
|
|
1.355
|
|
0.124
|
|
Note: ***p<0.01; **p<0.05; and *p<0.1
Financial inclusion also backs the accomplishment of financial resources in all sections of the economy at an inexpensive rate that could help to lubricate the wheel of the economy (Bhasker, 2013). These findings are consistent with the findings of some previous studies such as Dixit and Ghosh (2013), Onaolapo (2015), and Sharma (2016). According to the world bank (2014), financial inclusion helps the easy provision of financial services to the larger population of the society by increasing the number of financial institutions. As a result, the growth of the economy increases which in turn improves the living standard of the people and reduces poverty.
The control variables EC and Trade helps the economy to grow as well – a 1% rise in EC improves the economic growth of the country by 0.853%, 1.739%, and 1.628% and a 1% rise in Trade improves the economic growth of the economy by 0.032%, 0.039%, and 0.012%. However, a 1% rise in the POP only improves the economic growth in the first model by 0.458% whereas it is not statistically noticeable in the second and third models.
Now we will discuss the long-run estimates of CO2 models. The estimated coefficients of BB and BC are positively significant, whereas the estimated coefficient of Insurance is significantly negative. In the elasticity form, we can elaborate these results by saying that a 1% rise in the bank branches and credit facilities by banks increases the CO2 emissions by 0.015% and 0.1417%. However, a 1% rise in Insurance decreases the CO2 emissions by 0.0181%. Theory suggests that financial inclusion can affect the environment positively or negatively. Our results are suggesting that improved financial inclusion due to the increased number of bank branches and credits facilities help the financial sector to develop and grow which is considered as a driver in nurturing the economy due to the surge in the availability of production and consumption loans that also increase the energy demand and thus give rise to CO2 emissions (Frankel and Romer, 1999). On the other side, as the economy grows due to better financial inclusion of the society more sophisticated and advanced technologies developed in the production process can help to reduce CO2 emissions. Similarly, the availability of credit facilities also speeds up the investment in renewable energy projects that also exert less burden on the environment. Banks provide individual loans for energy-efficient products such as LEDs, DC inverters, fuel-efficient cars, etc., besides banks also provide easy credit to the house owners for installing solar energy. The positive impact of financial inclusion on CO2 emissions is supported by le et al. (2020), however, Renzhi and Baek (2020) found an inverted U-shape relationship between CO2 emissions and financial inclusion.
The variable of energy consumption exerted a positive impact on the CO2 emissions in all the models by the amount of 1.259%, 1.992%, and 1.635%. Conversely, a 1% rise in Trade reduces the CO2 emissions by 0.004% only in model six, whereas in models four & five the impact of Trade is insignificant. Finally, the estimated coefficient of POP (0.557%) is significant and negative in model four, while insignificant in models five & six. In the short run, the estimates in the GDP models are providing us with an inconclusive picture as most of them are insignificant and appeared with mixed signs at most lags. Similarly, the short-run estimates in all CO2 models are mostly insignificant and provide inconclusive results.
Table 4, provide the results of the Granger causality which confirm one-way causality running from GDP→BC, GDP→Insurance, CO2→BC, Insurance→CO2. However, bi-directional causality is found between GDP↔BB. For detailed results see Table 4.
Table 4: Panel causality test results
Null Hypothesis:
|
W-Stat.
|
Zbar-Stat.
|
Prob.
|
Null Hypothesis:
|
W-Stat.
|
Zbar-Stat.
|
Prob.
|
BB → GDP
|
4.446
|
1.925
|
0.054
|
BB → CO2
|
3.444
|
1.047
|
0.295
|
GDP → BB
|
4.195
|
1.704
|
0.088
|
CO2 → BB
|
3.535
|
1.126
|
0.260
|
BC → GDP
|
2.835
|
0.513
|
0.608
|
BC → CO2
|
3.199
|
0.832
|
0.406
|
GDP→ BC
|
7.523
|
4.621
|
0.000
|
CO2 → BC
|
4.851
|
2.280
|
0.023
|
INSURANCE → GDP
|
2.093
|
-0.138
|
0.890
|
INSURANCE → CO2
|
5.105
|
2.502
|
0.012
|
GDP → INSURANCE
|
5.182
|
2.570
|
0.010
|
CO2 → INSURANCE
|
2.947
|
0.611
|
0.542
|
EC → GDP
|
3.154
|
0.792
|
0.428
|
EC → CO2
|
5.019
|
2.427
|
0.015
|
GDP → EC
|
3.533
|
1.124
|
0.261
|
CO2 → EC
|
5.356
|
2.722
|
0.007
|
TRADE → GDP
|
3.795
|
1.354
|
0.176
|
TRADE → CO2
|
3.94
|
1.482
|
0.139
|
GDP → TRADE
|
7.742
|
4.813
|
0.000
|
CO2 → TRADE
|
10.713
|
7.418
|
0.000
|
POP → GDP
|
5.853
|
3.158
|
0.002
|
POP → CO2
|
2.721
|
0.413
|
0.680
|
GDP → POP
|
11.27
|
7.906
|
0.000
|
CO2 → POP
|
11.023
|
7.689
|
0.000
|
BC → BB
|
3.505
|
1.100
|
0.271
|
BC → BB
|
3.505
|
1.100
|
0.271
|
BB → BC
|
2.656
|
0.356
|
0.722
|
BB → BC
|
2.656
|
0.356
|
0.722
|
INSURANCE → BB
|
2.943
|
0.607
|
0.544
|
INSURANCE → BB
|
2.943
|
0.607
|
0.544
|
BB → INSURANCE
|
3.732
|
1.299
|
0.194
|
BB → INSURANCE
|
3.732
|
1.299
|
0.194
|
EC → BB
|
5.538
|
2.882
|
0.004
|
EC → BB
|
5.538
|
2.882
|
0.004
|
BB → EC
|
4.188
|
1.699
|
0.089
|
BB → EC
|
4.188
|
1.699
|
0.089
|
TRADE → BB
|
3.177
|
0.813
|
0.416
|
TRADE → BB
|
3.177
|
0.813
|
0.416
|
BB → TRADE
|
3.918
|
1.462
|
0.144
|
BB → TRADE
|
3.918
|
1.462
|
0.144
|
POP → BB
|
3.975
|
1.512
|
0.131
|
POP → BB
|
3.975
|
1.512
|
0.131
|
BB → POP
|
25.33
|
20.232
|
0.000
|
BB → POP
|
25.33
|
20.232
|
0.000
|
INSURANCE → BC
|
2.548
|
0.261
|
0.794
|
INSURANCE → BC
|
2.548
|
0.261
|
0.794
|
BC → INSURANCE
|
1.458
|
-0.694
|
0.488
|
BC → INSURANCE
|
1.458
|
-0.694
|
0.488
|
EC → BC
|
6.12
|
3.392
|
0.001
|
EC → BC
|
6.120
|
3.392
|
0.001
|
BC → EC
|
4.468
|
1.944
|
0.052
|
BC → EC
|
4.468
|
1.944
|
0.052
|
TRADE → BC
|
4.072
|
1.597
|
0.110
|
TRADE → BC
|
4.072
|
1.597
|
0.11
|
BC → TRADE
|
3.967
|
1.505
|
0.132
|
BC → TRADE
|
3.967
|
1.505
|
0.132
|
POP → BC
|
3.031
|
0.685
|
0.494
|
POP → BC
|
3.031
|
0.685
|
0.494
|
BC → POP
|
3.595
|
1.179
|
0.239
|
BC → POP
|
3.595
|
1.179
|
0.239
|
EC → INSURANCE
|
5.806
|
3.116
|
0.002
|
EC → INSURANCE
|
5.806
|
3.116
|
0.002
|
INSURANCE → EC
|
5.171
|
2.561
|
0.011
|
INSURANCE → EC
|
5.171
|
2.561
|
0.011
|
TRADE → INSURANCE
|
3.082
|
0.729
|
0.466
|
TRADE → INSURANCE
|
3.082
|
0.729
|
0.466
|
INSURANCE → TRADE
|
5.157
|
2.548
|
0.011
|
INSURANCE → TRADE
|
5.157
|
2.548
|
0.011
|
POP → INSURANCE
|
2.659
|
0.359
|
0.720
|
POP → INSURANCE
|
2.659
|
0.359
|
0.720
|
INSURANCE → POP
|
9.153
|
6.05
|
0.000
|
INSURANCE → POP
|
9.153
|
6.050
|
0.000
|
TRADE → EC
|
7.232
|
4.367
|
0.000
|
TRADE → EC
|
7.232
|
4.367
|
0.000
|
EC → TRADE
|
7.802
|
4.866
|
0.000
|
EC → TRADE
|
7.802
|
4.866
|
0.000
|
POP → EC
|
4.548
|
2.014
|
0.044
|
POP → EC
|
4.548
|
2.014
|
0.044
|
EC → POP
|
6.054
|
3.334
|
0.001
|
EC → POP
|
6.054
|
3.334
|
0.001
|
POP → TRADE
|
4.631
|
2.087
|
0.037
|
POP → TRADE
|
4.631
|
2.087
|
0.037
|
TRADE → POP
|
2.623
|
0.327
|
0.744
|
TRADE → POP
|
2.623
|
0.327
|
0.744
|
Note: ***p<0.01; **p<0.05; and *p<0.1