This section of the study focuses on interpreting the empirical outcomes of the econometric estimations. We presented an overview which encompasses the assessment of central tendencies and dispersion of the defined parameters in the previous section, which is demonstrated in Table 3given below.
Table 3
|
EP
|
EG
|
REN
|
OC
|
GCF
|
Mean
|
0.429568
|
3.671707
|
1.082526
|
2.719100
|
1.412765
|
Median
|
0.468457
|
3.684712
|
1.133569
|
2.820074
|
1.405248
|
Maximum
|
0.648165
|
4.069551
|
1.558652
|
3.351352
|
1.639884
|
Minimum
|
0.148443
|
3.208584
|
0.358878
|
2.222753
|
1.133354
|
Std. Dev.
|
0.154999
|
0.255716
|
0.378162
|
0.282122
|
0.114614
|
Skewness
|
-0.291196
|
-0.228872
|
-0.422492
|
-0.178690
|
0.108502
|
Kurtosis
|
1.626879
|
1.840436
|
1.836996
|
2.073032
|
2.791088
|
Jarque-Bera
|
4.912753
|
3.432009
|
4.563690
|
2.179602
|
0.200372
|
Probability
|
0.085745
|
0.179783
|
0.102096
|
0.336283
|
0.904669
|
Observations
|
53
|
53
|
53
|
53
|
53
|
EG is ranked as the highest average followed by OC, GCF, OC and REN with ecological footprint having the lowest average. Coincidently, a similar ranking is also confirmed with respect to median. This indicates that EG is on the upsurge in Malaysia. With respect to symmetry, all series display negative skewness with the exception of GCF; however, all series indicate a low tail peakedness of less than 3. Moreover, the Jarque-Bera test and its probability value also demonstrate that all series are normally distributed. In addition, RADAR chart is depicted as illustrated by Figure 3 below to showcase the descriptive statistics of the observed variables.
Furthermore, the matrix of Pearson correlation exhibiting the pairwise association amongst the underlined series is investigated in Table 4. We discovered a positive and significant association between OC and ecological footprint, indicating that nature of oil consumption towards environmental degradation in Malaysia. An akin positive association is evident between the ecological footprint and these variables (EG, REN and GCF). Correlation analysis, on the other hand, is insufficient to reach a resolution in the empirical literature. Hence, this research will conduct additional econometric analysis to either disprove or validate the stated association.
Table 4
Pearson correlation analysis
Observations
|
EF
|
EG
|
REN
|
OC
|
GCF
|
EF
|
1
|
|
|
|
|
EG
|
0.9724*
|
1
|
|
|
|
OC
|
0.9769
|
0.9931
|
1
|
|
|
REN
|
0.8115*
|
0.8728*
|
0.8611*
|
|
|
GCF
|
0.3624*
|
0.3029**
|
0.3425**
|
0.272429
|
1
|
* and ** portrays 0.01 and 0.05 significance level
|
Subsequently, this research proceeds to investigate the stationarity properties of the series as a crucial requirement in econometrics modeling in order to avoid erroneous analysis and inappropriate policy prescribed. The stationarity nature of the observed variables is summarized in Table 5 and Table 6 presented below.
Table 5 Conventional unit roots outcome
|
ADF
|
PP
|
|
I(0)
|
I(1)
|
I(0)
|
I(1)
|
EF
|
-2.4365
|
-9.3475*
|
-2.4365
|
-9.3814*
|
EG
|
-1.7810
|
-6.3093*
|
-1.8863
|
-6.3141*
|
REN
|
-2.3978
|
-5.5976*
|
-2.0937
|
-5.4584*
|
OC
|
-0.5615
|
-7.3020*
|
-0.6018
|
-7.3062*
|
GCF
|
-1.9341
|
-6.5675*
|
-2.0143
|
-6.5661*
|
* and ** depicts significance level of 0.01 and 0.05
|
Table 6 Structural break unit roots outcome
|
I(0)
|
I(1)
|
EF
|
-4.7740 (1991)
|
-10.4139* (1988)
|
EG
|
-2.9782 (1992)
|
-7.1217* (1998)
|
REN
|
-3.5593 (1983)
|
-6.3504* (1989)
|
OC
|
-2.8461 (1993)
|
-8.0196* (1981)
|
GCF
|
-4.5064 (1998)
|
-7.1311* (1998)
|
* depicts significance level of 0.01; structural breaks are in parentheses
|
The conventional unit roots test (ADF and PP) reveal that all series are integrated at first level. Also, the structural break unit also reveals that ecological footprint, EG, REN, OC, GCF are integrated at first level with the break level of 1988, 1998, 1989, 1981 and 1998 respectively. Hence, permitting to discover for a long run equilibrium association between the variables, as stated by the Pesaran Bounds testing approach expressed in Table 7given below. The bounds test using the Kripfganz and Schneider critical values establishes an equilibrium association between the observed series. This is asserted by the F-statistics and t-statistics having a greater value than both critical values (lower and upper value) at a 5% statistically significant level, implying convergence between the observed variables during the period of study. Therefore, this suggests a long-term association between ecological footprint and its determinants for an effective policy architecture in Malaysia.
Table 7
ARDL Approach to Cointegration
|
Model 1
|
Model 2
|
F-statistic
|
4.6785*
|
4.3395*
|
T-statistic
|
-5.0982*
|
-5.4549*
|
Kripfganz and Schneider critical values
|
|
1%
|
5%
|
10%
|
|
LB
|
HB
|
LB
|
HB
|
LB
|
HB
|
F-statistic
|
3.74
|
5.06
|
2.86
|
4.01
|
2.45
|
3.52
|
T-statistic
|
-3.43
|
-4.60
|
-2.86
|
-3.99
|
-2.57
|
-3.66
|
Diagnostic Check
|
|
Model 1
|
Model 2
|
χ2 Normality
|
0.7618 (0.6832)
|
0.9632 (0.6177)
|
χ2 LM
|
0.1912 (0.8268)
|
0.0432 (0.9578)
|
χ2 Heteroscedasticity
|
1.1683 (0.3401)
|
1.0305 (0.4479)
|
χ2 Ramsey
|
0.3076 (0.7602)
|
0.1814 (0.8571)
|
* depicts significance level of 0.05; p-value in parentheses
|
For the ARDL model’s goodness of fit, we conducted several post estimation tests as mentioned in the previous section of our study. These diagnostics tests are showcased in Table 7 above, which reveals that the residual of these models do not have a heteroscedasticity, misspecification, and serial correlation issue. The residual of these models are also normally distributed. Furthermore, the CUSUM and CUSUMSQ tests indicate that the models are stable at a 5% level of significance, which is illustrated in Figure 4 and Figure 5 given below.
To examine the magnitude and effect of the long run association of the ecological footprint and its determinants, the coefficient for the long and short term analysis is computed as summarized in Table 8 given below. Moreover, the long run association is corroborated by the error correction model, which demonstrates a negative and significant with its coefficient as 0.5105 for Model 1 and 0.5041 for Model 2. This suggests that the rate of convergence in the place of short shock is 51.05% for Model 1 and 50.41% for Model 2.
Table 8
Variable
|
Model 1
|
Model 2
|
Coefficients
|
T-statistics
|
Coefficients
|
T-statistics
|
EG
|
1.4070*
|
3.8542
|
1.2948*
|
3.5114
|
OC
|
0.3579*
|
3.6956
|
0.3418*
|
3.5528
|
REN
|
-0.1575**
|
3.6956
|
-0.1758**
|
-2.4462
|
GCF
|
-0.0233
|
-0.2397
|
-0.2757**
|
-2.3069
|
DUM
|
|
|
0.0325
|
1.4130
|
ΔEG
|
1.4070*
|
4.3307
|
1.2948*
|
4.1208
|
ΔREN
|
-0.0775
|
-1.8343
|
-0.0865**
|
-2.1205
|
ΔGCF
|
-0.0233
|
-0.2709
|
-0.1908**
|
-2.5943
|
ECT(-1)
|
-0.5105
|
-5.0982
|
-0.5041*
|
-5.4549
|
* and ** depict a significance level of 0.01 and 0.05; p-value in parentheses
|
As it is illustrated by Table 8, economic growth, renewable energy consumption and oil consumption are the underlying factors of ecological footprint in the long run. According to the estimation results, economic growth exhibits a positive and significant association with the ecological footprint in Malaysia not just in the long-term, but also in the short-term. This indicates that a 1.40% and 1.29% upsurge in ecological footprint is triggered by a 1% increase in EG as reported in Model 1 and Model 2 respectively. This is consistent with the previous research by Alola et al., (2021) in China, Kirikkaleli et al., (2021) in Turkey and Kihombo et al., (2021) in WAME economies, which establishes a positive interaction between EG and ecological footprints. Economic expansion is unattainable unless goods and services are produced and consumed. Consequently, as income levels continue to expand, there will be an upsurge in the consumption of resources (such as water, food and energy), residential sector, generation waste in the construction sector, transportation sector, industrial sector, and the land use, among others. Thus, as ecological footprint becomes more intense, environmental degradation worsens. Furthermore, this outcome is understandable given Malaysia's transition from an agrarian to an industrial and service-based economy with a heavy reliance on energy. Malaysia's average energy consumption growth rate in the industrial, transportation, and residential sectors has been between 6% and 7% during the last four decades.
Furthermore, with respect to Malaysia’s energy mix, the consumption of oil has a significant and positive interconnection with ecological footprint only in the long run, suggesting that the increase in oil consumption intensifies ecological footprint, thereby contributing to environmental degradation in both models. This conclusion suggests that increased oil consumption for other energy-intensive economic operations underpins the association between economic growth and ecological footprint. Although this energy resource promotes and boosts economic growth, its usage raises ecological footprint. This conclusion backs up the findings of earlier empirical research conducted on Pakistan by Majeed et al., (2021). Furthermore, the research by Adebayo et al., (2021) confirmed a similar interconnection between the CO2 emission and oil consumption in Japan. Oil consumption is the largest chuck of energy sources in Malaysia, accounting for around 36.83% of the total energy consumption. However, owing to the advent and development of renewable energy sources, as well as the transition of other fossil energy, Malaysia’s reliance on oil consumption has gradually decreased in recent years. This assists the economy in reducing its ecological footprint produced by the excessive consumption of oil.
Moreover, renewable energy has a significant and negative interconnection with ecological footprint not just in the long term, but in the short term. The reduction in ecological footprint by 0.16% can be realized by the increase in renewable energy by 1% in Model 1; whereas, in Model 2, the ecological footprint will decrease by 0.18%. This seems plausible given that numerous prior research show that RN reduces EF, thereby promoting environmental quality; for example, Akinsola et al., (2021) for Brazil; Rafique et al., (2021) for selected ten economies; and Iorember et al., (2021) for South Africa. Energy consumption is required to maintain continuous economic expansion; although, only clean energy sources such as geothermal, tidal, wind, sun, and hydropower could assist in achieving long term sustainability in terms of growth. Moreover, using these energy sources will not only promote a sustainable ecosystem without impeding economic growth, but it can also assist governments in meeting their environmental goals.
However, it is evident that the adverse effect of oil consumption on the ecosystem outmatch the positive effect of renewable energy on the environment. By discovering this, we concluded that the present manufacturing processes are insufficient to satisfy the standards for carbon-intensity and energy-efficiency. This condition is undesirable for the long term sustainable growth. Hence, Malaysia needs to re-engineer its long term economic policy to ensure the extensive use of cleaner energy in the industrial and residential sectors. To ensure a cleaner ecosystem, policymakers must pinpoint pollution-intense industries where the use of clean energy resources like biomass, water, air, solar and wind can be expanded.
Also, as depicted in Table 8, gross capital formation exerts an insignificant effect on ecological footprint not just in the long term, but in the short term as well. Although, having incorporated the dummy variable in Model 2, it is evident that there is a negative and significant association between gross capital formation and ecological footprint not just in the long term, but in the short term as well, which is in conformity with the study of Majeed et al., (2021) in Pakistan. This indicates that the gross capital formation of Malaysia is effective in decreasing ecological footprint, suggesting thus investment is channeled towards eco-friendly technologies for the production of goods. Finally, the dummy variables have a positive and insignificant effect on ecological footprint not only in the long run but also in the short run. This positive linkage could be possibly owing to the increase in GDP, which in turn increases oil consumption, which results in environmental degradation.
The robustness analysis for Model 1 and 2 is summarized in Table 9 given below. The findings reveal that economic growth and oil consumption significantly impact ecological footprint, but renewable energy exhibits a negative effect on ecological footprint. Conversely, between domestic capital investment and ecological footprint, an insignificant association was reported. These outcome is in accordance with the outcomes of the ARDL estimators.
Table 9
Robustness estimator outcome
Variable
|
FMOLS
|
FMOLS with breaks
|
DOLS
|
DOLS with breaks
|
Coefficients
|
T-statistics
|
Coefficients
|
T-statistics
|
Coefficients
|
T-statistics
|
Coefficients
|
T-statistics
|
EG
|
1.2252*
|
2.9630
|
1.1954*
|
3.0820
|
1.1138*
|
2.3982
|
1.1041*
|
2.4126
|
REN
|
-0.0783***
|
-1.9907
|
-0.0778**
|
-2.1071
|
-0.0742***
|
-1.6800
|
-0.0759*
|
-1.7418
|
OC
|
0.3678**
|
2.6667
|
0.3518*
|
2.7133
|
0.3427**
|
2.2113
|
0.3344*
|
2.1861
|
GCF
|
0.0009
|
0.0163
|
0.0277
|
0.5051
|
0.0156
|
0.2429
|
0.0570
|
0.8979
|
DUM
|
|
|
0.0421
|
1.1065
|
|
|
0.0348
|
0.7913
|
* and ** depict a significance level of 0.01 and 0.05
|
Having established the long run effect of ecological determinants, this current study proceeded to examine the causal interconnection by applying the spectral BC causality approach, which is useful in detecting the causal connection between ecological footprint and its determinants at different frequencies. However, the conventional Granger causality method is incapable of performing this operation. Figures 6 to Figure 9 display the outcomes of the causality interconnection of ecological footprint with its determinants. From the Figures 6 to Figure 9, the green and red plain line represents the 10% and 5% level of significance and the t-statistics of the BC causality approach is represented by the dash-line. Figure 6 indicates the unidirectional causal connection from economic growth to ecological footprint is evident only in the short run, signifying that only in the short run economic growth is a predictor of ecological footprint. A similar finding is seen between oil consumption and ecological footprint in Figure 7, where the unidirectional causal connection from oil consumption to ecological footprint is evident only in the short run. Thus, oil consumption is a predictor of ecological footprint in the short term. Also, Figure 8 displays the causality association between renewable energy and ecological footprint. The unidirectional causal connection from renewable energy to ecological footprint is evident in the medium and long term. Hence, renewable energy is a predictor of ecological footprint in the medium and long term. Finally, Figure 9 indicates the unidirectional causal connection from domestic capital investment to ecological footprint is evident only in the short run; signifying that only in the short run domestic capital investment is a predictor of ecological footprint. Based on these outcomes, it is obvious that economic growth, oil consumption, renewable energy and domestic capital investment are the predictive factors of ecological footprint in Malaysia. Hence, any initiatives aimed at enhancing economic growth, oil consumption, renewable energy and domestic capital investment will have a significant impact on the ecological footprint.