4.1. Summary statistics and normality test
The empirical results are analyzed as follows: Our analysis starts by providing a breakdown of variable summary statistics and normality test. It then shows preliminary results of cross-sectional dependence, unit root, and co-integration tests. The last and main part presents the results of panel quantile regression.
Table 1 provides the summary statistics of all variables in the natural logarithm. The means of consumption and production ecological footprint are 1.72 and 1.64, which are equivalent to 5.91 and 5.81 global hectares per capita, respectively. The average level of shadow economy is 2.75 (17.03% of official GDP). As of the year 2015, Mexico ranks first in terms of shadow economy (6.94%) while Switzerland ranks last (28.07%).
Table 1
Variable
|
No. Obs
|
Mean
|
Std. Dev.
|
Min
|
Max
|
EFC
|
672
|
1.721
|
0.323
|
0.846
|
2.875
|
EFP
|
672
|
1.642
|
0.472
|
0.739
|
2.669
|
SHA
|
672
|
2.747
|
0.422
|
1.818
|
3.644
|
OPE
|
672
|
4.332
|
0.534
|
2.814
|
6.012
|
ENE
|
672
|
8.184
|
0.423
|
6.959
|
9.152
|
REN
|
672
|
2.258
|
0.996
|
-0.813
|
4.097
|
GDP
|
672
|
10.205
|
0.899
|
6.133
|
11.627
|
Note: Variables are in natural logarithm. |
Figure 1 depicts that the relationship between shadow economy and ecological footprint does not follow a linear but instead a non-linear pattern. At each sub-distribution of ecological footprint, an initial increase of shadow economy leads to an escalation in ecological footprint but a further increase reduces ecological footprint. It provokes the idea that at there exists different inverted U-shaped relationships between shadow economy and ecological footprint in different sub-distributions of ecological footprint.
We check the normality of the variables, which is the basic assumption of a conditional mean regressions model, by employing the quantile-quantile plot as well as the Shapiro-Wilk and Skewness-Kurtosis test. First, the quantile-quantile plot is a visual expression of variable distribution around a diagonal line. It is demonstrated from Figure 2 that all scatter diagrams deviate from the diagonal lines. Therefore, none variable follows the normal distribution.
Second, the test by Shapiro and Wilk (1965) where the null hypothesis that a sample comes from a normally distributed population is adopted. As shown in Table 2 Panel A, the null hypothesis is rejected at a 1% significance level. The skewness-kurtosis test by Jarque and Bera (1981) in Table 2 Panel B also confirms the non-normality of the unconditional distribution of variables. Figure 3 exhibits the univariate kernel density for the residuals, which shows the strong deviation from the normal distribution. Overall, both the results of quantile-quantile plot and two residual tests imply that the panel data are not normally distributed, suggesting the implementation of panel quantile regressions.
Table 2. Shapiro-Wilk and Skewness-Kurtosis test for normality
Panel A. Shapiro-Wilk test
Variable
|
W
|
V
|
z
|
Prob>z
|
EFC_residual
|
0.868
|
57.988
|
9.888
|
0.000
|
EFP_residual
|
0.919
|
35.596
|
8.700
|
0.000
|
Panel B. Skewness-Kurtosis test
Variable
|
Pr(Skewness)
|
Pr(Kurtosis)
|
adj chi2(2)
|
joint Prob>chi2
|
EFC_residual
|
0.000
|
0.000
|
50.850
|
0.000
|
EFP_residual
|
0.000
|
0.060
|
49.630
|
0.000
|
Note: The residuals are obtained from estimating equation (1) using ordinary least squares.
4.3. Panel regression results
The present study proceeds with conditional mean methods before analyzing the panel quantile regression results. Table 6 reports the estimation results of fixed effects with Driscoll and Kraay standard errors and fully modified ordinary least squares. There are several noticeable outcomes. First, although the coefficients of shadow economy and its square are positive and negative, respectively, only those obtained from production ecological footprint by fixed effects model are statistically significant. The environmental impact of trade openness is inconsistent between the two estimation methods. While the sign of trade openness coefficients is negative in the fixed effects model, they are positive in the fully modified ordinary least squares model. Similar properties are found for the relationship between economic development and ecological footprint. However, the results consistently produce the harmful effects of energy intensity and beneficial effects of renewable energy. Although these findings only act as references for the Canay (2011) and Powell (2016) regressions, they support the implementation of such panel quantile regressions.
Table 6
Estimation results from conditional mean methods
|
(1)
|
(2)
|
(3)
|
(4)
|
|
Fixed effects with Driscoll and Kraay standard errors
|
Fully modified ordinary least squares
|
|
Consumption ecological footprint
|
Production ecological footprint
|
Consumption ecological footprint
|
Production ecological footprint
|
SHA
|
0.084
|
4.224***
|
0.232
|
0.138
|
|
(0.376)
|
(1.151)
|
(0.957)
|
(0.369)
|
SHA_sq
|
-0.030
|
-1.012***
|
-0.047
|
-0.029
|
|
(0.074)
|
(0.276)
|
(0.229)
|
(0.072)
|
OPE
|
-0.087
|
-0.012
|
0.127***
|
0.072
|
|
(0.059)
|
(0.027)
|
(0.023)
|
(0.049)
|
ENE
|
0.496***
|
1.392***
|
1.151***
|
0.663***
|
|
(0.110)
|
(0.047)
|
(0.039)
|
(0.105)
|
REN
|
-0.054***
|
-0.068***
|
-0.072***
|
-0.055***
|
|
(0.017)
|
(0.020)
|
(0.017)
|
(0.018)
|
GDP
|
0.317
|
-27.071***
|
0.138
|
0.701***
|
|
(0.225)
|
(5.478)
|
(4.554)
|
(0.201)
|
GDP_sq
|
-0.011
|
1.268***
|
-0.015
|
-0.042***
|
|
(0.013)
|
(0.256)
|
(0.213)
|
(0.011)
|
Constant
|
-3.922***
|
130.085***
|
-8.381
|
-6.832***
|
|
(1.387)
|
(28.490)
|
(23.685)
|
(1.216)
|
Note: Variables are transformed into natural logarithm before estimation. Standard errors are in brackets. *** < 0.01, ** < 0.05, * < 0.1. |
We use nine quantiles for a comprehensive understanding of the environmental effects of shadow economy and other decisive factors. The panel quantile regression is capable of modelling the entire conditional distribution and controls for the unobserved heterogeneity. Therefore, it facilitates the analysis at the extreme of the distribution, which is very helpful in proposing policy recommendations. The results obtained from Canay (2011) and Powell (2016) are reported in Table 7 and Table 8, respectively. In each table, we consecutively present the estimation outcome for consumption ecological footprint and production ecological footprint. At a first glance, we realize the estimation results of Canay (2011) and Powell (2016)’s methods are quite similar.
The results of quantile regression indicate that the effect of shadow economy on ecological footprint (of both consumption and production) are heterogeneous at all quantiles. The finding confirms our hypothesis H1. Table 7 Panel A and Table 8 Panel A show that the coefficients of shadow economy and its squares are positively and negatively significant at most quantiles (except for 10th quantile in Canay (2011) estimation). It confirms the existence of an inverted U-shaped relationship between shadow economy and ecological footprint of consumption. The finding confirms our hypothesis H2. This result is in line with Elgin and Öztunali (2014a, 2014b) and Yang et al. (2021) that claim that a higher shadow economy initially worsens environmental quality but to a certain threshold, it helps control environmental degradation. The non-linear association between the underground sector and the ecological footprint of production may be due to the change in the relative strength of the deregulation effect over the scale effect and vice versa by the degree of informality. Initially, the existence of a small-scale shadow economy could not create enough positive environmental externalities to offset the environmental costs of its deregulation effect. Only when the size of the shadow economy reaches a sufficiently high level, the scale effect of underground activities prevails to save energy consumption and creates a significant reduction in ecological footprint of production.
Table 7. Canay (2011) estimation results
Panel A. Consumption ecological footprint
|
10th
|
20th
|
30th
|
40th
|
50th
|
60th
|
70th
|
80th
|
90th
|
SHA
|
0.195
|
0.454**
|
0.517***
|
0.627***
|
1.081***
|
1.105***
|
1.344***
|
1.234***
|
1.369***
|
|
(0.203)
|
(0.221)
|
(0.196)
|
(0.206)
|
(0.220)
|
(0.212)
|
(0.223)
|
(0.254)
|
(0.215)
|
SHA_sq
|
-0.035
|
-0.077*
|
-0.085**
|
-0.101***
|
-0.187***
|
-0.195***
|
-0.235***
|
-0.220***
|
-0.248***
|
|
(0.039)
|
(0.041)
|
(0.036)
|
(0.038)
|
(0.039)
|
(0.037)
|
(0.038)
|
(0.045)
|
(0.041)
|
OPE
|
0.057***
|
0.039***
|
0.029***
|
0.032*
|
0.000
|
-0.032**
|
-0.036**
|
-0.024**
|
-0.046***
|
|
(0.011)
|
(0.009)
|
(0.011)
|
(0.017)
|
(0.017)
|
(0.014)
|
(0.016)
|
(0.010)
|
(0.016)
|
ENE
|
0.461***
|
0.418***
|
0.412***
|
0.420***
|
0.444***
|
0.479***
|
0.493***
|
0.456***
|
0.383***
|
|
(0.028)
|
(0.025)
|
(0.023)
|
(0.031)
|
(0.030)
|
(0.025)
|
(0.024)
|
(0.017)
|
(0.021)
|
REN
|
-0.042***
|
-0.037***
|
-0.029***
|
-0.027***
|
-0.028***
|
-0.021***
|
-0.014
|
-0.045***
|
-0.073***
|
|
(0.005)
|
(0.005)
|
(0.006)
|
(0.010)
|
(0.010)
|
(0.008)
|
(0.009)
|
(0.007)
|
(0.010)
|
GDP
|
-0.476***
|
-0.767***
|
-0.939***
|
-0.972***
|
-1.050***
|
-1.177***
|
-1.361***
|
-1.808***
|
-2.050***
|
|
(0.169)
|
(0.217)
|
(0.079)
|
(0.147)
|
(0.165)
|
(0.184)
|
(0.224)
|
(0.099)
|
(0.064)
|
GDP_sq
|
0.030***
|
0.045***
|
0.053***
|
0.056***
|
0.059***
|
0.065***
|
0.076***
|
0.100***
|
0.114***
|
|
(0.008)
|
(0.011)
|
(0.004)
|
(0.008)
|
(0.009)
|
(0.009)
|
(0.012)
|
(0.006)
|
(0.004)
|
Constant
|
-0.930
|
0.554
|
1.409***
|
1.301*
|
1.130
|
1.638*
|
1.996*
|
4.595***
|
6.227***
|
|
(0.896)
|
(1.073)
|
(0.438)
|
(0.720)
|
(0.789)
|
(0.862)
|
(1.071)
|
(0.565)
|
(0.401)
|
No. Obs
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
dSHA/dEFC
|
|
19.069
|
20.930
|
22.286
|
18.000
|
17.002
|
17.454
|
16.520
|
15.801
|
Panel B. Production ecological footprint
|
10th
|
20th
|
30th
|
40th
|
50th
|
60th
|
70th
|
80th
|
90th
|
SHA
|
1.553***
|
0.831**
|
1.898***
|
2.246***
|
2.483***
|
3.557***
|
3.375***
|
4.152***
|
4.083***
|
|
(0.178)
|
(0.373)
|
(0.241)
|
(0.222)
|
(0.292)
|
(0.430)
|
(0.485)
|
(0.668)
|
(0.374)
|
SHA_sq
|
-0.299***
|
-0.161**
|
-0.354***
|
-0.414***
|
-0.448***
|
-0.648***
|
-0.614***
|
-0.765***
|
-0.823***
|
|
(0.033)
|
(0.065)
|
(0.044)
|
(0.040)
|
(0.054)
|
(0.079)
|
(0.089)
|
(0.120)
|
(0.068)
|
OPE
|
-0.037***
|
-0.097***
|
-0.129***
|
-0.127***
|
-0.126***
|
-0.161***
|
-0.154***
|
-0.187***
|
-0.279***
|
|
(0.011)
|
(0.020)
|
(0.013)
|
(0.013)
|
(0.018)
|
(0.024)
|
(0.029)
|
(0.040)
|
(0.022)
|
ENE
|
0.933***
|
1.154***
|
1.063***
|
1.036***
|
1.028***
|
0.910***
|
0.882***
|
0.765***
|
0.732***
|
|
(0.020)
|
(0.051)
|
(0.030)
|
(0.022)
|
(0.037)
|
(0.046)
|
(0.049)
|
(0.061)
|
(0.040)
|
REN
|
0.093***
|
0.138***
|
0.143***
|
0.152***
|
0.167***
|
0.158***
|
0.165***
|
0.109***
|
0.071***
|
|
(0.008)
|
(0.012)
|
(0.011)
|
(0.007)
|
(0.010)
|
(0.013)
|
(0.015)
|
(0.022)
|
(0.011)
|
GDP
|
0.402***
|
0.765***
|
0.365***
|
0.185
|
0.043
|
-0.311
|
-0.518**
|
-0.902***
|
-0.627***
|
|
(0.081)
|
(0.107)
|
(0.111)
|
(0.115)
|
(0.136)
|
(0.216)
|
(0.222)
|
(0.159)
|
(0.232)
|
GDP_sq
|
-0.033***
|
-0.053***
|
-0.030***
|
-0.019***
|
-0.010
|
0.011
|
0.022*
|
0.044***
|
0.026**
|
|
(0.005)
|
(0.006)
|
(0.006)
|
(0.006)
|
(0.008)
|
(0.012)
|
(0.012)
|
(0.009)
|
(0.013)
|
Constant
|
-8.902***
|
-11.148***
|
-10.040***
|
-9.594***
|
-9.413***
|
-8.153***
|
-6.710***
|
-4.797***
|
-4.158***
|
|
(0.354)
|
(0.651)
|
(0.646)
|
(0.631)
|
(0.768)
|
(1.161)
|
(1.188)
|
(1.086)
|
(1.337)
|
No. Obs
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
dSHA/dEFC
|
13.423
|
13.207
|
14.597
|
15.068
|
15.978
|
15.558
|
15.617
|
15.085
|
11.948
|
Note: Variables are transformed into natural logarithm before estimation. Standard errors are in brackets. *** < 0.01, ** < 0.05, * < 0.1. The turning point of shadow economy has been recalculated to its original form. |
On the other hand, as the sum of ecological footprint of production and net ecological footprint of trade, the inverted U-shaped relationship between shadow economy and ecological footprint of consumption could be explained through the interactions among the scale effect, deregulation effect, and trade-related effect of informality. Our estimation results indicate that trade openness causes environmental deterioration in countries with low and medium levels of ecological footprint, whereas higher trade activities improve environmental quality in more environmentally degraded countries (see below). Given that the growth of the shadow economy helps foster cross-border exchange (OECD, 2017; Zagoršek et al., 2009), the trade-related effect of informality on consumption ecological footprint is positive (support the deregulation effect) in countries with better environmental quality yet negative (enhance the scale effect) where is characterized by poorer ecological conditions. However, the findings regarding the inverted U-shaped relationship between informality and both the ecological footprint of production and that of consumption indicate that the trade-related effect, either positive or negative, is not strong enough to offset the difference between the deregulation effect and the scale effect to create significant differences between the two indicators. Based on Canay (2011) estimation, the threshold equals 19.07% at the 20th quantile, increasing to 22.29% at the 40th percentile then decreasing gradually to 15.80% at the 90th quantile. The corresponding turning points obtained from Powell (2016) are 19.47%, 22.26%, and 15.99%. This may be because at low and medium levels of ecological footprint, both trade-related and deregulation effects increase ecological footprint as the shadow economy expands. Meanwhile, in countries with a higher level of environmental deterioration, the trade-effect supports the scale effect to alleviate the overall ecological footprint. Consequently, a higher level of scale effect is required to offset the negative impacts of shadow economy in countries with lower levels of ecological footprint.
For the year ended 2015, most countries with the lower level of environmental quality in terms of consumption ecological footprint (than other countries in our sample), e.g. Luxembourg, United States, Denmark, and Australia, have relatively low levels of shadow economy. By further controlling the informal sector, they can obtain a better environmental quality. In contrast, countries with higher levels of environmental quality, e.g. Mexico, Turkey, Hungary, Spain, Portugal, are facing the expansion of the informal sector. These countries need double efforts to reduce shadow economy considerably to fall behind the turning point. Only at the low levels of shadow economy (lower than those of more environmentally degraded countries), they can attain the environmental benefit of controlling the informal sector.
With regard to other explanatory variables, we also find the existence of heterogeneity. First, although energy intensity causes environmental degradation at all quantiles (Salman et al., 2019; Xie et al., 2021), its impacts varied significantly. Notably, the impact follows an N-shaped pattern where it reaches the high level at the 10th and 70th quantile.
Second, renewable energy has a beneficial impact on the consumption ecological footprint. This result is in line with those of Anwar et al. (2021), Chen and Lei (2018), Cheng et al. (2021), Khan et al. (2020), and Gyamfi et al. (2021). The highest benefit is observed at the extreme high quantiles.
Third, we find two contrasting effects of international trade on consumption ecological footprint. For the low to the medium distribution of ecological footprint, higher trade activities with foreign partners significantly degrade environmental quality. However, higher international trade helps improve the situation in countries with a high level of environmental deprivation. The heterogeneous impact of trade openness on environmental quality across countries is mentioned in both theoretical framework and empirical evidence. From the consumption perspective, Tayebi and Younespour (2012) contend that the direction of the trade-environmental quality nexus depends on the factor endowment of a country. Capital-abundant nations tend to obtain environmental benefits from trade by exporting more capital-intensive goods (with high environmental impacts) and importing more labor-intensive goods (cleaner products). In contrast, labor-abundant countries may bear environmental costs from trade by exchanging labor-intensive goods for capital-intensive products. From the production perspective, trade openness facilitates investment and production activities, and hence, increases energy demand and degrades the environment (Cole, 2006; Shahbaz et al., 2013; Koengkan et al., 2018). Previous studies provide empirical evidence about the harmful impacts of trade openness (Asici and Acar, 2016; Demiral et al., 2021; Shahbaz et al., 2020) or beneficial (Qi et al., 2019). Others affirm both the positive and negative forces of higher trade activities on the ecological conditions across nations (Chen and Lei, 2018; Wang et al., 2018; Zhu et al., 2016). Due to the heterogeneity of factor endowments and production capacities among OECD countries, these two contrasting roles of trade openness could be witnessed in this study. The higher level of the ecological footprint may imply a higher level of industrialization that leaves more environmental costs while awarding more accumulation of capital. Correspondingly, countries with more environmental degradation (as compared to others in our sample) are more likely capital-abundant agents that obtain environmental benefits from international trade.
Last, the relationship between economic development and environmental degradation follows a U-shaped pattern as the coefficients of GDP and its squares are negatively and positively significant. This outcome is inconsistent with the EKC hypothesis (Grossman and Krueger, 1991; Panayotou, 1993) and empirical findings of previous research (Anwar et al., 2021; Cheng et al., 2021; Salman et al., 2019; Zhang et al., 2016) that recommends an inverted U-shaped relationship between the two factors. Instead, our findings fit the U-shaped part of the N-shaped association in the economic growth - environmental deterioration nexus, as found in Sinha et al. (2017), Álvarez-Herránz et al. (2017), Allard et al. (2018), and Caravaggio (2020). In this study, our sample covers OECD countries since 1990. From that time, their economic growth may reach a sufficiently high level where the technical effect dominates the scale effect to create a significant positive contribution to environmental quality right at the early phase. Meanwhile, the increase of ecological footprint, as witnessed in the latter stage, could be due to the “technical obsolescence” effect that happens when the margin for successive improvements in the technological efficiency is exhausted or there is a diminishing return of the technological advances in reducing pollution (Opschoor and Vos, 1989; Torras and Boyce, 1998). The threshold of GDP per capita where the environmental impact of higher income changes from beneficial to harmful increases with degradation level of environmental quality. More specifically, the turning point increases when the ecological footprint increases among OECD countries. In other words, the threshold of GDP per capita where the “technical obsolescence” could be witnessed is lower in countries with less economic degradation. Therefore, it is alerted that the continuous enhancement of environmental quality may signal the coming exhaustion of technical efficiency in reducing the ecological footprint. Therefore, other measures should be taken to save the environment. In contrast, more efforts for technical progress in countries with a high level of environmental deprivation could be still rewarded with improved ecological conditions. It is noteworthy that as of the year 2015, all countries in our sample have income per capita higher than the turning points at all quantiles. This finding provides vigorous support for the dominant and harmful scale effect of economic development on environmental quality in OECD countries.
Table 7 Panel B and Table 8 Panel B present the estimation results for production ecological footprint, which are somewhat different from those for consumption ecological footprint. First, although an inverted U-shape still captures the linkage between shadow economy and ecological footprint of production, the threshold is lower than the threshold of ecological footprint of consumption at all quantiles. The finding confirms our hypothesis H3. It means that for production ecological footprint, shadow economy only needs to increase by a lower level to obtain its beneficial effects. This may be because while the ecological footprint of both production and consumption are influenced by the deregulation effect and the scale effect, the consumption ecological footprint is additionally affected by the trade-related effect (originating from export-import activities). Our findings indicate that, in general, the trade-related effect would fuel the deregulation effect, which dampens the ecological footprint of consumption. Consequently, the size of shadow economy needs to expand up to a higher threshold to dominate both the deregulation effect and the trade-related effect and create significant improvement in environmental quality. In this regard, the net ecological footprint of trade of OECD countries is mostly positive.
Table 8. Powell (2016) estimation results
Panel A. Consumption ecological footprint
|
10th
|
20th
|
30th
|
40th
|
50th
|
60th
|
70th
|
80th
|
90th
|
SHA
|
0.124*
|
0.380***
|
0.521***
|
0.695***
|
0.990***
|
1.154***
|
1.333***
|
1.377***
|
1.375***
|
|
(0.072)
|
(0.088)
|
(0.102)
|
(0.156)
|
(0.153)
|
(0.168)
|
(0.133)
|
(0.106)
|
(0.100)
|
SHA_sq
|
-0.024*
|
-0.064***
|
-0.088***
|
-0.112***
|
-0.168***
|
-0.202***
|
-0.233***
|
-0.245***
|
-0.248***
|
|
(0.013)
|
(0.016)
|
(0.020)
|
(0.027)
|
(0.028)
|
(0.030)
|
(0.024)
|
(0.018)
|
(0.018)
|
OPE
|
0.048***
|
0.042***
|
0.047***
|
0.028**
|
0.005
|
-0.031***
|
-0.028**
|
-0.018***
|
-0.045***
|
|
(0.007)
|
(0.005)
|
(0.013)
|
(0.012)
|
(0.017)
|
(0.010)
|
(0.012)
|
(0.005)
|
(0.004)
|
ENE
|
0.457***
|
0.436***
|
0.443***
|
0.402***
|
0.436***
|
0.462***
|
0.476***
|
0.446***
|
0.385***
|
|
(0.013)
|
(0.018)
|
(0.025)
|
(0.028)
|
(0.027)
|
(0.037)
|
(0.031)
|
(0.028)
|
(0.020)
|
REN
|
-0.045***
|
-0.035***
|
-0.026***
|
-0.031***
|
-0.028***
|
-0.021***
|
-0.019***
|
-0.045***
|
-0.071***
|
|
(0.003)
|
(0.003)
|
(0.004)
|
(0.005)
|
(0.007)
|
(0.006)
|
(0.007)
|
(0.006)
|
(0.005)
|
GDP
|
-0.494***
|
-0.691***
|
-0.549*
|
-1.176***
|
-1.137***
|
-1.318***
|
-1.464***
|
-1.790***
|
-2.059***
|
|
(0.058)
|
(0.135)
|
(0.293)
|
(0.269)
|
(0.233)
|
(0.369)
|
(0.287)
|
(0.096)
|
(0.064)
|
GDP_sq
|
0.031***
|
0.041***
|
0.032**
|
0.067***
|
0.064***
|
0.073***
|
0.082***
|
0.099***
|
0.115***
|
|
(0.003)
|
(0.007)
|
(0.016)
|
(0.015)
|
(0.013)
|
(0.020)
|
(0.016)
|
(0.005)
|
(0.003)
|
Constant
|
-0.628**
|
0.175
|
-0.685
|
2.301*
|
1.652
|
2.312
|
2.589*
|
4.342***
|
6.223***
|
|
(0.302)
|
(0.727)
|
(1.596)
|
(1.372)
|
(1.262)
|
(1.918)
|
(1.487)
|
(0.764)
|
(0.563)
|
No. Obs
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
dSHA/dEFP
|
13.423
|
13.207
|
14.597
|
15.068
|
15.978
|
15.558
|
15.617
|
15.085
|
11.948
|
Panel B. Production ecological footprint
|
10th
|
20th
|
30th
|
40th
|
50th
|
60th
|
70th
|
80th
|
90th
|
SHA
|
1.524***
|
0.747***
|
1.734***
|
2.309***
|
2.540***
|
3.411***
|
3.508***
|
4.150***
|
3.938***
|
|
(0.045)
|
(0.255)
|
(0.234)
|
(0.168)
|
(0.288)
|
(0.164)
|
(0.303)
|
(0.231)
|
(0.172)
|
SHA_sq
|
-0.294***
|
-0.146***
|
-0.327***
|
-0.425***
|
-0.464***
|
-0.621***
|
-0.640***
|
-0.773***
|
-0.796***
|
|
(0.008)
|
(0.049)
|
(0.043)
|
(0.031)
|
(0.054)
|
(0.030)
|
(0.055)
|
(0.036)
|
(0.031)
|
OPE
|
-0.039***
|
-0.087***
|
-0.133***
|
-0.123***
|
-0.131***
|
-0.160***
|
-0.151***
|
-0.213***
|
-0.278***
|
|
(0.003)
|
(0.011)
|
(0.013)
|
(0.008)
|
(0.015)
|
(0.013)
|
(0.011)
|
(0.032)
|
(0.007)
|
ENE
|
0.926***
|
1.186***
|
0.996***
|
1.089***
|
0.981***
|
0.918***
|
0.882***
|
0.740***
|
0.732***
|
|
(0.003)
|
(0.065)
|
(0.059)
|
(0.054)
|
(0.060)
|
(0.021)
|
(0.031)
|
(0.033)
|
(0.015)
|
REN
|
0.091***
|
0.147***
|
0.128***
|
0.161***
|
0.156***
|
0.159***
|
0.160***
|
0.100***
|
0.074***
|
|
(0.002)
|
(0.017)
|
(0.013)
|
(0.009)
|
(0.015)
|
(0.005)
|
(0.007)
|
(0.012)
|
(0.005)
|
GDP
|
0.371***
|
0.930***
|
-0.114
|
0.486
|
-0.194
|
-0.331***
|
-0.400**
|
-1.048***
|
-0.654***
|
|
(0.025)
|
(0.291)
|
(0.423)
|
(0.333)
|
(0.318)
|
(0.119)
|
(0.182)
|
(0.215)
|
(0.076)
|
GDP_sq
|
-0.032***
|
-0.062***
|
-0.003
|
-0.036*
|
0.003
|
0.011*
|
0.016
|
0.052***
|
0.028***
|
|
(0.001)
|
(0.016)
|
(0.023)
|
(0.018)
|
(0.017)
|
(0.006)
|
(0.010)
|
(0.012)
|
(0.004)
|
Constant
|
-8.641***
|
-12.098***
|
-7.101***
|
-11.481***
|
-7.895***
|
-7.884***
|
-7.420***
|
-3.707**
|
-3.867***
|
|
(0.114)
|
(1.683)
|
(2.593)
|
(2.090)
|
(2.114)
|
(0.747)
|
(1.322)
|
(1.686)
|
(0.450)
|
No. Obs
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
672
|
dSHA/dEFP
|
13.241
|
19.468
|
19.302
|
22.257
|
19.038
|
17.399
|
17.471
|
16.613
|
15.993
|
Note: Variables are transformed into natural logarithm before estimation. Standard errors are in brackets. *** < 0.01, ** < 0.05, * < 0.1. The turning point of shadow economy has been recalculated to its original form. |
Second, higher energy intensity is detrimental to the quality of the ecosystem. The magnitude of the environmental effect of energy intensity reaches the highest level at the 20th quantile before declining along with the degradation of environmental quality. It means that in countries with ruined eco-system, energy intensity is not the main culprit but other factors.
Third, it is interesting to find that the coefficients of renewable energy are positive at all quantiles. In fact, a modern energy structure fails to lessen environmental damage but aggravates it through the rebound effect. Specifically, the shift to more renewable energy consumption may lower the market price of some other polluting energy sources and thus, raises demand for that resources among manufacturers (Yang and Li, 2017). This would end up with higher total energy consumption along the whole supply chain. Moreover, the exploitation and application of renewable energy may require the construction of new infrastructure systems (Font Vivanco et al., 2014) and the inputs of other “brown” capital goods (with more harmful effects on the environment) (Jenkins et al., 2011; Rosenbaum, 2019).
Forth, international trade helps to reduce the ecological footprint of production, though the magnitude of the effect escalates with the degradation of ecological quality. This may be because the production sector benefits from the trade-related R&D spillover effects when joining the global value chains (Runge, 1994; Helpman, 1998). In more detail, international trade flows of eco-products could accelerate the diffusion of green technologies (Bi et al., 2015; Costantini and Crespi, 2008; Franco and Marin, 2015; Jiang and Liu, 2015; Tarancón and del Río, 2007) and better management methods (Bakhsh et al., 2017; Haider Zaidi et al., 2019) for lowering energy use and enhancing environmental quality. Moreover, trade openness also fosters the international expansion of the supply chains that, in turn, facilitates the transfer of polluting capital goods and dirty industry (producing pollution-intensive goods), especially from developed countries to the developing economies (Suri and Chapman, 1998; Copeland and Taylor, 2013). Our findings are backed by previous empirical research that supports the positive contribution of higher trade activities on the ecological conditions, such as Chen and Lei (2018), Wang et al. (2018), and Zhu et al., 2016).
Last, at low quantiles, the relationship between production activities and ecological footprint follows an inverted U-shaped pattern. At high quantiles, this pattern is reverted to be a U-shaped form.
To sum up, we find several distinct heterogeneities existing in the roles of shadow economy and many decisive factors with different quality of the environment. These findings reinforce our adoption of panel quantile regression and explain the reason for the inconsistent and insignificant effects of explanatory variables on ecological footprints found in conditional mean regressions. Figure 4 further illustrates our arguments.
Table 9
The Dumitrescu and Hurlin (2012) causality test
Null hypothesis
|
\(\stackrel{-}{Z}\) statistics
|
Causality flow
|
Null hypothesis
|
\(\stackrel{-}{Z}\) statistics
|
Causality flow
|
EFC≠˃SHA
|
-0.4516
|
EFC ← SHA
|
SHA≠˃OPE
|
3.2878***
|
SHA ↔ OPE
|
SHA≠˃EFC
|
5.0781***
|
OPE≠˃SHA
|
2.7259***
|
EFC≠˃OPE
|
0.8579
|
EFC ← OPE
|
SHA≠˃ENE
|
7.9222***
|
SHA → ENE
|
OPE≠˃EFC
|
8.9166***
|
ENE≠˃SHA
|
1.3594
|
EFC≠˃ENE
|
5.5741***
|
EFC ↔ ENE
|
SHA≠˃REN
|
5.8481***
|
SHA ↔ REN
|
ENE≠˃EFC
|
4.9772***
|
REN≠˃SHA
|
3.2331***
|
EFC≠˃REN
|
3.3895***
|
EFC ↔ REN
|
SHA≠˃GDP
|
2.8668***
|
SHA ↔ GDP
|
REN≠˃EFC
|
7.6612***
|
GDP≠˃SHA
|
5.9993***
|
EFC≠˃GDP
|
0.8389
|
EFC ← GDP
|
OPE≠˃ENE
|
10.4726***
|
OPE → ENE
|
GDP≠˃EFC
|
5.9782***
|
ENE≠˃OPE
|
0.2982
|
EFP≠˃SHA
|
1.2161
|
EFP ← SHA
|
OPE≠˃REN
|
2.2877**
|
OPE ↔ REN
|
SHA≠˃EFP
|
7.7784***
|
REN≠˃OPE
|
4.2078***
|
EFP≠˃OPE
|
1.7120*
|
EFP ↔ OPE
|
OPE≠˃GDP
|
1.9027*
|
OPE ↔ GDP
|
OPE≠˃EFP
|
10.0714***
|
GDP≠˃OPE
|
2.4962**
|
EFP≠˃ENE
|
7.8205***
|
EFP ↔ ENE
|
ENE≠˃REN
|
5.5762***
|
ENE ↔ REN
|
ENE≠˃EFP
|
2.5746***
|
REN≠˃ENE
|
15.0223***
|
EFP≠˃REN
|
2.9393***
|
EFP ↔ REN
|
ENE≠˃GDP
|
1.7496*
|
ENE ↔ GDP
|
REN≠˃EFP
|
13.6311***
|
GDP≠˃ENE
|
9.3060***
|
EFP≠˃GDP
|
0.8956
|
EFP ← GDP
|
REN≠˃GDP
|
1.2006
|
REN ← GDP
|
GDP≠˃EFP
|
7.3235***
|
GDP≠˃REN
|
8.2011***
|
Note: *** < 0.01, ** < 0.05, * < 0.1. |
This paper also adopts Dumitrescu and Hurlin (2012) test to assess the Granger causal relationship between interested variables. Table 9 presents the results on a pairwise basis. We find uni-directional associations running from shadow economy to ecological footprint and energy intensity. It implies that the shadow economy can exert its influence on environmental quality directly and indirectly through energy usage. Another one-way relationship exists involving income per capita and ecological footprint. In contrast, ecological footprint, and energy intensity, renewable energy have bi-directional causal linkages. It is noticeable that while the effect from trade openness to consumption ecological footprint is one-way directional, it is a two-way directional effect in the case of production ecological footprint. This result infers that the production activities in OECD countries are more sensitive to environmental changes than the consumption activities, which may be resulted from the environmental regulations, for example. Shadow economy and trade openness, renewable energy, and income per capita have two-way effects. Overall, the causal interaction of shadow economy to all the variables proves that shadow economy is an important factor to consider in abating environmental degradation.