Do natural resources affect environmental quality in MINT Economies? The role of tourism and financial development

Abstract

With the growing nature of the ecological footprint, research studies focus on exploring new determinants of environmental degradation. Moreover, the role of natural resources and energy consumption in environmental quality has gained much attention in the literature. However, tourism raises the demand for energy consumption and extraction of natural resources. This research study investigates the influence of natural resources, tourism, and renewable energy in MINT countries, using novel Cross-Sectional Auto Regressive Distributive Lag (CS-ARDL) methodological techniques and employing yearly data from 1995 to 2018. The study also applied recently developed Kónya (Econ Model 23:978–992, 2006) causality to identify the causal relationship between the variables of the heterogenous panel. The result shows that tourism, natural resources, and economic growth are positively associated with the ecological footprint in the long-run. However, renewable energy consumption negatively impacts ecological footprint in both in short-run and the long-run. Further, the study explored a bidirectional causality between economic growth and ecological footprint in MINT countries. Finally, based on the empirical results, the study recommends that the authorities in MINT countries revisit their tourism, natural resources, and economic activities policies to enhance the environmental quality and reduce the ecological footprint.

Appendix I

$${H}_{i,t}=\sum_{I=0}^{{P}_{w}}{\alpha }_{I,i}, {W}_{i,t-1}+\sum_{I=0}^{{P}_{z}}{\lambda }_{I,i}{Z}_{i,t-I}+{\varepsilon }_{i,t}$$

(6)

This equation represents the autoregressive distributed lag (ARDL), while adding cross-sections, the above equation can be change to the Eq. 10 to incorporate CSD issue.

$$H_{it}=\sum_{I=0}^{a_w}\alpha_{I,i},1+\sum_{I=0}^{a_z}\lambda_{I,i}Z_{i,t-I}+\sum_{I=0}^{a_x}\theta_i{'},I{\overline X}_{t-I}+\varepsilon_{i,t}$$

(7)

All variables’ averages can be stated as;

$${\overline{X} }_{t-I}={\overline{H} }_{i,t-I}{\overline{Z} }_{i,t-1}$$

(8)

${H}_{i,t}$ represents the dependent variables (EFP), ${a}_{w}$, ${a}_{z}$ and ${a}_{x}$ shows the lags of predictors, and ${Z}_{i,t}$ indicates the independent variable. Additionally, X displays CSD average to take control of the occupied possessions (Liddle 2018). Further, the following are the equations for the mean group (MG) and long-run values.

$${\widehat{\phi }}_{MG}=\frac{1}{N}\sum_{i=1}^{N}\widehat{\phi }$$

(9)

$${\widehat{\phi }}_{CS-ARDL,i}=\frac{\sum_{I=0 }^{{P}_{z}}{\beta }_{I,i}^{Pw}}{1-{\sum }_{I=0} }$$

(10)

Similarly, equations for CS-ARDL short-run can be states as follows;

$$\Delta H_{i,t}=\delta_i\left[H_{i,t-1}-\phi_iZ_{i,t}\right]-\sum_{I=1}^{a_w-1}\alpha_{I,}i\Delta_IH_{i,t-1}+\sum_{I=1}^{a_{ws}-1}\lambda_{I,}i\Delta_IZ_{i,t}+\sum_{I=0}^{a_x}\theta_i{'},I{\overline X}_{t-I}+\varepsilon_{i,t}$$

(11)

whereas, ${\Delta }_{I}=t-(t-1)$

$$\widehat{{\delta }_{i}}=-\left(1-\sum_{I=1}^{{a}_{w}}{\widehat{\alpha }}_{I,t}\right)$$

(12)

$${\phi }_{i}=\frac{\sum_{I=0}^{{a}_{z}}{\lambda }_{I,i}^{aw}}{\widehat{{\delta }_{i}}}$$

(13)

$${\widehat{\phi }}_{MG}=\frac{1}{N}\sum_{i=1}^{N}\widehat{\phi }$$

(14)