1. Introduction
As reported by the Global Footprint Network (GFN), India’s ecological footprint exceeds its biocapacity by 171 percent, thus suggesting a significant imbalance in the interplay of its population needs and the nation’s earth supplies, i.e., the load capacity factor (LCF) [
1]. Although several reasons could be linked with the increase in the ecological footprint of a country, trade liberalization that allows the importation of biocapacity aspects, the liquidation of national ecological assets, and atmospheric pollution arising from carbon dioxide (CO
2) emissions are some of the reasons adduced to the ecological footprint deficit by the GFN. In the case of India, though the country has mostly suffered from trade imbalance over the years [
2], the role of trade in economic development cannot be over-emphasized, especially in merchandise and services. Although the contribution of trade to the country’s gross domestic product (GDP) decreased from about 56 percent in the 2011–2012 period to about 45 percent in 2021 [
3], the country has continued to experience growth in its exports and imports. This alludes to the potential reason attributed to the ecological deficit, i.e., importation of biocapacity aspects is India’s trade imbalance. For instance, even though export values of all traded commodities increased from over 291 billion United States Dollars (USD) in the period 2020/2021 to over 422 billion USD in 2021/2022 (approximately 44 percent increase), the value of the imported goods and services across the same period increased by 55 percent (i.e., imported products were worth ~394 percent during 2020/2021 and ~613 percent in 2021/2022) (Ministry of Commerce and Industry, 2022). Beyond the above-mentioned factors, the energy consumption mix [
4,
5] and financial development, among others [
6,
7], are found to be contributing to India’s environmental quality.
Given the above-mentioned potential drivers of the declining LCF in India (evidently illustrated in
Figure 1), how variables such as trade, financial system development, energy efficiency, renewables, and a host of other variables affect LCF still remain unclear. Therefore, the current study is geared towards exposing more fundamental factors driving the ecological footprint–biocapacity nexus. Specifically, the objective of this study is to determine whether the efficient utilization of conventional energy and a clean energy transition influence the LCF capacity factor in India. Additionally, the roles of financial development and trade openness alongside dynamic growth are also explored. As mentioned above, trade makes a significant contribution to the GDP of India, and this justifies the need to examine its influence on the country’s LCF. Moreover, the role of the (in)efficient utilization of conventional energy, i.e., non-clean energy efficiency, has rarely been explored in the literature, which, thus, also justifies the direction of this study. Additionally, the LCC hypothesis was tested in this study by deploying an estimation approach known as Dynamic Autoregressive Distributed Lag (DyARDL). Considering the above-mentioned benefits of the study’s primary objective, this study is therefore poised to yield a significant contribution to the existing literature in several ways: First, the study diverts from the conventional way of determining the drivers of environmental sustainability by applying the LCF as a measure of environmental quality. This is perhaps expected to provide better findings than other measures such as CO
2 emissions, greenhouse gas emissions, ecological footprints, etc. Second, this study tests the validity of the LCC hypothesis for India by incorporating variables such as trade and financial development. Third, Dynamic Autoregressive Distributed Lag (DyARDL) simulations were applied. This approach makes it possible to assess the short-run and long-run impacts of both positive and negative changes in explanatory variables on the LCF.
The remaining sections of this study are arranged as follows.
Section 2 reviews the related studies.
Section 3 describes the data and empirical model for the study.
Section 4 presents the results and discussion of the findings. The final section, i.e.,
Section 5, concludes and make policy suggestions based on the findings of the study.
4. Empirical Results
For the DyARDLS model results to be valid, the order of integration of any variable under consideration must not be second difference, i.e., I(2). In other words, the variables’ order of integration can be at level, i.e., I(0), or first difference, i.e., I(1) [
30,
31,
32,
33]. Also, the co-integration relationship must be between the variables under consideration. For these reasons, three widely used unit root tests, namely, Augmented Dickey–Fuller [
34], Phillips–Perron [
35], and Zivot–Andrews [
36] (ZA) were first used to make sure the order of integration of any of the variables was not I(2) (corroborated in the literature [
37,
38,
39,
40,
41]) also apply these unit roots). The unit root test results are reported in
Table 3. The results of these unit root tests disclose that the null hypothesis of non-stationary could be rejected at the first difference for all variables, which implied that the order of integration for all the variables employed in this study was I(1). The implication of these results is that no variable exceeded the order of integration allowed to proceed with the estimation of the DyARDLS.
Table 4 reports the outcomes of the information criterion for lag selection. From the table, two out of five different criteria, selected the same value; that is, the lag order of three was selected by the LR and FPE, whereas one was selected by the SC and HQ. As was mentioned by [
42], the AIC, SC, and HQ are among the most widely used criteria, and therefore we chose the lag order of one selected by the SC and HQ in this study.
Since the order of integration of any study variable was not I(2), and the optimal lag length was one, we then applied the [
29] Pesaran–Shin–Smith (PSS) cointegration test of to assess whether there was a co-integration relationship between the variables under consideration or not. In the study, we used the critical values of [
43] for the lower and upper bounds of each significance level, since these values give more robust and reliable outputs, especially for the small sample size as in this study. According to the results of the PSS cointegration test reported in
Table 5, the null hypothesis of no co-integration relationship could not be true, since the F-statistic value (4.77) was greater than the upper bound value (4.00) at the 5% significance level, and the absolute value of the t-statistic (5.27) was greater than the absolute value of the upper bound (4.99) at the 1% significance level. Overall, these results prove that a valid co-integration relationship existed between the study variables.
After the requirements for estimating our model were attained, i.e., having variables that are integrated of not more than I(1) and also cointegrated, we then applied the DyARDLS method to obtain the short- and long-run parameters of the estimates. The statistically significant short- and long-run coefficients estimated by the DyARDLS are plotted in
Figure 2. At a glance, it is evident that GDP had the highest short- and long-run coefficients. Since these coefficients were positive, we concluded that there is a positive association between GDP and the LFC in India. Statistically, a 1% increase in GDP surged the LFC in India by 4.87% in the short run and 1.29% in the long run, thus implying that a positive contribution of income to environmental damage decreased over time. On the other hand, the short-term and long-term parameters of the GDPSQ were negative, which means that there was a negative linkage between the GDPSQ and the LFC. A 1% rise in the GDPSQ decreased the LFC by 0.41% and 0.12% in the short term and long term in India. This result discloses that increasing GDP not only reduces the positive influence of income on environmental damage but also, after a certain point, begins to harm the environment in India. Also, both the GDP and GDPSQ parameters clearly show that there was an inverted U-shaped relationship between income and the LFC. This reveals that the LCC hypothesis is not valid for India; however, the inverted U-shaped interaction between income and LFC signifies that the EKC assumption is perhaps valid for India.
Furthermore,
Figure 2 demonstrates that the short- and long-term coefficients of both non-renewable energy efficiency and renewable energy use were positive, thus indicating that the LFC (i.e., environmental quality) was positively affected by non-renewable energy efficiency, or clean energy use, in India. Specifically, a 1% increase in non-renewable energy efficiency increased the LFC by 0.28% in the short run and by 0.34% in the long run, while a 1% increase in renewable energy use resulted in an approximately 0.09% increase in the LFC in both the short and long term. These results reveal that, although non-renewable energy efficiency and renewable energy use are favorable factors for environmental sustainability, non-renewable energy efficiency had a much greater impact on environmental quality than renewable energy use in India. Lastly, it is seen that financial development and trade had very close negative coefficients that were statistically significant in the long run. This means that financial development and trade exerted almost the same negative effect on the LFC in India in the long run. Specifically, a 1% upsurge in financial development and trade decreased the LFC by 0.07% and 0.06% in the long run, respectively. These results divulge that both financial development and trade degrade the environment in India, albeit to a small extent.
We performed a set of diagnostic tests to assess whether the findings of this study from the DyARDLS method were robust and reliable. The outputs of the diagnostic tests are reported in
Table 6. The results in
Table 6 show that the model used in the analysis had no serial correlation, heteroscedasticity, ARCH effect, misspecification, and non-normality issues. The results of diagnostic tests also support that the findings obtained from the DyARDLS method were reliable and robust.
As previously stated, one distinguishing feature of the DyARDLS method is that it automatically generates impulse–response plots that project how a counterfactual positive and negative change in an independent variable affects the future path of the dependent variable while holding the remaining independent variables constant. We examined the impulse–response plots obtained from the DyARDLS method to investigate the dynamic impact of a one percent positive and negative change in our independent variables on the LFC in India.
Figure 3a–f show the response of the LFC a 10-year period to ±1% counterfactual change occurring in the second year for GDP, GDP
2, non-renewable energy efficiency, renewable energy use, financial development, and trade, respectively.
The impulse–response plots in
Figure 3a,b show that a 1% positive (negative) change in GDP increased (decreased) the LCF, while a 1% positive (negative) change in GDPSQ had the opposite effect in India. The fact that GDP increased and GDPSQ decreased the LCF reveals that there was an inverted U-shaped linkage between income and LCF, and, therefore, the LCC hypothesis is not valid for India. Also, the impulse–response plots in
Figure 3c,d demonstrate that the LCF responded positively (negatively) in India to the impulse of a 1% increase (decrease) in non-renewable energy efficiency and renewable energy use. Notably, the impact of non-renewable energy efficiency on the LCF was greater than renewable energy use. These results suggest that India should increase renewable energy usage, as well as make more efficient use of non-renewable energy.
Furthermore, the impulse–response plots in
Figure 3e,f illustrate that a 1% positive (negative) change in both financial development and trade declined (rose) the LCF by almost the same amount in India. This means that increasing financial development and trade have had an adverse effect on India’s environmental quality, and, thus, India should ensure that the incomes generated by financial development and trade openness are shifted to more environmentally friendly investments with the necessary incentives by determining the current usage areas.
Finally, the study findings are summarized in
Figure 4. As can be seen from the figure, the y-axis represents the coefficients expressed in absolute values, which also connotes the size of the bubbles. The −1 and +1 changes in a certain variable have the same bubble size. The x-axis reveals the explanatory variables in the model. The GDP (GDPSQ) had a positive (negative) impact on the LCF both in the short and long run; that is, income had an inverted U-shaped impact on the LCF, and, thus, the LCC hypothesis is not valid in India. The impact of non-renewable energy efficiency and renewable energy use on the LCF was positive in India, both in the short and long run. In addition, the impact of non-renewable energy efficiency on the LCF was greater than renewable energy use. At last, financial development and trade had nearly the same magnitude of long-term negative impact on the LCF in India.
Discussion of Findings
From the results of this study, it is clear than an increase in GDP reduced the LCF, while its square term increased the LCF. This suggests that the LCC hypothesis is not invariably supported by this study. The failure of the result of this study to uphold the LCC hypothesis for India suggests that the EKC hypothesis is obviously valid for India. Therefore, our finding is consistent with [
44], who found that the EKC assumption was established in India after controlling for the effect of energy consumption and democracy. Also, our finding is in agreement with the recent findings documented by [
11,
12] who found that, there was no evidence supporting the LCC assumption for France and South Korea, respectively. Conversely, our finding is not supported by the main conclusion put forward by [
14] that significant evidence in support of both the EKC and LCC assumptions was found for the top ten tourism destinations. The implication of the confirmation of the EKC assumptions is that an increase in economic growth is associated with environmental damage until a threshold value is established. On the other hand, the confirmation of the LCC assumption implies that growth is associated with environmental improvement. This is a signal that green growth is associated with the development pattern in these two economies, i.e., France and South Korea.
Furthermore, our results show that both nonrenewable energy efficiency and renewable energy utilization dampened the level of environmental degradation in India by improving the level of the LCF. The plausible explanation for the positively significant effect of nonrenewable energy efficiency established in this study suggests that fossil fuels, when used efficiently, can lead to environmental improvement. This finding is possibly pointing to the fact that technological advancement is a vehicle that promotes energy efficiency. By this revelation, it means that, in addition to the environmental lessening effect of renewable energy, nonrenewable energy efficiency can guarantee a sustainable environment. Therefore, the positive effect of renewable energy is consistent with [
15], who found that financial globalization and renewable energy promoted the LFC in India. Similarly, our results are also consistent with [
16], who found financial globalization, renewable energy, and non-renewable energy stimulated the LFC, while economic growth dampened it in the case of Brazil. Furthermore, the negative effect of trade and financial development is in agreement with [
18], who showed that trade and financial development reduced the LFC.
Furthermore, the negative effects of financial development and trade suggest that, as India is opening up to trade and stimulating financial development policies, the level of the LCF is reducing, which is thereby increasing environmental degradation. Therefore, the negative effect of trade on the LCF is in consonant with [
45] who established that the operational behaviors of the MNCs through trade promotes environmental damage in the African countries. Meanwhile, the current result is contrary to [
7] while a mixed result is portrayed in [
46].
5. Conclusions and Policy Recommendations
The need to protect the environment from the consequences of increasing levels of carbon dioxide emissions and other significant climate change gives rise to growing calls and alarms to drastically curb CO2 emissions. To this extent, several attempts have been made to transition from fossil fuels to renewable energy consumption by governments of various countries within the frameworks of the United Nations Framework Convention on Climate Change (UNFCCC). In this study, we investigated whether the efficient utilization of conventional energy and renewable (clean) energy triggered environmental quality in the case of India. We chose India because the country is a large consumer of fossil fuels in the world and has had large emissions of CO2 over the years. To achieve our objective in this paper, a dynamic ARDL model was applied, which was efficient in the presence of complicated in-sample parameters that distort statistical inferences. The empirical results prove that the effect of both non-renewable energy efficiency and renewable energy utilization on the LCF in both the long run and short run was positive and statistically significant. This remarkably means that an increase in both non-clean energy efficiency and clean energy utilization can positively impact the LCF and, hence, improve environmental quality both in the long and short term in India. Conversely, in both long- and short-term results, we found that an increases in financial development and trade openness degraded environmental quality by reducing the degree of the LCF. Furthermore, the results show that, in both the long run and short run, a rise in GDP exerted a positive pressure on the LCF, while an increase in the GDP squared dampened the degree of LCF. This means that income had an inverted U-shaped impact on the LCF and, hence, failed to validate the LCC hypothesis in India in both the long and short terms.
Policy Recommendation
Based on the findings of this study, the following policy implications have been formulated to guide policymakers in achieving environmental sustainability. First, since the result prove that non-renewable energy efficiency promotes environmental sustainability by reducing the degree of the LCF, we suggest that policymakers should formulate policies that enhance the efficient utilization of non-renewable energy. Specifically, this can be achieved by embarking on awareness campaigns and educating households and firms, as well as industries, that are end-users of energy in the country. Second, to achieve a sustainable environment, the share of renewable energy in the energy mix has to increase significantly. To do this, we suggest that policymakers should implement some effective policies such as subsidies, tax holidays, tax credits, and a host of others to attract huge clean and renewable energy investments from both domestic and foreign investors. Such policies will help to increase the amount of renewable energy generation to meet the demand of households, firms, and industries. Third, since financial development reduces the LCF, which increases environmental degradation in India, our study suggests that appropriate technologies that reduce the level of energy consumption should be employed. Fourth, since trade openness has a negative impact on the LCF, there is a need to strengthen environmental regulations to combat the environmental effects of trade. In other words, a strong and stringent environmental policy, such as a carbon tax, resources tax, pollution tax, transport tax, etc., should be put in place as a country is opening its trade policies. Fifth, economic growth disturbs environmental sustainability, and, hence, we suggest that investments in green technologies to transition toward green growth be encouraged by policymakers. This means that economic activities should be shifted to more environmentally friendly ventures with the appropriate incentives. It is hoped that our study contributes to the body of knowledge on the role of non-renewable energy efficiency and renewable energy in achieving environmental sustainability in India.
Despite the policy relevance of the investigation, its associated weakness can be improved upon in future study. For instance, the findings of this study may not be suitable for developed and low-income developing countries because of the different economic characteristics. Therefore, we suggest that similar studies with the same methodology be conducted for other countries––both developed and developing countries. This will provide comprehensive findings on how non-renewable energy efficiency and renewable energy work towards achieving environmental sustainability in the world.