Testing the EKC hypothesis in India: moderating role of trade openness

The contemporary era has witnessed a tremendous industrial growth as a result of which energy consumption has also increased. This has significantly boosted the release of carbon emission at an alarming rate (Murshed et al., 2021). As such as, carbon emission surged by 63.6% over a thirty two year period from 1990–2022 (IEA, 2022) since, carbon dioxide is dominant in the class of the greenhouse gases (Hossain et al., 2023). Thereby its increase in the environment was witnessed as a threat to environmental sustainability and it forced policy makers to adopt policies for reducing environmental degradation. The early attempt towards this direction was made in 1992 by United Nations Framework Convention on Climate Change (UNFCC) in the form Kyoto protocol which is voluntary and unbinding agreement to combat environmental degradation by signatory countries (Zafar, Qin, & Zaidi, 2020). As part of its efforts, the Kyoto Protocol put forth proposals with a key emphasis on transforming energy policies to mitigate environmental impact (Pata, 2024). India also endorsed to the second agreement of Kyoto protocol (Doha amendment) to mitigate the carbon emission levels. With this, India became 80th country to ratify the second agreement of Kyoto protocol.

India is the fastest-growing emerging economy, and as per IMF statistics, it is the fifth largest in terms of nominal GDP and the third largest by purchasing power parity. It is making tremendous efforts in all directions, pushing further its capacity to become a developed economy. However, the goal of becoming a developed economy by 2047 (Press information bureau, 2024), will require huge investment in social and economic overheads, besides requiring high energy consumption, as the modern Indian economy is energy-driven and uses both renewable and non-renewable sources of energy for the production of real GDP (US Energy Information Administration, 2021). International trade is yet another factor that adds to the growth of the GDP of countries (Kumari et al., 2023), and India is no exception to this. Globally, the Indian economy has expanded tremendously, particularly after the reforms of the 1990s. India has positioned itself as a favourite export destination for many countries, and as such, India is also a major exporter of many goods to many other countries. This trade openness has, although proven to be a growth accelerator for India, however the environmental impact of trade openness within countries can vary, depending on alterations in trade patterns driven by shifts in global supply chains. Trade openness is also intrinsically related to resource consumption, particularly in manufacturing activities and transportation sector. Developing economies which are mostly involved in resource consumption for the production of goods and services mostly experience environmental degradation (Kirikkaleli, Güngör, & Adebayo, 2022). Pollution levels associated with trade can be mitigated through stringent environmental policy followed by these trading nations. Further the trading nations can develop physical infrastructure to cope up with the environmental issues arising on greater demand for trade related logistics.

In synchronization with above, the scientific community of researchers have tried to explore the impact of various macroeconomic factors on environmental quality. In line with this the seminal paper by Grossman and Krueger (1991) laid the foundation of EKC hypothesis by exhibiting an inverted U-shaped relationship between economic growth and environmental degradation. This inverted U-shaped relationship is mainly explained by three effects; the Scale effect, the Composition effect and the Technique effect. The scale effect is manifested by the increase in demand of goods and services which increases the need for industrial activities to sustain such demand. This increase in industrial production is sustained through fossil fuels consumption that are abundant, cheap and easy to transport but at same time detrimental to environment (Sarkodie & Strezov, 2018). In the second phase, composition effect operates which is either positive or negative, depending upon whether the country is involved in industrialization or has shifted toward service sector growth (Sarkodie & Strezov, 2018). Further the third phase of EKC hypothesis is also explained by technique effect which explains the notion that as income levels of an economy increases beyond certain threshold, a nation’s ability to allocate more resources to research and development, adopt cleaner and more efficient technologies, and enforce stricter environmental regulations increases, thereby resulting in improved environmental quality.

Expanding the scope of the EKC hypothesis, numerous macroeconomic factors are integrated into the analysis to assess the validity of the EKC. The studies like Acaroğlu, Kartal, and García Márquez (2023), Rej, Bandyopadhyay, Murshed, Mahmood, and Razzaq (2022), Rana and Sharma (2019) identified the existence of inverted U-shape EKC, some studies identified U-shaped EKC (Mehmood & Tariq, 2020; Dogan & Inglesi-Lotz, 2020) However, some studies found the N-shaped EKC (Hossain et al., 2023; Jahanger et al., 2023) and inverted N-shaped EKC (Farooq, Bhanja, Rather, & Dar, 2024; Bandyopadhyay and Rej, 2021). Contrarily few studies did not find the existence of EKC (Pata & Tanriover, 2023; Dogan, Ulucak, Kocak, & Isik, 2020; Hasanov, Mikayilov, Mukhtarov, & Suleymanov, 2019). Further, the literature on this phenomenon expanded on the lines of examining the moderating role of financial development (Udeagha & Breitenbach, 2023), urbanization (Kirikkaleli & Kalmaz, 2020), trade openness (Sharif, Uddin, & Alexiou, 2022), renewable and non-renewable energy consumption (Anwar, Siddique, Dogan, & Sharif, 2021) in the EKC hypothesis. These studies confirm the significant moderating role of these variables in the EKC hypothesis.

From the above-mentioned literature we find that the literature concerning the phenomenon of EKC in emerging nations is in infancy stage and also the results concerning the shape of EKC is inconclusive. We therefore re-examine the existence and shape of EKC in world’s fifth largest economy (IMF) India, where prior studies have found inconclusive results with regard to shape of EKC. For instance, Rej et al. (2022), Rana and Sharma (2019) found inverted U-shaped EKC); Villanthenkodath, Gupta, Saini, and Sahoo (2021) found U-shaped EKC. On the other hand, Hossain et al. (2023), Pal and Mitra (2017) found the N-shape EKC and studies like Farooq et al. (2024) and Bandyopadhyay and Rej (2021) supported the argument for the presence of inverted N-shaped EKC. Also, we build on the recent string of EKC literature that examined the impact of trade openness on environmental degradation (Sharif et al., 2022; Jun, Mahmood, & Zakaria, 2020). It is asserted that although trade openness increases GDP growth of nations it also tends to increase the carbon emission which can be detrimental to the growth of nations in the long run (Wang, Zhang, & Li, 2023; Nasir, Canh, & Le, 2021). Conversely, another set of studies (Pata, Alola, Erdogan, & Kartal, 2023a, Pata, Dam, & Kaya, 2023b; Khan, Weili, & Khan, 2022) found trade openness contributing to environmental quality vis-à-vis the positive externalities of trade openness. However, these positive externalities are different for nations on account of their income levels and economic structures. Therefore, nations face different environmental consequences on account of trade liberalization (Antweiler, Copeland, & Taylor, 2001). Except the study by Sharif et al. (2022) conducted on panel dataset, no other study has explored the moderating role of trade openness in EKC hypothesis. Therefore we complement this literature and make additional contribution by examining both the direct as well as moderating role of trade openness in shaping the EKC trajectory within Indian context. Further this study extends the EKC literature by examining the nonlinear dynamics of economic growth and environmental degradation for the investigation of more complex relationships, such as multiple turning points in the growth-environment nexus. Thus this study is first to incorporate moderating role of trade openness in cubical EKC in the Indian context, thereby providing realistic insights for countries like India which is undergoing transitions in trade policies from protectionism to liberalization (1991 reform era).

In the.

The remaining sections of the study are organized as follows. Section 2 “Literature review” provides comprehensive overview of studies on the employed variables. Section 3 presents the “Data sources, Variable definition, and methodology” and section 4 deals with “Empirical results”. Lastly section 5 sums up the whole findings with “Conclusion”.

Since the inception of work on EKC hypothesis by Grossman and Krueger in 1991, many other researchers have delved into exploring the validity and existence of relationship between economic growth and environmental degradation. The existing literature highlights two major perspectives. One body of research focuses on carbon emissions (CO₂) as a proxy for environmental degradation (Thio, Tan, Li, Salman, Long, Sun, & Zhu, 2022; Koc & Bulus, 2020; Bilgili, Nathaniel, Kuşkaya, & Kassouri, 2021). Another line of inquiry adopts a broader approach by incorporating the Ecological Footprint (EF) as a comprehensive measure of environmental impact, encompassing multiple dimensions of environmental degradation (Ulucak & Khan, 2020; Caglar, Mert, & Boluk, 2021; Mehmood, 2022). More recently, researchers have begun exploring environmental quality through the Load capacity curve (LCC) framework. The LCC framework evaluates the balance between environmental load and ecological capacity, offering deeper insights into sustainability (Pata & Karlilar Pata, 2024). A study by Chen, Rehman, Luo, and Ali (2022) supports the existence of inverted U-shaped EKC hypothesis in China. Further the studies like (Acaroğlu et al., 2023) in Turkey (Nazir, Nazir, Hashmi, & Ali, 2018), in Pakistan (Rana & Sharma, 2019), in India also substantiated the argument of inverted U-shaped EKC in literature. However, some studies challenge the conventional EKC framework by identifying a U-shaped Kuznets Curve, indicating a resurgence of environmental degradation after an initial decline (Mehmood & Tariq, 2020; Dogan & Inglesi-Lotz, 2020). In addition, alternative patterns of the EKC relationship have been documented, with studies finding evidence for N-shaped and inverted N-shaped environmental Kuznet curves. For instance (Hossain et al., 2023; Jahangir et al., 2023) identified an N-shaped EKC, while (Farooq et al., 2024; Bandyopadhyay and Rej, 2021) found an inverted N-shaped EKC. These variations indicate that the economic growth-environmental degradation nexus exhibits different patterns across countries and time periods, necessitating a broader perspective in EKC-related research.

Energy consumption has long been identified as key determinant of productive activities with negative environmental outcomes. Consequently the energy consumption and its production over the years has become contentious and extensively debated issue in the academic literature and policy making (Alsagr & Van Hemmen, 2021). In particular the deployment of nonrenewable energy is directly linked to carbon emissions and ecological footprint. Therefore to overcome this growing challenge it is asserted to focus towards clean sources of renewable energy that can promote the transition to long-term sustainability (Pata, 2024, Usman and Makhdum, 2021). Further the development of renewable energy infrastructure is considered as a crucial in solving environmental issues, as it plays key role in reducing reliance on fossil fuels and promoting a sustainable energy future (Pata et al., 2023a, b). In this context, a study by Samour and Pata (2022) investigated the impact of US interest rate fluctuations and oil price volatility on renewable energy adoption in Turkey. The study confirmed that US interest rate has significant spillover effect on the consumption of renewable energy via the channels of income and local interest rate. Besides Magazzino, Toma, Fusco, Valente, and Petrosillo (2022) explored the nexus between renewable energy economic growth and carbon emission from five Scandinavian countries, where the empirical results suggest that renewable energy deployment is potent policy instrument in curbing environmental pollution. Similarly, Acaroğlu et al. (2023) validated the EKC for Turkey by using carbon emissions and ecological footprint as dependent variables. Their findings further emphasize the crucial role of renewable energy in reducing environmental degradation, while highlighting the adverse impact of coal consumption on environmental sustainability.

Trade openness (TO) facilitate economies to grow faster vis-à-vis the enhanced volume of trade and income (Raihan, Tanchangya, Rahman, & Ridwan, 2024). However, this growth brings in certain environmental issues, for instance the expansion in trade results in the establishment of more industrial units which necessitated the use of greater energy resources and thereby causing an expansion in carbon dioxide emissions (Shahbaz, Nasreen, Ahmed, & Hammoudeh, 2017). Various studies found support to the fact that TO increases environment degradation (Sharif et al., 2022; Jun et al., 2020). However, when different stages of growth are taken into account, some studies have claimed that TO have positive impacts on the environment (Jun et al., 2020). For instance, in the initial stage of economic development, a country prioritizes industrialization and economic growth over environmental concerns, and trade openness exacerbates environmental degradation due to higher production and resource extraction to fulfill the growing demand of consumers and producers (Rafindadi & Usman, 2019). However, in the second stage as economic development progresses countries may experience a transition phase under composition and technique effect wherein they employ cleaner technology and adopt best sustainable practices thereby creating cleaner and better environmental conditions to live in (Fang, Huang, & Yang, 2018). Nevertheless, trade openness can mitigate these adverse consequences by fostering the transfer of innovation, research and development (R&D), environmentally sustainable technologies across nations and thereby offset the detrimental impacts associated with the scale effects. Against this backdrop, we expect trade openness to play a significant moderating role in the economic growth and environmental degradation nexus as found by Sharif et al. (2022).

While extensive literature has explored the EKC hypothesis in various contexts, the shape of the EKC remains inconclusive, highlighting several critical research gaps. Firstly, the moderating role of trade openness within the EKC framework remains unexplored, particularly in the Indian context. Given India’s rapid economic expansion and increasing trade liberalization, understanding how trade openness influences the environmental Kuznets curve is crucial for formulating sustainable economic policies. Secondly, recent studies emphasize the importance of analyzing the nonlinear dynamics of the EKC. However, limited research has examined these dynamic relationships within India’s growth-environment nexus, leaving a significant gap in understanding how different phases of development affect environmental sustainability.

For the purpose of this study annual data for India on carbon dioxide emissions (CO₂), Gross domestic product per capita (GDP); Primary energy consumption (EU) and Trade openness (TO) for the period 1965 to 2022 has been used in this study. Table 1 presents the description of variables and sources of the data.

Having understood that researchers have found diverse results with regard to the shape of EKC in India like the inverted U-shaped EKC (Rana & Sharma, 2019) N-shaped EKC (Hossain et al., 2023) inverted N-shaped EKC (Bandyopadhyay and Rej, 2021). As some studies in India have found N-shaped and inverted N-shaped EKC therefore to account for this possibility we incorporate three forms of GDP in our model, first GDP at level, then square form of GDP and also the cubic form of GDP. Further, we have also incorporated energy use and trade openness as independent variables in the model. Further, for this study we have used carbon dioxide emissions as measure of environmental degradation. The EKC hypothesis is stated in log-linear form because of their superiority over linear models in the efficiency of the estimated coefficients. Accordingly Eqs (1) and (2) are written as follows:

The sign and significance on $α1,α2and$ $α3$ in Eq. (1) determines the shape of EKC while sign and significance on $α1,α2and$ $α3$ in Eq. (2) determines the shape of EKC by accounting for moderating role of TO in the EKC.

We start the econometric analysis by testing the order of integration of the variables using unit roots tests like Dickey-Fuller Generalized Least Squares (DF-GLS) and Phillips Perron test (PP). Further, autoregressive distributive lag (ARDL) bounds test is used to establish the co-integration among the variables. Also the short run and long run relationship between the variables is established with the help of ARDL model. We also use fully modified ordinary least squares (FMOLS) and dynamic Ordinary Least Squares (DOLS) to check robustness of the results. Lastly, to test the causality among the variables we use Granger causality and Toda Yamamoto tests.

We apply ARDL bounds test co-integration method to ascertain long-run relationships. This technique is better because it produces consistent & efficient estimates on small sample and address endogeneity issues as well. Further, it provides estimates of both long-run and short-run parameters and it allows optimal lag associated with each variable. Following ARDL models are specified:

In Eqs (3) and (4) Δ is the first difference operator and $∅0$ represents the intercept. Further, $∅1$ to $∅6$ in Eq. (3) and $∅1$ to $∅5$ in Eq. (4) represents the long-run coefficients while as $β1j$ to $β6j$ in Eq. (3) and $β1j$ to $β5j$ in Eq. (4) represents the short-run coefficients. K1 to K6 in Eq. (3) and K1 to K5 in Eq. (4) represent the lag length of each variable selected through Akaike information criteria (AIC). $μt$ in Eqs (3) and (4) represent the noise term.

The above mentioned model runs under the null hypothesis, i.e. H0: ϕ1 = ϕ2 = ϕ3 = ϕ4 = ϕ5 = ϕ6 = 0 for Eq. (3) and H0: ϕ1 = ϕ2 = ϕ3 = ϕ4 = ϕ5 = 0 for Eq. (4).

If H₀ is rejected, then we use ARDL model to measure the short run & long run estimations. The empirical model with error correction term (ECT) is specified in Eq. (5) to (6).

In Eqs (5) to (6) $ω1$ represents the coefficient on error correction term which should be negative and statistically significant.

The casual link between the variables is estimated through Granger causality and Toda Yamamoto causality test. The Toda Yamamoto causality test is superior version of conventional Granger causality since it estimates causality even when there is no co-integration and variables are integrated of any order. This test also allows for structural breaks in the series.

Table 2 presents the descriptive statistics of all the variables of the study. The results highlight that for all the variables the difference between mean and median variables is minimal. Further, the smaller values pertaining to standard deviation in Table 2 also reveals that the data points of all the variables are close to the mean and show less variability over the period of time. The figures of Kurtosis reveal that all the variables present lower tails than normal distribution since the value of kurtosis for all the variables is less than 3. Lastly, the values of skewness for all the variables assert that all the variables are slightly skewed towards right, thereby indicating that data is evenly distributed around the mean ^[1].

In time series analysis the need for stationarity of variables is important to circumvent inefficient and biased estimates in subsequent econometric modeling. We have harnessed Dickey-Fuller Generalized Least Squares (DF-GLS) and Phillips-Perron (PP) test to achieve this objective. The test statistics reported in Table 3 for DF-GLS test indicate that the variables lnCO₂, lnGDP, lnTO, lnEU and interaction terms of ln(GDP×TO), ln (GDP²×TO), ln(GDP³×TO) and ln(EU×TO) exhibit unit root at levels and becomes stationary at first difference. Similarly, the result of PP test suggests that all variables possess unit roots at level and becomes stationary after first differencing. These results of unit root tests suggest that all the variables exhibit order of integration I(1) or I(0) and none of the variables are integrated of order I(2). Further the outcomes suggest that we can proceed with co-integration testing under ARDL methodology.

The examination of the long term relationship among the variables has been investigated by utilizing ARDL bounds test of co-integration. The results of the ARDL bound test for co-integration of variables are presented in Table 4. The F statistics surpasses the upper bound critical values at a 1% significance level, thereby confirming the presence of long term relationship between the variables incorporated in basic and interaction models. Therefore, we conclude that both the basic and interaction model is co-integrated.

The outcomes of ARDL model including CO₂ emission as dependent variable (basic and interaction model) are illustrated in Table 5. The results presented in Table 5 reveal that coefficients of GDP, GDP² and GDP³ are negative, positive and negative respectively. Also these coefficients on GDP, GDP² and GDP³ are significant at 1% level. This outcome leads to the conclusion that inverted N-shaped EKC hypothesis is evident in India in long run, both in basic and the interaction model. The findings of the negative effect of GDP on CO₂ emission is explained by scale effect which is very weak in the phase of pre-industrialization era due to slow growth of income leading to low demand of goods, resources and energy use, thus low pressure on environmental resources. However as the Indian economy further progresses, the scale effect gears its pace and enters into the industrialization era in which the demand for industrial goods rises which in turn leads to resource and mineral extraction thereby causing environment degradation to increase in second stage. In addition to this as economy gets further boosted by economic growth (GDP³), the environmental degradation is dampened, brought in by growing concern for environment that compels nations to over ride the scale effect by strengthening the structural shifts and technological progress. These outcomes of the long term inverted N-shaped EKC align with the findings of Bandyopadhyay and Rej (2021) and Farooq et al. (2024). Further, the short-run results presented in column (5) of Table 5 suggests the non-existence of EKC in India during short-run, since the short run coefficients of GDP, GDP² and GDP³ are insignificant. In addition to this Table 5 incorporates the interaction terms of TO with GDP and EU to unravel the moderating effect of trade openness in EKC hypothesis in India. The results reveal that inverted N-shaped EKC exists in long-run as found previously but again couldn’t validate the existence of EKC in short run as evident from column 7. This outcome, following the moderation of TO via GDP, emphasizes that India manages volume and composition of international trade in such a manner that allows composition and technique effect to override the negative effects of scale effect in the third stage of EKC.

Further, we also calculate turning points in basic and interaction model by following (Farooq & Dar, 2022). Firstly, in the basic model (without interaction) we find turnaround points equal to USD 607.84 and USD 1224.14. Contrarily, in the interaction model we find turnaround points equal to USD 121.51 and USD 992.27. Comparing the second turnaround point in the basic and interaction model it can be ascertained that the second turnaround point occurs early in the interaction model, implying that trade openness plays a significant moderating role in EKC hypothesis in India and thereby helps to improve the environment quality in India. These results are indicative of the fact that if India puts additional focus on policies related to external trade India will be able to harness positive externalities from the external trade that will improve the environmental quality. It is pertinent to mention that positive externalities from the external trade will arrive to India only when policymakers will come with policies that address the climate change on account of enhanced external trade and allow transfer of cleaner technologies to India.

The above results are also supported by the findings with regard to the TO variable. As we hold all the other explanatory variables constant a 1% increase in TO leads to 0.06 % decrease in CO₂ emissions (Table 5) in the long run within the basic model. This implies a positive contribution of Trade openness to the environmental quality in long run. We find similar results for the TO variable in the short run. These results hold that Trade openness is a contributing factor to environmental quality in India. These results are in line with Sinha and Shahbaz (2018) who found trade openness contributing to environmental quality in India, mainly due to experiencing of technological spillovers through trade. Further, we find that the coefficients on EU variable are positive and significant both in long-run and short-run and also in basic and interaction model. These results assert that energy consumption triggers the pollution levels in long run as well in short run. The results align with the study by Aydin and Turan (2020) within BRICS economies and Ahmad et al. (2023) in case of India. Finally, the estimated values and signs of ECT in Table 5 indicate that our model reaches its long run equilibrium at a significant level. The R² values of 0.999 (with interaction) and 0.999 (with interaction) reveal that our model affirms the high goodness of fit.

Further, we also conducted a comprehensive set of diagnostic tests, as presented in Table 5 and Figures 1 and 2, to check the credibility of our model and validate our findings. The outcomes of Breusch-Godfrey serial correlation LM test indicate the no serial auto correlation, confirming no systematic patterns in the residuals. In addition, the Breusch-Pagan-Godfrey test verifies that there is no heteroskadestacity, indicating constant variance of residuals across observations. Lastly, the Jarque-Bera results provide evidence in support of existence of normality in the model. Additionally, CUSUM & CUSMUSQ test results presented in Figures 1 and 2 reveal that the results of these tests fall within the critical bounds, hence confirm that the coefficients are stable in the long run.

In addition to ARDL analysis we employed two other econometric methodologies; FMOLS and DOLS to corroborate our findings from the ARDL analysis. The results of both the methodologies presented in Table 6 support the existence of inverted N-shaped EKC in India. The coefficient values of GDP, GDP² and GDP³ in basic model are negative, positive and negative at 1% significance level. Further the interaction variables ln (GDP×TO), ln (GDP²×TO), ln(GDP³×TO) also withholds the validity of inverted N-shaped EKC and all are being significant at 1% level. Further, with FMOLS and DOLS results we also find that the second turnaround point occurs early in the interaction model, implying that trade openness plays a significant moderating role in EKC hypothesis in India and thereby helps to improve the environment quality in India.

Table 7 presents the results of Granger Causality test and Toda Yamamoto causality test, to offer insights into the causal relationships between the variables. It is evident from Table 7 that there exists causality between CO₂ emission and TO. This result suggests that TO granger cause CO₂ emission and thus outlines the need for a cautious approach in adopting policies to mitigate emission levels. In addition to this interaction term of TO with GDP, GDP², GDP³ and EU is also found to granger cause CO₂ emissions. These findings are in line with the study of Pata (2019) where the results also suggest that unidirectional causality runs from per captia real income and trade openness to per captia carbon emission. Another approach of Toda Yamamoto causality also reveals that EU granger causes CO₂ emissions and this result aligns with the research by Dou, Zhao, Malik, and Dong (2021) who find similar outcomes in China-Japan-ROK Free Trade Agreement (FTA) countries. Also TO and interaction term of LnEU*TO causes CO₂ emissions to increase thereby revealing important policy insights for policy makers to remain careful while framing policies for trade and economic growth as both seem to interact and impact the environment.

This paper aimed to analyze the existence and shape of EKC in India over a period of 1965–2022. The findings of the study reveal detrimental influence of EU on CO₂ emissions and a significant positive impact of TO on environmental quality. Further, our results validate the existence of inverted N-shape EKC in both the basic and interaction model. In addition, Causality results of Granger test and Toda Yamamoto test revealed existence of causality between CO₂ emission and Trade openness, thereby emphasizing the need for a comprehensive and balanced approach to achieve economic growth via trade openness that is aligning with environmental sustainability. The empirical findings concerning the negative environmental impact attributed to EU suggests India’s significant reliance on non-renewable energy resources. To effectively address this issue, it is imperative for India to advocate for promotion of low carbon energy alternatives and adoption of renewable energy sources (the solar, wind and hydel power generation) in the energy mix at large scale. Further India can mitigate the challenges associated with the change in energy use by providing low interest loans to support investments in development of cleaner energy technologies.

The inverted N-shaped EKC obtained in this study implies that growth and carbon emission share important relation. While acknowledging the importance of economic growth for a country like India, it becomes imperative to address the associated environmental degradation while pursuing economic development. In light of this, government is urged to proactively introduce and implement interventionist policies in form of stringent environmental regulations to curb carbon emissions, establish carbon pricing mechanism to internalize environmental costs, foster public awareness and education, monitor and enforce green practices. Further, green financing initiatives can play a pivotal role in fostering environmental sustainable projects.

A key finding of this study is the significant moderating role of trade openness in shaping the EKC trajectory. The interaction model reveals that second turnaround point occurs early, implying that India’s trade policies facilitate a transition towards cleaner production. Moreover, the negative long-run coefficient of trade openness on carbon emissions underscores its positive contribution to environmental quality, supporting the Porter Hypothesis, which argues that trade induced technological advancements can drive sustainability. This outcome suggests that India’s economy is following structural transformation as evident from its growing emphasis on IT services and high tech-manufacturing. Also India has shifted its focus towards technology transfer and clean energy adoption, a prime example of which is National Solar Mission, where India imports seventy five percent of its photovoltaic cells and modules from China to accelerate its renewable energy transition. Hence the policy implication of this result suggests that India should welcome trade openness and reorient trade policies from time to time to sustain the benefits that it is currently harnessing. Further India can focus to collaborate with technology providers, research institutions in order to stay informed about emerging technologies to address the arising technological obsolescence from time to time.

The supplementary material for this article can be found online.