The impact of economic growth, trade openness and manufacturing on CO2 emissions in India: an autoregressive distributive lag (ARDL) bounds test approach

Purpose – The purpose of this study is to examine the impact of economic growth, trade openness and manufacturing on CO2 emissions in India. Design/methodology/approach – The studyemployed autoregressive distributivelag (ARDL) bounds test approachand usesCO2emissions,trade,manufacturingandGDPper capitatoexamine therelationship using an annual time series data from World Development Indicators during 1971 to 2016. Findings – Resultsdepictthatthereexistsalong-runrelationshipbetweenCO2emissionsandothervariables. Trade openness significantly reduces CO2 emissions, whereas manufacturing and GDP have a significant and positive impact on CO2 in the long run. Research limitations/implications – The findings of the study contribute to the body of knowledge by providing new evidence on the relationship between developmental metrics and the environment. These findings are critical for policymakers and regulatory bodies to focus on economic development without jeopardizing environmental degradation. Practical implications – In order to keep its commitment to sustainability, India needs to develop policies that encourage cleaner production methods and establishment of non-polluting industries. Simultaneously, it must disincentivize industries that emit CO2 by policy frameworks such as carbon taxes, pollution taxes or green taxes. Originality/value – None of studies examine at how these environmental factors interact in India. Kilavuz and Dogan (2020) used the same variables, but their scope was limited to Turkey. As a result, the study is the first to examine this relationship for India, contributing to the body of knowledge on economic growth, manufacturing, trade openness and environmental concerns.


Introduction
The genesis of globalization was based on the idea of free trade amongst countries as promulgated by Adam Smith in his seminal work -The Wealth of Nations. Historically, production and trade have been our bedrock of economic growth spanning across ancient civilizations to the contemporary world. Climate change as an objective reality has dawned upon our collective conscience in the late 19th century. It is lately that governments have realized the significance of environmental conservation and its perils in case of any further degradation, thus leading to policy formulation around sustainable development (Chowdhury et al., 2021;Dale et al., 2020;Xu et al., 2021). This realization has led to several multilateral initiatives being undertaken by the conscience-stricken international community through the way of globally inclusive platforms such as the Stockholm Conference, Montreal Protocol, Rio Convention, Kyoto Protocol, the recent Paris Agreement, etc. (Wang and Wiser, 2002;Jacquet and Jamieson, 2016).
Modern day consumerism and consumption have imparted substantial momentum to economic growth, which has been largely responsible for the financial upliftment of many countries. Nevertheless, climate change has been a negative externality of this relentless human pursuit. An ample of studies indicates that economic growth often contributes to environmental degradation (Bekun et al., 2019;Song, 2021). The environmental Kuznets curve (EKC) hypothesis proposed by Grossman and Krueger (1995) delineates that during the initial stages of a country's economic development, its environmental degradation increases but gradually subsides at the turning point after attaining a certain level of industrialization. In the context of developing countries, it becomes imperative for policymakers to optimize the trade-off between economic growth and ecological conservation.
Globalization has been an important proponent of trade openness for emerging economies. From a theoretical sense, trade openness has three far-reaching implications on pollution viz. scale effect, composition effect and technology effect (Antweiler et al., 2001). The scale effect implies that an increase in trade leads to an increase in energy consumption, which in turn causes increased environmental degradation. The composition effect revolves around the premise that comparative advantages inherent to a country decide its production composition for the prevalence of either labor or capital-intensive industries. As per the factor endowment hypothesis (FEH), the latter is more polluting, in nature, than the former. Third, the technology effect expounds that increased trade causes greater facilitation of technology transfer amongst trading partners, which results in the adoption of cleaner and more efficient practices.
Developing economies like India tend to have a major share of non-renewable energy sources as a part of their energy consumption mix. Moreover, being amongst the most densely populated country, the scope for raising ecological footprint increases . The increased energy demand for powering industrial activity of a developing nation leads to elevated pollution levels. This is in concurrence with several studies that have investigated the nexus between manufacturing and CO2 emissions (Canh et al., 2019;Rahman and Kashem, 2017;Zafar et al., 2020).
Our research aims at exploring the interplay between economic growth, trade openness and degree of industrialization with carbon emissions in the context of India. We have deployed the autoregressive distributive lag (ARDL) bounds test approach as the statistical methodology for ascertaining empirical relationships amongst the variables under study. There exists a dearth of studies about emerging economies, and our study on one of the largest emerging economies, India, shall prove to be an important source of knowledge for understanding such implications.
2. Literature review EKC hypothesis is the most popular theory that links the effect of economic growth on the environment; it was the first empirical study of Grossman and Krueger (1995). The theory asserts that there is an inverted relationship between pollution and economic growth. As the economy grows beyond a certain level, it tries to achieve technological advances, which will lead to control the pollution. Shahbaz and Sinha (2019) and Purcel (2020)  Trade openness indicates the degree to which an economy is open to trade across the world economies. It helps countries to increase exports that intend to increase domestic production, by increasing the scale of industries, which leads to increased pollution (Jun et al., 2020). Concerning the relationship between pollution and trade openness, there seems to be no consensus. Across the several countries studied, pollution levels have increased with increased trade openness (Al-Mulali et al., 2016;Jun et al., 2020;Lin, 2017;Wen and Dai, 2020). However, few believe that the increase in trade openness reduces pollution (Ghazouani et al., 2020;Kohler, 2013;Shahbaz et al., 2017). While few observe differences in the relationship based on the economic income. For example, Wang and Zhang (2021) observed that the relationship between pollution and trade openness is positive for low-income countries, while it is negative for high-and middle-income countries. Greater trade openness and FDI are expected to increase emissions in developing economies with fewer environmental regulations (Sajeev and Kaur, 2020). Jayanthakumaran et al. (2012) also observed a negative relationship in China and a positive relationship in the case of India.
Manufacturing positively contributes toward pollution levels in industrialized economies (Rauf et al., 2018). The primary reason for pollution from manufacturing is that they heavily rely on fossil fuel energy. Industrial production and energy consumption have significant positive impacts on carbon emissions (Rahman and Kashem, 2017). To achieve higher economic growth, manufacturing activities require higher energy consumption, and in absence of abundant renewable energy sources, these activities rely more on non-renewable sources. Energy consumption and economic growth have a positive and statistically significant association (Esen and Bayrak, 2017). Hence, the use of non-renewable sources leads to environmental degradation. Industrial production is the driving force of CO2 emissions (Hocaoglu and Karanfil, 2011). Zafar et al. (2020) examined the role of industrialization in environmental pollution for 46 Asian countries. They observed a significant and positive impact of industrialization on carbon emissions. Most of the researchers have observed a positive relationship between manufacturing activities and pollution (Banerjee and Rahman, 2012;Canh et al., 2019;Lin et al., 2014).
A large number of studies have examined the linkages between economic growth and environment after the most popular EKC theory Grossman and Krueger (1995). Most of the studies found a positive relationship between economic growth and pollution (Alshehry and Belloumi, 2015;Menyah and Wolde-Rufael, 2010;Pao and Tsai, 2011;Park and Hong, 2013;Song, 2021). Rajpurohit and Sharma (2021) observed that moderate economic growth and moderate financial development increase carbon emissions, whereas exponential growth and financial development decrease carbon emissions. Dey and Tareque (2020) observed a negative impact of external debt on GDP growth. Shabbir et al. (2020) examined the role of natural resources in economic growth for Pakistan during 1972-2016. They examined the relationship of population density, water renewable resources, CO2 and deforestation on the GDP. They observed a negative and significant relationship of all the variables with GDP. Rasool et al. (2020) examined the curvilinear relationship between environmental pollution and economic growth in India during 1971-2014. They identified that energy consumption, economic growth and financial development have negative effects on the environment.
India is one of the fastest-growing economies of the world and it is also the third-largest carbon emitter. With the ambitions of high economic and industrial growth, the emissions are going to increase substantially because of the reliance on carbon emissive fuels. Due to this, along with India, the world is also vulnerable to climate change and faces threats from rising sea levels, floods, droughts and health hazards. Therefore, our research aims to find the cointegration relationship of carbon emissions in the Indian context.

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Our research is the most recent and is closely linked to the literature on environmental variables that are responsible in India. None of these studies investigates how these environmental factors interact in India. Kılavuz and Do gan (2020) used the same variables, but their reach was limited to Turkey. As a result, our research is the first to look into this relationship for India, adding to the body of knowledge on economic development, manufacturing, trade openness and environmental issues. Table 1 summarized the empirical evidence on the relationship of the variables with CO2 emissions.

Data and variables
The current research examines the relationship between carbon dioxide emissions, trade openness, manufacturing and economic growth. The variables in this study are time series data from 1971 to 2016, with data sources and descriptions mentioned in Table 2.

Model
For the study, we employed the ARDL bounds test approach by Pesaran et al. (2001). The ECM representation of ARDL is formulated with reference to CO2 in Eq (2) in order to examine cointegration among the variables defined in Eq (1): Here Δ represents the change; β and ∅ represent in long run and short run, respectively; ε t represents the white noise terms.
The null hypothesis of no cointegration among the variables is rejected if the F-statistics obtained is above the upper bound values, and the null hypothesis is not rejected if the F-statistics is below the lower bound critical values. The presence of a long-run relationship is considered inconclusive if the F-statistics is between the upper and lower bound values.

Procedure
As a first step, the stationarity of the data is tested using the unit root tests to ensure that none of the variables are of order I(2). Later, the ARDL model is performed on the selected variables with AIC as lag length criteria. The bounds test is performed to check the existence of a longterm relationship between the variables. After confirming the cointegration relationship amongst the variables, the long-run and short-run elasticities of the variables are estimated.
Finally, model stability is tested by performing various diagnostic and stability tests.

Results
The primary condition of ARDL states that the series should not be integrated at the order I(2) to prevent spurious results. As a result, the null hypothesis of unit roots is validated using two unit root tests namely, ADF and PP. We have assessed the order of integration at a 1% significance level from both tests. The results of unit root tests are shown in Table 3

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The results of the unit root tests in Table 3 show that none of the variables are stationary at the 1% level, but they become stationary at their first difference. This ensures that none of the variables are integrated at I(2).
The bounds test was used to evaluate the long-run relationship between the variables, as shown in Table 4. The null hypothesis states that there is no cointegration relationship between the variables. If the calculated F-statistic is greater than critical [I(1)], there exists a cointegration relationship in the model. If the F-statistic is less than the critical value [I(0)], there is no cointegration. However, if the F-statistic falls between the critical limits, it is concluded as inconclusive.
The results reveal that the F-statistic is greater than the critical values of the upper bound at a 1% significance level. Therefore, we can reject the null hypothesis of no long-run relationship between CO2 emissions and other variables. This concludes that there exists a cointegration among CO2 emissions, trade openness, manufacturing and economic growth. Once the cointegration relationship is established, we then estimated the long-run coefficient of the model. Table 5 presents the long-run elasticity of CO2 emissions regarding the independent variables. Trade openness has a significant negative impact on CO2 emissions, whereas manufacturing and GDP have a significant and positive impact on CO2 emissions in the long run.  Sajeev and Kaur (2020) and Sharma and Kautish (2020).
The coefficient of À1.194 for trade indicates that a 1% increase in trade results in a decrease of 1.19% in CO2. With increased global trades, there will be a faster and larger inflow of information, resources and cleaner technology, which will aid in the reduction of environmental burdens. Our results are consistent with that of Ghazouani et al. (2020), Kohler (2013) and Shahbaz et al. (2017 Table 3.

Results of unit root tests
The impact of growth metrics on CO2 emissions Manufacturing has a significant positive long-run relationship with CO2. The coefficient of manufacturing indicates that a 1% increase in manufacturing leads to a 6% increase in CO2 emissions. This means that manufacturing activities positively lead to CO2 emissions as the industrial sector is highly reliant on energy input generated from carbon emitting fossil fuels. Our study is consistent with Hocaoglu and Karanfil (2011), Banerjee and Rahman (2012), Lin et al. (2014), Rahman and Kashem (2017), Rauf et al. (2018), Canh et al. (2019 and a more recent study of Zafar et al. (2020).
In India, economic growth has a significant and positive impact on CO2 emissions. Every 1% increase in GDP leads to a 2.56% increase in CO2 emissions. As India is an emerging country, its growing economic activity necessitates a higher energy demand that is more reliant on fossil fuels, resulting in a rise in CO2 emissions. This result is consistent with Menyah and Wolde-Rufael (2010), Pao andTsai (2011), Park andHong (2013), Alshehry and Belloumi (2015), Rasool et al. (2020), Rajpurohit and Sharma (2021) and Song (2021).
Once the long-run relationship is established, it is now required to check the adjustments (error correction model) toward the long-run equilibrium relationship. Table 6 displays the short-run relationship.
The change in manufacturing is having a significant and positive impact on CO2 emissions. In the short run, increased trade openness lowers CO2 emission levels. This negative relationship may mean that trade is an environmental quality improver. In the short run, for every 1% increase in trade openness, there will be a 0.11% decrease in CO2 emissions. On the other hand, an increase in manufacturing output by 1% leads to an increase of 0.26% in CO2 emissions. As expected, the error correction term ECM (À1) is negative and significant, which indicates how quickly the dependent variable adjusts to the long-run equilibrium point. Any disequilibrium in the dependent variable is returned to equilibrium in one period at a speed of 8.80%. Different diagnostic measures, such as normality, heteroscedasticity and serial correlation, are used to determine the model's stability. The Jarque-Bera test is used to verify the model's normality assumption. The null hypothesis of normally distributed residuals is accepted. The null hypothesis of homoscedastic disturbance terms is accepted using the Breusch-Pagan test. The Ramsey RESET test indicates that the  CO2 model is a good fit. Finally, to validate the estimated ARDL model stability of the longrun coefficients with the short-run dynamics, we used the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares (CUSUMSQ). Figure 1 plots the CUSUM and CUSUMSQ.

Discussion
The economic growth in India has been largely driven by consumption, urbanization and industrialization. The energy requirements to supplement this growing demand has been largely met through non-renewable energy sources. Hence, economic growth and CO2 emissions in India have grown in tandem. Improving the existing skewed energy mix of India and increasing the share of cleaner alternative energy sources must take precedence in any policy planning considerations.
The negative relationship between trade openness and carbon emissions can be justified by technology effects. As trade opens up a gateway for spillover effects, cleaner and efficient technological practices become mainstream across partner nations. Hence, India should encourage preferential trade policies, with a particular emphasis on technological value addition, which can be cultivated through reciprocal trade liberalization and the removal of trade barriers. Ultimately, trade openness persuades a virtuous cycle that benefits the economy by increasing employment opportunities, softening capital flows and fostering a competitive environment.
Industrialization in India has witnessed enormous momentum in recent decades. Manufacturing has been a substantial contributor to the GDP of India. Nonetheless, it has also been a precursor for rising CO2 emissions. To uphold its commitment toward environmental conservation, India must formulate policies around incentivizing cleaner methods of production or setting up the so-called non-polluting industries. Parallelly, it must disincentivize CO2 emissions by manufacturing industries through policy mechanisms such as carbon tax, pollution tax or green tax. 2.787 (0.106) Note(s): *, ** indicates 1% and 5% statistical significance respectively Source(s): Authors' calculations from Eviews Table 6. Short-run coefficient estimates The impact of growth metrics on CO2 emissions The scope of the study can be extended by considering urbanization and renewable energy generation, which are considered important proxies for economic growth and reduction of CO2 emissions, respectively. Fossil fuels are the primary source of energy for most of the emerging economies; hence, the study can also be extended by considering this relationship for a panel of emerging economies.

Conclusions
The present study explores the long-run and short-run relationship between economic growth, trade openness and manufacturing with CO2 emissions in the Indian context. The ARDL bound test approach is employed to establish this relationship. For this study, annual time series data from 1971 to 2016 were used. Empirical findings reveal that the variables under research have a long-term relationship.
The results of the long-run relationship are congruent with the empirical research. Trade openness has a considerable negative association with CO2 emissions. With expanded global trades, there will be a faster and larger inflow of information, resources and cleaner technologies, which will contribute to the reduction of environmental burdens. Manufacturing and GDP have a significant and positive relationship with CO2 emissions in the long run. As the industrial sector is heavily reliant on energy input generated from carbon emitting fossil fuels, this means that manufacturing operations have a positive impact on CO2 emissions. Because India is a developing country, its increasing economic activity needs a higher energy demand, which is more reliant on fossil fuels, increasing CO2 emissions. In order to keep its commitment to sustainability, India needs to implement regulations that encourage cleaner manufacturing methods and the establishment of non-polluting industries. Simultaneously, it must disincentivize industries that emit CO2 by policy frameworks such as carbon taxes, pollution taxes or green taxes.