Environmental sustainability with the role of green innovation and economic growth in India with bootstrap ARDL approach

This paper applies a novel Bootstrap Autoregressive Distributed Lag (BARDL) approach to investigate the relationship between green innovation (GI), economic growth (GDP), drama and film (D&F) industry, and environmental sustainability in India for the 1995 to 2020 period. The data has been checked for its stationarity by applying the Zivot and Andrews (ZA) unit root test, and the cointegration test results suggest a long-run equilibrium relationship between the variables. The empirical finding of long-run estimates reveals that 1% augments of GI, GDP, and D&F industry increase CO₂ emissions by −0.079, 0.566%, and 0.143%, respectively. Furthermore, the main results indicate that GDP and the D&F industry have statistically significant positive effects on CO₂ emissions, and GI has statistically significant negative effects on CO₂ emissions. The GI leads to lower environmental damage by reducing carbon emissions. Regarding causal relationships, bidirectional causality is found between D&F and CO₂ emissions, GI and CO₂ emissions. In addition, a unidirectional causality is also revealed from GDP to CO₂ emissions. Based on the finding of this study, policy implications are suggested for India.

Introduction

Developing countries are facing the issue of environmental deterioration. Environmental deterioration is harmed by using energy consumption for economic growth, but the consequences of environmental deterioration cannot be ignored. Indian economy is growing rapidly same trend in the future. India’s economy depends on industrial production, agriculture, foreign investment, and film industries, and agriculture is the important dominant sector of the nation, but due to repaid growth of the industrial sector in India, the agricultural land is cutting. Besides this, the rapid increase in population causes degradation; India is the top ranked country in Asian nations that faces the issue of deforestation. An increase in financial development and the industrial sector’s use of energy for growth causes ecological deterioration. India is facing high energy demand for which traditional energy sources are used to meet its fast-rising demand for energy. (Khan M. K. et al., 2019, Khan F et al., 2019) started that traditional energy sources emit CO₂ emissions, contributing to the deterioration of ecological quality. (Malik, 2021) stated that environmental deterioration affects the environment and health of human beings in India. (Shahbaz et al., 2013a, Shahbaz et al., 2013b) Stated that fossil fuels use for daily life, the expulsion of massive smoke from factories and the consumption of wood as an energy resource to boost carbon dioxide. CO₂ emissions have a devastating effect on the economy and other sectors such as agriculture and forestry.

Due to policies that increase income, developing countries are expanding their economies and urbanizing their populations, which could endanger the environment. Energy use and CO₂ emissions could rise due to growing urbanization in developing nations (He et al., 2017). The boosted energy demand in South Asian nations and their massive dependence on fossil fuels has created an alarming situation for ecological qualities, GHG emissions and pollution (James et al., 2013). Moreover, Countries have used various energy policies due to the aforementioned international agreements to reduce CO2 emissions. Governments impose penalties for excessive consumption or credits and subsidies for modernization and the use of clean energy to encourage adherence to the policies. Aiming for a net-zero energy footprint and raising public awareness of new technologies, these policies can succeed if they are set at an appropriate level. However, developing nations are also a source of growing concern, and it is crucial to develop environmental policies that support economic growth and are consistent with the goals set. Finding long-term causal effects on particular policies is essential and relevant when implementing these new global programs.

The potential benefits of technology innovation have contributed significantly to its dramatic growth in recent years (Najmi et al., 2020; Shu et al., 2022). For instance, technological innovation aids in reducing environmental pollution and improving resource use quality. It also increases green technologies’ cost-effectiveness (Ielasi et al., 2018; Manigandan et al., 2021). The adoption of clean and green technologies is likely to be hampered by certain hindrances, both locally and globally, although there is agreement among the nations regarding their progress towards sustainable growth (Biresselioglu et al., 2018; Su et al., 2021). Regularly, eco-friendly technology is considered relatively desirable, particularly for emerging nations. Nevertheless, such technologies have the potential to extend into the future as well (Yu et al., 2022). Furthermore, a company is more likely to invest in such technologies, particularly if they decrease misappropriation and ineffectiveness by enhancing the productivity of the company (Guo et al., 2022).

There are two ways in which the media, specifically the drama and film industries (D&F), can contribute to the drive for pollution reduction, environmental preservation, and, most importantly, global warming mitigation. The public can first convey the importance of environmental protection by creating and developing relevant content and producing investigative reports, short films, and awareness campaigns (Chen and Valdmanis, 2019). The second aspect could be the industry’s role as a consumer of energy and resources, with each scene having a different demand and each idea having a different production setup. As a result, this industry will likely contribute to the rise in pollution and environmental degradation (Brereton, 2014). It is noted that because of being non-ecologically friendly, this industry has always been criticized for its neglect (Lopera-Mármol and Jiménez-Morales, 2021). Furthermore, despite their ability to draw and shape the societal narrative, the television sector in general, and drama and film, have been reported as the main contributors to CO₂ emissions, operations of socially unequal, pollutions-releasing, and endeavors (Brereton, 2014; Forster et al., 2020). Based on the findings of the 2019 report, the industry involved in the audio and video sector is said to generate over two million tons of carbon per year (Salami et al., 2019).

India is one of the most populous countries whose economy primarily relies on manufacturing enterprises which are the major causes of pollution spreading into the air, adversely affecting the natural resources and health of living beings. The manufacturing organizations taking care of the environmental and social concerns of the general public have sustainable business development. On the other hand, the countries which are only interested in profits and do not care for environmental quality and social well-being have a backward position in the market. There are multiple contributions that the current study attempts to make. Firstly, the present study explores the effects of the drama and film industry (D&F) and economic growth (GDP) and green innovations together on the environmental performance (EP) in India, which possess the potential both in terms of D&F and GDP but also emits pollution significantly. Secondly, to the best of the author’s knowledge, this is the first study to analyze the green innovations, economic growth, and drama and film industry indices as the independent variable in this country. Thirdly, the current study is statistically sound, based on the employment of the unique and recent technique suggested by McNown et al. (2018) named “Bootstrap Autoregressive Distributed Lag” which is far robust and rigorous in terms of predictability, especially when the dataset possesses weak statistical power or low sample size.

The structure of this article is as follows: the second section is a literature review. The third section provides data and methods. Empirical results and discussion have been discussed in the fourth section. Finally, the last section provides the conclusion and policy recommendation.

Literature review

Green tech innovation effect on CO₂ emissions

Growing international concern about green development has changed several countries from simply working to achieve growth to developing a sustainable economic model to protect environmental health (Lorek and Spangenberg, 2014). Without the former, true green evolution would not be possible (Yue et al., 2021). Technology and scientific innovations are important to green growth (Song et al., 2019), and researchers have confirmed this assertion in several parts of the world (e.g., (Padilla-Pérez and Gaudin, 2014) in the central US (Grover, 2013), in India).

In recent years, green technology innovations have become a global tool for reducing CO2 emissions (Weina et al., 2016; Zhang et al., 2016; Nikzad and Sedigh, 2017; Murshed and Alam, 2021). With rising anxiety over climate change, energy technology innovation was the focus of several researchers. Even though many studies unpack the story of complex networks among the previous and energy consumption, the effect the technological innovation exerts on carbon footprint has not yet been extensively debated. Applying the spatial economic model (Ahmad et al., 2018, Shao et al., 2021), technological innovations based on renewable materials have reduced carbon emissions in China, while traditional energy technology restructuring has failed to reduce environmental damage. Furthermore, the author observed that technological innovation’s impacts on toxic emissions were transregional. That means that changes in the geographical region that enjoys energies from renewables will enable carbon mitigation in the surrounding regions (Mohsin et al., 2021).

Moreover, some previous studies argued that the impacts of green innovation on CO₂ emissions vary due to different factors, income, time, and other things (Jaffe et al., 2002). According to several reports (Goh et al., 2017; Wang et al., 2019; Adedoyin et al., 2020), the former efficiently addresses the trade exchanges among economic development and environmental protection because it improves energy efficiency, which is necessary for carbon reduction efforts. Thus, not all agree with this assertion. For instance, (Salman et al., 2019) found no substantial role of energy technology patents in reducing ambient carbon emission in Thailand; green energy technology patents drive down air pollution only in Eastern Thailand. Similarly, (Wang et al., 2021) revealed that technological innovations improve ecological performance in China but have no crucial effect on diluting CO₂ emissions.

Regarding the effect of green innovation on environmental damage, scientists have different views. Some researchers believe that green innovation can decrease CO₂ emissions by using alternative clean energy, carbon capture and storage technologies, and improvements in energy efficiency. For example, Chine’s datasets from thirty provinces from 2005–2010 (Feng and Yuan, 1984) proposed that green innovation and research and development (R&D) investment have significant technical spillover effects, which can significantly encourage reducing carbon emissions. Also, scrutinizing the panel dataset of OECD member nations from 2001 to 2015 (Ganda, 2019) suggested that patent output can significantly impact CO₂ emissions. Although innovations and technological investment have different effects on carbon emissions in various nations, there is still evidence suggesting that technological innovation capacity improvements can effectively contribute to CO₂ emission reduction. (Meirun et al., 2021) the BARDL approach explores the dynamics of green innovations in carbon dioxide emissions in the Singapore economy. In an economy like Singapore, there is also intense population density growth, so there is a need to explore the trend of green innovation and CO₂ emissions. The study results from 1991–2017 by BARDL indicate the negative effect of technological innovations on carbon emission (Wu et al., 2020). Explore the role of energy technology and innovations in reducing CO₂ emissions from a spatial perspective. Numerous findings have examined the relationship between energy tech innovations and energy consumption, while they also point out that the association between green discovery and CO₂ emissions has not received enough attention. The research results show that green tech innovations’ play a significant role in reducing carbon emissions.

Drama and film industry effect on CO₂ emissions

The drama and film industry is one of the strategic locomotive industrial sectors of the economy, which contributes significantly to society’s moral development and economy (İncekara et al., 2013). Film production involves various steps, from film concept and production to marketing and distribution. Moreover, this industry needs high energy resources and enough time to run its operations (Meilani, 2021). Climate change is a major issue and is highly dependent on fossil fuel consumption, mainly from greenhouse gases accelerating global warming caused by industry (Collins et al., 2013). During the production process of films and plays, substantial amounts of waste are generated, and the high use of energy resources causes air and noise pollution. The drama industry’s ecological footprint consists of raw material in sets, plastic bottles, photocopies, lighting equipment, other waste materials and gasoline.

Furthermore, with the enormous investments made in France, Europe, and the United States, the drama and film industry generates large amounts of greenhouse gas emissions (Corbett, 2006). Moreover, the British Academy of Television and Film Art aims to decrease CO₂ emissions through sustainable film production by 2050. The existing literature provides few studies to assess the drama and film industry’s effect on environmental sustainability. (Ahmed et al., 2020, Sun et al., 2022, Victory, 2014). Conducted qualitative studies to emphasize the significance of environmentally friendly practices for the film industry to lessen its environmental impact. The studies have emphasized the role of the production manager as an environmental manager responsible for taking several sustainable initiatives during production and implementing environmentally-friendly policies.

(Malmodin and Lundén, 2018) explained the carbon footprints of the media and entertainment industry with previously predicted footprint size. By adopting the use of energy and life cycle greenhouse gas emissions dataset, the report found that the carbon footprints of the media and entertainment industry decreased in size with time due to the adoption of advanced information technology worldwide. (Tsaurai, 2019, Murshed et al., 2022, Najmi et al., 2021) investigated communication and information technology as supporting variables for the entertainment and media industry. The report found that the development of communication and information technology is helping the entertainment and media industry to reduce barriers to achieving a sustainable environment in developing nations.

A recent study by (Hu et al., 2021) has discussed the importance of the drama and film industry’s contribution to Chinese financial development and the impact of the COVID-19 pandemic on the industry. During the slow economic activities in COVID-19, the drama and film stock returns also faced a significant drop in the long run, and low investment was observed. In contrast, digital technology has increased drama, film stock returns, and viewership in the short run. However, in the face of financial uncertainty, China’s drama and film industry have a less ecological effect from an environmental perspective. Moreover, (Meilani, 2021) examined the role of carbon footprint in the drama and film industry and found that the drama and film industry plays a major role in corban emissions. However, by gathering data from different sustainable’ production reports and literature, the study also examined whether environmentally friendly movements in move production, like the movement of the green output, play an important role in decreasing CO₂ emissions.

Economic growth effect on CO₂ emissions

Since (Kraft & Kraft, 1978) examined the relationship between economic growth and CO₂ emissions for the first time, this topic has gradually caught the attention of many scholars (Wu, 2012; Herrerias et al., 2013; Dong et al., 2016; Zhou et al., 2017; Román-Collado et al., 2018). (Du and Lin, 2015) studied changes in the degree of dependence of Chinese economic growth on fossil fuels from 1985 to 2005 and discovered that during this period, Chinese economic growth has always remained strongly dependent on fossil fuels. Since fossil-fuels is Chines major energy resource, it is of immense practical importance to investigate China’s GDP growth dependence on coal-related CO2 emissions.

In the past few years, (Pao & Tsai, 2010) investigated the link between economic growth, energy use and environmental sustainability. Most scholars have been done for developed countries such as the candidate nations and European members (Kasman and Duman, 2015). Early scholars on the same subject have generally concluded that energy use and economic growth cause carbon emissions. (Jiang et al., 2019; Alam et al., 2022) Employ panel Granger causality test to examine the linkage between economic development and carbon dioxide emissions to investigate the effects of institutional factors as a significant contributor to rising carbon emissions by employing the U.S. sectoral-level data. Their research found a Granger causality relationship between the US economy and CO₂ emissions growth.

Scholars on the effects of urbanization and economic growth on CO2 emissions have been conducted over several years. A firstly notable study detected imbalances between rural and urban CO₂ emissions in the 1985s (Heil and Wodon, 1997). After that, a scholar on the relationship between economic development and carbon emission gradually increased (Stocchero et al., 2017). A more current study applying the ARDL model and cointegration approaches attempt to explain the relationship between economic growth and carbon emission. These findings help an EKC (inverted U-shaped curve) linkage between economic growth and CO₂ emissions for eastern and central European countries (Atici, 2009), Romania (Shahbaz et al., 2013a), Malaysia (Saboori and Sulaiman, 2013), Pakistan (Nasir and Ur Rehman, 2011); and India (Tiwari, et, al., 2013; Pachiyappan et, al., 2021); when economic growth and social reaches a certain stage, CO₂ emissions gradually decrease (Sheldon, 2017; Alam, 2022) indicating that there is a decoupling among economic growth and CO₂. emissions. However, some studies do not support an EKC association for Turkey (Ozturk and Acaravci, 2010), and the key factors affecting the decoupling association between economic growth and CO₂ emissions are economic scale, energy use intensity, industrial structure, CO₂ emissions intensity, and land economic output (Alam et al., 2016; Ganda, 2019). Granger causality indicates that economic performance and carbon emissions have a bidirectional causality connection (Chandia et al., 2018). The summary of the literature review is listed in Table 1. Based on this literature review, we conclude that most studies use ARDL and nonlinear ARDL models; for example, Pachiyappan et al. (2021) apply a nonlinear ARDL model to show the nonlinear relationships between environmental pollution, carbon emissions, and health expenditure in India. The results show that positive carbon emissions and environmental degradation impact health spending in the long and short runs, while negative environmental pollution and carbon emissions decrease expenditure on health. (Neog and Kumar Yadava, 2020) evidence of a nonlinear cointegrating association between carbon emissions and remittances in both the long and short runs. In this study, we use three BARDL models to study the relationship among the sample variables.

TABLE 1

TABLE 1. Summary of the literature review.

Data and methodology

Data description

To evaluate the role of the film and drama stock prices, and green innovation toward environmental sustainability in China, this study uses the consumption-based CO₂ emissions as the proxy of the sustainable environment (endogenous variable). At the same time, exogenous variables of India’s drama and film industry share price are explained through Drama and Film index. However, the other exogenous variable green innovation, represents the number of green patents registered in a year. Lastly, GDP is reflected through real gross product measured in USD. The data for CO₂ and GDP is extracted from WDI, whereas the observations for all variables are gathered from 1995 to 2020. The data sources and variables details are shown in Table 2, and the data is graphically presented in Figure 1.

TABLE 2

TABLE 2. Synopsis of Data description and variable source.

FIGURE 1

FIGURE 1. Plots of study variables.

Model specification

This study examines the impact of green innovations, economic growth (GDP), and the drama and film industry (D&F) on a sustainable environment in India. Green innovation, drama, and film prices play a vital role in sustaining the attainable climate by reducing CO₂ emissions. For the study calculation of the sustainable environment, the following log-linear econometric model was used, as recommended by (Ozturk & Acaravci, 2013).

$In\mathrm{C}{{\mathrm{O}}_2}_t={\beta }_0+{\beta }_{1}InG{I}_t+{\beta }_{2}InGD{P}_t+{\beta }_{3}InD{F}_t+e_t(1)$

In Eq. 1, CO₂, GI, GDP, and DF stands for carbon dioxide emissions, green innovations, economic growth, and drama and film prices; e is the residual error term support to independently and identically distributed (i.i.d), respectively. Figure 2 illustrates a comprehensive explanation of the econometric strategy of the present study.

FIGURE 2

FIGURE 2. The framework of econometric strategy.

The long-run relationship between green innovations, economic growth, drama and film, and CO₂ emissions are then investigated by applying the bootstrap autoregressive distributed lag (BARDL) for cointegration proposed by (McNown et al., 2018) developing on the ARDL bounds-testing framework of (McNown et al., 2018) determined that these tests have the appropriate size and sensible power properties. To verify the existence of a cointegration nexus, they propose an additional test on the lagged explanatory variables as a complement to the existing F- test and t-test (Pesaran et al., 2001). It is necessary to apply these three tests to distinguish between the cases of cointegration and non-cointegration degenerate cases (Goh et al., 2017).

To address this problem, the bootstrap ARDL approach involves a significance test on the coefficients of the lagged explanatory variables. (McNown et al., 2018) performed Monte Carlo Simulations (MCS) and indicated this new test has reasonable size and power properties that are superior to the asymptotic test in the ARDL bound test.

The ARDL model is a dynamic single equation error correction specification. In general, a four-variables ARDL (q, p, r, s) can be written as:

$\begin{array}{l}\Delta InC{O_2}_t=c_0+{\displaystyle \sum }_{i=1}^q{\beta }_i\Delta InC{O_2}_{t-i}+{\displaystyle \sum }_{j=0}^p{\beta }_j\Delta InG{I}_{t-j}+{\displaystyle \sum }_{k=0}^r{\beta }_k\Delta InGD{P}_{t-k}\\ +{\displaystyle \sum }_{l=0}^s{\beta }_l\Delta InD{F}_{t-l}+{\displaystyle \sum }_{u=1}^t{\chi }_u{D}_{t,u}+w_t\end{array}(2)$

Where i, j, k, l, and u are indices of lags: $i=\mathrm{1,2},...,q,j=\mathrm{1,2},...,p,$ $k=\mathrm{1,2},...,r,l=\mathrm{1,2},...,s,t$ denotes the periods$t=1...T$, $c_0$ is a constant term, CO₂ is the dependent variable, GI, GDP, and FDI denote the explanatory variables, β_i are coefficients on the lags of the endogenous variable, β_j, β_k, β_l, β_m and β_n are coefficients on the lags of the exogenous variables, $D_{t,u}$ is a dummy variable, ${\chi }_u$are the coefficient of the uth dummy variables and$w_t$ is the i. i.d residual error term with mean zero and finite variance ${\sigma }^{\mathit{2}}$. The constant is ignored for brevity.

Eq. 2 can be re-parameterized and expressed in an error correction model representation as:

$\begin{array}{l}\Delta InC{O_2}_t=c_0+{\displaystyle \sum }_{i=1}^{q-1}{\eta }_i\Delta InC{O_2}_{t-i}+{\displaystyle \sum }_{j=1}^{p-1}{\alpha }_j\Delta InG{I}_{t-j}+{\displaystyle \sum }_{k=1}^{r-1}{\beta }_k\Delta InGD{P}_{t-k}\\ +{\displaystyle \sum }_{l=1}^{s-1}{\delta }_l\Delta InD{F}_{t-l}{\displaystyle \sum }_{u=1}^t{\upsilon }_u{D}_{t,u}+{\varphi }_{1}InC{O_2}_{t-1}+{\varphi }_{2}InG{I}_{t-1}+{\varphi }_{3}InGD{P}_{t-1}\\ +{\varphi }_{4}InD{F}_{t-1}+w_t\end{array}(3)$

Where ${\varphi }_1=1-{\displaystyle \sum }_{i=1}^q{\beta }_i$; ${\varphi }_2=1-{\displaystyle \sum }_{j=0}^p{\beta }_j$; ${\varphi }_3=1-{\displaystyle \sum }_{k=0}^r{\beta }_k$; ${\varphi }_4=1-{\displaystyle \sum }_{l=0}^s{\beta }_l$; and ${\eta }_i$, ${\alpha }_j$, ${\beta }_k$, ${\delta }_l$, ${\chi }_m$, and${\gamma }_n$ are functions of the original parameters in Eq. 3. The derivation of Eq. 3 from Eq. 2 is the standard re-normalization that is employed in transforming a vector auto-regression in levels in its error-correction from Eq. 3 will be estimated by the ordinary least square (OLS) method. According to (McNown, Yan Sam, et al., 2018), cointegration between CO₂ and GI, GDP, and DF, involves rejection of all three of the following null-hypothesis:

i) F-test based on all relevant terms of error correction (indicated as F₁):

$H_0:{\varphi }_1={\varphi }_2={\varphi }_3={\varphi }_4=0;$ against $H_1\ne {\varphi }_1\ne {\varphi }_2\ne {\varphi }_3\ne {\varphi }_4\ne 0.$

ii) F-test based on all of the explanatory variable’s terms (indicated as F₂):

$H_0:{\varphi }_1={\varphi }_2={\varphi }_3={\varphi }_4=0;$against $H_1:{\varphi }_1\ne {\varphi }_2\ne {\varphi }_3\ne {\varphi }_4\ne 0.$

iii) t-test based on the lagged dependent variable (indicated as T):

$H_0={\varphi }_1=0;$ against $H_1\ne {\varphi }_1\ne 0.$

One point to note here is that the traditional ARDL approach generates only the critical values of the bounds test for F₁ and T-tests. However, F₂-test statistics for the lagged explanatory variables are ignored. Using the bootstrap approach, ARDL suggested by (McNown et al., 2018) can provide critical values for all three tests. At the same time, in our effort to deliver empirically robust outcomes, we used the critical values tabulated by (McNown et al., 2018) in this research.

Empirical results and their discussion

The descriptive statistics of the variables of the present study are indicated in Table 3. Based on the outcomes, CO₂ emissions varied between 0.923 and 1.922, with an average value of 1.471 and a standard deviation of 0.321. Similarly, the green innovation (GI) minimum and maximum values are 0.832 and 1.631, respectively, whereas the variable’s average value is 1.082 with a standard deviation of 0.249. The mean value of D&F is 2.041, with minimum and maximum ranges of 1.074 and 3.021, with a standard deviation of 0.273. Moreover, the GDP has the highest mean value of 4.331, which varies between 2.451 and 6.412, with a standard deviation of 0.681. Additionally, the Jarque-Bera test rejects the null hypotheses of normal distribution for each variable under consideration at a 5% level of significance.

TABLE 3

TABLE 3. Descriptive Statistics results.

Table 4 presents the outcomes of the Zivot and Andrews (2002) unit root tests. The ZA unit root test with structural break indicates that all variables are non-stationarity in levels, but they become stationary by differencing them. This implies that all the variables are integrated of 1^st-order or I (1). To circumvent the low predicting power of this unit root in the presence of structural breaks, the ZA non-stationary test is applied. The outcomes of this test, as demonstrated in Table 4, indicates that the variables are non-stationary in their levels. They become stationary after 1^st-difference. Once the unit root test properties are detected, we move to the cointegration analysis.

TABLE 4

TABLE 4. Zivot and Andrews unit root test results.

After the estimation of unit root, in the next stage, the cointegration of the data in this study was estimated by bootstrap ARDL which many researchers suggest as an alternative to evaluate more robustly when compared with the other traditional ARDL methods (Sinha and Shahbaz, 2018). As already discussed in greater detail in the methodology, in bootstrap ARDL, the result is the generation and consequently estimated by three test statistics that evaluate three various aspects of the model, which are the whole model estimated by T-statistics, lagged values of the response variables estimated by F₁-statistics, and traditional aspect which is the assessment of lagged values of the explanatory variables estimated by F₂-statistics, which justify the selection of bootstrap ARDL over other related traditional ARDL methods. However, choosing a suitable lag specification is compulsory for bound testing while determining the accurate cointegration in the data; Failure to do so may affect of adversely the validity of the outcomes. Therefore, Bayesian information criteria (BIC) were selected for enhanced power properties appropriate for ascertaining lag specification (McNown et al., 2018). Firstly, the bootstrap ARDL cointegration results are presented in the 2^nd column of Table 5. Since all three test statistics (T_DV, T_IV, and F_PSS) are found to be statistically significant at the 1% level, we can claim that there is cointegration between the variables in the model. Moreover, populace equivalency is ensured by an investigation test, Q-statistics; this test confirms the presence of normality and conformation of the outcome’s generation of the output through the Jarque-Bera test. In addition, the tests also confirm the absence of multicollinearity, which tends to generate distorted outcomes (Pesaran et al., 2001) and hence is not the focus of concern in this study. The outcomes of the statistics are summarized and presented in Table 5.

TABLE 5

TABLE 5. Outcomes of Bootstrap ARDL cointegration approach.

After complying with all parameters for robustness as mentioned above, the bootstrap ARDL was used to evaluate the cointegration of short- and long-run keeping carbon emissions as response variables and GI, GDP, and D&F as independent variables. The outcomes of the BARDL approach for these coefficients are reported in Table 6. Precisely, while focusing the level of cointegration among green innovation (GI) on CO₂ emissions, the green innovation (GI) was reported to be negative and significant at a 5% level in the long-run ($\beta$ = −0.079, $p$< 0.05) and negative and 1% level of significance in the short-run ($\beta$ = −0.031, $p$< 0.01). This means that a 1% increase in GI will decrease carbon emissions by 7.9% in the long run and 3.1% in the short run. This is very interesting and needs time where the world faces environmental degradation; it has been found that more innovative towards environmentally-friendly technologies will improve the environmental quality by decreasing the levels of CO₂ emissions (Meirun et al., 2021; Hu et al., 2022).

TABLE 6

TABLE 6. BARDL long and short-run estimates and diagnostic test.

While focusing on the level of cointegration among drama and film industry (D&F) on CO₂ emissions, the (D&F) was reported to be positive and 1% level of significance in the long-run ($\beta$ = 0.143, $p$< 0.05) and positive and 5% level of significance in the short-run ($\beta$ = 0.067, $p$< 0.01). This means that a 1% increase in D&F will increase the CO₂ emissions by 14.3% in the long run and 06.7% in the short run. Thus, it has been proven that the drama and film industry consumes more energy in indoor and outdoor production, traveling, and post-production activities such as distribution and launching and cause high CO₂emissions. These results confirm the study results of the European broadcast union, which states that the world’s audio-video sector is responsible for more than 2% of the total CO₂ emissions yearly (Ecopord.com). On the other hand, while focusing on the level of cointegration among GDP on CO₂ emissions, the GDP was reported to be positive and statistically significant at a 1% level in the long-run ($\beta$ = 0.566, $p$< 0.05) and positive and statistically significant at 1% level in the short-run ($\beta$ = 0.231, $p$< 0.01). It means that a 1% increase in GDP will increase carbon dioxide emissions by 56.6% in the long-run and 23.1% in the short run, respectively. It shows that high economic growth causes damage to the environment by emitting more greenhouse gas emissions into the atmosphere. The rise in financial development or per capita income accelerates financial activity, non-renewable energy use, global warming, and environmental pollution, thus raising the risk of potential loss of ecosystems. This evidence is consistent with several studies, such as those (Baz et al., 2020; Sharif et al., 2020; Meirun et al., 2021; Murshed et al., 2021). Overall outcomes illustrated in Table 6 favor the BARDL approach to express the dynamic relationship between variables.

In addition to these, while evaluating the model’s predictability, all three predictors can explain the carbon emissions by 78%, which is referred to as the explanation of the variation in the criterion variable reflected by the value of R-square. Apart from that, the model was also reported to be free from autocorrelation, which is gauged by the measure of Durbin Watson. Lastly, while assessing the robustness and rigorousness of the model, the values of CUSUM and CUSUMsq clearing reflects that the model is legitimate and appropriate in terms of stability. All of the aforementioned statistical measures are also reported in Table 6.

Table 7 reports the results of the granger causality test. In the short-run, this study has found bidirectional causal links between drama and the film industry and carbon emissions (D&F$\leftrightarrow$CO₂) and between green innovation and carbon emissions (GI$\leftrightarrow$CO₂). We have also found a unidirectional causality from economic growth to carbon emissions (GDP$\to$CO₂).

TABLE 7

TABLE 7. Granger causality results.

Conclusion and policy implications

Environmental researchers have urged safeguarding nature, fauna, and flora, whereas if neglected, there will be abnormalities in the rainfalls, droughts, storms, etc. That is also expected to be more lethal and destructive because of the imbalance caused to the environment. Among the few potential solutions that could contribute to environment conservation by eliminating pollution is to create awareness among the masses and public, for which the potential of the role and the significance of contribution by the film and drama industry cannot be ignored.

The Indian drama and film industries are responsible for contributing efficiently to a sustainable environment. Hence, it is crucial to assess the influence of the drama and film industry, per capita GDP, and green innovation on CO₂ emission in India. No previous research has studied the drama and film industry’s effect on CO₂ emissions. Nevertheless, the audio and visual industries are well-known for waste generation and excess energy sources. This study has covered the gap by applying the BARDL approaches to identify the dynamic relationship between GI, D&F, GDP, and CO₂ emissions. The study used the yearly data of variables from 2000 to 2020; before using the BARDL approach study, stationarity tests of ZA test with a structural break to find the integration order of the observations. The test outcomes indicate that all the observations are stationarity in 1^st order. The results of the bootstrap ARDL can be used to identify the existence of cointegration between variables.

Moreover, the BARDL long and short-run dynamic approach results show the response of the endogenous variable against each exogenous variable’s negative and positive variation. The green innovation (GI) was reported to be negative and statistically significant at a 5% level in the long-run and negative and statistically significant at a 1% level in the short run. The drama and film industry (D&F) was reported to be positive and statistically significant at a 1% level in the long run and positive and statistically significant at a 5% level in the short run. In contrast, economic growth (GDP) was reported to be positive and statistically significant at a 1% level in the long and short run. While comparing the beta parameters, economic growth was reported to be a higher predictor of a sustainable environment, followed by the drama and film industry and green innovation. This sector contributes to the Indian economy. Furthermore, the Granger causality test results reveal that green innovation, drama and the film industry have a bidirectional causal link with carbon emissions.

This paper offers several policy recommendations for reducing CO₂ emissions in several Indian economic sectors. Concerning Indian environmental policies for the industrial and manufacturing sectors, these sectors are hugely dependent on fossil fuel-based energy. Shifting to renewable energy sources, such as solar, gas, and coal-based energy as the primary energy sources is strongly recommended. Second, the government should improve environmentally friendly, long-term policy frameworks for manufacturing and industrial sectors to instigate like policies in related sectors. These measures and policy structures may also help improve management capability, foster public awareness of energy efficiency, and support green technology in the industrial sector. Third, India has been attempting to mandate an environmentally friendly industry and means for renewable energy and efficient energy policy but still has not achieved the target level. Fourth, the country should reduce CO₂ emissions through renewable energy efficiency; for example, a study of neighboring countries, Shao et al., 2022 and Shahzad et al., 2022, analyzed that technological progress in the various sectors of China, particularly for industrial (drama and film) and agriculture sectors are feasible for economic growth and CO₂ emissions reduction.

The green technological approaches include green filming, a resource-efficient concept to provide a sustainable contribution to climate change. Green filming controls the energy supply efficiently, transport, waste, and catering. The government should encourage green filming by introducing specific production houses and providing funding to fulfill the sustainable criteria. Furthermore, there is a need for knowledge sharing to create awareness and proper training in this industry to reduce environmental damage. Feature film production is time-consuming and resource-consuming; therefore, the green screen production process is highly suggested at every production stage; filmmakers can decrease the environmental impact of production activities. Filmmakers should emphasize paperless product planning, selection of closed sites and locations, reusable production materials, and minimal water and energy consumption. In the future, an overall review of all the world’s leading film and theater industries will provide comprehensive countermeasures and suggestions for a concerted contribution to achieving sustainable environmental development goals. In addition, technological advances such as using hybrid vehicles for transportation, green screens or GGI boxes to reduce emissions, and using LED and solar power supply generators in manufacturing companies will positively impact the environment. Therefore, the general legal efforts of the government to improve the growing awareness and sustainable environment will green the film and drama sector.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

PD conceptualized, wrote the original draft, conducted the econometric analysis, and analyzed the findings; MA and UK compiled the literature review and generated the graphical illustrations, reviewed and edited the manuscript; MP and AK wrote the introduction and compiled the literature review and contributed to the methodology section; AK and SM supervised and reviewed the entire study. All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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