Key drivers of consumption-based carbon emissions: empirical evidence from SAARC countries

To devise an appropriate climate policy dealing with environmental degradation, reliable measurement of CO2 emissions is essential. In the recent past, most researchers have utilized production-based emissions in their studies, ignoring the important role of consumption-based emissions in environmental degradation. Therefore, the present research examines the drivers of consumption-based CO2 emissions in SAARC nations over the period 1990 to 2018. By employing traditional and second-generation panel cointegration methodologies, the study, more specifically, explores the link between consumption-based CO2 emissions and its five macroeconomic determinants, namely, GDP growth, energy consumption, FDI, trade openness (measured by composite trade share index), and urbanization. The study also applies the FMOLS and DOLS techniques for calculating the long-run elasticities of regressors with respect to the explained variable. The results establish a cointegration relationship between the variables and validate an “N-shaped EKC” for the SAARC region. It is also found that in the long run, energy consumption and urbanization amplify the consumption-based CO2 emissions while FDI and trade openness improve the environmental quality by plummeting emissions. Most importantly, the study rejects the “pollution-haven hypothesis” for the SAARC region based on the outcomes of FDI and trade openness. Lastly, based on the results, some policies are recommended for the abatement of environmental degradation in SAARC countries. As the SAARC nations rely heavily on fossil-based energy, it is suggestive for these economies to enhance the level of energy efficiency and augment the share of renewable energy sources in the energy mix. Furthermore, the policy designers in this region should encourage trade openness and liberalize inward FDI for containing consumption-based emissions.


Introduction
Today, climate change is one of the critical issues facing humanity. Global warming caused by GHGs, particularly CO 2 emissions, which constitute around 60 percent of the total greenhouse gases in the atmosphere , presents unappalled risks to human lives and properties. Both anthropogenic (human) and natural economic expansion practices triggered these environmental issues . With the growing impetus on growth augmenting policy initiatives involving rapid industrialization and urban infrastructure development, pollution as a negative externality continues to pose a threat to environmental sustainability. This phenomenon is more pronounced, especially across the emerging market economies. In developing countries, rising populations, income levels, and energy use mainly based on fossil fuels are leading to a rapid increase in GHGs emissions (Chandler et al. 2002;Hanif et al. 2019). Presently, CO 2 emission from developing economies is growing at a faster rate than those of developed economies. If this rate persists, they will surpass the developed countries within a matter of decades (Ertugrul et al. 2016). Combating these environmental challenges is particularly difficult for least developed and developing economies as they set very ambitious growth targets to increase their masses' living standards. Nevertheless, along with developed nations, it is also the responsibility of developing countries to prevent environmental degradation by designing viable policies that leverage natural synergies between climate protection and development goals. This has triggered our interest in investigating the determinants of CO 2 emissions in the SAARC region, one of the world's developing regions, having contrasting economic profiles.
The SAARC member countries, including India, Bangladesh, Pakistan, Sri Lanka, Nepal, Bhutan, and the Maldives have contrasting economic profiles, energy consumption patterns, and energy portfolios. For emitting a high level of CO 2 , India and Pakistan are responsible for the highest anthropogenic environmental degradation in the region (Latief et al. 2021). While South Asia has historically experienced low GHG emission, high population growth coupled with rapid urbanization and industrialization is pushing the region toward a more carbon-intensive development path (Shrestha et al. 2012). Over the decade 2001-2011, the countries in the region have experienced a population increase of 130 million, which is more than Japan's total population (Ellis and Roberts 2016). South Asian Economies rely heavily on fossil fuels to meet their energy demand, resulting in greenhouse emissions, and pollution, overall holding serious implications for the environmental sustainability and health of the population (Wijayatunga and Fernando 2013). For instance, in Pakistan, 90 percent of CO 2 is emitted from the use of fossil fuels (Waqih et al. 2019). Therefore, the countries in the region need to examine their resource and energy options so as to adopt a low-carbon development path for more inclusive and sustainable economic growth.
The evidence on the relationship between renewable energy usage and economic growth variables with CO 2 emissions has been well documented across different geographies and time periods all over world (Mohsin et al. 2018;Sun et al. 2021). Among key macroeconomic variables, trade openness has been found to be significantly explaining the cause of CO 2 emissions in various studies (Adams and Klobodu 2017;Ertugrul et al. 2016). Ertugrul et al. (2016) have argued in explaining the pollution haven hypothesis (PHH) that with rising incomes, the clean environment is also demanding, which leads to the relocation in countries with less environmental concern for high CO 2 emission industries (Kukla-Gryz 2009) and where income is selected for the trade between income and pollution. This was also verified in a research conducted by Gökmenoğlu and Taspinar (2016), which examined the instance of Turkey. While trade openness is important in determining CO 2 emissions, the findings from the existing literature are contradictory in nature (Dogan 2015;Nasir and Rehman 2011;Shahbaz et al. 2013). Especially for the SAARC region, it remains as a testable proposition. SAARC region holds a significant position being the most populous region comprising more than one-fifth of the world population and the world's fastest-growing region (WDI, 2020). Better prospects of growth imply a surge in energy demand which primarily is fulfilled via nonrenewable resources. Further, there has been a shift in the biodiversity of the region due to frequent episodes of extreme weather events. Such events are projected to be more frequent and hence an issue of policy concern for the region.
To achieve sustainable development goals 2030, sustainable consumption with sustainable production cycles are the key factors. Production-based carbon emissions have been the subject of extensive research, with a significant amount of effort devoted to understanding their consequences. Very few studies (Knight and Schor 2014; have considered consumption-based carbon emissions to assess environmental degradation. The consumption-based approach towards carbon emissions is distinctive because it considers the global supply chain that contributes to emission generation and distinguishes between emissions generated in one country from those used in another (Khan et al. 2020;Safi et al. 2021). Therefore, the current study adds to the knowledge base through the proxy to environmental degradation of consumption-based carbon emissions (CCO 2 ). In selected SAARC countries, trends of CCO 2 are presented in Fig. 1. During the sample period, it displays an increasing trend. The validity of the environmental Kuznets curve (EKC), N-shaped, and the PHH are investigated in this paper. In addition, the study also examines the impact of FDI, trade openness, energy consumption, urbanization, and economic growth on CCO 2 emissions in SAARC countries during 1990-2018.
The earlier studies in the literature have used traditional measures to estimate trade openness. Trade openness in the estimation model is considered as the proxy for technological progress. According to Gozgor (2017) and Waugh and Ravikumar (2016), the trade potential index (TPI) can be used as a proxy for technological progress. On the other hand, an open economy is defined by Squalli and Wilson (2011) as one that has a relatively high proportion of trade in total economic activity and significant engagement and interconnectivity with the rest of the world. These two dimensions are significant because they are concerned with actual trade flows rather than anticipated trade flows. With this backdrop, the present paper analyzes the determinants of CCO 2 emissions in SAARC countries during 1990-2018. In a number of ways, this work contributes to the literature. First, CCO 2 emissions are employed as a proxy for environmental deterioration rather than ecological and production-based CO 2 emissions. Second, the trade openness approach of Squalli and Wilson (2011) is used instead of the traditional trade to GDP ratio. Third, to the authors' knowledge, no previous study linking economic growth to consumption-based CO 2 emissions has been carried out in the SAARC region. Therefore, this research adds to the existing knowledge for examining the impact of economic growth, energy consumption, FDI, trade openness, and urbanization on CCO 2 emission in the context of this under-researched economic bloc. Lastly, this study explores the possible N-shaped association between economic growth and environmental degradation while using CCO 2 emissions as a proxy for ecological degradation. The existing literature on the N-shaped relationship between economic growth and environmental degradation has mainly considered the production-based CO2 emissions. The remainder of the paper is organized as follows: Sect. 2 presents the literature review. The theoretical framework for the investigation is included in Sect. 3. Section 4 contains information on the data and techniques used in this research. Section 5 contains the findings and conclusions. Section 6 gives a conclusion as well as some policy recommendations.

Linkage between economic growth and CO 2 emissions
The EKC theory asserts that environmental degradation grows with output in the early phases of economic growth but afterward diminishes. The EKC hypothesis is explained by three distinct mediums: scale, composition, and method influences (Grossman and Krueger 1995;Antweiler et al. 2001). According to the scale effect, emissions tend to rise due to greater economic activities that are harmful to the environment. As industrial technology gets more environmentally friendly and efficient, the technique effect tends to reduce emissions. The composition effect states that as an economy's industries get cleaner, emissions decrease. The EKC hypothesis's importance and stability in explaining the relationship between production and pollution motivated a quest for both empirical and theoretical explanations. Apergis (2016) analyzed data from fifteen countries from 1960 to 2013 to assess the validity of the EKC hypothesis using both panel and time series cointegration methods. They identified an inverse U-shape connection between CO 2 emissions and economic growth in twelve of the fifteen countries investigated. The remaining three countries appeared to support the EKC theory but only in particular quantiles. Similarly, studies by Ang (2007), Halicioglu (2009), Nasir and Rehman (2011), Shahbaz et al. (2015, Tutulmaz (2015), Ahmad et al. (2016), Alam et al. (2016), Dong et al. (2016), Chakravarty and Mandal (2016), Sapkota and Bastola (2017), Rehman and Rashid (2017), Gill et al. (2018), Bekun et al. (2019, Sharif et al. (2019), Pan et al. (2019), and Ahmed and Le (2021) confirmed the validity of EKC hypothesis. Whereas studies by Zoundi (2017), Aye and Edoja (2017), Neve and Hamaide (2017), and Lotz and Dogan (2018) concluded that there is no association between output and pollutants. A summary of these studies is explained in Table 1.

Linkage between energy consumption and CO 2 emissions
The second strand investigates the link between energy consumption (EC) and carbon dioxide emissions (CO 2 ). Ito (2017), for example, obtained data for 42 industrialized countries from 2002 to 2011, using the GMM and PMG models, and discovered an inverse link between EC and carbon emissions. Cai et al. (2018) examined the link between EC and CO2 emissions for G7 countries from 1965 to 2015 using the bootstrap ARDL bound test, finding unidirectional causality from clean EC to CO 2 emissions. Chen et al. (2021) observed a statistically insignificant influence of EC on CO 2 emissions for 36 OECD nations between 1970 and 2016. They employed the random effects model (REM) and panel quantile regression using the fixed-effects (FE) methods of moments. Other studies, such as Zhang (2011), Alam et al. (2011), Ocal andAslan (2013), Magazzino et al. (2021), Banday and Aneja (2019), and Ocal and Aslan (2013), investigated the causal relationship between EC and CO 2 emissions and came up with mixed results. Table 2 explains the summary of these investigations.

Linkage between FDI and CO 2 emissions
The third focuses on the relationships between FDI and CO 2 . The pollution haven hypothesis (PHH) was explored for panel data of 28 Chinese provinces by Ahmad et al. (2021), who discovered the existence of PHH in seven provinces with varying levels of development.  used three composite financial inclusion measures to examine the relationship between FDI and CO 2 emissions for a sample of 21 Asian countries from 2004 to 2014. The study demonstrates PHH's validity and revealed that greater financial inclusion contributed to greater CO 2 emissions. PHH is also supported by research ). Mujtaba and Jena (2021) analyze the asymmetric effects of FDI on CO 2 emissions in India. Their study supports the PHH hypothesis. Kim (2019) employed the VECM model to assess the causative link for 57 developing countries from 1980 to 2013, finding no direct short-run causality between FDI and CO 2 emissions, rejecting the prevalence of PHH in these countries. He et al. (2020) adopted the bootstrap autoregressive distributed lagged model (ARDL) approach on a panel of BRICS countries. They observed a poor association between FDI and carbon emissions. Table 3 provides a summary of these studies.

Linkage between trade openness and CO 2 emissions
The final section is concerned with the relationship between trade openness (TO) and CO 2 emissions. Zhang Saidi and Mbarek (2017) used time series data from 1990 to 2013 to examine the relationship for emerging economies and found that TO was an insignificant determinant of CO 2 emissions. Table 4 summarizes the findings of these studies.

Studies on consumption-based carbon emissions
As mentioned in the introduction, a couple of studies (Knight and Schor 2014;Baloch et al. 2021;He et al. 2021;Kirikkaleli and Adebayo 2021) have considered consumption-based carbon emissions to assess environmental degradation. He et al. (2021) investigated the effect of globalization and financial development on consumption-based carbon emissions for Mexico using a dual adjustment approach. The findings revealed that economic growth and energy consumption worsen environmental quality whereas globalization and financial development improve it. Kirikkaleli and Adebayo (2021) investigated the impact of public-private partnership and renewable energy consumption on CCO 2 emissions, finding an inverse relationship  between public-private partnership investment and carbon emissions whereas renewable energy lowers carbon emissions.  used wavelet tools to investigate the impact of globalization, technical innovation, and renewable energy consumption on environmental degradation for Japan for the time period 1990Q1 to 2015Q4. The results reported a negative relationship with economic growth, technological innovation, and globalization and a positive relationship with the usage of renewable energy.  used nonlinear ARDL for the time period 1990-2018 to explore the main drivers of CCO 2 for Chile. The empirical estimation indicates that usage of renewable energy improves environmental quality whereas reduction in economic growth leads to environmental degradation. However, only a few studies have been conducted for the SAARC region. Rehman and Rashid (2017) have used FMOLS and DOLS approaches to detect the presence of EKC and PHH hypotheses in SAARC countries. The authors examined the impact of energy consumption, GDP, and population growth, as well as CO 2 emissions, on environmental degradation, and predicted bidirectional causality between CO 2 emissions and EG. Afridi et al. (2019) analyzed SAARC countries for panel data from 1980 to 2016. The authors advocated the EKC hypothesis and obtained N-shaped EKC by incorporating a cubic function. The results reported a negative relationship between CO 2 and TO and a positive with the rest of the variables. Using panel data from 1986 to 2014, Waqih et al. (2019) examined the relationship between foreign direct investment and carbon dioxide for SAARC countries. The study implemented panel ARDL and FMOLS and validated the PHH and EKC hypotheses in the short run and the absence of PHH in the long run. Dar and Asif (2019) investigated the impact of renewable energy consumption, trade liberalization, real income, agricultural contribution, and urbanization on carbon emissions in five SAARC countries from 1990 to 2013. The study used Pedroni and Kao cointegration techniques as well as Granger causality tests and found no evidence of PHH. Khalid et al. (2021) investigated the impact of trade openness, financial development, economic growth, and primary and renewable energy utilization on environmental quality in SAARC countries using panel data from 1990 to 2017. In the case of SAARC countries, the authors predicted that financial development would be fragile.

Theoretical Framework
This study looks at economic growth, energy consumption, foreign direct investment (FDI), trade openness, and urbanization as key factors of consumption-based CO 2 emission in the SAARC region. The study documented that economic growth and environmental quality are frequently emphasized to be interconnected (Iheonu et al. 2021). Moreover, sustaining economic growth is in doubt unless environmental sustainability is ensured as well. Grossman and Krueger (1995) noticed a nonlinear association between a country's economic growth and its environmental quality. They hypothesized that as an underdeveloped economy begins to rise, the increase in national income initially causes the deterioration of environmental qualities. Demand for energy within the economy is predicted to rise, most of it met by fossil fuels. Thus, the exploitation and consumption of nonrenewable energy sources damage the ecosystem. Moreover, the EKC hypothesis states that once national income reaches a certain threshold level, additional economic growth improves environmental quality. Numerous studies link economic growth and environmental quality (Iheonu et al. 2021). As a result, the ability to sustain economic growth is in doubt unless environmental sustainability is ensured. Hence, the relationship between economic growth and environmental quality has become a prominent research area recently. EKC hypothesis shows a nonlinear relationship between economic growth and environmental quality. According to this theory, when an underdeveloped economy starts to expand, an increase in national income causes environmental quality deterioration. This can be termed as scale effect, which emphasizes economic well-being over environmental quality, thus resulting in a trade-off between these two variables. Furthermore, economic growth leads to continued increases in national income, resulting from a composition effect, further deteriorating environmental quality. The decomposition effect occurs when input criteria change, especially during modernization (Zhang et al. 2020). Energy demand is predicted to rise throughout industrialization, which is often supplied by fossil fuels. Thus, extracting and burning nonrenewable energy sources damage the ecosystem. The EKC hypothesis states that as the national income increases to a threshold level, further increase in national income leads to environmental improvement. It is termed as technique effect (Muhammad and Long 2021), which highlights the importance of technical innovation in eliminating the trade-off between economic growth and environmental quality (Sarkodie 2018). Some studies have found an N-shaped association between environmental deterioration and economic growth instead of an inverted U-shaped relationship. It implies that throughout the initial phase of economic development, economic expansion raises the amount of environmental pollution until the first critical juncture is reached and then diminishes till the second crucial juncture. But, once the second critical juncture is reached, the environmental deterioration resumes its upward shift. Because an increase in the level of income has a significant impact on environmental emissions, environmental changes occur in specific economic settings. This also suggests that environmental emissions will restore their upward trajectory if the government's renewal energy sources initiatives are not implemented and enforced over the second juncture for the implementation of energy legislation. The final stage demonstrates technological innovation to generate economically efficient goods that support the mitigation of environmental emissions (Sinha and Shahbaz 2018).
Urbanization is frequently considered in terms of economic modernization; it is a demographic factor that affects household energy use patterns by increasing urban density and changing the structure of human behavior (Barnes et al. 2010). The existing literature highlighted three important theories, i.e., ecological modernization, urban environmental transmission, and compact city theories, explaining how urbanization affects the natural environment. Ecological, environmental theory can be seen as an important indicator of societal transformation, a significant indication of progress (Poumanyvong and Kaneko 2010). As societal transformation takes place in the society from the low to the middle phase of development, the environment may deteriorate because in the development process, growth in the economy takes priority over environmental sustainability. In the higher stage of development, environmental deterioration became more critical; governments sought to make their nations more environmentally friendly. Technological innovation, urbanization, and change from a manufacturing-based economy to a service-based economy all have the potential to mitigate the negative environmental impact of economic growth. The urban environmental transmission theory is linked with environmental problems with urbanization at the city level (Jacobi et al. 2010). In the modern era, cities have frequently become more prosperous as a result of expanding their manufacturing base, which results in industrial pollution issues, which affect land, air, and water resources. The compact city theory is primarily concerned with the advantages of rapid urbanization. Rising urban density helps to achieve economies of scale in developing public infrastructure like schools, hospitals, electricity, water supply, etc. As a result, these economies of scale improve environmental quality (Capello and Camagni 2000).
The EKC hypothesis is also affected by energy consumption. The energy push emission hypothesis asserts that when an economy's aggregate energy consumption increases, it is more likely to boost greenhouse gas emissions . Furthermore, it is well accepted that environmental consequences of energy use rely on the types of energy resources used (Ito 2017). It is widely accepted that burning fossil fuels accelerates environmental damage (Pata 2018). The combustion of fossil fuels boosts greenhouse gas emissions into the atmosphere, resulting in considerable environmental degradation. The pollution haven hypothesis and the pollution halo hypothesis are two theories that explain how foreign direct investment (FDI) influences environmental performance. According to population haven hypothesis, FDI brings polluting industries to poor countries because of low environmental restrictions, which degrades the quality of the environment. Consequently, poor countries become pollution haven by bringing efficient technical industries to underdeveloped countries; FDI benefits the environment. Trade openness has three environmental effects: scale, technique, and composition (Antweiler et al. 2001). The scale effect assumes that more trade openness leads to increased exports, contributing to greater economic activity. As a result, hazardous emissions further damage the ecosystem. Moreover, the technique effect effectively reduces emissions since trade openness allows for the importation of advanced technology, positively affecting the environment. According to the composition effect, increased trade openness will enhance the environment if the country has a comparative advantage in environmentally friendly industries. Thus, the environmental impact of trade openness is uncertain because it depends on other factors.

Data and model specification
This study analyses annual data from 1990 to 2018 to estimate the major determinants of CCO 2 emissions in the SAARC countries. Bangladesh, Nepal, India, Pakistan, and Sri Lanka are sample SAARC countries. These countries have been chosen solely on the basis of the availability of data in each of them. CCO 2 emissions are employed as a proxy for environmental degradation in this study as the dependent variable. CCO 2 emissions have a significant advantage over production-based CO 2 emissions. In recent years, many emerging economies have seen a substantial reduction in production-based CO 2 emissions due to sustained economic growth (Iqbal et al. 2021). According to Davis and Caldeira (2010), rather than actual reductions in carbon emissions, reported reductions in production-based CO 2 emissions are mainly the result of PHH. Therefore, consumption-based CO 2 emissions play a critical role in ensuring a fair distribution of responsibility among nations. Moreover, this study used economic growth, energy consumption, FDI inflows, trade openness, and urbanization as the explanatory variables. These variables are selected on the basis of the review of literature and their significance with respect to environmental degradation in SAARC countries. The study utilized (see Squalli and Wilson 2011) composite trade share (CTS) approach rather than trade percent GDP to estimate trade openness in the presence of technological progress. The definition of trade openness and how it is measured have been ambiguous (Ngouhouo et al. 2021;Udeagha and Ngepah 2021). Trade openness is traditionally assessed by using the trade to GDP ratio (Kumar et al. 2021). Traditional approaches to trade openness fail to fully influence economic growth as they ignore the country's openness to world trade because it incentivizes larger economies by labeling and portraying them as closed economies as a consequence of their higher GDP (Squalli and Wilson 2011). It is calculated using the formula below.
where X is the export, M is the import, and GDP is the country's gross domestic product. Detailed explanations of the variables and data sources are provided in Table 5.
Following a review of the literature, we hypothesized that GDP, EC, FDI, TO, and URB all had an impact on CCO 2 emissions and developed the following empirical model: where i and t denote the country and year, respectively. The model also adds the square and cube of per capita GDP to assess the validity of "EKC hypothesis" and "N-shaped" for sample SAARC countries. The model is redefined by taking natural logarithms of all these variables to deal with heteroskedasticity as follows in Eq. (3): (1) where βs denotes the elasticities to be evaluated and denotes the error term that accounts for random effects.

Cross-sectional dependence, panel unit root tests, and panel cointegration tests
We begin by employing cross-sectional dependence (CSD) tests. In addition to socioeconomic and cultural similarities, the selected SAARC nations have various bilateral and multilateral ties in commercial and scientific fields. Moreover, some of the SAARC countries share a common border and may affect the transborder pollution effect. As a result, the interdependence among them is high. We check the crosssectional dependence among sample countries by employing CD and scaled LM tests proposed by Pesaran et al. (2004) as well as the Breusch-Pagan LM test. The equation for the CD test is presented in Eq. (4).
where T denotes the time period, N is the sample size and P ij is the sample estimate of correlation errors for each cross section of country i and j defined as follows in Eq. (5).
T signifies the time, N denotes the sample size, and P ij denotes the correlation error sample estimate for each cross section of the country i and j as specified in Eq. (5).
The stationarity of target variables is checked prior to the empirical estimation of the panel model because all of the panel cointegration tests are based on a presupposition that the variables of order I(0), I(1), or a mix of both are integrated and no variables of order I(2) or beyond are integrated. To begin, the study uses four well-known firstgeneration panel unit root tests: Fisher ADF, Fisher PP, Im, Pesaran, and Shin (IPS) and Levin, Lin, and Chu (LLC). The stationarity properties have been investigated separately with the only intercept and with both trend and intercept. In contrast to the alternative hypothesis of no unit root, the null hypothesis of the unit root test confirms the presence of a unit root in the series.
The first-generation unit root tests may give biased results when there is heterogeneity and cross-sectional dependence. As a result, two second-generation panel unit root tests: cross section ADF (CADF) and cross-sectionally augmented IPS (CIPS) are used. Both tests were developed by Pesaran (2007). As a consequence, two root unit tests of Pesaran (2007) of second generation are being performed:

ADF-cross section (CADF) and IPS-cross-sectional increment (CIPS).
After validating that all of the variables in this study are I(0) and I(1), the study applies four types of panel cointegration tests to determine the existence of a long-run linkage between the variables: Pedroni (1999Pedroni ( , 2000, Kao (1999), and Johansen Fisher panel cointegration). Finally, the study use the Westerlund (2007) cointegration test to resolve cross-sectional dependence among selected nations. This is an error correction-based test that is resilient to crosssectional dependence.

FMOLS, DOLS, and DH panel model
After determining the long-run relationship, we intend to compute the long-run elasticities of all explanatory variables included in our model with respect to the explained variable. For this, we have employed panel FMOLS and DOLS methods suggested by Pedroni (2001Pedroni ( , 2004. These methods can be expressed as presented in Eq. (9) and Eq. (10): The heterogeneous panel causality test of Dumitrescu and Hurlin (DH) (2012) is utilized for determining the causal link of the panel variables with reference to CCO 2 emissions after measuring the elastics of Y, squared Y, C, FDI, and TO. A causal relationship between the variables is asserted by the null hypothesis versus the alternative hypothesis, which says that there is a causal linkage among the variables, in DH test. Table 6 summarizes descriptive statistics for the variables under discussion. The lower part of Table 6 shows the correlation matrices among the variables. The correlation analysis manifests that Y, EC, FDI, TO, and URB are significantly and positively correlated with CCO 2 emissions. Similarly, EC, FDI, and TO have a significant and positive correlation with Y. TO is also positively correlated with FDI. Table 6 also indicates a strong correlation between the explanatory variables, which implies the likelihood of multicollinearity. The observed high collinearity is probably due to the inclusion of the square term of Y so as to validate the "EKC hypothesis." Allison (2012) and Waqih et al. (2019) argued that this term neither affects standard error nor probability of the model; thus, it has no effects on results. Nevertheless, to safeguard our panel model from the potential multicollinearity, we apply a VIF test for explanatory variables. The results

Empirical Results and Discussions
show that the mean VIF value is 1.67 and the individual VIF values for lnY, lnEC, lnFDI, and lnTO are 2.01, 1.75, 1.84, and 1.09, respectively, are well below the critical value. The results for the CSD test are presented in Table 7. Following our findings, we conclude that cross-sectional dependency exists in our sample of five SAARC nations, rejecting the null hypothesis of "no cross-sectional dependence." Table 6 Descriptive statistics and correlation matrix "***," "**," and "*" indicate the level of significance at 1%, 5%, and 10%, respectively   Table 8 and Table 9 show the unit root tests of first generation. All variables, with the exception of lnFDI, have a unit root at a level, and there is no unit root at the first difference, according to the findings of these tests. Only lnFDI is found stationary at both level and first difference.
The results for CADF and CIPS are presented in Table 10. The results of CADF show that lnCCO 2 , lnY, lnY 2 , lnY 3 , lnTO, and lnURB are stationary at first difference, while lnFDI is stationary at both level and first difference. CIPS results indicate that all panel variables have a unit root at the level, but they become stationary when the first difference is taken. In conclusion, all of the variables are I(0) and I(1) in both unit root tests of first and second generation.
The results of the Pedroni cointegration tests are displayed in Table 11. Four out of seven statistics (two within dimension and two between dimensions) reject the "null hypothesis of no cointegration" indicating that Y, square of Y, cube of Y, EC, FDI, TO, and URB have a long-run association with CCO 2 emissions in our sample of five SAARC countries. The Kao cointegration test is tabulated in Table 12. The results of this test corroborate Pedroni cointegration's conclusions by implying that the variables are in long-run equilibrium. Table 13 shows the results of Johansen Fisher's cointegration test. The reported trace and  Table 8, 3 out of 4 statistics refute the null hypothesis of no cointegration, which has been accepted as true, at a level of significance of less than 5%. In summary, we conclude from all four tests of co-integration test that there is strong empirical evidence that the target variables have a long-running association in the countries of SAARC throughout the period 1990 to 2018. Finally, the long-run elasticities of all explanatory variables using FMOLS and DOLS methods are summarized in Table 15. The results of FMOLS and DOLS expose that per capita GDP growth has a significant and positive effect on consumption-based CO 2 emissions (CCO 2 ) in the SAARC region. These outcomes imply that economic growth in South Asian countries comes at the cost of environmental degradation, including air pollution and carbon emissions. The results further indicate that the squared-GDP (GDP 2 ) is negatively associated with CO 2 emissions. More precisely, FMOLS estimation shows that a 1% increase in squared GDP minimizes CCO 2 emission by 4.89% if all other factors are considered constant. Similarly, according to the DOLS approach, when squared-GDP grows by 1%, CCO 2 emissions will fall by 0.334%. The positive and negative effects of GDP and its square term, respectively, on CCO 2 emissions confirm the presence of an EKC in the region under study. This evidence corroborates the findings of Saboori and Sulaiman (2013), Farhani and Shahbaz (2014), Kasman and Duman (2015), Shahbaz et al. (2016aShahbaz et al. ( , 2016b, Dogan and Seker (2016), Le and Ozturk (2020) and Pablo-Romero et al. (2017) in regard to the ASEAN countries, MENA region, African countries, new EU members, EU 27 countries, 47 EMDEs countries, and OECD countries. Studies by Knight and Schor (2014), Baloch et al. (2021), , He et al. (2021), and Kirikkaleli and Adebayo (2021) have considered consumption-based carbon emissions to assess environmental degradation. Our results regarding the positive and negative impact of GDP and its square term, respectively, on CCO 2 emissions endorse the findings of Knight and Schor (2014), Baloch et al. (2021), and  with respect to high-income countries, OECD countries, and Chile, respectively. However, the results contradict the conclusion of Kirikkaleli and Adebayo (2021), who found an insignificant connection between economic growth and consumption-based CO2 in the case of India. This is because India has achieved a certain degree of energy efficiency gains and is thus in a position to pursue economic expansion strategies without being overly worried about emissions. We also investigate whether an N-shaped EKC exists in SAARC nations by including the cubic term of per capita GDP in our empirical model. The results highlight that the cubic term of GDP (GDP 3 ) is positively linked with Table 13 Johansen Fisher panel cointegration test "***," "**," and "*" indicate the level of significance at 1%, 5%, and 10%, respectively  CCO 2 emissions, thereby confirming an N-shaped relationship between economic growth and CCO 2 emissions. This relationship pattern implies that at the initial phase of economic growth, an increase in income entails a higher level of CO 2 emission. Further increase in income level leads to an improvement in environmental quality by reducing CO 2 emission. Lastly, in the third stage, the environment again deteriorates after a certain income level is achieved. One possible explanation for this behavior of EKC in SAARC nations is that the economy of the region is mainly driven by India, where technological obsolesce effect may be in operation as the country is still heavily dependent on fossil fuels to fulfill its ambitious development projects. Therefore, the technical effect is being outweighed by the scale effect in SAARC countries. These findings regarding the existence of N-shaped EKC corroborate the studies of Alvarez-Herranz et al. (2017), Bekun et al. (2019), and Zhang et al. (2020). However, these outcomes contradict Gyamfi et al. (2021). The findings uncover a positive and highly significant link between energy usage and CCO 2 emissions in SAARC countries. The results of the FMOLS method reveal that when energy consumption rises by 1%, CCO 2 emissions increase by 0.378%, all other factors remaining the same. These results endorse the previous findings of Arouri et al. (2012) for MENA countries; Ozturk and Acaravci (2013) for Turkey; Shahbaz et al. (2018) for a sample of high, middle, and low-income countries; Tang and Tan (2015) for Vietnam and Baek (2015) for Arctic countries; and Hanif et al. (2019) for emerging Asian economies. Like other developing countries, SAARC nations are confronting multiple economic challenges and are struggling to improve the living standards of their populations. To achieve their ambitious growth targets and produce more goods, they are using more energy, which mainly originates from fossil fuels in SAARC countries. This, in turn, generates CO 2 emissions and other toxic gases. Additionally, as Hanif et al. (2019) pointed out, the combustion of fossil fuels discharges more CO 2 emissions in Asian developing countries as they use less efficient and oil-based technologies (Table 16).
Contrary to the "pollution haven hypothesis," our results indicate that inward FDI diminishes CCO 2 emissions in SAARC countries. However, this relationship is statistically insignificant. A positive relationship between FDI and CO2 emissions is also reported by Zhu et al. (2016) for ASEAN-5 countries and Waqih et al. (2019) for SAARC countries. However, this outcome is contrary to those reported by Baek and Koo (2008), Chandran and Tang (2013), Shahbaz et al. (2018), and Khan and Ozturk (2019). This positive effect of FDI experienced by SAARC countries may be attributed to adopting modern technology and approaches designed for improving environmental quality.
Trade openness is found to boost the environmental quality in SAARC countries by plummeting CCO 2 emissions.
The coefficients of trade openness obtained from FMOLS and DOLS are − 0.013 and − 0.014, respectively, indicating that increased trade openness leads to a decline in CCO 2 emissions. These findings are the same as Dogan and Seker (2016) for 23 top renewable energy-using nations, Shahbaz et al. (2013) for Indonesia, and Zhang et al. (2017) for 10 newly industrialized countries but differ from Shahbaz et al. (2016aShahbaz et al. ( , 2016b for BRICS countries. This is an interesting finding as the conventional wisdom supports the view that trade openness would degrade the environment in developing countries. Nevertheless, there are some plausible explanations for a negative association between trade openness and CCO 2 emissions in the SAARC region. First, the extent of trade openness in SAARC nations is still meager to affect CO 2 emissions positively. Second, trade openness can encourage the transfer of innovative technologies from industrialized to developing economies, reducing CO 2 emissions (Zhang et al. 2017;Dauda et al. 2021). In other words, in line with the "pollution halo effect," knowledge spillover from engaging with industrialized nations promotes green growth in host countries by diminishing emissions. Third, India, which is the dominant economy in the SAARC region, is paying more attention to the environmental impact of a trade by drawing lessons from industrialized nations. Interestingly, the evidence we have drawn from FDI and trade openness rejects the "pollution haven hypothesis" in SAARC countries favoring the "pollution halo hypothesis" from 1990 to 2018.
The results also demonstrate that urbanization in SAARC member countries significantly contributes to CCO 2 emissions. More precisely, the results obtained from FMOLS show that a 1% rise in urbanization increases CCO 2 emissions by 1.495% at less than 1% level of significance. This outcome endorses the findings of Kasman and Duman (2015), Turkekul (2016), andHanif (2018). Despite offering various economic and social benefits to the lives of the people, rapid urbanization creates greater demand for transportation, public utilities, and other services. The higher demands for these services put increasing pressure on natural resources, thereby generating negative externalities, including GHGs emissions, health issues, and water contamination (Hanif et al. 2019). South Asia is experiencing rapid growth of urban population; according to an   Table 17. The findings support the unidirectional causality that runs from economic growth to CCO 2 emissions and the unidirectional causality that runs from squared economic growth to CCO 2 emissions. These outcomes align with . The findings also support the unidirectional causality that runs from cube of economic growth to CCO 2 emissions. The results also reveal the evidence of unidirectional causality from economic growth to energy consumption, squared economic growth to energy consumption, trade openness to energy consumption, cube of economic growth to energy consumption, cube of economic growth to urbanization, and urbanization to trade openness. Interestingly, the findings show bidirectional causality between urbanization and CCO 2 emissions, urbanization and economic growth, urbanization and squared GDP, and urbanization and energy consumption.

Conclusion and Policy Recommendations
This research is an empirical attempt to determine the key drivers of consumption-based carbon dioxide emissions in SAARC nations for a time span of 1990 to 2018. We investigate the effects of economic growth, energy consumption, trade openness, and FDI on consumption-based CO2 emissions within the EKC framework. Unlike most previous researches, our study utilizes consumption-based CO2 emissions as a proxy for environmental deterioration. Further, instead of considering nominal trade openness or trade potential index as a proxy of trade openness, this paper considers composite trade share for the same. The study employs both the first and second-generation unit root tests, revealing all the variables are stationary either at level or at first difference. After confirming all the variables under the present research are I(0) and I(1), we performed firstand second-generation cointegration tests. The findings of these cointegration tests show that the target variables have strong empirical evidence of a long-run equilibrium relationship. Apart from establishing a traditional inverted U-shaped association between income and CCO2 emissions as evidenced by the negative and positive coefficients of GDP and squared-GDP, our findings reveal the presence of an N-shaped EKC in the SAARC region. Besides, we find that energy consumption and urbanization are rapidly degrading the environment in the region by emitting CCO2, while FDI and trade openness are found to improve the environmental quality in SAARC countries by reducing CO 2 emissions. Although the impact of FDI on CCO2 emission is proved positive, the relationship is not significant. The negative elasticities of FDI and trade openness found in the study reject the "pollution haven hypothesis" in SAARC countries favoring the "pollution halo hypothesis" over the period 1990 to 2018. DH causality test results support the unidirectional causality running from economic growth to CCO2 emissions and from squared economic growth to CCO2 emissions. The results also reveal the evidence of unidirectional causality from economic growth to energy consumption, squared economic growth to energy consumption, and trade openness to energy consumption. Due to the unavailability of data for Bhutan and Maldives, only Bangladesh, Nepal, India, Pakistan, and Sri Lanka are considered for this study. Population and usage of renewable resources are other significant determinants of consumption-based carbon emissions that are not considered in this study due to the unavailability of data for some SAARC countries. The N-shaped hypothesis can be explored for other economic regions by employing FMOLS and DOLS methods.

Policy Implications
Some of the policy recommendations emanating from this study are: The findings show that in the SAARC area, trade openness, and FDI have a negative impact on carbon dioxide emissions. At the same time, GDP and energy consumption are the primary drivers of environmental degradation: 1. As the findings highlight an N-shaped association between economic growth and CCO2 emissions, the "***," "**," and "*" indicate the level of significance at 1%, 5%, and 10%, respectively SAARC countries, especially India, which is the dominant economy in the region, should revise their current policies by considering the technological obsolesce effect to avoid further environmental degradation in the future. The authorities in the region need to continually encourage the creation/upgradation of new technologies so as to boost environmental quality. 2. The result manifests that SAARC nations are now using a high level of energy, which is causing environmental degradation. Therefore, it is suggestive for these economies to enhance the level of energy efficiency and augment the share of renewable energy sources in the energy mix. Additionally, they should pay more attention to environmental protection by implementing policies and designing action agendas. 3. The importance of the scale effect in economic activity must be addressed, and SAARC countries should invest in new technologies that are both environmentally benign and efficient. 4. As urbanization is found detrimental for environmental quality in the SAARC region, the member countries should slow down the pace of urbanization by creating income-generating avenues in rural areas. Although urbanization enhances the wellbeing and living standards of people, its pace should be monitored in such a way that it does not worsen the environmental quality. Additionally, the authorities can raise the awareness of the urban population about the necessity of green energy through awareness programs and open dialogues with civil society at both the micro and macro levels. 5. The region must also concentrate on strategies to reduce emissions by fostering industries that use green and cleaner energy sources. Aside from that, the area should focus on green projects in a shared forum and use advanced carbon-reduction mechanisms (cap and trade, carbon permits) to reduce emissions. 6. SAARC countries' energy usage is heavily reliant on nonrenewable sources. Therefore, alternative energy resources should be discovered for both economic growth and environmental protection. Furthermore, nonrenewable resources must be used wisely and effectively. To reproduce the intended results, policies must be developed in a shared forum. 7. On the government's front, national policies targeting consumption-based carbon emissions should also be designed to combat environmental degradation in order to ensure harmony between energy consumption, foreign direct investment, trade openness, and environment. Creating awareness and disseminating information at a mass level regarding the harmful effects of rising carbon emissions and resulting environmental unsustainability is one of the mediums.

Author contribution Komal Kanwar Shekhawat and Arvind Kumar
Yadav have done the introduction, literature, review, and method section. Pushp Kumar has made the analysis. Md Sahnewaz Sanu has written the result and discussion section. Pushp Kumar has done the overall formatting of the paper. All authors have read and approved the manuscript.
Data Availability Data will be made available upon request.

Declarations
Ethics approval and consent to participate Not applicable.

Consent for publication Not applicable.
Competing interests The authors declare no competing interests.