Environmental costs of political instability in Pakistan: policy options for clean energy consumption and environment

Using time series data of Pakistan from 1990 to 2019, this study explores the asymmetric effects of political instability on clean energy consumption and CO2 emissions. The results from the traditional ARDL model show that political stability lessens environmental damage by reducing CO2 emissions in the long run. However, when we used the nonlinear ARDL approach, we found that political instability not only reduces the consumption of clean energy but also leads to damage environmental quality in the long run in Pakistan,while political stability not only increases the consumption of clean energy but also helps improve environmental quality in the short run in Pakistan. Thus, macroeconomic policies to promote expansion in clean energy consumption will directly stimulate green economic growth and environmental quality.


Introduction
The main worldwide distress faced by the existing world, environmentalists, and scholarly researchers is the persistently declining quality of the environment. The obstinate upsurge in greenhouse gas emissions is normally considered a fundamental cause behind climatic change and environmental degradation (Uzar 2020). The persistent environmental distresses have encouraged many governments to adopt political choices to tackle environmental issues. Pollution emissions are generated from developing, developed, and emerging economies. The task onward is not easy to handle and needs decisions from political segments.
Apparently, environmental performance and economic expansion are provoking trade-offs, which shows that spreading growth rates are the major cause behind inevitable environmental degradation. However, if the use of coal and fossil fuel is converted with the consumption of clean energy, then the climatic loss can be decoupled from development (Majeed and Luni 2019). Presently, international institutes, domestic governments, political scientists, energy experts, and environmental economists are considering the significance of clean energy sources in the process of production. More specifically, pollution emissions are alleviated after the Kyoto Plan which was signed in 1997 and being implemented in 2005. Furthermore, clean energy has been declared as the 17 th sustainable development goal to control and regulate environmental issues. Institutional quality plays a significant role in the utilization of natural resources and the sustainability of the environment (Abdala 2008). Due to political instability, climatic regulations and rules become less effective; and thus, pollution emissions deteriorate the quality of the environment. Al-Mulali and  investigated the dynamics that influence environmental quality in 14 MENA economies and found that political stability is a fundamental determinant that contributes significantly to improving the quality of the environment. Additionally, the industrial sector, trade sector, urbanization, and energy consumption deteriorate the quality of the environment. Previous studies overlooked the dynamics of political economy in investigating its influence on carbon emissions and energy consumption though several studies focused on the effect of democracy on the degradation of environment (Adams et al. 2016;Torras and Boyce 1998). For instance, Raleigh and Urdal (2007) emphasize that dynamics of political economy, specifically the political regime, is important in shaping the outcomes of environment. The previous studies on the factors of environmental pollution and energy consumption provide contradictory findings depending upon expansion phases (Adams and Klobodu 2017). The study of Adams and Klobodu (2017) investigates the nexus between environmental degradation and the political environment. The study measured the political environment through bureaucracy quality and democracy. The findings of the study demonstrate that both measures of political environment mitigate pollution emissions and contribute significantly to improving environmental quality.
A vast body of literature suggests that good-quality institutions help in environmental regulation and carbon emissions mitigation. Environmental regulation is repressed due to financial management, bureaucratic inertia, red tape, and high corruption. Good-quality institutions remove these determinants of anti-environmental regulation and pave the path for the better organization of the quality of the environment (Panayotou 1997). In the case of MENA economies, Goel et al. (2013) investigated the effects of shadow economy and corruption on carbon emission. The study found mixed outcomes. In general, the study revealed that higher levels of shadow economy and corruption are associated with lower levels of carbon emissions.
Many political scientists, energy experts, and environmentalists have started considering the political features of environmental problems since the 1990s. The existing literature also highlighted the significance of political institutions in enhancing quality of the environment. For instance, the study of Congleton (1992) demonstrates that democracy contributes to improving environmental reforms. Many other studies have confirmed this finding, with few exemptions (Barrett and Graddy 2000;Midlarsky 1998). This study belongs to the emergent literature that highlights political indicators in affecting environmental and energy concerns.
The existing body of literature on environmental performance and political economy is questioned on two major grounds. Firstly, the political stability aspect is not measured adequately. Secondly, the empirical findings are doubtful because these studies provide empirical estimates based on out-of-date estimation techniques. The theoretical foundations of environmental performance and political stability can be drawn back to the groundbreaking study of Grossman and Krueger (1995) which highlighted a U-shaped linkage between environmental pollution and GDP per capita. This association is known as the "Environmental Kuznets Curve (EKC)," and this linkage is largely debated in the literature; still, to date the empirical literature has not reached any conclusive evidence (Majeed and Mazhar 2020). It is mainly due to the reason that several studies adopted the linkage between emissions and growth as an automatic process. In reality, it is not possible as the pioneering study of Grossman and Krueger (1995) proclaims that EKC mainly depends upon rejoinders of public policies that are grounded in government provision for environmental regulation. Subsequently, a new dispute has appeared which emphasizes political stability and background in determining the environmental quality.
Grossman and Krueger's statement has merits as there is a need for a clean environment that produces its automatic supply. But, political scientists claim that environmental reforms and regulations largely depend on the smooth functioning of markets. Market failure-related issues mainly hamper the effectiveness and implementation of environmental policy. Another reason is that information about the effects and causes of the problems are asymmetric. Furthermore, a socially cohesive and efficient performance demands the public policy structure based on mechanisms of collective actions. Therefore, investigating the asymmetric effects of environmental and political performance remains a fundamental research query and this study tries to fill this vacuum. It is believed that political stability ensures environmental quality and free flow of information in a democratic setup flourished policy learning (Barret and Graddy 2000).
In contrast, the existing literature also denotes that the world's top democratic regimes have been proved to be laggard towards environmental conservation (Battig and Bernauer 2009;Burnell 2012;Bohmelt et al. 2016). Battig and Bernauer (2009) denote that the main cause is the freedom of individuals in democracies, particularly in the transportation sector. Literature also denotes magnitude of democracy matters such as the form of the electoral system (Bohmelt et al. 2016) and the level of inclusiveness (Bohmelt et al. 2016), instead of democracy per se, are supportive for environmental quality.
The abovementioned studies provide diverse impacts of political economy on quality of the environment. These studies conclude that political economy exerts diverse impacts based upon the type of political indicators such as development phases of economies, geographical bases, forms of democracy, and electoral system, which in return have asymmetric impacts on energy and environment. Thus, there is a need for more refined conclusive evidence to untangle the intricate association of political stability on environmental quality and clean energy.
Our study will contribute to the current literature in the following manners. Firstly, to the best of the authors' knowledge, this study is a pioneer of its kind that introduces the role of political instability in pollution emissions and clean energy framework. Secondly, it takes into consideration diverse dimensions for measuring political instability; however, previous studies considered a single feature of political instability. Thirdly, our study also examines the asymmetric relationship between political instability, environmental pollution, and clean energy by employing the non-linear autoregressive distributive lags (NARDL) technique. The findings of this study will contribute to the provision of appropriate policy choices to achieve the environmental and energy concerns of Pakistan's economy.

Model and methods
As far as the theoretical underpinnings are concerned, we observed that political stability is a key element to applying stringent environmental policies (Purcel 2019). In the presence of political stability, the policy practitioners are in a better position to initiate new and better policies to control environmental pollution (Congleton 1992). In addition, political stability paves a way to initiate new projects which are based on cleaner energy production and consumption. However, in the presence of political unrest and chaos, it is not possible for any government to initiate new policies and programs which are based on low emissions and cleaner energy use (Uzar 2020). Similarly, due to financial development, consumers are able to take loans and can buy energy-intensive goods, which in turn bring an increase in emissions. In addition, different durable goods also increase the use of energy. Another essential indicator is the GDP growth rate; this is particularly relevant for developing economies. As developing economies grow, their growth rate is majorly coming from the industrial sector. In addition, a higher level of GDP is connected with a higher standard of living and most people are able to buy more energy-intensive goods, for example, air conditioners, refrigerators, dishwashers, and also personal cars for commuting, etc. Therefore, higher GDP growth rates are interconnected with higher levels of emissions and energy use. Based on the previous studies (Al-Mulali and Ozturk 2015 and Sofuoğlu and Ay 2020), we have constructed our empirical model in Eq. (1) and Eq. (2). We examine the nonlinear impacts of political instability on clean energy consumption and environment. Therefore, we adopt the following clean energy consumption and CO 2 emissions model specification: As can be seen, in Eqs. (1) and (2), we have considered political stability (PS) as the main determinant of clean energy consumption and environment. Previous literature noted that political stability often leads to pro-environmental behaviors in green economic growth. So, we expect an estimate of 1 to be positive in Eq. (1), but estimates of 1 to be negative in Eq. (2). We used economic growth (EG) and financial development (FD) as control variables in empirical analysis. Equations (1) and (2) give us only the long-run nexus among concern variables. To assess short-run impacts, Eqs. (1) and (2) must be expressed in an error-correction format as follows: Equations (3) and (4) reported normalized estimates via OLS. The short-run effects are reported in the estimates of "first differenced" variables and long-run estimates are β 2 ,β 3 , and β 4 . The linear ARDL specification is introduced by Pesaran et al. (2001) and has some additional advantages over the time series econometric technique. Long-run and short-run effects can be estimated in one step. To avoid the problem of spurious regression, Pesaran et al. (2001) recommend two tests for cointegration, for instance, F-test and ECM or t-test. Indeed, under ARDL approach, variables could be a grouping of I(1) and I(0). We prolonged the literature so that we can explore the asymmetric impact of political stability (PS) on clean energy consumption and environment. Shin et al. (2014) introduce the partial sum concept to decompose (ΔPS) into twofold new time series. Two new time series generating variables are as follows: where PS + t reveals the partial sum of positive shock, inferring political stability, and PS − t reflects the partial sum of (1) negative shock, inferring political instability. Two new time series are used to replace PS t in Eqs. (3) and (4) to arrive at: Error-correction models such as (7) and (8) are generally known as asymmetric ARDL, whereas Eqs. (3) and (4) are mentioned as the symmetric ARDL. Shin et al. (2014) validate linear and nonlinear models that are subjected to the same estimation method and diagnostics tests. For nonlinear ARDL, we can test a few other asymmetry hypotheses. First, if two partial sum factors have different lag orders in the short run, then it is a sign of short-run asymmetry. Second, short-and long-run asymmetric effects of political stability on clean energy and CO 2 emissions will be confirmed through the Wald test.

Data and variable construction
This section will elucidate the data and variables' construction. First, we will elaborate on our dependent variables. To accomplish our objectives, we have two dependent variables in this study, i.e., clean energy use and CO 2 emissions as proxies to assess the impact of political stability on clean energy and environmental quality. Whereas our most essential independent variable is political stability, we have included the political stability index, which takes values from 0 to 100. Furthermore, financial development and (7) economic growth are used as control variables in our empirical analysis. Financial development is the domestic credit which is extended to the private sector. On the other hand, we have taken the GDP growth rate as a proxy for GDP. We obtained all datasets from the World Bank, except political stability that we have extracted from ICRG. In Table 1, the means of CE, CO 2 , PS, EG, and FD are 3.171%, 11.65kt, 47.77, 4.159%, and 21.88% years, respectively.

Empirical findings and discussion
The prime objective of the present study is to assess the asymmetric impacts of political stability on clean energy and CO 2 emissions in the case of Pakistan. To specify the appropriate model, the first step is to check the stationarity of the data by using Phillips-Perron (PP) and augmented Dickey-Fuller (ADF) unit root test statistics. In Table 2, ADF test statistics values reveal that CE, CO 2 emissions, and PS are non-stationary at I(0); and thus, they become stationary at I(1), i.e., first difference. However, the rest of the two variables, namely, EG and FD, are stationary at I(0). After checking the stationarity of the included variables, we can infer that we can apply the NARDL approach for the empirical estimation. Meanwhile, if we carefully observe the PP statistics values, then we can find out that CE, PS, and FS are non-stationary at I(0) but they are stationary at I(1).
The rest of the two variables are stationary at I(0). Table 3 give us mixed order of integration in the structural break unit root tests. Next, as our data is an annual time series, the analysis applied a maximum of two lags, and we imposed Akaike Information Criterion (AIC). According to Hakkio and Rush (1991), co-integration is a long-run phenomenon and it provides better results when data is stretched over a long period instead of a large number of observations. In the past, some good studies have been conducted clearly for 29 observations in a time series nonlinear hypothesis dimension (Usman et al. 2021 andUllah et al. 2021). As there are a lot of variables for which weekly, monthly, or quarterly data  is not available, hence, a recent study is using annual data to capture the asymmetric impact of political instability on clean energy and the environment. Table 4 depicts the empirical estimated results of ARDL and NARDL models to quantify the asymmetric and nonasymmetric impacts of political stability on clean air and CO 2 emissions in Pakistan. First, we will discuss the estimated results of the ARDL model and then we will explain the NARDL model for clean air and also for CO 2 emissions. In the first column of Table 4, we have reported the empirical coefficients of the ARDL model for the clean energy model. We observe that the coefficient of political stability is positive and significant at 5%. It implies that a 1% increase in political stability will increase the use of cleaner energy by 2.987% in the case of Pakistan. We can infer that political stability is positively influencing the use of cleaner energy. On the other hand, the 1-year lag value of political stability is also positively associated with the use of cleaner energy in the case of Pakistan. It is indicated from Table 4 that in the short run one 1% increase in the 1-year lag value of political stability is causing a 2.130% increase in the use of clean energy. However, we could not find a significant difference between the current and lag values of political instability on the use of clean energy.
Next, we have concluded from the empirical results that economic growth is positively associated with the use of clean energy in Pakistan in the short run. In the same context, a 1% increase in economic growth leads to a 0.101% increase in the use of clean energy and it is significant at 5%. Meanwhile, the one-period lag of economic growth is also positively and significantly associated with the use of clean energy in Pakistan. It elaborates that with an increase in GDP, the economy invests in cleaner and environmentally friendly technologies. Our results are in line with previous studies such as Ahmed and Long (2013), Javid and Sharif (2016), and Khan and Ullah (2019). Similarly, we have found that financial development is negatively associated with the use of clean energy in Pakistan. According to the empirical estimates for the short run, we can observe that a 1% increase in financial development is leading to a 1.290% decrease in the use of cleaner energy in Pakistan. Our results are consistent with the previous literature on the impact of financial development on environmental quality, for example, Zhang (2011), Ozturk andAcaravci (2013), Dogan and Turkekul (2016), Lahiani (2020), and Shoaib et al. (2020). These studies are of the view that financial development is causing a deterioration in overall environmental quality. Overall, we can justify the negative impacts of financial development through three effects, namely, capitalization effect, technology effects, and income effect.
Next, we will explain the long-run coefficients of the clean energy model. We observe that in the long run, political stability is negatively associated with the use of clean energy; however, it is insignificant. In a similar study, Carlsson and Lundström (2003) also found the insignificant impact of political stability on environmental quality. Similarly, Sarkodie and Adams (2018) also explained the insignificant association between political stability and environmental degradation in South Africa. Furthermore, economic growth is positively associated with the use of cleaner energy, even though it is not significant. In the same context, Sharma (2011) also found a positive and insignificant impact of GDP on environmental quality for the sample of 69 countries. Financial development is also negatively associated with the use of cleaner energy but it is also insignificant. Few other studies in literature such as Dogan and Turkekul (2015) and Abid (2016) also found an insignificant association between these two variables of interest. Now, we will discuss the empirical results of the ARDL model for the CO 2 model. We found a few very interesting insights in this regard. First, we will explain the short-run empirical estimates of the ARDL model. We can infer that in the short run the political stability is negatively associated with CO 2 emissions but it is insignificant. Meanwhile, we have included a one-period lag value of political stability, which positively influences the use of CO 2 emissions but it is insignificant. Our results are consistent with the previous literature, where different researchers found an insignificant relationship between political instability and CO 2 emissions, for example, Carlsson and Lundström (2003) and Sarkodie and Adams (2018). Meanwhile, in the short run, economic growth is positively and significantly associated with the level of CO 2 emissions. It implies that a 1% increase in economic growth will lead to the 0.006% increase in CO 2 emissions and it is significant at 5%. We found immense literature on the positive and significant impact of GDP on CO 2 emissions (Sharma 2011;Kasperowicz 2015;Ullah et al 2020). Furthermore, the one-period lag of economic growth is negatively and significantly associated with the level of CO 2 emissions in Pakistan.
In the previous literature on the impact of financial development on CO 2 emissions, we observe that financial development can also spur manufacturing activities, therefore considered to be the most important source to upsurge the CO 2 emissions. In the present study, we can conclude from the short-run empirical analysis that financial development is positively associated with the level of emission in the short run and it is significant at 5%. Meanwhile, a 1-year lag value of financial development is negatively influencing the CO 2 emissions and it is significant at 10%.
In the long run, we observe that political stability is negatively and significantly associated with CO 2 emissions, i.e., a 1% increase in political stability, in the long run, causes an increase in CO 2 emission by 1.267% and it is significant at 10%. According to the previous literature, political stability is one of the most prominent factors to improve environmental quality by increasing clean energy consumption. In the presence of political stability, the government can impose strict environmental regulations which in turn help improve the environmental quality in the economy. Our results are consistent with previous literature, for example, Gani (2012aGani ( , 2012b, Lau et al. (2014), andBhattacharya et al. (2017). Abid (2016) explained that political stability is positively influencing the environmental quality in the case of selected Sub-Saharan African countries. Al-Mulali and  examined the impact of political stability on environmental quality in the case of the MENA region and concluded that political stability is helping improve the environmental quality in these economies. Similarly, economic growth is also positively surging the level of CO 2 emissions but it is not significant in our ARDL model. On the other hand, our financial development is positively and significantly causing an increase in CO 2 emissions and the coefficient is significant at 5%. We found immense literature which is supporting the positive association between financial development and CO 2 emissions. For example, Komal and Abbas (2015) assessed that financial development is positively associated with CO 2 emissions in the case of Pakistan. Besides, Khan et al. (2020) revealed that financial development is upsurging the level of CO 2 emissions. Shahzad et al. (2017) applied the ARDL test and found a long-run and positive association between financial development and CO 2 emissions in the case of the Pakistani economy. Abbasi and Riaz (2016) also supported the positive impact of financial development on CO 2 emission.
We have explained the results of diagnostic tests in Table 4. First of all, F tests' values are confirming the presence of co-integration for the clean energy model as well as the CO 2 emission model. Furthermore, to know about the existence of serial correlation in our estimated models, we have applied a Lagrange multiplier test. Its coefficient is insignificant in both of the models. Therefore, we can conclude that there is no serial correlation in these estimated models. At the same time by looking at the values of the RESET test, we can conclude that there are no model specification errors in our estimated models. In the end, we are concerned with the stability of the parameters in both of the estimated models. Thus, we have applied two tests, i.e., CUSUM test and the CUSUMSQ test. Here, "S" implies stability whereas "US" implies instability. However, we can see in Table 4 that both of our models are indicating the stability of the parameters. Now, we will explain the asymmetric effects of political instability on clean energy use and CO 2 emissions in the case of Pakistan. First, we will explain the short-run coefficients of the clean energy use model. As column 2 of Table 4 reveals, in the short run, a positive shock in political stability is positively associated with the use of clean energy and it is significant at 10%. Moreover, a negative shock in political stability is also negatively and significantly associated with clean energy use in Pakistan. The empirical results are supporting the asymmetries in the relationship between political stability and the use of clean energy in the case of Pakistan. In the same regard, Purcel (2019) explained the relationship between political stability and environmental degradation in the case of lower and middle-income countries and concluded an inverted U-shaped relationship. Meanwhile, a positive shock in the 1-year lag value of political stability is also positively associated with the use of clean energy and it is significant at 5%. We can infer from our empirical analysis that economic growth is positively influencing the use of clean energy in the case of Pakistan. The empirical results are supported by previous literature: Nasir and Rehman (2011), Ahmed and Long (2013), Javid and Sharif (2016), and Khan and Ullah (2019). We can infer from our empirical estimation that financial development exerts a negative influence on the use of clean energy. We found immense evidence from the existing literature in this regard, for instance, Haseeb et al. (2018), Pata (2018), and Gokmenoglu and Sadeghieh (2019).
In the long run, a positive shock in political stability is negatively influencing the use of clean energy; however, it is not significant. Similarly, a negative shock in political stability is negatively and significantly associated with the use of clean energy. Also, Rizk and Slimane (2018) indicated that political stability can lower CO 2 emissions. In the case of economic growth, we have observed that in the long run, economic growth is exerting a positive influence on the use of clean energy. Ahmed and Long (2013) and Javid and Sharif (2016) also found the same evidence in the case of Pakistan. In the long run, the financial development is negatively and insignificantly associated with the use of clean energy. On the same lines, Ding et al. (2018) also found an insignificant association between financial development and CO 2 emissions for China and 219 trading partners for the time period 2004 to 2014.
Next, we will discuss the asymmetric impacts of political stability on CO 2 emissions in the case of Pakistan. The empirical results of the short-run estimates of the NARDL model reveal that a positive shock in political stability is negatively associated with CO 2 emissions and also a negative shock in political stability is positively associated with the level of CO 2 emissions. Therefore, in the case of Pakistan, we can infer the presence of asymmetries between political stability and CO 2 emissions. Similarly, in the short run, financial development is a prominent source of CO 2 emissions in our empirical analysis. As indicated by our empirical analysis, a positive shock in financial development is causing an increase in CO 2 emissions.
In the long run, a positive shock in political stability is exerting a negative influence on CO 2 emissions and a negative shock in political stability is causing an upsurge in CO 2 emissions. We found strong evidence of asymmetries in the said relationship in the case of Pakistan. According to our empirical analysis, economic growth is positively associated with CO 2 emissions in the long run. There is immense literature available in the same context, for instance, Tamazian et al. (2009), Shahbaz et al. (2013, and Dogan and Seker (2016). Financial development is proved to be an important determinant of an increase in CO 2 emissions. Our results are consistent with those of Ozturk and Acaravci (2013) and Farhani and Ozturk (2015). These studies are of the view that financial development is actually giving way to the entrance of heavy industries into the economy and therefore upsurging the CO 2 emissions.
In the end, we will discuss the results of diagnostic tests, in the case of NARDL models for clean energy use and CO 2 emissions. The F test values validate the joint significance of long-run estimates for both models, i.e., clean energy and CO 2 emissions. Besides, the critical values of the F test also confirm the existence of cointegration in both models. To check the serial correlation, we have applied the LM test and by looking at the estimated coefficients of LM tests we could not find any evidence of serial correlation. Besides, to check the correct model specification, and also stability of the parameters we have applied three tests, RESET, CUSUM, and CUSUMsq tests. These tests validate the correct model specification as well as parameter stability. According to the estimated values of the goodness of fit measures tests, we can infer that models are well fitted. We applied the Wald test to confirm the asymmetries in the model. We can infer from the estimated coefficients of Wald tests that both the short-and long-run asymmetries are existing in our estimated models.

Conclusion and implications
The key purpose of this paper is to investigate the asymmetric impact of political instability on clean energy consumption and CO 2 emissions in Pakistan. To achieve this objective, the time-series data NARDL approach is employed for the period 1990-2019. The finding of the study has revealed that political stability has a positive and significant impact on clean energy consumption in short run. However, the short-run effects of political stability lower CO 2 emissions in Pakistan; this finding is in accordance with the theory. However, CO 2 emissions in Pakistan is not affected by the political instability in the short run. Moreover, in the long run, political stability did not show any significant and robust effects on clean energy consumption and CO 2 emissions. Conversely, in the long run, political instability has only revealed negative and significant effects on clean energy consumption but it has a positive impact on CO 2 emissions in Pakistan. The asymmetric results show that policy instability has a more dramatic and robust impact on clean energy consumption and CO 2 emissions than political stability. This finding is consistent with that of Al-Mulali and , who argues that political instability leads to lower public-private clean energy investments and, hence, lower clean energy consumption and more carbon emissions.
Based on empirics, some specific implications can be made for the deployment of clean energy and environment. The institution should reduce the conflicts and political instability in Pakistan, which is essential for social, economic, and political performance. The conflicts and political instability not only weaken clean energy consumption and production performance, but they also decline the environmental regulations. Thus, Pakistan needs to develop a strong political and institutional framework for environmental quality. Pakistan needs to take measures to improve governance and redesign the stable economic policies of energy and environment.
The empirical study generally provides robust evidence for Pakistan's economy. Moreover, Pakistan has a unique experience of political instability along with a weak institutional setup and high environmental vulnerability. Therefore, an empirical analysis using a nonlinear estimation approach are quite useful for Pakistan. Therefore, we cannot extend the empirical analysis for other economies, which is one of the limitation of our study. The current study is limited to only Pakistan's economy. The researcher's similar empirical studies can be continued to other highly politically unstable economies, mainly by using the fresh panel asymmetric ARDL approach. Future studies can also focus on the impacts of numerous socioeconomic factors such as health, education, social security and instability, economic instability on clean energy consumption, and CO 2 emissions. Further empirical inquiry is required to seek better measures and channels through which political instability affects clean energy consumption and CO 2 emissions. Future research should use a different measure of political instability in analysis for robust analysis.
Author contribution This idea was given by Muhammad Tayyab Sohail. Parvez Ahmed Shaikh, Muhammad Tayyab Sohail, and Muhammad Tariq Majeed analyzed the data and wrote the complete paper, while Zubaria Andlib read and approved the final version.

Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations
Ethics approval Not applicable Consent to participate I am free to contact any of the people involved in the research to seek further clarification and information.

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Competing interests The authors declare no competing interests.