Introduction

An earth-wide temperature has been boosted and environmental changes are the burning question of today’s world. Environmental problems do not only affect the human activates but also our economies, legislative issues, and ways of life (Kocak and Sarkgunesi 2018). Business activities are one of the factors which increase environmental degradation. In order to improve the various economic activities, countries have to sacrifice the environmental climate or other natural resources (Shahbaz et al. 2018). Similarly, the most important and sensitive problem of environment is climate change and it is caused due to the extraction of carbon dioxide and greenhouse gas emissions which are produced due to energy consumption (Bekun et al., 2019). The Intergovernmental Panel on Climate Change (2014) stated that environmental degradation is mainly generated caused to economic activity, population, energy consumption, and technology and climate policies. Environmental degradation has been increasing the past couple of decades which has been damaging not only to the environment but also human life. It was 11,190 million in 1965 which has been increased to 33,444 million in 2017 with an average growth rate of 37.5% (BP Statistical Report 2017). Economic growth can only be achieved with the help of increase in economic activity which cannot be done without environmental degradation because for economic growth, most of the firms focus on mass production (Wu et al., 2015). The issue of the environmental degradation is the most important topic for the government and policymakers of every country and is trying to find out the way to achieve the development and growth in economic condition without its impact on environment (Bilgili and Ulucak 2018).

In today’s world, the developed and developing countries main idea is that (1) they should reduce environmental degradation by reducing energy usage and improving the technology by expenditure on it and brining technological innovation in effective and efficient devices and (2) they should try to use renewable and alternative clean source of energy technologies with low-carbon extraction (Won et al. 2017; Oikonomou et al. 2009). The environmental degradation can be reduced with the help of development in technologies and encouraging people to use energy in the most effective manner (Kahouli 2018; Won et al. 2017; Herring et al., 2007). Garrone and Grilli (2010) found the effect of energy innovation technology on environmental degradation for 13 developed nations spread during the time of 1980–2004. Findings showed a positive relationship with energy effectiveness by reducing the environmental degradation Cheng et al. (2017). Lee and Min (2015) found that the abundance of the effect of energy innovation on atmosphere and economic growth on the sample data of Japan covering the period of 2001–2010 showed a positive and significant connection between energy innovation with budgetary performance which directly reduces the environmental degradation. Energy innovation mostly focuses on energy-saving devices which changes the energy intensity of environmental degradation and greenhouse emissions. It finds various ways to produce energy by an alternative source of energy such as clear energy (Álvarez-Herránz et al. 2017). Energy innovation in the part of energy lessens the energy consumption and decreases the force of energy transformation by diminishing the generation expenses of sustainable power source (Garrone and Grilli 2010). Energy innovation likewise helps for the creation of perfect and low-carbon generation forms with any damage to financial development (Chen and Xu 2010; Ockwell et al. 2010). Energy innovation is the best and cost-effective way to energy conversion and reduction of environmental degradation because it fulfills the demand of energy by an alternative source of energy which reduces the environmental degradation (Chen and Lei, 2018). Energy innovation not only improves the production activity but also improves the efficiency of labor with economic growth by reducing environmental degradation. The energy innovation increases the renewable energy supply free from environmental degradation and change in the climate structure (Chen and Lei, 2018; Sadorsky, 2014; Sharif et al., 2019). If energy innovation is used on a large scale, it will protect the climate and improve energy security (Irandoust, 2016). Energy innovation is cost-effective and it is the most reliable way to lower carbon (Bayer et al., 2013). Through energy innovation, cost of the production and production time can be reduced and due to that, pollution will automatically reduce without change in the production activity which will not harm the economic growth (Cho and Sohn 2018). Energy innovation is cleaner than fossil energy because it extracts low greenhouse gasses as compared to fossil energy consumption (Asdrubali et al., 2015; Odeh and Cockerill, 2008). Technologies which convert energy into low intensity of environmental degradation are really helpful in order to reduce environmental degradation; it uses not only an efficient but also an effective way of energy without waste of resources. Energy innovation technologies believe in maximum output with minimum input (Su and Moaniba 2017).

European countries have been a new avenue to discuss environmental friendly and energy innovation implementation for sustainable growth. In 2008, a plan was launched namely the Strategic Energy Technology (SET) to focus on innovation activities and energy research within European countries. This plan was initially decided to support European countries’ policies on renewable energy, economic growth, climate change, energy security, energy-efficiency, and global competitiveness. The SET plan implementation started from a wide range of activities within the European industries to impact the short-term (to 2020) and prolong its benefits in the long run (to 2050). The SET Commission later proposed a revised and comprehensive plan to establish a target-oriented integration among different sectors in Europe. The revised SET plan mainly focused on key functional areas such as energy-efficiency, sustainable environment, smart systems, and renewable energy. The Commission also included two priority areas namely, nuclear power and carbon storage. These six important areas are responsible to correspond to innovative methods to achieve sustainable growth. On the other side, the future of the energy innovation system and sustainable environment is unclear for the European Union (EU) countries (Kim and Wilson, 2019). This required extensive empirical evidence to understand the issues pertaining to the EU’s energy innovation and environmental planning. These future uncertainties can be answered through empirical evidence.

Therefore, this research intends to focus on the important areas of environmental degradation, energy innovation and economic growth. For this purpose, our investigation is designed to answer the following: What effect will energy innovation diminish environmental degradation with any damage to economic development in European countries? How can energy innovation improve the environmental degradation? And what are the trends of main European countries’, like France and Turkey, environmental degradation in the upcoming period? How will other control variables be helpful in order to cut out the impact of environmental degradation? Numerous examinations have discovered the connection between environmental degradation with energy consumption and economic growth but very few studies have investigated the impact on energy innovation and economic growth on environmental degradation. The contribution of the study is that firstly, this study is using large and current data to discover the connection between environmental degradation with energy innovation and economic growth. Secondly, most researchers have investigated the co-relation and linkage between energy consumption and environmental degradation or energy innovation on environmental degradation by utilizing the appropriate techniques as per their work mostly environmental Kuznets curve (EKC) proposed by Crossman and Krueger (1992). To the best of the knowledge, it is found that very few studies have been conducted to investigate the relationship between energy innovation and economic growth with environmental degradation in European countries.

Literature review

This study discussed the relationship of environmental degradation with gross domestic product and energy innovation which increases the level of income on cost of environmental damages (Ang 2007). Also, study appeared environmental degradation which might be influenced by consumption of energy and trade (Liu 2005; Coondoo and Dinda 2008). Moreover, this study investigated the connection between the environmental degradation with urbanization just as different factors, for example, gross domestic product, import and export, energy consumption, and discovered fascinating outcomes (Sharma 2011; Hossain 2011; Shahbaz et al. 2013). Finally, the study discussed the influence of energy innovation expenditure on environmental degradation and how energy innovation devices play an important role in decreasing of environmental degradation and increasing in the economic growth of the countries because it is a paramount for production process (Andersson et al., 2011; Çalışkan, 2015).

Environmental degradation, economic growth, and energy usage

Environmental degradation is the cause of polluted gasses due to massive use of energy, since energy consumption gives a higher level of production and economic growth but on the cost of environmental degradation (Arouriet al. 2012; Acaravci and Ozturk 2010; Lean and Smyth 2010; Apergis and Payne 2009). Ang (2007) explored the causality middle of economic growth, environmental degradation, and consumption of energy covering the period of 1960–2000 for France. The results showed a strong relationship among the variables. In causality, result explained that economic growth is the main factor which increases the usage of energy and emission in the long run but unidirectional causality in the short run. Similarly, Apergis and Payne (2009, 2019) agreed with Ang (2007) and explored the same relationship between environmental degradation and related determents by using two-panel vector error correction model (VECM) covering the period of 1974–2004 of six Central American countries and eleven counties autonomous kingdoms covering the timeframe of 1992–2004. The results clarified that economic growth has upset the U-shape and energy consumption has a direct and significant relationship with environmental degradation. Be that as it may, in the short run, there is a unidirectional causal relationship with consumption of energy and economic growth with environmental degradation, while between energy usage and economic growth have a bidirectional causality, but very strong bidirectional causality in the long run. Another study showed a similar result which is investigated by Acaravci and Ozturk (2010); they used a nineteen-European country data set by using ARDL bounds testing technique in order to analysis the co-integration over the period of 1960–2005 with data source of World Bank indicators. The examination demonstrated that environmental degradation has a positive and huge association with economic growth and energy in the majority of the 19 European countries.

Environmental degradation, energy innovation, economic growth, and trade

In previous studies, it is recommended that the environmental damages from business activities should be avoided (e.g., Farhani et al. 2014; Jayanthakumaran et al. 2012; Jalil and Mahmud 2009. Ang 2007; Halicioglu 2009). Ang (2009) inspected the relationship of environmental degradation with import and export, consumption of energy and economic growth by utilizing the time period of 1953–2006 of China. The finding clarified that with more consumption of import and export, economic growth and energy use raises the environmental degradation. In a comparative manner, another examination bolstered the comparative relationship of environmental degradation with import and export, economic growth, and energy usage covering the period of 1960–2005 in Turkey by utilizing the ARDL bound testing technique. Findings uncover the outcomes that there is a direct and significant connection between environmental degradation with import and export, economic growth, and energy use. Then again, another examination has diverse discoveries about the import and export with environmental degradation that is directed by Jalil and Mahmud (2009) covering the time of 1975–2005 by utilizing a quadratic connection between the environmental degradation and economic growth and bolstered the hypothesis of EKC theory. The outcome demonstrated that in the long-run dimension of environmental degradation can be resolved with the help of economic growth and consumption of energy while import and export have a positive and insignificant effect on environmental degradation. Notwithstanding, Jayanthakumaran et al. (2012) tried to find out the relationship of import and export and environmental degradation with other control variables over the period of 1971–2007 by using the data of China and India. Variable data are collected from the World Bank but environmental degradation data is collected from SP Statistical. The result indicated that economic growth, structural changes, and energy usage are the determent of environmental degradation in China but no causal relationship in India. As of late, Farhani et al. (2014) have investigated the composition by inquiring about the strong association between environmental degradation, economic growth, energy usage, and import and export receptiveness and the bounds of the testing technique to manage co-coordination and the ARDL approach for covering the season of 1971–2008 in Tunisia. The observational outcomes showed that nearness of two causal long-run associations between the components. In the short run, makers show the nearness of three unidirectional Granger causality relationship, which continue running from economic growth, squared gross domestic product, and, energy use to environmental degradation.

Environmental degradation, economic growth, energy, trade and urbanization

Urbanization is the factor which was introduced in the model in order to check environmental degradation (carbon dioxide emission) by work of Shahbaz et al. (2013), Ozturk and Acaravci (2013), and Jalil and Feridun (2011) who researched the effect of development of economy, import and export, energy consumption, economic growth, and urbanization on environmental degradation over the time of 1953–2006 in China by utilizing an ARDL bound testing; the investigation demonstrated that expenditure on energy innovation has an insignificant effect in long kept running on environmental degradation. Then again, urbanization, economic growth, import and export, and energy consumption have a critical effect on environmental degradation and affirmed the EKC theory. Hossain (2011) additionally broke down the connection between environmental degradation and urbanization with different factors, for example, energy consumption, import, and export and economic growth covering the time of 1971–2007 of recently industrialized nations (NIC) with the assistance of time arrangement information; the exploration demonstrated there is a critical linkage between environmental degradation with urbanization, import and export, energy usage, and economic growth in the short and long run both. But on the other hand, there are numerous unidirectional easygoing connections of each factor with environmental degradation. Moreover, Sharma (2011) has additionally analyzed similar factors relationship by covering the 69 nations for a worldwide board utilizing dynamic board information which shows over the information estimate comprise of 1985–2005. The investigation uncovers that import and export, economic growth and consumption of energy positively affect environmental degradation while urbanization negatively affects environmental degradation. The examination of Farhani et al. 2014) corresponds both systems for 11 MENA countries covering the timeframe of 1980–2009. The results were in favor of the legitimacy of EKC hypothesis and exhibited a critical effect of components on environmental degradation with referencing a huge respect for the job of the urbanization joining in the natural harms. Notwithstanding, another examination bolsters the relationship which is directed by Shahbaz (2013), the study investigated the connection between money-related flimsiness and the natural harms with the assistance of economic growth, energy usage, urbanization, and import and export covering the time of 1971–2009 dataset of Pakistan by utilizing ARDL bound testing method for discovering co-mix and ECM to confirm the short- and long-run relationship. Findings of the study demonstrated that a critical and long-run connection between the factors and helps to increase the rate of environmental degradation.

Energy innovations and environmental degradation

It is argued that innovation changes the structure of the countries and creates impact on the environment. Therefore, energy innovation is the key to increase the level of production and economic development (Çalışkan, 2015; Andersson et al., 2011). The significance of the technology advancement is clarified in the Balsalobre-Lorentea et al. (2018, p. 358) study which portrays that there is a solid and complete connection between financial development and environmental degradation; the innovation which decreases impact is the primary factor in environmental degradation. Energy innovation is the main way to reduce environmental degradation. This is the main cause many studies have been conducted and argued on technologies that are helpful to make better the quality of the environment (Brock and Taylor, 2005; Dinda, 2004). Due to the recent subject and its significance on the decrease of environmental degradation, an examination is directed by Tang and Tan (2013) expressed that a critical connection between economic growth, energy consumption, and energy innovation covering a time of 1962–2007 by taking the information of 24 European countries by utilizing the ARDL bound testing. Similarly, Fei et al. (2014) also show that energy innovation technologies are extremely essential for environmental degrading in New Zealand and Norway. Countless studies found a significant and negative connection between innovation development and environmental degradation. Zhao et al. (2013) considered the power business in China in 1980–2010 by utilizing the auto-relapse dispersion slack (ARDL) show. The last Granger causality test showed that energy innovation could diminish environmental degradation. Kumar et al. (2012) and Irandoust (2016) similarly fixated on the dynamic association between energy innovation and environmental degradation.

Methodology

To examine the relationship between environmental degradation, economic growth, and energy innovation, this study used a panel data of 33 European countries over a sample period from 1996 to 2017. The study used economic growth, renewable energy, energy consumption, urbanization, import, and export as a predictor of environmental degradation. All variables are converted into per capita by dividing the variables with total population except urbanization. The study employed following functional model for environmental degradation:

$$ \mathrm{Environmental}\ \mathrm{degradation}=f\ \left(\mathrm{economic}\ \mathrm{growth},{\mathrm{economic}\ \mathrm{growth}}^2,\mathrm{energy}\ \mathrm{innovation},\mathrm{urbanization},\mathrm{import},\mathrm{export}\right) $$
(1)

The econometric form of Eq. (1) can be expressed as follows:

$$ \mathrm{COE}=\upalpha +{\beta}_1{\mathrm{GDP}}_{\mathrm{nt}}+{\beta}_2{{\mathrm{GDP}}^2}_{\mathrm{nt}}+{\beta}_3{\mathrm{RE}}_{\mathrm{nt}}+{\beta}_4{\mathrm{ENC}}_{\mathrm{nt}}+{\beta}_5{\mathrm{URB}}_{\mathrm{nt}}+{\beta}_6{\mathrm{M}}_{\mathrm{nt}}+{\beta}_7{\mathrm{X}}_{\mathrm{nt}}+{\epsilon}_{\mathrm{nt}} $$
(2)

The above Eq. (2) represents the panel regression model. The symbolic explanation, variable measurement, definition, and data sources are reported in Table 1 while ϵ in Eq. (2) denotes error term.

Table 1 Variable description
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Estimation techniques

In the current study, we observed that variables have long-run relationship by using a bootstrap co-integration technique; similarly, we have also employed fully modified ordinary test square (FMOLS) in order to examine the long run effect of energy consumption, energy innovation, export and import, economic growth, square of economic growth, and urbanization on environmental degradation. Finally, we applied a Coint, and CD and CIPS to find out the short-run causal relationship between of energy consumption, energy innovation, export and import, economic, square of economic growth, and urbanization on environmental degradation.

CD and CIPS test

In the beginning, we examine that either the data we have collected has the features of cross-sectional dependence or independence. For this purpose, we have opted for the Pesaran’s cross-sectional dependence test. This is the biggest issue which should be solved before applying panel unit root test. The issue with cross-sectional dependence is that the older version of unit root test is less effective for the panel series. That is why in the current study, we have used Pesaran CIPS unit root test and it depends on the hypothesis of cross-sectional dependence. Unit root test is very vital for the panel co-integration models. The test is employed to find out the sequence of incorporation of variables. If variables are combined at equal level, i.e., (1), then this will give a hint that the collected data set has I issue of unit root problem at the level and are stationary at first difference. Therefore, the conclusion is that variables in the study have a long-run equilibrium relationship.

Panel co-integration technique

In this study, we used bootstrap panel co-integration introduced by Westerlund to know the long-run relationship between the variable on the total sample of 33 European countries. The observation is highly fruitful if time series component is smaller of every cross-section. Due to this kind of nature in the test, most of the scholars have recommended and used the bootstrapping panel co-integration technique to find out the long-run relationship between the variables. There was an issue in the old technique; it was in the favor of null hypothesis means no co-integration even many studies are strongly in the favor of co-integration. Ignoring the fact of traditional technique, Westerlund has introduced the new panel co-integration test based on structural rather than residual dynamics. The result showed that these tests are limited towards normal distribution and are very good if we talk about the level of persistence and consistency. Westerlund has proved that structural test is more effective in terms of size and accuracy and provides better results as compared to the residual-based test by Pedroni. By knowing the fact this study analyses the impact and effect of economic growth, square of economic growth, urbanization, energy consumption, import, and export on environmental degradation.

In this study, panel co-integration test is used which was introduced by Westerlund and Persyn. Assessment of the hypothesis of co-integration is conducted with the help of two tests (i) panel test and (ii) group mean test. Westerlund made four types of statistical tests, Gt, Ga, Pt, and PA, on the basis of error correction model and all the tests are normally distributed. The PT and Gt are calculated with the help of standard error parameter of the error correction model. On the other hand, Pa and Ga consist of the standard errors introduced by Newey and West which are settled with the help of autocorrelations and heteroskedasticity.

For this test, purpose variables are considered as stationary at first difference. This test assesses the nonattendance of co-integration by deciding if mistake correction is available for the entire gathering and furthermore in individual panel members. Due to the presence of co-integration, long-run parameter is found. Consistency of the estimators is affected due to the variation in error variance across the groups in cross-sectional analysis. To vanish that issue, generalized least squares method (GLS) can be used but there will still be the variance variability, like the correlation of the squared residuals which will be there with regression in every group; in the group there are two main factors which are the reason of heteroskedasticity issue that are differences in variance of the residual terms conditioned or unconditional. Therefore, fully modified ordinary least square (FMOLS) is used in order to eliminate both the issue creating the heteroskedasticity.

Long-run elasticity

According to panel data set analysis, the implementation of ordinary least square (OLS) is known to be biased and its distribution focuses on the annoyance constraint. According to Pedroni, due to the annoyance constraints, there is an existence of serial correlation and endogeneity between repressors in case of regression results. Therefore, we used FMOLS model in order to handle this issue. The technique only considered on the nonparametric method for solving the problem of endogeneity and serial correlation. That is why we employed the FMOLS technique to investigate the long-run equilibrium relationship.

Heterogeneous panel causality test

We assessed the short-run bivariate causal relationship between the variables with the help of that framework which helps the heterogeneity of the models in the cross-sections. Dumitrescu and Hurlin have introduced this panel causality technique. This technique is more reliable for the stationary data by using fixed coefficients in the vector auto-regression (VAR) model. This approach uses heterogeneous measurement and log structure in the cross-section using both assumptions. For few cross-sections, we tested the null hypothesis of no causal relationship and then we tested alternative hypothesis for casual relationships. In the last, Wald statistics are calculated to every cross-section separately for verifying the Granger non-causality. Dumitrescu and Hurlin (2012) believe that in homogeneous panel causality test qualified to a normal distribution no causality hypothesis, whereas T shows to limitless fist and N shows to limitless (Table 2).

Table 2 Descriptive statistics
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Data analysis and findings

The cross-sectional dependence and unit root tests

Table 3 shows the results for the CIPS unit root and CD test. Results of CD test explain that almost every country in the Europe region rejects the null hypothesis at the 1% level of significance for every variable of cross-section independence by showing the importance of cross-sectional dependence. Therefore, we applied newly introduced CIPS unit root test in place of traditional method of unit root test. For the discussion of the cross-sectional dependence, we used the newly CIPS unit root test for the data series. The results of this method at first-order differential also reject the null hypothesis for every variable. Hence, the outcomes of CIPS unit root explored that variables are non-stationary in nature at the level and show a stationary nature at first level difference. Hence, according to result it is an indication that there is co-integration relationship in the long run between the variables.

Table 3 Results of cross-sectional dependence and CIPS unit root test
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Findings of panel co-integration tests

Table 4 verifies the outcomes of co-integration test and Pedroni results. Both of the test shows that there is no co-integration at the 1% level of significant means rejecting the null hypothesis because within the dimension two tests that are (panel ADF statistics and panel PP statistics). Similarly, two tests between the dimension (group ADF statistics and group PP statistics) agreed with the rejection result. That is why only four tests from the seven tests show that variables go in the same way and together in long-run equilibrium in model of carbon emission. Second-generation co-integration test can also be used in order to find out the co-integration between the variables. The outcomes of the bootstrap panel co-integration are mentioned in Table 5. Within the dimension and between the dimensions outcomes are reported in this study. The outcomes show that rejection of null hypothesis while acceptance of an alternative hypothesis. Hence, variables are co-integrated in the second-generation test in the long run of environmental degradation model.

Table 4 Results of Pedroni (Engle-Granger-based) panel co-integration
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Table 5 Results of Westerlund (2007) bootstrap panel co-integration
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Findings from fully modified ordinary least square (FMOLS)

We used FMOLS technique in order to find out the long-run linkage between the variables. This technique is invented by Philips and Hansen and then later it is more refined by the Pedroni. The reason of the selection of this technique is that they resolve the issue of autocorrelation and endogeneity and prove results of robust. The study finds out the long-run estimation by using the dynamic ordinary least square (DOLS) coefficients and FMOLS. The outcomes of the FMOLS have been mentioned individually for every model in Table 6. For the calculation of the long-run beta, two very similar type of approaches have been used at the 10% level of significance; Table 6 indicates that FMOLS method has been used for environmental degradation model, the outcomes of panel estimate given the view that the impact of environmental degradation on economic growth is close to 0.395; a 1% increase energy consumption will raise the environmental degradation by 1.050; a 1% increase urbanization will raise the environmental degradation by 0.188; a 1% increase in export and import will raise the environmental degradation by 0.303 and 0.060, while a 1% increase energy innovation will decrease the environmental degradation 0.178.

Table 6 Results of long-run analysis through FMOLS
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The model shows that all the variables help to increase environmental degradation apart from energy innovation is only variable in the study which reduces environmental degradation in the long run. That is why government should make a huge amount of budget for energy innovation so that environmental degradation can be reduced and countries become eco-friendly with the help of technologies. We also found out the effect of environmental Kuznets curve (EKC) hypothesis between environmental degradation and economic growth in most of the countries who are the creator of environmental degradation. In our investigation of this, we add an additional variable square of gross domestic product (GDP2) in the model. It can be shown in the outcomes of Table 6 that economic growth has a positive and significant impact on environmental degradation whereas square of gross domestic product has a negative and significant impact on environmental degradation. This proves Kuznets curve hypothesis existence that is there is a U-shaped (inverted) relationship between environmental degradation and economic growth. It means economic growth helps to increase in environmental degradation in the initial mode but after a specific time period, it helps to reduce environmental degradation. Findings are great that countries should not think about the increase in environmental degradation in starting at a cost of economic growth because after sometime it will help to make the country eco-friendly with the help of technologies.

Heterogeneous panel causality test

The causal relationship between economic growth, energy innovation, energy consumption, urbanization, import, and export with environmental degradation is analyzed with the help of heterogeneous panel causality test. The outcomes are reported in Table 7. The outcomes show that bidirectional causal relationship present between every variable with environmental degradation. This means that variables help to cause environmental degradation in a model of carbon dioxide apart from energy innovation.

Table 7 Results of heterogeneous panel causality test
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Conclusion

This study aims to find out the role of economic growth, energy consumption, energy innovation, import and export and urbanization on environmental degradation by utilizing a multivariate structure and panel data sets of 33 countries who emit carbon dioxide emissions in European countries covering the period of 1996 to 2017. The result of the study guides us to investigate the linkage between economic growth, energy consumption, and energy innovation, urbanization, import, and export with environmental degradation by using panel data of mostly covered countries of European countries. The outcomes of the current study support the basic analysis in the literature of economic growth, urbanization, energy consumption, import, and export have a positive and significant impact on environmental degradation, while energy innovation has a negative and significant impact on environmental degradation. In other words, economic growth, import, export, urbanization, and energy consumption are the main factors to increase environmental degradation and disturbance while energy innovation helps to reduce the rate of environmental degradation as a result the quality of environment improves.

Policy implications

Based on the current study and outcomes, it can be suggested that the government of those countries who are top rankers in the environmental degradation should invest more in the department of energy innovation so that devices which can reduce the intensity of energy and helps to reduce the level of carbon dioxide can be invented. Furthermore, they should educate people about the disadvantages of environmental degradation not only in their personal life but also for the business, animals, and non-living things so that consumption of energy should be utilized in the best and effective manner without its waste. Additionally, government should encourage the businessmen and every people of the country to install equipment which are helpful to reduce the intensity of the carbon and are eco-friendly by giving tax and other intensives. Moreover, they still reduce the level of import and export so that fuel consumption can be reduced to a certain level and try to use other way of transportation which uses renewable sources of energy without damaging the environment. Finally, they should focus to increase the economic growth so that at a certain point it starts to help to decrease the environment degradation.