relationship between output, renewableenergyconsumption and internationaltrade for a sample of 69countries during the period 1980-2007. In the short-run, Granger causality tests show that there is evidence of bidirectional causality relationship between output and trade (exports or imports), and a unidirectional causality relationship running fromrenewableenergyconsumption to trade. However, in the short-run, there is evidence of no causality running fromtrade to renewableenergyconsumption. In the long-run, the error correction term provides that there is evidence of bidirectional causality relationship between output, trade and renewableenergyconsumption. Long-run estimations show that all coefficients are positive and statistically significant. Policies recommendations are that, in the long-run, internationaltrade enables countries to benefit from technology transfer and to build the human and physical capacities needed to produce more renewable energies, while increasing their output. Therefore, more trade openness could be a good policy for combating global warming as it incites the use of renewable energies.
The ARDL approach to cointegration is convenient to measure the dynamic relations set out in equations (2, 3 and 4) for each individual country of the EU-15. However, if one wishes to test the dynamic relationship set out in equation (1) as a panel, the ARDL approach cannot be used. In this case, the GMM technique which was introduced by Hansen (1982) seems to be a good choice econometrically. The GMM 4 is based on an inclusion to the regression of lagged endogenous variables as instrumental variables. One of the important advantages of the GMM is that many estimators like Ordinary Least Squares (OLS) and instrumental variables are considered as special cases, making the GMM flexible in use. The superiority of the GMM test is in the avoidance of heteroskedasticity and autocorrelation by allowing a weighting matrix to account for them using orthogonality conditions. In order to check the validity of instrumental variables applied to the regression, the test for over- identifying restrictions developed by Sargan (1958) is employed. The GMM technique also requires the variables in question to be stationary.
emissions, economic growth (i.e., real output), renewableenergyconsumption and health expenditures in the case of a panel framework. In this work, we consider another driver that may control the environmental situation. Thus, it is really interesting to think about the role of health expenditures in the mitigation of emissions when renewableenergy is used for production. In other words, the expansion of health expenditures is significant if growth in renewableenergy is strong enough. The installation of solar photovoltaic or wind mills could be a good idea to feed health facilities in electricity. Moreover, encouraging developing countries to adopt clean technologies turns out to be a good policy to stimulate higher health quality and decreased carbon emissions levels. In particular, this study will investigate the dynamic causal links between CO 2 emissions, real GDP, renewableenergyconsumption and
Renewableenergy is not only directly included in production as an input, but it also indirectly affects economic growth. In 2014, the renewableenergy sector employed 9.2 million people. The number of people employed is expected to rise to 24.4 million by 2030 (IRENA, 2016a). In addition, the global GDP is expected to rise from 0.6% to 1.1%, and global welfare is expected to rise between 2.7% to 3.7% by 2030 due to the increased consumption of renewableenergy (IRENA, 2016b). According to InternationalEnergy Outlook (2016), the consumption of renewableenergy, the most rapidly increasing source of energy, will increase by an average of 2.6% per year between 2012 and 2040. In cases where the countries around the world sustain their energy plans and policies, the share of renewableenergyconsumption in total energyconsumption, which amounted to 18.4% in 2014 will rise to 21% in 2030 (IRENA, 2016a).
Energy is fundamental to sustain the development of nations. Particularly, fossil fuel energy has been the most component used worldwide. However, the expansion of energy-consuming activities in the developed and emerging countries, and waste in rich countries (especially the Gulf countries) lead to two major concerns: the depletion of the most easily accessible energy resources (mainly oil) and correspondingly, the problem of global warming caused by the rapidly increasing emissions of greenhouse gases such as carbon dioxide (CO2) and methane. This global nature of energy challenges requires that renewableenergy resources be appropriately managed and used. Renewableenergy is commonly defined as energy generated from solar, wind, geothermal, tide and wave, wood, waste and biomass. Contrarily to conventional energy, renewableenergy is clean, safe and inexhaustible. Therefore, it is growing fast around the world and according to expectations it will edge out many conventional energy components and occupies a leading position in the overall share of energyconsumption. For example, in China wind power generation increases more than generation from coal and passes nuclear power output (REN21, 2013).
Recently, numerous empirical analysis studies prove that renewableenergyconsumption plays a vital role for combating global warming (e.g. Apergis et al., 2010 and Sadorsky, 2009a) and the increase of output (e.g. Apergis and Payne, 2010a, 2010b, 2011, 2012; Menegaki, 2011, Ocal and Aslan, 2013; Sadorsky, 2009). The results from these papers are different depending on the selected data, period, and methodology used for the empirical analysis (ARDL, panel cointegration, variance decomposition, Toda-Yamamoto, p anel random effect model, panel error correction model). The direction of causalities come from these papers have been established using various techniques such as Granger causality, and Toda-Yamamoto causality. However, some of them suggest the existence of bidirectional causality between renewableenergyconsumption and economic growth. It means that these studies support the feedback hypothesis. Thus, this hypothesis exposes that any increase in the share of renewableenergy in total energy use will increase output and it supports that any increase in economic growth (real GDP) causes an increase in renewableenergyconsumption. The Toda-Yamamoto causality test results in Ocal and Aslan (2013) show that there is a unidirectional causality running from economic growth to renewableenergyconsumption. This result supports the conservation hypothesis which argues that any change in economic growth will change renewableenergyconsumption but otherwise is not supported. For the United States, only one causal relationship running from biomass-waste-derived energyconsumption to real GDP has been founded in Yildirim et al. (2012) during the period 1949- 2010. This findings support the growth hypothesis which means that any reduction in the consumption of renewableenergy will affect economic growth. Menegaki (2011) investigates the causality between renewableenergyconsumption and economic growth for a panel of twenty seven European countries during the period 1997-2007. The neutrality hypothesis is supported in the empirical test. However, the result involves that no causality between economic growth and renewableenergyconsumption.
the hydrocarbon-led energy sector  16 and reports 33% of diesel and natural gas electricity production and 6% of coal. The Israel nation needs to adopt the use energyrenewableenergyconsumption by reducing the use of non-renewableenergy resources and ensure sustainable development by lowering the carbon emissions. Recently, these nine nations relied heavily on foreign trade. Thus, low-carbon emissions energy-mix will not have a considerable and unfavorable impact on economic growth. To maintain the stability of economic growth and sustainability development, the countries has to encourage the use of renewableenergy resource by increasing energy efficiency, furthermore, which will ensure stable economic growth and sustainability growth. The consumption of renewableenergy sources has grown for the vast majority of nations in the last two decades. The results of the implementation of renewableenergy are unique across nations due to many factors, as addressed in various reports by international bodies. All the nations have to encourage the use of renewableenergyconsumption as well as allocate more funding to renewableenergy projects, it’s not only reduced carbon emissions but also ensure sustainable growth. Therefore, the policy makers and governments need to reframe the new policies, which will help to ensure future aspects.
It is contended that renewableenergy can play an important role in “decarbonizing” energy, which is a key aspect of climate change mitigation. Renewables currently contribute to 19.3% to global energyconsumption and contribute significantly to the levelling off of carbon emissions [ 12 ]. A study by Fang [ 13 ] revealed that renewableenergyconsumption could help to reduce carbon emissions by roughly 8.2% by the year 2050. The deployment of renewableenergy technologies also has distinct economic advantages. Renewables have the potential to reduce dependency on imported fuel and solve the issues of energy access for over 1.4 billion people across the globe who remain “energy poor” [ 14 ]. Renewableenergy deployment can also help to create jobs [ 15 ] and foster the development of small-scale industries in the rural areas of developing countries [ 16 ]. According to Fang [ 13 ] an increase in renewableenergyconsumption by 1% point increases GDP per capita by 0.12% points. However, some studies have also gathered contradictory evidence on the inter-linkages between economic growth and renewableenergyconsumption. For instance, empirical studies by Apergis and Payne [ 17 – 19 ] and Salim et al. [ 20 ] show that renewableenergy production does not have net positive effect on employment generation. Other studies by Hall et al. [ 21 ] and Weibbach et al. [ 22 ] revealed that with the current level of technology, the Energy Returns on Investment (EROI) for renewableenergy are three times lower than fossil fuel energy. Both these studies concluded that with the current level of technology it is highly unlikely that renewableenergy will be a viable energy alternative in the near future.
Although several plausible nonlinear models have been used in the empirical economics literature, we prefer smooth transition regression (STR) modelling approach. The STR modelling approach has several advantages over other nonlinear models (see, for example, Teräsvirta and Anderson, 1992; Granger and Teräsvirta, 1993). First, STR models are theoretically more appealing over simple threshold and Markov regime switching models, which impose an abrupt change in coefficients. Instantaneous changes in regimes are possible only if all economic agents act simultaneously and in the same direction. Second, the STR model allows for modelling different types of nonlinear and asymmetric dynamics depending on the type of the transition function. In particular, a STR model with a first-order logistic transition function is more convenient for modelling the interaction between energyconsumption and output growth rate if the dynamic interrelationships between the variables depend on the phases of business cycles. On the other hand, a STR model with an exponential or second-order logistic transition function is more convenient if, for example, the interaction between the variables depend not on the sign but on the size of fluctuations in variables. Finally, STR modelling approach allows one to choose both the appropriate switching variable and the type of the transition function unlike other regime switching models that impose both the switching variable and function a priori.
Before moving to formal modelling, the diagnostic tests including cross-sectional dependence, heteroskedasticity and serial correlation are checked. The results of the diagnostic tests for non-renewable and renewableenergy use models are presented in Appendix A Table 4.4. The results of the different cross-section dependence tests under both random and fixed effects estimations show that the null hypothesis of no cross-sectional dependence is rejected in both non-renewable and renewableenergy use models under all of the used tests —Friedman, Frees, and Pesaran— meaning the residuals of the two models are correlated. The results of heteroskedasticity based on a modified Wald test indicate the existence of the problem of heteroskedasticity at a 1% level of significance in both models. Finally, the findings of serial correlation test based on Wooldridge suggest that the two models suffer from a positive serial correlation. In the case of the existence of cross-section error dependence, in addition to heteroskedasticity and serial correlation, conventional panel estimators (such as fixed or random effects) can result in misleading inference and even inconsistent estimators (Phillips and Sul 2003). Pesaran (2006) proposes an estimation method, called Common Correlated Effects (CCE), which allows for unobserved factors to be correlated with exogenous regressors and idiosyncratic components to be independent across countries. Furthermore, this estimator holds under different situations such as serial correlation in errors, unit roots in the variables and possible contemporaneous dependence of the observed regressors with the unobserved factors (Coakley et al. 2006; Kapetanios and Pesaran 2007; Kapetanios et al. 2011; Pesaran and Tosetti 2011). Therefore, in this study, to account for the cross-sectional dependence in the data, the common correlated effects (CCE) estimator by Pesaran (2006) is employed 15 . A brief review of the structure of this approach is provided in Appendix B.
The stationarity of countries current account variables is tested using the ADF unit root test procedure. After that, panel unit root tests are applied. In recent years some tests for unit root within panels are developed in the literature. Levin and Lin (1992, 1993), Im, Pesaran and Shin (1997), Maddala and Wu (1999), Kao (1999) and Quah (1994) have developed panel unit root tests. In this study Im, Pesaran and Shin (hereafter IPS) are used. We briefly describe the IPS model:
Our paper differs from previous studies by applying the new heterogeneous panel cointegration technique to investigate the relationship between energyconsumption and GDP across 7 Asian countries. This paper contributes the following. First, we use a cointegration test for a panel of countries which provides more powerful tests and allows us to increase the degrees of freedom compared to the cross-section approach. Next, we use the full-modified OLS (FMOLS) technique to estimate the cointegration vector for heterogeneous cointegrated panels, which correct the standard OLS for the bias induced by the endogeneity and serial correlation of the regressors. Finally, we specify and estimate an error correction model appropriate for heterogeneous panels, which distinguishes between long-run and short-run causality. In this paper we use a different direction to overcome the short span of data and the distortions of a small sample. Since the power of an individual unit root test can be distorted when the span of data is short , we use a panel unit root test. The power of the traditional cointegration test  is that multivariate systems with small sample sizes can be severely distorted. To this end, we need to combine information from time series and cross-section data once again, and thus we use a panel unit root test and heterogeneous panel cointegration tests.
by the Schwarz Bayesian criterion (SBC) on the unrestricted model in each state, conditional to a maximum lag of 1. Nevertheless, there are several criteria for this methodology's accuracy, effectiveness, and credibility. First, the existence of a long-run connection between interest variables requires the coefficient on the term of error correction to also be harmful and not below -2. Secondly, A significant presumption for the continuity of the ARDL model is whether the independent variables are regarded as exogenous as well as the eventual residuals of the error correction model are not correlated in any way. Third, the comparable size of T and N is essential: in the average estimators, all should be huge to choose the diverse panel process to avoid the bias.  suggests that heterogeneity treatment is critical to understanding the cycle of development. Failure to meet these criteria will, therefore, produce various PMG estimates . The Pooled Mean Group Model, including the long-term relationship among variables, may emulate the
The independent variables in this study have all been shown to be related to economic growth in previous studies. Real gross fixed capital formation (K) is measured in constant 2010 US dollars. Gross fixed capital formation (formerly gross domestic fixed investment) includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. According to the 1993 SNA, net acquisitions of valuables are also considered capital formation. Total labor force, measured in millions, comprises people ages 15 and older who meet the International Labor Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labor force includes the armed forces, the unemployed, and first-time job-seekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector.
This test is carried out to check for the presence of cointegration which is a check for long run relationship between exchange rate, real oil price and real interest rate differential variables. The paper utilise panel cointegration tests due to Pedroni (1998), Kao (1999) and Maddala and Wu (1999). The tests proposed in (Pedroni, 1998) are residual-based tests which allow for heterogeneity among individual members of the panel, including heterogeneity in both the long-run cointegrating vectors and in the dynamics. Two classes of statistics are considered in the context of the Pedroni (1998) test. The panel tests are based on the within dimension approach (i.e. panel cointegration statistics) which includes four statistics: panel v-statistic, panel ñ-statistic, panel PP-statistic, and panel ADF-statistic. These statistics essentially pool the autoregressive coefficients across different countries for the unit root tests on the estimated residuals. These statistics take into account common time factors and heterogeneity across countries. The group tests are based on the between dimension approach (i.e. group mean panel cointegration statistics) which includes three statistics: group ñ-statistic, group PP-statistic, and group ADF-statistic. These statistics are based on averages of the individual autoregressive coefficients associated with the unit root tests of the residuals for each country in the panel. All seven tests are distributed asymptotically as standard normal. Of the seven tests, the panel v-statistic is a one-sided test where large positive values reject the null hypothesis of no cointegration whereas large negative values for the remaining test statistics reject the null hypothesis of no cointegration.
This technique combines in a system the relevant regressions expressed in first-differences and in levels. First-differencing checks for unobserved heterogeneity and omitted variable bias, as well as for time- invariant component of the measurement error. It also corrects endogeneity bias (time-varying component) via instrumenting the explanatory variables. Instruments for differenced equations are obtained from values (levels) of explanatory variables lagged at least twice, and instruments for levels equations are lagged differences of the variable. Estimating two equations in a system GMM reduced potential bias and imprecision associated with a simple first-difference GMM estimator (Arrellano and Bover, (1995), Blundell and Bond (1998)) ‡ . Alonso-Borrego and Arellano (1999), and Blundell and Bond (1998) point out that when explanatory variables are persistent over time, lagged levels of these variables make weak instruments for regression in differences, and instrument weakness in turn influences the asymptotic and the small-sample performance of the difference estimator. Asymptotically, variance of the coefficients will rise, and in small
When a nation tries to traverse along the path to achieve economic growth, it has to rely on its resource pool, which includes the natural and intellectual resources. During the earliest phases of this economic growth, a nation relies on the pool of natural resources, as it is easier to utilize and consume. Consumption of the natural resources helps the nations to grow, while this pattern of consumption deteriorates the environmental quality of these nations. Continuous consumption of natural resources gradually raises the level of environmental degradation, and this is the time, when the nations start to embrace the intellectual resources in pursuit of alternate energy sources. However, owing to the high implementation cost, it might not always be possible for the nations to carry out the implementation of alternate energy sources, as the implementation cost might have implications on the economic growth pattern itself. Therefore, in order to boost the industrialization in a nation, majorly fossil fuel consumption takes place in pursuit of energy generation. Because of the environmental degradation caused by the consumption of fossil fuel-based solutions, the biocapacity of the nation is hampered, as the absorptive capacity of the land, water, and air of the nation might not be sufficient for the waste generated in the due course of economic growth. This carrying capacity of the nation is generally referred to as the “Ecological footprint”. In general, eco logical footprint is “the aggregate area of land and water that is claimed by participants in this economy to produce all the resources they consume and to absorb all their wastes they generate on a continuous basis, using prevailing technology” . Now, as the world has ushered in the regime of Sustainable Development Goals (SDGs), it is gradually turning out to be more important for the nations around the globe to comply with the SDG objectives by 2030. Therefore, the nations are in pursuit of redesigning their energy and environmental policies, so that they can create the basis for addressing the SDG objectives by having a control over the environmental degradation created by them, by means of the ecological footprint.
As to the first aspect, the numerous studies have examined the relationship between trade and FDI. The results of these studies vary considerably from country to country and from industry to industry. Blomström et al. (1988) found that the relationship between FDI and exports is negative in some industries suggesting that FDI and exports are alternatives. Belderbos and Sleuwaegen (1998) reached the same conclusion. Svensson (1996) used firm level data for Sweden to estimate the impact of FDI on exports. Svensson found a negative linkage between exports and FDI for finished goods and a positive relationship between exports of intermediate goods and FDI. Blonigen et al. (2004) found that tariff-jumping FDI has significant larger negative effects on the US domestic firms' exports than other types of FDI. They argued that Trade frictions (commercial policy, distance and transportation cost, etc.) encourage foreign producers to "jump" trade barriers by replicating similar plants in different markets. Such investment patterns are called horizontal FDI. Beugelsdijk et al. (2008) argued that horizontal FDI and trade are largely substituting and an increase in trade decreases such investment. In contrast, cost gaps may encourage producers to break up the production process, putting labor intensive stages of production in low wage countries, and the more capital intensive stages of production (R&D, assembly, headquarter services, etc.) in industrialized countries. Such investment patterns are called vertical FDI.
RenewableEnergy is a fundamental part of the energy sector and because of benefits provided to the society and economy their role is increasing with reference to data of internationalEnergy agency. RenewableEnergy accounted for 13.1% in global total primary energy supply (further in the next TPES, Total Primary Energy Supply) in 2004 and 2009. However, it is expected to increase the share of fossil energy sources such as oil, coal and natural gas (Müller et al. 2011). Biomass and waste are the noticeable types of RenewableEnergy, representing 9.9% in global TPES and 75.9% in global RenewableEnergy supply in 2009. However, their share in global RenewableEnergy has a decreasing trend. The second largest type of RenewableEnergy is Hydro. It accounted for 2.3% in global TPES and 17.7% in global RenewableEnergy supply in 2009. This is by 0.1 and 1.0 percentage points less than in 2004. It is expected that during 2009- 2035 the volume of hydro power will be increasing by 2.1% a year and will exceed the growth rates of fossil fuel and nuclear energy; however, the share of it will have a tendency to reduce (Müller et al. 2011).
From the respective Lagrange multiplier and likelihood ratio test statistics of 10310.62 and 727.46, the assumption of no groupwise heteroscedaticity is rejected. This suggests that there is heterogeneity among each country’s export activities. A simple OLS regression of a straightforward pooling of all observations without considering heterogeneity will lead to an unacceptable degree of aggregation bias or even meaningless results. In addition, according to the respective Lagrange multiplier and likelihood ratio test statistics of 25869.26 and 1520.38, the time effects should be considered in the estimation. Finally the significant Hausman statistic of 21.73 indicates that the two-way random effects model performs better than the two-way fixed effects model. The estimates for the country and year dummies are not reported. We simply note that these dummies help pick up country-specific and cyclical factors. The results in Table 3 are consistent with expectations. In any regression, the coefficients of all the variables are highly significant and have the expected signs. The results suggest that the economic similarity, market size, R&D and FDI stocks and similarity are the powerful determinants of bilateral trade. The geographical distance remains the most powerful in explaining bilateral trade, which is consistent with the results from most gravity model based empirical studies. The distance in relative endowment has a negative sign and is highly significant in all regressions. This is consistent with new trade theory as intra-industry trade plays a more important role than inter-industry trade in OECD countries.