Energy is recognised as a fuel for economicgrowth and industrial development. Energy along with other factors of production (such as labour and capital) is a vital input and necessary requirement for economic and social development (Ghali and El- Sakka 2004). In fact, energy industry through its vital products, which serve as inputs into nearly every good and service in the economy, acts as a contribution to sustainable economicgrowth (World Economic Forum 2012). Since the beginning of industrialisation, the rapid pace of economicgrowth in most countries has been accompanied by a large consumption of energy. Industrialisation by increasing wages and accelerating urbanisation creates an additional boost in energy demand. For instance, in China, energyconsumption increased by more than 150% over the past ten years and China turned out to be the world’s largest consumer of energy in 2010, surpassing the US (The World Bank 2011). However, the use of energy, especially fossil fuels as the major energy sources has many adverse environmental effects. The consumption of energy in terms of non-renewables 1 is a significant contributor to stationary energy greenhouse gas (GHG) emissions. Greenhouse gases are potentially essential to keep the earth’s temperature warm. However, extra greenhouse gases, which are caused by man-made activities, absorb more and more heat and cause global warming 2 . Global warming causes climate change, which is one of the greatest challenges facing policy makers at every level, from global and international to national, regional and local. Global climate change threatens to disrupt the well-being of society, undermine economic development and alter the natural environment, making it a key policy concern of this century.
In addition, population growth, rapid urbanization and economicgrowth put pressure on the existing infrastructure and the demand for new investment is relatively high. The total demand for investment in the energy sector in MENA countries is expected to exceed 30 billion dollars per year over the next 30 years, or about 3% of the total GDP of the region (which is three times higher to the world average), (WDI, World Development Indicators 2013). Specifically, continues rising and volatile fuel prices put pressure on the financial resources of MENA Net Oil Importing Countries (NOIC). Given the appearance of renewable energy in the discussion of a sustainable energy future, it is essential to comprehend the dynamics between renewable energyconsumption and economicgrowth, which this paper attempts to deal with. While the literature on energyconsumption and economicgrowth has been extensively examined in the literature (Ozturk, 2010; Sharma, 2010; Payne, 2010a, b;
Recently, contributions have exposed that the world is facing severe problems with energy depletion in consequence of the unbalanced availability between finite energy resources and population growth as well as industrial development. The available quantity of finite-based energy resources was expected to last between 30-150 years 2 . Additionally, According to the International Energy Agency (IEA, 2012), production from gas and oil reserves will drop to about 40-60% by 2030. Besides, Huntington (2009) exposed that this type of energy use was also vulnerable to disruptions caused by major events in the world, such as war, monopolistic behaviors (e.g. by OPEC 3 ) and commonly more depending on the political stability of the net oil producing countries . These circumstances indeed slowed down the economic development in most countries in the world. Not only finite energy resources availability became the immediate concern, but also the environmental degradation whereas oil and coal exploitation ultimately led to forest destruction, biodiversity extinction as well as natural disasters.
is encouraged by using more oil and additional oil use is also needed to accommodate the energy demand associated with economicgrowth. Lee and Chang (2005) scrutinise the stability between energyconsumption and GDP in Taiwan during the period of 1954 - 2003. They exploit the information from aggregate in addition to various disaggregated measures of the consumption of energy, including coal, oil, gas, and electricity. Then cointegration tests that accounts for structural breaks are employed. The key ﬁnding is that the directions of causation between GDP and the use of diﬀerent energy resources are mixed. They point out that there are feedback eﬀects between GDP and both over- all energy and coal consumption. However, there is a unidirectional causality running from oil consumption to economicgrowth. Additionally, unidirectional causalities ﬂowing from gas and electricity use to economicgrowth are discovered. Another recent article developed by Ziramba (2015) assesses the long-run and causal relationships between oil consumption and economicgrowth in South Africa over the period 1970 - 2008. Using multivariate framework, he ﬁnds that capital, oil consumption, and economicgrowth have a stable long-run relationship. Also, he ﬁnds that there is a unidirectional causality from oil consumption to economicgrowth, which implies that oil consumption play a vital role in stimulating economicgrowth both directly and indirectly as a complement to other inputs in the production process. Park and Yoo (2014) have used annual data cover- ing the period 1965-2011 for Malaysia to examine the linkage between oil consumption and economicgrowth. On the basis of the cointegration approach results, they ﬁnd that the two variables have feedback impacts on each other. This means that an increase in oil consumption directly aﬀects economicgrowth, and an increase in economicgrowth need more oil usage as well. Thus, in order not avoid any adverse impacts on economicgrowth in Malaysia, energy policies should endeavor to overcome the constraints on oil consumption.
The negative results for public R&D needs some qualification. Taken at face value they suggest publicly performed R&D crowds out resources that could be alternatively used by the private sector, including private R&D. There is some evi- dence of this effect in studies that have looked in details at the role of different forms of R&D and the interaction between them. 30 In particular, it is found that defence research performed by the public sector does indeed crowd out private R&D, partly by raising the cost of research. However, there are avenues for more complex effects that regression analysis cannot identify. For example, while busi- ness-performed R&D is likely to be more directly targeted towards innovation and implementation of new innovative processes in production (leading to improve- ment in productivity), other forms of R&D (e.g. energy, health and university research) may not raise technology levels significantly in the short run, but they may generate basic knowledge with possible “technology spillovers”. The latter are difficult to identify, not least because of the long lags involved and the possi- ble interactions with human capital and associated institutions. 31
Compared to previous studies (see table1), this paper used simultaneous equations based on structural modeling to study of the nexus between energyconsumption, CO2 emissions and economicgrowth in the Middle East and North Africa (MENA) region. As we can see, about the emerging economies, our literature review generally indicates that little attention has paid to smaller emerging economies, particularly in MENA region. This region has some of the largest energy reserves in the world. Yet, while the region is trying to industrialize and modernize its economies, there are the challenges of the carbon emissions. Moreover, energyconsumption is the most significant source of pollution and, in terms of particulate matter concentrations; MENA represents the second most polluted region in the world – after South Asia – and the highest CO 2 producer per dollar of output. The model allows examining at the
In other hand, studies such as Keppler (2006) for China, Narayan and Smyth (2008) for G7 Countries, Apergis and Danuletiu (2012) for Romania, Karagöl et al. (2007), for Turkey found unidirectional causality running fromenergyconsumption to economicgrowth. Moreover, bidirectional causality was found by Apergis and Payne (2009) for 11 countries of the Commonwealth of Independent States, by Ozturk et al. (2010) for lower-middle income, by Lee and Lee (2010), Bekle et al. (2010) for 25 OECDCountries, by Pao et al. (2014) for Brazil, by Rezitis and Ahammad (2015) for South and Southeast Asian countries, by Vafaeirad et al. (2015) for 7 Asian countries, by Al-mulali and Mohammed (2015) for Emerging countries, Osigwe and Arawomo (2015) for Nigeria and Khobai and Roux (2017) for south Africa. In addition to them, Erdal et al. (2008), Kaplan et al. (2011), Akpolat and Altıntaş (2013), Bayar (2014), Çakmak (2015) for Turkey reached the similar conclusion. In some studies, like Ozturk et al. (2010) for upper-middle income, Kalyoncu et al. (2013) for Georgia and Azerbaijan results indicated no causality between energyconsumption and economicgrowth. Similarly, Jobert and Karanfil (2007), Ozturk and Acaravci (2010), Çetin and Seker (2012) for Turkey emphasized the same conclusion in their studies.
Adhikari and Chen (2012) take the 80 developing countries data for the period of 1990 to 2009 to examine the long run relationship between energyconsumption and economicgrowth. To check the relationship between dependent and independent variables panel unit root test, panel co-integration and panel dynamic OLS technique are applied. Three income group lower, middle and higher income group are made for all 80 countries. The empirically results shows long run cointegration between energyconsumption and economicgrowth for the whole 80 countries and as well as for each income group of countries. Study ﬁnds the huge relation. The ﬁndings clearly suggest that energyconsumption has a statistically signiﬁcant and positive impact on economicgrowth in the long run for these all 80 developing countries.
The paper examines the impact of energyconsumption on economicgrowth and environmental quality and also verifies the existence of the Environmental Kuznets Curve (EKC) hypothesis in Nigeria. The Autoregressive Distributed Lag (ARDL) approach was used to estimate data covering 1981-2015 period. The result of the first model reveals evidence of inverse and significant impact of energyconsumption on economicgrowth. Capital and trade openness show evidence of positive and significant impact on economicgrowth but labour reveals a negative and significant impact on economicgrowth. The result of the second model suggests that energyconsumption is significant and positively related to environmental quality. As such, greater consumption of primary energy such as petroleum and natural gas increase carbon emissions which subsequently reduce environmental quality. Trade openness was also found to improve environmental quality. Furthermore, the test for EKC hypothesis did not reveal any evidence of its existence in Nigeria. This could result from the fact that growth level has not been expanded to a certain threshold beyond which additional expansion can reduce carbon emissions and improves environmental sustainability. The study recommends that efficiency in the use of conventional energy will go long way in reducing energy-related carbon emissions and enhance environmental sustainability. While improving human capital development will enhance the impact of labour force on economicgrowth in Nigeria.
After establishing that economicgrowth is linked in the long-run with energyconsumption, price index, labor and capital, we need to examine the causality between the five variables. Panels A, B and C of Table 9 report the results of the short-run and long-run Granger- causality tests for each panel data set. The optimal lag structure of two years is chosen using the Schwarz Bayesian Criterion. In panel A which includes all countries of our sample, Eq. (14a) shows that energyconsumption, labor force and capital have a positive and statistically significant impact in the short-run on economicgrowth whereas price index has negative impact on economicgrowth. The sum of the lagged coefficients indicates that energyconsumption (0.337) has greater impact on real GDP than labor force (0.279) and less than real gross fixed capital formation (0.413). 17 This highlights the importance of energy in the economicgrowth process in African countries. Moreover, the error correction term is statistically significant at 5% and denotes the speed of adjustment to long-run equilibrium. In term of Eq. (14b), it appears that economicgrowth and capital have positive impact on energyconsumption, whereas, labor has no impact on energyconsumption. On the other hand, the impact of price index is negative in the short-run. Then, there is complementarity between energy usage and capital, and substitutability between price index and energyconsumption. The statistically significance at 1% of the error correction term suggests that energyconsumption responds to deviations from long-run equilibrium. In regards to Eq. (14c), it is not surprising that energyconsumption has positive impact on price index, and the other variables are not significant. There is also no evidence for long-run adjustment in price index, because the error correction term is statistically insignificant. With respect to Eq. (14d), both real GDP and capital have a positive and statistically significant impact on labor in the short- run, while the impact of energy is statistically insignificant. Finally, in Eq. (14e), real GDP and energyconsumption have a positive and significant impact on capital, whereas price index have negative impact and labor is statistically insignificant. In terms of the long-run dynamics, based on the statistical significance of the error correction terms from Eqs. (14d)- (14e), capital and the labor force each responds to deviations from long-run equilibrium. Overall, the relationship between energyconsumption and economicgrowth is characterized by bidirectional causality, in both the short and long-run.
between energyconsumption and economicgrowth will not be discussed. Huang et al. (2008) supports the results of our study in their finding that there is no causality between energyconsumption and economicgrowth in China, India and South Africa. This finding is called the neutrality hypothesis. In countries where this hypothesis is valid, energyconsumption has no contribution to the growth so more conservative energy policies should be followed to minimize the negative impact of energyconsumption on the environment. Within this scope, technologies that can increase efficiency in energyconsumption can be utilized. Instead of energyconsumption based on fossil fuels, measures can be taken to promote the use of clean and renewable energy such as solar energy, wind energy, nuclear energy and natural gas. In our study, the causality relation between economicgrowth and energyconsumption for Russia is parallel to the Zhang (2011) analysis, and this relation is called the conservation hypothesis in the literature. In this respect, since there is no energy dependence on economicgrowth in Russia, so energy conservation-based energy policies can be implemented without hindering economicgrowth. Finally, test results approve the validity of feedback hypothesis for Brazilian economy, that is the causality relationship for Brazilian economy is bi-directional and also parallel to Pao and Tsai (2011) findings. The policies applied in energy or economy should be considered very carefully in such situation according to the fact that any change in economicgrowth or any change in energyconsumption will affect the other.
To address this issue, Arnold et al. (2011) apply the Pooled Mean Group (PMG) estimator, …rst developed by Pesaran, Smith and Shin (1999), to examine the link between tax structure and growth. Their PMG estimations specify the model in a less restrictive way by relaxing the homogeneity restriction on some of the slope coe¢ cients in the growth equation. Arnold et al. (2011) show that given the level of tax revenue as a share of GDP, raising more tax revenue from taxes on income is associated with lower income per capita in the long run. Moreover, the authors suggest a "tax and growth ranking" in terms of e¤ects on income per capita, with recurrent taxes on immovable property at the top of this ranking, followed by consumption and other property taxes, personal income taxes, and …nally corporate income taxes. These …ndings lend empirical support to the policy recommendations as described in the OECD’s Current Tax Agenda.
trade openness and found the negative relationship between them, while on contradictory to the above study, Copeland and Taylor (1995) pointed out that factor endowment in each country also affects the trade. Depending upon the environment policy of the country, comparative advantage also affects the environmental degradation. Magani (2004) verified empirically that with a 1% increase in trade openness will lead to 0.58% of carbon emission. He used the data of 63 developed and developing regions. However, Dean (2002) found the relationship between environmental quality and trade openness in the case of China and states that trade openness worsen the environment quality. Liddle (2001) pointed out that increase in income lead to strictness in the environmental regulation, therefore more energy efficient technologies were adopted in order to save the environmentfrom degradation. Grossman and Krueger (1991) empirically investigate the relationship between free trade agreements and the environment in North America by using cross- section and panel data.
In addition to individual findings pertaining to specific countries, the following general conclusions can be drawn from these studies. When observing economic indicators, more developed OECDcountries expectedly are ranked higher than less developed ones. However, after adding undesirable environmental indicators, performance rankings change, and the relative performance of most developed countries declines. Therefore, an increase in GDP per capita should be unequivocally associated with a growing demand for higher environmental quality. Moreover, due to the robust and positive correlation between energyconsumption and undesirable outputs, countries producing high undesirable outputs have an extreme potential to save the optimum energy. Studies from the third group underline the significance of energy supply and/or consumption, usually combining three types of indicators – economic, environmental and energetic (Färe et al., 2004; Zhou et al., 2006; Zhou et al., 2007; Zhou & Ang, 2008; Simsek, 2014; Rashidi et al., 2015; Rashidi & Saen, 2015; Woo et al., 2015), (Makridou et al., 2016), (Sueyoshi et al., 2017), (Mardani et al., 2015). The most employed energetic indicators are different types of energyconsumption (oil, gas, coal, power). It may be said, roughly speaking, that researchers progressively shifted their focus fromeconomic efficiency in the 1990s to environmental efficiency in 2000s, and then to energy efficiency in 2010s. International institutions’ financial assistance program effect on the economicgrowth is important just in the long run (Fidrmuc & Kostagianni, 2015). Panel data model applied for studying economicgrowth determinants in the EU (Simionescu et al., 2016) show lagged GDP growth rate to have the largest effect on the current economicgrowth (along with the employment rate).
We introduce the technique of disaggregated GDP and total energy into a bivariate framework, while taking into account cross-sectional dependency and heterogeneity. Cross-sectional dependency implies a variation of the intercept in countries and overtime. While until 2010 the majority of the studies did not consider cross-sectional dependency (Mehrara 2007; Lee and Chang 2008; Huang et al. 2008; Narayan and Smyth 2008; Ozturk 2010), a more recent trend of literature underlines the risks of inconsistency and misleading inferences that can affect the exploitation of first generation of commonly used panel unit root tests and cointegration tests, since several of them assume independence (Kapetanios et al. 2011). Also, we consider the heterogeneous dynamics which prevails for most aggregate country level data by identifying the existence of structural breaks to obtain consistency in our cointegration analysis. Such an identification beforehand is of utmost importance, for a break introduces spurious unit root behaviour in the cointegration relationship so that the hypothesis of no cointegration is difficult to reject (Gregory et al. 1996).
The causal relationship between energyconsumption and economicgrowth has been examined extensively in a number of countries in recent years, with conflicting results. Three views exist regarding the relationship between energyconsumption and economicgrowth. The first view, which posits that energyconsumption Granger-causes economicgrowth, has been supported by studies like those of Chang et al. (2001) for the case of Taiwan; Wolde-Rufael (2004) for Shanghai; Lee (2005) for the case of developing countries; Altinay and Karagol (2005) for Turkey; Chiou-Wei et al. (2008) for Taiwan, Hong Kong, Malaysia and Indonesia; Akinlo (2009) for Nigeria; Odhiambo (2009a) for Tanzania; Odhiambo (2010) for the case of South Africa and Kenya; Chu (2012) for the case of 13 countries; Dergiades et al. (2013) for Greece; Muhammad et al. (2013) for Pakistan; Odhiambo (2014) for the case of Uruguay and Brazil; Abosedra et al. (2015) for Lebanon; Iyke (2015) for Nigeria; Tang et al. (2016) for Vietnam; Rahman (2017) for the case of Asian populous countries; Saidi et al. (2017) for the case of the European countries; Cai et al. (2018) for the case of Canada, Germany and the US; Le and Quah (2018) for the case of 14 selected countries in the Asia and the Pacific region; Bekun et al. (2019) for South Africa; and more recently Rahman et al. (2020) for the case of China when coal and oil consumption are used as proxies for energyconsumption.
international trade has been considered as an advent of rapid economicgrowth. Industrial manufacturing export in China, India and Brazil is on the rise, therefore the manufactured products which are exported to different parts of the world requires higher energyconsumption. Suri and Chapman (1998) discussed that Industrial manufacturing export for all developing countries is rising. They also concluded that, the growth rate in this section is higher for developing countries. The other interesting aspect to this argument is that the demand for these products from these economies is increasing at a faster rate and the clients being the developed economies. This is because of the availability of these products at a much cheaper rate because of the low cost resources in developing economies, especially in China, India and Brazil. In this paper Industrial exports share in total exports is used as a proxy for industrial export.
It is important to understand more how sensitive people are or their motivations in order to design appropriate communication policy to sensitize them on the energy transition. More specifically, the opinions of household can help to target specific envi- ronmental issues that would serve to boost both the adoption of renewable energy and the reduction of energy use. In this paper, we make a distinguish between environmental problems as general issue and specific environmental problems such as climate change, resource depletion, pollution, etc. The former is compared to other general issues such as unemployment, economic crisis, etc. Acceptedly, this consideration of environmental mo- tivation which is a proxy may not correspond to the true environmental conscientiousness which is a private information. Our results show that environmental motivations have mixed effects on both investment in energy efficiency and adoption of renewable energy. Namely, people who think that environmental issues are generally more important than other issues ( unemployment, economic crisis, etc.) are more likely to invest in renewable energy. This is consistent with results in Gerpott and Mahmudova (2010) and Zoric and Hrovatin (2012). Investments in renewable energy are mostly undertaken to reduce CO2 emissions and less probably to save money. People for whom environmental issues are the priority and who are aware that renewable energy is an alternative energy that is clean and helps protect environment, will have more motivations to overcome barriers that they may encounter to adopt renewable energy. On contrary, additionally to reduce CO2 emissions, investments in energy efficiency are also for money saving. Then, people who intend so save their energy bill can have motivations to invest in energy efficiency as well as people who are pro-environmental. Therefore, it does not have a significant effect on their investment decision in energy efficiency. However, when it comes to com- pare specific environmental issues between themselves, people who think that climate change problem is the priority are more likely to invest in energy efficiency, while those who choose resource depletion problem as priority are less motivated to invest in energy efficiency. They may prefer alternatives energy which do not rely on depletable energy resources. Though there is no significant effect of resource depletion issues on investment in renewable energy, the coefficient is positive.
Katircioglu et al. (2013) studied the relationship between energyconsumption of G7 countries with income and international trade. They observed these variables to be influencing the level of energy demand in these countries. In another related research by Akinlo A. (2008) who studied the causal effect of energyconsumption on the economic performances of eleven sub-Saharan African countries. Using Autoregressive distributed lag, he found that energy demand is positively correlated with economicgrowth in only seven of these countries. Using another test (Granger causality test) he found that there is bidirectional relationship between energy demand and economicgrowth in only three of these countries. He further discovered that economicgrowth granger cause energyconsumption in two of these countries. In another set of two countries, he discovered neutrality of this relationship. Similarly, absent of any relationship is observed in another set of three countries. He suggested a unique energy policy peculiar to each country’s nature of the relationship between energy demand and economicgrowth .
Perotti (1999) provides a theoretical model and empirical evidence to indicate that the effects of fiscal policy depend on the initial conditions. He first lays out a simple model where government expenditure shocks are positively correlated with private consumption in normal times, but are negatively correlated with them in bad times. Besides, Easterly and Rebelo (1993) show that there is a strong positive relationship between government size and per capita income both across a large sample of countries at a point in time and for a panel of 28 countriesfrom 1870 to 1988. It seems that the effect of expanding government size should be correlated with different economic conditions, such as the scale of the economy, the economicgrowth and private consumption. Furthermore, the quantile regression model can consider several different regression curves that correspond to the various percentage points of the distributions and can help us to determine whether the effect of expanding government size will lead to changes in the different economicgrowth conditions. Previous studies indicate that the results vary widely even when they deal with the same groups of countries in the OECD. While some studies find that the relationship is negative (Landau, 1985; Saunders, 1985, 1986; Fölster and Henrekson, 2001; Dar and AmirKhalkhali, 2002), other studies report mixed results (Conte and Darrat ,1988; Ghali,1998). The relationship between the two variables may vary in different regression curves that correspond to the various percentage points of the distributions and not only to the conditional mean distribution of a given sample. In this paper, we employ the quantile regression model to examine the whole spectrum of the relationship between government size and economicgrowth in 24 OECDcountries, and provide plausible explanations for the divergent outcomes reported in previous empirical studies.