Narayan and Prasad (2008) examined European countries included Czech Republic, Iceland, Italy, Portugal and Slovak Republic and found causality running from energy consumption to economic growth. Hatemi-J and Irandoust (2005) examined Sweden, Narayan and Prasad (2008) examined Finland, Hungary and Netherlands and they found same results. Hondroyiannis (2002) in his paper found two-way relationship for Greece. Erol and Yu (1987) for West Germany and Aktas and Yilmaz (2008) for Turkey found the same results. Yu and Choi (1985) for Poland and United Kingdom and Narayan and Prasad (2008) for Belgium, Denmark, France, Germany, Ireland, Luxembourg, New Zeland, Norway, Poland, Spain, Sweden, Switzerland and Turkey investigated causal relationship and found no evidence of causal relationshipbetween these variables. Although there are so much study examining countries all over the world, especially European countries, there is not enough study examining Romania. Romania is the newest member of European Union with Bulgaria. Romania has renewable energy sources like hydro power and wind power energy in the different part of the country. This country has also big capacity about petroleum and metan gas. These energy sources are important advantages for Romania among European Union countries. Because of these reasons to investigate the direction of causality for Romanian economy is important.
Data analysis. The analysis of the relationshipbetween renewable energy consumption and economic growth will be started after the tendencies of selected variables are presented. Time series of Lithuanian realGDP and RES gross inland consumption will be analyzed in this paper. Agreeably to Yoo & Ku (2009) realGDP in national currency Litas (LTL) instead of GNP as a measure for economic growth was chosen. The decision to select GDP instead of GNP was influenced by the fact that energy consumption of the specific country is related to goods and services produced within the country but not outside it. Chontanawat et al. (2008) recommended including final energy consumption (consumption of industry, construction, agriculture, transport, fishing, commercial and public services, as well households) into the investigation. Narayan & Prasad (2008) investigated the causality running from electricityconsumption to realGDP in 30 OECD counties and used only industrial electricityconsumption. RES (wind, hydro, geothermal, biomass, biofuel) gross inland consumption will be analyzed in this paper. The examined data are of the year 1990-2009.
Electricity is one of the basic elements of the daily routine of human’s life (i.e., personal usage to industrial production). Most often, it is claimed that the amount of electricityconsumption is directly attributed to the economic growth of a particular country. Due to the expansion of the global economy as well as the increase of income per capita, more demands are created for electrical-based equipment. Even in the rural area, people are eagerly connecting to the electric grid, gaining access to road transportation, and purchasing energy- used assets like electrical appliances and vehicles. These activities are also common for small, medium and large manufacturing industries, which are heavily relying on energy i.e., electricity, and thus contributing to the GDP. Given the possibility of the contribution of electricityconsumption on economic growth, many scholars have extensively conducted the study on electricityconsumption and economic growth nexus over the past two decades. Many studies have investigated the relationshipbetweenelectricityconsumption and economic growth due to the complex links between these two variables (Ciarreta & Zarraga, 2010).
After establishing long-run relationshipbetween the variables, we the proceeded to investigate the long-run and short-run impacts of electricityconsumption, exports and credit on economic growth using the associated ARDL and ECM. The results for long run are presented in Table 4, and the findings of the study found that there is a positive and statistically meaningful relationshipbetween economic growth, electricityconsumption, financial development and exports in Turkey. The study found that the electricityconsumption prospects to be positively and statistically related with economic growth. The findings of this study discovered that a 1% increase in electricityconsumption will lead to a corresponding increase of 0.303% in economic growth in Turkey holding another factors constant. Like in this line, the results indicate that financial development has positive and meaningful relationship with realGDP per capita. As a result, a 1% increase in financial development increases economic growth by 0.082 in the long run. The Turkish exports were, on the other hand, found to be positively related with economic growth. The result of the long-run analysis indicates that any 1% increase in the Turkish exports will have a significant impact on economic growth by a cumulative rise of 0.045%.
With electricityconsumption from the year 2001 till 2013, we used cross-state panel data to analyze the EC- GDP nexus. In state context, we use per capita income in the state as quasi for per capita GDP. In Section 2, we denote the equations used to address the causality. In Section 3, we perform several estimations for the causality testing. Section 4 concludes the paper.
The first group comprises of studies that find unidirectional causality running from energy consumption (both aggregate and disaggregate level) to GDP. Yang (2000) found unidirectional causality running from natural gas to GDP for Taiwan. Wolde-Rufael (2004) found unidirectional Granger causality from coal, coke, electricity, and total energy consumption to realGDP. Sari and Soytas (2004) found that waste had the largest initial impact, followed by oil on realGDP. However, lignite, waste, oil, and hydropower explained the larger amount of GDP variation among energy sources within the 3-year horizon respectively. Awerbuch and Sauter (2006) found that RES had a positive effect on economic growth by reducing the negative effects of oil prices volatility either by providing energy supply security or otherwise. Ewing et al. (2007) found that shocks arise due to NRES consumption like coal, gas and oil had more impact
finds bidirectional causalitybetween the two variables. Ho and Siu (2007) find unidirectional causality running from electricityconsumption to realGDP in Hong Kong. Chen et al. (2007) find that the directions of causalitybetweenelectricityconsumption and realGDP are mixed among ten Asian economies when the data for individual countries are analyzed. However, bidirectional causality is found in the panel data analysis. Narayan and Smyth (2009) use a panel dataset in the Middle Eastern countries to examine the relationshipbetweenelectricityconsumption and GDP and find bidirectional causation between the two variables. Chandran et al. (2010) examine the relationshipbetweenelectricityconsumption and realGDP for Malaysia during 1971 and 2003. They find that electricityconsumption, realGDP and price are cointegrated. In addition, there is a unidirectional causality running from electricityconsumption to economic growth. Sami (2011) finds that real per capita income causes electricityconsumption in Japan. Faisal and Nirmalya (2013) find that electricityconsumption does not cause growth in India, but there is bidirectional causalitybetween the two variables in Pakistan. Halkos and Tzeremes (2014) use a sample of 35 countries over the 1990-2011 period to examine the relationshipbetweenelectricityconsumption from renewable sources and GDP. They find that electricityconsumption from renewable sources will not cause higher GDP in emerging and developing countries.
7 Our empirical investigation has two dimensions. The first is to examine the long-run relationshipbetween carbon electricityconsumption and realGDP, while the second is to examine the short-run dynamic causal relationshipbetween the variables. The basic testing procedure requires three steps. The first step is to test whether the variables contain a panel unit root to confirm the stationarity of each variable (Engle and Granger, 1987). This is done by using the Levin and Chu test, (LLC, 2002), the Im et al. test (Im, Pesaran and Shin (IPS, 2003)), the Augmented Dickey – Fuller test (F-ADF) (Maddala and Wu, 1999; Choi, 2001) and finally Breitung (2000) test. The second step is to test whether there is a long-run cointegrating relationshipbetween the variables. This is done by the use of the Johansen-Fisher (Maddala and Wu, 1999; Kao, 1999; Pedroni, 1999, 2004) methods. Finally, the last step, if all variables are I(1) (integrated of order one) and cointegrated (Masih and Masih, 1996), short-run elasticities can be computed using the vector error correction model (VECM) method suggested by Engle and Granger (1987). In this case, an error correction mechanism exists by which changes in the dependent variables are modeled as a function of the level of the disequilibrium in the cointegrating relationship, captured by the error-correction term (ECT), as well as changes in the other explanatory variables to capture all short-term relations among variables (Pao and Tsai, 2010).
This study examines the causalitybetweenelectricityconsumption and economic growth for Thailand during 2000Q1 and 2014Q2. The bounds test in a trivariate framework is employed. The causality tests are performed using ECMs to detect long- run causations between the two variables. The empirical results show the existence of long-run bidirectional causal relationshipbetweenrealGDP and electricityconsumption. The sources of long-run linkages are found from the ECTs in both directions. In addition, there exists short-run bidirectional causalities between the two variables. The limitation of the present study is that the availability of time series data of electricityconsumption in a short time span, even though the long-run relationships are found
In time series econometrics most recent studies have tended to focus on VAR and VEC models and cointegration approach. For example Asafu and Adjaye (2000) investigated the causal relationshipbetween energy use and income in four Asian countries using cointegration and error correction mechanism. They found that causality runs from energy use to income in India and Indonesia and bi-directional causality in Thailand and Philippines. Yang (2000), found bi-directional causalitybetween energy consumption and GDP in Taiwan and this results contradicts with Cheng and Lai (1997) results. Soytas and Sari (2003) found bidirectional causality in Argentina and unidirectional causality from GDP to energy consumption in Italy and South Korea, and from energy consumption to GDP in Turkey, France, Germany and Japan. Paul and Bhattacharya (2004) found bidirectional causalitybetween energy consumption and economic growth in India. Using cointegration analysis Wietze and and Van (2007) found that unidirectional causality from GDP to energy consumption in Turkey. Dirck (2008) used the cointegartion approach to study the causal relationshipbetweenelectricityconsumption and economic growth for the panel of 15 European countries. He found the unidirectional causality from electricityconsumption to economic growth for Greece, Italy, and Belgium, and from economic growth to electricityconsumption for Great Britain, Ireland, Netherland, Spain and Portugal, no causality is found in Austria, Germany, Denmark, Finland, France, Luxembourg, and Switzerland. Narayan, Narayan and Popp (2010) used the cointegartion approach to study the causal relationshipbetweenelectricityconsumption and economic growth for six different panels of 93 countries. They found bidirectional causalityrelationshipbetween
Marathe, 2007; Wolde-Rufael, 2006). Yet, a handful of studies have reported neutral causal relation betweenelectricityconsumption and economic growth (Mozumber and Marathe, 2007; Jumbe, 2004; Asafu-Adjaye, 2000). The findings from the studies vary not only across countries, but depend also on methodologies within the same country (Akinlo, 2009; Wolde-Rufael, 2006; Soytas and Sari, 2003). Despite this numerous studies, none have focused on the causal relationshipbetween energy consumption and GDP for Togo. Wolde-Rufael (2006) examined the cointegration and Granger causality using Pesaran et al (2001) procedure. His study failed to establish a cointegration relationshipbetweenGDP and electricityconsumption for Benin.
Toda and Yamamotoo (1995) has been applied with maximum lag order 2 to investigate the direction of causalitybetweenelectricityconsumption per capita, realGDP per capita and capital use per capita. The results are reported in Table-6 indicated that bidirectional causality is founds betweenelectricityconsumption and economic growth. This empirical evidence provides support to findings of energy literature such as Yang (2000) for Taiwan, Yoo (2005) for Korea, Zamani (2006) for Iran, Zachariadis and Pashouortidou (2007) for Cyprus, Tang (2008, 2009) and Lean and Smyth (2010) for Malaysia, Hondroyiannis et al. (2002) and Tsani (2009) for Greece, Odhiambo (2009a) for South Africa, Ouédraogo (2010) for Burkina Faso and Lorde et al. (2010) for Barbados but contrast with Kayhan et al. (2010). Kayhan et al. (2010) reported unidirectional running from electricityconsumption to economic growth. The findings of Kayhan et al., (2010) may be biased due to ignorance of relevant variable such as capital stock as pointed out by Lütkepohl (1982) that omissions of important variables provide biased and inappropriate results on relationshipbetweenelectricityconsumption and economic growth. No causal relation is found in bivariate system due to neglected variables which affect electricityconsumption and economic growth relation. Our findings are more consistent because we have use trivariate system and covered long data span from 1980- 2008 while Kayhan et al. (2010) used 2001-2010. This finding implies that electricity conservation policies may retard economic growth by reduction in electricityconsumption in an economy and fluctuations in economic growth furthermore reduces demand for electricity due to feedback effect from economic growth to electricityconsumption.
Abstract: This paper investigates the causal relationshipbetweenelectricityconsumption and GDP in Slovenia, for the time period 1990-2010 and in Albania and Bulgaria for the time period 1980-2010. The causality is tested with the Granger causality test. But first we check whether or not the time series of GDP and electricityconsumption are stationary. The augmented Dickey-Fuller (ADF) test and Phillips-Perron test are used to evaluate whether these two series have unit root.
This paper contributes to the literature on the causal relationshipbetween energy and realGDP and improves on previous empirical research on African countries by using panel data co-integration and causality tests. Previous studies on the energy- GDPrelationship in African countries have made use of single country time series unit root and co- integration tests which have been shown to have low power (Maddala and Wu, 1999). Using data for 14 countries in Sub-Saharan Africa, we improve on existing studies on African countries in 2 main ways. Firstly, we make use of panel unit root and co-inte- gration tests and thereby address the low power crit- icisms of single country unit root and co-integration tests (Maddala and Wu, 1999). Secondly, by using a panel analysis, we exploit both the time-series and cross-section dimension of our data thereby increasing the number of observations and degrees of freedom, thus improving the efficiency of causal- ity tests (Hurlin and Venet, 2001).
Since the reform and opening up, China’s foreign trade, which is playing a significance role in the world, has become more and more important. But the proportion of China's total import value in the GDP cannot match the average level of developed countries. Obviously, the for- eign trade is closely related to economic growth in China. The importance of foreign trade for a country is increas- ingly prominent, though many researchers like Xu Qifa and Jiang Cuixia (2002) focused on the research related to the contribution of foreign trade on GDP growth, re- searchers particularly only focused on one region, a province as an example, literature related to the research on the economic development of east China is rare. Since the reform and opening up, foreign trade in east has ex- perienced rapid development. From 1981 to 2008, ex- ports and imports in east increased from 8.564 billion dollars to 2289.189 billion dollars. The increase of fore- ign trade is faster than the increase of GDP, and the pro- portion of foreign trade in GDP is increasing too. Howe- ver, is there serious internal logical causalitybetweenGDP and foreign trade? or, is there long term or short termcausality between them? Thus, this paper will try to research and discover it.
There are numerous arguments in favor of the pursuit of this export-led development strategy: first, trade expansion will bring about enhanced productivity through increased economies of scale in the export sector, positive externalities on non-exports and through increased capacity utilization. Second, exports may affect productivity through encouraging better allocation of resources driven by specialization and increased in efficiency, which in turn generate dynamic comparative advantage via reduction in costs for a country that facilitates exports (Mahadevan, 2007). Third, through encounters with international markets, trade will facilitate more diffusion of knowledge (especially in the process of interaction with foreign buyers and learning by doing gains) and more efficient management techniques which will have a net positive effect on the rest of economy and enhance overall economic productivity. Fourth, export growth also promotes capital accumulation and accumulation of foreign exchange and thus enables the importation of capital and intermediate inputs necessary in the production of goods exports. Through this link export growth has been analyzed as the engine of economic growth (Bhagwati and Srinivasan, 1978; Krueger, 1978; Kavoussi, 1984). These factors notwithstanding, the empirical evidence on export-led growth strategy (ELG) remain inconclusive at best. Empirical evidence that has found strong support for ELG include Krueger (1978), Bhagwati (1978), Tyler (1981), Kavoussi (1984), and Balassa (1978; 1985), and more recent studies such as Afxentiou and Serletis (1992), Serletis (1992), Bahmani-Oskooee and Asle (1993), Durraisami (1996), Henriques and Sadorsky(1996), Liu et al. (1997), Ghatak et al. (1997) and Al- Yousif (1999). Others, notably Jung and Marshal (1985), Chow (1987), Ahmad and Kwan (1991), Afxentiou and Serletis (1991), Bahmani-Oskoee et al. (1991), Dodaro (1993), and Greenaway and Seaford (1994) have failed to provide unambiguous support for ELG while using recent advances in time series econometrics and longer time periods. A number of other variables have also impacted on the relationshipbetween exports and economic growth, such as imports, real effective exchange rate and capital expenditure. The failure to address the role of these macroeconomic variables will result in specification bias or spurious regression (Al-Yousif, 1999; Shan and Sun, 1998; Riezman et al., 1996). Thus more recent empirical studies (such as Asafu- Adjaye and Chakraborty, 1999) have carried out ELG hypothesis test beyond the traditional two- variable relationship by taking into account other important macroeconomic variable in their investigation.
Another reason would be that Vietnam has been in its early period of development, and most people have relatively low income. Annual per capita GDP in 2010 (at constant price) was USD 712; percentage of the poor was 9.5%, and many people just got out of the national poverty line; nearly 68.1% of population located in rural areas (General Statistics Office, 2011). So when income increases, individuals or households try to get their basic needs rather than electricity-intensive goods at least in the short-run. Moreover, rural economy is based on agricultural production, so expansions of this production due to an increase in income would not have significant effects on electricityconsumption, at least in the short-run. These characteristics would explain that economic growth does not statistically affect electricityconsumption in the short-run in Vietnam.
Power is an important infrastructure for economic growth and for elaborating on the quality of life. The Ministry of Power, 2013 has placed power in the list of concurrent subjects under the constitution of India with the center and state. After the independence of India, the state electricity boards were the sole utilities responsible for generation, transmission & distribution of electricity (Singh, 2020). To strengthen the efforts of the state in bridging the yawning gap between demand and supply of power, it was decided in mid-seventies to setup generation stations and associated high voltage transmission lines at the center level (Singh & Kaur, 2020). India launched its energy sector reforms in 1992 with a thrust on power sector reforms. Over the period of time, the limit of power plants has increased from a pitfall 1,713 Megawatts (MW) in 1950 to 3,57,875 MW as on 30.09.2019 and similarly, power generation increased from about 5.1 Billion Units (BU) to 1,249.3 BU in the year 2018-19 (Singh & Vashishtha, 2019b). As delineated in Figure 2 the maximum energy-intensive sector is the industrial sector accounting for 41 percent of total energy consumption in the year 2018-19.
Some studies focus on the relationshipbetweenelectricityconsumption and economic growth for a single country. A few of those studies are Amusa and Leshoro (2013) for Botswana, Atif and Siddiqi (2010) for Pakistan, Khobai, Abel, and Le Roux (2016) for South Africa, Shahbaz, Sbia, Hamdi, and Ozturk (2014) for United Arab Emirates, Ogundipe and Apata (2013) and Chindo, Abdulrahim, Waziri, Huong, and Ahmad (2015) for Nigeria, Zaman (2015) for Pakistan, Zhao, Zhao, Han, He, and Guo (2016) for North China, Tang and Tan (2012) for Portugal. The analysis has been done for specific groups of countries also. Some of such contributions are Joyeux and Ripple (2011) for 30 OECD and 26 non-OECD countries by, Shakeel, Iqbal, and Majeed (2014) for South Asian countries, Khanna and Rao (2009) for developing countries, Campo and Sarmiento (2013) for 10 Latin American countries, Hamdi et al. for BIICS countries, and Dhungel (2017) for South Asian economies. Certain industry level studies are also there which examine the relationshipbetweenelectricityconsumption and output in different sectors. For example, Mushtaq (2008) examined the relationship for the agricultural sector of Pakistan and Lu (2017) examines for seventeen Taiwanese industries. Khandker, Samad, Ali, and Barnes (2014) studied the relationship at the micro level using household data for India to examine the impact of rural electrification on reduction of poverty.
Abstract. Relevant literature suggests that the most important determinant of health care spending is realGDP. Moreover, there is considerable evidence that health care spending rises at a faster rate than realGDP. This paper uses recently developed tests for the existence of a long run relationship to analyze the links between health care spending and GDP. We are, particularly, interested in estimating the elasticity parameter. The aim of the paper is to provide a new method of analysis to those used in recent papers on this subject. Typically in applied analysis, testing for the existence of cointegration and causality can only be carried out once the time series properties of the data have been established. For example, tests for cointegration require the variables to integrated of the same order, typically I(1), prior to estimation. By eliminating the need for unit root pre- testing, the tests applied here considerably simplify the inference procedure. They also reduce the potential for distortions in the inference due to the unknown properties of the testing sequence. Our findings include robust evidence that, for Pakistan, the income elasticity for health care spending is greater than one and that the elasticity value is stable over the estimation period.