Assessment of Causality Relationship between Renewable Energy Consumption and Economic Growth in Lithuania

Bobinaitė, Viktorija; Juozapavičienė, Aldona; Konstantinaviciute, Inga

doi:10.5755/j01.ee.22.5.969

Cited by 22 publications

(11 citation statements)

References 32 publications

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“…Such analysis were performed in (Anwar, Arshed, & Nabeela Kousar, 2017) on a panel of 29 OIC countries, data period 1990-2014; in (Alper, & Oguz, 2016) (Bozkurt, & Destek, 2015) on a panel of OEDC countries, data period 1980OEDC countries, data period -2012in (Tugcu, Ozlturk, & Aslain, 2012) on a panel G-7 countries, in (Okyay, Ebru, & Fatih, 2014) on a panel of developed countries in Europe, data period 1990Europe, data period -2011in (Chontanawat, Hunt, & Pierse, 2008) on a panel of 100 countries, data period 1960-2000 or 1971-2000. Similar studies for the case of individual countries and for different periods of time can be found for Saudi Arabia (Wada, 2017), for China (Lin, & Moubarak, 2014), for Turkey (Ocal, & Aslan, 2013), for Brazil (Pao, & Fu, 2013), for U.S. (Stern, 2000;Yildirim, Sarac, & Aslan, 2012), for Lithuania (Bobinaite, Juozapaviciene, & Konstantinaviciute, 2011).…”

Section: Literature Reviewsupporting

confidence: 74%

Impact of Renewable Energy Consumption on Economic Growth: Evidence From European Union Countries

Şoavă

Mehedințu

Sterpu

et al. 2018

Technological and Economic Development of Economy

118

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Abstract. This study examines the causal relationship between economic growth and renewable energy consumption using data for 28 countries of European Union, taken from Eurostat database for years from 1995 to 2015. In addition, motivated by EU Directive 2009/28/EC, the tendency of the share of renewable energy consumption into the final energy consumption is analysed. Various panel data techniques implemented in EViews are used. The empirical results suggest a positive impact of renewable energy consumption on economic growth, and emphasize bidirectional or unidirectional Granger causalities between the two macroeconomic indicators, for each country in the panel. These results justify the political decisions of EU concerning the necessity of increasing the renewable energy consumption, and prove that this type of energy consumption has a strong positive impact on economic growth. Thus, the inclusion of such policies in future EU and national strategies is further motivated. Finally, by means of linear regression, an increasing trend was found for the ratio between renewable energy consumption and final energy consumption for all but one of the EU countries.

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Section: Literature Reviewsupporting

confidence: 74%

Impact of Renewable Energy Consumption on Economic Growth: Evidence From European Union Countries

Şoavă

Mehedințu

Sterpu

et al. 2018

Technological and Economic Development of Economy

118

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“…Notable studies of the Lithuanian scientists include more general investigations at macroeconomics level. Bobinaitė et al (2011) assessed the causality relationship between renewable energy consumption and economic growth in Lithuania; Smaliukienė et al (2012) investigated interrelationship between energy consumption and the Lithuanian economic growth; Konstantinavičiūtė et al (2010) examined the dynamic of energy demand; Dudzevičiūtė (2013) did research on economic structural changes and energy consumption.…”

Section: Empirical Studies' Review and Research Methodologymentioning

confidence: 99%

Energy Intensity in the Lithuanian Manufacturing Sector

Dudzevičiūtė¹

2013

JSSI

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There is major concern with Lithuania's industrial development because its manufacturing structure is increasingly dependent on the consumption of energy. In spite of the Lithuanian energy intensity decrease more than 35 percent in the last decade, the energy required to produce a unit of output in Lithuania twice exceeds the average of the European Union countries. This paper investigates the energy intensity from a production theoretic framework and uses annual data of 1998-2011 to measure energy intensity in the Lithuanian manufacturing sector. The investigation compares energy intensity in manufacturing across different activities, based on several models. The results of the research show considerable variation in energy intensity across the activities. Based on energy intensity ratio, the Lithuanian manufacturing activities are classified into three categories, such as high energy-intensive, moderate energyintensive and low energy-intensive. The research reveals a strong and negative interrelationship between intensity of energy consumption and manufacturing production. Over a period of 1998-2011, the contribution of high energy-intensive industries to total manufacturing value added was increasing and amounted to 30 percent at the end of 2011. Finally, the research provides insights, that restructuring of the activities from energy intensive industries towards more technologically advanced ones might potentially lead to higher energy efficiency and it could be one of the most important routes to sustainable development.

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“…In Lithuania, Bobinaite et al [34] examined the linkage between economic growth (GDP) and renewable energy consumption (RES) by employing unit root, co-integration and Granger causality tests. Here, they have found a unidirectional relationship running from RES gross inland consumption to real GDP in the short run.…”

Section: Literature Reviewmentioning

confidence: 99%

A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

Sutthichaimethee

Chatchorfa

Suyaprom

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

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Assessment of Causality Relationship between Renewable Energy Consumption and Economic Growth in Lithuania

Cited by 22 publications

References 32 publications

Impact of Renewable Energy Consumption on Economic Growth: Evidence From European Union Countries

Impact of Renewable Energy Consumption on Economic Growth: Evidence From European Union Countries

Energy Intensity in the Lithuanian Manufacturing Sector

A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

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