Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union

Mehedințu, Anca; Sterpu, Mihaela; Şoavă, Georgeta

doi:10.3390/su10051515

Cited by 50 publications

(37 citation statements)

References 37 publications

(53 reference statements)

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“…As the world's states have adopted sustainable policies and decisions to encourage clean economic development, the share of renewable energy consumption in gross inland energy consumption has had an increasing trend since 1990 [24]. Therefore, we considered it appropriate, alongside the three above-mentioned indicators, to analyze the relationship with renewable energy consumption (from gross inland energy consumption).…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions: Testing Environmental Curves Hypotheses on EU Countries

2018

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This study analyses the relationship between per capita greenhouse gas (GHG) emissions, gross domestic product, gross inland energy consumption, and renewable energy consumption for a panel of 28 countries of European Union in the period 1990–2016. Two theoretical models, a quadratic and a cubic one, are used to estimate the shape of the environmental curve and to test the Kuznets hypothesis. The panel cointegration approach proved the existence of long-run equilibrium relations among the four macroeconomic indicators. Empirical estimations, using panel data techniques, as well as heterogeneous regression for each individual country in the panel, show non-conclusive evidence for the environmental Kuznets curve (EKC) hypothesis. The least square estimates, with the variables in log per capita form, reveal that the inverted U-shaped EKC hypothesis is verified for the panel and for 17 of the 28 EU countries. Estimates of the cubic model show that the environmental curve has an inverted N-shaped form. These results do not hold when the values are in non-logarithmic form. In addition, the estimations for all models show that an increase of gross energy consumption leads to an increase of GHGs, while an increase of renewable energy consumption leads to a reduction in GHG emissions.

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Section: Brief Literature Reviewmentioning

confidence: 99%

Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions: Testing Environmental Curves Hypotheses on EU Countries

2018

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“…In order to estimate and forecast the share of annual REMW into RPT different regression models were used. The general time regression model can be written as follows [46]:…”

Section: Modelsmentioning

confidence: 99%

The Effect of Remittances on Poverty in the Emerging Countries of the European Union

2019

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In this paper we study the evolution of remittances and risk of poverty threshold for nine emerging countries in the European Union and analyzed the evolution and trend of the share of remittances in the risk of poverty threshold. The analysis was performed on data taken from the Eurostat database for the period 2005–2017. The statistical analysis of the data showed that the evolution of both remittances and risk of poverty threshold was heavily influenced by the global economic crisis. Although after the crisis, the risk of poverty threshold has seen a growing trend in all emerging countries, the remittances have experienced sinuous variations, dramatic declines for some of the countries (drastically for Romania and Latvia) and significant increases for others (Hungary). The results of the analysis using time-dependent regression models lead to the conclusion that, although the share of remittances in risk of poverty threshold diminished abruptly after the 2009 economic crisis, in the short term it is expected to maintain a growth trend for most of the analyzed countries (Bulgaria, Czechia, Hungary, Lithuania, Poland, Romania, and Slovakia), followed downward tendency after 2018 for Bulgaria and Romania, and after 2020 for Hungary and Lithuania. For Latvia and Estonia, both quadratic and cubic models estimate a decreasing evolution.

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“…Jiang [67] take advantage of ARIMA to calculate China's coal consumption and price from 2016 to 2030. Mehedintu et al [68] used five single methods to predict the share of renewable energy consumption in total consumption in 2020, including ARIMA model. It can be seen that ARIMA model is widely used, and the accuracy of prediction results is also accepted by researchers.…”

Section: Review Of Arima Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union

Cited by 50 publications

References 37 publications

Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions: Testing Environmental Curves Hypotheses on EU Countries

Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions: Testing Environmental Curves Hypotheses on EU Countries

The Effect of Remittances on Poverty in the Emerging Countries of the European Union

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

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