On the Impact of GDP per Capita, Carbon Intensity and Innovation for Renewable Energy Consumption: Worldwide Evidence

Tudor, Cristiana; Şova, Robert

doi:10.3390/en14196254

Cited by 39 publications

(18 citation statements)

References 58 publications

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“…The findings are consistent with the fixed effects model’s results: there are positive correlations between RE capacity, GDP per capita, RE policies, and R&D. RE policies and R&D spending cause REC to expand by 0.06%, 0.02%, and 0.02%, respectively, with a 1% growth in GDP per capita. Recent research has discovered similar results (Gershon and Emekalam, 2021 ; Tudor and Sova, 2021 ). According to Sadorsky (2009), for G7 countries, a 1% increase in GDP per capita boosts RE consumption by 3.5%.…”

Section: Resultssupporting

confidence: 60%

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Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries

Kilinc‐Ata

Dolmatov

2022

Environ Sci Pollut Res

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The importance of using renewable energy (RE) sources has increased significantly in recent times, especially considering the growing concerns about climate change problems and rising fossil fuel prices, which pose a significant threat to the national economies. Therefore, empirical studies that can be used both domestically and internationally in harmony can be created in line with rising investments in RE. However, there has no more analysis of RE investments from the viewpoint of investors in the literature up to this point, and it is crucial to highlight the best investor practices when deploying RE. This research provides theoretical and empirical support for the factors influencing RE investments; used in this analysis are newly constructed panel data on 34 OECD countries and the 5 BRICS countries that range from 2000 to 2020. Specifically, the generalized moment method (GMM), robustness check, fixed and random effects models, panel unit testing, and other panel regression techniques were employed in the study to analyze the determinants of RE investment. The main findings of this paper suggest that economic growth, RE policy, and R&D expenditures all have a statistically significant and positive relationship with RE capacity. Furthermore, RE investment is inversely relative to energy use, electricity use, and carbon (CO 2 ) emissions. As a result, rigorous governmental or state regulation (policy, R&D) is essential for RE investment.

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Section: Resultssupporting

confidence: 60%

“…According to Sadorsky (2009), for G7 countries, a 1% increase in GDP per capita boosts RE consumption by 3.5%. Recent research has discovered similar results (Gershon and Emekalam, 2021 ; Tudor and Sova, 2021 ). According to Sadorsky (2009), for G7 countries, a 1% increase in GDP per capita boosts RE consumption by 3.5%.…”

Section: Resultssupporting

confidence: 60%

Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries

Kilinc‐Ata

Dolmatov

2022

Environ Sci Pollut Res

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“…GDP per capita (GDP): GDP is the most important statistic in macroeconomics. While GDP growth can solve problems such as financing difficulties, talent shortages, and misallocation of resources for green innovation [ 59 ], it can also increase national income per capita and stimulate social groups’ pursuit of a green life when their material needs are satisfied, thus driving the state, society, and enterprises to strengthen their support for green innovation [ 60 ]. Therefore, we used the natural logarithm of per capita GDP to measure the level of a country’s economic development.…”

Section: Methodsmentioning

confidence: 99%

How Government Size Expansion Can Affect Green Innovation—An Empirical Analysis of Data on Cross-Country Green Patent Filings

Wen

Zhang

et al. 2022

IJERPH

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The expansion of government size will have dual effects on a country’s green innovation. An appropriately sized government size increases marginal productivity and stimulates the development of green innovation by increasing government expenditure. On the contrary, an excessively sized government creates a huge administrative agency, which not only increases the tax burden but also damages social welfare by excessive intervention. Therefore, the effect of government size on green innovation is not linear. In order to prove this proposition, this study examines the impact of government size on green innovation in 166 countries between 1995 and 2018, using a two-way fixed effects model. The results reveal an inverted U-shaped relationship between government size and the level of green innovation, indicating that optimal government size may maximize a country’s green innovation output. The results further suggest that this inverted U-shaped relationship is mainly influenced by environmental regulations and financial support. Finally, our heterogeneity analysis demonstrates that the inverted U-shaped relationship is more pronounced for countries with high organizational inertia and more R&D expenditure than for those with low organizational inertia and less R&D expenditure. This finding makes up for the research gap between government size and green innovation and provides a reference for countries to formulate the optimal government size to improve the level of green innovation.

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“…The selection of the variables for the current research targeted the development of a three-dimensional construct (economic-environmental-renewable energy) considered to be explanatory for getting new insights on the existing relation between the economic performance of the targeted countries and their advances towards renewable energy usage. Thus, the seven indicators considered highly suggestive for revealing the nexus between economic, environmental and energy production dimensions, were chosen based on significant studies as follows: oil consumption (Gallo et al, 2010;Mukhtarov et al, 2020;Guo et al, 2021), gas consumption (Dong et al, 2017(Dong et al, , 2018Ummalla and Samal, 2019;Çıtak et al, 2021), CO 2 emissions (Dong et al, 2017(Dong et al, , 2018Ummalla and Samal, 2019;Çıtak et al, 2021), GHG (Dong et al, 2017(Dong et al, , 2018Ummalla and Samal, 2019;Çıtak et al, 2021), GDP per capita (Simionescu et al, 2019;Tudor and Sova, 2021) and GDP from industry (Simionescu et al, 2019;Tudor and Sova, 2021).…”

Section: Dataset Descriptionmentioning

confidence: 99%

Renewable energy strategy analysis in relation to environmental pollution for BRICS, G7, and EU countries by using a machine learning framework and panel data analysis

Cristea

Zamfir

Simionov

et al. 2022

Front. Environ. Sci.

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The present research uses machine learning, panel data and time series prediction and forecasting techniques to establish a framework between a series of renewable energy and environmental pollution parameters, considering data for BRICS, G7, and EU countries, which can serve as a tool for optimizing the policy strategy in the sustainable energy production sector. The results indicates that XGBoost model for predicting the renewable energy production capacity reveals the highest feature importance among independent variables is associated with the gas consumption parameter in the case of G7, oil consumption for EU block and GHG emissions for BRICS, respectively. Furthermore, the generalized additive model (GAM) predictions for the EU block reveal the scenario of relatively constant renewable energy capacity if gas consumption increases, while oil consumption increases determine an increase in renewable energy capacity until a kick point, followed by a decrease. The GAM models for G7 revealed the scenario of an upward trend of renewable energy production capacity, as gas consumption increases and renewable energy production capacity decreases while oil consumption increases. In the case of the BRICS geopolitical block, the prediction scenario reveals that, in time, an increase in gas consumption generates an increase in renewable energy production capacity. The PCA emphasizes that renewable energy production capacity and GHG, respectively CO2 emissions, are highly correlated and are integrated into the first component, which explains more than 60% of the variance. The resulting models represent a good prediction capacity and reveal specific peculiarities for each analyzed geopolitical block. The prediction models conclude that the EU economic growth scenario is based on fossil fuel energy sources during the first development stage, followed by a shift to renewable energy sources once it reaches a kick point, during the second development stage. The decrease in renewable energy production capacity when oil consumption increases indicates that fossil fuels are in trend within the G7 economy. In the case of BRICS, it is assumed that gas consumption appears because of increasing the industrial capacity, followed by the increase of economic sustainability, respectively. In addition, the generalized additive models emphasize evolution scenarios with different peculiarities, specific for each analyzed geopolitical block.

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On the Impact of GDP per Capita, Carbon Intensity and Innovation for Renewable Energy Consumption: Worldwide Evidence

Cited by 39 publications

References 58 publications

Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries

Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries

How Government Size Expansion Can Affect Green Innovation—An Empirical Analysis of Data on Cross-Country Green Patent Filings

Renewable energy strategy analysis in relation to environmental pollution for BRICS, G7, and EU countries by using a machine learning framework and panel data analysis

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