Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA

Ulussever, Talat; Depren, Serpil Kılıç; Kartal, Mustafa Tevfik; Depren, Özer

doi:10.1007/s11356-023-26050-0

Cited by 26 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early studies focused on unveiling the eff ect of traditional economic indicators (e.g., investments, trade openness, foreign direct investment, urbanization) on the environment (Nurgazina et al, 2022). The second wave of the research has investigated the eff ect of input-based variables (e.g., electricity, renewable energy consumption and non-renewable energy consumption) on environmental performance (Pata and Çağlar, 2021;Ulussever et al, 2023;Adebayo et al, 2023;Akhayere et al, 2023;Kartal, 2023;Pata, Erdoğan, Ozkan, 2023;Ullah et al, 2023;Wang, Chandavuth, et al, 2023). Besides, most of the empirical research has adopted the EKC hypothesis (Shahbaz and Sinha, 2019;.…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Kiliç Depren,

Erdogan,

Tevfik Kartal

et al. 2024

Politicka ekonomie

Self Cite

View full text Add to dashboard Cite

This research investigates the effect of political stability and geopolitical risk on environmental sustainability (ES) by considering R&D investments in nuclear and renewable energy. Considering the high political stability and recent energy crisis and increasing geopolitical risk, the study focuses on three leading European countries. We use the load capacity factor, include data between 1985/1 and 2020/12, and apply quantile on quantile regression (QQ), Granger causality in quantiles (GQ), and quantile regression (QR) models. The study finds that in higher quantiles (i) increasing political stability stimulates the ES in Sweden and the United Kingdom; (ii) increasing geopolitical risk supports the ES in France; (iii) R&D investments increase the ES in all the countries; (iv) there are generally causal effects from the explanatory variables to the ES except some quantiles (0.45-0.50) in all the countries; (v) the power effects of the variables differ according to countries, quantiles and variables.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Kiliç Depren,

Erdogan,

Tevfik Kartal

et al. 2024

Politicka ekonomie

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most research in the literature has focused on CO 2 emissions using low-frequency (e.g., annual) data. However, the use of low-frequency data can be misleading because it only considers aggregate conditions over time and neglects the dynamic changes in the nexus over much longer periods and frequencies 7 , 8 . High-frequency data can address such issues and provide much more novel insights.…”

Section: Introductionmentioning

confidence: 99%

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach

Kartal,

Ulussever,

Pata

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.

show abstract

Natural gas supply cuts and searching alternatives in Germany: A disaggregated level energy consumption analysis for environmental quality by time series approaches

Kartal

Depren

Ayhan

2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA

Cited by 26 publications

References 64 publications

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Time and frequency analysis of daily-based nexus between global CO2 emissions and electricity generation nexus by novel WLMC approach

Natural gas supply cuts and searching alternatives in Germany: A disaggregated level energy consumption analysis for environmental quality by time series approaches

Contact Info

Product

Resources

About