Editorial

2 pages, 154 KiB

Open AccessEditorial

by Periklis Gogas and Theophilos Papadimitriou

Energies 2022, 15(14), 5212; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145212 - 19 Jul 2022

Cited by 2 | Viewed by 1484

In the intersection between economics and engineering, energy economics has been an active research topic for more than 150 years [...] Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

Research

Jump to: Editorial, Review

21 pages, 9841 KiB

Open AccessArticle

Geopolitical Risk as a Determinant of Renewable Energy Investments

by Floros Flouros, Victoria Pistikou and Vasilios Plakandaras

Energies 2022, 15(4), 1498; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041498 - 17 Feb 2022

Cited by 39 | Viewed by 4176

Abstract

The advent of various initiatives around the globe in shaping an energy transition towards a “greener” energy production future sparked a research interest towards the determinants that will shape their success. In this paper, we depart from the relevant literature evaluating the potential [...] Read more.

The advent of various initiatives around the globe in shaping an energy transition towards a “greener” energy production future sparked a research interest towards the determinants that will shape their success. In this paper, we depart from the relevant literature evaluating the potential effect of geopolitical tensions on renewable energy investments, building on an explicit quantitative approach that provides clear empirical evidence. In doing so, we compile a large panel of 171 economies and measure the effect of geopolitical risk on “green” investing as measured by popular geopolitical risk indices, while controlling for all major variables proposed by literature. Our flexible Autoregressive Distributed Lag model with heterogenous effects across economies suggests that geopolitical risk has a significantly measurable effect on green investments both in the short and the long run. In fact, our results suggest that proper model specification is robust across alternate risk assessments. Overall, our study has direct policy implications suggesting that renewable energy could be an important part of our energy mix only if we take into account its linkages with geopolitical tensions. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

14 pages, 1324 KiB

Open AccessArticle

Short-Term Load Probabilistic Forecasting Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Reconstruction and Salp Swarm Algorithm

by Tianyu Hu, Mengran Zhou, Kai Bian, Wenhao Lai and Ziwei Zhu

Energies 2022, 15(1), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/en15010147 - 27 Dec 2021

Cited by 5 | Viewed by 2176

Abstract

Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical [...] Read more.

Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy (S_E). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

15 pages, 239 KiB

Open AccessArticle

COVID-19 and the Energy Price Volatility

by Apostolos G. Christopoulos, Petros Kalantonis, Ioannis Katsampoxakis and Konstantinos Vergos

Energies 2021, 14(20), 6496; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206496 - 11 Oct 2021

Cited by 29 | Viewed by 2331

Abstract

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. [...] Read more.

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. A general econometric panel model is applied to investigate the relationship between COVID-19 infection and death announcements with oil price volatility. The paper uses data from six geographical zones, Europe, Africa, Asia, North America, South America, and Oceania for the period 21 January 2020 until 13 May 2021 and the empirical findings show that COVID-19 deaths affected oil volatility significantly. This conclusion is confirmed by a second stage analysis applied separately for each geographical area. The only geographical area where the existence of correlation is not confirmed between the rate of increase in deaths and the volatility of the price of crude oil is Asia. The conclusions of this study clearly suggest that COVID-19 is a new risk component on top of economic and market uncertainty that affects oil prices and volatility. Overall, our results are useful for policy-makers, especially in the case of a new wave of infection and deaths in the future. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

22 pages, 35872 KiB

Open AccessArticle

Assessment of the Target Model Implementation in the Wholesale Electricity Market of Greece

by Filippos Ioannidis, Kyriaki Kosmidou, Kostas Andriosopoulos and Antigoni Everkiadi

Energies 2021, 14(19), 6397; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196397 - 06 Oct 2021

Cited by 9 | Viewed by 2907

Abstract

The European Union Target Model aims to integrate European energy market by removing barriers to trade and align markets. The most important goals of the Target Model are to provide consistent prices, enhance liquidity, support cross boarder trading, facilitate interconnections, and coordinate the [...] Read more.

The European Union Target Model aims to integrate European energy market by removing barriers to trade and align markets. The most important goals of the Target Model are to provide consistent prices, enhance liquidity, support cross boarder trading, facilitate interconnections, and coordinate the use of transmission system capacity. In that context, the smooth operation of both forward and spot markets is a core development that directly affects the good operation of the wholesale market. This paper examines the application of the Target Model in the wholesale electricity market of Greece and its impact on electricity prices. The study explores the time period before the implementation of the Target Model, which took place on November 2020, and the first nine months of its execution. Based on the feedback received by the rest of the European countries, which are already part of the European Single Market, this crucial period of time is considered transitional, when many distortions and unethical behaviors take place. Empirical findings indicate a relatively successful implementation of the Target Model in Greece, with price disorders mostly met in the Balancing Market. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

58 pages, 25009 KiB

Open AccessArticle

A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

by James Ming Chen and Mobeen Ur Rehman

Energies 2021, 14(19), 6099; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196099 - 24 Sep 2021

Cited by 7 | Viewed by 2480

Abstract

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. [...] Read more.

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Graphical abstract

13 pages, 5000 KiB

Open AccessArticle

Forecasting Natural Gas Spot Prices with Machine Learning

by Dimitrios Mouchtaris, Emmanouil Sofianos, Periklis Gogas and Theophilos Papadimitriou

Energies 2021, 14(18), 5782; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185782 - 14 Sep 2021

Cited by 18 | Viewed by 4886

Abstract

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and [...] Read more.

The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

15 pages, 4170 KiB

Open AccessArticle

Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

by Rangan Gupta and Christian Pierdzioch

Energies 2021, 14(14), 4173; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144173 - 10 Jul 2021

Cited by 11 | Viewed by 1433

Abstract

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to [...] Read more.

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

11 pages, 1058 KiB

Open AccessArticle

Oil Price Uncertainty, Globalization, and Total Factor Productivity: Evidence from the European Union

by Svetlana Balashova and Apostolos Serletis

Energies 2021, 14(12), 3429; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123429 - 10 Jun 2021

Cited by 6 | Viewed by 2015

Abstract

This paper uncovers linkages between oil price uncertainty, total factor productivity (TFP) growth, and critical indicators of knowledge production and spillovers. It contributes to the literature by investigating the effects of oil price volatility on TFP growth, controlling for two different channels for [...] Read more.

This paper uncovers linkages between oil price uncertainty, total factor productivity (TFP) growth, and critical indicators of knowledge production and spillovers. It contributes to the literature by investigating the effects of oil price volatility on TFP growth, controlling for two different channels for TFP growth; benefits from the quality of the national innovation system and from adopting new technologies. We use an unbalanced panel for 28 European Union countries for the period from 1990 to 2018. We find that oil price uncertainty has a negative and statistically significant effect on TFP growth, even after we control for technological advancements and the effects of globalization. We also find that the scale of research and innovation and international trade are positive contributors to TFP growth. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

16 pages, 255 KiB

Open AccessReview

Towards a Global Energy-Sustainable Economy Nexus; Summing up Evidence from Recent Empirical Work

by Angeliki N. Menegaki

Energies 2021, 14(16), 5074; https://0-doi-org.brum.beds.ac.uk/10.3390/en14165074 - 18 Aug 2021

Cited by 8 | Viewed by 1193

Abstract

The recent trend in New Economics is the establishment of measures of sustainable wealth and welfare which take into account all the parameters of economic, environmental, and social life and progress, juxtaposed to the conventional and myopic GDP. This review summarizes results from [...] Read more.

The recent trend in New Economics is the establishment of measures of sustainable wealth and welfare which take into account all the parameters of economic, environmental, and social life and progress, juxtaposed to the conventional and myopic GDP. This review summarizes results from a series of recent papers in the energy-growth nexus field, which have perused a proxy for the sustainable GDP instead of the conventional GDP and discusses the difference in results and policy implications. The energy-growth nexus field itself has generated a bulk of work since the seminal study of Kraft and Kraft (1978), but still the field needs new perspectives in order to generate results with a consensus. The bidirectional causality between energy consumption and sustainable economy provides evidence for the Feedback Hypothesis, a statement that essentially warns that it is too early for sustainability to be feasible without fossil energy consumption, and vice versa. The unidirectional causality reveals, on the one side, that an economy cannot grow without the plentiful consumption of energy (the Growth Hypothesis) and, on the other side, that the growth of the economy fuels energy consumption (the Conservation Hypothesis). Failure to corroborate causality between energy consumption and economic growth is evidence for the Neutrality Hypothesis. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

28 pages, 16409 KiB

Open AccessReview

Renewable Energy, Economic Growth and Economic Development Nexus: A Bibliometric Analysis

by Henrique Oliveira and Víctor Moutinho

Energies 2021, 14(15), 4578; https://0-doi-org.brum.beds.ac.uk/10.3390/en14154578 - 28 Jul 2021

Cited by 28 | Viewed by 3819

Abstract

The present research aims to conduct a systemic review on Renewable Energy, Economic Growth and Economic Development and look for links between the papers published between 2008 and May 2021. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, it [...] Read more.

The present research aims to conduct a systemic review on Renewable Energy, Economic Growth and Economic Development and look for links between the papers published between 2008 and May 2021. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, it was possible to reach a sample of 111 articles selected by Web of Science and a sample of 199 academic articles selected by Scopus in that specific period. The analysis of the group of Renewable and Non-renewable Energy Consumption, Economic Growth and Economic Development shows that most of the articles published in this subsample use the quantitative methodology in economic sciences. The results indicate that research on the subject has a growing trend and that most of the articles are post-2015 publications. In addition, China has been the leading nation in published works. The journal Renewable and Sustainable Energy Reviews is considered the most relevant in this category, and Sustainability has the most publications. Finally, a research gap was identified to be explored, lacking studies aimed at understanding the consumption of renewable energies and economic development and studies that focus on renewable energies and economic growth in less developed economies. Full article

(This article belongs to the Special Issue Emerging Trends in Energy Economics)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Emerging Trends in Energy Economics

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI