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The purpose of this study is to evaluate the energy and exergy prices and carbon emission equivalents of the jet kerosene (Jet A-1) fuel considering 12 months data for an air craft used in the air transport sector in Turkey.

In the selection of the energy resources, one of the most important factors besides the need is the price of the energy resources. To use and save the energy resources efficiently, the prices should be evaluated in terms of exergy too. In this context, the exergy prices and carbon emission equivalents of the jet kerosene fuel have been examined.

According to analysis results, after January 2020, a steady decline in energy prices has been obtained until April 2020. In this regard, directly proportional changes have been obtained in exergy prices. The minimum exergy price of the fuel is calculated as 74.36 US cents/kWh for April 2020, while the maximum exergy price of the fuel is calculated as 150.02 US cents/kWh for September 2019. The minimum exergy price based carbon emission equivalents for the jet kerosene fuel is determined as 1,099.98 US cents/kg for April 2020, while the maximum exergy price based carbon emission equivalents for the jet kerosene fuel is found to be 2,219.29 US cents/kg for September 2019.

The new contribution has been made to the open literature by examining the energy and exergy prices of the jet kerosene fuel. In addition, the carbon emission equivalents of the jet kerosene fuel have been determined not only energy but also exergy methods.

In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural gas, fuel oil, the authors compared the effectiveness of forecasting models of generalized autoregressive heteroscedasticity (Generalized Autoregressive Conditional Heteroscedastic model, GARCH) with regression of support vectors for futures contracts. GARCH models are a standard tool used in the literature on volatility, and the vector machine nonlinear regression model is one of the machine learning methods that has been gaining huge popularity in recent years. The authors have shown that the accuracy of volatility forecasts for energy and aluminum prices significantly depends on the volatility proxy used. The model with correctly defined parameters can lead to fewer prediction errors than GARCH models when the square of the daily yield is used as an indicator of volatility in the evaluation. In addition, it is difficult to choose the best model among GARCH models, but forecasts based on asymmetric GARCH models are often the most accurate. The work is based on a model with a representative investor who solves the problem of optimizing utility in a two-period model. The key assumption of the model is the homogeneity of energy and aluminum investor preferences, that is, preferences do not change over time. There are also works with an attempt to solve this problem in a continuous state space. A completely new theory has been put forward that allows predicting the movement of the underlying asset without using historical data, so this topic is very relevant.

The current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium).

This study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings.

The results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label.

Previous research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.

Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

Clean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price.

The paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns.

Although, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods.

The study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility.

Results documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks.

In assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements.

To the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions.

The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups.

In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms.

The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study

This study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.

Daily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.

The empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.

This paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.

Energy represents an important component of production costs and therefore, an increase in energy prices directly impacts economic productivity, unemployment, inflation, and balance of payments equilibrium – often engendering currency devaluations. Until recently, the growth in demand for conventional fuels, mainly oil and gas, has widened imbalances between demand for and supply of energy. The effects of the surge in oil prices ripple across the entire global economy resulting in a redistribution of international liquidity. The latter creates global imbalances characterized by increasing balance of payment deficits and deteriorating the terms of trade, reducing the flow of non‐energy goods and services and increasing uncertainty of future global transactions. The aim of this paper is to shed some light on the impact of higher fuel prices on global liquidity management.

In the hybrid electricity market, renewable energy power generator faces the uncertainty of power market demand and the randomness of the renewable energy generation output. In order to improve the grid-connected quantity of green power, the purpose of this paper is to design the pricing mechanism for renewable energy power generator with revenue-sharing contract in a two-stage “multi-single” electricity supply chain which contains a single dominant power retailer and two kinds of power suppliers providing different power energy species.

Considering the dual uncertainties of renewable energy power output and power market demand, the authors design the full-cooperative contract decision-making model, wholesale price contract decision-making model and revenue-sharing contract decision-making model to compare and optimize grid-connected pricing in order to maximize profit of different parties in power supply chain. Then, this paper performs a numerical simulation, discusses the existence of the equilibrium analytical solutions to satisfy the supply chain coordination conditions and analyzes the optimal contract parameters’ variation characteristics and their interaction relationship.

The authors find that the expected profits of the parties in the hybrid power supply chain are concave about their decision variables in each decision-making mode. The revenue-sharing contract can realize the Pareto improvement for all parties’ interest of the supply chain, and promote the grid-connected quantity of green power effectively. The grid-connected price will reduce with the increase of revenue-sharing ratio, and this impact will be greater on the renewable energy power. The greater the competition intensity in power supply side, the smaller the revenue-sharing ratio from power purchaser. And for the same rangeability of competition intensity, the revenue-sharing ratio reduction of thermal power is less than that of the green power. The more the government subsidizing green power supplier, the smaller the retailer sharing revenue to it.

Facing with the dual uncertainties of green power output and market demand and the competition of thermal power in hybrid electricity market, this study can provide a path to solve the problem of renewable energy power grid-connecting. The results can help green power become competitive in hybrid power market under loose regulations. And this paper suggests that the government subsidy policy should be more tactical in order to implement a revenue-sharing contract of the power supply chain.

This paper studies the renewable energy electricity grid-connected pricing under the uncertainty of power supply and market demand, and compares different contract decision-making strategies in order to achieve the power supply chain coordination. The paper also analyzes the competition between thermal power and renewable energy power in hybrid electricity market.