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This paper aims to study the ongoing and emerging technological changes in the global energy sector from the frequently neglected perspective of their potential destructive impact on the Russian economy.

Having reviewed existing global energy forecasts made by reputable multilateral and national government agencies, major energy corporations and specialised consulting firms, the authors noticed that most of them are by and large based on the extrapolation of conventional long-term trends depicting gradual growth of fossil fuels’ demand and catching-up supply. Unlike this approach, the paper focuses on the possible cases when conventional trends are broken, supply–demand imbalances become huge and the situation in the global energy markets is rapidly and dramatically changing with severe consequences for the Russian economy, seriously dependent on fossil fuels exports. Revealing these stress scenarios and major drivers leading to their realisation are in the focus of the research. Based on the Social, Technological, Economic, Environmental, Political, Values (analytical framework) (STEEPV) approach, the authors start from analysing various combinations of factors capable to launch stress scenarios for the Russian economy. Formulating concrete stress scenarios and assessing their negative impact on the Russian economy constitute the next step of the analysis. In conclusion, the paper underlines the urgency to integrate stress analysis related to global energy trends into the Russian national systems of technology foresight and strategic planning, which are now in the early stages of development.

The analysis of global energy market trends and various combinations of related economic, political, technological and ecological factors allowed to formulate four stress scenarios particularly painful for the Russian economy. They include the currently developing scenario “Collapse of oil prices”, and three potential ones: “Gas abundance”, “Radical de-carbonisation” and “Hydrogen economy”. One of the most important conclusions of the paper is that technology-related drivers are playing the leading role in stress scenario realisation, but it is usually a specific combination of other drivers (interlacing with technology-related factors) that could trigger the launch a particular scenario.

This study’s approach is based on the assumption that Russia’s dependence on hydrocarbons exports as one of the main structural characteristics of the Russian economy will remain intact. However, for the long-term perspective, this assumption might not hold true. So, new research will be needed to review the stress scenarios within the context of radical diversification of the Russian economy.

This paper suggests a number of practical steps aimed at introducing stress analysis as one of the key functions within the energy-related sectoral components of the Russian national systems of technology forecasting and strategic planning.

The novelty of this paper is determined both by the subject of the analysis and approach taken to reveal it. In contrast to most of research in this area, the main focus has been moved from the opportunities and potential benefits of contemporary technology-related global energy shifts to their possible negative impact on the national economy. Another important original feature of the approach is that existing global energy forecasts are used only as a background for core analysis centred around the cases when conventional energy trends are broken.

China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC.

Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years.

Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency.

A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

The purpose of this paper is to explore whether the out-of-sample model bias plays an important role in predicting volatility.

Under the heterogeneous autoregressive realized volatility (HAR-RV) framework, we analyze the predictive power of out-of-sample model bias for the realized volatility (RV) of the Dow Jones Industrial Average (DJI) and the S&P 500 (SPX) indices from in-sample and out-of-sample perspectives respectively.

The in-sample results reveal that the prediction model including the model bias can obtain bigger R², and the out-of-sample empirical results based on several evaluation methods suggest that the prediction model incorporating model bias can improve forecast accuracy for the RV of the DJI and the SPX indices. That is, model bias can enhance the predictability of original HAR family models.

The author introduce out-of-sample model bias into HAR family models to enhance model capability in predicting realized volatility.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

This paper aims to investigate whether the best statistical model also corresponds to the best empirical performance in the volatility modeling of financialized commodity markets.

The authors use various p and q values in Value-at-Risk (VaR) GARCH(p, q) estimation and perform backtesting at different confidence levels, different out-of-sample periods and different data frequencies for eight financialized commodities.

They find that the best fitted GARCH(p,q) model tends to generate the best empirical performance for most financialized commodities. Their findings are consistent at different confidence levels and different out-of-sample periods. However, the strong results occur for both daily and weekly returns series. They obtain weak results for the monthly series.

Their research method is limited to the GARCH(p,q) model and the eight discussed financialized commodities.

They conclude that they should continue to rely on the log-likelihood statistical criteria for choosing a GARCH(p,q) model in financialized commodity markets for daily and weekly forecasting horizons.

The log-likelihood statistical criterion has strong predictive power in GARCH high-frequency data series (daily and weekly). This finding justifies the importance of using statistical criterion in financial market modeling.

First, this paper investigates whether the best statistical model corresponds to the best empirical performance. Second, this paper provides an indirect test for evaluating the accuracy of volatility modeling by using the VaR approach.

The purpose of this paper is to propose a simple regression‐based method of forecasting daily electricity demand, which may serve as a more accurate benchmark for short‐term forecasts.

In order to make more efficient use of the calendar effects in electricity demand, including weekend, and seasonal effects, while maintaining the parsimony of the forecasting model, the authors match the demand on each day of an entire year with the average of the corresponding days in recent years. This matching‐day approach substantially simplifies the modeling procedure of complex periodicity in electricity demand without loss of information.

With daily data on electric power system load in New England, the authors' method provides quite accurate forecasts. The mean absolute percentage error (MAPE) (2.1 percent) is significantly lower than those of the seasonal ARIMA and exponential smoothing method, and also comparable to the performance of more sophisticated methods in the literature.

The authors' method needs to be modified or augmented by other techniques when the periodicity is not stable due to time trends, economic crises, and other factors.

The management of electric utility providers as well as professional forecasters may use this method as a handy benchmark.

While previous studies focus mainly on accuracy of forecasts, the method presented in the paper is developed with the balance between accuracy and ease of use in mind.

Hydrogen fuel cells could play an important role in meeting the challenges of the Two Degrees Scenario. The purpose of this paper is to review the development of this technology in South Africa with the aim of understanding how the country can transform its existing socio-technical systems and act to support a hydrogen-based technological innovation system (TIS).

A mixed methods approach has been followed in this study. Secondary data analysis was used initially to build a profile of South Africa’s present energy system, followed by a stakeholder survey of the emerging hydrogen economy. Respondents were selected based on a convenience/snowball sampling approach and were interviewed using a semi-structured questionnaire, covering opportunities for South Africa in the global hydrogen economy; sources of competitive advantage; the present phase of development; the maturity of each function and the main weaknesses within the TIS; and finally the appropriate policy instrument to remedy the weakness and/or maximise opportunities for local companies.

The research has shown that the hydrogen economy is still at a pre-competitive level and requires ongoing government support to ensure an energy transition is realised. In particular, it is important that niche experimentation, a proven strategy in respect of successful sustainability transitions, is further pursued. Importantly, the net cost of hydrogen-based transportation, which is still several times larger than the cost of transport based on the internal combustion engine (ICE), must be reduced, especially in the key applications of public transport and underground vehicles. Furthermore, the development of digital technologies to manage supply fluctuations in energy grids must be accelerated.

The South Africa economy will be severely affected by the replacement of the ICEs with battery electric vehicles due to the country’s reliance on ICEs for platinum demand. Fuel cells represent a new market for platinum but the hydrogen TIS is still at a vulnerable point in its development; without policy support, it will not contribute to a successful socio-technical transformation, nor provide an alternative outlet for platinum.

Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank.

After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software.

The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained.

The authors did not find similar studies or research studies conducted in Brazil.

Analysis of relationship between energy and growth offers the sustainable energy pathway for a country’s sustainable economic development. This study aims to focus on the evaluation of the Pakistan’s energy system using long-run energy alternative planning (LEAP) modeling framework through different growth scenarios.

Principal component analysis has been adopted for indicators index formation. Study period of 1980 to 2030 is covered by forward and backward simulations in LEAP software.

The study reveals that current energy policy does not have the potential to lead the country toward a desired goal of economic sustainability.

In falling off scenario, negative growth rate (-5 per cent) assumption is also debatable; LEAP shows an error in the analysis and takes the last positive available value for any further analysis as a default. This case could have been simply omitted from results but for research contribution, the computations for this case are also reported.

Long-range energy alternative planning model has been applied to answer the corresponding question for simulation period of 1980 to 2030 to better compare the past trend and future expectations. Critical analysis of four selected scenarios (BAU, moderate, advanced and falling off) indicate that energy policy of Pakistan is poorly managed to maintain energy system’s effectiveness.

As far as statistical difference is concerned, early years have more fluctuation; however, from 2009, curve flattens for energy consumption and energy demand. The increasing demand of energy impacts the society and hence disturbs all sectors.

Policymakers have been so dragged off from the main route to sustainability, despite all odds there is a huge unexplored potential in the country for use to move in step with the world for a better tomorrow. The study educates the policymakers to comprehend the future energy scenarios and make rational decisions based on the study outcomes.

The purpose of this paper is to investigate the effect of service quality and relationship quality on customer loyalty in different stages of the relationship life cycle in online banking services.

A total of 651 Iranian online banking customers participated in the research by completing questionnaires. The research hypotheses were tested using structural modeling technique.

The results showed that the relationship quality on customer loyalty in online banking services is affected by the relationship life cycle. The results also showed that online service quality, in the form of Utilitarian quality and Hedonic quality, has a positive effect both directly and indirectly on customer loyalty through online relationship quality.

In this paper, the relationship dynamics was achieved through adding the relationship life cycle variable to the model. However, the study was a cross-sectional research and different results might be obtained if data was collected longitudinally.

In an online banking service, the role of relationship quality in the prediction of customer loyalty is reduced as the relationship ages. Therefore, marketers need to consider other marketing actions to continue their relationship with the customer in the long run.

This paper examines customer loyalty to online banking services from dynamic perspective by introducing relationship life cycle as a moderating variable for the first time. Therefore, the main contribution of this paper is to develop the relationship marketing literature in the field of relationship dynamics and to challenge the effectiveness of relationship marketing in the long run.