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This article examines the influence of specific time series attributes on the efficacy of fuel demand forecasting. By utilising autoregressive models and Markov chains, the research aims to determine the impact of these attributes on the effectiveness of specific models. The study also proposes modifications to these models to enhance their performance in the context of the fuel industry’s unique fuel distribution. The research involves a comprehensive analysis, including identifying the impact of volatility, seasonality, trends, and sudden shocks within time series data on the suitability and accuracy of forecasting methods. The paper utilises ARIMA, SARIMA, and Markov chain models to assess their ability to integrate diverse time series features, improve forecast precision, and facilitate strategic logistical planning. The findings suggest that recognising and leveraging these time series characteristics can significantly enhance the management of fuel supplies, leading to reduced operational costs and environmental impacts.

Heterogeneity is a theme acquiring more and more prominence in the energy economic literature from both a modelling and policy-making perspective. We show that useful empirical evidence on this subject can be obtained by applying a parsimonious multivariate cointegration analysis that makes use of the increasingly available time series data on energy demand. We find that there is substantial heterogeneity in the demand for fuels from UK firms belonging to different subsectors, with price and level of production having different degrees of importance in the fuel choice, and with evidence of both substitutability and complementarity between fuels. Moreover, we show that fuel demand for the industrial sector as a whole is considerably more elastic than most estimates presented in the literature, finding which has direct relevance for policies aimed at influencing industrial fuel consumption through fuel switching.

This study introduces an innovative deep-learning approach for fuel demand estimation in maritime transportation, leveraging a novel convolutional neural network, bidirectional, and long short-term memory attention as a deep learning model. The input variables studied include vessel characteristics, weather conditions, sea states, the number of ships entering the port, and navigation specifics. This study focused on the ports of Jazan in Saudi Arabia and Fujairah in the United Arab Emirates, analyzing daily and monthly data to capture fuel consumption patterns. The proposed model significantly improves prediction accuracy compared with traditional methods, effectively accounting for the complex, nonlinear interactions influencing fuel demand. The results showed that the proposed model has a mean square error of 0.0199 for the daily scale, which is a significantly higher accuracy than the other models. The model could play an important role in port management with a potential reduction in fuel consumption, enhancing port efficiency and minimizing environmental impacts, such as preserving seawater quality. This advancement supports sustainable development in maritime operations, offering a robust tool for operational cost reduction and regulatory compliance.

The aim of this paper is to provide demand elasticities for the three main fuels used in Brazil: gasoline, ethanol and diesel. We used a panel data approach at municipal level for the period between 2007 and 2016. The innovation in this study is in its introduction of a new instrumental variable for prices, combining three taxes and municipal distance from state capital. The main results are as follows: i) the gasoline, ethanol and diesel demands are price elastic, meaning that all own-price elasticities are greater than one; ii) ethanol consumption is more elastic when the CNG price is added as an explanatory variable, but this does not apply to gasoline; iii) an increase in GDP positively affects the demand for gasoline and diesel (less than proportionally), but does not affect demand for ethanol; iv) fleet size impacts the consumption of all fuels, except when the CNG price is excluded from the ethanol model; v) the ethanol-to-gasoline price ratio is a relevant variable for the demand of both gasoline and ethanol.

Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship’s average fuel consumption. However, although there are much models available to predict a ship’s consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship’s average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required.

The active industrial activities, urbanization, globalization and rapid economic development have stimulated the energy demand of newly industrialized countries, prompting renewable energy to be replaced by cheaper and more readily available fossil fuel energy, thus leading to environmental pollution. This study therefore explores the impact of economic complexity, technological innovation and structural transformation on energy efficiency, fossil fuel energy demand and renewable energy generation in newly industrialized countries (NICs). This study differs from earlier studies in that it focuses on improving energy efficiency, renewable energy generation, and reducing fossil fuel energy as indicators of energy conservation and sustainability. This study employs the cross-sectional autoregressive distributed lag (CS-ARDL) technique to investigate both short- and long-term associations and examine the robustness of the results over the period 1985–2023. The results show that in the long run, each unit increase in economic complexity can improve energy efficiency by 0.265%, promote renewable energy generation by 0.327%, and reduce fossil fuel energy demand by 0.228%. Technological innovation has significant incremental impacts on energy efficiency and renewable energy, while having an adverse impact on fossil fuel energy in both the short- and long-term. However, GDP can greatly improve energy efficiency and renewable energy generation while boosting fossil fuel energy demand, but only in the long term. Industrial value added significantly reduces energy efficiency and renewable energy generation, while contributing to fossil fuel energy demand in both short- and long-term. The causality test support feedback hypothesis between GDP and fossil fuel energy. Comparative analysis shows that economic complexity and technological innovation contribute more to supporting energy efficiency and energy production than to mitigating fossil fuels, indicating the necessity of structural adjustment of economic activities and technological innovation. Policymakers should consider the empirical findings of this study to pave the way for a more sustainable and resilient economic and environmental future for newly industrialized countries through energy transition measures that focus on economic maturity and technological innovation to produce renewable energy.

Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.

Based on the panel data of 306 cities in China from 2002 to 2012, this paper investigates China’s road transport fuel (i.e., gasoline and diesel) demand system by using the Almost Ideal Demand System (AIDS) and the Quadratic AIDS (QUAIDS) models. The results indicate that own-priceelasticitiesfordifferentvehiclecategoriesrangefrom−1.215to−0.459(byAIDS)andfrom −1.399 to−0.369 (by QUAIDS). Then, this study estimates the air pollution emissions (CO, NOx and PM2.5) and public health damages from the road transport sector under different oil price shocks. Compared to the base year 2012, results show that a fuel price rise of 30% can avoid 1,147,270 tonnes of pollution emissions; besides, premature deaths and economic losses decrease by 16,149 cases and 13,817.953 million RMB yuan respectively; while based on the non-linear health effect model, the premature deaths and total economic losses decrease by 15,534 and 13,291.4 million RMB yuan respectively. Our study combines the fuel demand and health evaluation models and is the first attempt to address how oil price changes influence public health through the fuel demand system in China. Given its serious air pollution emission and substantial health damages, this paper provides important insights for policy makers in terms of persistent increasing in fuel consumption and the associated health and economic losses.

Biofuels offer new opportunities for African countries. They can contribute to economic growth, employment, and rural incomes. They can become an important export for some countries and provide low-cost fuel for others. There is also a potentially large demand for biofuels to meet the rapidly growing need for local fuel. Abundant natural resources and low-cost labor make producing biofuel feedstock's a viable alternative to traditional crops; and the preferential access available to most African countries to protected markets in industrial countries provides unique export opportunities. Biofuels also bring challenges and risks, including potential land-use conflicts, environmental risks, and heightened concerns about food security. This book examines the potential of African countries to produce biofuels for export or domestic consumption and looks at the policy framework needed. It is part of the effort by the World Bank's Africa region to examine critical issues that affect the region and to recommend policies that effectively address these issues while providing an enabling environment for the private sector. The book is intended to inform policy makers and the larger development community of the global and domestic market opportunities facing biofuel producers, as well as the challenges of producing biofuels, in the Africa region.

Over the past decade, Vietnam has emerged as one of the world's fastest growing economies. Fossil fuel use, which is a dominant energy source and vital for economic growth, have been increasing considerably. Undoubtedly, the projection of fossil fuel demand is essential for a better understanding of energy needs, fuel mix, and Vietnam's strategic development. This paper provides an outlook for coal, oil, and gas demand in Vietnam to 2050. The projection is based on the calibrated results from a hybrid model (that combines a GTAP-R version for resources, and a micro simulation approach) and an energy database. Under the baseline scenario (business as usual), from 2018 to 2050, the demand for coal, oil products, and gas are expected to increase by a factor of 2.47-fold, 2.14-fold, and 1.67-fold, respectively. Emissions are also projected to increase. Because fossil fuels are the dominant source of carbon emissions in Vietnam, it follows, going forward, that an effective fuel-mix strategy that encourages the development of renewables and energy efficiency is essential.