Gasoline Demand Research Articles

‘On the Road Again’: A 118 country panel analysis of gasoline and diesel demand

Abstract The current paper contributes to the literature on the relationship between economic growth, fuel prices, and the demand for gasoline and diesel within the transportation sector by assembling a wide panel dataset of fuel consumption and prices for 35 OECD and 83 Non-OECD countries. The unbalanced data spans 1978–2016, with the full 39 years of data for 36 countries. In addition, our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. The OECD panel price elasticity for gasoline is around −0.7 or about three times that for the non-OECD panel; whereas, the OECD price elasticity for diesel is only modestly larger (in absolute terms) than the non-OECD elasticity (−0.3 and −0.2, respectively). For gasoline, the non-OECD GDP elasticity is around 1.0 or about twice that for OECD countries. For the OECD panel, the diesel GDP elasticity is about three times that of the OECD GDP elasticity for gasoline; whereas, for the non-OECD panel, the two GDP elasticities (for gasoline and diesel) are about the same. For non-OECD countries, subpanels based on geography and income produced mostly similar results. We found no evidence of GDP or price asymmetric effects for the 1978–2016 period. Lastly, the large (at least for the OECD panel) and statistically significant transportation price elasticities reported here provide stark contrast to the economy-wide energy price elasticities calculated in Liddle and Huntington (2020a).

The effect of fuel prices on traffic flows: Evidence from New South Wales

Abstract Understanding how traffic flows respond to fuel price changes is useful for traffic management. This study uses a dataset of 11.9 million hourly observations from 118 traffic count stations over 2010–2017 to investigate the relationship between gasoline prices and traffic flows in the state of New South Wales, Australia. The findings suggest that higher gasoline prices reduce traffic flows, with an average effect size of −0.04 in the hourly estimates. The elasticity is particularly pronounced during off-peak periods, both on weekdays (−0.10) and weekends (−0.07). In contrast, a positive effect of gasoline prices on traffic flows is observed for peak periods on weekdays (0.06). Evidence is also obtained that afternoon peak-hour speeds are faster when gasoline prices are higher, consistent with a lowering of traffic density. The research also finds a negative price elasticity of gasoline demand and that people are more likely to use public transport when gasoline prices are higher. The findings suggest that fuel excise plays a role in both reducing overall road dependence and alleviating the severity of some peak-hour traffic jams.

FORECASTING GASOLINE DEMAND IN INDONESIA USING TIME SERIES

Fuel is an essential commodity in both the economy and society. Indonesian fuel demand continues to increase annually, whereas fuel production has decreased. Gasoline accounts for more than 50% of fuel consumption for transportation. A reliable gasoline product demand forecast is required to plan the gasoline supply. The objective of this study is to forecast the demand for total gasoline and its three components, which are gasoline 88, gasoline 90, and gasoline 92. This study compared the Holt–Winters additive model and autoregressive integrated moving average for the time-series data for the 2017–2019 period. Because the Holt–Winters additive model generates more accurate results, it was applied to predict the total demand for gasoline during 2020–2022. The results of the combination of the Holt–Winters model and a neural network to forecast gasoline 92 demand had lower errors than the individual Holt–Winters method. The forecast results show that total gasoline demand is forecasted to increase, but the components indicate a different trend. Gasoline 92 and gasoline 88 decreased, but gasoline 90 increased.Keywords: Forecasting, time series, gasoline demand, Holt-Winters, Neural NetworkJEL Classifications: Q4, Q47DOI: https://doi.org/10.32479/ijeep.9982

The Effect of Fuel Prices on Traffic Flows: Evidence from New South Wales

Understanding how traffic flows respond to fuel price changes is useful for traffic management. This study uses a dataset of 11.9 million hourly observations from 118 traffic count stations over 2010–2017 to investigate the relationship between the gasoline price and traffic flows in the state of New South Wales, Australia. The findings suggest that higher gasoline prices reduce traffic flows, with an average effect size of –0.04 in the hourly estimates. The elasticity is particularly pronounced during off-peak periods, both on weekdays (–0.10) and weekends (–0.07). In contrast, a positive effect of gasoline prices on traffic flows is observed for peak periods on weekdays (0.06). Evidence is also obtained that afternoon peak-hour speeds are faster when gasoline prices are higher, consistent with a lowering of traffic density. The research also finds a negative price elasticity of gasoline demand and that people are more likely to use public transport when gasoline prices are higher. The findings suggest that fuel excise plays a role in both reducing overall road dependence and alleviating the severity of some peak-hour traffic jams.

Counterflow ignition and extinction of FACE gasoline fuels

The demand for petroleum-derived gasoline in the transportation sector is on the rise. For better knowledge of gasoline combustion in practical combustion systems, this study presents experimental measurements and numerical prediction of autoignition temperatures and extinction limits of six FACE (fuels for advanced combustion engines) gasoline fuels in counterflow flames. Extinction limits were measured at atmospheric pressures while the experiments for autoignition temperatures were carried out at atmospheric and high pressures. For atmospheric pressure experiment, the fuel stream consists of the pre-vaporized fuel diluted with nitrogen, while a condensed fuel configuration is used for ignition experiment at higher chamber pressures. The oxidizer stream is pure air. Autoignition temperatures of the tested fuels are nearly the same at atmospheric pressure, while a huge difference is observed as the pressure is increased. Unlike the ignition temperatures at atmospheric pressures, minor difference exists in the extinction limits of the tested fuels. Simulations were carried out using a recently developed gasoline surrogate model. Both multi-component and n-heptane/isooctane mixtures were used as surrogates for the simulations. Overall, the n-heptane/isooctane surrogate mixtures are consistently more reactive as compared the multi-component surrogate mixtures. Transport weighted enthalpy and radical index analysis was used to explain the differences in extinction strain rates for the various fuels.

Debiased machine learning of conditional average treatment effects and other causal functions

Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularisation bias. We then project the signal onto a set of basis functions, which grow with sample size, to get the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference results automatically target this function. When basis functions are group indicators, the best linear predictor reduces to the group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.

A mixed integer nonlinear programming approach for integrated bio-refinery and petroleum refinery topology optimization

Abstract The conversion of biomass into gasoline and diesel in bio-refinery process is an attractive process given its carbon neutral and sustainable nature. The economics of bio-refinery can be improved via integration with petroleum refinery, whereby bio-refinery intermediates can be processed into gasoline and diesel in the well-established petroleum refinery processing units, i. e., hydrocracking (HC) and fluidized catalytic cracking (FCC) units. However, the integration of the new bio-refinery into the existing petroleum refinery may not give the optimum solution given the capacities constraints of the existing petroleum refinery upgrading units such as FCC and HC units. Thus, this work proposed a superstructure comprising new bio-refinery and new petroleum refinery block diagrams. The superstructure was formulated into mixed integer nonlinear programming (MINLP) model. The model was coded into general algebraic modeling system (GAMS) platform and solved using global optimum solver, LINDOGLOBAL. The model application was demonstrated using representative case study. The model results showed that the optimum integrated bio-refinery and petroleum refinery topology favors the upgrading of bio-refinery intermediates using petroleum refinery HC unit under one-through operation mode with a marginal increase in the profit of about 0.39% compared to the second optimum case of upgrading bio-refinery intermediate using petroleum refinery FCC unit under gasoline operation mode. Thus, the decision in selecting the most suitable topology can be made based on the market demand for gasoline and diesel as the topology that uses FCC maximizes gasoline production and the topology that uses HC maximizes diesel production.

Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020.

A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia

This paper develops a novel AI and data-driven predictive model to analyze and forecast energy markets, and tests it for gasoline demand of Saudi Arabia. The AI model is based on a genetic algorithm (GA), artificial neural network (ANN), and data mining (DM) approach for time-series (TS) analysis, referred to as GANNATS. The GANNATS predictive model was successfully designed, trained, validated, and tested using real historical market data. Results show that the model yields accurate predictions with robust key performance indicators. A double cross-validation of the model verified that Saudi Arabia's gasoline demand declined by 2.5% in 2017 from its 2016 level. The model forecasts that Saudi gasoline demand will maintain a mild growth over the short-term outlook. Variables impact and screening analysis was performed to identify the influencing factors driving the gasoline demand. The recent decline in Saudi gasoline demand is primarily attributed to the improvements in vehicle efficiency, lifting of fuel price subsidies, declining population growth, and changes in consumer behavior. This paper enriches existing knowledge of best practices for forecasting domestic and global gasoline demand. In addition, the methodology presented improves on traditional econometric models and enhances the predictability and accuracy of forecasts of gasoline demand.

Gasoline demand in Saudi Arabia: are the price and income elasticities constant?

ABSTRACT The paper investigates gasoline consumption in case of oil-exporting country applying Time-varying Coefficient Cointegration approach to the data from 1980 to 2017. Empirical estimations show that long-run income and price elasticities are not constant and are responsive to price and income fluctuations in the period considered. The income elasticity of gasoline demand increased until 2014, peaking at 0.151, following growth in disposable income, before declining to 0.136 in 2017. However, consumers do not stop driving when their disposable incomes fall, resulting in a less elastic response of gasoline demand to income. Price elasticities sit in the range of −0.31 to −0.05, becoming less elastic when prices are low and vice versa. The findings of the study may be useful in successful implementation of energy price reforms and implementation of environmental policies.

Price and income elasticities of oil demand in Mauritius: An empirical analysis using cointegration method

There are no studies that quantify the effects of price and income on the demand of fuel oil, gasoline and diesel oil in Mauritius. In line with increasing retail prices for these commodities, there is no estimation of the effect that increasing price, income and other variables like electricity and vehicle stock have on demand of petroleum products which affects effective policymaking in this area. This study uses an ARDL–ECM model to determine consumer response on petroleum demand when price and income in Mauritius changes. The study determines the income and price elasticities of demand which is a needed parameter in transportation and taxation policies. It was found that gasoline is price and income inelastic in the short–run, diesel oil is income inelastic in the long–run and fuel oil is inelastic in the short–run but becomes elastic in the long–run, other variables being not significant. The effect of rising electricity consumption on fuel oil demand was to a ratio of 1: 1.1 on average. Vehicle stock is estimated to cause unprecedented increase in gasoline demand by nearly 3.1% on average. Results are essential for transportation and energy taxation policymaking.

‘On the Road Again’: A 118 Country Panel Analysis of Gasoline and Diesel Demand

The current paper contributes to the literature on the relationship between economic growth, fuel prices, and the demand for gasoline and diesel within the transportation sector by assembling a wide panel dataset of fuel consumption and prices for 35 OECD and 83 Non-OECD countries. The unbalanced data spans 1978-2016, with the full 39 years of data for 36 countries. In addition, our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. The OECD panel price elasticity for gasoline is around -0.7 or about three times that for the non-OECD panel; whereas, the OECD price elasticity for diesel is only modestly larger (in absolute terms) than the non-OECD elasticity (-0.3 and -0.2, respectively). For gasoline, the non-OECD GDP elasticity is around 1.0 or about twice that for OECD countries. For the OECD panel, the diesel GDP elasticity is about three times that of the OECD GDP elasticity for gasoline; whereas, for the non-OECD panel, the two GDP elasticities (for gasoline and diesel) are about the same. For non-OECD countries, subpanels based on geography and income produced mostly similar results. We found no evidence of GDP or price asymmetric effects for the 1978-2016 period. Lastly, the large (at least for the OECD panel) and statistically significant transportation price elasticities reported here provide stark contrast to the economy-wide energy price elasticities calculated in Liddle and Huntington (2020a).

Elasticity Analysis of Fossil Energy Sources for Sustainable Economies: A Case of Gasoline Consumption in Turkey

The current paper investigates the gasoline demand relationship in the case of Turkey, utilizing different econometric techniques and using quarterly data spanning from 2000Q1 to 2019Q1. The estimation results revealed that income and gasoline price are the main drivers of consumption. The found long-run income, price and auto stock elasticities are 0.25, −0.27 and −0.80, respectively. In addition, it is concluded that in the short-run, gasoline demand does not respond to changes in income, price and car stock. The estimation results also showed that the contribution of commercial and public car stock on gasoline demand is higher than that for private auto stock. Based on the finding of the study it is concluded that to achieve the optimal use of gasoline in line with providing high quality transport services, firstly, policies before the 2012 period can be followed. Secondly, channels such as pricing mechanism and taxation policies can be used in this framework.

FUEL DEMAND ELASTICITIES IN BRAZIL: A PANEL DATA ANALYSIS WITH INSTRUMENTAL VARIABLES

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.

Simultaneous Inference of the Partially Linear Model with a Multivariate Unknown Function

In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for the U.S. gasoline demand where the income and price variables are contaminated by Berkson errors. The empirical results strongly suggest that the linearity of the U.S. gasoline demand is rejected. The results are also used to propose an alternative form for the demand.

Uloga hibrida kukuruza u aktuelnim trendovima proizvodnje bioetanola

Bioethanol is a biofuel that is mostly used as a replacement for fossil fuels worldwide with yearly production reaching nearly 110 billion liters in 2019. Trends of producing this alternative fuel are rising and maize is considered as one of the best renewable raw materials for the production of fuel ethanol due to the high content of starch in the grain. Taking into account that Serbia is one of the most prominent maize producers in Europe, the surpluses of this crop could be directed towards bioethanol production. Even though there is no organized production and consumption of bioethanol as an automotive fuel in Serbia, the Serbian Government has recently introduced some new regulations regarding biofuels. However, due to the reduction of economic activities since the onset of COVID-19 pandemic in 2020, the global demand for crude oil has fallen sharply, negatively affecting the gasoline demand, and thus for bioethanol, which makes the future of this alternative fuel production notably uncertain.

Econometric Analysis of Demand for Petrol in India, 1966-2019

This study uses single-equation dynamic models to estimate petrol demand in India. Estimated long-run elasticities are higher than their short-run counterparts, which is in line with expectations based on the existing literature. We find price elasticities of -0.418 (long-run) and -0.189 (short run), which indicates that when price increases by 10%, demand tends to reduce by approximately 4% as consumers adjust their consumption behaviour. Prices appear to be more elastic in India rather than USA where studies estimate petrol elasticities to be in the range of -0.02 to -0.04 in the short term. We further find evidence that long-run elasticities are not as high as estimated elsewhere. We address issues around modelling of habit formation, habit persistence, and unobserved heterogeneity. Results are essential for transportation policymaking, especially in the context of taxation, understanding price stability, estimating the effects of duty increases on demand, and the potential implications for carbon taxes. The results are also important for wider policy considerations such as climate protections goals, reducing local emissions, dependency on fossil fuels, and strategic energy security.

Green Transportation and Information Uncertainty in Gasoline Distribution: Evidence from China

ABSTRACT The green vehicle routing problem of distributing gasoline to retail gasoline stations in a way that is not just the most cost-effective overall but also reduces its own carbon emissions needs to account for uncertainty from multiple sources. Therefore, in this paper we study how to do this, simultaneously reducing the cost and minimizing the environmental impact in the presence of information uncertainty. Among the sources of uncertainty are random demand by gas stations and uncertain travel time to them by tankers, which we build into our model. We also design a three-stage heuristic algorithm. In the first stage, the chance constraints of the model are converted into their deterministic equivalents. In the second stage, gas stations are clustered, based on their distance and demand for gasoline. In the third stage, we use a genetic algorithm to solve the model based on the first two stages. Finally, we use our simulation results to propose how gas stations can optimize their cost of gasoline distribution.

Google Search Explains Your Gasoline Consumption!

We show that including Google Search Volume Index (GSVI) for bus and train, as proxies for the willingness of the consumers to use public transportation services, improves the explanatory power of the estimated demand for the United States retail gasoline. We find that gasoline consumption is negatively related to the search for public transportation services. Our results demonstrate a showcase for utilizing real-time data to improve quantitative energy policy analysis.

Engineering dual bed hydrocracking catalyst towards enhanced high-octane gasoline generation from light cycle oil

Processing of light cycle oil (LCO) by hydrotreating and subsequent hydrocracking process harbors tremendous significance due to more stringent environmental regulations and growing demand for high-octane gasoline. However, the commercial single bed hydrocracking catalysts normally over-saturate multi-ring aromatics in LCO, leading to increased H2/energy consumption and reduced octane number. Herein, novel dual bed catalyst strategy is first proposed to enhance the hydrocracking performance, which contains fresh and used Ni-Mo/HY-Al2O3 catalysts as upstream and downstream bed catalyst, respectively. In order to select suitable downstream catalyst, the used industrial Ni-Mo/HY-Al2O3 catalysts in reactor after running for almost 1 year were divided into almost 20 layers through sampling along the axial positions, and then systematically analyzed by multi-techniques, including XPS, HRTEM, Py-IR, TPD, TGA and XRD. It is found that the first 20% of all catalyst layers suffer from serious deactivation due to coke formation, leading to the loss of Ni-Mo-S phase and acidic sites. Employing this first 20% of used catalysts as downstream catalyst, the weaker hydrogenation performance could avoid excessive hydrogenation and maintain mono-aromatics content. As expected, the designed dual bed catalyst leads to superior octane number, higher liquid yield of C5+ and lower H2 consumption than the commercial single bed catalyst. This strategy not only offers a scenario to boost the hydrocracking performance, but also sheds new light on the efficient utilization of used catalyst.