Gasoline Demand Research Articles

Élasticités et effet rebond des véhicules légers au Canada

Although reducing greenhouse gas emissions is an imperative, environmental policies can run into two pitfalls: the rebound effect and inelastic demand for gasoline. If a vehicle is more fuel-efficient but is used more often, this reduces the expected energy savings (rebound effect). Similarly, increasing the price of gasoline via carbon pricing has little effect on demand if it is not very sensitive (inelastic) to price. To determine the value of these parameters for light vehicles in Canada, I use data for the ten provinces between 2000 and 2019 to estimate a simultaneous three-equation model of aggregate demand for kilometers traveled, vehicle fleet and fuel efficiency. While the rebound effect is worth 9 % in the short term and 30 % in the long term, gasoline demand is found to be inelastic in both cases. These values are broadly consistent with those found in the literature. A rise in gasoline prices therefore reduces purchasing power rather than consumption. Similarly, in the long term, a 10 % increase in fuel efficiency would increase distance travelled by 3 %.

Lignocellulosic Residues from Fruit Trees: Availability, Characterization, and Energetic Potential Valorization

Reducing the carbon footprint of energy production is one of the most pressing challenges facing humanity today. Lignocellulosic biomass residues from fruit production industries show promise as a viable energy source. This paper presents a study of the Italian context concerning the utilization of orchard lignocellulosic residues for energy production as electricity or bioethanol. The potential of various orchard residues was assessed through chemical and physical analyses, and an equivalent electrical energy of about 6441.62 GWh or an amount of 0.48 Mt/y of bioethanol was obtained based on the average annual dry residue mass availability of about 3.04 Mt/y. These data represent 9.30% of the national electrical energy production from renewable sources, as well as 6.21% of the Italian demand for gasoline in 2022. Electricity generation from these residues has shown its potential as a reliable and sustainable baseload power source, as well as a source of renewable transportation fuel. The studied process could be a valuable reference to expand these concepts on a global scale to achieve a greener and more sustainable energy future.

Experimental Study of the Impact of Ethanol Content on Partially Premixed Combustion With Ethanol-Gasoline Blends

Abstract As ethanol production increases and the demand for gasoline in spark ignition engines decreases over the next few decades, the use of these fuels in compression ignition systems is expected to increase. This raises the question of how much ethanol-in-gasoline is the right blend for compression ignition. This work experimentally studied E10, E30, E50, E75, and E100 in a split injection partially premixed compression ignition strategy. It was found that, separately, the cool flame reactivity of E10 and the high cooling potential of E75 and E100 enable heat release rate control in partially premixed combustion. E30 and E50 did not show the same control authority. E30 performed the worst due to having a small amount of cool flame reactivity and a low cooling potential. It was found that NOx emissions were not significantly impacted by ethanol content, but the smoke emissions were. E100 produced near zero smoke, but all the ethanol-gasoline blends produced some smoke. When 20% of the fuel was injected during the mid-dle of the compression stroke, E10, E30, E50, and E75 all produced low, but nonzero smoke emissions. When the second injection was moved near top dead center, the smoke emissions of E75 stayed low but the smoke emissions of E10, E30, and E50 all rose to a filter smoke number greater than 0.3. These results indicate that low- and high-ethanol content gasolines are both viable paths forward for partially premixed combustion systems, but medium-ethanol content gasolines should be avoided.

Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity

SummaryWe propose a per‐cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two‐way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively well in finite samples in either dimension. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. We find significant elasticity heterogeneity and more elastic gasoline demand on average than with standard estimators.

Heterogeneity in the pass-through from oil to gasoline prices: A new instrument for estimating the price elasticity of gasoline demand

We propose a new instrument for estimating the price elasticity of gasoline demand that exploits systematic differences across U.S. states in the pass-through of oil price shocks to retail gasoline prices. We show that these differences are primarily driven by the cost of producing and distributing gasoline, which varies with states’ access to oil and gasoline transportation infrastructure, refinery technology, and environmental regulations, creating cross-sectional gasoline price shocks in response to an aggregate oil price shock. Time-varying estimates do not support the view that the gasoline demand elasticity has declined in absolute value to near zero since the 1980s. The elasticity was stable near −0.3 until the end of 2014. It rose to about −0.2 in 2015–16, but has remained stable since 2016. Gasoline demand is more responsive in states with lower personal income, higher unemployment rates and lower urban population shares. There is no evidence for an asymmetry in the elasticity with respect to positive and negative gasoline price shocks. We illustrate how these elasticity estimates inform the recent policy debate about the impact of gasoline tax holidays on consumers’ discretionary income and about the demand destruction from the spike in gasoline prices after the invasion of Ukraine.

Debiasing the Price Elasticity of Gasoline Demand with Double Machine Learning

Debiasing the Price Elasticity of Gasoline Demand with Double Machine Learning

Simulation of a rapid compression machine for evaluation of ignition chemistry and soot formation using gasoline/ethanol blends

Due to the projected decline of demand for gasoline in light duty engines and the advent of ethanol as a green fuel, the use of gasoline/ethanol blend fuels in heavy duty applications are being investigated as they are projected to have lower cost and lower lifecycle green house gas (GHG) emissions. In heavy duty engines, the primary mode of combustion is mixing controlled combustion where wide range of mixture conditions (equivalence ratio) exist. Soot emissions of these fuels in richer conditions are not well understood. The goal of this research is to evaluate some commercially available soot modeling codes for the particulate matter emissions from gasoline/ethanol fuel blends, especially at fuel rich conditions. A Rapid Compression Machine (RCM) is modeled in a three-dimensional numerical simulation using CONVERGE computational software using a reduced chemical kinetic mechanism with SAGE chemistry solver and a RANS k-ϵ turbulence model with a sector model including the creviced piston. The creviced piston is used in the experimental setup to reduce boundary layer effects and to maintain a homogeneous core in the reaction cylinder. Computational fluid dynamics simulations are conducted for different gasoline-ethanol fuel blends from E10 (10% ethanol v/v) to E100. The fuel blend is modeled as a surrogate mixture of toluene, iso-octane, n-heptane for gasoline content, and ethanol. The computational results were validated against experimental results using pressure measurements and laser extinction diagnostics. Different soot models are investigated to evaluate their capability of predicting the sooting tendencies of fuel blends, especially in richer conditions experienced during mixing-controlled combustion. The experimental combustion characteristics such as the ignition delay of different blends of fuel are reasonably well predicted. The Particulate Size Mimic (PSM) model accurately predicts the soot generation characteristics of the different fuels, but the Hiroyasu-NSC model falls short in this regard. For accurate prediction of soot with the PSM model, the thermodynamic conditions during combustion must be accurately modeled. While the current computational modeling tools can produce accurate results for the prediction of particulate matter emissions, there is much work to be done in improving our understanding of the underlying fundamental processes.

Why has the OECD long-run GDP elasticity of economy-wide electricity demand declined? Because the electrification of energy services has saturated

Understanding the income/GDP and price elasticities of energy/electricity demand is important for forecasting demand and evaluation of the potential impact of policies. Recent work on the GDP elasticity of economy-wide electricity demand has suggested that this elasticity is both substantially smaller than that of economy-wide energy demand and possibly has declined substantially over time. We assemble the largest OECD country panel to date to analyze the GDP and price elasticities of economy-wide electricity demand. We use an approach that addresses several key aspects of long-panel, macro modeling—cross-sectional dependence, country heterogeneity, and dynamic adjustments—and approximates temporal heterogeneity. We find that: (1) the GDP elasticity of economy-wide electricity demand has declined over time; and (2) that elasticity is now possibly as low as 0.2. In addition, we produce evidence that the reason for that decline is the saturation of the electrification of energy services in OECD countries. Hence, were more energy services to become electrified (e.g., transport), we would expect the GDP elasticity of economy-wide electricity demand to increase (e.g., toward 0.5—the GDP elasticity for gasoline demand). This saturation finding is important for forecasts of electricity consumption as well as for climate change mitigation policy; the finding is important because suggests that substantially increasing the electrification of energy services—one of two prongs in a carbon mitigation strategy—will/may lead to an increase in the GDP elasticity for electricity as well as to the potentially rapid increase in electricity consumption.

The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach

This study contributes to the body of literature on modeling and predicting gasoline demand by using nonlinear econometric techniques. For this purpose, dynamic model averaging (DMA) and Bayesian model averaging (BMA) combined with Artificial Bee Colony (ABC) are used to forecast gasoline consumption in the United States. The article’s independent variables include demographic characteristics, economic activity, income, driving expenditures, automobile price, and road availability for annual data from 1960 to 2020. In the proposed model, not only may the coefficients and elasticity of a predictor of gasoline demand change over time, but other sets of predictors can also emerge at different periods. Moreover, this study aims to automate the process of picking two forgotten variables of the DMA model using the ABC model. Our findings indicate that dynamic model averaging significantly improves forecasting performance when compared to basic benchmark techniques and advanced approaches. Additionally, integrating it with an Artificial Bee Colony (ABC) may result in improved outcomes when time-varying forgetting variables are present. The findings of this research provide policymakers in the fields of energy economics and the environment with helpful tools and information.

Optimization of Syngas Quality for Fischer-Tropsch Synthesis

While fossil oil reserves have been receding, the demand for diesel and gasoline has been growing. In recent years, syngas of biomass origin has been emerging as a viable feedstock for Fischer-Tropsch (FT) synthesis, a process for manufacturing synthetic gasoline and diesel. This paper reports the optimization of syngas quality to match the FT synthesis requirement. The optimization model maximizes the thermal efficiency under the constraint of H 2 / C O ≥ 2.15 and operating conditions of equivalent ratio ( E R = 0.0 -1.0), steam to biomass ratio ( S B = 0.0 -5.0), and gasification temperature ( T g = 500 °C-1300°C). The optimization model is executed using the optimization section of the Model Analysis Tools of the Aspen Plus simulator. The model is tested using eleven (11) types of municipal solid waste (MSW). The optimum operating conditions under which the objective function and the constraint are satisfied are E R = 0 , S B = 0.66 -1.22, and T g = 679 -763°C. Under optimal operating conditions, the syngas quality is H 2 = 52.38 -58.67 mole percent, lower heating value L H V = 12.55 -17.15 MJ/kg, and N 2 = 0.38 -2.33 mole percent. From an economic point of view, 12.98% to 33.12% of biomass is used as fuel for steam generation, drying, and pyrolysis. The generalized optimization model reported could be extended to any other type of biomass and coal.

Determining optimal fuel delivery strategies under uncertainty

During the preparation before a hurricane makes landfall, affected individuals may be asked to evacuate. Large and small-scale evacuations can cause rapid increases in the demand for gasoline fuel. However, during a hurricane vessels carrying gas may be delayed and/or rerouted, adding to the difficulty of providing the necessary gas in affected areas. In this work, we determine alternate delivery locations and times for vessels carrying fuel that are scheduled to arrive and deliver fuel at ports impacted by an approaching hurricane. Motivated by Hurricane Irma in Florida, we develop a multi-period stochastic scheduling model that incorporates hurricane (weather) advisories, fuel delivery schedules, port storage capacities, and port docking capacities. Our model determines the best schedule based on two objectives: (1) minimize the total unmet demand at each port, and (2) minimize inequities in unmet demands among the ports. We also present a case study and a numerical experiment based on fuel delivery data from ports in Florida. Among our key findings is that port availability is the driving factor in determining feasible schedules for vessel gas deliveries. We also present a scheduling heuristic that dynamically adapts to weather advisories so as to minimize the impact of unmet demand in the affected areas.

Review of Molybdenum Disulfide Research in Slurry Bed Heavy Oil Hydrogenation.

With the growing demand for gasoline and diesel fuel and the shortage of conventional oil reserves, there has been extensive interest in upgrading technologies for unconventional feedstocks such as heavy oil. Slurry bed reactors with high tolerance to heavy oil have been extensively investigated. Among them, dispersive MoS2 is favored for its excellent hydrogenation ability for heavy oil even under harsh reaction conditions such as high pressure and high temperature, its ability to effectively prevent damage to equipment from deposited coke, and its ability to meet the requirement of high catalyst dispersion for slurry bed reactors. This paper reviews the relationship between the structure and hydrogenation effectiveness of dispersive molybdenum disulfide, the hydrogenation mechanism, and the improvement of its hydrogenation performance by adding defects and compares the application of molybdenum disulfide in heavy oil hydrogenation, desulfurization, deoxygenation, and denitrification. It is found that the current research on dispersive molybdenum disulfide catalysts focuses mostly on the reduction of stacking layers and catalytic performance, and there is a lack of research on the lateral dimensions, microdomain regions, and defect sites of MoS2 catalysts. The relationship between catalyst structure and hydrogenation effect also lags far behind the application of MoS2 in the precipitation of hydrogen, etc. Oil-soluble and water-soluble MoS2 catalysts eventually need to be converted to a solid sulfide state to have hydrogenation activity. The conversion history of soluble catalysts to solid-type catalysts and the key to their improved catalytic effectiveness remain unclear.

The Response of Consumer Spending to Changes in Gasoline Prices

This paper estimates how overall consumer spending responds to changes in gasoline prices. It uses the differential impact across consumers of the sharp drop in gasoline prices in 2014 for identification. This estimation strategy is implemented using comprehensive, high-frequency, transaction-level data for a large panel of individuals. The average estimated marginal propensity to consume (MPC) out of unanticipated, permanent shocks to income is approximately one. This estimate accounts for the elasticity of demand for gasoline and potential slow adjustment to changes in prices. The high MPC implies that changes in gasoline prices have large aggregate effects. (JEL D12, G51, L11, L71, L81, Q35)

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand

Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models

Accurate fuel demand forecast is crucial to meet the demand. India is aiming to roll out 20% ethanol blended gasoline by 2025 and planning to make this transition complete by 2030. But due to lack of ethanol availability this target cannot be achieved yet. So, accurate forecast of ethanol demand based on future gasoline demand is important. In this study, various linear and non-linear regression models and Autoregressive integrated moving average (ARIMA) models are developed and compared for forecasting gasoline demand in India. Historical gasoline consumption data from 1997 to 2021 is used to develop and evaluate these models. ARIMA (1,1,0) model is found to be the most accurate among all the developed models for this specific time series. Forecast made with ARIMA (1,1,0) model shows that ethanol demand for achieving 20% blending will be 9777 million Litres by 2025 and 11247 million Litres by 2030. Hence, considering the result of this study and the present domestic ethanol production it can be said that India will continue to face ethanol shortage to meet future blending targets.

Economic analysis of the benefits to petroleum refiners for low carbon boosted spark ignition biofuels

A refinery modeling framework is developed to estimate the benefits of blending high-quality biofuels directly with refinery gasoline components to produce premium grade fuels. The results offer a change in paradigm - instead of biofuels being competitors to fossil fuels, biofuels can add value to refineries’ product slates, because of their favorable properties. This potential value can be characterized by calculating the breakeven value (BEV), as defined below. The proposed modeling framework incorporates extensive data from (1) projected product demands over the next few decades, (2) crude oil and refinery products pricing, and (3) fuel specifications. The complete refinery models serve as a basis for assessing the value of biofuels, assuming profitability remains the same for representative petroleum refinery configurations. Resulting valuations varied widely with BEVs observed between $10-$120/bbl given the considered blending levels and crude prices. Further, BEV was correlated with the fuel octane ratings such as octane numbers (research, RON and motor octane numbers, MON) and both antiknock index (AKI, average of RON and MON) and sensitivity (S, difference between RON and MON), with a slightly higher correlation with the sensitivity. However, the expected decrease in gasoline demand for the upcoming years could negatively impact biofuels demand and value, in a business-as-usual scenario. The analysis also showed high valuations in smaller refineries since they can enhance the capabilities for producing specialty, high-value fuels/products, and introduce high octane-barrels into otherwise constrained blending operations. Additional implications towards refiners include opportunities to rebalance operations, access to high-value fuel markets, and synchronization with broader transportation industry trends. Furthermore, results indicate the value of Co-Optima boosted spark ignition (BSI) efficiency gains can extend to refiners to incentivize decarbonization and diversified feedstock production.

Forecasting of Bioethanol Production in the Sudan

Gasoline demand of the Sudanese transportation sector is increasing. To meet the demand many measures have been developed in the last few years. These included efficiency improvement and supplement with bioethanol. The latter is blended with gasoline in the range of 5-27%; the blend is also called E85 and E90. The aim of this research was to forecast bioethanol production from molasses of Sudanese Sugar Factories by assuming the design capacities of bioethanol factories in Sudan from 2016 to 2030. Data on current consumption and production of gasoline by refineries as well as the potential production were obtained from relevant sources such as Ministry of Petroleum. The data were analyzed using forecasting models. Mainly two models namely a trend model and an econometric model were used. For econometric model, data on population, gasoline prices and gross domestic product were collected as well, while the trend model is time series dependent only. The results revealed that beyond the year 2021 Sudan production of gasoline will not meet the demand. Bioethanol mixed with gasoline at 10% is a feasible option to supplement gasoline. The study also revealed that the production potential of bioethanol in sugar industry will meet the demand with a surplus in year 2021.

Transition to energy‐efficient and clean transportation system in Nigeria: Evidence from transportation fuels demand analysis

AbstractThis paper examines the prospects and challenges of transition from the current inefficient and highly polluting road transportation system in Nigeria to a highly efficient and environmentally friendly road transportation system. The study used time‐series data from 1981 to 2019 within the framework of demand for gasoline and diesel in the structural time‐series estimation model, where the concept of the asymmetric price increase and underlying energy demand trends were incorporated into the models. The findings of the study show that both gasoline and diesel are highly price inelastic, and the UEDT is generally stochastic but exhibits a positive trend in a large part of the estimation period. The conclusion and policy implication of the result suggests that governments should combine incentives and public enlightenment on the importance of energy efficiency as well as transport infrastructure improvements with gradual adjustments of domestic prices in the short run. However, in the long run, policy should be extended towards massive investments in the transformation and modernization of the whole transportation sector in line with sustainability, efficiency and environmentally friendly goals in the transportation system of the country.