Gasoline Consumption Research Articles

Analysis of Carbon Footprint from a Drilling Project in Niger Delta, Nigeria

Abstract: The oil and gas industry plays a significant role in the release of carbon emissions into the atmosphere. Therefore, it is crucial to accurately gauge and minimize its carbon footprint which requires the thorough measurement of emissions and the identification of the primary sources of carbon emissions. By doing so, we can then determine the most effective methods for reducing these emissions. This study aims to precisely quantify and decrease the carbon footprint associated with drilling operations. To achieve this, we evaluated the diesel and petrol consumption from an onshore drilling project in the Niger Delta and used an emissions model to assess carbon dioxide (CO2) emissions from a drilling rig, thus gaining a comprehensive understanding of current emission levels and the potential for reduction. The data collected included the daily fuel consumption for power generation, transportation, and handling vehicles. The CO2 emissions resulting from fuel consumption were calculated and measured to be 103.5 metric tonnes. Our analysis determined that the primary contributor to the emissions was the energy generation on the site, primarily from the generators. Additionally, it was found that the circulating system on the rig was the main source of CO2 emissions. The study underscores the necessity for long-term impact assessments of drilling fluids and new technologies, emphasizing the need for innovative solutions to further decrease emissions.

Regulatory impact of informality on gasoline consumption efficiency in Africa: A proposed two-part complementary hypothesis test

This study provides new evidence for the regulatory impact of informality on gasoline efficiency in Africa. I propose a Two-Part Complementary Hypothesis (hereafter referred to as the TPCH test), advocating a differential approach to promoting gasoline efficiency: (1) an inverted U-shaped relationship between informality and gasoline inefficiency, and (2) a U-shaped relationship between government regulation and informality, with a significant level effect. The findings indicate an inverted U-shaped effect of informality on gasoline efficiency and a level-negative effect of regulation on informality. These results suggest a differential strategy for enhancing gasoline efficiency. Government regulation is more effective in economies at the pre-saturation stage (characterized by normal growth levels of informality) but proves ineffective in economies at the post-saturation stage (characterized by abnormal growth levels of informality), where energy-saving behaviors may be self-motivated. This is corroborated by the inefficiency equation, where indicators of good governance, such as the rule of law, control of corruption, and regulatory quality, are statistically significant in advancing energy efficiency goals. Gasoline efficiency performance varies across countries, with the higher performers also being the continent's most economically advanced. However, these estimates risk downward bias if outliers or unobserved/observed heterogeneity are not considered.

THE INFLUENCE OF NITROUS OXIDE ADDITION TO THE AIR CHARGE ON THE INDICATORS OF A SPARK IGNITION ENGINE UNDER FULL LOAD OPERATION

The article presents the methodology and research results on the influence of nitrous oxide addition to the air charge on the energy and environmental indicators, as well as the fuel economy of a spark ignition engine with feedback and a three-way catalytic converter under full load conditions. Full load engine modes are often used in conditions of intense traffic of vehicles in cities and settlement. Increasing energy indicators is relevant for sports vehicles and stationary energy installations operating at full load. When determining the most efficient method of achieving high engine energy indicators, it is necessary to take into account the method's impact on the engine's environmental indicators and fuel economy. Several methods for increasing the energy indicators of spark ignition engines are known. One of the most utilized methods is enriching the fuel-air mixture. Engine operation on enriched fuel-air mixture leads to increased gasoline consumption and deterioration of environmental indicators, especially when using a catalytic converter. Therefore, research into methods that simultaneously improve the energy and environmental indicators of the engine in the mentioned operating modes without increasing fuel consumption is relevant. One such method is the use of oxygen-containing compounds, in particular nitrous oxide, added to the air charge. The article proposes a methodology for assessing the method's impact on engine performance indicators in relative terms compared to fuel-air mixture enrichment. We conducted a study of indicator indicators of engine operation, namely: energy indicators based on average indicator pressure, fuel efficiency indicators based on indicator coefficient of useful action, environmental indicators based on concentrations of pollutants in exhaust gases. Calculations using experimental data showed that by adding various amounts of nitrous oxide to the engine's air charge, it is possible to achieve higher energy indicators, improve environmental indicators without worsening fuel economy compared to fuel-air mixture enrichment.

Investigating the Impact of Economic Growth on Pollution Index in Emerging Market Countries

This research aims to provide answers to two problems: to analyze the influence of economic, ecological, and demographic variables on environmental quality and which variables of the three have more influence on environmental quality. Using panel data analysis, with 24 Emerging Markets countries as the research objects and a series for the period between 2013 to 2022, it was found that: (a) the variables of economic growth, greenhouse gases, petrol consumption, and population influence the pollution index, while carbon emissions have no significant effect on the Pollution Index in Emerging Market countries; (b) the variables that have the greatest influence on the pollution index in emerging market countries are ecological and demographic variables compared to economic variables. Based on the findings, the implications for policy implementation are: (a) providing understanding to the public about ecology; (b) increasing population must be balanced with increasing education for the community to provide information about maintaining environmental quality; and (c) directing a sustainable economic development model in all countries through an appropriate and consistent policy framework. Theoretically, this research contributes to sharpening and understanding the flow of thinking of the Environmental Kuznets Curve (EKC) theory. Doi: 10.28991/HEF-2024-05-03-07 Full Text: PDF

Comparative study on combustion and emission of ternary-fuel combined supply SI engine with oxyhydrogen/ethanol/gasoline by different injection modes of fuel

The combination of renewable energy and multifuel combined supply is an efficient way to deal with the energetic crisis and the pollution of environment. Ethanol, gasoline and oxyhydrogen participate in combustion by means of three independent supply paths, which can improve engine power and reduce gasoline consumption and gaseous pollutant emission at the same time. However, the optimization of ternary-fuel combined supply mode is a problem that must be clarified. In this study, the engine performance at four direct injection pressure (DIP), six direct injection timing (DIT), five intake manifold absolute pressures (MAP) and four speeds is studied under the condition of equal ethanol injection ratio and gasoline injection ratio. The experimental results show that ethanol direct injection forms a mixture with a better stratified combustion state than gasoline direct injection. Moreover, oxyhydrogen negatory pressure inhalation (ONPI) can effectually increase Pmax and IMEP, reduce APmax, CA 10–90, CA 0–10, CO emission, HC emission and particulate emission, but increase NOx emission. However, ethanol direct injection (EDI) can reduce NOx emission produced by ONPI, and oxyhydrogen can attenuate the negatory effect on mixture combustion due to the excessive latent heat of vaporization of ethanol. NO emission in EDI mode is reduced by an average of 28.39 % compared to gasoline direct injection (GDI) mode under different MAPs. In conclusion, the combustion and emission characteristics of the engine in EDI + gasoline port injection (GPI) + ONPI mode are better than those in ethanol port injection (EPI) + GDI + ONPI mode. And the control strategy of "9–11 MPa DIP+250–300°CA BTDC DIT +16 L/min oxyhydrogen negative pressure inhalation volume (ONPIv)" can improve the performance of EDI + GPI + ONPI engine while reducing emission.

Weighted penalized m-estimators in robust ridge regression: an application to gasoline consumption data

ABSTRACT The OLS and ridge regression (RR) estimators are adversely affected, when the problem of multicollinearity and y-direction outliers occur together. The robust ridge regression with penalized parameters offers biased estimators with lower variance than OLS and RR. The optimal penalized parameter value is crucial for balancing variance and bias. This study proposes three new weighted robust penalized M-estimators to address both multicollinearity and y-direction outliers. Their performance is evaluated against OLS, RR and existing penalized M-estimators using Monte Carlo simulations based on mean squared error (MSE). The new estimators exhibit lower MSE in the presence of multicollinearity and y-direction outliers. A real data application on gasoline consumption demonstrates the superior performance of the new estimators.

A Hybrid Grey System Model Based on Stacked Long Short-Term Memory Layers and Its Application in Energy Consumption Forecasting

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model’s performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model’s prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting.

Carbon Taxes and CO2 Emissions: A Replication of Andersson (American Economic Journal: Economic Policy, 2019)

Abstract Do carbon taxes reduce CO2 emissions in the countries that adopt it? Andersson (2019. Carbon taxes and CO2 emissions: Sweden as a case study. American Economic Journal: Economic Policy, 11(4), 1–30) provides a clear, affirmative answer. His article has been widely cited as evidence that carbon taxes “work.” To check whether the estimates from Andersson (2019) are reliable, I replicate his article using its publicly available data and codes. I modify his synthetic control method (SCM) by using a more restricted set of control units (excluding one potentially treated unit). I also use a more efficient methodology, the Prais-Winsten estimator, to estimate price effects on gasoline consumption. In addition, I compute prediction intervals (PIs) and add these to the SCM estimates, using the newly developed scpi R package. My best estimate is that carbon taxes reduced per-capita CO2 emissions in Sweden’s transport sector by 7.7%, confirming Andersson’s main finding. I then extend Andersson’s approach to the Norwegian transport sector, estimating a smaller reduction of 2.4%. However, this effect falls within the PIs of the estimates assuming no carbon taxes. When I extend the analysis to the national level in Sweden, I estimate wide PIs and obtain inconclusive results.

Dynamic delay-based game approach in car and petrol supply chains under government intervention

Effective decision-making in a temporal context is challenged by the enduring impacts of delays, where decisions made in a certain period are influenced by choices made in preceding periods. This paper delves into the analysis of decision-making in a dynamic game system characterized by delay impact on decisions. Notably, it pioneers the application of a game approach to detect the intricate relationship between car and petrol supply chains operating under conditions of dynamism and uncertainty. The marginal profit method is applied to deal with the dynamic game model. Furthermore, this article investigates the improvement of green car levels, on member profits. In the car supply chain, the car manufacturer sells two distinct product types: green and regular. In this paper, green cars refer to cars with low petrol consumption, and as gas consumption decreases, the green level of the car ascends. This study marks the first instance where optimal production capacity allocation is examined within the framework of delay and uncertainty. Considering the government's initiative to curtail petrol consumption in automobiles, the effects of the government's supporting role are also analyzed in this paper. As a result, the greenness level witnessed a 4.1-fold increase, leading to an 81.7% surge in the demand for green cars, consequently boosting the profit of the entire car supply chain. Several numerical experiments of a real case were examined to evaluate the performance of the proposed models. Finally, some sensitivity analyses were applied to the principal parameters of the model to extract managerial insights.

Does artificial intelligence reduce corporate energy consumption? New evidence from China

Artificial intelligence is playing a significant role in addressing the energy crisis. This study selected data from manufacturing companies listed on China's A-share market from 2011 to 2022 and calculated the total energy consumption for the first time. The data include the usage of coal, natural gas, gasoline, diesel and water consumption, electricity usage, and centralized heating. The data were then matched and merged with robot usage data from the International Federation of Robotics to empirically study the impact and mechanism of artificial intelligence on energy consumption levels. Our findings reveal that energy consumption decreases by 0.20 % with a one-unit increase in artificial intelligence applications by a corporation, indicating artificial intelligence can significantly reduce energy consumption. The mechanisms by which artificial intelligence affects energy consumption include technological innovation and digital transformation. Additionally, a heterogeneity analysis revealed that applying artificial intelligence in state-owned enterprises, high-tech companies, and non-heavy-pollution industries can further reduce energy consumption. Our study also provides important practical implications for formulating and optimizing global energy policies to achieve sustainable development goals.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

Traffic situation in Yerevan and ways to modernize with the help of current problem of traffic congestion

Introduction. This article addresses the pressing issue of road traffic congestion in the capital of Armenia, Yerevan, and proposes a solution using the Automated Traffic Management System (ATMS). The introduction underscores the significance of effective traffic management in ensuring the safety and comfort of urban residents.Materials and methods. The analysis of the current situation raises concerns about traffic density, congestion, inefficient use of road resources, and their impact on the environment and citizens’ quality of life. In turn, the exploration of ATMS as a solution includes an explanation of the system’s concept, its benefits, and successful global implementation examples. The implementation plan for ATMS encompasses stages from data collection to traffic management, along with necessary technical and infrastructural changes. The assessment of costs and benefits underscores the economic and social components of anticipated outcomes.Conclusions. The introduction of automated traffic management systems (ATMS) leads to a significant reduction in transport delays at intersections by 10–20%, which reduces gasoline consumption for movement by 30%. The number of traffic accidents at intersections is reduced by 10–15%. This leads to economic benefits, improved road safety and improved environmental conditions in cities.

Estimating the Energy Demand and Carbon Emission Reduction Potential of Singapore’s Future Road Transport Sector

About 20% of the world’s CO2 emissions originate from transport. Many countries are committed to decarbonizing their transport sector. Singapore pledged to electrify a whole host of its land transportation fleet, which includes private cars, public buses, ride-hail vehicles, and motorcycles. This paper proposes a simple empirical framework to estimate the future energy demand after 100% electrification has been realized for nine selected road transport vehicle sub-classes and to calculate the carbon emission reduction potential based on various scenarios. The present energy demand for each vehicle sub-class is first calculated based on parameters like petrol and diesel consumption, heat value and density of petrol and diesel, population of vehicle type, and average mileage per vehicle sub-class. Several scenarios are presented, and an analysis is carried out to derive a range of emission factors which are used to estimate the carbon emission reduction potential. Relative to the present day, the future energy demand estimates reveal an overall reduction of 73.60%. Full electrification and a “clean” power generation mix could lead to an emission reduction as high as 93.64% across all vehicles sub-classes, with private cars having the highest reduction potential.

US gasoline response to vehicle fuel efficiency: A contribution to the direct rebound effect

This study measures the response of gasoline consumption to improved vehicle fuel efficiency (miles per gallon). Although an inverse relationship exists, the percentage decline is always less than the percentage efficiency improvement. As usually measured by past researchers, the long-run response in this study is approximately 80% of the efficiency improvement. The remaining 20% is the direct rebound effect and comports well with previous estimates. However, this rebound estimate escalates to 40–50% if horsepower or vehicle size are controlled. Even larger estimates (about 70%) are possible if carmakers change both fuel efficiency and horsepower when required to meet energy efficiency standards. Larger rebound effects are also possible when VFE improvements also reduce gasoline prices, but these price reductions may also improve welfare.

Exploring the Relationship Between CO2 Emissions and Fuel Consumption in Road Transport: Empirical Evidence from Chad

Over the period 2008-2019, this study examines the linear impact of petroleum product (PP) consumption in Chad, PP prices, the vehicle fleet and urbanisation on CO2 emissions on the one hand, and on the other, it determines the causal links that exist between the various variables studied. This study opts for the augmented Dickey-Fuller and Phillips-Perron tests to verify the stationarity of the variables. The ARDL model is then estimated and diagnostic tests are performed to confirm the validity of the model. To confirm the existence of long-term relationships, the bounds test was applied. Finally, the Toda-Yamamoto causality test was used to capture the influences present between the series under study. The main results show that, in the long term, a 1% increase in gasoline consumption would lead to a 1.03% increase in CO2 emissions and the linear impact of diesel consumption is positive and insignificant on CO2 emissions. In the short term, gasoline and diesel consumption have a positive and insignificant impact on CO2 emissions. In terms of causality, there is a unidirectional causality from gasoline consumption to CO2 emissions and a bidirectional causality between CO2 emissions and diesel consumption. This study is the first to simultaneously link CO2 emissions, PP consumption, PP prices, the vehicle fleet and urbanisation in general, and particularly in the case of Chad. It therefore adds to the literature on the simultaneous relationship between CO2 emissions, PP consumption, the car fleet and urbanisation in a global and restricted context. This study could guide Chadian oil pollution management decision-makers in adopting policies related to the effects of PP consumption in the road transport sector.

Deep Learning Algorithm for Optimization of Wait Time at a Traffic Signal Controlled Intersection for Smart Traffic Management

Abstract: The urban landscape is constantly shifting and adapting as a result of factors such as population increase, immigration, and changes in the economy, politics, and culture. Concerns like growing road obstruction, transit delays, pollution levels, consumption of gasoline, etc. have resulted from higher traffic volumes in metropolitan areas. Life expectancy, road safety, environmental quality, and commute times are all impacted by traffic congestion. At junctions, traffic lights play a crucial role in regulating vehicular flow. The traditional pre-timed traffic systems have been ineffective due to the dramatic growth in lights become insufficient in managing traffic clearance which leads to heavy traffic congestion at intersection. Another major issue is that the Emergency vehicles like ambulance, police van, fire brigade experience prolonged waiting time to cross the intersection. For these reasons, development of smart intersections with the aid of technologies such as fuzzy logic, neural networks, artificial intelligence, multiprocessor systems etc. can help to provide the solution and improve the urban traffic management problems. Smart cities are increasingly adopting solutions by using adaptive and intelligent approach to develop smart traffic lights to improve the traffic flow at intersections. The current study on smart traffic light system makes contribution in developing intelligent and adaptive systems to resolve traffic congestion problems at single and connected intersections with an objective to improve traffic clearance and prioritize passage of Emergency vehicle at an isolated intersection. Another problem is the one generally faced by pedestrians as they have to wait for a prolonged interval of time for getting way to cross the lane of major intersection specially at peak traffic hours. This problem has also been considered in the study. System performance has been tested for a twenty-four-hour traffic pattern at an isolated intersection. The outcomes of the simulations showed that the suggested design outperformed the standard fixed-time method in terms of automotive clearing, rescue vehicles passing, and pedestrians wait time.

A lasso regression-based forecasting model for daily gasoline consumption: Türkiye Case

Gasoline is one of the most sought-after resources in the world, where the need for energy is indispensable and continuously increasing for human life today. A shortage of gasoline may negatively affect the economies of countries. Therefore, analysis and estimates about gasoline consumption are critical. Better forecast performance on gasoline consumption can serve the policymakers, managers, researchers, and other gasoline sector stakeholders. This study focuses on forecasting daily gasoline consumption in Türkiye using a lasso regression-based methodology. The methodology involves three main stages: cleaning data, extracting/selecting features, and forecasting future consumption. Additionally, Ridge Regression is employed for performance comparison. Results from the proposed methodology inform strategies for gasoline consumption, enabling more accurate planning and trade activities. The study emphasizes the importance of daily forecasts in deciding import quantities, facilitating timely planning, and establishing a well-organized gasoline supply chain system. Application of this methodology in Türkiye can pave the way for globally coordinated steps in gasoline consumption, establishing efficient gasoline supply chain systems. The findings provide insights for establishing a smooth and secure gasoline collection/distribution infrastructure, offering effective solutions to both public and private sectors. The proposed forecasting methodology serves as a reference for ensuring uninterrupted gasoline supply and maximizing engagement between customers and suppliers. Applied and validated for Türkiye, this methodology can guide global efforts, fostering planned approaches to gasoline consumption and enhancing supply chain systems.

Using Renewable Energy Sources in the Province of Manitoba

This paper explores the potential for additional renewable energy options in the Canadian province of Manitoba, which already produces nearly 100% of its electrical energy using hydro power. It considers the perspectives of the national and provincial governments with regard to the future of electric transportation. It also consolidates available data collected in the province. Finally, it explores the possibilities of future applications of the concept of smart grid in the context of how this may assist the optimal energy source mix for this province. There are reasons other than economics which could drive Manitobans to invest in renewable energy sources different from hydro power. The electric vehicle (EV) is a prime example of a load that should be supplied by new renewable energy. The reduction in the consumption of petrol, the reduction of pollution, and the education in energy efficiency justify the use of renewable energies in EVs.

Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence.

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

Does the electric vehicle industry help achieve sustainable development goals?—evidence from China

Electric vehicles (EVs) are regarded as a key low-carbon technology to address sustainability challenges like climate change and air pollution. However, the real-world environmental impacts resulting from rapid EV adoption remain uncertain. This study develops a theoretical framework to quantify the impacts of EV adoption on energy consumption, carbon emissions, and air quality. Then we empirically investigates the influence of surging EV uptake in China using provincial panel data from 2015 to 2020. Our results indicate that increased EV stocks significantly reduced gasoline consumption but boosted coal-based power demand, shifting emissions and air pollutants from transportation to the electricity sector rather than yielding absolute reductions. We find important regional heterogeneity based on differences in grid generation profiles. In provinces more reliant on coal power, the environmental impacts were more severe. The findings also reveal a spatial spillover effect, with emissions transferred from net power-importing regions to exporters. Overall, the rapid EV transition alone appears insufficient to guarantee emissions cuts and environmental gains. Complementary efforts across sectors are essential to align industrial promotion with sustainability objectives. The empirical evidence informs integrated policy design and metrics to maximize decarbonization as EVs are deployed globally. Future research can build on this study by expanding geographical scope, incorporating projections, adopting a multi-disciplinary lens, leveraging microdata, and applying cutting-edge analytical techniques. Pursuing these directions will further advance knowledge on sustainable EV transitions.