Search results

In recent years, people have started to realize the importance of environmental protection, and in particular the problem of global warming. Consequently, many governments have started to view decreasing carbon emissions as a priority. Green transportation is one of the policies that is relevant to these efforts. This research aims to optimize the routing plan with minimizing fuel consumption.

In this research, a model is proposed for calculating the total fuel consumption when given a routing plan. Three factors which greatly affect fuel consumption of transportation – transportation distance, transportation speed and loading weight – are taken into consideration. Then a simple Tabu Search is used to optimize the routing plan and an experimental evaluation of the proposed method is performed.

It is shown that the proposed method provides substantial improvements over a method based on minimizing transportation distances.

The experimental results show that the routing plans found by the proposed method require less fuel consumption than that found by optimizing methods in which the distance travelled was minimized. That means that, if the distribution center can transport goods using vehicles with better fuel consumption, and the drivers can drive in the such a way as to reduce the discharge of carbon, then the proposed method can be a strategy for the continuous improvement of fuel consumption.

Consumption of fossil fuels due to their non-renewability has always been one of the fundamental problems among energy-related issues. Major dependence of mining equipment and activities on energy, fuel and adequate fuel allocation has become of great importance in fuel consumption of mines. Therefore, this study aims to propose a model for optimal fuel allocation for mining industry.

In this study, partial least squares structural equation modeling (PLS-SEM), as one of the well-known statistical methods, is used to model and analyze fuel consumption pattern in mine industry.

To show the applicability of the proposed model, the study investigates the model for a real mine in Iran. In this regard, real data of important factors affecting mine fuel consumption are collected. Results of statistical models construct a general formula to calculate the fuel consumption based on three main variables.

Policymaking is one of the important tasks in energy-related organizations. One of the main sectors that uses noticeable amount of fossil fuels is mining industry. Despite the government subsidy for mining in Iran, there is a significant price difference between the price of allocated fuel for mine and the price of the same fuel in the open market. Therefore, this study implements PLS-SEM approach to formulate the fuel consumption pattern under all possible fuel consumption indicators to enable policymakers to make reliable decision for future purposes.

Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

Non-fossil fuels are receiving increasing attention within the context of addressing global climate challenges. Based on a review of non-fossil fuel consumption in major countries worldwide from 1985 to 2015, the purpose of this paper is to analyze trends for global non-fossil fuel consumption, share of fuel consumption and inequality.

The similarities were obtained between the logarithmic mean divisia index and the mean-rate-of-change index decomposition analysis methods, and a method was proposed for complete decomposition of the incremental Gini coefficient.

Empirical analysis showed that: global non-fossil fuel consumption accounts for a small share of the total energy consumption, but presents an increasing trend; the level of global non-fossil fuel consumption inequality is high but has gradually declined, which is mainly attributed to the concentration effect; inequality in global non-fossil fuel consumption is mainly due to the difference between nuclear power and hydropower consumption, but the contributions of nuclear power and hydropower to per capita non-fossil fuel consumption are declining; and population has the greatest influence on global non-fossil fuel consumption during the sampling period.

The main contribution of this study is its analysis of global non-fossil fuel consumption trends, disparities and driving factors. In addition, a general formula for complete index decomposition is proposed and the incremental Gini coefficient is wholly decomposed.

Forklift trucks are generally operated with frequent accelerations and stops, reverse and operations of load handling. This way of operation increases the energy losses and consequently the need for reduction of fuel consumption from forklift customers. This study aims to build a model to replicate the performance of forklifts during real operations and estimate fuel consumption without building a real prototype.

AVL Cruise has been used to simulate forklift powertrain and hydraulic circuit. The driving cycles used for this study were in accordance with the standard VDI 2198. Artificial neural networks (ANNs), trained by the results of AVL Cruise simulations, have been used to forecast the fuel consumption for a large set of possible driving cycles.

The comparison between simulated and experimental data verified that AVL Cruise model was able to simulate the performance of real forklifts, but the results were only valid for the specified driving cycle. The ANNs, trained by the results of AVL Cruise for a certain number of driving cycles, have been found effective to forecast the fuel consumption of a larger number of driving cycles following the prescriptions of the standard VDI 2198.

A new method based on ANN, trained by AVL Cruise simulation results, has been introduced to forecast the forklift fuel consumption, reducing the computational time and the cost of experimental tests.

The purpose of this paper is to provide an assessment of the effects of fuel efficiency improvements on four‐wheeler ownership, fuel consumption, fuel imports and emissions for personal transportation in the context of India. The paper also aims to measure the rebound effect induced by this policy.

The paper relies on a system dynamics model to analyse the problem. A causal loop model was developed initially, which was transformed to a stock and flow diagram. Simulation was carried out to capture the effects of fuel efficiency improvements in the four‐wheeler sector of India.

The study has revealed that a policy of fuel efficiency improvements is favourable to the Indian four‐wheeler sector growth, but this policy could result in an increase in fuel consumption and therefore a corresponding increase in the fuel imports and emissions in the country. This policy also induces direct rebound effect that adds up to the already alarming fuel consumption levels.

The study is limited to four‐wheeler passenger cars in India and gasoline as the transport fuel.

The paper offers a system dynamics model that can aid the government, vehicle manufacturers, and environment protection groups to further analyse policies regarding fuel efficiency improvements, fuel price adjustments, four‐wheeler ownership, energy consumption and emission, and to obtain some useful policy insights before those policies are implemented.

In this paper, the authors investigate that the increasing level of fossil fuel combustion in the industrial sector has been considered the prime cause for the emissions of greenhouse gas. Meanwhile, the research focusing on the impact of fossil fuel consumption on the emission of CO₂ is limited for the developing countries containing Vietnam. This study applied the autoregressive distributed lag (ARDL) approach with structural breaks presence, and the Bayer–Hanck combined cointegration method to observe the rationality of the environmental Kuznets curve (EKC) hypothesis in the dynamic relationship between the industrialization and carbon dioxide (CO₂) emission in Vietnam, capturing the role of foreign direct investment (FDI) inflows and the fossil fuel consumption over the period of 1975–2019. The outcomes revealed the confirmation of cointegration among the variables and both short and long-run regression parameters indicated the evidence for the presence of a U-shaped association between the level of industrial growth and CO₂ emission that is further confirmed by employing the Lind and Mehlum U-test for robustness purpose. The results of Granger causality discovered a unidirectional causality from FDI and fossil fuel consumption to CO₂ emission in the short run. For the policy points, this study suggests the use of efficient and low carbon-emitting technologies.

In order to test for consistency and robustness of the cointegration analysis, this study also applied the ARDL bound testing method to find out long-run association among variables with the existence of the structural break in the dataset. The ARDL method was preferred to other traditional cointegration models; because of the smaller dataset, the results obtained from the ARDL method are efficient and consistent and equally appropriate for I(1) and I(0) variables.

The short-run and long-run causal associations among variables have been observed by employing the error correction term (ECT) augmented Granger-causality test that revealed the presence of the long-run causality among variables only when the CO₂ emission is employed as a dependent variable. The outcomes for short-run causality indicated the presence of unidirectional causality between consumption of fossil fuel and CO₂ emission, where the fossil fuel consumptions Granger-cause CO₂ emission. Industrial growth has also been found to have an impact on fossil fuel consumptions, however not the opposite. This advocates that the policies aimed at reducing the fossil fuel consumptions would not be harmful to industrial growth as other energy efficient and cleaner technology could be implemented by the firms to substitute the fossil fuel usage.

The study explored the dynamic relationship among FDI, consumption of fossil fuel, industrial growth and the CO₂ emission in Vietnam for the time period 1975–2019. The newly established Bayer–Hanck joint cointegration method and the ARDL bound testing were employed by taking into account the structural breaks in the dataset.

The high costs of collection and transportation of municipal solid waste (MSW) on the overall waste management budget (sometimes more than 75 per cent) makes it an issue to be urgently addressed for improvement. The paper aims to focus on the optimisation of routing networks for waste collection/transportation.

The paper proposes herein the application of geographic information system (GIS) 3D route modelling for waste collection/transportation to optimise the route according to the minimum fuel consumption criterion to different municipalities of the island of Santo Antao of Cape Verde.

The optimisation for the lowest fuel consumption yields 52 per cent savings in fuel, when compared to that for the shortest distance, even travelling a 34 percent longer distance, which shows the importance of considering simultaneously the relief of the territory and the lowest fuel consumption criterion when optimising vehicle routes.

With such a supporting decision tool savings in fuel are huge, the efficiency of management systems is improved and the environmental impact during daily operation is reduced. The GIS 3D route modelling takes into account the effects of both the road inclination and the vehicle load.

The originality of the work lies in the chosen approach. To optimise vehicle routes the criterion of minimum fuel consumption rather than the commonly used shortest distance is used, since fuel consumption is the factor reflecting actual costs relative to MSW management.

It is postulated that operator-dependent factors have a substantial impact on the fuel consumption and emissions of heavy-duty construction vehicles, such that by adopting different operator practices, savings in fuel used and lower emissions and costs can be achieved. Accordingly, the purpose of this paper is to examine the emission and cost effects of a driver-training programme aimed at fuel efficiency and construction truck operators.

A study was conducted on an urban excavation involving truck-and-trailer vehicles, serviced by an excavator, over an approximately 50 km trafficked urban circuit. Field observations, before and after training, and including those on a control truck operator, gave fuel usage, emissions data, speed, travel times, routing, production and operational costs.

Compared to baseline values, trained drivers saw a reduction in their fuel consumption by an average of 8.5 per cent, reducing to 7.7 per cent after several weeks following training. Importantly, this occurred without increased travel time or lost production.

The number of drivers participating in the research was dictated by the nature of the project involved. A larger sample, and research involving a closer study of braking, acceleration and gearshift behaviour, would help confirm this paper’s results and provide further insight.

The paper demonstrates quantitative benefits (reduced unit emissions and reduced unit costs) associated with construction equipment operator training.

The research is original, being the first related to driver training of heavy-duty construction equipment and incorporating production measurements, the first involving a study of equipment operators unaware that they were being observed, and the first involving a control.