scholarly journals Association of Carbon Emissions and Circular Curve in Northwestern China

2019 ◽  
Vol 11 (4) ◽  
pp. 1156 ◽  
Author(s):  
Yaping Dong ◽  
Jinliang Xu ◽  
Menghui Li ◽  
Xingli Jia ◽  
Chao Sun
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Research Work ◽  
Field Tests ◽  
Quantitative Model ◽  
Northwestern China ◽  
Low Carbon ◽  
Emission Data ◽  
Circular Curve

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design.

scholarly journals Estimation of Vehicle Carbon Emissions in China Accounting for Vertical Curve Effects

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yaping Dong ◽  
Jinliang Xu
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Vertical Profile ◽  
Carbon Emission ◽  
Emission Rate ◽  
Field Tests ◽  
Emission Model ◽  
Low Carbon ◽  
Conversion Model ◽  
Longitudinal Slope

Predicting vehicle carbon emissions on vertical curve sections can provide guidance for low-carbon vertical profile designs. Given that the influence of vertical curve design indicators on the fuel consumption and CO2 emissions of vehicles are underexplored, this study filled this research gap by establishing a theoretical carbon emission model of vehicles on vertical curve sections. The carbon emission model was established based on Xu’s vehicle energy conversion model, the conversion model of energy, fuel consumption, and CO2 emissions. The accuracy of the theoretical carbon emission model and the CO2 emission rules on vertical curve sections were verified by field test results. Field tests were carried out on flat sections, longitudinal slope sections, and various types of vertical curve sections, with five common types of vehicles maintaining cruising speed. The carbon emission rate effects on the vertical curve are closely related to the gradient and irrelevant of the radius. On the vertical profile composed with downhill/asymmetric/symmetrical vertical curve with a gradient greater than the balance gradient, the carbon emission rate is determined by the gradient and radius. The influence of the gradient on carbon emissions of vehicle on these vertical profiles was more significant than the radius. The radius is irrelevant to the carbon emission rate on the other forms of vertical profile. These results may benefit highway designers and engineers by providing guidelines regarding the environmental effects of highway vertical curve indexes.

scholarly journals A Model for Estimating Passenger-Car Carbon Emissions that Accounts for Uphill, Downhill and Flat Roads

2020 ◽  
Vol 12 (5) ◽  
pp. 2028
Author(s):  
Jinliang Xu ◽  
Yaping Dong ◽  
Menghua Yan
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Mechanical Energy ◽  
Maximum Error ◽  
Low Carbon ◽  
Passenger Cars ◽  
Proposed Model ◽  
Longitudinal Slope ◽  
Main Factor

The geometric longitudinal slope line of a given road significantly effects the carbon emissions of vehicles traversing it. This study was conducted to explore the carbon emission rules of passenger cars on various highway slopes. The law of conservation of mechanical energy, the first law of thermodynamics and the vehicle longitudinal dynamics theory were utilized to determine the influence of slope design indicators on fuel consumption. The energy conversion, fuel consumption, and carbon emission models of passenger cars on a flat straight road, uphill road, and downhill road sections were derived accordingly. Two types of passenger cars were selected for analysis. A field test was carried out to verify the proposed model where the vehicle maintained a cruise speed on flat straight road, uphill road and downhill road with equal gradient and mileage, and continuous longitudinal slope to gather fuel consumption data. The proposed model showed strong accuracy and a maximum error of 9.97%. The main factor affecting the vehicle’s carbon emissions on the continuous longitudinal slope was found to be the average gradient. For a round-trip longitudinal slope with a small gradient, the main factor affecting the vehicle’s carbon emissions is speed: higher speed results in higher carbon emissions. The results of this study are likely to provide the data for support and a workable reference for the low-carbon highway design and operation.

scholarly journals Carbon Emission Estimation of Assembled Composite Concrete Beams during Construction

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1810
Author(s):  
Kaitong Xu ◽  
Haibo Kang ◽  
Wei Wang ◽  
Ping Jiang ◽  
Na Li
Keyword(s):  
Life Cycle ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Composite Beams ◽  
Total Carbon ◽  
Construction Process ◽  
Low Carbon ◽  
Carbon Emission Reduction ◽  
The Government

At present, the issue of carbon emissions from buildings has become a hot topic, and carbon emission reduction is also becoming a political and economic contest for countries. As a result, the government and researchers have gradually begun to attach great importance to the industrialization of low-carbon and energy-saving buildings. The rise of prefabricated buildings has promoted a major transformation of the construction methods in the construction industry, which is conducive to reducing the consumption of resources and energy, and of great significance in promoting the low-carbon emission reduction of industrial buildings. This article mainly studies the calculation model for carbon emissions of the three-stage life cycle of component production, logistics transportation, and on-site installation in the whole construction process of composite beams for prefabricated buildings. The construction of CG-2 composite beams in Fujian province, China, was taken as the example. Based on the life cycle assessment method, carbon emissions from the actual construction process of composite beams were evaluated, and that generated by the composite beam components during the transportation stage by using diesel, gasoline, and electric energy consumption methods were compared in detail. The results show that (1) the carbon emissions generated by composite beams during the production stage were relatively high, accounting for 80.8% of the total carbon emissions, while during the transport stage and installation stage, they only accounted for 7.6% and 11.6%, respectively; and (2) during the transportation stage with three different energy-consuming trucks, the carbon emissions from diesel fuel trucks were higher, reaching 186.05 kg, followed by gasoline trucks, which generated about 115.68 kg; electric trucks produced the lowest, only 12.24 kg.

Carbon Emission of Mining Industry’s Energy Consumption in Heilongjiang Province

2015 ◽  
Vol 1092-1093 ◽  
pp. 1597-1600
Author(s):  
Zhong Hua Wang ◽  
Xin Ye Chen
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Mining Industry ◽  
Calculation Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Heilongjiang Province ◽  
Low Carbon

The need to reduce carbon emission in Heilongjiang Province of China is urgent challenge facing sustainable development. This paper aims to make explicit the problem-solving of carbon emission to find low carbon emission ways. According to domestic and foreign literatures on estimating and calculating carbon emissions and by integrating calculation methods of carbon emissions, it was not possible to consider all of the many contributions to carbon emissions. Calculation model of carbon emissions suitable to this paper is selected. The carbon emissions of energy consumption in mining industry are estimated and calculated from 2005 to 2012, and the characteristics of carbon emission are analyzed at the provincial level. It makes the point that carbon emissions of energy consumption in mining industry can be reduced when we attempt to alter energy consumption structure, adjust industrial structure and improve energy utilization efficiency.

scholarly journals A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7559
Author(s):  
Lisha Li ◽  
Shuming Yuan ◽  
Yue Teng ◽  
Jing Shao
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Influencing Factors ◽  
Carbon Emission ◽  
Estimation Method ◽  
Civil Aviation ◽  
Accurate Estimation ◽  
Low Carbon ◽  
Fuel Price ◽  
Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

scholarly journals Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

scholarly journals Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

2020 ◽  
Vol 12 (19) ◽  
pp. 8118
Author(s):  
Tu Peng ◽  
Xu Yang ◽  
Zi Xu ◽  
Yu Liang
Keyword(s):  
Sustainable Development ◽  
Environmental Pollution ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Intelligent Transportation ◽  
Exhaust Emissions ◽  
Low Carbon ◽  
Vehicle Exhaust ◽  
The Sustainable Development ◽  
Low Carbon Emission

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

scholarly journals The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2706 ◽  
Author(s):  
Feng Wang ◽  
Mengnan Gao ◽  
Juan Liu ◽  
Wenna Fan
Keyword(s):  
Network Structure ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Geographical Location ◽  
Network Effect ◽  
Quadratic Assignment ◽  
Spatial Correlations ◽  
Emission Data ◽  
Correlation Relationship ◽  
Four Blocks

Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

scholarly journals Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Longlong Leng ◽  
Yanwei Zhao ◽  
Zheng Wang ◽  
Hongwei Wang ◽  
Jingling Zhang
Keyword(s):  
Fuel Consumption ◽  
Carbon Emission ◽  
Time Windows ◽  
Travel Distance ◽  
Low Carbon ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Total Cost ◽  
Location Routing ◽  
Self Adaptive

In this paper, we consider a variant of the location-routing problem (LRP), namely, the regional low-carbon LRP with reality constraint conditions (RLCLRPRCC), which is characterized by clients and depots that located in nested zones with different speed limits. The RLCLRPRCC aims at reducing the logistics total cost and carbon emission and improving clients satisfactory by replacing the travel distance/time with fuel consumption and carbon emission costs under considering heterogeneous fleet, simultaneous pickup and delivery, and hard time windows. Aiming at this project, a novel approach is proposed: hyperheuristic (HH), which manipulates the space, consisted of a fixed pool of simple operators such as “shift” and “swap” for directly modifying the space of solutions. In proposed framework of HH, a kind of shared mechanism-based self-adaptive selection strategy and self-adaptive acceptance criterion are developed to improve its performance, accelerate convergence, and improve algorithm accuracy. The results show that the proposed HH effectively solves LRP/LRPSPD/RLCLRPRCC within reasonable computing time and the proposed mathematical model can reduce 2.6% logistics total cost, 27.6% carbon emission/fuel consumption, and 13.6% travel distance. Additionally, several managerial insights are presented for logistics enterprises to plan and design the distribution network by extensively analyzing the effects of various problem parameters such as depot cost and location, clients’ distribution, heterogeneous vehicles, and time windows allowance, on the key performance indicators, including fuel consumption, carbon emissions, operational costs, travel distance, and time.

scholarly journals Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 782
Author(s):  
Na Zhang ◽  
Zijia Wang ◽  
Feng Chen ◽  
Jingni Song ◽  
Jianpo Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Quantitative Research ◽  
Carbon Emission ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Low Carbon ◽  
Demand Forecast ◽  
Passenger Demand ◽  
Urban Rail

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.

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