Analysis of road transportation energy consumption demand in China

Chai, Jian; Lu, Quanying; Wang, Shouyang; Lai, Kin Keung

doi:10.1016/j.trd.2016.08.009

Cited by 86 publications

(40 citation statements)

References 27 publications

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“…Previous studies have shown that transport energy demand can be projected through top-down models (e.g., Zhang et al [13], Lu et al [14], and Chai et al [15]); however, to determine the impact of technological change, the energy demand projection for the road transport sector should be conducted using a bottom-up model [16]. Other studies have implemented a bottom-up model for projecting the transport energy demand (e.g., Eom and Schipper [17], Ma et al [18], Baptista et al [19], Ko et al [20], and Deendarlianto et al [21]).…”

Section: Introductionmentioning

confidence: 99%

Modeling Future Energy Demand and CO2 Emissions of Passenger Cars in Indonesia at the Provincial Level

et al. 2019

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The high energy demand and CO2 emissions in the road transport sector in Indonesia are mainly caused by the use of passenger cars. This situation is predicted to continue due to the increase in car ownership. Scenarios are arranged to examine the potential reductions in energy demand and CO2 emissions in comparison with the business as usual (BAU) condition between 2016 and 2050 by controlling car intensity (fuel economy) and activity (vehicle-km). The intensity is controlled through the introduction of new car technologies, while the activity is controlled through the enactment of fuel taxes. This study aims to analyze the energy demand and CO2 emissions of passenger cars in Indonesia not only for a period in the past (2010–2015) but also based on projections through to 2050, by employing a provincially disaggregated bottom-up model. The provincially disaggregated model shows more accurate estimations for passenger car energy demands. The results suggest that energy demand and CO2 emissions in 2050 will be 50 million liter gasoline equivalent (LGE) and 110 million tons of CO2, respectively. The five provinces with the highest CO2 emissions in 2050 are projected to be West Java, Banten, East Java, Central Java, and South Sulawesi. The projected analysis for 2050 shows that new car technology and fuel tax scenarios can reduce energy demand from the BAU condition by 7.72% and 3.18% and CO2 emissions by 15.96% and 3.18%, respectively.

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Section: Introductionmentioning

confidence: 99%

Modeling Future Energy Demand and CO2 Emissions of Passenger Cars in Indonesia at the Provincial Level

et al. 2019

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“…However, the relationship between the economy and transportation is not a simple linear relationship. Therefore, the characteristics of the relationship between social development and transportation demand are the premise for analyzing road energy demand [12].…”

Section: Introductionmentioning

confidence: 99%

Measuring the Influences and Impacts of Signalized Intersection Delay Reduction on the Fuel Consumption, Operation Cost and Exhaust Emissions

Al-Arkawazi

2018

CivileJournal

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With the rapid urban expansion and economic development, vehicle fuel dissipation and exhaust emissions have been identified as major energy wastage and urban air pollutions in Kalar City and Iraqi Kurdistan Region in general. Traffic congestion is a growing problem in Kalar City and other urban areas. As it increases, the delay at the transportation network will increase. Any increase of the delay in the transportation network will reflect negatively by increasing the delay at the signalized intersections. Therefore, a study on delay and its relation to fuel consumption, operation cost and emissions at signalized intersection are necessary. This paper, studies the influences and impacts of signalized intersection delay reduction on the fuel consumption, operation cost, and exhaust emissions. A simulation is carried out to evaluate the existing conditions of selected intersections by estimating the intersection delay, operation cost, and emissions. The simulation results show that fuel consumption, operation cost, and emissions are high and directly proportional to the intersection delay. To reduce intersection delay, a signal timing optimization is carried out to the selected intersections. The optimization results show that the delay reduction has a significant influence and impacts in reducing; fuel wastage, operation cost, and exhaust emissions.

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“…Chai et al. use multiple regression models to forecast the road transportation energy consumption in China. In order to overcome the limitations of the linear statistical method, ANN have been used to forecast the demand of transport energy.…”

Section: Introductionmentioning

confidence: 99%

Use of group method of data handling for transport energy demand modeling

Teng

Xiao

et al. 2017

Energy Science & Engineering

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As transport sector takes a big share of the whole energy consumption in China, it is crucial to predict its energy demand. To forecast China's transport energy demand, group method of data handling (GMDH) was introduced. The model can help policymakers' select influential variables and build prediction models automatically. Furthermore, it can reduce the negative impact of the noise in the Chinese statistical data. To produce comparable results, four of the six data sets used in this paper contain the same variables as in previously published research. Artificial neural networks (ANN), GMDH, multiple linear regression (MLR), and support vector machine (SVM) models were trained using fivefold cross-validation. The performance of these models was measured in terms of coefficient of determination and root mean square error. Results showed that GMDH achieved better performance than the other models. Finally, projections were made with two scenarios. Both of the projected results showed that the energy demands peak in certain years and then decrease gradually. This study suggests that GDP is not the essential variable, while urbanization rate is an important variable to forecast the transport energy demand in China. It also suggests that Chinese government needs to prepare for the development and deployment of transport energy. 303

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Analysis of road transportation energy consumption demand in China

Cited by 86 publications

References 27 publications

Modeling Future Energy Demand and CO2 Emissions of Passenger Cars in Indonesia at the Provincial Level

Modeling Future Energy Demand and CO2 Emissions of Passenger Cars in Indonesia at the Provincial Level

Measuring the Influences and Impacts of Signalized Intersection Delay Reduction on the Fuel Consumption, Operation Cost and Exhaust Emissions

Use of group method of data handling for transport energy demand modeling

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