Development and application of China provincial road transport energy demand and GHG emissions analysis model

Peng, Tianduo; Ou, Xunmin; Yuan, Zhenhong; Yan, Xiaoyu; Zhang, Xiliang

doi:10.1016/j.apenergy.2018.03.139

Cited by 157 publications

(45 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, compared with the study by Wu et al [12], which also used the Gompertz model to predict the number of vehicle ownership, the prediction result of this paper is higher than theirs, which may be due to fact that Wu et al [12] set the saturation level of vehicle ownership rate for China directly in their study, while we set the saturation level of vehicle ownership rate in each province separately. In comparison with the study by Peng et al [16], which also established a bottom-up model and forecast the vehicle ownership provincially, our prediction results are slightly higher. There could be two reasons: Firstly, in the setting of the saturation level of the vehicle ownership rate, Peng et al [16] referred to the saturation level of France, and set the saturation level of the provinces unlimited to the purchase restriction policies to 376 vehicles per thousand people, while the limited provinces were uniformly set to 250 vehicles per thousand people.…”

Section: Fuel Demandcontrasting

confidence: 74%

“…In comparison with the study by Peng et al [16], which also established a bottom-up model and forecast the vehicle ownership provincially, our prediction results are slightly higher. There could be two reasons: Firstly, in the setting of the saturation level of the vehicle ownership rate, Peng et al [16] referred to the saturation level of France, and set the saturation level of the provinces unlimited to the purchase restriction policies to 376 vehicles per thousand people, while the limited provinces were uniformly set to 250 vehicles per thousand people. In our study, we comprehensively considered the provincial differences in economic status, population distribution, regional structure, and restriction policies, and then reasonably measured the saturation level of the vehicle ownership rate in each province.…”

Section: Fuel Demandcontrasting

confidence: 74%

“…As China's vehicle ownership is still in a period of rapid growth and is not saturated, future vehicle ownership, which is regarded as the basis of predicting fuel demand by many researchers [12][13][14][15][16], is the most important factor affecting China's fuel demand. International experience shows that vehicle ownership rate of a country increases slowly at the lowest income levels, followed by an increasing rate of growth as income rises, and then slows down as saturation is approached.…”

Section: Related Literaturementioning

confidence: 99%

“…In our study, we comprehensively considered the provincial differences in economic status, population distribution, regional structure, and restriction policies, and then reasonably measured the saturation level of the vehicle ownership rate in each province. Secondly, Peng et al [16] set the fuel consumption rate and the annual miles traveled by different vehicle types of each province separately when establishing the bottom-up model. The refinement of these indicators placed more emphasis on the influence of other factors, such as technological progress on fuel demand, thus leading to a lower forecast result.…”

Section: Fuel Demandmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China

et al. 2019

View full text Add to dashboard Cite

Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.

show abstract

Section: Fuel Demandcontrasting

confidence: 74%

Section: Fuel Demandcontrasting

confidence: 74%

Section: Related Literaturementioning

confidence: 99%

Section: Fuel Demandmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Compared to other existing LEAP-based 2050 China models [20,21], our model is distinct in the level of complexity and detail in modeling the building sector [22] and in using physical drivers such as building floorspace and infrastructure needs for projecting heavy industrial production that captures saturation points [23]. Other China modeling studies have evaluated the potential impacts of accelerated electrification, but most focused on transport without consideration for industry or commercial buildings, two dominant and rapidly growing sectors in China's energy system [12,[24][25][26]. Most other studies also do not explicitly model the linkage between electrification and power sector decarbonization [27], or have done so only for other regions [28][29][30][31] or only for selected sectors [24,32,33].…”

Section: Introductionmentioning

confidence: 99%

Energy and CO2 implications of decarbonization strategies for China beyond efficiency: Modeling 2050 maximum renewable resources and accelerated electrification impacts

et al. 2019

View full text Add to dashboard Cite

• China LEAP model used to assess multi-sector decarbonization strategies to 2050. • Energy, CO 2 impacts of accelerated electrification and renewables are assessed. • 2025 CO 2 peak feasible under 4 strategies, peak levels vary from 10.2 to 10.7 GtCO 2. • Faster electrification's CO 2 impact depend on pace of power sector decarbonization. • Demand-side renewable has similar CO 2 reduction impact as power decarbonization.

show abstract

Electric vehicle lifecycle carbon emission reduction: A review

Gao,

Xie,

Yang

et al. 2023

Carbon Neutralization

View full text Add to dashboard Cite

Under the global carbon neutrality initiative, carbon emissions from the transportation sector are becoming increasingly prominent due to the growth in vehicle ownership. And electric mobility may be a potentially effective measure to reduce road traffic carbon emissions and achieve a green transformation of transportation. This paper systematically collates the relevant carbon accounting standards for the automotive industry and elaborates the current status of road transport greenhouse gas emissions by combining the data from the International Energy Agency. And by comparing the lifecycle carbon footprint of various energy types of vehicles, the necessity and feasibility of electric mobility to reduce carbon emissions are discussed. However, the comparison of vehicle lifecycle carbon footprints shows that electric vehicles (EVs) are not as environmentally friendly as expected, although they can significantly reduce road traffic carbon emissions. The high carbon emissions from the manufacturing process of the core components of EVs, especially the power battery, reduce the low‐carbon potential of electric mobility. Therefore, the carbon emission reduction strategies and outcomes of automakers in the automotive industry chain have been further reviewed. Finally, focusing on vehicle power batteries, this article reviews the technologies such as refined management and echelon utilization that can make EVs more environmentally friendly and promote carbon neutrality in the transportation sector.

show abstract

Development and application of China provincial road transport energy demand and GHG emissions analysis model

Cited by 157 publications

References 27 publications

Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China

Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China

Energy and CO2 implications of decarbonization strategies for China beyond efficiency: Modeling 2050 maximum renewable resources and accelerated electrification impacts

Electric vehicle lifecycle carbon emission reduction: A review

Contact Info

Product

Resources

About