Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets

Zeng, Yuan; Tan, Xianjun; Gu, Baihe; Wang, Yi; Bing, Xu

doi:10.1016/j.apenergy.2016.06.130

Cited by 52 publications

(28 citation statements)

References 38 publications

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“…However, because passenger car ownership in China is still in the boom period, there are not enough data to fit the development of passenger car ownership due to the lack of inflection point data. Therefore, asymptotic line of the NE-SR is determined in advance according to the pattern of the six selected countries, which is similar with the treatment in the researches of Chinese car ownership using the Gompertz function [6,9,10].…”

Section: Forecasting Chinese Vehicle Ownershipmentioning

confidence: 99%

“…It can be observed in Table 3 that the three errors of NE-SR such as MAD, mean absolute percentage error (MAPE) and root mean squared error (RMSE) for the five countries are all lower than those of Gompertz function. Here, the MAD, MAPE and RMSE are expressed as Equations (1), (8) and (9). Therefore, the NE-SR is of higher precision than the Gompertz function by SPSS.…”

Section: Validation Of New Functionmentioning

confidence: 99%

“…Furthermore, the Gompertz function has been found to best fit the historical vehicle ownership data among these three functions [8], and a variety of researches studied the environment and transportation policy by assuming the growth of vehicle ownership as Gompertz function. For example, the future vehicle energy demand and greenhouse gas (GHG) were estimated depending on Gompertz function [9,10]. The effects of the two license quota policies on car ownership levels were compared and the delays in the process of personal motorization in Shanghai and Beijing were examined in [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Lian

Tian

et al. 2018

Sustainability

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Section: Forecasting Chinese Vehicle Ownershipmentioning

confidence: 99%

Section: Validation Of New Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Lian

Tian

et al. 2018

Sustainability

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show abstract

“…Zeng and colleagues [34] predicted baseline GHG emissions of different motor vehicles of Chinese cities from 2013 to 2035, and suggest that GHG emissions from tank to wheel and well to wheel in all cities will continuously increase yet at different rates. Particular, technology improvement or renewable development in power generation sector can have direct mitigation impacts, given that the power sector is the major consumer of fossil fuel energy and producer of GHGs.…”

Section: Technology and Renewable Energymentioning

confidence: 99%

Understanding the energy consumption and greenhouse gas emissions and the implication for achieving climate change mitigation targets

et al. 2016

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“…The Gompertz model and prediction model from our previous study [31] are used here to predict the total vehicle ownership and the population of each type of vehicles except for motorcycles. The model is as follows:…”

Section: Prediction Model For Vehicle Ownershipmentioning

confidence: 99%

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

Tan

Zeng

et al. 2018

Sustainability

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This study, using Chongqing City of China as an example, predicts the future motor vehicle population using the Gompertz Model and the motorcycle population using the piecewise regression model, and predicts and analyzes fuel consumption and greenhouse gas (GHG) emissions of motor vehicles from 2016 to 2035 based on the bottom-up method under different scenarios of improving the fuel economy of conventional vehicles, promoting alternative fuel vehicles, and the mixed policy of the above two policy options. The results indicate that the total population of motor vehicles in Chongqing will increase from 4.61 million in 2015 to 10.15 million in 2035. In the business-as-usual scenario, the road-transportation energy demand in Chongqing will keep increasing from 2015 and will peak in 2030, before it begins to decline by 2035. The trends for the tank to wheel (TTW) and well to wheel (WTW) GHG emissions are similar to that of energy demand. The WTW GHG emissions will increase from 24.9 Mt CO 2 e in 2016 to 50.5 Mt CO 2 e in 2030 and will then gradually decline to 48.9 Mt CO 2 e in 2035. Under the policy scenarios of improving fuel economy of conventional fuel passenger cars, promoting alternative fuel vehicles, and their mixed policy, direct energy consumption and TTW and WTW GHG emissions from 2016 to 2035 will be reduced to different levels. It is also found that the two types of policies have a hedging effect on the direct energy-consumption saving, TTW, and WTW GHG emission reductions. Sensitivity analysis of key parameters and policy settings is conducted to investigate the impact of their changes on the vehicle population projection, direct energy demand, and WTW GHG emissions. Some policy implications are suggested to provide reference for the formulation and adjustment of Chongqing's, or even China's, low-carbon road transportation policies in the future based on the analysis results.

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Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets

Cited by 52 publications

References 38 publications

Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression

Understanding the energy consumption and greenhouse gas emissions and the implication for achieving climate change mitigation targets

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

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