Analysis of Future Vehicle Energy Demand in China Based on a Gompertz Function Method and Computable General Equilibrium Model
Analysis of Future Vehicle Energy Demand in China Based on a Gompertz Function Method and Computable General Equilibrium Model
复制标题
基于Gompertz函数法和可计算一般均衡模型的中国未来汽车能源需求分析
作者：
Wu Tian;Zhang Mengbo;Ou Xunmin
本文提出了一个基于低、中、高增长情景的到2050年中国汽车库存和道路车辆能源需求预测模型。为了推导出国内生产总值（GDP）依赖的Gompertz函数，使用递归动态可计算一般均衡（CGE）模型估计了中国的GDP。Gompertz函数是利用北美、环太平洋地区和欧洲汽车发展趋势的历史数据来估算的，以克服中国汽车保有量长期数据不足的问题。结果表明，在低、中、高增长情景下，2050年预计汽车保有量分别为3亿、4.55亿和4.63亿辆。此外，到2020年，中国汽车保有量的增长将超过Gompertz曲线的拐点，但在2014-2050年期间不会达到饱和点。在主要道路车辆类别中，汽车是最大的能源消费者，其次是卡车和公共汽车。中国汽车需求的增长主要取决于人均GDP。车辆饱和水平仅影响Gompertz曲线的形状，人口增长对车辆需求的影响微弱。预计2050年道路车辆的总能耗分别为380万吨、5.75亿吨和5.86亿吨石油当量。
This paper presents a model for the projection of Chinese vehicle stocks and road vehicle energy demand through 2050 based on low-, medium-, and high-growth scenarios. To derive a gross-domestic product (GDP)-dependent Gompertz function, Chinese GDP is estimated using a recursive dynamic Computable General Equilibrium (CGE) model. The Gompertz function is estimated using historical data on vehicle development trends in North America, Pacific Rim and Europe to overcome the problem of insufficient long-running data on Chinese vehicle ownership. Results indicate that the number of projected vehicle stocks for 2050 is 300, 455 and 463 million for low-, medium-, and high-growth scenarios respectively. Furthermore, the growth in China’s vehicle stock will increase beyond the inflection point of Gompertz curve by 2020, but will not reach saturation point during the period 2014–2050. Of major road vehicle categories, cars are the largest energy consumers, followed by trucks and buses. Growth in Chinese vehicle demand is primarily determined by per capita GDP. Vehicle saturation levels solely influence the shape of the Gompertz curve and population growth weakly affects vehicle demand. Projected total energy consumption of road vehicles in 2050 is 380, 575 and 586 million tonnes of oil equivalent for each scenario.