城镇化背景下中国石油需求峰值预测——基于交通部门和化学品制造部门的情景分析被引量：3

Peak Oil Demand Forecast in China in the Context of Urbanization: A Scenario Analysis Based on Transportation Sectors and Chemical Manufacturing Sectors

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摘要控制中国石油消费总量过快增长,推动石油消费尽早达峰,对应对气候变化、保障能源安全具有重要意义。本文使用LMDI方法对石油消费总量进行因素分解和定量分析,并对人均GDP、人口、城镇化率、产业结构等自变量合理假定,构建BP模型分情景预测中国石油消费峰值和峰值时间。结果表明:(1)人均GDP和城镇化率是推动中国石油消费增长的主要因素,人口、石油结构对石油消费增长的贡献较小;石油强度是抑制石油消费增长的主要因素,而产业结构因素的抑制效果不明显。(2)BP模型分情景的预测结果显示:基准情景下,2037年达到峰值,峰值量为11.42亿吨;强化情景下,2037年达到峰值,峰值量为7.71亿吨;温控情景下,2025年达到峰值,峰值量为6.37亿吨。建议使用行政手段协调经济增长与石油消费,交通运输领域推进技术和模式创新、石化化工领域采取需求减量、高效替代以及原料多元化等措施,控制石油消费总量过快增长。 Controlling the excessive growth of China’s oil consumption and promoting the peak of oil consumption as soon as possible is crucial to coping with climate change and ensuring energy security. In this paper, LMDI method is used to discuss the main factors that influence the growth of China’s oil consumption. On the basis of independent variables such as per capita GDP, population, urbanization, and the industrial structure, a BP neural network model to predict the peak and its time of China’s oil consumption in three scenarios. The results show that:(1)Per capita GDP and urbanization are the main factors driving the increase in China’s oil consumption, while the contributions of population and the oil structure are relatively small. The oil intensity is the main factor inhibiting oil consumption, while the effect of the industrial structure is not obvious.(2)The prediction results of BP model by scenarios are shown: Under the bench-marked scenario, the peak will be reached in 2037, with a value of 1.142 billion tons. Under the enhanced scenario, the peak will be reached in 2037, with a value of 771 million tons. Under the temperature control scenario, the peak will be reached in 2025, with a value of 637 million tons. Corresponding administrative measures are proposed to coordinate economic growth and oil consumption. The measures include promoting technological and model innovation in transportation industry, and demand reduction, efficient substitution and raw material diversification in chemical manufacturing industry.

作者刘晓燕张一清宋晓倩 Liu Xiaoyan;Zhang Yiqing;Song Xiaoqian

机构地区鲁东大学环渤海发展研究院山东工商学院工商管理学院上海交通大学中国城市治理研究院

出处《宏观经济研究》 CSSCI 北大核心 2021年第11期79-92,共14页 Macroeconomics

基金泰山学者工程专项经费(No.tsqn201909149) 国家自然基金青年项目“面向生态系统服务的资源枯竭型城市绿色转型研究”(72004130) 教育部人文社科青年项目“基于计算试验的我国资源型城市低碳转型机理及政策研究”(18YJC630148) 上海市软科学研究重点项目(21692194800)的资助。

关键词石油消费峰值总量控制情景预测 Peak oil consumption Total volume control Scenario prediction

分类号 F426.22 [经济管理—产业经济]