亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Modeling, Prediction and Analysis of New Energy Vehicle Sales in China Using a Variable-Structure Grey Model

变量(数学) 计量经济学 中国 统计 经济 数学 地理 数学分析 考古
作者
Bo Zeng,Hui Li,Cuiwei Mao,You Wu
出处
期刊:Social Science Research Network [Social Science Electronic Publishing]
被引量:6
标识
DOI:10.2139/ssrn.4111807
摘要

At present, the new energy vehicle (NEV) industry in China is at a huge risk of overheated investment and overcapacity. An accurate prediction of China’s future NEV market is of great significance for the Chinese government to control the growth of the industry at a reasonable speed and the production on a reasonable scale. To this end, a new grey prediction model with a variable structure was established considering the data characteristic of small sample size of China’s NEV sales. In the new model, the value range and optimization space of the order r were expanded, and the definitions and structures of two operators, the grey accumulating operator and grey inverse operator, were unified. Meanwhile, the new model had good structural variability and was fully compatible with other grey models of the same type. The performance of the model was tested with different data sequences, and results showed that the comprehensive performance of this model was better than that of other similar models. Lastly, the model was employed to predict China’s NEV sales. Results showed that the sales were expected to be 3.03 million in 2030, which indicates that China’s NEV market will continue to grow, but at a significantly slower rate. The government and enterprises need to take corresponding measures to promote the healthy development of China’s NEV industry.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
丘比特应助KamilahKupps采纳,获得10
2秒前
Doc_d完成签到,获得积分10
4秒前
江流儿完成签到,获得积分10
14秒前
15秒前
jeff完成签到,获得积分10
20秒前
23秒前
26秒前
粽子发布了新的文献求助10
28秒前
大个应助琳毓采纳,获得30
30秒前
小圆圈发布了新的文献求助10
35秒前
汉堡包应助十柒采纳,获得10
35秒前
科研通AI6.1应助霖霖采纳,获得10
38秒前
三三发布了新的文献求助10
40秒前
令狐冲完成签到 ,获得积分10
41秒前
吞吞完成签到 ,获得积分10
50秒前
冬序拾柒完成签到,获得积分20
51秒前
54秒前
56秒前
ykssss完成签到,获得积分20
56秒前
57秒前
atmcymed发布了新的文献求助10
59秒前
冬序拾柒关注了科研通微信公众号
59秒前
fgmy发布了新的文献求助10
1分钟前
1分钟前
赘婿应助爱听歌笑柳采纳,获得10
1分钟前
1分钟前
1分钟前
852应助科研通管家采纳,获得10
1分钟前
atmcymed完成签到,获得积分10
1分钟前
干净的琦应助科研通管家采纳,获得10
1分钟前
在水一方应助科研通管家采纳,获得10
1分钟前
1分钟前
小边发布了新的文献求助10
1分钟前
医研完成签到 ,获得积分10
1分钟前
粽子发布了新的文献求助10
1分钟前
小蘑菇应助白衣渡姜采纳,获得10
1分钟前
1分钟前
1分钟前
研友_VZG7GZ应助粽子采纳,获得10
1分钟前
leo发布了新的文献求助10
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
First commercial application of ELCRES™ HTV150A film in Nichicon capacitors for AC-DC inverters: SABIC at PCIM Europe 1000
Feldspar inclusion dating of ceramics and burnt stones 1000
Digital and Social Media Marketing 600
Zeolites: From Fundamentals to Emerging Applications 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5987954
求助须知:如何正确求助?哪些是违规求助? 7409397
关于积分的说明 16048746
捐赠科研通 5128608
什么是DOI,文献DOI怎么找? 2751779
邀请新用户注册赠送积分活动 1723142
关于科研通互助平台的介绍 1627089