西部地区新能源上市公司财务效率评价
Evaluation of Financial Efficiency of New Energy Listed Companies in Western China
行金玲 1刘梦珠 1赵玉姝1
作者信息
- 1. 西安工业大学经济管理学院,西安 710021
- 折叠
摘要
为了优化新能源上市公司的财务效率,以我国西部12家新能源上市公司为重点研究对象,选取总资产、总负债、主营业务成本、期间费用4个投入指标和净利润、主营业务收入2个产出指标,构建新能源上市公司财务效率评价指标体系.采用DEA-Malmquist模型评价我国西部新能源上市公司2020至2022年财务效率.结果表明:西部地区新能源行业上市公司综合效率存在两极分化现象,综合效率非有效是受纯技术效率和规模效率的共同影响;高负债的资本结构和高投入的期间费用限制了财务效率的提高;企业比较注重技术创新并具有好的发展能力,技术效率是影响财务效率的重要因素.企业应加强对环境的应变能力、加大技术创新投入、调整资本结构并合理规划负债的方式来促进财务效率的提升.
Abstract
In order to optimize the financial efficiency of listed new energy companies,12 new energy listed companies in western China were selected as the key research objects,and four input indica-tors including total assets,total liabilities,main business costs,and period expenses,and two output indicators including net profit and main business income are selected to construct the finan-cial efficiency evaluation index system of new energy listed companies.The DEA-Malmquist model is used to evaluate the financial efficiency of new energy listed companies in western China from 2020 to 2022.The results show that the comprehensive efficiency of listed companies in the new energy industry in western China is polarized,and the ineffectiveness of the comprehensive effi-ciency is influenced by both pure technical efficiency and scale efficiency.The capital structure with high liabilities and high input of period expenses restrict the improvement of financial efficien-cy.Enterprises that pay attention to technological innovation have good development ability,and technical efficiency is an important factor affecting financial efficiency.Enterprises should strengthen their adaptability to the environment,increase investment in technological innovation,adjust their capital structure,and reasonably plan their liabilities to promote the improvement of financial efficiency.
关键词
财务效率/上市公司/新能源企业/DEA-Malmquist模型Key words
financial efficiency/listed companies/new energy enterprises/DEA-Malmquist model引用本文复制引用
基金项目
国家社会科学基金(22BRK007)
陕西省社会科学基金(2022R060)
出版年
2024
NETL
NSTL
万方数据