Assessing the nexus mechanism between energy efficiency and green finance

被引:24
|
作者
Zhang, Dongyang [1 ]
Awawdeh, Ala Eldin [2 ]
Hussain, Muhammed Sajjad [3 ]
Quang-Thanh Ngo [4 ]
Vu Minh Hieu [5 ]
机构
[1] Capital Univ Econ & Business, Zhangjialukou 121, Beijing 100070, Peoples R China
[2] Emirates Coll Technol, Dept Management, Abu Dhabi, U Arab Emirates
[3] Univ Lahore, Lahore, Pakistan
[4] Univ Econ Ho Chi Minh City, Sch Govt, Ho Chi Minh City, Vietnam
[5] Van Lang Univ, Fac Business Adm, Vietnam 45 Nguyen Khac Nhu,24 Dist 1, Ho Chi Minh City, Vietnam
关键词
Financing efficiency index; Assessment; Energy efficiency finance; Data envelopment analysis; Chinese provinces; MEANINGFUL ENVIRONMENTAL INDEXES; CO2; EMISSION; COMPOSITE INDICATORS; OIL-RECOVERY; SUSTAINABILITY; REDUCTION; COUNTRIES; DEA;
D O I
10.1007/s12053-021-09987-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The objective of this study is to measure the energy efficiency index based on green finance data from 114 listed companies of China during 2009 to 2018 by using the slack-based data envelopment analysis (DEA) model to measure the energy efficiency and econometric estimation to measure the impact of debt and equity financing. The proposed research study purpose is to develop a comprehensive assessment financing efficiency index based on energy efficiency index. Results shows that 1% increase in equity financing increases the financing efficiency by 0.002, while bond financing is negatively associated with financing efficiency, whereas 1% rise in debt financing decreases the financing efficiency by -0.019 whereas coefficients of bank financing and market capitalization. Jilin and Shandong provinces are best-performing provinces in terms of financing efficiency, whereas the Henan and Hubei provinces are worst-performing provinces in overall sample. For every 1% increase in research and development, Jilin and Shandong reduce carbon dioxide emissions by considerable amount, respectively. The key findings indicate that banks continue to dominate the Chinese credit industry. A significance of arithmetic mean aggregation as mathematical tool and DEA to construct an eco-friendly index of Chinese have been discussed. The proposed research study highlights irregularities of data issues and to follow appropriate properties index number. The current study may fit in the broader spectrum and developing an eco-friendly index and developing the ranking index based on multiple indicators.
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页数:18
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