Green investment efficiency in the Chinese energy sector: Overinvestment or underinvestment?

Liu, Liyun; Zhao, Zhenzhi; Zhang, Mingming; Zhou, Dequn

doi:10.1016/j.enpol.2021.112694

Cited by 58 publications

(16 citation statements)

References 73 publications

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“…This negative effect is more evident in small-and medium-sized businesses (Han et al, 2019;Wanting, X., 2020;Yin X., 2021). At the enterprise level, most of the literature focuses on the relationship between green credit policies and firms' investment and financing profiles (Liu et al, 2019;Huang and Lei, 2021;Li et al, 2022;Liu et al, 2022). For example, Liu et al (2022) demonstrated that the Chinese energy sector receives an overall excessive level of green investment (overinvestment) when compared with the estimated optimal investment level.…”

Section: Empirical Reviewmentioning

confidence: 99%

“…At the enterprise level, most of the literature focuses on the relationship between green credit policies and firms' investment and financing profiles (Liu et al, 2019;Huang and Lei, 2021;Li et al, 2022;Liu et al, 2022). For example, Liu et al (2022) demonstrated that the Chinese energy sector receives an overall excessive level of green investment (overinvestment) when compared with the estimated optimal investment level. Zhang Q. et al (2021) found that GCGs affect the size of loans to environmentally friendly manufacturing firms, with small-and medium-sized firms receiving more loans than larger ones.…”

Section: Empirical Reviewmentioning

confidence: 99%

“…Jaffe and Palmer (1997) and Brunnermeier and Cohen (2003) have empirically confirmed that environmental regulation promotes American corporate innovation to a certain extent. The literature on the impact of green credit policies has also paid attention to enterprises' credit financing costs (Su and Lian, 2018;Liu et al, 2019;Jin et al, 2021;Li et al, 2022), investments (Huang and Lei, 2021;Liu et al, 2022), pollutant emissions Xiao et al, 2022), and firm performance (Ding et al, 2020;Yao et al, 2021;Zhang and Vigne, 2021). Nevertheless, there is a lack of literature on the relationship between GCGs and green innovation in firms, especially in HPEs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Can the green credit policy stimulate green innovation of heavily polluting enterprises in China?

Xiong

Masron

Gondo

2023

Front. Environ. Sci.

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Introduction: The requirement for more funds has been a significant challenge for China’s heavily polluting enterprises (HPEs) to embark on green innovation.Methods: This study takes China’s 2012 Green Credit Guidelines (GCGs) as a quasi-natural experiment to examine their effect on the quality and quantity of green innovation in HPEs. Using the data of Chinese listed companies from 2007 to 2020 and the difference-in-differences (DID) model.Results: We found that the Green Credit Guidelines could significantly improve the number of enterprises’ green innovation but not their quality.Discussion: As part of the potential solutions, this study proposes 1) the implementation of specific green fiscal policies to complement the existing green credit policies by the government, 2) a more comprehensive range of green financing products by financial institutions, and 3) the active development of funding from non-bank sources, such as venture capital or commercial credit.

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Section: Empirical Reviewmentioning

confidence: 99%

Section: Empirical Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Can the green credit policy stimulate green innovation of heavily polluting enterprises in China?

Xiong

Masron

Gondo

2023

Front. Environ. Sci.

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“…Recent literature on investment has documented the attention of investors toward green investments (Liu et al 2022;Ren et al 2022b;Pham and Huynh 2020;Doval and Negulescu 2014). Low carbon investments support building resilient infrastructure and stimulate sustainability.…”

Section: Introductionmentioning

confidence: 99%

Do green bonds offer a diversification opportunity during COVID-19?—an empirical evidence from energy, crypto, and carbon markets

Yadav

Kumar

Mukherjee

et al. 2022

Environ Sci Pollut Res

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The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), Baruník and Krehlík (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.

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“…A wide variety of models have been used to evaluate efficiency, including investment-cash flow sensitive (FHP) models (Francis et al, 2013), Vogt models (Aggarwal and Goodell, 2014), Richardson in-vestment expenditure models (Khanghah et al, 2015;Ben et al, 2017;Liu et al, 2022), stochastic frontier analysis (SFA) models (Sun et al, 2019;Zhang et al, 2022), and DEA models (Giacalone et al, 2020;Tavassoli et al, 2022;Wu and Lin, 2022). Studies have shown that in the energy industry, the BCC model, which is one of the models underlying the DEA model, is one of the most commonly used efficiency evaluation models.…”

Section: Introductionmentioning

confidence: 99%

Investment merger efficiency evaluation of energy enterprises: From a perspective of energy internet

Deng

Cao

Wei

et al. 2022

Front. Environ. Sci.

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Evaluating the efficiency of energy enterprises’ investment merger in the energy Internet sector can be a visual measure of the effectiveness of their investment merger in this sector. It is crucial for energy enterprises to achieve strategic transformation and expand their market share. To this end, this study aims to investigate the efficiency of energy enterprises’ investment merger in the energy Internet sector by constructing an efficiency evaluation index system and evaluation model with the characteristics of the sector. Based on which the TOPSIS model is used to distinguish the differences in the efficiency of the same enterprises’ investment merger in different years. The results of the validation on international power enterprises show that the size of the enterprise has an important influence on its investment merger efficiency in this field. In addition, the evaluation index system and method constructed prove to be effective in evaluating and differentiating the annual investment merger efficiency of energy enterprises in the energy Internet field.

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Green investment efficiency in the Chinese energy sector: Overinvestment or underinvestment?

Cited by 58 publications

References 73 publications

Can the green credit policy stimulate green innovation of heavily polluting enterprises in China?

Can the green credit policy stimulate green innovation of heavily polluting enterprises in China?

Do green bonds offer a diversification opportunity during COVID-19?—an empirical evidence from energy, crypto, and carbon markets

Investment merger efficiency evaluation of energy enterprises: From a perspective of energy internet

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