Guest editorial: Venture capital and green finance for sustainable development
Bibliographic record
Abstract
China Finance Review International has this special issue dedicated to "venture capital and green finance for sustainable development".Nowadays, ESG, green finance and venture capital have been hot topics for many academic journal publications.Thus, there is a large body of knowledge on these topics, and there is strong competition for high-quality paper submissions.This special issue has received several submissions, and after a rigorous peer review process, a collection of eight papers have been selected for publication, including one survey paper, two papers on venture capital and five empirical papers, with authors from the UK, Canada, China and Thailand.This issue contributes to this large body of knowledge in several aspects.Firstly, a paper presents an excellent overview of the vast literature on ESG.Paper #1 "A survey on ESG: investors, institutions and firms" provides an overview of the academic literature on ESG through the perspectives of investors, institutions and firms.It describes how ESG is measured and notes problems with the measurement of and quality of ESG data and discrepancies between different measures of ESG.Secondly, five papers present studies of ESG and green finance in the China and Asia context.Paper #2 "Shaping corporate ESG performance: role of social trust in China's capital market" expands the comprehension of the determinants of ESG and underscores the influential role of social trust as an informal institution in enhancing a firm's ESG performance.Paper #3 "Environmental, social and governance (ESG) -augmented investments in innovation and firms' value: a fixed-effects panel regression of Asian economies" shows that innovation is advantageous to the firms' market value, by decomposing innovation into traditional innovation and environmental innovation.Paper #4 "The impact of China's green credit policy on enterprise digital innovation: evidence from heavily-polluting Chinese listed companies" provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, also providing a basis to further improve green credit policies.The paper shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation.Paper #5 "The deterrent effect of central environmental protection inspection: evidence from Chinese listed companies" takes the central environmental protection inspection (CEPI) as an exogenous shock to study the reaction of the stock market in China.This paper shows that polluting firms in the supervised provinces were negatively impacted by the CEPI within 20 trading days of the event day, and its effects spread to the polluting firms in the neighboring provinces.Paper #6 "The environmental externality of economic growth target pressure: evidence from China" sheds light on to what extent economic growth target pressure has a negative externality of pollution in China and how this pressure may conflict with environmental protection.Thirdly, two papers contribute to cross-disciplinary studies on the topics of venture capital.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".