The Impact of Political Connections on Corporate Green Innovation: The Mediating Effect of Corporate Social Responsibility and the Moderating Effect of Environmental Public Opinion
Bibliographic record
Abstract
The construction industry is the lifeblood of the national economy; thereby, to some extent, the green transformation of the construction industry is representative of the industrialization levels of modern construction, especially in China. Based on the panel data of A-share listed companies in China’s construction industry from 2014 to 2019, this work studies the influence mechanism of political connection on corporate green innovation by establishing a multiple regression model, analyzes the realization path of corporate social responsibility as an mediating variable on the impact of political connections on corporate green innovation, and reveals the role boundary of environmental public opinion as a moderating variable on the impact of political connections on corporate green innovation. The results show that political connection has a significant positive impact on corporate green innovation, and this impact exists in both state-owned and non-state-owned enterprises, as well as in the eastern region and the central and western regions of China. Moreover, corporate social responsibility plays a partial mediating role in the relationship between political connection and corporate green innovation, and serves as an effective value transfer intermediary, a benefit balance mechanism, and a risk avoidance method. Political connections urge enterprises to be more socially responsible, thus affecting green innovation. Additionally, environmental public opinion strengthens the positive impact of political connections on corporate green innovation, especially in non-state-owned enterprises and in the eastern regions with a higher degree of marketization. The research conclusions provide a new theoretical reference for promoting the transformation of green innovation and achieving high-quality development in the construction industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".