How GDP Manipulation by Local Government Affects Corporate Greenwashing in China
Bibliographic record
Abstract
Firms frequently face a tradeoff between the advantages of upholding sustainability and ESG performance and the expenses associated with participating in ESG initiatives. This tension leads to an increase in greenwashing practices, which ultimately undermines genuine sustainability efforts and misleads stakeholders. Motivated by this trend, our study examines the influence of a macro-level factor, specifically local city-level governments’ GDP manipulation, on the extent of firms’ greenwashing, highlighting how government behavior can distort sustainable business practices. Using the data of the publicly traded Chinese manufacturing companies during the period of 2007–2019, we find a positive and significant relationship between the extent to which firms engage in greenwashing and the extent of local city-level governments’ GDP manipulation. Additional analysis reveals that firms’ financial constraints and external monitoring are the channels through which governments influence firms’ greenwashing. In addition, the finding of a positive association between firm greenwashing and government GDP manipulation is more pronounced in regions with a less developed marketization index, in periods before China’s anti-corruption campaign, in state-owned firms, and in firms at the business life cycle of the mature stage. Our study addresses a gap in the literature by demonstrating how government economic interventions influence firms’ sustainability performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".