Does Blockchain Help Make the World Better? Analyzing the Effect of Blockchain Adoption on Environmental, Social, and Governance Performance of Firms
Bibliographic record
Abstract
ABSTRACT Blockchain technology (BCT) use has been touted to have many corporate benefits including enhancing sustainability. However, there is limited understanding of whether and how it can help achieve sustainability outcomes, particularly along environmental, social, and governance (ESG) dimensions. Based on a sample of 6,063 firm-year observations, we find that a firm’s BCT adoption leads to a 4.62 percent increase in the firm’s ESG performance compared to those that do not adopt BCT, underscoring its sustainability practices. In addition, firms with ESG-focused BCT adoption exert a 7.62 percent increase in ESG performance compared to firms that use BCT without necessarily that focus. Our results are also robust to difference-in-differences (DiD) and dynamic analyses, alternative sample specifications, and different dependent variable specifications. Overall, we provide novel empirical evidence to justify characterizing blockchain as an impactive technology for sustainability. Data Availability: Data are available from the public sources cited in the text.
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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 | 0.003 | 0.018 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".