Sustainable Empowerment: Digital Transformation and Carbon Emissions as a Catalyst for Enterprise ESG Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines whether digital transformation and carbon emissions can be catalysts for ESG performance.The sample comprises 17 manufacturing companies, and a quantitative approach was used by analyzing data from several large manufacturing companies listed on the IDX in 2019 -2023 that have implemented a digital transformation strategy.Our research uses the two-way GMM method with StataMP 17.The results of this study show a significant positive relationship between digital transformation and ESG performance and a significant negative relationship between carbon emissions and ESG performance.By integrating the analysis of digital transformation and carbon emissions, this study offers a new holistic view and provides strategic recommendations for companies to optimize ESG performance amidst the dynamic changing demands of the latest regulations, thus providing empirical guidance for policymakers and management in sustainability efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it