Firm Sustainable Development Goals and Firm Financial Performance through the Lens of Green Innovation Practices and Reporting: A Proactive Approach
Bibliographic record
Abstract
The current global economy demands synergy between ecological responsiveness and proactive business models. To analyze these dynamics, the objective of this study is to simultaneously investigate the effects of green innovation practices concerning the sustainable development goals (SDG) and financial performance of firms. This study also advocates for the injection of green innovation reporting into sustainable reporting for greater disclosure. Data from sixty-seven companies from five continents and the top five blue chip firms for each country are collected through content analysis, with the generalized least squares (GLS) approach used to test a causal relationship hypothesis. The results indicate mixed findings, with green product innovation showing positive relationships with returns on equity (ROE) and returns on investments (ROI). At the same time, green process innovation shows negative relationships with returns on assets (ROA) but shows a positive impact on returns on investments (ROI) and firm SDGs. In contrast, green service innovation shows an insignificant relationship with financial performance and SDGs. On the other hand, non-operational green innovation variables and green marketing positively affect returns on assets and investment, showing significant negative impacts on returns on equity. However, green organizational innovation shows an insignificant relationship with firm financial performance and SDGs. In addition, this study also shows that the Australia/New Zealand region is the leader in green innovation reporting, followed by Europe, Asia, Africa, and lastly, North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".