Compliance Behavior in Environmental Tax Policy
Bibliographic record
Abstract
This study examines compliance behavior in the context of environmental tax policies, highlighting the essential role that these policies play in achieving the objectives of the Sustainable Development Goals (SDGs). Environmental taxes are crucial instruments for reducing environmental damage and increasing energy efficiency. Nevertheless, taxpayer compliance, which is impacted by several variables, including social acceptability, regulatory quality, and perceptions of fairness, is a key component of these policies’ efficacy. In contrast to earlier research, which frequently concentrated on certain kinds of tax or discrete policy mechanisms, this study takes a broad approach, looking at a range of environmental taxation instruments. Emerging trends, significant factors influencing compliance behavior, and noteworthy contributions from eminent authors and organizations are all identified via bibliometric and scientometric analyses. To create fair and effective environmental tax policies, interdisciplinary approaches and international collaboration are required. Along with presenting policies to improve environmental regulation compliance, this study offers insightful advice for businesses that can help them innovate toward sustainability and adjust to shifting policy. It also provides a solid theoretical base for future researchers by highlighting important areas that require more investigation, especially when it comes to the wider effects of environmental taxes on various industries.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".