Ecological Taxation as an Instrument for Implementing ESG Policies in the Context of Sustainable Development
Bibliographic record
Abstract
The article examines ecological taxation as an instrument for State regulation of sustainable development in the context of implementing ESG (Environmental, Social, and Governance) principles. The article analyzes the increased role of fiscal mechanisms in response to contemporary climate challenges that require a transformation of tax policy towards greening. The author highlights the necessity of integrating environmental, social, and economic factors into the development of tax strategies aimed at promoting environmentally responsible behavior among economic agents. The research covers international experience in the implementation of carbon taxes, fiscal incentives for green investments, tax credits, and preferences that support the execution of corporate ESG strategies. Examples from the European Union, Canada, Singapore, Chile, and Africa are examined, enabling the identification of common approaches to implementing tax innovations and assessing their efficiency in achieving environmental and social goals. The article also highlights the social aspects of ecological taxation, particularly the risks of regressive impacts on low-income households and the importance of compensatory mechanisms, such as climate dividends. Significant attention is given to the institutional conditions for implementing tax changes, particularly the quality of administration, digital infrastructure, transparency in the distribution of revenues, and communication with the public. The article covers various directions of ecological taxation application, including its link to the green transformation of energy, the development of low-carbon technologies, and the modernization of industry. The need for coordination of tax, climate, and social policies is emphasized in order to ensure an effective and fair transition to a sustainable economy.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".