Russian Companies’ Motivations for Making Green Investments
Bibliographic record
Abstract
The purpose of this study is to identify the most significant motivations for Russian companies to make green investments. This article presents a multiple regression model based on panel data, designed to assess the impact of various factors on green investments made by Russian companies. To create this model, the authors used annual data for 83 regions of the Russian Federation for the period from 2011 to 2020. According to calculations made in this paper, the growth of green investments in the economy is due to the inflow of foreign direct investment, the increase in the collection of fees for negative impact on the environment, the increase in the production of extractive products and the growth of CO2 emissions. At the same time, the total volume of investments is not affected by indicators assessing the environmental factor, but is affected by the inflow of foreign direct investments and the level of business concentration. The obtained results mean that the main motivators that encourage Russian companies to make green investments today are the opinion of foreign investors, global decisions to reduce greenhouse gases and the partial tightening of national environmental legislation. This indicates that the degree of a companies’ integration into the global economy is of great importance for its propensity to make green investments in Russia. Therefore, special approaches are needed from the state in order to create incentives for green modernization of the national economy. This study expands our understanding of the role that green investments can play in the economy and the motivation for companies to make them, thus contributing to the existing literature on this subject.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".