Impact of environmental innovation on country socio-economic development
Bibliographic record
Abstract
Global warming and deterioration of the ecological situation trigger the necessity of innovation development and implementation to reduce the negative impact of industry on the environment. It is considered that the oil industry is one of the most environmentally damaging industries. Therefore, the implementation of environmental innovation in the oil industry becomes crucial. This paper is dedicated to identifying environmental innovation impact on country socio-economic development parameters in countries specialized in oil extraction and production. The article realized a bibliometric analysis with VOSviewer v.1.6.16 to identify critical contextual directions of scientific research on environmental innovation. In the paper, it is developed and tested a scientific hypothesis about the positive influence of environmental innovation on country socio-economic development (CO2 emissions from manufacturing industries and construction, electricity production from oil sources, employment in industry, and industry value added are chosen as proxies of environmental innovation, while GDP growth, current account balance, foreign direct investment and gross fixed capital formation – as proxies of country socio-economic development). Under testing of the research hypothesis, it is realized several procedures: 1) correlation analysis aimed at identification of strongly correlated explanatory variables and their elimination to avoid multicollinearity problem; 2) comprehensive analysis of descriptive statistics aimed at identification of data sufficiency; 3) identification of model specification with Hausman test (random or fixed effects model); 4) regression modeling and characteristics of its results (in this research, it is developed four regression models with different dependent variables). Technically all these procedures are realized in Stata 12/SE software. Research is realized based on data for 9 countries specializing in oil extraction and production, such as Azerbaijan, Canada, Brazil, the Russian Federation, Saudi Arabia, Oman, Romania, the Republic of Yemen, and the Islamic Republic of Iran. The time horizon is 2005-2019 (or the available last year). Bibliometric and panel data regression analysis allows concluding that oil-producing countries' environmental innovation improves oil enterprises' competitiveness and stimulates socio-economic growth in these countries.
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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".