The Impact of Innovation and Information Technology on Greenhouse Gas Emissions: A Case of the Visegrád Countries
Bibliographic record
Abstract
The rapid growth of negative consequences from climate changes provokes divergent effects in all economic sectors. The experts proved that a core catalyst which bootstrapped the climate changes was greenhouse gas emission. This has led to a range of social, economic, and ecological issues. Such issues could be solved by extending innovation and information technology. This paper aimed to check the hypothesis that innovation and information technology allowed for a reduction of greenhouse gas emissions. The author used such methodology as OLS, fully modified OLS (FMOLS), dynamic OLS (DMOLS), Dicky-Fuller and Phillips-Perron tests. The research is informed by the report of the World Economic Forum, World Data Bank, Eurostat for the Visegrád countries (Hungary, Poland, Check Republic, Slovakia) for the period of 2000–2019. The findings were confirmed in models without control variables, and an increase of 1% of patents led to reducing greenhouse gas (GHG) emissions by 0.28% for Poland, 0.28% for Hungary, 0.38% for the Slovak Republic and 0.46% for the Czech Republic. At the same time, for the models with control variables, only Hungary experienced a statistically significant impact. There, an increase of patents by 1% led to reduction of GHG emissions by 0.22%. The variable R&D expenditure was statistically significant for all countries and all types of models (with and without control variables). The increase of R&D expenditure provoked a decline of GHG emissions by 0.29% (without control variables) and 0.11% (with control variables) for Poland, by 0.26% (without control variables) and 0.41% (with control variables) for Hungary, by 0.3% (without control variables) and 0.23% (with control variables) for the Slovak Republic and by 0.54% (without control variables) and 0.38% (with control variables) for the Czech Republic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".