Forecasting Green Technology Diffusion in OECD Economies Through Machine Learning Analysis
Bibliographic record
Abstract
An accelerating global shift towards sustainable development has made the diffusion of green technologies a critical area of focus, particularly within OECD economies. This study aims to use a machine-learning approach to explore the future diffusion of green technology across OECD countries. It provides detailed forecasts from 2023 to 2037, highlighting the varying rates of green technology diffusion (GTD) among different nations. To achieve this, the Autoregressive Integrated Moving Average (ARIMA) model is employed to offer new evidence on how the progress of green technology can be predicted. Based on empirical data, the study categorizes countries into high, moderate, and low GTD growth. The findings suggest that Japan, Germany, and the USA will experience significant growth in GTD, while countries like Australia, Canada, and Mexico will see moderate increases. Conversely, some nations, including Ireland and Iceland, face challenges with low or negative GTD values. The study concludes that applying this machine-learning model provides valuable insights and future predictions for policymakers aiming to enhance green technology adoption in their respective countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".