Environmental Technology Strengths : International Rankings Based on US Patent Data
Bibliographic record
Abstract
Patent information has been used by economists and researchers in the field of innovation to analyse current and forecast future technological directions. The recent surge in patenting activities in developed countries reaffirms the strong position of the patent system in a globalised world dominated by market mechanisms. This paper analyses the technological position of the top twelve foreign patenting countries in the USA, namely Australia, Canada, France, Germany, Italy, Japan, Korea, the Netherlands, Sweden, Switzerland, Taiwan and UK, using four technological strength indicators based on patent data. These are the technological specialisation index (for national technological priorities), patent share (for global impact), citation rate (for further knowledge development) and rate of assigned patents (for market potential). The technological strength indicators are calculated for patents relating to environmental technologies between 1975 and 2000. These technologies are expected to have a significant impact on society, the economy and the natural environment as they have the potential to reduce the effects of global climate change. The empirical findings demonstrate that the expertise and strengths in environmental technologies are concentrated in a relatively small number of countries, namely Germany, Canada and Japan. Nevertheless, these countries show different priorities, being more successful in some aspects of technology strengths than in others.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.031 | 0.035 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".