Innovation patterns in climate change mitigation technologies in Korea’s building sector: A cross-country comparison
Bibliographic record
Abstract
The building sector is one of South Korea’s major sources of greenhouse gas emissions. This study analyzes systematic growth patterns and characteristics of technological innovation in greenhouse gas reduction technologies in this sector to derive policy implications for promoting innovation. Patent time-series data from 1990 to 2022 were constructed for climate change mitigation technologies in the building sector and its four constituent technology groups. A generalized logistic function was used to examine the dynamics of technological innovation. In addition, patent data from Japan, Canada, the United States, France, and Germany were analyzed to compare technological innovation characteristics with those of South Korea. The results indicated that, although Korea’s technologies generally exhibit high potential saturation levels of innovation, the activation of innovation has been delayed, and its growth rate remains relatively moderate compared with advanced countries. As of 2025, all analyzed countries appeared to be approaching their respective saturation points of innovation. To further reduce greenhouse gas emissions in the building sector, future policy measures should strengthen technological innovation systems to induce new innovation cycles centered on emerging technologies. The findings of this study provide valuable foundational evidence for developing future technology policies in the building sector.
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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 | 0.001 |
| 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 | 0.000 |
| 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".