Policy implications of technological innovations, domestic material consumption and renewable energy consumption for achieving sustainable development goals in G7 economies
Bibliographic record
Abstract
Despite significant progress by G7 member countries towards achieving Sustainable Development Goals (SDGs), challenges persist in meeting targets for SDGs 7, 9, 12, and 13. A critical gap lies in policy frameworks addressing the impact of domestic material consumption on CO2 emissions. While current policies propose emission mitigation solutions, they often overlook the pivotal roles of domestic material consumption, renewable energy adoption, and technological innovations in achieving carbon neutrality. This study responds to the need for policy realignment by integrating these factors into sustainability strategies. To provide empirical insights, this study examines the influence of renewable energy consumption, domestic material consumption, and technological innovations on CO2 emissions using data from 1985:Q1 to 2021:Q4 across G7 nations. Employing Wavelet Quantile Regression, the analysis captures associations across different time periods and quantiles. The findings reveal that domestic material consumption generally increases CO₂ emissions, except in the cases of Canada and the USA. Renewable energy consumption shows a consistent trend of reducing emissions, albeit with mixed outcomes observed in Japan and Canada. Technological innovations tend to lower CO2 emissions, except for instances in France and the UK where the impact is positive. Based on these findings, G7 nations are urged to prioritize reductions in domestic material consumption, accelerate the transition to renewable energy sources, and foster technological innovations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".