Impacts of government attention on achieving Sustainable Development Goals: Evidence from China
Bibliographic record
Abstract
• China’s government attention on the SDGs has generally increased over time. • Government attention intensity impacts the SDGs more than text similarity and tone. • Government attention promotes coordinated and balanced progress of the SDGs. • Financial input boosts the impact of government attention on promoting the SDGs. The Sustainable Development Goals (SDGs) are crucial in tackling the sustainability challenges and emerging issues faced by humanity, with government attention being a significant factor in promoting their successful achievement. However, there is limited quantitative research systematically examining the impacts of government attention on SDGs progress. This study employs text analysis and a panel regression model to analyze the impacts of government attention intensity, text similarity, and tone on the achievement of SDGs, utilizing data extracted from China’s Government Work Reports spanning the decade from 2010 to 2020. The findings reveal that the Chinese government attention to the SDGs has generally increased over time. The heightened focus has notably bolstered the achievement of the SDGs, with the most significant impact observed post-2015. Government attention intensity was identified as the most impactful factor. Moreover, government attention intensity, text similarity, and tone have positively influenced the coupling coordination relationship between 17 SDGs, as measured by the coupling coordination degree, leading to a more harmonious and balanced achievement of socioeconomic and environmental goals in China. Financial investment served as a moderating factor, enhancing the positive impacts of attention intensity, text similarity and tone on the promotion of SDGs attainment. The effects of government attention on SDGs progress were notably positive in the eastern region, exhibiting greater significance in areas with stronger governance capacity compared to those with weaker governance capacity. This study provides insightful information for enhancing the modernization and efficiency of China’s national governance system, promoting SDGs at local and global scales, and fostering sustainable transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".