Does Technological Innovation Promote Haze Pollution Control? New Evidence Based on Panel Threshold Model and Spatial Econometric Model
Bibliographic record
Abstract
Since the reform and opening up, China’s rapid economic growth mainly depends on the industrial development mode of “high energy consumption and high pollution,” which has caused serious haze pollution. In order to achieve the goal of haze control and sustainable development, we need to give full play to the role of technological innovation. Empirical analysis of the haze control effect of technological innovation has theoretical significance and practical value. Based on the panel data of 30 provinces in China from 2005 to 2018 and the PM2.5 concentration data published by the atmospheric composition analysis group of Dalhousie University, this study selects R&D personnel input and technology market turnover to represent the level of technological innovation and uses the panel data model, threshold effect model, and spatial Durbin model to empirically analyze the impact of technological innovation on haze pollution control. The empirical results show that 1) technological innovation can significantly reduce the PM2.5 concentration of the province, showing a positive haze control effect; 2) technological innovation indicates a negative indirect effect on PM2.5 concentration, confirming the “technology spillover effect,” that is, technological innovation also has a haze control effect on the surrounding provinces; 3) with the increase in the province’s economic aggregate, the haze control effect of technological innovation shows a trend of “high low high,” and the role of technological innovation is the lowest in the stage of economic transformation; and 4) from the perspective of regional differentiation, the haze control effect of technological innovation is the largest in the central region, and the smallest in the western region. Technological innovation indicates a positive haze control effect on all regions at all stages of economic development. This study provides policy suggestions for the government and enterprises to use innovation for cleaner production and sustainable development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".