Regulation, Emissions and Productivity: Evidence from China’s Eleventh Five-Year Plan
Bibliographic record
Abstract
Leveraging the sharp changes in environmental regulation embedded in China’s 11th Five-Year Plan (FYP), which covered the period from 2006 to 2010, we characterize the degree to which the plan softens trade-offs between emissions and output. We document that the 11th FYP is associated with modest changes in average or total sulphur dioxide (SO2) emissions among manufacturers, but a sharp decline in the variance in the distribution of emissions intensity. Extending well-known distributional estimators to characterize dynamic firm-level responses to policy change, we find large causal declines in emissions intensity in the upper quantiles of the distribution, modest evidence of increases in the lower quantiles and no change in the middle quantiles. Differential changes in firm-level emissions intensity are consistent with the differential investment in emissions-mitigating technology, energy switching and productivity improvements. Interpreted through the lens of a resource misallocation framework, China’s 11th FYP increased aggregate productivity and output by 1.8% and 10.2%, respectively, through improved resource allocation. Our model suggests efficient regulation could have further increased aggregate productivity by 3.5% and output by 4.7% without any increase in aggregate emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".