Vegetation cover mitigates the economic impacts of warming
Bibliographic record
Abstract
# Data and Code for the Publication "Vegetation cover mitigates the economic impacts of warming" ## Structure - Base folder `/` includes the main Stata `.do` file to replicate all regression tables and a Python script for figure generation.- File `base_pnas2.dta` contains the cleaned panel dataset used for all estimations.- File `Vegetation_code_finalf.do` is the Stata script that reproduces all tables (Table 1 and Supplementary Tables S1 to S5).- File `matrices2.xlsx` provides the basis for calculating marginal effects, including vegetation and temperature interaction terms.- Figures (`ME_FIG2.png`, `NDVI_MEs_mapped2.png`) are generated using the Python script. ## 1) Replication of Results - Open Stata.- Run the file `Vegetation_code_finalf.do` to reproduce: - Main Table 1 (baseline specification), - Robustness checks (Table S1), - Alternative standard errors (Table S2), - Extreme heat specifications (Table S3), - Alternative vegetation measures (Table S4), - Heterogeneous effects by vegetation types (Tables S5A–S5C). ## 2) Marginal Effects and Visualizations - Open Python (preferably in a Jupyter environment).- Use the Excel file `matrices2.xlsx` to extract interaction coefficients and standard errors.- The marginal effects and plotting of conditional effects are handled in a separate Python script. ## 3) Figures - `ME_FIG2.png` shows the main marginal effect of temperature conditional on vegetation.- `NDVI_MEs_mapped2.png` maps the spatial heterogeneity of marginal effects using NDVI and temperature coefficients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".