Urban expansion drives both loss and compensation in city vegetation productivity
Bibliographic record
Abstract
Urbanization alters vegetation productivity by both direct (ω d ) and indirect (ω i ) effects. The direct effect is from the change of vegetated area indicated by impervious surface intensity (ISI), while indirect effects arise from changes in urban environmental factors, such as near-surface air temperatures, precipitation, urban heat island (UHI) intensity, and population density (POP). The respective contributions of ω d and ω i to vegetation net primary productivity (NPP) under various phases of urbanization are not well quantified. Using multisource remote-sensing data from 1990 to 2020, we analyzed the spatiotemporal variation in urban expansion and the effect that ω d and ω i had on NPP in the megalopolis of Beijing, China, over 5-year intervals. During this period, Beijing underwent significant planar expansion rates of about 58.9 km 2 /yr. Annual mean loss of NPP by ω d was estimated to be about 77.1 g C/(m 2 ·yr) during the 1990-2020 period, while annual mean improvement to NPP by ω i amounted to an increase of 28.9 g C/(m 2 ·yr). The NPP losses were partially offset by NPP improvements in the order of 18.6 %-69.3 %. The impact of forcing variables on NPP varied spatially. Air temperature, precipitation, UHI, POP, and ISI explained about 13.8 %, 23.2 %, 23.7 %, 14.7 %, and 24.6 % of the spatial variation in NPP. The impact of air temperature on NPP was related to available moisture, negatively affecting NPP in regions with water deficits. Our findings demonstrate the dual impact of urbanization on vegetation and underscore the necessity for spatially adaptive ecological management strategies in regions experiencing rapid urban growth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".