Biochar Benefits Green Infrastructure: Global Meta-Analysis and Synthesis
Bibliographic record
Abstract
Urbanization has degraded ecosystem services on a global scale, and cities are vulnerable to long-term stresses and risks exacerbated by climate change. Green infrastructure (GI) has been increasingly implemented in cities to improve ecosystem functions and enhance city resilience, yet GI degradation or failure is common. Biochar has been recently suggested as an ideal substrate additive for a range of GI types due to its favorable properties; however, the generality of biochar benefits the GI ecosystem function, and the underlying mechanisms remain unclear. Here, we present a global meta-analysis and synthesis and demonstrate that biochar additions pervasively benefit a wide range of ecosystem functions on GI. Biochar applications were found to improve substrate water retention capacity by 23% and enhance substrate nutrients by 12–31%, contributing to a 33% increase in plant total biomass. Improved substrate physicochemical properties and plant growth together reduce discharge water volume and improve discharge water quality from GI. In addition, biochar increases microbial biomass on GI by ∼150% due to the presence of biochar pores and enhanced microbial growth conditions, while also reducing CO 2 and N 2 O emissions. Overall results suggest that biochar has great potential to enhance GI ecosystem functions as well as urban sustainability and resilience.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".