Modelling the net environmental and economic impacts of urban nature-based solutions by combining ecosystem services, system dynamics and life cycle thinking: An application to urban forests
Bibliographic record
Abstract
Nature-based solutions (NBS) are gaining relevance as sustainable urban actions because of their potential to provide multiple benefits in the form of ecosystem services (ES), and thus mitigate urban challenges. This paper presents an original semi-dynamic modelling framework that simultaneously considers i) ES supply and demand dynamics, ii) negative environmental impacts, externalities, and financial costs derived from NBS, and iii) life cycle NBS impacts beyond the use phase. Compared to other models, it also aims to be valuable for urban planning actions at site level, i.e., for evaluating the net impacts of specific urban NBS projects. To validate the modelling framework, a proof-of-concept model for urban forests is developed and tested for a case study in Madrid (Spain). The modelling framework is split in two interrelated parts: foreground (dynamic modelling) and background (static modelling). In the foreground, the environmental impacts derived from the use phase of an NBS project are quantified considering its spatio-temporal dynamism, by making use of system dynamics. In the background, the environmental impacts derived from the rest of the life cycle phases of the NBS are quantified making use of steady state life cycle impact assessment. The net economic impact of the NBS project, considering both financial values and externalities, is eventually calculated in the background encompassing all the life cycle phases. Results from the case study illustrate how planning, design, and management decisions over the entire life cycle of an urban forest can influence the net environmental and economic performance of this type of NBS. A discussion is provided to inform on how the modelling framework can help moving beyond the state-of-the-art, and how the derived model can be used for sustainability assessments of urban NBS projects.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".