Multidimensional Valuation of Trees at Thompson Rivers University: An Ecological, Cultural, and Socio-Economic Exploration
Bibliographic record
Abstract
This study investigates the ecosystem services provided by the 1,806 trees on the TRU campus, focusing on their economic, environmental, and social contributions. Through established methodologies, the research quantifies key ecosystem services, including carbon storage and sequestration, stormwater management, energy savings, and aesthetic benefits using benchmarked valuation techniques. Using field data, the total appraisal value of the campus trees was determined to be ~ $34.3 million CAD, with an annual ecosystem service yield at the minimum of ~ $343,000 CAD. The analysis revealed carbon storage values ranging from 361 to 542 tons, contributing $61,404 to $92,106 CAD, and annual carbon sequestration of 5.4 to 54 tons, valued at $910 to $9,211 CAD. The total air pollution removed by campus trees was estimated to be 64 kg/year, corresponding to an economic value of approximately $4,620 CAD/year. Stormwater interception was calculated at 3,066 m³ annually, yielding cost savings of $7,970 CAD by reducing the burden on stormwater infrastructure. Energy savings, derived from reduced heating and cooling demand, added $15,942 CAD annually, while aesthetic contributions, measured through hedonic pricing, amounted to $76,297 CAD per year. While the primary focus is on quantifiable benefits, the research acknowledges the broader role of the forest in enhancing biodiversity, supporting cultural values, and fostering mental well-being. This research provides a framework for understanding the value of urban forests in academic settings and highlights the need for proactive policies to sustain and enhance these benefits. The findings serve as a resource for decision-makers and contribute to the growing body of knowledge on integrating green infrastructure into campus planning and sustainability initiatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".