Green Building Contractors 2025: Analyzing and Forecasting Green Building Contractors’ Market Trends in the US
Bibliographic record
Abstract
With population growth, the demand for building construction is continuously increasing. This comes at the price of the built environment where the building sector is contributing to large energy consumption and carbon footprint releases. To encourage sustainable construction, contractors need to see the market benefit of “going green”. Previous studies of green building contractors (GBCs) mainly relied on qualitative discussions and lacked studying the market performance which drives contractors’ decisions the most. This paper collects GBC revenue data from the Engineering News-Record magazine for the top 100 GBCs over a 13-year period and performs trend analysis to assess the market performance of GBCs and time series analysis to forecast future revenues. In addition, k-means clustering technique was used to divide the firms into subsets of similar behavior to understand growth trends for different firm sizes. The results show a continuous increase in green building revenues (GBRs), where commercial office buildings contribute the most to it. Furthermore, the firm ranks responsible for most of the growth are identified; mainly the top 9. Predictions show the expected steady increase in GBR in the upcoming years which is anticipated to reach 83 billion USD in 2025. The findings inform contractors considering executing green buildings by understanding the market trends and forecasted revenues. Moreover, contractors who are already in the green building business can use this information to increase their revenues in their respective market subset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".