Carbon impacts of hardwood lumber processing in the northeastern United States
Bibliographic record
Abstract
Carbon emission from hardwood lumber processing in different-sized sawmills under varying energy sources, management strategies, and potential carbon offsetting capacity through useful life (service life) of lumber in the northeastern United States was analyzed using analytical statistics such as analysis of variance (ANOVA), mixed-effect model, principal component analysis, and Monte Carlo simulation. Data obtained from a regional sawmill survey (Pennsylvania, New York, Ohio, and West Virginia), energy audit of sawmills, public databases, and relevant literature were analyzed for the gate-to-gate life cycle inventory framework. Results showed that mean carbon emission (megagrams (Mg) per thousand cubic metres (TCM)) for lumber processing significantly differs among sawmill sizes. The total carbon emission from electricity consumption and wood residue of lumber processing was approximately 62.5%, 80.3%, and 66.2% of carbon stored in lumber processed for small, medium, and large sawmills, respectively. Efficient management and potential opportunities of improvement in sawmills can significantly reduce carbon emission (10.96% ± 1.57%) from hardwood lumber processing. Carbon stock from lumber production could be enhanced by either reducing carbon emission from energy consumption or decreasing lumber export quantity. The carbon emission–loss ratio (CELR) suggested that after 100 years, nearly 50% of carbon stored in lumber would be still available for carbon accountability. Electricity generation from either a single resource (natural gas) or mixed resources as is the case in RFC EAST (eGrid subregion) would be beneficial in lowering carbon emission from sawmill processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".