Historical Carbon Footprinting and Implications for Sustainability Planning: A Case Study of the Pittsburgh Region
Bibliographic record
Abstract
This study estimates fossil-based CO(2) emissions and energy use from 1900-2000 for Allegheny County, PA. Total energy use and emissions increased from 1900 to 1970, reflecting the significant industrial, economic, and population growth that occurred in Allegheny County. From 1970 to 2000, Allegheny County experienced a 30% decrease in total emissions and energy use from peak values, primarily because of a decline in industrial activity (40% decrease in value added) and the loss of a quarter of its population. Despite these dramatic economic and demographic transitions, per capita emissions remained stable from 1970 to 2000, buoyed by relatively stable or slightly increasing emissions in the commercial and transportation sectors. Allegheny County's history suggests the scale of change needed to achieve local emissions reductions may be significant; given years of major technological, economic, and demographic changes, per capita emissions in 1940 were nearly the same in 2000. Most local governments are planning emissions reductions rates that exceed 1% per year, which deviate significantly from historical trends. Our results suggest additional resources and improved planning paradigms are likely necessary to achieve significant emissions reductions, especially for areas where emissions are still increasing.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".