Mapping Local Economic Benefits of Oil and Gas Development in Colorado
Bibliographic record
Abstract
Abstract Development and operation of oil and gas assets can contribute positive social gains to local, regional and national communities. Local economic benefits of oil and gas development and production can include new business opportunities and employment generated for communities, royalties paid to mineral and land owners, and taxes paid to governments. On a per employee basis, the oil and gas industry is a high-output, high-wage industry that has a multiplier effect on local economies. In addition to generating thousands of direct jobs and wages, the oil and gas sector purchases goods and services from other industries, thereby building transferrable skillsets and creating broad capacity in the community. Memberships and donations made to local universities and organizations can also benefit the community. This paper focuses on the economic benefits to local communities, and across the state, from the investment in exploration, development and operation of a specific unconventional resource asset in Colorado, USA. The investment spend by an operator (ConocoPhillips) is mapped from direct contracts through subcontracting with sub-suppliers. The mapping shows geographically dispersed benefits as they cascade through the oil and gas supply chain into the local communities in oil producing counties and beyond into counties without oil production. Investment with large national companies is mapped to increased employment throughout Colorado. Direct spend with Colorado-based companies is inventoried to show benefits to small suppliers and contractors. Spend on local supporting services, such as retail, restaurants and lodging with hundreds of businesses is documented. The framework of local economic benefits mapped for a specific Colorado unconventional asset development and operation is typical for unconventional asset developments and may be employed by similar developments in other geographies. This approach to mapping local economic benefits may also be employed in other types of oil and gas project developments to comprehensively identify positive social impacts. A Local Economic Benefit Fact Sheet was created from this assessment and is used to communicate key messages to external stakeholders in Colorado. The fact sheet is included in Appendix 1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".