Net-Zero Emissions With Renewable Energy Certificates: A Public Policy for a Massachusetts Municipal Light Plant
Bibliographic record
Abstract
The promotion, desire and need for renewable energy generation and transmission to electric grids to provide clean, non-carbon-based power has increased in recent years with more focus on climate change mitigation in both the public and private sectors. Renewable Energy Certificates, also known as “RECs” are the established public policy mechanism for incentivizing, verifying, tracking and supporting renewable energy. REC markets are created and managed by state governments to allow selling, purchasing and trading of these “green commodities'' to substantiate environmental attribute claims. A new legislation in Massachusetts requires all stakeholders, businesses and sectors to reduce emissions, which means electric utilities, both public and private, must participate in REC markets to green their power supply portfolios that they provide to consumers. This paper explores and analyzes the role of REC markets, monetary policy, trends, stakeholders, participants, and the current public policy debates in this area. A specific public policy making case is explored for this research, the Municipal Light Plant in Shrewsbury, MA, utilizing RECs to achieve a 100% non-carbon power supply or “net-zero” emissions. A financial analysis based on REC market research and debate is conducted to inform a rules-based and judgement-based fiscal Power Supply Policy and Greenhouse Gas Emissions Standard for SELCO (Shrewsbury Electric and Cable Operations), a public electricity utility.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".