Demand-side management from a sustainable development perspective : experience from Quebec (Canada) and India
Bibliographic record
Abstract
This book describes the implementation of energy efficiency and demand-side management (DSM) projects in Quebec and India. The efficient use of energy and DSM are economical and environmentally sound approaches to power generation. The application of DSM is important in industrialized countries as well as developing countries and economies in transition. In the early 1970s, Quebec re-examined its energy supply in the wake of the oil crisis and joined forces with Hydro-Quebec and the Agence de l'efficacite energetique (AEE) to offer ongoing support for implementing energy-efficiency programmes and to promote hydroelectricity. Quebec's investment in DSM has proven to be very profitable in terms of its impact on society, the environment and the economy. The AEE promotes partnerships and the sharing of technology expertise with developing countries. This book describes the initiatives in India regarding energy efficiency improvements and DSM. In particular, it covers the work being done by governmental and non-government organizations. It also covers the legislative measures of the Energy Conservation Act and the Electricity Act of 2001. The scope and potential for DSM projects in India was also described with particular reference to the industrial, commercial, residential and agricultural sectors. Energy efficiency was shown to be the best means of achieving sustainable development for both developed and developing countries. refs., tabs., figs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".