The Role of Forecasts in Planning for Energy Infrastructure: A Historical Look at Past Futures in Postwar Quebec
Bibliographic record
Abstract
Forecasts play a central role in the development of energy infrastructure. Since building energy infrastructure is long and costly, energy system planners try to anticipate future demand to avoid both shortages and overcapacity. But energy demand forecasts aren’t neutral: they represent a certain vision of the future that forecasters hope to bring into being. This article uses a historical case study to open the black box of forecasting and the world it contains. It studies electricity demand forecasts made by Hydro-Québec, one of the biggest industrial firms in North America, from the 1960s to the 1980s. Based on linear extrapolation models forecasting exponential demand and endless growth, the state-owned firm embarked on huge hydroelectric megaprojects with deep consequences on the environment and Indigenous lands. The energy crisis of the 1970s, by disturbing energy systems, led to criticism from the provincial government and civil society towards Hydro-Québec’s bullish forecasts that justified its expansionist agenda. This uncertain context favored other methods of predicting the future, like scenario analysis, and brought scrutiny towards the hydroelectric powerhouse’s business. At the crossroads of business history, energy history, and science and technology studies, the article argues that energy forecasts are used by actors like energy suppliers and governments to produce and project power relations onto the future. They become performative when powerful interests coalesce around their vision of the future to implement it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".