Powering Change: The Critical Role of Women and Youth in Sustainable Energy Transformation
Bibliographic record
Abstract
How do we build economic systems that recognise and work within the biophysical limits of our finite planet while simultaneously reducing poverty and inequality? This has become a defining question of our time, and the global transition to clean energy is increasingly considered an important vehicle via which we might address this ‘trilemma.’ Concerns about environmental sustainability and fossil fuel insecurity have encouraged countries around the world to transition to low-carbon energy supplies derived from clean renewables such as solar, hydro, bioenergy, geothermal and wind. Since producing and distributing clean energy is more labour intensive than producing and distributing fossil fuels, this shift is creating new employment opportunities, as well as addressing energy poverty in remote or under-served communities everywhere in the world. Although there is tremendous potential to create employment and opportunities for entrepreneurship in clean energy almost everywhere in the world, there is a growing concern that women, who are already underrepresented in the energy sector, will become even more marginalised if gender equity policies and programmes are not proactively planned and implemented. Without appropriately targeted training, education, apprenticeships, employment placement, financial tools and supportive social policies, transitioning to clean energy may exacerbate existing gender inequities and hinder global poverty alleviation goals, including the SDGs. Empirical data on the participation of women and youth in the clean energy sector remains weak and scattered, and so do policy interventions designed to optimise their participation. This is precisely what Canada’s International Development Research Centre (IDRC) is trying to accomplish via its Clean Energy for Development: A Call to Action (CEDCA) initiative, which supports 12 research projects that operate in 27 countries across three thematic bases: clean energy transition, micro-, small and medium-sized enterprises (MSMEs), and women and youth. This research for policy and practice report showcases three of these research projects and draws out rigorous evidence to inform policymaking that advances the participation of women and youth in the clean energy sector.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".