Regulation for Renewable Energy Development: Lessons from Sri Lanka Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban emissions represent approximately 40% of Canada's current GHG emissions and the need to implement Integrated Community Energy Solutions (ICES) is now broadly recognized.A more consistent approach for characterizing energy and emissions opportunities in communities and the provision of more accurate and comprehensive information to planning processes is required.Integrated Community Energy Models (ICEMs) employ Geographical Information Systems (GIS) to integrate spatial information on a community's land use, building stock, transportation and energy systems and socio-economic characteristics.Using future scenarios, ICEMs support the prioritization of opportunities for energy efficiency and renewable and district technology integration, better enabling planning, policy development and investment decisions.This paper describes organizations forwarding ICES and ICEM development and selected enabling provincial legislation.Three case-studies are presented: the Energy Density Mapping Strategy for the cities of Guelph and Hamilton, Ontario, the Spatial Community Energy Carbon and Cost Characterization (SCEC 3 ) model for the City of Prince George, British Columbia and the Energy Asset Mapping project in the Strait-Highlands Region, Nova Scotia.For each, core model aspects, required data, highlighted results and their integration into community planning processes are discussed.The article concludes with next steps for implementation and future research and development of ICEMs in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it