Evaluating the Energy Metabolic System in Sri Lanka
Bibliographic record
Abstract
Fast growing economy of Sri Lanka with an annual GDP growth rate of 5% has significantly increased demand for energy. As energy supply must grow in a sustainable way to meet the demand, concern over the environmental impact of energy flows have been gaining attention during policy development and implementation. Therefore, there is a need of comprehensively evaluating energy metabolic system in Sri Lanka to identify resource dependencies of the country that must be addressed to increase the sustainability. A conceptual energy metabolic model was developed identifying economic, social and demographic variables affecting energy demand, transformation and supply and GHG emissions in Sri Lanka. Developed model was used to evaluate the current energy flows and forecast the behaviour of energy metabolism while assessing the sustainability of the energy system using number of sustainability indicators. Developed model indicates an average annual growth rate of 4.06% in energy demand, 4.17% in non-renewable energy supply and 3.36% in GHG emissions. Transport sector has the highest GHG emissions percentage of 73%. Sustainability evaluation of the energy metabolic system shows that Sri Lanka is becoming more efficient and less energy intensive over the years. However, increase in GHG emissions per capita and emission intensity has a negative impact on the environmental sustainability while increase renewable energy share in total energy supply can be considered positive. The findings of the research give new insights to the energy system of Sri Lanka which enable energy planners to implement policies to transition towards a more secure and sustainable energy system.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".