A Biodiversity Literacy: A Systematic Literature Review of Conceptual Frameworks, Educational Strategies, and Policy Implications (2015-2025)
Bibliographic record
Abstract
Energy efficiency helps mitigate climate change, while energy efficiency assessment promotes sustainability in the building sector.Santa Elena, a province of Ecuador, is a region facing climate challenges due to high temperatures and scarce energy resources.Therefore, energy efficiency assessment is a crucial factor in promoting conservation strategies aligned with the Sustainable Development Goals (SDGs).The objective of this research is to evaluate the energy efficiency of homes in the Santa Elena province of Ecuador through energy modelling, considering energy consumption and material types to propose sustainability measures.This study adopted a three-phase approach: the first phase consisted of establishing a baseline through surveys of homes in the area; the second phase evaluated housing types based on their materials, aligning with international standards, using Open Studio energy modelling software.The third phase involved a technical comparison of energy consumption and thermal comfort to propose sustainability strategies that focus on reducing carbon dioxide emissions (CO2).Based on the results obtained, the most common housing types in Santa Elena were evaluated: brick (52.74%), concrete (13.58%), and bamboo (12.27%).When modelling these housing types without HVAC systems, an annual energy consumption of 1639 kWh was obtained for the brick and concrete home and 1556 kWh for the bamboo.However, with the implementation of HVAC systems, particularly in brick and concrete homes with thermal conductivities of 1.13 W/m² and 0.72 W/m² , respectively, consumption rose to 5392.09 kWh for the brick home and 5686.56 kWh for the concrete home.These results prove that materials with greater thermal insulation capacity contribute to energy conservation strategies in homes.In conclusion, this research highlights the significance of evaluating energy efficiency in various housing types to promote sustainable strategies that prioritise the use of natural resources, efficient thermal insulation, and reduced energy consumption and CO2 emissions.It is worth noting that this line of research aligns with the SDGs (7, 11, and 13), promoting education and culture to ensure a sustainable future for generations.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".