CARBON EMISSION INTENSITY FOR MELAKA GREEN TECHNOLOGY CITY STATE
Bibliographic record
Abstract
The study analyzes the carbon emission absolute number and intensity for Melaka state as a response to the Malaysia voluntary reduction target of up to 40 percent in terms of emissions intensity of gross domestic product, GDP by the year 2030 compared to 2005 levels. It is manifested in the vision of Melaka Green Technology city state by 2020. The Global Protocol for Community-Scale Greenhouse Gas Emissions, GPC method deploys to calculate greenhouse gas, GHGs emission resulted from the various development activity in Melaka. This method classified GHG emissions into four (4) mains sectors: stationary energy, transportation, waste, and agriculture, forestry, and other land use (AFOLU) by using secondary data from related government agencies at the state level. The computation by BASIC+ reporting level resulted from an output of GHGs emission translated into carbon emission. Steady increase of GHG emissions was captured from 8,859,802 tCO2e (2013) to 8,911,173 tCO2e (2017). Further, carbon emission intensity calculates by gathering the carbon emission absolute number to Melaka’s population. The emission per-capita increase from 6.19 tCO2e (2013) to 6.88 tCO2e (2017), indicates each person contributes to the increment of GHGs emissions for Melaka state. However, the decrease of emission intensity records from 0.189 tCO2e (2013) to 0.176 tCO2e (2017) compared to an increase in population growth. The study concludes certain influences of the aggressive green technology initiative effect, such as renewable energy, LED street lighting, solar valley, smart metering in the building, electric public bus, no plastic bag, waste recycling to the overall carbon emission intensity of Melaka Green technology City State.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".