Forecasting CO2 Emissions in Malaysia Through ARIMA Modelling: Implications for Environmental Policy
Bibliographic record
Abstract
Carbon dioxide (CO2), a prominent constituent of greenhouse gases, has a vital impact on environmental pollution and the occurrence of global warming.Malaysia is categorised as the primary contributor to CO2 emissions among the ASEAN countries.Malaysia's total CO2 emissions had a significant increase, surging by a factor of nine, from 28 Mt in 1980 to 262.2 Mt in 2020.This indicates analysing the significance of CO2 emissions is an urgent concern in Malaysia.Therefore, the objective of this study is to forecasts the magnitude of CO2 emissions that will be discharged in Malaysia during a span of ten years, specifically from 2021 to 2030.This study utilises quantitative modelling, namely auto-regressive integrated moving average (ARIMA) analysis, to assess the yearly time series data of CO2 emissions in Malaysia spanning from 1970 to 2020.The findings reveal that Malaysia's CO2 emissions are expected to continue rising in the next ten years, albeit with a gradual decline.This finding contributes to the body of knowledge and provides Malaysian policymakers with an opportunity to strengthen their current economic and environmental policies.This, in turn, could help create a safer environment and mitigate the negative impacts of CO2 emissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".