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.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".