Carbon Storage Technology and Monitoring Strategies for Malaysia: A Review
Bibliographic record
Abstract
Rising global temperatures and intensifying climate extremes in 2024, as reported by the World Meteorological Organisation, underscore the urgent need for effective carbon management solutions. Carbon capture and storage (CCS) is a strategic technology for meeting the net zero carbon emission target of Malaysia by 2050. This study reviews the technical readiness and monitoring strategies of leading CCS projects worldwide, including Sleipner (Norway), Quest (Canada) and Aquistore (Canada), with a particular focus on site characterisation, injection operations and long-term containment monitoring. By analysing these global case studies, the paper identifies key insights into measurement, monitoring and verification (MMV) systems such as seismic imaging, distributed acoustic sensing and pressure tomography that are essential for secure and effective CO2 storage. The review highlights both challenges and successes across a range of geological storage sites, emphasising the need for advanced predictive modelling techniques and the development of standardised regulatory frameworks to ensure long term storage security. These lessons provide a foundation for strengthening carbon storage capabilities in Malaysia, particularly at offshore sites which are progressing towards operational readiness. The paper concludes that further integration of advanced monitoring technologies and comprehensive predictive modelling is critical to reinforce long-term containment assurance. Adoption of comprehensive, end-to-end carbon storage processes aligned with international best practices will enhance stakeholder confidence. These strategies will support a substantial transition to a low carbon energy system and contribute significantly to achieving net zero carbon emissions by 2050.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".