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This study employs advanced modelling to assess the effectiveness of Malaysia’s current energy policies in achieving a low-carbon future. By optimising a 100% renewable energy mix, including energy storage, the research identifies pathways to decarbonise the power sector while minimising costs. These findings will inform the development of future policies.

This study employs the Stockholm Environment Institute-developed Low Emissions Analysis Platform (LEAP) and Next Energy Modeling system for Optimization (NEMO) to construct and optimise a comprehensive Malaysian power sector model. The model encompasses both electricity supply, including diverse electricity generation sources and demand across key sectors. Three scenarios – existing policy, optimised existing policy and more ambitious policy (near-zero emissions) – are analysed.

Solar photovoltaic (PV) is the dominant technology, but realising its full potential requires significant grid upgrades. While natural gas expansion underpins Malaysia’s decarbonisation strategy, solar and storage offer a cleaner and potentially cost-effective alternative. Rapid technological advancements in clean energy increase stranded asset risk for new gas power plants. Malaysia’s abundant bioenergy resources need more tapping. This can contribute to decarbonisation and rural development. Transitioning to a fully renewable grid necessitates substantial investments in energy storage and grid infrastructure. While falling battery costs and regional interconnection can mitigate costs, careful consideration of potential disruptions and cost fluctuations is essential for resilience.

Energy sector modelling results are inherently dependent on input assumptions, such as future technology costs, resource availability and fossil fuel prices. These factors can be highly uncertain. While this study did not conduct sensitivity analyses to explore how variations in these assumptions might affect the results (e.g. cost variations across scenarios, technology mix fluctuations), the core findings provide valuable insights into potential decarbonisation pathways for Malaysia’s power sector. Future studies could build upon this work by incorporating sensitivity analyses to provide a more comprehensive understanding of how key results might change under a wider range of future possibilities.

This study co-optimises a 100% renewable energy mix for Malaysia, incorporating a comprehensive range of renewable resources, battery and pumped hydro storage. The research also provides a unique perspective on the interplay of philosophical underpinnings, psychological maturity and energy policy.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.