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Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition.

ABSTRACT: We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright's law) or time (Moore's law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies.

SUBMITTER: Meng J

PROVIDER: S-EPMC8271727 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesHigh-quality evidence of effectiveness and cost-effectiveness is rarely available and relevant for health policy decisions in low-resource settings. In such situations, innovative approaches are needed to generate locally relevant evidence. This study aims to inform decision-making on antenatal care (ANC) recommendations in Rwanda by estimating the incremental cost-effectiveness of the recent (2016) WHO antenatal care recommendations compared to current practice in Rwanda.MethodsTwo health outcome scenarios (optimistic, pessimistic) in terms of expected maternal and perinatal mortality reduction were constructed using expert elicitation with gynaecologists/obstetricians currently practicing in Rwanda. Three costing scenarios were constructed from the societal perspective over a 1-year period. The two main inputs to the cost analyses were a Monte Carlo simulation of the distribution of ANC attendance for a hypothetical cohort of 373,679 women and unit cost estimation of the new recommendations using data from a recent primary costing study of current ANC practice in Rwanda. Results were reported in 2015 USD and compared with the 2015 Rwandan per-capita gross domestic product (US$ 697).ResultsIncremental health gains were estimated as 162,509 life-years saved (LYS) in the optimistic scenario and 65,366 LYS in the pessimistic scenario. Incremental cost ranged between $5.8 and $11 million (an increase of 42% and 79%, respectively, compared to current practice) across the costing scenarios. In the optimistic outcome scenario, incremental cost per LYS ranged between $36 (for low ANC attendance) and $67 (high ANC attendance), while in the pessimistic outcome scenario, it ranged between $90 (low ANC attendance) and $168 (high ANC attendance) per LYS. Incremental cost effectiveness was below the GDP-based thresholds in all six scenarios.DiscussionImplementing the new WHO ANC recommendations in Rwanda would likely be very cost-effective; however, the additional resource requirements are substantial. This study demonstrates how expert elicitation combined with other data can provide an affordable source of locally relevant evidence for health policy decisions in low-resource settings.

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Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition.

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