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Learning, economies of scale, and knowledge gap effects on power generation technology cost improvements.

ABSTRACT: Cost reductions are essential for accelerating clean technology deployment. Because multiple factors influence costs, traditional one-factor learning models, solely relying on cumulative installed capacity as an explanatory variable, may oversimplify cost dynamics. In this study, we disentangle learning and economies of scale effects at unit and project levels and introduce a knowledge gap concept to quantify rapid technological change's impact on costs. Our results show that a substantial proportion of cost declines in several technologies is attributable to economies of scale rather than learning processes. Thus, relying on one-factor learning may underestimate cost declines during upscaling periods for technologies with strong economies of scale effects and overestimate reductions for those approaching maximum size. Notably, the knowledge gap concept can endogenously capture how rapidly technology sizes can evolve through learning. These insights can improve decision-making and highlight the benefits of separating learning and economies of scale effects to estimate technology costs.

SUBMITTER: Pratama YW

PROVIDER: S-EPMC11761306 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature

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Learning, economies of scale, and knowledge gap effects on power generation technology cost improvements.

Pratama Yoga W YW Gidden Matthew J MJ Greene Jenna J Zaiser Andrew A Nemet Gregory G Riahi Keywan K

iScience 20241219 1

Cost reductions are essential for accelerating clean technology deployment. Because multiple factors influence costs, traditional one-factor learning models, solely relying on cumulative installed capacity as an explanatory variable, may oversimplify cost dynamics. In this study, we disentangle learning and economies of scale effects at unit and project levels and introduce a knowledge gap concept to quantify rapid technological change's impact on costs. Our results show that a substantial propo ...[more]

PMID: 39868052

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Project description:Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in growth performance and gas consumption/production of three wild-type strains and one genetically engineered strain in two independent quadruplicate chemostat bioreactor experiments: 1) with molecular hydrogen and carbon dioxide; and 2) with sodium formate. In the first experiment, we found the highest methane production rate for Methanothermobacter thermautotrophicus ΔH, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. We collected statistically reliable transcriptomics and proteomics data sets from these steady-state bioreactors, which we integrated within our genome-scale metabolic models. The implementation of an pan-model that contains combined reactions from all three microbes allowed us to perform an interspecies comparison of the complete omics data set. While the observed differences in the growth behavior cannot be fully explained, the comparison enabled us to identify crucial differences in growth-related pathways, such as formate anabolism. In the second experiment, we found stable growth with a M. thermautotrophicus ΔH plasmid-carrying strain on formate with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. The results of the two studies demonstrate the advantages of an integrative approach using fermentation and omics data with genome-scale modeling for the investigation of lesser studied metabolisms, and the biotechnological potential of Methanothermobacter spp. as production platform hosts.

Dataset Information

Learning, economies of scale, and knowledge gap effects on power generation technology cost improvements.

Publications

Learning, economies of scale, and knowledge gap effects on power generation technology cost improvements.

Similar Datasets

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