Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model

Shuwei Zhang, Nico Bauer, Guangzhi Yin and Xi Xie

Technological Forecasting and Social Change, 2020, vol. 151, issue C

Abstract: Empirical studies on technology advancement and regional diffusion indicate that technology learning is a multi-scale process driven by factors such as globally traded equipment and local accumulation of experience. This understanding should be incorporated in energy-economy models to improve the model representation and policy recommendations. The present study augments the large-scale, integrated assessment model REMIND for this purpose. To answer the research question on how multi-level learning affects technology diffusion and the regional costs of mitigation policies, alternative variants of multi-scale learning are implemented and calibrated to one set of regional-specific cost data. The results show that purely local learning leads to similar technology diffusion patterns as fully global learning, since the learning rates are set equal, and all regions are calibrated to their cost levels and specific capacity. Relative to these two, the combination of global and local learning leads to slower deployment of learning technologies and increases the mitigation cost if the cost disparity persists across regions, e.g. due to incomplete spillover. Our modelling exercise suggests that the choice of learning rates at different levels matter and calls for better data quality.

Keywords: IAM model; Technology multi-scale learning; Incomplete spillover; Learning rate (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:151:y:2020:i:c:s0040162518320134

DOI: 10.1016/j.techfore.2019.119765

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