Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

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


Zhang,  S.
External Organizations;


Bauer,  Nicolas
Potsdam Institute for Climate Impact Research;

Yin,  G.
External Organizations;

Xie,  X.
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available

Zhang, S., Bauer, N., Yin, G., Xie, X. (2020): Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model. - Technological Forecasting and Social Change, 151, 119765.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_23528
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.