In:
Geoscientific Model Development, Copernicus GmbH, Vol. 14, No. 5 ( 2021-06-01), p. 3121-3140
Kurzfassung:
Abstract. Process-based models are powerful tools for simulating the economic impacts of climate change, but they are computationally expensive. In order to project climate-change impacts under various scenarios, produce probabilistic ensembles, conduct online coupled simulations, or explore pathways by numerical optimization, the computational and implementation cost of economic impact calculations should be reduced. To do so, in this study, we developed various emulators that mimic the behaviours of simulation models, namely economic models coupled with bio/physical-process-based impact models, by statistical regression techniques. Their performance was evaluated for multiple sectors and regions. Among the tested emulators, those composed of artificial neural networks, which can incorporate non-linearities and interactions between variables, performed better particularly when finer input variables were available. Although simple functional forms were effective for approximating general tendencies, complex emulators are necessary if the focus is regional or sectoral heterogeneity. Since the computational cost of the developed emulators is sufficiently small, they could be used to explore future scenarios related to climate-change policies. The findings of this study could also help researchers design their own emulators in different situations.
Materialart:
Online-Ressource
ISSN:
1991-9603
DOI:
10.5194/gmd-14-3121-2021
DOI:
10.5194/gmd-14-3121-2021-supplement
Sprache:
Englisch
Verlag:
Copernicus GmbH
Publikationsdatum:
2021
ZDB Id:
2456725-5