The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Some Contributions of Integrated Assessment Models of Global Climate Change
Open accessAbstract
This article reviews the use of integrated assessment models (IAMs) in climate policy and research at the global scale. Two different types of IAMs are discussed. First, there are models that focus on climate change mitigation options and climate change impacts in some detail without necessarily valuing or aggregating all possible impacts into a single measure of projected climate damages. Here they are called detailed process (DP) IAMs. A second class of IAMs are much more highly aggregated and focus on calculating carbon emissions trajectories and carbon prices that maximize global welfare. Here these models are referred to as aggregate benefit–cost analysis (BCA) IAMs. Early IAMs of both types were introduced about 30 years ago and by now have been applied to many important policy and research design issues. Continual advancements in physical and economic system understanding, modeling techniques, and computational power should continue to open up many additional opportunities for using these models to provide relevant information to decision makers. While the models can be improved in many areas, much of the uncertainty that exists reflects a lack of complete scientific understanding of the systems involved rather than limitations of one or another approach to model construction and use.
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Cites
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Biochar in climate change mitigation
Lehmann, J., Cowie, A., Masiello, C. A., Kammann, C., Woolf, D., Amonette, J. E., Cayuela, M. L., Camps-Arbestain, M., & Whitman, T. (2021) · Nature Geoscience · doi:10.1038/s41561-021-00852-8 · Cited by 970
GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., et al. (2019) · Geoscientific Model Development · doi:10.5194/gmd-12-677-2019 · Cited by 543
Quantifying the economic risks of climate change
Diaz, D., & Moore, F. (2017) · Nature Climate Change · doi:10.1038/nclimate3411 · Cited by 366
Tackling climate change with machine learning
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The missing risks of climate change
Rising, J., Tedesco, M., Piontek, F., & Stainforth, D. A. (2022) · Nature · doi:10.1038/s41586-022-05243-6 · Cited by 310
Metadata
- Authors
- Weyant, J.
- Venue
- Review of Environmental Economics and Policy
- Publisher
- University of Chicago Press
- Published
- 2017-01-01
- Volume
- 11
- Issue
- 1
- Pages
- 115-137
- ISSN
- 1750-6816 (ISSN-L)
- 1750-6824
- OpenAlex
- W2594741346
- Cited by
- 465
- References
- 96
- FWCI
- 45.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
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