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Cited by 409

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus' Framework Supports Deep Cuts in Carbon Emissions

Open access
Dietz, Simon, Stern, Nicholas·2015·The Economic Journal

Abstract

‘To slow or not to slow’ (Nordhaus, 1991) was the first economic appraisal of greenhouse gas emissions abatement and founded a large literature on a topic of worldwide importance. We offer our assessment of the original article and trace its legacy, in particular Nordhaus's later series of ‘DICE’ models. From this work, many have drawn the conclusion that an efficient global emissions abatement policy comprises modest and modestly increasing controls. We use DICE itself to provide an initial illustration that, if the analysis is extended to take more strongly into account three essential elements of the climate problem – the endogeneity of growth, the convexity of damage and climate risk – optimal policy comprises strong controls. Nordhaus, W.D. (1991). ‘To slow or not to slow: the economics of the greenhouse effect’, Economic Journal, vol. 101(407), pp. 920–37.

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Metadata

Authors
Dietz, S., & Stern, N.
Venue
The Economic Journal
Published
2015-03-01
Volume
125
Issue
583
Pages
574-620
ISSN
  • 0013-0133 (ISSN-L)
  • 1468-0297
OpenAlex
W3121371609
Cited by
409
References
49
FWCI
76.88
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus' Framework Supports Deep Cuts in Carbon Emissions. https://app.cochat.ai/papers/10.1111/ecoj.12188
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Dietz, S., & Stern, N. (2015). Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus' Framework Supports Deep Cuts in Carbon Emissions. The Economic Journal. https://doi.org/10.1111/ecoj.12188

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.