2023
DOI: 10.33774/coe-2023-5v93l-v3
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Realising the social value of impermanent carbon credits

Abstract: Efforts to avert dangerous climate change by conserving and restoring natural habitats are hampered by widespread concerns over the credibility of methods used to quantify their net long-term benefits. We develop a novel, flexible framework for estimating the long-run social benefit of impermanent carbon credits generated by nature-based interventions which integrates three substantial advances: the conceptualisation of the permanence of a project’s impact as its additionality over time (relative to a statisti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 11 publications
0
3
0
Order By: Relevance
“…This discount rate is compatible with an increase in the SCC of 2% per year as well as the implied Ramsey discount rate using the mean pure time preference rate and elasticity of marginal utility of the expert survey reported in Drupp et al (2018). Balmford et al (2023) provide sensitivity analysis of eP to discount rate choice.…”
Section: Permanencementioning
confidence: 69%
“…This discount rate is compatible with an increase in the SCC of 2% per year as well as the implied Ramsey discount rate using the mean pure time preference rate and elasticity of marginal utility of the expert survey reported in Drupp et al (2018). Balmford et al (2023) provide sensitivity analysis of eP to discount rate choice.…”
Section: Permanencementioning
confidence: 69%
“…This is 1.3% time discounting over an assumed 1.7% GDP growth rate. Balmford et al (2023) provide sensitivity analysis of eP to discount rate choice.…”
Section: Permanencementioning
confidence: 99%
“…Finally the permanence of (additionality-leakage) in the evaluation period is estimated using the approach from (Balmford et al, 2023) (Section 6.4).…”
Section: Algorithm Sketchmentioning
confidence: 99%