CRED: A new model of climate and development

Ackerman, Frank; Stanton, Elizabeth A.; Bueno, Ramón

doi:10.1016/j.ecolecon.2011.04.006

Cited by 34 publications

(21 citation statements)

References 10 publications

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“…Instead, we analyze sensitivity to their exclusion in Appendix C in the Supplementary Material, and find that the exclusion of panel estimates has little effect on our primary results. 10 In the former methodology, Weitzman (2012) estimates impacts of 50 and 99% of global GDP for 6 and 12 • C increases (Ackerman et al 2012), respectively, based on the share of human habitat that will be inhospitable. While the 12 • C estimate is based on a global limit from Sherwood and Huber (2010), the 6 • C estimate internalizes regional limits, such as the Middle East becoming inhospitable by the end of the century under a Business As Usual scenario (Pal and Eltahir 2015).…”

Section: History Of Global Climate Damage Estimatesmentioning

confidence: 99%

“…Nordhaus and Yang (1996) is based on Nordhaus (1994a). 22 With respect to dropping estimates based on already included estimates, we make exceptions for Hanemann (2008), as adjusted by Ackerman et al (2012), Meyer and Cooper (1995), Roson and Van der Mensbrugghe (2012) and Bosello and Parrado (2014), where we believe that the authors used the original estimates (Nordhaus 2008;Fankhauser 1995;Tol 2002) as a starting point for their own estimates, such that the final estimates represent a significant departure from the original estimates. We only conduct sensitivity analysis to these two previous exceptions, given that ENVISAGE (Roson and Van der Mensbrugghe 2012) and ICES (Bosello and Parrado 2014) overlap with FUND only in minor ways.…”

Section: Datamentioning

confidence: 99%

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Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Howard

Sterner

2017

Environ Resource Econ

196

100

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Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4 • C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature-damage relationship, we find a three-to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four-to five-fold.

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Section: History Of Global Climate Damage Estimatesmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Howard

Sterner

2017

Environ Resource Econ

196

100

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“…보다 자세한 내용은 다음의 문헌들에서 살펴볼 수 있다. RICE (Nordhaus, 2010), FUND (Anthoff and Tol, 2014a), MERGE (Manne and Richels, 2005), WITCH (Bosello and de Cian, 2014), CETA-M (Peck and Teisberg, 1999), GRAPE (Kurosawa, 2004), AIM/Dynamic (Masui et al, 2006), CRED (Ackerman et al, 2013a), AD-RICE(de Bruin, 2014), PAGE (Hope, 2011). …”

Section: 사회후생함수 유형화unclassified

Comparative Analysis of Regional Integrated Assessment Models of Climate and the Economy

Hwang

2016

Environmental and Resource Economics Review

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: An integrated assessment model of climate and the economy (IAM) has been a standard tool for the economic analysis of climate change and policy recommendations. Since policy measures to address climate change take places at a national level, a regional integrated assessment model of climate and the economy (RIAM) is gaining more importance. A RIAM is a useful tool for the assessment of regional (or national) impacts of climate change. This paper investigates the main features of the currently available RIAMs. The focus is social welfare functions and the regional aspects of climate change. The comparative analysis shows that there is a huge gap between the economics of climate change and its applications to RIAMs. As an application, this paper examines the effect of social welfare functions on optimal solutions of the RICE (Regional Integrated model of Climate and the Economy) model. It is found that optimal climate policy such as carbon tax or emissions control rate is very sensitive to the assumptions on social welfare functions of RIAMs. It is better for each country to have their own RIAM as a basic tool for national climate policy-making and for international bargaining in greenhouse-gas mitigation. This is because a country's own preferences such as efficiency, equity, and sustainable development as well as national circumstances can be reflected in RIAM. The Republic of Korea has not developed its own RIAM yet. The comparative analysis and the numerical model in this paper can be a stepping stone for the development of such a national model.

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“…Nordhaus (2008) and most other studies by contrast use a 'descriptive'approach in which the welfare function of the representative agent has to be calibrated to …t empirical observations. 2 This approach, Nordhaus argues, "does not make a case for the social desirability of the distribution of incomes over space or time under existing conditions". Instead, using the descriptive approach…”

Section: Introductionmentioning

confidence: 99%