A simple estimate for the social cost of carbon

Zhen, Zaili; Li, Tian; Ye, Qian

doi:10.1016/j.egypro.2018.09.243

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…The intergenerational issues associated with climate change identifies it as an externality associated with carbon dioxide and other GHG emissions because it involves costs that are borne by future generations who have not created the emissions [34][35][36][37][38]. Climate change economists have introduced the concept of "social costs of carbon," which externalizes the externalities of these emissions by denoting the damages caused by them with a monetary value [35,[39][40][41]. It is for this reason that climate policy experts have advocated for a carbon price to achieve the "right price" as well as incentivize the investments in low-carbon technologies.…”

Section: The Risks Of Stranding In the Energy Sectormentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

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Section: The Risks Of Stranding In the Energy Sectormentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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“…Comparison and assessment of electricity generation technologies, supply chains, and estimated costs of electricity production should be based on comparable measures, which tackle the use of resources as well as health and environmental hazards. A social opportunity cost encompasses the society's outlays and the costs connected with using and processing None energy resources and encountering unfavorable environment changes as well as degraded human welfare (Zhen et al 2018). It embraces both direct costs and external costs in the entire energy transformation chain.…”

Section: Costs Of Electricity Productionmentioning

confidence: 99%

The externalities of energy production in the context of development of clean energy generation

Bielecki

Ernst

Skrodzka

et al. 2020

Environ Sci Pollut Res

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In this paper, we present a comparative review of the externalities of electricity production. First of all, the environmental impact is considered. A discussion of the influence of various electricity production processes on human health follows. The studies are conducted in the context of historical development. Current trends, as well as a historical background that resulted in the changes that can be observed today, are presented. The considerations are supported by a few case studies. Analysis of perspectives for the development of electricity generation methods, in particular the indication of clean energy sources and the perspectives of their exploitation, is the main aim of this paper.

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“…The previous meta-analysis of the social cost of carbon (Tol, 2018) was extended with estimates reported in Anthoff and Emmerling (2019), Bretschger and Pattakou (2019), Budolfson et al (2017), Daniel et al (2019), Dayaratna et al (2020), Ekholm (2018), Faulwasser et al (2018, Golub and Brody (2017), Guivarch and Pottier (2018), Hafeez et al (2017), Hänsel and Quaas (2018), Kotchen (2018), Moore et al (2017), Nordhaus (2015), Okullo (2020), Ricke et al (2018), Scovronick et al (2017), Tol (2019), Yang et al (2018) and Zhen et al (2018). The Budolfson and Faulwasser estimates were digitized from graphs.…”

Section: Appendix a New Estimates Of The Social Cost Of Carbonmentioning

confidence: 99%

Kernel density decomposition with an application to the social cost of carbon

Tol¹

2020

Preprint

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A kernel density is an aggregate of kernel functions, which are itself densities and could be kernel densities. This is used to decompose a kernel into its constituent parts. Pearson's test for equality of proportions is applied to quantiles to test whether the component distributions differ from one another. The proposed methods are illustrated with a meta-analysis of the social cost of carbon. Different discount rates lead to significantly different Pigou taxes, but not different growth rates. Estimates have not varied over time. Different authors have contributed different estimates, but these differences are insignificant. Kernel decomposition can be applied in many other fields with discrete explanatory variables.

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A simple estimate for the social cost of carbon

Cited by 16 publications

References 13 publications

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

The externalities of energy production in the context of development of clean energy generation

Kernel density decomposition with an application to the social cost of carbon

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