Social cost of carbon under shared socioeconomic pathways
Introduction
After the Paris Agreement, countries have increasingly taken actions to address climate change. Social cost of carbon (SCC), which balances the social costs resulting from emission reductions with the incremental costs of regulation policy has been widely used to provide policy guidance. The US government has relied on the SCC estimates provided by the Interagency Working Group (IWG) as a basis for taxing and implementing regulation policies (Revesz et al., 2017). The IWG SCC estimates started in 2010 and were updated with new scientific developments in 2013 and 2016, resulting in policy benefits of more than $1 trillion (Nordhaus, 2017). The SCC is also increasingly being adopted for regulations at the state level, resulting in regulatory policies in California, New York and Minnesota (California, 2016; Larson, 2016; Minnesota, 2016).
Given the wide range of social and climate interactions included in the calculation, SCC estimation is necessarily complex and highly uncertain (Pindyck, 2013). Damage functions and social welfare discounts are considered the two major contributors to this uncertainty (Cai et al., 2016; Diaz and Moore, 2017; Heal and Millner, 2014; Howarth et al., 2014; Pycroft et al., 2014); however, any discussion of these issues is necessarily based on the underlying socioeconomic assumptions. Economic development can alter emission flow patterns (Mi et al., 2017), and—because the SCC is defined by social welfare—population and economic projections are fundamental determinants in its estimation. Scovronick et al. (2017) investigated the influence of future population growth on the SCC, Dietz and Stern (2015) and Moore and Diaz (2015) considered the impacts of climate on economic growth as the drivers of SCC uncertainty. However, the demographic and economic assumptions are only two aspects of the socioeconomic assumption, which may be associated with a wide range of political, technological and environmental contingencies. If the China-US trade war continues developing and becomes a regional rivalry, it may well alter the long-term and global trends, resulting in different SCC patterns.
The SSP framework was initially proposed by Moss et al. (2010) and Van Vuuren et al. (2012), but the quantified and qualified version was published seven years later by Riahi et al. (2017). It include five SSPs which cover the broad spectrum of future challenges to mitigation and adaptation and translate this into consistent narratives of future developments that are quantified for diverse fields like demography, economic growth and convergence, energy, land-use, air pollution, policies, and trading (O’Neill et al., 2017; Riahi et al., 2017). The SSP framework greatly facilitates integrated analyses of mitigation and adaptation. Pizer et al. (2014) revealed the importance of considering the new SSP framework into SCC estimates. However, the current IWG models are too simple to directly quantify the SSP narratives. Therefore, we choose the SSP characteristics quantified by C3IAM (Wei et al., 2018), and use its result to re-estimate the parameters in DICE, in order to characterize the five SSPs. The C3IAM couples CGE with economic growth theory, which result can be used to update the mitigation function in DICE, while match the GDP trajectory with DICE as they both rooted in the economic growth theory.
Our paper estimates the SCC under the five socioeconomic scenarios; we also extend our research by considering the uncertainty caused by damage functions and the social welfare discount rate. The most important innovation of this study is to extent current research by computing the SCC under different socioeconomic pathways (SSPs), rather than considering the demographic and economic separately. The results demonstrate the need to avoid regional rivalries and fossil-fueled development, which can raise the current SCC or induce much heavier mitigation pressures by the end of this century. The SCC value provides a carbon price benchmark for policy makers who hold different attitudes towards the future and is an important reference for future research under the various SSPs.
Section snippets
Overview of the methodology
Dynamic Integrated Climate Economy model (DICE) is one of the three models used by the U.S. government to provide latest SCC estimation, and also been widely used for SCC discussion by scholars (Crost and Traeger, 2014; Moore and Diaz, 2015; Scovronick et al., 2017). Four parameters in DICE are subjected to the socioeconomic assumptions, namely the population, total factor productivity (TFP), carbon intensity, and the mitigation functions. To characterize the SSPs in DICE, these parameters need
Evaluating the SSP outcomes in DICE
As shown in Fig. 1, the socioeconomic assumption is accompanied by a particular emission trajectory. The emission patterns differentiate under each SSP, leading to increases in atmospheric concentrations, which indicate the long-term temperature trends. Temperature is the direct indicator of climate change and produces different degrees of climate damage, which further determine the SCC. Therefore, we chose emission, concentration and temperature to illustrate the major outcome of SSP in the
Conclusions
Paris Agreement had promoted more countries to implement climate policy, and the cost-benefit of climate policy is a good point for nations to start. As the SCC internalize the CO2 externality, its value will be helpful to provide regulatory policy guidance. The term has been used for carbon tax, tradable obligations or renewable portfolio standards (Burke, 2016). However, SCC estimation relies heavily on future assumptions (e.g., mitigation and adaptation challenges, population growth and
Acknowledgements
The authors gratefully acknowledge the support from the National Key R & D Program (Grant No. 2016YFA0602603), the National Natural Science Foundation of China (Grant Nos. 71521002, 71642004, 71673026). The paper also benefitted from the participants at a seminar at Beijing Institute of Technology.
References (40)
- et al.
The SSP4: a world of deepening inequality
Global Environ. Change
(2017) - et al.
The marker quantification of the shared socioeconomic pathway 2: a middle-of-the-road scenario for the 21st century
Glob. Environ. Change
(2017) - et al.
SSP3: AIM implementation of shared socioeconomic pathways
Glob. Environ. Change
(2017) - et al.
Risk mitigation and the social cost of carbon
Glob. Environ. Change-Hum. Policy Dimensions
(2014) - et al.
Fossil-fueled development (SSP5): an energy and resource intensive scenario for the 21st century
Glob. Environ. Change
(2017) - et al.
Future growth patterns of world regions – a GDP scenario approach
Glob. Environ. Change
(2017) - et al.
The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century
Glob. Environ. Change
(2017) - et al.
The economic impact of extreme sea-level rise: ice sheet vulnerability and the social cost of carbon dioxide
Glob. Environ. Change-Hum. Policy Dimensions
(2014) - et al.
The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview
Glob. Environ. Change
(2017) - et al.
Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm
Glob. Environ. Change
(2017)
A proposal for a new scenario framework to support research and assessment in different climate research communities
Glob. Environ. Change
Opportunities for advances in climate change economics
Science
Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction
Nat. Clim. Change
Optimal CO2 mitigation under damage risk valuation
Nat. Clim. Change
Quantifying the economic risks of climate change
Nat.Clim. Change
Endogenous growth, convexity of damage and climate risk: how nordhaus’ framework supports deep cuts in carbon emissions
Econ. J.
Social cost of carbon: domestic duty
Science
Social cost of carbon: global duty
Science
Agreeing to disagree on climate policy
Proc. Natl. Acad. Sci. U. S. A.
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