Responsible researcher: Bruno Benevit
Original title: The Social Cost of Carbon with Economic and Climate Risks
Authors: Yongyang Cai and Thomas S. Lontzek
Intervention Location: All countries
Sample Size: 10000 simulations
Sector: Environment
Variable of Main Interest: Social cost of carbon
Type of Intervention: Economic and climate risk
Methodology: DSICE
Summary
The choice of policies to manage interactions between the economy and climate is substantially affected by uncertainty regarding future economic and climate conditions. In this sense, the objective of this study was to understand how the social cost of carbon is affected by both economic and climate risks. Presenting a novel Dynamic Stochastic Integration of Climate and Economy (DSICE) model, the authors demonstrate that the social cost of carbon is substantially affected by both economic and climate risks, involving a stochastic process with significant variation.
Climate change, widely associated with the global emission of carbon dioxide (CO2), has several impacts on economic productivity. Rising temperatures imply a reduction in agricultural production, an increase in cooling costs and an easier spread of diseases (Cai and Lontzek, 2019). There is evidence that rising temperatures also increase the likelihood of severe droughts and floods (Wuebbles, 2016). Consequently, rising sea levels cause coastal flooding, which could lead to the submersion of coastal areas.
Furthermore, climate change has the potential to cause irreversible changes beyond a certain critical point. From a given critical point, a small disturbance can qualitatively alter the state or development of the climate system (Pindyck, 2011). In this sense, understanding the relationship between the economic implications of climate change and the Social Cost of Carbon (CSC) is essential for sustainable economic development. Specifically, it is important to verify how uncertainty surrounding variables such as economic growth, risk aversion and extreme weather events affect the CSC.
The context of economic and climate risks is increasingly present in our rapidly changing world. Climate change, driven mainly by greenhouse gas emissions, has generated a series of significant impacts on the global economy. Increases in average temperatures, extreme weather events, rising sea levels and changes in precipitation patterns are just some of the direct consequences of these changes. These extreme weather events have the potential to adversely affect agriculture, infrastructure, public health and several other economic sectors.
Furthermore, the context of economic and climate risks also involves the uncertainty and variability associated with climate and economic projections. Predicting future climate behavior and its economic implications involves a multiplicity of factors. Uncertainty is exacerbated by the lack of global consensus on effective actions to mitigate climate change.
The Social Cost of Carbon (CSC) assesses the economic cost resulting from CO2 emissions into the atmosphere. It represents the additional cost associated with emitting an extra ton of CO2, considering the economic damage resulting from global warming. These damages include losses to agriculture, impacts on infrastructure and adaptation costs. The CSC is fundamental for political decisions, guiding the definition of emissions reduction targets and the implementation of mitigation strategies.
The methodology used in this study was based on the use of the Stochastic Dynamics of Climate and Economy (DSICE) computational method, which integrates economic and climate models, considering stochastic and irreversible elements. DSICE contains a model for the climate and a model for the economy, using a five-dimensional system (two for temperature and three for the carbon cycle).
Specifically, the climate model was composed of three modules: carbon systems, temperature and climate hotspots. In the carbon system, two sources of carbon emissions are assumed in each period: an industrial source, related to economic activity, and an exogenous source, resulting from biological processes in the soil. The temperature system monitors atmospheric and ocean temperatures, measured in Cº above the pre-industrial level. Climate hotspots are modeled by a Markov chain process, including the probability of critical events occurring, the expected duration of the process resulting from that event, the mean and variance of long-term impacts on economic productivity, and the dependence on factors climatic.
The economic part of DSICE consisted of a simple stochastic growth model, assuming that production generates greenhouse gas emissions and that global output is affected by the state of the climate. The world's capital stock was analyzed in trillions of dollars throughout each year, considering economic aspects such as the production function, population growth, increased productivity, carbon intensity in production and the impacts caused by temperature levels. The stochastic growth of the productivity factor was calibrated in order to bring the resulting consumption process closer to the empirical data. The model considers a utility function based on the preferences proposed by Epstein-Zin (Epstein and Zin, 1989), allowing the distinction between risk preferences and consumption smoothing preferences.
The social cost of carbon is defined as the marginal cost of atmospheric carbon, which can be either consumption or capital, as there are no adjustment costs. The central planner establishes such that the private and social costs of carbon are equated according to a given Pigovian carbon tax. The authors also computed the internal rate of return on invested capital, defined by the rate used to discount the additional consumption caused by an extra unit of capital in 2005. Finally, the study presents several computations with different parameters and sensitivity analyzes to verify of critical point/change processes.
The results of this study indicated that the use of Epstein-Zin preferences plays a fundamental role in modeling risk aversion and intertemporal elasticity of substitution. These preferences have a significant impact on CSC, which tends to increase as risk aversion grows, particularly in scenarios involving critical climate change.
By incorporating long-term risk, the study revealed that CSC itself is a stochastic process subject to considerable uncertainty. This implies that climate policy formulation must take into account uncertainties, including the possibility of extreme weather events, and consider the feasibility of mitigation policies, such as geoengineering and carbon capture, which can be seen as too costly when analyzed only under models. deterministic.
Furthermore, evidence from this study highlighted the influence of elements of critical change in the climate system on CSC. The threat posed by these events leads to substantial and immediate increases in CSC, even in scenarios where the probability and impact of the events are moderate. This suggests that the CSC can reach considerable values without the need for catastrophic climate events, simply by plausibly assuming uncertain and irreversible climate changes. Internal rate of return analysis also points to the importance of applying a lower discount rate when assessing damage caused by critical change events, due to its lower correlation with total consumption compared to damage to production.
Finally, the study highlights DSICE's ability to handle complex nine-dimensional models at the expense of simpler models. In this sense, the consideration of factors such as productivity shocks, dynamic preferences and stochastic elements of critical changes in the climate system was demonstrated to be possible and robust.
The study introduced DSICE, a computational method designed to examine the economic and climate implications arising from the interaction between the economy and climate. In this context, the article analyzed the CSC and the optimal carbon tax in stochastic and irreversible climate change scenarios. The analysis considered uncertainties related to economic growth and preferences regarding risk. DSICE also integrated “turning” elements in the climate system, such as extreme weather events, in order to assess how these uncertainties could impact climate policy. The evidence from this study emphasizes the relevance of risk aversion for the formulation of public policies related to the containment of climate change.
References
CAI, Y.; LONTZEK, TS The Social Cost of Carbon with Economic and Climate Risks. Journal of Political Economy , vol. 127, no. 6, p. 2684–2734, Dec. 2019.
EPSTEIN, LG; ZIN, SE Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework. Econometrica , v. 57, no. 4, p. 937, Jul. 1989.
PINDYCK, RS Fat Tails, Thin Tails, and Climate Change Policy. Review of Environmental Economics and Policy , vol. 5, no. 2, p. 258–274, 1 Jul. 2011.
WUEBBLES, DJ Setting the Stage for Risk Management: Severe Weather Under a Changing Climate. In : GARDONI, P.; MURPHY, C.; ROWELL, A. (Eds.). . Risk Analysis of Natural Hazards . Cham: Springer International Publishing, 2016. p. 61–80.
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