Principal investigator: Bruno Benevit
Original title: The Social Cost of Carbon with Economic and Climate Risks
Authors: Yongyang Cai and Thomas S. Lontzek
Location of the Intervention: All countries
Sample Size: 10000 simulations
Sector: Environment
Primary Variable of Interest: Social cost of carbon
Type of Intervention: Economic and climate risk
Methodology: DSICE
Summary
The choice of policies to manage the interactions between the economy and the climate is substantially affected by uncertainty regarding future economic and climatic conditions. In this sense, the objective of this study was to understand how the social cost of carbon is affected by both economic and climatic 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 climatic risks, involving a stochastic process with significant variation.
- Policy Problem
Climate change, largely associated with global carbon dioxide (CO2) emissions, has several impacts on economic productivity. Increased temperatures lead to reduced agricultural production, increased cooling costs, and facilitated disease spread (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, potentially leading to the submersion of coastal areas.
Furthermore, climate change has the potential to cause irreversible changes beyond a certain tipping point. Beyond that tipping 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 (SCC) is essential for sustainable economic development. Specifically, it is relevant to examine how uncertainty surrounding variables such as economic growth, risk aversion, and extreme weather events affects the SCC.
- Policy Implementation Context
The context of economic and climate risks is increasingly present in our rapidly changing world. Climate change, driven primarily 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 various 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 multitude of factors. Uncertainty is exacerbated by the lack of global consensus on effective actions to mitigate climate change.
- Evaluation Details
The Social Cost of Carbon (SCC) assesses the economic cost resulting from CO2 emissions into the atmosphere. It represents the additional cost associated with the emission of an extra ton of CO2, considering the economic damage resulting from global warming. This damage includes losses in agriculture, impacts on infrastructure, and adaptation costs. The SCC is fundamental for policy decisions, guiding the definition of emission reduction targets and the implementation of mitigation strategies.
- Method
The methodology employed in this study was based on the use of the Dynamic Stochastic Climate and Economy (DSICE) computational method, which integrates economic and climate models, considering stochastic and irreversible elements. DSICE consists of a climate model and an economic model, 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 tipping points. The carbon system assumes two sources of carbon emissions 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 °C above pre-industrial levels. Climate tipping points are modeled using a Markov chain process, including the probability of occurrence of tipping events, the expected duration of the process resulting from that event, the average and variance of long-term impacts on economic productivity, and the dependence on climatic factors.
The economic component of DSICE consisted of a simple stochastic growth model, assuming that production generates greenhouse gas emissions and that world output is affected by the state of the climate. The world capital stock was analyzed in trillions of dollars over each year, considering economic aspects such as the production function, population growth, productivity increases, carbon intensity in production, and the impacts caused by temperature levels. The stochastic growth of the productivity factor was calibrated to approximate the resulting consumption process to 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, since there are no adjustment costs. The central planner establishes that the private and social costs of carbon are equated according to a given Pigovian carbon rate. 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 analyses to verify critical point/change processes.
- Main results
The results of this study indicated that the use of Epstein-Zin preferences plays a key 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 the CSC itself is a stochastic process subject to considerable uncertainty. This implies that climate policymaking must take uncertainties into account, including the possibility of extreme weather events, and consider the feasibility of mitigation policies, such as geoengineering and carbon capture, which may be seen as too costly when analyzed solely under deterministic models.
Furthermore, the evidence from this study highlighted the influence of critical climate change elements on the CSC. The threat posed by these events leads to substantial and immediate increases in the 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 change. The internal rate of return analysis also points to the importance of applying a lower discount rate when assessing the damage caused by critical climate change events, due to their lower correlation with total consumption compared to production damage.
Finally, the study highlights DSICE's ability to handle complex nine-dimensional models as opposed to simpler ones. In this sense, it has proven possible and robust to consider factors such as productivity shocks, dynamic preferences, and stochastic elements of critical changes in the climate system.
- Lessons in Public Policy
The study presented DSICE, a computational method designed to examine the economic and climate implications arising from the interaction between the economy and the climate. In this context, the article analyzed the CSC and the optimal carbon tax in scenarios of stochastic and irreversible climate change. The analysis considered uncertainties related to economic growth and risk preferences. DSICE also integrated "turning" elements in the climate system, such as extreme weather events, in order to assess how these uncertainties can impact climate policies. The evidence from this study emphasizes the relevance of risk aversion for the formulation of public policies related to containing climate change.
References
CAI, Y.; LONTZEK, TS The Social Cost of Carbon with Economic and Climate Risks. Journal of Political Economy, v. 127, n. 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. Econometrics, v. 57, no. 4, p. 937, Jul. 1989.
PINDYCK, RS Fat Tails, Thin Tails, and Climate Change Policy. Review of Environmental Economics and Policy, v. 5, no. 2, p. 258–274, 1 Jul. 2011.
WUEBBLES, DJ Setting the Stage for Risk Management: Severe Weather Under a Changing Climate. Em: GARDONI, P.; MURPHY, C.; ROWELL, A. (Eds.). . Risk Analysis of Natural Hazards. Cham: Springer International Publishing, 2016. p. 61–80.
