ABM-IAM: optimal climate policy under bounded rationality and multiple inequalities

We propose a framework that allows to integrate social (tipping) dynamics into a DICE-style IAM model, in which a policy maker determines abatement effort and savings. The policy maker maximizes the sum of social welfare and a political penalty/reward, depending on whether the majority of the population opposes (penalty) or supports (reward) more ambitious abatement policies. The social process itself depends inter alia on observable climate impacts. We provide numerical simulations that illustrate the impact of the tipping process on policy choices which in turn are built around a total cost of carbon that embraces both the "classical" social cost of carbon and a political cost of carbon. Our initial findings illustrate (i) the considerable scope for political penalties (rewards) to stifle (boost) abatement policies; (ii) an incentive for the policy maker to distort policies in a way that boosts political support; and (iii) a considerable deviation between the total cost of carbon and the social cost of carbon. We argue how the model can be used for the purpose of understanding climate policy making from a "social dynamics" perspective.

This article quantifies the impact on optimal climate policy, of both damage elasticity and equilibrium climate sensitivity uncertainty, under separable preferences for risk and intergenerational inequality. The primary findings are as follows. (1) Such preferences can depress the social cost of carbon (SCC) when calibration aims at matching actual economic outcomes, countering the prevailing view that the SCC is greater with separable than with conventional entangled preferences. (2) Damage elasticity uncertainty has larger effects on climate policy than equilibrium climate sensitivity uncertainty, even under high impact tail risk of the latter. (3) Risk aversion decisively strengthens optimal climate policy under joint damage and climate sensitivity uncertainty, than with a single source of uncertainty alone. Indeed, failing to account for the interaction between damage and climate sensitivity uncertainty underestimates the cost of climate change by more than US dollars 1 trillion.

Dark Clouds or Silver Linings? Knightian Uncertainty and Climate Change

Optimal climate policy under tipping risk and temporal risk aversion

This paper aims at providing a consistent framework to appraise alternative modeling choices that have driven the so-called “when flexibility” controversy since the early 1990s, dealing with the optimal timing of mitigation efforts and the social cost of carbon (SCC). The literature has emphasized the critical impact of modeling structures on the optimal climate policy. We estimate within a unified framework the comparative impact of modeling structures and investigate the structural modeling drivers of differences in climate policy recommendations. We use the integrated assessment model (IAM) RESPONSE to capture a wide array of modeling choices. Specifically, we analyse four emblematic modeling choices, namely the forms of the damage function (quadratic vs. sigmoid) and the abatement cost (with or without inertia), the treatment of uncertainty, and the decision framework, deterministic or sequential, with different dates of information arrival. We define an original methodology based on an equivalence criterion to compare modeling structures, and we estimate their comparative impact on two outputs: the optimal SCC and abatement trajectories. We exhibit three key findings: (1) IAMs with a quadratic damage function are insensitive to changes of other features of the modeling structure, (2) IAMs involving a non-convex damage function entail contrasting climate strategies, (3) Precautionary behaviors can only come up in IAMs with non-convexities in damage.

The Comparative Impact of Integrated Assessment Models' Structures on Optimal Mitigation Policies

The Social Cost of Carbon and Inequality: When Local Redistribution Shapes Global Carbon Prices

The Trade-Off between Intra- and Intergenerational Equity in Climate Policy

We provide a theoretical framework and detailed bioeconomic simulations to examine privately and socially optimal dairy farm management in the presence of nutrient runoff and greenhouse gas emissions. Dairy farms produce milk by choosing herd size, diet, fertilization and land allocation between crops, as well as (discrete) manure storage and spreading technologies and the number of milking seasons. We show analytically that a critical radius emerges for the choice of land use between silage and cereal cultivation and fertilizer types (mineral and manure). Both privately and socially optimal manure application rates decrease with application distance. We characterize the optimal climate and water policy instruments for dairy farming. A detailed bioeconomic simulation model links farm management decisions with their impacts on climate and water quality. We numerically solve the social and private optima and the features of optimal climate and water policy instruments. We show that using only climate instruments provides considerable water co‐benefits, and in the same vein, the use of water quality instruments provides considerable climate co‐benefits. Climate policies lead to a reduction in herd size, as measures relating to manure management and spreading are relatively inefficient at reducing climate emissions. There is much more leeway for adapting to water policies than to climate policies, because dairy farms have multiple measures to reduce their nutrient loads.

Many studies project that climate change can cause a significant number of excess deaths. Yet, in integrated assessment models (IAMs) that determine the social cost of carbon (SCC) and prescribe optimal climate policy, human mortality impacts are limited and not updated to the latest scientific understanding. This study extends the DICE-2016 IAM to explicitly include temperature-related mortality impacts by estimating a climate-mortality damage function. We introduce a metric, the mortality cost of carbon (MCC), that estimates the number of deaths caused by the emissions of one additional metric ton of CO2. In the baseline emissions scenario, the 2020 MCC is 2.26 × 10‒4 [low to high estimate −1.71× 10‒4 to 6.78 × 10‒4] excess deaths per metric ton of 2020 emissions. This implies that adding 4,434 metric tons of carbon dioxide in 2020—equivalent to the lifetime emissions of 3.5 average Americans—causes one excess death globally in expectation between 2020-2100. Incorporating mortality costs increases the 2020 SCC from $37 to $258 [−$69 to $545] per metric ton in the baseline emissions scenario. Optimal climate policy changes from gradual emissions reductions starting in 2050 to full decarbonization by 2050 when mortality is considered.

Measurement of China's provincial social cost of carbon under the integrated socioeconomic-climate framework

How expensive should CO2 be? Fuel for the political debate on optimal climate policy

Abrupt positive feedback and the social cost of carbon

How Expensive Should CO<sub>2</sub> Be? Fuel for the Debate on Optimal Climate Policy

Long-term decisions, such as decisions about carbon abatement, are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and nonmarket goods, and it investigates the optimal climate policy when social preference for them is also changed by climate policy in a seminal climate-economy model: the Dynamic Integrated Model of Climate and the Economy (DICE) model with a single objective to maximize market goods consumption. We amend DICE to study an optimization problem that trades off the values of market and nonmarket goods, considering endogenous preferences. The relative price of non-market goods grows over time because of increases in both relative scarcity and the appreciation that represents the preference of it. As nonmarket goods are increasingly valuable relative to market ones, climate damages on nonmarket goods are increasingly expensive. The social cost of carbon, which aggregated climate impact on both goods, is consequently higher. Because abatement decision affects the valuation of nonmarket goods in the utility function, unlike previous climate-economy models, we solve the model iteratively by taking the obtained abatement rates from the last run as inputs in the current run. The results in baseline calibration advocate a more stringent climate policy, where endogenous social preference to climate policy raises the social cost of carbon further by roughly 12%–18% this century. Moreover, neglecting changing social preference leads to an underestimate of nonmarket goods damages by 15%. Climate policy is self-reinforced if it favors the more expensive consumption good. The proposed method can be applied to other long-term problems with endogenous preferences. Funding: H. Liao appreciates the funding from the National Natural Science Foundation of China [Grants 71925008, 72293603, and 72488101].