Social Cost of Greenhouse Gases -- OPTiMEM and the Heat Conjecture(s)

TL;DR

This work presents OPTiMEM, an Ocean-Heat-Content–driven climate-economy framework that links carbon emissions, ocean heat uptake, and economic damages to compute social costs of greenhouse gases. It introduces a heat-conjecture that damages scale with ocean heat content rather than temperature alone, validates the model against NOAA datasets, and proposes a long-horizon carbon bond to implement real-world discounting. The methodology integrates carbon consumption, EEI, OHC, and a diffusion-physics approach to forecast damages across multiple GHGs (CO$_2$, CH$_4$, N$_2$O, F-gases) under baseline and tipping-point scenarios, including permafrost thaw. Key contributions include a novel long-term discounting mechanism, a dynamically evolving SC-GHG framework across gas species, and a comprehensive NOAA-based risk analysis that emphasizes outlier-weather events and tipping-points. The practical impact lies in offering a physics-grounded, finance-ready alternative to IAMs for policy appraisal and long-range climate risk assessment, while clearly delineating uncertainties and the need for further integration of land/cryosphere processes.

Abstract

Despite well-meaning scenarios that propose global CO2 emissions will decline presented in every IPCC report since 1988, the trend of global CO2 increase continues without significant change. Even if any individual nation manages to flatten its emissions, what matters is the trajectory of the globe. Together the gulf between climate science and climate economics, plus the urgent need for alternative methods of estimation, provided the incentives for development of our Ocean-Heat-Content (OHC) Physics and Time Macro Economic Model (OPTiMEM) system. To link NOAA damages to climate required creating a carbon consumption model to drive a physics model of climate. How fast could carbon be burned and how much coal, oil and natural gas was reasonably available? A carbon model driving climate meant burning the carbon, and modelling how the earth heated up. We developed this using the most recent best greenhouse gas equations and production models for CO2, CH4, N2O, and halogenated gases. This developed an ocean heat content model for the globe. Each step is validated against Known carbon consumption, CO2, temperature, and ocean heat content. This allows a physics founded model of climate costs to be projected.

Figures (59)

• Figure 1: Gigatons of CO$_2$ equivalent greenhouse gases emitted by yearClimateWatch2021DataExplorerGHG. Upper dots global emissions, lower crosses, USA emissions. USA emissions as percentage of global emissions declined from high of 18% of global emissions to 12% due to global growth by remaining fairly flat. However, cost per tonne increases with global CO$_2$, not national CO$_2$. Given the longevity of the climate problem, the CO$_2$ equivalent metrics have understated the climate impact of CO$_2$ relative to other GHGs, leading to unwarranted emphasis on GHGs like CH$_4$Tans_2022UseAbuseOfC-14andC-13InAtmosphericCO2.
• Figure 2: Expert heuristic estimate of economic damages---interesting, but not a viable path forward.This graph presents a relation between temperature ($\Delta$T) and damages that simply does not exist. We present this graph to show the evolution of our thinking over time. Here we have responded to the criticisms regarding quadratic (exponential) growth, smooth curves, catastrophic events and discontinuities STANTON2009Inside30ClimateModelsPindyck2017UseandMisusePindyck2013PolicyDilemmaPindyck2013WhatmodelstellWEITZMAN2010WEITZMAN2012Keen2020appallinglykeen2021erroneousKeen2022EstimatesTippingCannotBeReconciled, providing 3 layers of "cloud", where the actual results could be likely to land. The heavy smooth black curve is the expert's mean. IAMs non-market damages quadratic function (long dash light red) nordhaussztorc2016Rdice. The small blue/red/gray heat map points suggest probability potential for deviation from the expert's mean curve. Blue dots is highest probability damages years up to 1 bad year in 10, red dots is intermediate 1 year in 100, gray dots is lowest probability, up to 1 year in 1000. (These are, in concept, like 10 year, 100 year and 1000 year storms.) Three large red dots are Swiss Re published estimates SwissReInstClimate. The smooth curves should not occur in the real world. Instead, a random walk centred on a curve, bounded by the blue/red/gray probability regions should occur. However, again, the relationship shown here does not actually exist.
• Figure 3: Declining Discount Rates In the Green Book$f(x) = -1.1997 \cdot 10^{-5} \cdot x + 0.004976$ (eq. \ref{['eq:HMTreasuryGreenBookFittedCurve']}) $R^2 = 0.988 \qquad$ 0 intercept = 414.77 years
• Figure 4: Declining Discount Rates using Formula $\rho \, 1$ such that $\overline{\rho}$ =0.50% for $t$=0-30. $P_S \approx6.667$ determined from equation \ref{['eq:IntegralsF1-3']}
• Figure 5: Declining Discount Rates using Formula $\rho \, 2$ such that $\rho = 0.50\%$.$P_S \approx 0.424$ determined from Formula $\rho \,2$