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Building Interpretable Climate Emulators for Economics

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Representing deforestation-driven loss of land carbon storage raises the optimal social cost of carbon by up to 17 US$ per tonne of CO2 within a DICE-2016-style model.

desk verdict A transparent, reproducible emulator framework whose static-box calibration deserves publication, but whose headline land-use SCC results rest on an acknowledged, uncalibrated r=1 assumption and should be treated as a scenario until sensitivity analysis is added. read the letter →

arxiv 2411.10768 v2 pith:Z2YW3IQN submitted 2024-11-16 econ.EM cs.CEcs.LG

Aryan Eftekhari , Doris Folini , Aleksandra Friedl , Felix Kübler , Simon Scheidegger , Olaf Schenk This is my paper · ORCID
classification econ.EMcs.CEcs.LG
keywords climateemulatorscarboncycleboxmodelsintegratedassessmentsocialcostofland-usechangepatternscalingDICE-2016
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the way a climate emulator represents the land biosphere is not a neutral modeling detail: it changes the optimal climate policy. Within a DICE-2016-style integrated assessment model, the authors compare a three-box carbon cycle, a four-box version with a static land reservoir, and a four-box version whose land storage capacity shrinks one-for-one with land-use emissions. The static land box leaves outcomes essentially unchanged, but the shrinking-sink version leaves nearly 80 GtC more carbon in the atmosphere by 2100, adds roughly 0.2 °C of warming, and raises the optimal social cost of carbon by up to 17 USD per tCO2 (roughly 12–14%). The same framework, through pattern scaling, also quantifies how the choice of regional warming pattern and present-day baseline propagates into local damages. The authors conclude that omitting deforestation-driven loss of land carbon storage causes systematic underpricing of carbon.

What carries the argument

The central object is the generalized linear multi-reservoir carbon-cycle operator A (a mass-conserving, equilibrium-constrained box model) with a time-dependent land-biosphere equilibrium mass. The decisive element is the update $\tilde{m}^L_{t+1} = \tilde{m}^L_t - r\, e^L_t$ with $r=1$, which shrinks the land reservoir's carbon holding capacity in lockstep with land-use emissions, forcing a larger share of emitted carbon to remain in the atmosphere. Calibration fits the operator's fluxes and equilibrium masses to the multi-model mean of the 100 GtC pulse-decay benchmark, with penalty terms for dynamic timescales, equilibrium masses, and ocean-to-land uptake ratios; a second layer rescales eigenvalues to emulate fast and slow extremes. For spatial downscaling, the workhorse is the linear pattern-scaling relation $\Delta T^z = \Delta T^{AT}\, \beta^z$ combined with an observational climatology to obtain absolute regional temperatures.

What would settle it

Estimate r directly from data: compare cumulative land-use emissions against observed losses of global vegetation and soil carbon stocks; an r of, say, 0.3 instead of 1 would cut the 0.2 °C and 17 USD effects proportionally. Equivalently, drive the 4PR-X emulator with historical land-use emissions from 1850 onward and check whether the simulated atmospheric CO2 trajectory remains within the observed ice-core and Mauna Loa record; a systematic divergence would falsify the one-for-one sink-loss mechanism.

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Extended reading notes

Core claim

Representing the land biosphere as a dynamic reservoir whose equilibrium carbon mass falls in proportion to land-use emissions (the 4PR-X model) materially alters the climate-economy outcome relative to the static three- and four-box emulators. Under business-as-usual and optimal mitigation, the 4PR-X emulator projects atmospheric carbon about 6% higher (~80 GtC) and global-mean temperature about 0.2 °C higher by 2100, raising the optimal social cost of carbon by 11.9–13.9% in 2020–2050 (up to 17 USD per tCO2). The fourth reservoir alone does nothing: the static 4PR model reproduces 3SR behavior almost exactly. The result is driven by an explicit mechanism: each ton of deforestation carbon emits CO2 and simultaneously removes one ton of permanent land storage capacity, so more carbon remains airborne; the paper explicitly flags that the one-to-one relationship (r=1) is an assumption.

Load-bearing premise

The whole policy effect rests on the assumption that each ton of carbon emitted through land-use change permanently removes one ton of carbon storage capacity from the land biosphere in the emulator; if the true loss is smaller or delayed, the extra warming and higher carbon price shrink accordingly.

Editorial extensions

If this is right

  • DICE-type integrated assessment models that omit deforestation-driven loss of land carbon storage underestimate future atmospheric carbon, warming, and the optimal carbon price needed to offset them.
  • Policy evaluations of carbon capture and storage are overly optimistic unless land-use change is controlled: the 4PR-X model shows that CCS alone leaves temperatures about 7% higher in 2100 if deforestation continues.
  • Adding a static land-biosphere box to a three-box carbon cycle changes atmospheric carbon, temperature, and SCC by negligible amounts; the improvement comes only when the land reservoir's capacity responds to land-use emissions.
  • Pattern-scaling choices carry real economic weight: anchoring regional warming to different present-day climatologies can shift 2100 regional mean temperatures by up to roughly 3 °C, which can flip a region from a relative winner to a relative loser in a hump-shaped damage function.
  • Emulator calibration to present-day rather than pre-industrial conditions changes the trajectory but preserves the qualitative ordering, with present-day initialization yielding higher accumulation and higher SCC.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the r=1 assumption is relaxed to a value calibrated against observed biomass-loss data, the SCC uplift would likely shrink but remain positive for any r>0; the paper's headline numbers should be read as an upper-bound estimate of the deforestation feedback.
  • The same shrinking-sink mechanism could be ported to other compact climate models used for policy (impulse-response or reduced-complexity emulators), where land-use emissions are currently treated purely as an atmospheric source rather than as a reduction in future uptake capacity.
  • A testable extension is to couple the dynamic land reservoir with a simple carbon-cycle non-linearity such as saturating CO2 fertilization; under high emissions that non-linearity would reinforce the 4PR-X effect, making the 0.2 °C a lower bound at high concentration.
  • The pattern-scaling uncertainty decomposition suggests that spatial integrated assessment models should treat the warming pattern and the baseline climatology as separate, hedgeable uncertainty sources rather than pooling them into one damage-function risk.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper develops an open-source framework for building interpretable, physically constrained linear box-model carbon-cycle emulators for use in economic integrated assessment models. Three emulators are constructed and compared within a DICE-2016-style model: a three-reservoir model (3SR), a four-reservoir model with a static land box (4PR), and a four-reservoir model with a dynamic land capacity that declines with land-use emissions (4PR-X). The authors find that 3SR and 4PR give nearly identical results, while 4PR-X produces substantially higher atmospheric carbon, temperature, and social cost of carbon by 2100. A second contribution propagates global-mean temperature projections to regional scales via pattern scaling using the Lynch et al. (2017) CMIP5 pattern library, and illustrates how choices of warming pattern and observational baseline affect regional damage estimates.

Significance. If the quantitative claims are upheld, the paper makes a useful methodological contribution: it provides a transparent, reproducible, and physically interpretable alternative to black-box climate emulators, with code publicly available and validation against the Joos et al. (2013) pulse-decay benchmark as well as ZECMIP and RCP-style tests. The finding that a static land box hardly changes DICE outcomes while a dynamic land-capacity representation materially increases warming and SCC is a policy-relevant result for the IAM community. The pattern-scaling module is a useful plug-and-play addition, clearly separating emulator, pattern, and baseline uncertainties. However, the headline quantitative results for 4PR-X are driven by an uncalibrated parameter, and the accuracy claims in the abstract and Section 3.1.4 are contradicted by the paper's own Appendix A.2, so the current version does not yet establish the magnitude of the claimed effects.

major comments (3)
  1. [Section 2.1, Eq. (7); Tables 3-5] The central quantitative result—approximately 80 GtC higher atmospheric carbon, 0.2°C extra warming, and 12–14% higher SCC in 4PR-X relative to the static models—is generated entirely by the assumption r=1 in Eq. (7), which sets the decline in equilibrium land-biosphere capacity equal to one-for-one land-use emissions. The paper explicitly acknowledges in Section 2.1 that 'a one-to-one correspondence ... may not be guaranteed' and that 'r could be different from 1,' yet no sensitivity analysis is reported. Because all differences between 4PR-X and the static models scale with r, the quantitative headline is conditional on this uncalibrated assumption. I request a sensitivity analysis over a plausible range of r (e.g., 0.25, 0.5, 0.75, 1) for the BAU and optimal mitigation runs, and reporting of how the 2100 atmospheric carbon, temperature, and SCC differences respond. If results shrink substantially for lower r, the abstract and conclusions should be reframed as qualitative.
  2. [Abstract; Section 3.1.4; Appendix A.2, Figure 21] The abstract and Section 3.1.4 claim that the three- and four-box emulators 'reproduce the historical and long-run evolution of atmospheric CO2 and global temperature to within about 5% and 3%, respectively.' This is contradicted by Appendix A.2, which states that the PI-calibrated 3SR and 4PR models 'systematically underestimate atmospheric CO2' under RCP scenarios, and Figure 21 shows these models falling well below the CMIP5 RCP concentration trajectories. The 5%/3% accuracy figure appears to refer to the atmospheric-pulse-decay fit error (Figures 15 and 24), not to historical or scenario concentration accuracy. Please clarify precisely which metric the accuracy claim refers to, and either qualify or remove the broad claim.
  3. [Appendix A.2, Table 11, Section 4.2] The 4PR-X model's equilibrium land-biosphere mass in 2015 is 258 GtC (Table 11), far below the Ciais et al. (2014) active land-pool estimate of ~550 GtC cited in Section 3.1.1, and below the 4PR value of 387 GtC. The paper acknowledges this in Section 4.2, noting that the low 2015 land content is 'slightly outside the estimated range.' This implausibly low land pool is a direct consequence of applying r=1 cumulatively to historical land-use emissions, and it contributes to the elevated atmospheric burden in 4PR-X. The paper should discuss whether this initial-condition distortion unduly inflates the projected differences, and whether an r<1 calibration or a cap on the equilibrium-mass decline would yield more realistic land-pool trajectories.
minor comments (4)
  1. [Table 6] The table header uses '3PR' as the column label, which is inconsistent with the '3SR' nomenclature used throughout the paper.
  2. [Appendix A.1.1, Figure 16 caption] The caption states that 'the q1 penalty function enforces an approximately equal mass of carbon absorption between the oceans and the land biosphere,' but this is the role of the q3 penalty (reservoir absorption ratios), not q1 (dynamic timescales). Please correct the reference.
  3. [Section 5.2, Table 7] The comparison of anchoring choices in Table 7 conflates two differences: the baseline period (1961-1990 for model climatologies vs 1991-2020 for ERA5) and the use of model versus observed climatology. The statement that the anchor choice can shift 2100 regional means by 'up to 2.5-3°C' should be decomposed into these two effects, since the baseline-period difference alone can explain part of the spread.
  4. [Figure 12 text] There is a typo in the paragraph above Figure 12: 'model undertainty' should be 'model uncertainty.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 4PR-X results are conditional on an explicitly flagged r=1 assumption, not on a fitted parameter renamed as a prediction or on a load-bearing self-citation chain.

full rationale

The central quantitative claim (about 80 GtC, 0.2°C, and 12–14% SCC differences) is generated by the 4PR-X operator in Eq. (34) with the land equilibrium mass evolving according to Eq. (7) at r=1. This is an explicit structural assumption, and the paper flags its uncertainty: Section 2.1 states, 'In our simplified setting we adopt a one-to-one correspondence (r = 1)... The proportionality factor r could be different from 1.' The paper never fits r to the atmospheric data whose response it then reports; the pulse-decay calibration in Section 3.1.1 constrains the transfer coefficients and equilibrium masses under a fixed land equilibrium mass, and the q3 penalty targets a pulse-absorption ratio, not land-use-driven capacity loss. The 4PR-X-versus-static divergence is therefore a conditional scenario output, not a disguised refit of the same target. The direction of the effect (a shrinking land sink leaves more carbon in the atmosphere) is indeed built into Eqs. (7) and (34), but the paper presents this as the modeled mechanism and explicitly cautions that r may differ from 1, so the headline numbers are not presented as an independently measured empirical prediction. External anchors do exist for the parts of the framework that are claimed as predictions: the pulse-decay calibration uses the external Joos et al. (2013) multi-model mean with out-of-sample validation for t>250 (Appendix A.1.1), the zero-emissions commitment experiments are checked against MacDougall et al. (2020), and the pattern-scaling analysis uses the external CMIP5 pattern library of Lynch et al. (2017). Self-citations to Folini et al. (2024) supply the temperature module and calibration-data justification, but they are not load-bearing for the land-use channel that drives the paper's main new results. Accordingly, no circular step meets the required standard of exhibiting a specific reduction of a claimed prediction to its own fitted inputs or to a self-citation chain.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

No new physical entities are postulated; the land biosphere, ocean layers, and atmosphere are standard reservoirs, and the dynamic land capacity in 4PR-X is a modification of an existing reservoir's equilibrium value, not a new entity. The main latent assumption is the r=1 conversion in Eq. (7), which is the load-bearing ad hoc choice.

free parameters (3)
  • r (land-use emission to equilibrium-mass conversion factor) = 1 (assumed, not fitted)
    Eq. (7) sets the fraction of land-use emissions that permanently reduce land-biosphere equilibrium mass to 1. All 4PR-X increases in atmospheric carbon, temperature, and SCC scale with this number; the paper states r could differ from 1.
  • Penalty weights ρ1, ρ2, ρ3 = 1e-2, 1e-4, 1e-4 (3SR: ρ3=0)
    Chosen by grid search in Section 3.1.3 to balance fit error and physical constraints. They shape the calibrated transfer coefficients but are not the source of the 4PR-X result.
  • Extreme scaling factors c_mu+ and c_mu- = 3SR: 0.4746 and 2.4559; 4PR: 0.4701 and 2.4074
    Fitted in Eq. (14) to the µ+ and µ- envelopes of Joos et al. (2013) to build the weighted operator Aα for extreme scenarios. The headline α=0 results do not depend on them.
assumptions (5)
  • domain assumption Linear box model with mass conservation and eigenvalues in (-1,0]
    Eqs. (1)-(4); the entire CCE framework assumes carbon fluxes are linear in reservoir masses and that equilibrium ratios are fixed except for the 4PR-X land capacity.
  • domain assumption Two-layer energy balance temperature model with logarithmic CO2 forcing
    Eqs. (16)-(18), from Geoffroy et al. (2013) with κ=1.2; used to convert carbon mass changes into global temperature changes.
  • domain assumption Pattern scaling linearity with stationary βz
    Eqs. (27)-(28); inherited from Santer et al. (1990) and Lynch et al. (2017), assumes local warming scales linearly with global mean warming.
  • domain assumption Inverted-U regional damage function
    Eq. (31) and Table 8, taken from Krusell and Smith (2022); used to convert local absolute temperatures into regional TFP changes.
  • ad hoc to paper r=1 one-for-one land capacity reduction
    Eq. (7); not calibrated, and the paper notes r could differ from 1. This is the key assumption driving the 4PR-X results.

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Pith. "Pith review of Building Interpretable Climate Emulators for Economics." pith.science (2026). https://pith.science/paper/Z2YW3IQN

@misc{pith2026241110768,
  author       = {Pith},
  title        = {Pith review of: Building Interpretable Climate Emulators for Economics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z2YW3IQN}},
  note         = {Machine review of arXiv:2411.10768}
}
read the original abstract

We introduce a framework for developing efficient and interpretable climate emulators (CEs) for economic models of climate change. The paper makes two main contributions. First, we propose a general framework for constructing carbon-cycle emulators (CCEs) for macroeconomic models. The framework is implemented as a generalized linear multi-reservoir (box) model that conserves key physical quantities and can be customized for specific applications. We consider three versions of the CCE, which we evaluate within a simple representative agent economic model: (i) a three-box setting comparable to DICE-2016, (ii) a four-box extension, and (iii) a four-box version that explicitly captures land-use change. While the three-box model reproduces benchmark results well and the fourth reservoir adds little, incorporating the impact of land-use change on the carbon storage capacity of the terrestrial biosphere substantially alters atmospheric carbon stocks, temperature trajectories, and the optimal mitigation path. Second, we investigate pattern-scaling techniques that transform global-mean temperature projections from CEs into spatially heterogeneous warming fields. We show how regional baseline climates, non-uniform warming, and the associated uncertainties propagate into economic damages.

Figures

Figures reproduced from arXiv: 2411.10768 by the authors.

Figure 1
Figure 1. Stylized schematic of an IAM. In their structural form, an IAM in macroeconomics typically links three components: (i) an economic module that generates fossil-fuel CO2 emissions; (ii) a CE, that is, a reduced-form physical model that maps those emissions into atmospheric CO2 concentrations and global-mean temperature T AT; and (iii) a damage module that transforms the resulting temperature trajectory into economic … view at source ↗
Figure 2
Figure 2. The left panel depicts the non-zero pattern of the operator matrix A for the four-reservoir carbon￾cycle emulator (4PR), whose reservoirs are the atmosphere (A), upper ocean (O1), deep ocean (O2), and land biosphere (L). Black squares indicate fixed, non-zero coefficients, while colored squares mark free parameters that are calibrated (cf. Equation (4) below). Matrix rows (“To”) correspond to recipient reservoirs an… view at source ↗
Figure 3
Figure 3. Simulated atmospheric CO2 decay after a 100 GtC pulse under PI equilibrium conditions with experimental data from Joos et al. (2013). In the left and right panels, we show the decay trajectories of different models for 0–500 and 0–100 years, respectively. Shown is the simulated pulse decay across various test models (thin gray lines), including ESMs and EMICs. Shown also is the multi-model mean (µ, thick black line)… view at source ↗
Figures from the paper (29 more)
Figure 4
Figure 4. Figure 4: The top panel shows the average absolute error (∥mA − y µ ∥1/T), and the bottom panel shows the dynamic timescales τ for the 3SR (left) and 4PR (right) models across varying ρ1, with ρ2 = ρ3 = 10−4 fixed. The average absolute error represents the mean absolute differen…
Figure 5
Figure 5. Figure 5: The simulation results for the BAU case (µt = 0). In [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]
Figure 6
Figure 6. Figure 6: Share of output lost to temperature damages (left) and the resulting monetary loss (right) [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: traces the same variables under the optimal-mitigation policy. The ordering of outcomes, 4PR-X above 4PR and 3SR, mirrors the BAU experiment. The optimal run additionally displays the mitigation rate and the SCC (bottom-right panel). Both series are consistently higher…
Figure 8
Figure 8. Figure 8: Atmospheric carbon concentrations (left panel) and atmospheric temperature (right panel) under the full abatement scenario (µt = 1). Consistent with the BAU and optimal-policy experiments, adding a static land-biosphere reservoir leaves climate trajectories practically…
Figure 9
Figure 9. Figure 9: Top panel: 30-year climatological mean near-surface air temperature (1991–2020) from the ERA5 reanalysis, taken as the observational reference. Bottom panels: temperature biases of two CMIP5 Earth￾System Models with respect to that reference, shown as model climatology…
Figure 10
Figure 10. Figure 10: WGI (v4) Reference Land and Land-Ocean Regions (Iturbide et al., 2020). resolution against computational tractability. Nevertheless, the optimal degree of regional aggregation ultimately depends on the specific objectives of the study at hand [PITH_FULL_IMAGE:figures…
Figure 11
Figure 11. Figure 11: Spatial warming patterns β z corresponding to a 1°C increase in global mean temperature, shown as gridded maps (left column) and regionally aggregated maps (right column). The patterns are generated using pattern scaling based on the ESMs MPI-ESM-LR (top row) and HadG…
Figure 12
Figure 12. Figure 12: Damages, expressed as D˜(T z 2100)/D˜(T z Baseline) − 1, assuming a global-mean warming of 2.65 °C since pre-industrial times (≈ 1.55 °C since 2015) under the 4PR-X scenario. Absolute temperatures stem from ERA5 (T z abs,c); pattern factors β z are taken from MPI-ESM-…
Figure 13
Figure 13. Figure 13: Relative change in damages D˜(T z 2100)/D˜(T z Baseline) − 1 as a function of present-day (x-axis) and future (y-axis) temperatures, based on expression Equation (32). Coloured points correspond to the cities listed in [PITH_FULL_IMAGE:figures/full_fig_p030_13.png]
Figure 14
Figure 14. Figure 14: Annual and cumulative CO2 emissions: total fossil-fuel and industrial sources (left panels) versus land-use-change sources (right panels) across RCP scenarios 2.6, 4.5, 6.0, and 8.5. A.1 Atmospheric Perturbation Tests Our first numerical experiment follows a standard …
Figure 15
Figure 15. Figure 15: Emulated pulse fraction (top panels) and absolute error (bottom panels) for a 100 GtC atmospheric pulse under PI equilibrium conditions, simulated with the 3SR and 4PR configurations. Grey lines reproduce the multi-model test ensemble of Joos et al. (2013); the dashed…
Figure 16
Figure 16. Figure 16: Relative atmospheric fraction of a 100 GtC pulse over 500 years, illustrating the influence of the penalty terms for Equilibrium-Mass Variability (q2) and Reservoir-Absorption Ratios (q3), simulated with the 3SR (left panels) and 4PR (right panels) emulators. The expe…
Figure 17
Figure 17. Figure 17: Fraction of a 100 GtC pulse absorbed by various reservoirs over a 500-year period using the weighted operator A α for the 3SR (left column) and 4PR (right column) model configurations; see Section 3.1.2 for further details. The benchmark simulation with α = 0 represen…
Figure 18
Figure 18. Figure 18: Trajectories of atmospheric CO2 concentration (top panel) and global-mean temperature (bottom panel) after emissions cease, shown for the 3SR and 4PR emulators. Thick solid lines correspond to α = −1, 0, and 1. Thin solid lines show simulations from MacDougall et al. …
Figure 19
Figure 19. Figure 19: Change in CO2 mass within each reservoir relative to the PI equilibrium condition, using the weighted operator A α in the 3SR (left panels) and 4PR (right panels) model configurations. The simulations are based on the experimental setup described in Appendix A.1.2. Mo…
Figure 20
Figure 20. Figure 20: The time dependent carbon mass as well as the equilibrium mass of the land biosphere, denoted as mL t and m˜ L t , respectively, are shown as fractions of their initial values (t = 0) for the 4PR and 4PR￾X case. Results are provided for both constant and time-dependen…
Figure 21
Figure 21. Figure 21: The top four panels illustrate the simulated atmospheric CO2 concentration, followed by the next four panels, which show the global-mean temperature change. A CCE should be able to reproduce atmospheric CO2 concentration as used in the RCP scenarios of CMIP5. This goa…
Figure 22
Figure 22. Figure 22: Reservoir CO2 anomalies relative to the pre-industrial equilibrium, simulated with the 4PR em￾ulator: constant operator (top panels) versus time-dependent operator (bottom panels). All runs follow the experimental protocol of Section 4 and use α = 0 [PITH_FULL_IMAGE:…
Figure 23
Figure 23. Figure 23: Analogous to [PITH_FULL_IMAGE:figures/full_fig_p042_23.png]
Figure 24
Figure 24. Figure 24: Analogous to [PITH_FULL_IMAGE:figures/full_fig_p043_24.png]
Figure 25
Figure 25. Figure 25: Analogous to [PITH_FULL_IMAGE:figures/full_fig_p043_25.png]
Figure 26
Figure 26. Figure 26: Optimal concentrations in the atmosphere (left panel) as well as the atmospheric temperature (right panel) for the present-day simulations. This more pronounced climate response to the emissions results in the need for higher mitigation efforts and the increased SCC. …
Figure 27
Figure 27. Figure 27: Optimal abatement (left panel) and the SCC (right panel) for present-day simulations. C A Practitioner’s Guide to Using Climate Emulators This appendix supplies additional information on the IAM used in the numerical experiments of Section 4 as well as summarizes prac…
Figure 28
Figure 28. Figure 28: Optimal atmospheric CO2 concentrations (left panel) and atmospheric temperature (right panel) in the higher damages scenario. 2020 2040 2060 2080 2100 Year 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fraction 3SR 4PR 4PR-X (a) Abatement 2020 2040 2060 2080 2100 Year 100 200 3…
Figure 29
Figure 29. Figure 29: Optimal abatement levels (left panel) and Social Cost of Carbon (SCC) (right panel) in the higher damages scenario [PITH_FULL_IMAGE:figures/full_fig_p046_29.png]
Figure 30
Figure 30. Figure 30: Optimal atmospheric carbon concentrations (left panel) and atmospheric temperature (right panel) for varying discount rate values. 2020 2040 2060 2080 2100 Year 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Fraction 3SR, ψ=0.69 3SR, ψ=2. 3SR, ψ=0.5 4PR, ψ=0.69 4PR, ψ=2. 4PR, ψ=…
Figure 31
Figure 31. Figure 31: Optimal abatement levels (left panel) and Social Cost of Carbon (SCC) values (right panel) across different discount rates [PITH_FULL_IMAGE:figures/full_fig_p047_31.png]
Figure 32
Figure 32. Figure 32: Regional warming β z (color-coded in degrees Celsius) for 46 regions (y-axis) across 41 models (x-axis), including the multi-model minimum, maximum, and range, under a global mean temperature increase of 1 °C [PITH_FULL_IMAGE:figures/full_fig_p049_32.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.