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Operationalizing the net-negative carbon economy

Nature volume 596, pages 377–383 (2021)Cite this article

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Abstract

The remaining carbon budget for limiting global warming to 1.5 degrees Celsius will probably be exhausted within this decade1,2. Carbon debt3 generated thereafter will need to be compensated by net-negative emissions4. However, economic policy instruments to guarantee potentially very costly net carbon dioxide removal (CDR) have not yet been devised. Here we propose intertemporal instruments to provide the basis for widely applied carbon taxes and emission trading systems to finance a net-negative carbon economy5. We investigate an idealized market approach to incentivize the repayment of previously accrued carbon debt by establishing the responsibility of emitters for the net removal of carbon dioxide through ‘carbon removal obligations’ (CROs). Inherent risks, such as the risk of default by carbon debtors, are addressed by pricing atmospheric CO2 storage through interest on carbon debt. In contrast to the prevailing literature on emission pathways, we find that interest payments for CROs induce substantially more-ambitious near-term decarbonization that is complemented by earlier and less-aggressive deployment of CDR. We conclude that CROs will need to become an integral part of the global climate policy mix if we are to ensure the viability of ambitious climate targets and an equitable distribution of mitigation efforts across generations.

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Fig. 1: Idealized global tax scheme with net carbon removal fund.
Fig. 2: Idealized ETS with intertemporal trade of carbon debt.
Fig. 3: The 2 °C (RCP 2.6) mitigation scenarios for a range of interest rates on carbon debt.
Fig. 4: The 1.5 °C (RCP 1.9) pathways under a conventional ETS or a CRO-ETS.

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Data availability

All data generated or analysed during this study are included in this published Article and its Supplementary Information.

Code availability

The source code of the numerical model used for generating the data used in this study is available at https://github.com/jobednar/CROmodel. The numerical model was calibrated using scenarios from the SSP scenario database hosted by IIASA (https://tntcat.iiasa.ac.at/SspDb/).

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Acknowledgements

We acknowledge financial support from the European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P as well as from the FWF grant P-31796 Medium Complexity Earth System Risk Management (ERM) and support from the HSE University Basic Research Program for A.B.

Author information

Authors and Affiliations

  1. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Johannes Bednar, Michael Obersteiner, Artem Baklanov, Fabian Wagner & Oliver Geden

  2. School of Geography and the Environment, University of Oxford, Oxford, UK

    Johannes Bednar, Michael Obersteiner, Myles Allen & Jim W. Hall

  3. HSE University, St Petersburg, Russian Federation

    Artem Baklanov

  4. National Center for Ecological Analysis and Synthesis (NCEAS), University of California, Santa Barbara, CA, USA

    Marcus Thomson

  5. German Institute for International and Security Affairs, Berlin, Germany

    Oliver Geden

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Contributions

M.O., F.W., M.T. and J.B. have contributed equally to identifying the knowledge gaps and main ideas of this Article, as well as sharpening the field of interest. J.B. acted as lead author and was primarily involved in formalizing and quantifying the ideas of the Article, as well as in drafting the main paper and developing the analytical and numerical methods. J.W.H. and M.O. supervised the development of the paper from the first draft throughout the review process. J.W.H., O.G., M.A., F.W. and M.T. contributed to the framing, conception and design of the work, as well as to the interpretation of the results. A.B. provided quality control of methods and the presentation of the results and contributed the mathematical proof of the equations derived in the Methods. O.G. contributed by markedly improving the policy relevance of the Article (for instance, by developing the EU implementation scenario). All authors were equally involved in the revision process and have approved the submitted version of the Article.

Corresponding author

Correspondence to Johannes Bednar.

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Peer review information Nature thanks David Stainforth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Schematic supply of emission allowances and CROs at a fixed point in time.

The supply of allowances is completely inelastic (emission cap), whereas the supply elasticity of CROs is determined by discounted future abatement costs, which increase as the demand for CROs increases, as well as interest costs, which can be controlled by managing authorities and financial institutions (dashed blue CRO supply curves). If CROs are traded on a market, they clear at the same price as allowances and thereby reduce the price of allowances. The larger the elasticity of the CRO supply curve, the lower the potential for price volatility (red arrows), as—for example—induced by a demand shock (dashed orange line). The sum of allowances and CROs issued equals net emissions. Abated emissions equal the difference between baseline emissions (green) and net emissions and consist of emission reductions and/or carbon removal.

Extended Data Fig. 2 Abatement costs and carbon debt of 1.5 °C (RCP 1.9) scenarios for six different MACCs of DACS and interest rates on carbon debt rd = 0 and rd = 0.08.

A definition of D is provided in the Methods. Abatement costs are discounted and expressed as a percentage of the GDP. Abatement costs are exclusive of interest costs. For each rate rd, 78 scenarios (13 scenarios as for the RCP 2.6 analysis times 6 DACS parameters) are grouped by DACS costs (low to high, that is, ‘LoCost’, ‘MedCost’ and ‘HiCost’) and DACS capacity limits (10% and 30% of baseline emissions, that is, ‘LoCap’ and ‘HiCap’). a, b, Median abatement costs as a function of median carbon debt D for rd = 0 (a) and rd = 0.08 (b). For rd = 0, we observe an inverse relation between the level of carbon debt and abatement costs; and the capacity limit is a stronger determinant of abatement costs than DACS deployment costs. This ‘discounting effect’ is reversed when rd = 0.08 and high levels of \(D\) are penalized. In this case, lower abatement costs are realized by lower carbon debt (and vice versa). For both rates rd ‘LoCost_HiCap’ DACS scenarios are characterized by the lowest abatement costs, however, at very different levels of D. When interest is invoked, DACS deployments costs become an increasingly important determinant of total abatement costs. c, d, Distribution of total carbon debt D (c) and abatement costs (d) for the median values shown in a, b. Boxes indicate the 25–75% interquartile ranges around medians (bold solid line), whiskers indicate minimum to maximum ranges, black dots mark outliers.

Extended Data Fig. 3 The 1.5 °C (RCP 1.9) pathways under a conventional ETS or a CRO-ETS.

a, b, A conventional ETS is used. c, d, A CRO-ETS is used. The underlying set of scenarios was filtered for those scenarios that achieved at least a 5% reduction in total carbon debt compared with their baselines (see Methods). a, c, Geometric median net emissions (solid line) and gross emissions from FFI, BECCS, LUC and DACS. Net emissions from a are also displayed in c (dashed line) and vice versa. The total carbon debt D is shown as a box-and-whiskers plot. Boxes indicate the 25–75% interquartile range around the median values (bold line), whiskers indicate minimum to maximum ranges, points mark the outliers. b, d, Annual mitigation costs as a percentage of GDP, including the share of average abatement costs attributed to emission reductions (ABM), to the compensation of residual emissions by CDR (RES) and to net-negative emissions (NNE) as well as expenditures for allowances (ETS) and interest costs (INT). Total mitigation costs (that is, ABM + RES + NNE + ETS + INT) from d are also displayed in b (dashed line) and vice versa. Box-and-whiskers plots show the total discounted abatement costs (that is, ABM + RES + NNE) as a percentage of GDP, the number above the chart indicates out-of-range outliers. Pie charts in d summarize the properties of the underlying set of scenarios (see Methods). The distribution of rd in CRO-ETS scenarios is depicted in c.

Extended Data Fig. 4 The 1.5 °C (RCP 1.9) pathways under a conventional ETS or a CRO-ETS.

a, b, A conventional ETS is used. c, d, A CRO-ETS is used. The underlying set of scenarios was filtered for those scenarios that achieve at least a 15% reduction in total carbon debt compared with their baselines (see Methods). a, c, Geometric median net emissions (solid line) and gross emissions from FFI, BECCS, LUC and DACS. Net emissions from a are also displayed in c (dashed line) and vice versa. The total carbon debt D is shown as a box-and-whiskers plot. Boxes indicate the 25–75% interquartile range around the median values (bold line), whiskers indicate minimum to maximum ranges, points mark the outliers. b, d, Annual mitigation costs as a percentage of GDP, including the share of average abatement costs attributed to emission reductions (ABM), to the compensation of residual emissions by CDR (RES) and to net-negative emissions (NNE) as well as expenditures for allowances (ETS) and interest costs (INT). Total mitigation costs (that is, ABM + RES + NNE + ETS + INT) from d are also displayed in b (dashed line) and vice versa. Box-and-whiskers plots show the total discounted abatement costs (that is, ABM + RES + NNE) as a percentage of GDP, the number above the chart indicates out-of-range outliers. Pie charts in d summarize the properties of the underlying set of scenarios (see Methods). The distribution of rd in CRO-ETS scenarios is depicted in c.

Extended Data Fig. 5 The 1.5 °C (RCP1.9) pathways under a conventional ETS or a CRO-ETS.

a, b, A conventional ETS is used. c, d, A CRO-ETS is used. The underlying set of scenarios was filtered for those scenarios that achieve at least a 45% reduction in total carbon debt compared with their baselines (see Methods). a, c, Geometric median net emissions (solid line) and gross emissions from FFI, BECCS, LUC and DACS. Net emissions from a are also displayed in c (dashed line) and vice versa. The total carbon debt D is shown as a box-and-whiskers plot. Boxes indicate the 25–75% interquartile range around the median values (bold line), whiskers indicate minimum to maximum ranges, points mark the outliers. b, d, Annual mitigation costs as a percentage of GDP, including the share of average abatement costs attributed to emission reductions (ABM), to the compensation of residual emissions by CDR (RES) and to net-negative emissions (NNE) as well as expenditures for allowances (ETS) and interest costs (INT). Total mitigation costs (that is, ABM + RES + NNE + ETS + INT) from d are also displayed in b (dashed line) and vice versa. Box-and-whiskers plots show the total discounted abatement costs (that is, ABM + RES + NNE) as a percentage of GDP, the number above the chart indicates out-of-range outliers. Pie charts in d summarize the properties of the underlying set of scenarios (see Methods). The distribution of rd in CRO-ETS scenarios is depicted in c.

Extended Data Fig. 6 The abatement costs and interest costs of the 1.5 °C (RCP 1.9) scenarios as function of the percentage of carbon debt reduction compared with the baseline scenario.

a, b, The abatement costs (a) and interest costs (b) of the 1.5 °C (RCP 1.9) scenarios is compared with the baseline scenario (in which rd = 0) for all 468 RCP 1.9 scenarios, grouped by the carbon debt interest rate (rd) and the cost and capacity parameters of DACS. DACS cost parameters range from low to high (that is, LoCost, MedCost and HiCost); capacity limits include 10% and 30% of baseline emissions (that is, LoCap and HiCap). a, Total discounted abatement costs excluding interest costs (that is, ABM + RES + NNE as in Fig. 4 and Extended Data Figs. 3–5). b, Total discounted interest costs (that is, INT as in Fig. 4 and Extended Data Figs. 3–5).

Extended Data Fig. 7 Schematic overview and illustrative repayment terms of RCP 1.9 scenarios.

a, Schematic overview of the CRO-ETS. The physical overshoot of a cumulative emission target, potentially amplified by outgassing of CO2 from the Earth’s stocks, subsequently necessitates carbon sequestration for returning to the target. For accrued carbon debt, CROs are issued, obliging emitters to compensate for a tonne of CO2 before a specified maturity—for example, by physically removing atmospheric CO2 or by acquiring an adequate quantity of allowances in the future. Similar to financial debt, CROs require debtors to pay interest to hedge physical and financial risks associated with carbon debt. Three earmarked financial resources are created under a CRO-ETS. (1) Revenues from auctioning allowances are recycled into the economy to the benefit of society. (2) Revenues from interest on carbon debt are targeted at managing risks—that is, by enabling additional carbon sequestration when Earth system risks (for example, permafrost thaw) and financial risks (for example, default risk of debtors) materialize. (3) Funds for repayment of the carbon debt are individually managed by debtors. b–e, The repayment term function TR(t) for the scenarios illustrated in Extended Data Fig. 3 (b), Extended Data Fig. 4 (c), Fig. 4 (d) and Extended Data Fig. 5 (e). Interest on carbon debt rd reflects the mean values of the distributions shown in Fig. 4c and Extended Data Figs. 3c, 4c, 5c. Bold lines indicate geometric median repayment terms derived from the scenarios presented in Fig. 4 and Extended Data Figs. 3–5. TR(t) maps the timing of carbon debt accrual to the time of its compensation (see Methods). For instance, in c, the carbon debt accrued in 2020 is compensated approximately 40 years later in scenarios with interest (rd = 0.058, yellow lines) and roughly 50 years later in scenarios for which rd = 0 (turquoise lines). As rd is increased, the net-zero year moves closer, indicating that carbon debt in 2020 is compensated earlier, whereas, in general, TR extends over longer periods. The increasingly flat net-negative emissions profile (when rd is increased) suggests that TR increases more rapidly in the beginning than when rd = 0 because the cumulative carbon debt at t grows faster than the cumulative net-negative emissions at t + TR(t). The point of inflection indicates where cumulative carbon debt begins to grow more slowly than cumulative net-negative emissions that compensate for that carbon debt. For instance, in d (yellow line), the cumulative carbon debt from 2030 onwards grows at a slower pace than the cumulative net-negative emissions approximately 63 years later.

Extended Data Fig. 8 MACCs.

a, The functional form of MACs,\(\,{\rm{MAC}}(a)=b[\tfrac{1}{\nu }((\tfrac{L-A}{a-A}{)}^{\nu }-1){]}^{c}\), is derived from the inverse generalized logistic function. It is relatively flexible with respect to replicating a wide range of MACCs derived from the SSP database. Here A = 1 and L = 0 are upper and lower asymptotes along the y axis. Notably, MAC(a = A) = ∞; therefore, A is a maximum abatement rate built into the MAC curve. b defines the y position of the pivot point. The x position of the pivot point is determined by ν and for ν = 1 it is exactly the middle of the interval (L, A), (L + A)/2. c defines the level of rotation with respect to the pivot point. b, Six stylized MACCs for DACS covering the literature range for costs from US$20 to US$1,000 per t CO2 (orange area). Low-cost MACCs (dotted lines) start at approximately US$50 per t CO2 and reach US$1,000 per t CO2 at abatement rates aDACS = 0.07 (low capacity, blue line) and aDACS = 0.27 (high capacity, red line) equivalent to approximately 3 and 12 Gt CO2 yr−1 at current emission levels, respectively. Medium-cost MACCs (dashed lines) start at US$250 per t CO2 and reach US$1,000 t CO2 at aDACS = 0.05 (low capacity, blue line) and aDACS = 0.22 (high capacity, red line), that is, roughly 2 and 10 Gt CO2 yr−1 at current emission levels, respectively. High-cost MACCs (solid lines) start at approximately US$500 per t CO2 and reach US$1,000 per t CO2 at aDACS = 0.03 (low capacity, blue line) and aDACS = 0.12 (high capacity, red line), amounting to roughly 1 and 5 Gt CO2 yr−1 at current emission levels, respectively.

Extended Data Table 1 Net and gross emission removal in illustrative pathways from the IPCC’s Special Report on global warming of 1.5 °C
Full size table
Extended Data Table 2 Combinations of models and SSPs used to calibrate the model in this study
Full size table

Supplementary information

Supplementary Information

This file contains a graphical representation of all 2 °C (RCP2.6) emission pathways discussed in the Results (specifically in Figure 3).

Supplementary Data

This file contains the data of all 2 °C (RCP2.6) pathways discussed in the Results as an Excel sheet.

Supplementary Information

SI1.3 contains a graphical representation of all 1.5 °C (RCP1.9) emission pathways discussed in the Results (specifically in Figure 4 and Extended Data Figures 7–9).

Supplementary Data

This file contains the data of all 1.5 °C (RCP1.9) pathways discussed in the Results as an Excel sheet.

Supplementary Information

This file contains a graphical representation of the marginal abatement cost (MAC) curves used in the numerical model of this study. The parameters of the MAC curves can be retrieved with the R package provided for using the numerical model.

Supplementary Information

This file illustrates carbon prices from the SSP scenarios compared to carbon prices and MAC from the numerical model of this study.

Supplementary Information

This file illustrates consumption loss and GDP loss from the SSP scenarios compared to abatement costs from the numerical model of this study.

Supplementary Information

This file illustrates net emissions from the SSP scenarios compared to net emissions from the numerical model of this study.

Supplementary Information

This file contains the analytical methods necessary to derive equation [16] in the Methods section.

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Bednar, J., Obersteiner, M., Baklanov, A. et al. Operationalizing the net-negative carbon economy. Nature 596, 377–383 (2021). https://doi.org/10.1038/s41586-021-03723-9

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