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Bioenergy-induced land-use-change emissions with sectorally fragmented policies

Nature Climate Change volume 13, pages 685–692 (2023)Cite this article

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Abstract

Controlling bioenergy-induced land-use-change emissions is key to exploiting bioenergy for climate change mitigation. However, the effect of different land-use and energy sector policies on specific bioenergy emissions has not been studied so far. Using the global integrated assessment model REMIND-MAgPIE, we derive a biofuel emission factor (EF) for different policy frameworks. We find that a uniform price on emissions from both sectors keeps biofuel emissions at 12 kg CO2 GJ−1. However, without land-use regulation, the EF increases substantially (64 kg CO2 GJ−1 over 80 years, 92 kg CO2 GJ−1 over 30 years). We also find that comprehensive coverage (>90%) of carbon-rich land areas worldwide is key to containing land-use emissions. Pricing emissions indirectly on the level of bioenergy consumption reduces total emissions by cutting bioenergy demand but fails to reduce the average EF. In the absence of comprehensive and timely land-use regulation, bioenergy thus may contribute less to climate change mitigation than assumed previously.

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Fig. 1: Bioenergy-induced LUC emissions, bioenergy production and EFs.
Fig. 2: Spatial allocation of LUC CO2 emissions and bioenergy production.
Fig. 3: Composition of emissions, BECCS efficiency and carbon prices.

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Data availability

The model runs and scenario data for this study are archived at Zenodo under a CC-BY-4.0 license75.

Code availability

REMIND is open source and available on GitHub. The model version used in this study is 2.1.2, which can be downloaded at https://github.com/remindmodel/remind/releases/tag/v2.1.2. MAgPIE is open source and available on GitHub. The model version used in this study is 4.2.1, which can be downloaded at https://github.com/magpiemodel/magpie/releases/tag/v4.2.1. Documentation can be found at https://rse.pik-potsdam.de/doc/magpie/4.2.1/.

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Acknowledgements

The research leading to these results has received funding by the DIPOL from the German Federal Ministry of Education and Research (BMBF) under grant number 01LA1809A (L.M., N.B. and J.S.). This work was supported by the NAVIGATE project funded by the European Union’s Horizon 2020 research and innovation programme under grant number 821124 (N.B., F.H. and J.S.), by the RESCUE project from the European Union’s Horizon Europe programme under grant number 101056939 (L.M.) and by the ARIADNE project from BMBF under grant number 03SFK5A (D.K. and G.L.).

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Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

    Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer & Elmar Kriegler

  2. Faculty of Organic Agricultural Sciences, University of Kassel, Witzenhausen, Germany

    Alexander Popp

  3. Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany

    Gunnar Luderer

  4. Faculty of Economics and Social Sciences, University of Potsdam, Potsdam, Germany

    Elmar Kriegler

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Contributions

L.M. performed the model experiments, analysed the scenarios, produced the figures and led the writing of the manuscript. L.M., N.B., F.H., G.L. and E.K. designed the study, the scenarios and the analysis. L.M., N.B., F.H., D.K., J.S., A.P., G.L. and E.K. contributed to the development of the models, the presented ideas and the text.

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Correspondence to Leon Merfort.

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Extended data

Extended Data Fig. 1 Primary energy biomass distribution.

The area plots show the share of biomass that is converted into different secondary energy carriers. The main conversion process is the biomass to liquids route via gasification and Fischer-Tropsch synthesis of lignocellulosic biomass (in our scenarios, all liquids derived from lignocellulosic biomass originate from that process). The biomass shown here originates from both purpose-grown energy crops and residues (see Extended Data Fig. 10, showing shares of different feedstocks). Please note that some conversion processes have a second energy carrier type as co-product. In particular in the biomass-based Fischer-Tropsch process electricity is co-produced, which makes up almost all of the electricity that is derived from lignocellulosic biomass.

Extended Data Fig. 2 Bioenergy emission factors derived with different metrics.

In addition to EFs in Fig. 1a we here explicitly show marginal 30-year EFs for the years 2035 and 2050 in green, 80-year average N2O EFs in bright red, as well as the averaged marginal 20-year EF in orange. Please note that by definition the 20-year marginal EF is simply the 30-year marginal EF multiplied with 1.5, since the accounting period for bioenergy production is divided by that factor. The boxplot shows the temporal variation of \({{EF}}^{{mar}30}(t)\) for years \(t\) between 2025 and 2070 (sample size of n = 9; bioTax40, bioTax40_noTrd are exceptions with n = 8, since the year 2070 is excluded, see ‘Emission factors over time’ in the SI). The minima and maxima of the box confine the inter-quartile range, the whiskers represent the 1st and 4th quartile, and the center states the median value. N2O EFs are converted to CO2-eq using a global warming potential of 26576 and are calculated equivalently to the 80-year average EF for CO2. It can be seen that the different policy assumptions have a rather small effect on the N2O EF with values between 4.6 and 7.2 kg CO2/GJbiofuel. While the N2O EF can mostly be neglected relative to the high CO2 EF in scenarios where LU policies are missing, for the UCP scenario cumulative N2O emissions until 2100 in carbon equivalents are roughly 40% of the cumulative CO2 emissions, since the EF for CO2 is relatively small.

Extended Data Fig. 3 Land cover change.

Shown are changes in the land cover with respect to 1995 of the four main land types represented in MAgPIE, ‘Forest’, ‘Other Land’, ‘Pasture and Rangelands’ and ‘Cropland’. The dark shading represents the land cover change that is attributed to bioenergy production (‘bioen.-induced’) and the light shading represents the land cover change that is observed in the respective counterfactual scenario (‘cf. baseline’), that is, the fraction that is not attributed to bioenergy. Areas classified as ‘Other Land’ comprise non-forest natural vegetation (such as savannahs or shrubland), abandoned agricultural land and deserts. Some of these areas also store substantial amounts of carbon.

Extended Data Fig. 4 Allocation of LUC CO2 emissions and bioenergy production by land-use characteristic.

In Fig. 2b only a selection of scenarios was presented. Here we show the allocation of emissions and bioenergy production for all policy assumptions. For a description, please refer to Fig. 2 in the main text.

Extended Data Fig. 5 The BECCS efficiency factor in 2050.

\({\eta }_{\text{BECCS}}\) is an indicator of how much of the sequestered carbon is effectively removed from the atmosphere if bioenergy-induced LUC emissions are subtracted. A negative efficiency indicates that bioenergy-induced LUC emissions exceed CDR savings by BECCS in the year 2050. For instance, \({\eta }_{\text{BECCS}}=-200 \%\) implies that emissions are 200% higher than savings, that is, three times as high. For the UCP policy setting, emissions and savings are just equal in 2050.

Extended Data Fig. 6 Share of biomass used with CCS and total production of primary energy biomass.

The share of biomass that is used in combination with CCS, shown on the left-hand side, is given as a fraction of total primary energy biomass production, that is, including traditionally used biomass and 1st generation biofuels (which cannot be combined with CCS) and residues. This total primary energy production is shown on the right-hand side for comparison. For clarity we highlight only a few scenarios with colors, grey lines refer to the other scenarios.

Extended Data Fig. 7 Liquids by primary energy carrier.

Biomass, coal and oil can be converted to liquid fuels and biomass and coal have an option to add CCS (E-fuels are not considered in this study). Shown are shares of all liquids that are produced from these primary energy carriers, either with or without CCS included. Shares are given in the year 2050 and 2100 for different policy settings.

Extended Data Fig. 8 Final energy mix.

Total final energy is compound of different energy carriers. The shares are shown for different policy settings in the years 2050 and 2100.

Extended Data Fig. 9 Global agricultural price index relative to 2010.

Black dots denote the agricultural price index of a given scenario. Orange bars represent the index of the respective counterfactual scenario without bioenergy production. The green bars indicate the difference between scenarios with and without bioenergy, that is, the fraction of the average price change for agricultural goods induced by allowing for bioenergy production. The agricultural price index is defined as the change of averaged prices of all agricultural commodities relative to the year 2010 (which has the value 100). Addítionally to the scenarios shown in the main text, we here show the additional scenario from the sensitivity analysis on pessimistic crop yields (UCP_bioYield50), see ‘Sensitivity analysis: Pessimistic yield assumptions’ in the SI. Please refer to ‘Food demand and prices’ in the SI for more details on the impact of policy assumptions on the food prices.

Extended Data Fig. 10 Bioenergy feedstocks to the energy system.

The area plots show the share of bioenergy from different sources. While today residues are almost exclusively used traditionally, that is, using the solid biomass for cooking and heating mostly in developing countries, in our scenarios this shifts within the next 30 years towards modern usage, mostly in form of creating second generation bio liquids from it (see also Extended Data Fig. 1).

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Supplementary Figs. 1–18, Tables 1–4 and Discussion.

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Merfort, L., Bauer, N., Humpenöder, F. et al. Bioenergy-induced land-use-change emissions with sectorally fragmented policies. Nat. Clim. Chang. 13, 685–692 (2023). https://doi.org/10.1038/s41558-023-01697-2

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