Potential and limitations of bioenergy for low carbon transitions

Abstract

Sustaining low CO 2 emissions pathways to 2100 may rely on electricity production from biomass. We analyze the economic effect of the availability of biomass resources and technologies with and without CCS within a general equilibrium framework. We assess the robustness of bioenergy with and without CCS for reaching the RCP 3.7 target with the hybrid model Imaclim-R. Global consumption is affected by the absence of CCS or biomass options, and biomass is shown to be a possible technological answer to the absence of CCS. As the use of biomass on a large scale might prove unsustainable, we show that early action is a strategy to reduce the need for biomass and enhance economic growth in the long term.

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Notes

  1. 1.

    The possibility of achieving negative emissions may introduce a downward bias in policy cost estimates (Tavoni and Tol 2010). Optimistic models will find stringent climate targets to be feasible and will report results for those targets. These cost estimates are likely to be lower than the values that would have been found by other (less optimistic) models, had the stringent climate scenarios been feasible.

  2. 2.

    These values are used in the model comparison exercise ADAM.

  3. 3.

    Appendix A provides more details.

  4. 4.

    The exogenous emissions trajectory used here was derived using a heuristic method satisfying the prescribed 2050 and 2100 emission budgets (and was not shaped based on technology availability).

  5. 5.

    For instance, total costs of oil-powered thermal plants are higher than those of other technologies. However, since they are very flexible, they provide balancing services and are used for peak demand more often than would be economically optimal based solely on their costs. Therefore, this advantage leads to a market share superior to what would be economically optimal. To reflect this fact, their apparent cost, used for investment and production decisions, is decreased by 4.5 % to 40 % according to the model region. However, the final production cost does not take into account this intangible cost.

  6. 6.

    A global supply curve for biofuels production is split into regional production according to the regional shares of liquid fuel production (i.e. petrol and coal-to-liquids). Biofuels are traded in the pool of liquid fuels.

  7. 7.

    Appendix E maps scenarios between this paper and the EMF27 study.

  8. 8.

    Other macroeconomic cost drivers and accompanying policies to mitigate costs (e.g. efficient tax recycling, improving price signal anticipation and infrastructures policies) are mentioned in Appendix G.

  9. 9.

    The share of nuclear (respectively renewables) is exogenously constrained because of acceptability issues (respectively intermittence), as explained in Section 2.2.2.

  10. 10.

    Carbon prices in scenario (1) are higher than in scenario (2), but lead to lower losses. Higher carbon prices show the greater difficulty to meet the instantaneous constraint on carbon emissions, but carbon tax revenues are recycled in the economy, allowing for funds reallocation and possibly a higher level of growth. This result is even more striking for scenario (3), which consistently displays higher carbon prices after 2075 than scenario (2). This is made possible by second-best features in the model, cf. Section 2.2.1.

  11. 11.

    These options could be replaced by other options such as more nuclear or renewables, if the constraints on their development were lifted.

  12. 12.

    The energy share in production costs for the overall economy follows the same pattern except for scenario (3): after a sharp peak before 2050, they decrease heavily to reach lower levels than the baseline after 2060. This may be attributed to price-driven energy efficiency in productive sectors following higher energy costs in the first period. Scenario (4) displays the same prices patterns and there is indeed a dip between 2050 and 2060 corresponding to the dip in scenario (3). However, the significant increase in carbon prices in scenario (4) prevents costs from decreasing as much after 2060. The decrease of the energy share in production costs after 2050 is less striking in scenarios with CCS compared to scenarios without CCS and seems to confirm that hypothesis.

  13. 13.

    Note that in the current version of Imaclim-R, the explicit land-use module is replaced by biofuel and woody biomass supply curves.

  14. 14.

    Assumptions vary on CCS, bioenergy, nuclear and renewables availability, energy efficiency, infrastructures investments and carbon tax recycling (cf. Appendix M).

  15. 15.

    GDP losses are expressed as compared to the baseline for each mitigation scenario.

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Acknowledgements

The authors wish to thank Henri Waisman, Thierry Brunelle, Patrice Dumas, François Souty and two anonymous reviewers for their careful examination of results, as well as the EMF27 study participants for their helpful comments. The authors acknowledge funding from the Chair “Modeling for Sustainable Development” led by ParisTech.

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Correspondence to Ruben Bibas.

Additional information

This article is part of the Special Issue on “The EMF27 Study on Global Technology and Climate Policy Strategies” edited by John Weyant, Elmar Kriegler, Geoffrey Blanford, Volker Krey, Jae Edmonds, Keywan Riahi, Richard Richels, and Massimo Tavoni.

Appendices

Appendices

A Imaclim-R World specifications

Imaclim-R World is a recursive, dynamic, multi-region and multi-sector hybrid CGE model (Waisman et al. 2012a). The 12 regions are USA, Canada, Europe, OECD Pacific, Former Soviet Union, China, India, Brazil, Middle-East, Africa, rest of Asia, rest of Latin America. The 12 sectors are divided as follows: three primary energy sectors (Coal, Oil, Gas), two transformed energy sectors (Liquid fuels, Electricity), three transport sectors (Air, Water, Terrestrial Transport) and four productive sectors (Construction, Agriculture, Industry, Services). It is calibrated for the 2001 base year by modifying the set of balanced input-output tables provided by the GTAP-6 dataset (Dimaranan 2006) to make them fully compatible with 2001 IEA energy balances (in Mtoe) and data on passengers’ mobility (in passenger-km) from Schafer and Victor (2000).

B Imaclim-R model schematics

Fig. 4
figure4

Imaclim-R model schematics

C Electricity technologies characteristics

Cf. Table 2.

Table 2 Electricity technologies characteristics

D Biomass supply curves in Imaclim-R World

D.1 Woody biomass supply curve for electricity production

In this section, woody biomass quantities in EJ refer to primary energy for electricity production (one needs to use the yields of 40 % for BIGCC and 40 % for BIGCCS to get secondary energy, added to the losses of the electricity sector to get final energy).

Table 3 Biomass supply curve for electricity in Imaclim-R World
Fig. 5
figure5

Woody biomass supply curve

D.2 Biomass supply curves for biofuel production

In this section, prices and quantities are given for final energy.

Fig. 6
figure6

Biomass supply curves for liquid fuels in Imaclim-R World

D.3 All biomass types

In this section, prices and quantities are given for final energy.

Fig. 7
figure7

Biomass supply curves for period 2050–2100

E Matching between EMF27 and paper scenarios

Table 4 Matching between paper scenarios and EMF27 study scenarios

F GDP Losses

Fig. 8
figure8

Macroeconomic cost profile of the RCP 3.7 climate policy under four scenarios

G Accompanying policies

G.1 Introduction

This annex briefly describes the formation of costs in the Imaclim-R World model. Appendix G.2G.2 introduces the second-best mechanisms that we want to emphasize. Section G.3G.3 explains the interplay between those mechanisms and climate policies. Section G.4G.4 concludes on the formation of costs.

G.2 Methods

G.2.1 Methods

As described in Section 2, the Imaclim-R World model embarks assumptions describing a second-best economy. The magnitude of the cost as revealed by the modeling exercise can be explained by the interaction between the energy-economy system and the choice of policy instruments for climate mitigation within the modeling framework of Imaclim-R. Imaclim-R models market imperfections, inertia, short-run adjustments constraints and imperfect expectations, which may induce higher costs than in the case of inter-temporal optimization with perfect foresight (Fisher et al. 2007). The sole policy instrument used in the EMF27 study is a uniform CO 2 tax imposed on CO 2 emissions from fossil fuels use. A single price signal may be suited to first-best modeling frameworks. However, in the case of imperfect markets (including imperfect labor markets) and imperfect foresight, the absence of accompanying instruments such as policies inducing a shift in investments towards public transportation infrastructure (Waisman et al. 2012a), labor policies (Guivarch et al. 2011) may partly explain the high macroeconomic cost of climate mitigation.

In this setting, the Imaclim-R World model takes as an input a given yearly emissions trajectory, abiding by the emissions constraints provided in the EMF27 protocol. Since Imaclim-R World is a recursive dynamic model, each year, the economy is subject to this emission constraint and a carbon tax is found to meet this constraint. There is however no optimization of the trajectory as a whole: the carbon value is therefore not only determined by the yearly constraint, but also by the state of the economy caused by past decisions. The carbon tax is therefore path-dependent. In addition, in the regular setting, agents are myopic by nature, especially with regards to carbon prices, and do not expect a rise in carbon prices due to more stringent concerns in the future. This myopia, in conjunction with inertia, is a first cause for a second-best economy. This first setting is represented by the blue curves.

Consequently, we implemented another expectations formation system in which agents anticipate carbon prices as a result of a better price signal following the goverments’ annoucements. In this second setting, since agents adapt their behavior to take into account expected carbon prices, their myopia does not prevent them from using technologies more suited to the future carbon constraints. Consequently, the red curves will improve adaptability to climate policies.

The imperfections of labor markets are another cause of the second-best economy. Imaclim-R World represents rigidities in labor and wages adjustments, causing climate policies to be potentially more problematic than in a first best scenario, especially in the case of a suboptimal fiscal system. One way of starting to solve this issue is to alleviate the burden on labor by redirecting the additional government income from the carbon tax towards lowering labor taxes. This recycling scheme, in addition to the improvement in carbon price expectations, will constitute the third set of scenarios, in yellow.

Finally, because of the strong inertia in capital, notably in infrastructures, installed long-lived capital might be ill-suited to an increased stringency of the carbon constraint. The final setting will therefore add to the previous scenarios an improvement in infrastructure installations, obeying other signals than prices. This setting will be shown in green.

G.3 Results

Figure 9 displays the full results of a simulation prepared on the EMF27G17 (Fig. 9a) and EMF27G9 (Fig. 9b) scenarios, i.e. the default scenario (optimistic on all four alternative technologies and conservative on energy efficiency progress). From these figures, the following “policy storylines” for the 550 ppm and 450 ppm targets:

Fig. 9
figure9

Macroecomic impact of accompanying policies—GDP variation w.r.t. baseline (% of MER real GDP)

Blue line, a succession of “command and control” decisions   is imposed on all sectors with no predictability of these public decisions; only the electricity sector and the rest of energy sectors work under semi-myopic expectation. This entails a world GDP loss of 10 % and 17 % (for 550 ppm and 450 ppm respectively) in 2030, 13.8 % and 25.2 % in 2050, 17.6 % and 16.4 % in 2100.

  • These costs are very high in the transition phase: a lower yearly growth rate of 0.3 % and 0.59 % respectively leads to 6.2 and 14 years of delay in economic growth in 2050 (i.e. the 2050 baseline GDP is reached in 2056 under a 550 ppm constraint and 2064 under a 450 ppm).

  • This pessimism can be justified in economic terms. First, the abatement profile is constrained by the 2050 carbon budget imposed in the exercise. An alternative profile with less abatement in the first period and more abatement in the second period would be less costly. Second, all the macroeconomic studies on the lessons of the oil shocks (lower than the carbon prices imposed by the carbon constraints considered here) demonstrate significant negative impacts of energy prices on growth in case of oil shocks. The “blue” scenarios can be interpreted of a succession of policy shocks with no formation of credible policy signals. The GDP losses then result from the inertia in the turnover of capital stocks (including end-use energy equipments of households) which causes a higher energy costs to propagate throughout the economy, higher prices on non-energy goods and a decrease of the purchasing power of wages.

  • Despite these pessimistic assumptions the GDP losses stabilize between 2050 and 2100. They are similar in both scenarios in 2100 because the 450 ppm constraint imposes faster learning-by-doing and a faster penetration of the bioenergy (BECCS de facto acts as a backstop technology). The growth rate is only 0.19 % lower, leading to a 9 year delay in economic growth (the 2100 GDP is reached in 2109).

Red line, “pure” carbon price signals:   in this scenario, price signals are launched in the form of a carbon tax (or carbon trading designed in such a way that the volatility of carbon prices remains low). A carbon price trajectory is “announced” by governmental agencies and, for example, the investments in 2030 are redirected in function of a 1000$/tCO2 carbon price in 2050 while the actual carbon price which propagates throughout the economy in 2030 is only 500$/tCO2. However, tax revenues are recycled in the economy in an (almost) lump-sum fashion, without any attention to the capacity of tax recycling to generate macroeconomic feedbacks which optimize social welfare.

  • Thanks to this “signal effect” (which could be improved), the GDP losses are reduced to 10.5 % and 21.6 % in 2050 for 550 ppm and 450 ppm respectively, causing a 4.5 and 11 years of growth delay.

  • In 2100 the growth delay is again almost similar for both constraints; it falls to 7.4 and 6.4 year (again better for the 450 ppm for the reasons mentioned above)

  • The time profile of this signal can be improved to obtain more optimistic results, but we did not go in this direction because most of the non energy industry works under a time horizon of less than 10 years; the infrastructure industry which works under a longer time horizon is treated below.

Yellow line, carbon price signals with an alternative recycling of revenues:   this recycling is not optimized per region and we use the carbon tax revenues to lower payroll taxes for the sake of simplicity. Other taxes should be cut in some regions but decreasing labor taxes present the advantage of cutting most of the propagation effect from energy costs to overall production costs. The result is impressive (it confirms the general validity of what L. Goulder calls the “weak form” of double-dividend, namely the minimization of social costs of abatement but no “negative costs”.

  • in 2050, the GDP losses are reduced to 5.5 % and 9.3 % for 550 ppm and 450 ppm respectively, causing a 2.2 and 4 year growth delay, which starts becoming rather low for 550 ppm (a 0.001 % decrease of the annual growth rate)

  • In 2100, the growth delay falls to 5.2 year in the 550 ppm scenario and 2.7 year in the 450 ppm (a 0.0006 % decline of the annual growth rate).

  • The transition costs remains higher (although moderate) in the 450 ppm scenario than in the 550 ppm scenario, but the 450 ppm scenario performs better over the long run; this is due to the fact that higher price signals over the short and medium term slow down the mobility needs and accelerate the penetration of biofuels as substitutes to gasoline. The 550 ppm scenario is characterized by a higher induction of mobility needs and a higher demand of oil for gasoline.

Green line, carbon price signals, recycling of their revenues and infrastructure policies:   the above policy package is complemented with infrastructure policies in the transportation sector which control the induction of road based mobility needs and the rebound effect due to more efficient cars; it also encompasses lower road and air freight transport and a lower freight content of production

  • in 2050, the GDP losses are reduced to 2 % for 550 ppm, causing a negligible nine months of growth delay. The incremental benefits of infrastructure policies is lower for under the 450 ppm constraint and the growth delay decreases slightly to 3.8 years

  • In 2100, the growth delays are similar again since the rebound effects of mobility demand is better controlled by non-price policies in the 550 ppm scenario; they are low on a century basis, 2.5 and 2.4 years.

G.4 Conclusion

In Imaclim-R World, a carbon tax is set up and one cannot avoid to be faced with question “what is the best way to recycle it?”; the mobility needs depend on many other signals than carbon prices and it would make more sense to try and control them through carbon prices coupled with other instruments.

H Sectoral CO 2 emissions abatement

Numbers over 100 % in Table 5b for electricity correspond to the negative emissions from bioelectricity paired with CCS. With this technology, the power sector can go over 100 %, when its net emissions are negative. This also explains the negative numbers in Table 5f, with negative emissions being attributed a negative weight in global emissions.

Table 5 Sectoral CO 2 emissions abatement (%)

I Electricity mixes

Fig. 10
figure10

Global electricity mix (EJ)

J Prices of goods and energy shares in production costs

Fig. 11
figure11

Prices of goods and energy shares in production costs

K Biomass

K.1 Production

Fig. 12
figure12

Biomass production in scenarios by type of biomass (EJ)

K.2 Negative emissions from BECCS

Fig. 13
figure13

Negative emissions from BECCS (GtCO 2)

L Consumption Price Index

Fig. 14
figure14

Consumption Price Index (Mitigation scenario compared to baseline scenario)

M Assumptions for the discussion

Table 6 Assumptions for the discussion

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Bibas, R., Méjean, A. Potential and limitations of bioenergy for low carbon transitions. Climatic Change 123, 731–761 (2014). https://doi.org/10.1007/s10584-013-0962-6

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Keywords

  • Electricity Price
  • Carbon Price
  • Computable General Equilibrium
  • Biomass Resource
  • Mitigation Cost