The transition in energy demand sectors to limit global warming to 1.5 °C

Abstract

Achieving an emission pathway that would be compatible with limiting the global temperature increase to 1.5 °C compared with pre-industrial levels would require unprecedented changes in the economy and energy use and supply. This paper describes how such a transition may impact the dynamics of sectoral emissions. We compare contrasted global scenarios in terms of the date of emission peaks, energy efficiency, availability of low-carbon energy technologies, and fossil fuels, using the global integrated assessment model IMACLIM-R. The results suggest that it is impossible to delay the peak of global emissions until 2030 while remaining on a path compatible with the 1.5 °C objective. We show that stringent policies in energy-demand sectors—industry and transportation especially—are needed in the short run to trigger an immediate peak of global emissions and increase the probability to meet the 1.5 °C objective. Such sector-specific policies would contribute to lowering energy demand and would reduce the level of the carbon price required to reach the same temperature objective. Bringing forward the peak of global emissions does not lead to a homothetic adjustment of all sectoral emission pathways: an early peak of global emissions implies the fast decarbonization of the electricity sector and early emission reductions in energy-demand sectors—mainly industry and transportation.

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Notes

  1. 1.

    Note that the residential sector refers to private housing only and is separate from the service (or composite) sector. We left the service sector out of the analysis as it represents less than 5% of total emissions in 2015 and as the representation of this sector is less detailed than other sectors in IMACLIM-R.

  2. 2.

    For a detailed description of the IMACLIM-R model, see: http://themasites.pbl.nl/models/advance/index.php/Model_Documentation_-_IMACLIM

  3. 3.

    See Bibas et al. (2015) for a thorough description of the representation of technical change in IMACLIM-R.

  4. 4.

    Non-CO2 GHG gases and emissions from land-use change are not modeled explicitly in this version of the model.

  5. 5.

    Note that global emissions are imposed over that period but not the sectoral shares of those emissions.

  6. 6.

    One family corresponds to one global emission constraint with a peak at year 2016, 2020, 2025, or 2030 and eight different combinations of technico-economic parameters.

  7. 7.

    Non-CO2 GHG gases and emissions from land-use change are not modeled explicitly in this version of the model. Cumulative CO2 emissions over 2010–2050 are compared with CO2 budgets from studies that account for the impact of non-CO2 gases on warming. Implicitly, this means that we are assuming non-CO2 gases emissions to be similar to the trends from those studies.

  8. 8.

    Threshold Exceedance Budget (TEB) and Threshold Avoidance Budget (TAB) are defined as follows: “TEB is the amount of cumulative carbon emissions at the time a specific temperature threshold is exceeded with a given probability in a particular multi-gas emission scenarios,” “TAB is the amount of cumulative carbon emissions over a given time period of a multi-gas emission scenario that limits global-mean temperature increase to below a specific threshold with a given probability” (definition from Rogelj et al. (2016), which detail the different types of carbon budget concepts used and their implications). TEB is expected to be higher than TAB.

  9. 9.

    Note that the results show direct emissions for the transportation and residential sector (i.e., excluding for instance the emissions associated with the production of electricity used for transportation). We account for both direct and indirect emissions in the industry sector.

References

  1. Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273–291.

    Article  Google Scholar 

  2. Allcott, H., & Mullainathan, S. (2010). Behavior and Energy Policy. Science, 327, 1204–1205.

    Article  Google Scholar 

  3. Anderson, K., & Peters, G. (2016). The trouble with negative emissions. Science, 354(6309), 182–183.

    Article  Google Scholar 

  4. Bager, S., & Mundaca, L. (2017). Making “Smart Meters” smarter? Insights from a behavioural economics pilot field experiment in Copenhagen, Denmark. Energy Research & Social Science, 28, 68–76.

    Article  Google Scholar 

  5. Bertoldi, P., Rezessy, S., Lees, E., Baudry, P., Jeandel, A., & Labanca, N. (2010). Energy supplier obligations and white certificate schemes: comparative analysis of experiences in the European Union. Energy Policy, 38, 1455–1469.

    Article  Google Scholar 

  6. Bertoldi, P., Rezessy, S., & Oikonomou, V. (2013). Rewarding energy savings rather than energy efficiency: exploring the concept of a feed-in tariff for energy savings. Energy Policy, 56, 526–535.

    Article  Google Scholar 

  7. Bibas, R., Méjean, A., & Hamdi-Cherif, M. (2015). Energy efficiency policies and the timing of action: an assessment of climate mitigation costs. Technological Forecasting and Social Change, 90(Part A), 137–152.

    Article  Google Scholar 

  8. Chapman, L. (2007). Transport and climate change: a review. Journal of Transport Geography, 15, 354–367.

    Article  Google Scholar 

  9. Clarke L., K. Jiang, K. Akimoto, M. Babiker, G. Blanford, K. Fisher-Vanden, J.-C. Hourcade, V. Krey, E. Kriegler, A. Löschel, D. McCollum, S. Paltsev, S. Rose, P. R. Shukla, M. Tavoni, B. C. C. van der Zwaan, and D.P. van Vuuren, 2014. Assessing transformation pathways. In: Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, UK.

  10. Creutzig, F., McGlynn, E., Minx, J., & Edenhofer, O. (2011). Climate policies for road transport revisited (I): Evaluation of the current framework. Energy Policy, 39(5), 2396–2406.

    Article  Google Scholar 

  11. Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Kadner, S., Minx, J. C., Brunner, S., Agrawala, S., Baiocchi, G., Bashmakov, I. A., Blanco, G., Broome, J., Bruckner, T., Bustamante, M., Clarke, L., Conte Grand, M., Creutzig, F., Cruz-Núñez, X., Dhakal, S., Dubash, N. K., Eickemeier, P., Farahani, E., Fischedick, M., Fleurbaey, M., Gerlagh, R., Gómez-Echeverri, L., Gupta, S., Harnisch, J., Jiang, K., Jotzo, F., Kartha, S., Klasen, S., Kolstad, C., Krey, V., Kunreuther, H., Lucon, O., Masera, O., Mulugetta, Y., Norgaard, R. B., Patt, A., Ravindranath, N. H., Riahi, K., Roy, J., Sagar, A., Schaeffer, R., Schlömer, S., Seto, K. C., Seyboth, K., Sims, R., Smith, P., Somanathan, E., Stavins, R., von Stechow, C., Sterner, T., Sugiyama, T., Suh, S., Ürge-Vorsatz, D., Urama, K., Venables, A., Victor, D. G., Weber, E., Zhou, D., Zou, J., & Zwickel, T. (2014). Technical summary. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, & J. C. Minx (Eds.), Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  12. Faruqui, A., & Sergici, S. (2010). Household response to dynamic pricing of electricity: a survey of 15 experiments. Journal of Regulatory Economics, 38, 193–225.

    Article  Google Scholar 

  13. Figueres, C., Schellnhuber, H. J., Whiteman, G., Rockström, J., Hobley, A., & Rahmstorf, S. (2017). Three years to safeguard our climate. Nature, 546(7660), 593–595.

    Article  Google Scholar 

  14. Fuss, S., Canadell, J. G., Peters, G. P., Tavoni, M., Andrew, R. M., Ciais, P., Jackson, R. B., Jones, C. D., Kraxner, F., & Nakicenovic, N. (2014). Betting on negative emissions. Nature Climate Change, 4(10), 850–853.

    Article  Google Scholar 

  15. Goodwin, P., Dargay, J., & Hanly, M. (2004). Elasticities of road traffic and fuel consumption with respect to price and income: a review. Transport Reviews, 24(3), 275–292.

    Article  Google Scholar 

  16. Grubler, A., Bai, X., Buettner, T., Dhakal, S., Fisk, D., Ichinose, T., Keristead, J., Sammer, G., Satterthwaite, D., Schulz, N., Shah, N., Steinberger, J., & Weiz, H. (2012). Urban energy systems. In Global energy assessment—toward a sustainable future (pp. 1307–1400). Cambridge, UK: International Institute for Applied Systems Analysis and Cambridge University Press.

    Google Scholar 

  17. Guivarch, C., & Hallegatte, S. (2013). 2C or not 2C? Global Environmental Change, 23(1), 179–192.

    Article  Google Scholar 

  18. Guivarch, C., Monjon, S., Rozenberg, J., & Vogt-Schilb, A. (2015). Would climate policy improve the European energy security? Climate Change Economics, 6, 1550008.

    Article  Google Scholar 

  19. Harmelink, M., Nilsson, L., & Harmsen, R. (2008). Theory-based policy evaluation of 20 energy efficiency instruments. Energy Efficiency, 1, 131–148.

    Article  Google Scholar 

  20. Hansen, J., Sato, M., Kharecha, P., Beerling, D., Berner, R., Masson-Delmotte, V., Pagani, M., Raymo, M., Royer, D. L., & Zachos, J. C. (2008). Target atmospheric CO2: where should humanity aim? The Open Atmospheric Science Journal., 2(1), 217–231.

    Article  Google Scholar 

  21. Hare, W. L., Cramer, W., Schaeffer, M., Battaglini, A., & Jaeger, C. C. (2011). Climate hotspots: key vulnerable regions, climate change and limits to warming. Regional Environmental Change, 11(Supplement 1), 1–13.

    Article  Google Scholar 

  22. IPCC. (2014). Climate change 2014: mitigation of climate change. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, & J. C. Minx (Eds.), Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  23. IEA, 2008. World energy outlook. Tech. rep., IEA/OECD, Paris, France.

  24. Iyer, G., Hultman, N., Eom, J., McJeon, H., Patel, P., & Clarke, L. (2015). Diffusion of low-carbon technologies and the feasibility of long-term climate targets. Technological Forecasting and Social Change, 90(Part A), 103–118.

    Article  Google Scholar 

  25. Jackson, R. B., Canadell, J. G., Le Quéré, C., Andrew, R. M., Korsbakken, J. I., Peters, G. P., & Nakicenovic, N. (2016). Reaching peak emissions. Nature Climate Change, 6(1), 710.

    Article  Google Scholar 

  26. Kriegler, E., Petermann, M., Krey, V., Schwanitz, V. J., Luderer, G., Ashina, S., Bosetti, V., et al. (2015). Diagnostic indicators for integrated assessment models of climate policy. Technological Forecasting and Social Change, 90(Part A), 45–61.

    Article  Google Scholar 

  27. Kunreuther, H., & Weber, E. U. (2014). Aiding Decision Making to Reduce the Impacts of Climate Change. Journal of Consumer Policy, 37, 397–411.

    Article  Google Scholar 

  28. Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., & Houghton, R. A. (2016). Global carbon budget 2016. Earth System Science Data, 8(2), 605–649.

    Article  Google Scholar 

  29. Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M., & Edenhofer, O. (2013). Economic mitigation challenges: how further delay closes the door for achieving climate targets. Environmental Research Letters, 8(3), 034033.

    Article  Google Scholar 

  30. McGilligan, C., Sunikka-Blank, M., & Natarajan, S. (2010). Subsidy as an agent to enhance the effectiveness of the energy performance certificate. Energy Policy, 38, 1272–1287.

    Article  Google Scholar 

  31. Millar, R. J., Fuglestvedt, J. S., Friedlingstein, P., Rogelj, J., Grubb, M. J., Matthews, H. D., Skeie, R. B., Forster, P. M., Frame, D. J., & Allen, M. R. (2017). Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nature Geoscience, 10, 741–747.

    Article  Google Scholar 

  32. Pichert, D., & Katsikopoulos, K. V. (2008). Green defaults: Information presentation and pro-environmental behaviour. Journal of Environmental Psychology, 28, 63–73.

    Article  Google Scholar 

  33. Riahi, K., Kriegler, E., Johnson, N., Bertram, C., den Elzen, M., Eom, J., Schaeffer, M., et al. (2015). Locked into Copenhagen pledges—implications of short-term emission targets for the cost and feasibility of long-term climate goals. Technological Forecasting and Social Change, 90(Part A), 8–23.

    Article  Google Scholar 

  34. Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D. P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlík, P., Humpenöder, F., Stehfest, E., & Tavoni, M. (2018). Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change, 8, 325–332.

    Article  Google Scholar 

  35. Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V., & Riahi, K. (2015). Energy system transformations for limiting end-of-century warming to below 1.5 °C. Nature Climate Change, 5(6), 519–527.

    Article  Google Scholar 

  36. Rogelj, J., Schaeffer, M., Friedlingstein, P., Gillett, N. P., van Vuuren, D. P., Riahi, K., Allen, M., & Knutti, R. (2016). Differences between carbon budget estimates unravelled. Nature Climate Change, 6, 245–252.

    Article  Google Scholar 

  37. Rozenberg, J., Hallegatte, S., Vogt-Schilb, A., Sassi, O., Guivarch, C., Waisman, H., & Hourcade, J. C. (2010). Climate policies as a hedge against the uncertainty on future oil supply. Climatic Change, 101(3–4), 663–668.

    Article  Google Scholar 

  38. Schafer, A., & Victor, D. G. (2000). The future mobility of the world population. Transportation Research Part A: Policy and Practice, 34, 171–205.

    Google Scholar 

  39. Schafer, A. (2012). Introducing Behavioral Change in Transportation into Energy/Economy/Environment Models (World Bank Policy Research Working Paper No. 6234). Washington, D.C: World Bank.

    Google Scholar 

  40. Sims, R., Schaeffer, R., Creutzig, F., Cruz-Núñez, X., D’Agosto, M., Dimitriu, D., Figueroa Meza, M. J., Fulton, L., Kobayashi, S., Lah, O., McKinnon, A., Newman, P., Ouyang, M., Schauer, J. J., Sperling, D., & Tiwari, G. (2014). Transport. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, & J. C. Minx (Eds.), Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  41. Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., Kato, E., Jackson, R. B., Cowie, A., & Kriegler, E. (2016). Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6(1), 42–50.

    Article  Google Scholar 

  42. Stocker, T. F. (2013). The closing door of climate targets. Science, 339(6117), 280–282.

    Article  Google Scholar 

  43. Suzuki, M. (2015). Identifying roles of international institutions in clean energy technology innovation and diffusion in the developing countries: matching barriers with roles of the institutions. Journal of Cleaner Production, 98, 229–240.

    Article  Google Scholar 

  44. Tanaka, K. (2011). Review of policies and measures for energy efficiency in industry sector. Energy Policy, 39, 6532–6550.

    Article  Google Scholar 

  45. van Vuuren, D., den Elzen, M., Lucas, P., Eickhout, B., Strengers, B., van Ruijven, B., Wonink, S., & van Houdt, R. (2007). Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Climatic Change, 81, 119–159.

    Article  Google Scholar 

  46. Waisman, H., Guivarch, C., & Lecocq, F. (2013). The transportation sector and low-carbon growth pathways: modelling urban, infrastructure, and spatial determinants of mobility. Climate Policy, 13(1), 106–129.

    Article  Google Scholar 

  47. Waisman, H., Guivarch, C., Grazi, F., & Hourcade, J. C. (2012). The IMACLIM-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight. Climatic Change, 114(1), 101–120.

    Article  Google Scholar 

  48. World Bank and Ecofys. 2017. Carbon pricing watch 2017. Washington, DC: World Bank.

  49. Zhou, N., McNeil, M., & Levine, M. (2011). Assessment of building energy-saving policies and programs in China during the 11th five year plan. Berkeley, CA: Lawrence Berkeley National Laboratory 19 pp.

    Google Scholar 

  50. Grübler, A., Nakicenovic, N., & Victor, D. G. (May 1999). Dynamics of energy technologies and global change. Energy Policy, 27(5), 247–280.

    Article  Google Scholar 

  51. IEA, 2007. World energy outlook. Tech. rep., IEA/OECD, Paris, France.

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Correspondence to Aurélie Méjean.

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Highlights

• It may be impossible to delay the peak of global emissions until 2030 while remaining on a pathway compatible with a 1.5 °C world, whatever the policies in place to lower energy demand across sectors.

• Stringent policies in energy-demand sectors—industry and transportation especially—are needed in the short run to trigger an immediate peak of global emissions and increase the probability to meet the 1.5 °C objective.

• Sector-specific policies reduce energy demand and reduce the level of the carbon price required to reach the same temperature objective by 25 to 50% in 2030.

• Bringing forward the peak of global emissions does not lead to a homothetic adjustment of all sectoral emission pathways.

• An early peak of global emissions implies the fast decarbonization of the electricity sector and early emission reductions in energy-demand sectors—mainly industry and transportation.

Appendix

Appendix

Parameters

This section provides a description of the parameters used for each scenario. Note that this description is very similar in its formulation to Guivarch et al. (2015).

  1. a)

    Energy demand

Energy-efficiency improvements

In each sector, the region with the lowest-energy intensity is defined as the leader and its energy efficiency is triggered by energy prices. After a delay, other regions catch up with the leader region. We build two hypotheses (see Table 3) using the following parameters: maximum annual improvement in the leader’s energy efficiency, other regions’ speed of convergence (% of the initial gap after 50 years), and asymptotic level of catch up (% of the leader’s energy efficiency).

Table 3 Parameters for energy-efficiency improvements

Development style of developing countries

This set describes either a mimetic development pattern for developing countries, which aim at adopting western lifestyles or a less carbon-intensive development pattern. We take into account infrastructure policies and the agents’ preferences for automobile transport and spacious individual dwelling (see Table 4).

Table 4 Parameter options for development patterns of developing countries
  1. b)

    Fossil fuel resources

The scenario alternatives on fossil fuel resources focus on the availability and prices of gas, coal, and coal to liquids.

In the model, global gas production capacities match demand growth until ultimately recoverable resources enter a depletion process. Variations in gas prices are indexed on variations of oil prices via an indexation coefficient (0.68, see Eq. 1) calibrated on the World Energy Model (IEA 2007). When oil prices increase by 1%, gas prices increase by 0.68%. Two alternative assumptions are used in this price indexation. Under the assumption of “relatively abundant and cheap” fossil fuel resources, this indexation disappears when oil prices reach US$80/barrel: beyond this threshold, fluctuations in gas prices only depend on production costs and possibly on the depletion effect. When depletion is reached, the price increases. Under the assumption of “relatively scarce and expensive” fossil fuel resources, gas prices remain indexed on oil prices regardless of fluctuations, but an additional price increase occurs when gas production enters its depletion phase. The price of gas in each region at year t is:

$$ {p}_{\mathrm{gas}}(t)={p}_{\mathrm{gas}}\left({t}_0\right)\cdotp {\tau}_{\mathrm{gas}}(t) $$
(1)

where pgas(t0) is the gas price in this region at year t0. As long as gas depletion has not started, τgas(t) in each region is:

$$ {\tau}_{\mathrm{gas}}(t)=0.68\cdotp \left(\frac{1}{3}\cdotp {\mathrm{wp}}_{\mathrm{oil}}(t)+\frac{2}{3}\cdotp {\mathrm{wp}}_{\mathrm{oil}}\left(t-1\right)\right)\cdotp \frac{1}{{\mathrm{wp}}_{\mathrm{oil}}\left({t}_0\right)} $$
(2)

where wpoil(t) is the world oil price at year t; wpoil(t0) is the world oil price at year t0. If depletion has started in this region, τgas(t) increases 5% each year, regardless of oil prices.

The oil market is modeled according to the following principles: (i) the OPEC can influence world oil prices before they approach a depletion constraint; (ii) oil supply cannot fully adapt to demand due to the geological nature of world oil reserves, i.e., the amount of economically exploitable reserves and technical constraints leading to inertias in the deployment of production capacities; and (iii) oil demand depends on agents’ decisions and on incentives aimed at increasing the production of alternatives to oil. The oil price reflects tensions between supply and demand:

$$ {p}_{k,\mathrm{oil}}=\sum \limits_jp\mathrm{I}{\mathrm{C}}_{j,\mathrm{oil},k}\cdotp \mathrm{I}{\mathrm{C}}_{j,\mathrm{oil},k}+\left({\varOmega}_{\mathrm{oil},k}\left(\frac{Q_{\mathrm{oil},k}}{{\mathrm{C}\mathrm{ap}}_{\mathrm{oil},k}}\right)\right)\cdotp {l}_{\mathrm{oil},k}\cdotp \left(1+{\mathrm{tax}}_{\mathrm{oil},k,w}\right)+{\pi}_{\mathrm{oil},k}\cdotp \frac{Q_{\mathrm{oil},k}}{{\mathrm{C}\mathrm{ap}}_{\mathrm{oil},k}}\cdotp {p}_{k,\mathrm{oil}} $$
(3)

Regional prices pk,oil are obtained by adding average regional production costs and a margin that includes both Ricardian and scarcity rents. In Eq. (3), ICj,oil,k is the intermediate consumption of goods from sector j to produce a unit of oil and pICj,oil,k is the intermediate consumption price for sector j for oil in region k. Qoil,k is the quantity of oil produced in region k. Ωoil,k is an increasing function of the utilization rate of production capacities Capoil,k in region k. loil,k is the quantity of labor per unit of oil produced in region k, taxoil,k,w is the labor tax rate in the oil sector in region . πk,oil is the markup rate in the oil sector in region k. The swing producer anticipates the level of capacities to reach a predefined target on the basis of projections of total oil demand and production in other regions.

Coal is treated in a different way than oil and gas in the model because coal resources are plentiful, which prevents coal production from entering a depletion process before the end of the twenty-first century. We describe the price formation on the world coal market in a reduced functional form linking variations in price to variations in production. This choice allows us to capture the cyclic behavior of this commodity market. Coal prices then depend on current production through an elasticity coefficient ηcoal: tight coal markets exhibit a high value of ηcoal (i.e., the price of coal increases if coal production increases). We make two assumptions for ηcoal. Under the assumption of “relatively abundant and cheap” resources, the sensitivity of an increase in coal price to an increase in coal production is quite low, so that the increase in coal production can be absorbed without price fluctuations (ηcoal = 1.3). Conversely, the increase in coal prices is very sensitive to any increase in coal production under the assumption of “relatively scarce and expensive” resources (ηcoal = 3).

The variants on coal-to-liquids (CTL) govern its ability to penetrate energy markets (Table 5, see Rozenberg et al. 2010 for details).

Table 5 Parameter options for coal-to-liquid penetration
  1. c)

    Low-carbon technologies

Technologies penetrate markets according to their profitability but are constrained by a maximum market share which follows an S-shaped curve (Grübler et al. 1999). We consider two alternatives for each group of technologies. The high availability assumption corresponds to a higher maximum market share and faster diffusion than under the low availability assumption. The model also represents endogenous learning for some new technologies: the cost of the technology decreases with the cumulative investment in that technology. This mechanism is governed by a learning rate, and two alternative values are considered for this learning rate.

Low-carbon power generation technologies

The technologies considered are nuclear power and renewables. In the low availability assumption, it is assumed that the new generation of nuclear power plants is not available at all. The parameters are described in Table 6.

Table 6 Parameter options for low-carbon electricity generation

Detailed result tables

This section provides the results of Figs. 4, 5, 7, 8, and 9 in table format (Table 7, 8, 9, 10, and 11).

Table 7 Average yearly growth rate of CO2 price (%) over the 2040–2050 period as a function of the date of the peak in global emissions for all feasible scenarios (corresponds to Fig. 4)
Table 8 CO2 price (USD/tCO2) in 2030 as a function of the date of the peak in global emissions for low energy demand scenarios and high energy demand scenarios (corresponds to Fig. 5)
Table 9 Date of sectoral emission peak as a function of date of global emission peak (corresponds to Fig. 7)
Table 10 Level of sectoral emissions at the peak (MtCO2) as a function of the date of the peak of global emissions (corresponds to Fig. 8)
Table 11 Annual growth rate of sectoral emissions after the peak (until the end of the period) as a function of the date of the global emission peak (corresponds to Fig. 9)

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Méjean, A., Guivarch, C., Lefèvre, J. et al. The transition in energy demand sectors to limit global warming to 1.5 °C. Energy Efficiency 12, 441–462 (2019). https://doi.org/10.1007/s12053-018-9682-0

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Keywords

  • Global integrated assessment model
  • Dynamics of sectoral CO2 emissions
  • Energy demand patterns
  • Peak of global emissions
  • Scenario feasibility
  • 1.5 °C