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.

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).

• 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

• 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 p_gas(t₀) is the gas price in this region at year t₀. As long as gas depletion has not started, τ_gas(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 wp_oil(t) is the world oil price at year t; wp_oil(t₀) is the world oil price at year t₀. If depletion has started in this region, τ_gas(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 p_k,oil are obtained by adding average regional production costs and a margin that includes both Ricardian and scarcity rents. In Eq. (3), IC_j,oil,k is the intermediate consumption of goods from sector j to produce a unit of oil and pIC_j,oil,k is the intermediate consumption price for sector j for oil in region k. Q_oil,k is the quantity of oil produced in region k. Ω_oil,k is an increasing function of the utilization rate of production capacities Cap_oil,k in region k. l_oil,k is the quantity of labor per unit of oil produced in region k, tax_oil,k,w is the labor tax rate in the oil sector in region . π_k,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 η_coal: tight coal markets exhibit a high value of η_coal (i.e., the price of coal increases if coal production increases). We make two assumptions for η_coal. 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 (η_coal = 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 (η_coal = 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

• 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)