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Analysis of the Relative Roles of Supply-Side and Demand-Side Measures in Tackling the Global 1.5 °C Target

  • Babak Mousavi
  • Markus Blesl
Chapter
Part of the Lecture Notes in Energy book series (LNEN, volume 64)

Abstract

This chapter explores, in a systematic manner, the required energy system transformations and the associated price-dependent energy-service demands reductions in order to hold the increase in global average temperature below 1.5 °C above pre-industrial levels. It also evaluates the macroeconomic implications of the climate mitigation policy. The analysis is carried out using the global hybrid TIAM-MACRO model. The major findings show that a rapid decarbonisation of all sectors in the global energy system is fundamental in achieving a 1.5 °C consistent goal. This requires a portfolio of supply-side and demand-side mitigation measures. While technological measures are essential to meet the decarbonisation target, reducing energy-service demands is found to be a mitigation measure that facilitates a cost-effective transition. In addition, energy-service demands reductions play an important role in offsetting the macroeconomic impacts of the climate policy. Finally, any overshoot of the energy sector carbon budget must be counterbalanced by a significant deployment of negative emissions technologies.

Key messages
  • Since biomass with CCS is a necessary option to move towards a 1.5 °C target, strong policies are needed to support efficient supply and consumption of sustainable biomass for energy purposes and to prioritise the use of biomass in sectors with limited mitigation options (e.g. industry).

  • A cost-effective mitigation strategy would benefit from policy instruments that aim at modifying consumer behaviour to further support the transition beyond technological mitigation measures.

  • Reducing energy-service demands plays a critical role in offsetting the additional pressure on the power sector resulting from the rapid and strong electrification of the energy sector.

  • TIAM-MACRO proves to be a suitable tool for analysis of the required energy system transformations and the associated macroeconomic implications in order to meet decarbonisation targets, at global and regional scales.

1 Introduction

Climate change is one the most critical global concerns that must be addressed by all nations in the world. In 2015, the Paris Agreement under the United Nations Framework Convention on Climate Change (UNFCCC) codified two ambitious long-term global targets: holding the increase in the global average temperature well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C. It is recognized that the latter goal is a notably safe guardrail and will significantly reduce negative impacts of climate change. As a result, policy instruments for achieving the 1.5 °C target have become of central interest in the current climate change debate.

To meet such an ambitious goal, there is no single mitigation option; instead, the energy sector offers a wide range of technological options, namely, energy efficiency improvement, shifting from high carbon-intensive fossil fuels to less carbon-intensive alternatives (e.g., switching from coal to natural gas), and the enhanced use of renewables, nuclear, and Carbon Capture and Storage (CCS). On the other hand, additional investments in cleaner technologies will, ceteris paribus, result in higher price of energy services and consequently reduce demand for energy-services which is considered as a mitigation measure (Fujimori et al. 2014). Hence, for a more systematic assessment of decarbonisation strategies, it is of crucial importance to take into account not only the contribution of technological options, but also the role that price-induced energy-service demands reductions will play.

This chapter aims at exploring the relative roles of energy-service demands reductions and technological measures in meeting the 1.5 °C target by 2100. To provide a deeper evaluation of future decarbonisation strategies, the interconnections between the energy system and the rest of the economy is taken into account to quantify the macroeconomic implications of the strategies. The analysis relies on a model-based energy scenario analysis and is centred at the global level which is critical in the context of climate change policies. To this end, the hybrid TIAM-MACRO model is applied, which combines the technological explicitness of the global TIAM model with the macroeconomic representation of the MACRO model.

One of the key parameters of the MACRO model is elasticity of substitution reflecting the degree to which energy-service demands can be replaced by the aggregate of capital and labour, as their relative prices change. In fact, this parameter determines how strong the economy reacts to energy-service price changes, caused by climate policy. In the original MACRO model, it is assumed that this parameter is constant over the planning time and across all regions. However, there is no evidence supporting this simplifying assumption. Therefore, for a better representation of the price-induced energy-service demands reduction, in one of the scenarios presented in this chapter, elasticity of substitution varies across regions and over the planning horizon as a function of regional incomes. The basic idea behind this function is that as a country/region becomes richer, it has higher flexibility to substitute its production’s inputs in order to prevent negative impacts of the climate policy on the economy.

2 Literature Review

Many studies have addressed the required energy system transformations in order to meet future climate targets. According to Dessens et al. (2016), who conducted a comprehensive review of model based studies, most of the reported scenarios present a significant role for electrification of end-use sectors coupled with a fast decarbonisation of the electricity sector. Moreover, they shed light on the necessity of deploying Biomass with CCS (BECCS) in combination with a full portfolio of other mitigation technologies in the power sector, especially in the second half of the century.

In contrast to studies that focused on technological mitigation measures, few attempts addressed the possible role of price-induced energy-service demand reduction in achieving the stringent 2 °C target. Examples include Kesicki and Anandarajah (2011) who applied the demand-elastic version of the TIAM model to analyse the possible role of energy-service demand reduction to tackle global climate change, and Pye et al. (2014) who used the same methodology to address the uncertainty associated with such service demand responses. The findings of these studies show that reducing energy-service demands plays a critical role in meeting such an ambitious climate target, ensuring a more cost-effective transition to a low carbon energy system.

Despite the need for more ambitious climate targets, only few studies have so far discussed the required energy system transformations to hold warming well below 2 °C, or even further below 1.5 °C by 2100. In fact, the only existing model-based analysis that discussed the energy system characteristics of mitigation pathways consistent with the 1.5 °C limit, was given by Rogelj et al. (2016). According to this study, achieving the target will require immediate attention to push mitigation in every individual sector of the economy. However, to be best knowledge of the authors no study has so far analysed the contribution of technological mitigation options with regard to price-induced energy-service demand reduction in meeting a 1.5 °C consistent decarbonisation goal. The present chapter aims at filling this gap in the literature.

On the other hand, there exist a large number of empirical studies that employed the VES production function with capital and labour inputs. Examples include Lovell (1973), Roskamp (1977), Bairam (1991), and Zellner and Ryu (1998). Most of the studies found the VES a more realistic approach compared to other approaches. However, no chapter so far has employed a VES production function in the context of energy system modelling. In this sense, this chapter opens avenues for future research by introducing a macroeconomic model with the VES specification.

3 Methodology: Focus on the Elasticity of Substitution

TIMES Integrated Assessment Model (TIAM) is a global multi-regional, technology-rich, bottom-up energy system model, maintained by Energy Technology Systems Analysis Program (ETSAP) (Loulou and Labriet 2008). The model encompasses current and future energy technologies in a detailed manner and aims to find the least-cost mix of technologies to fulfil a given set of energy-service demands under different energy and climate related policies. Therefore, it is considered as an appropriate tool to chapter the role of technological measures in combating climate change. However, as the model ignores feedback between energy-service demands and their prices, it is not able to fully address the role of energy-service demands. Furthermore, being restricted to the energy sector limits the ability of the model to account the repercussions on the rest of the economy. To bridge these gaps, TIAM is linked to MACRO which is a top-down macroeconomic model with an aggregated view of long-term economic growth. Since Kypreos and Lehtila (2015) extensively discussed the original TIAM-MACRO, we only discuss how the MACRO’s production function is generalized so as to support the VES specification.

The production function of the standard MACRO model is a nested, constant elasticity of substitution (CES) function between the aggregate capital and labour inputs and the energy-service demands, which allows substitution between the pair capital-labour and the energy-service demands.
$$Y_{r,t} = \left[ {a_{r} \left( {K_{ r,t}^{{kpvs_{r} }} \cdot L_{ r,t}^{{\left( {1 - kpvs_{r} } \right)}} } \right)^{{\rho_{r} }} + \mathop \sum \limits_{i} b_{r,i} \cdot DM_{r,t,i}^{{\rho_{r} }} } \right]^{{1/\rho_{r} }}$$
(1)
$$\rho_{r} = 1 - 1/\sigma_{r}$$
(2)
where,
\(Y_{r,t}\)

Annual production of region r in period t

\(K_{r,t}\)

annual capital of region r in period t

\(L_{r,t}\)

annual labour growth index of region r in period t

\(DM_{r,t,i}\)

annual energy-service demand in MACRO for commodity i of region r in period t

\(a_{r}\)

capital-labour constant for region r (determined in a base-year benchmarking procedure)

\(\rho_{r}\)

energy-service demand constant for commodity i in region r (determined in a base-year benchmarking procedure)

\(kpvs_{r}\)

share of capital in the value-added aggregate of region r

\(\rho_{r}\)

substitution constant for region r

\(\sigma_{r}\)

elasticity of substitution for region r

In the standard MACRO model, this parameter is assumed to be a constant for all model regions and over the long-term planning horizon (2015–2100). Under the constancy assumption, all regions in the model react similarly to a unit change in the relative prices of the production inputs. However, considering the structural differences between the world regions, this cannot reflect the reality. Moreover, the assumption represents that the behaviour of an economy does not change over time. Notwithstanding, considering the long-term horizon of the model, it can be argued that as time passes, the economy may react differently to a unit change in the relative prices. Therefore, for a deeper analysis of mitigation pathways, we generalize the production function of the MACRO model in a way that it allows the elasticity of substitution to vary not only across the model regions, but also over the time horizon.

Basically, to implement the VES approach, first step is to generalize the production function of the standard MACRO model (Eq.  (1) in order to support time dependent elasticity of substitution:
$$Y_{r,t} = \left[ {a_{r} \cdot K_{r,t}^{{kpvs_{r} \cdot\varvec{\rho}_{{\varvec{r},\varvec{t}}} }} \cdot L_{r,t}^{{\left( {1 - kpvs_{r} } \right) \cdot\varvec{\rho}_{{\varvec{r},\varvec{t}}} }} + \mathop \sum \limits_{i} b_{r,i} \cdot DM_{r,t,i}^{{\varvec{\rho}_{{\varvec{r},\varvec{t}}} }} } \right]^{{1/\varvec{\rho}_{{\varvec{r},\varvec{t}}} }}$$
(3)
$$\rho_{r,t} = 1 - 1/\sigma_{r,t}$$
(4)
where,
\(\rho_{r,t}\)

Substitution constant for region r in time period t

\(\sigma_{r,t}\)

Elasticity of substitution for region r in time period t

Once the assumption of constancy is dropped and the variability of the substitution elasticity is admitted, we face a variety of alternatives for defining the functional form of the elasticity of substitution. The resultant production function, therefore, depends on the assumptions involved in this function. To develop the function, it is assumed that as an economy becomes richer, it will have higher flexibility to replace energy-service demands with the other production inputs. To specify the regional elasticity of substitutions in the first planning period (2015–2020), adapted from World Bank (2017) and the investigation of Remme and Blesl (2006), the model regions are divided into high-income, middle-income and low-income regions with elasticities of 0.25, 0.2 and 0.15, respectively. To determine the elasticity values for the next years (i.e. 2021–2100) it is assumed that the elasticity of substitution of each region is a function of GDP (Gross Domestic Production) per capita growth of that region. However, in order to avoid overestimations and stay within the range proposed by Remme and Blesl (2006), the maximum elasticity of substitution (for India in 2100) is set to 0.5. Accordingly, the elasticities for all the other regions/time periods are adopted. Figure 1 depicts the given values for substitution elasticities.
Fig. 1

Applied elasticity of substitutions in the VES case

4 Scenario Definition

Four different scenarios are constructed for this chapter. While one of the scenarios does not involve any CO2 reduction policy, all others are subject to a carbon budget consistent with at least a 33% chance of limiting the average global temperature increase to 1.5 °C by 2100. In this context, the Base scenario is employed as a benchmark for the decarbonisation scenarios. For the purposes of the decarbonisation scenarios, the energy sector carbon budget (including emissions from industrial processes as well as fuel combustion) is calculated from an estimate of the total CO2 emissions budget for the given temperature limit (see IPCC 2014 and IEA 2017). Non-energy sector CO2 emissions that mainly arise through land-use, land-use change and forestry are given according to IEA (2017). The assumed carbon budget for the energy sector over the period 2015–2100 is presented in Table  1.
Table  1

CO2 budget assumptions in this chapter

Net anthropogenic warming with a probability of more than 33%

Total CO2 budget (2015–2100)

Non-energy CO2 emissions (2015–2100)

Energy sector CO2 budget (2015–2100)

<1.5 °C

570 GtCO2

-30 GtCO2

600 GtCO2

Due to uncertainties in the contribution of price-induced energy service demands reductions, the decarbonisation scenarios vary depending on their assumption concerning this mitigation measure. One of the decarbonisation scenarios (1.5D-DEM) ignores the interconnection between energy-service demands and their prices in order to rely only on technological mitigation measures. In contrast, the other two decarbonisation scenarios consider both types of mitigation options. While in one of them (1.5D) the elasticity of substitution is a constant value (0.25) for all regions/periods, to provide more insights into the role of price-induced energy-service demand reduction, in the other scenario (1.5D-VES), the elasticity varies as in Fig.  1. The start year of mitigation action in all decarbonisation scenarios is set to 2020. Table 2 reviews the scenarios of this chapter.
Table 2

Observed scenarios

Scenario name

Description

Base

No carbon budget limitation

1.5D

The carbon budget is consistent with the 1.5 °C target (Table 1)

Elasticity of substitution is set to 0.25 for all regions/time periods

1.5D-VES

The carbon budget is consistent with the 1.5 °C target (Table 1)

Elasticity of substitution varies as in Fig. 1

1.5D-DEM

The carbon budget is consistent with the 1.5 °C target (Table 1)

Service demands do not react to changes in their prices

5 Results

5.1 Primary Energy Consumption

Reaching the ambitious 1.5°C target requires a significant and rapid transition in the global energy system. In the Base, primary energy consumption more than doubles the 2014 levels to reach 1282 EJ in 2100 (Fig. 2). While fossil fuels continue to dominate primary energy consumption, their total share decreases from 82% in 2014 to 72% in 2100. The remaining primary energy mix in 2100 consists of 15% biomass and waste, 7% other renewables and 5% nuclear.
Fig. 2

Global primary energy consumption in the Base and 1.5D scenarios, 2014–2100

In the 1.5D, growth in primary energy consumption in 2100 compared to 2014 levels is limited to 80% and is around 256 EJ lower than in the Base. This features the need to accelerate the decoupling of primary energy consumption from economic growth. In this scenario, reliance on fossil fuels falls dramatically over the century. Most of the remaining fossil fuels in 2100 are used either in combination with CCS technologies or for feedstocks and non-energy purposes. Renewables overtake fossil fuels to dominate the primary energy mix by the share of 69% (565 EJ) in 2100. This is around 25% higher than today’s total primary energy consumption. The remainder of the primary energy mix in 2100 is nuclear with a share of 18%, highlighting that despite its challenges, nuclear is considered to be an essential part of the transition.

5.2 The Rapid Decarbonisation of the Power Sector

An initial step in an effective transformation of the energy system is decarbonisation of the power sector. Figure 3 shows that moving from the Base to the 1.5D entails deep and rapid changes in this sector.
Fig. 3

Changes in the global electricity generation mix in the 1.5D over 2020–2100 and in the 1.5D-DEM and 1.5D-VES in 2100, relative to the Base

In the 1.5D, electricity generation from renewables (in this chapter, renewables refer to all renewables excluding BECCS) grows dramatically over the century. This highlights the importance of policy instruments focusing on the challenges associated with renewables deployment. According to Mousavi et al. (2017), to develop renewable energies, especially in less-developed countries, governments should address not only economic barriers (e.g. fossil-fuel subsidies and high capital costs of renewables), but also non-economic challenges (e.g. lack of sufficient awareness and confidence) that they may face.

On the other hand, variable renewable sources which are non-dispatchable due to their fluctuating nature, account for around 75% of the increase in renewables-based generation in 2100. Considerable system flexibility is needed to integrate such a high level of variable renewables. In the 1.5D, this issue is reflected in the increased deployment of geothermal, hydro, biogas and Solar Thermal (STE) plants with storage, as well as fossil-fired plants with CCS.

Despite the long technical lifetime of conventional coal power plants, in the 1.5D, they are almost completely phased out by 2035. This raises concerns of stranded assets and affects current and near future investment decisions. However, CCS retrofits allow the continued use of fossil-fuels in the power sector for some decades.

One of the key measures in the shift from the Base to the 1.5D is BECCS. In fact, 20% share of BECCS turns the power sector into a source of net-negative emissions over the period 2040–2100. The negative emissions are crucially important to offset residual emissions in other sectors where direct mitigation is either technically too difficult or more expensive. Despite the advances made in CCS technology in recent years, no large BECCS power plant operates at a commercial scale. Therefore, achieving the ambitious target of the 1.5D requires continued investment in Research, development and deployment (RD&D) in this area. However, ahead of any of the concerns related to large-scale deployment of BECCS, the availability of sustainable and sufficiently large biomass supply over the world regions is critical. This elevates the importance of an effective system to support efficient production of biomass for energy purposes.

Figure 3 also illustrates that changes in energy-service demands have a notable impact on the electricity generation from renewables, in a long-term perspective. In the 1.5D-DEM, the majority of the additional generation in 2100 is supplied by wind and solar, which is primarily due to their relatively high technical potential. For instance, the global technical potential of solar energy is estimated to be 1600 EJ (Resch et al. 2008), which is around 280% of the global primary energy consumption in 2014.

5.2.1 End-Use Electrification and Decarbonising the Power Sector

In the 1.5D, the power sector is almost completely decarbonised by 2050 and share of electricity in final energy consumption increases from around 20% in 2020 to 62% in 2100 (Fig. 4). This conveys a clear message that a cost-effective strategy to reach the ambitious 1.5°C target is decarbonising the power sector and substituting fossil fuels in end-use sectors with the decarbonised electricity. More than 83% of the increase in global final electricity consumption by 2100 is derived by non-OECD countries, especially India. In relative terms, electricity consumption in the transport sector grows the most. The share of electricity in this sector increases dramatically from around 1% in 2020 to almost 50% in 2100.
Fig. 4

Share of decarbonised electricity in total global annual generation (left) and share of electricity in global annual final energy consumption (right)

5.2.2 Energy-Service Demands and Investment Needs in the Power Sector

Although more extensive deployment of energy efficient technologies reduces electricity demand in the decarbonisation scenarios in the short term, vast electrification of end-users leads to considerably higher electricity demand in these scenarios over the longer term. However, the level of electricity demand and the required investments in the power sector tightly depend on the level of energy-service demands (Fig. 5). In other words, reducing energy-service demands will significantly offset the increase in electricity demand and consequently reduce the level of investments in the power sector. Over the period 2020–2100, the 1.5D-DEM requires considerably higher investment levels, largely for renewables, compared to the 1.5D. In contrast, in the 1.5D-VES, the cumulative investment need is lower than in the 1.5D. In relative terms, China experiences the highest reduction in the cumulative investment levels (17%) in the 1.5D-VES compared with the 1.5D.
Fig. 5

Global final electricity demand by scenario (left) and cumulative additional investment in the decarbonisation scenarios relative to the Base (right)

5.3 The Relative Roles of Mitigation Measures

Shifting from the Base to the 1.5D needs a wide range of supply-side and demand-side mitigation options, with the major contributor being renewables (excluding BECCS), accounting for a 30% share in cumulative global CO2 emissions reductions over the period 2020–2100. Energy-service demands reductions play a substantial role with 20% contribution, while nuclear contributes 17%, BECCS 16%, fossil CCS 12%, fuel switching 4% and efficiency 1% (Fig. 6). The low contribution of efficiency improvement is due to the fact that energy efficiency measures, which are cost-saving over the long run, are already deployed in the Base, as these would be an integral part of a least-cost pathway.
Fig. 6

Global energy-related CO2 emissions reductions from the Base to the 1.5D by mitigation measures (left) and global cumulative energy-related CO2 emissions in the 1.5D

Despite the rapid decarbonisation of the global energy system which reaches carbon-neutrality by 2065 (the power sector reaches carbon-neutrality by 2040), meeting the 1.5 °C target results in an “overshoot” of the energy sector carbon budget that must be counterbalanced by a significant deployment of negative emissions technologies, especially after 2060 (Fig. 6). This sheds light on the key role of BECCS in this context. In fact, more than 300 GtCO2 of net negative emissions resulted from deployment of BECCS bring temperature back to the target level in 2100.

It is important to note that although BECCS is the most mature negative emissions technology, there are other options that deliver negative emissions such as direct air capture, biochar and lime-soda process (McGlashan et al. 2012). However, as their costs and potential are quite uncertain, they are not included in the current chapter.

The mitigation role of energy-service demands reductions is slightly higher (23%) in the 1.5D-VES (Table 3). However, the relative contributions of the other measures in this scenario do not notably differ from those in the 1.5D. In contrast, excluding energy-service demand reduction option in the 1.5D-DEM scales up deployment of the technological measures, especially renewables that contributes around 45%. Besides the relatively high technical potentials for renewables, this is due to the fact that renewables can be directly used by end-users, which provides higher flexibility to the system. In the 1.5D-DEM the direct-use of renewables (excl. Bioenergy) by end-use sectors is more than 40% compared to that in the 1.5D. Although the importance of BECCS is already recognized, its contribution does not vary among scenarios. As stated, this is mainly due to the limited availability of biomass for energy purposes.
Table 3

GtCO2 cumulative CO2 emissions reductions over the period 2020–2100 by mitigation measure and scenario

 

1.5D

1.5D-DEM

1-5D-VES

Service-demand

20

0

23

Efficiency

1

4

1

Renewables

30

45

29

Nuclear

17

17

17

Fossil switching

4

3

3

Fossil CCS

12

15

11

Biomass CCS

16

16

16

5.4 A Detailed Look at the Role of Energy-Service Demands Reductions

The level of energy-service demands reductions depends on the costs of alternative technologies and fuels available to meet the service demands, costs of the other production input factors (i.e. capital-labour) and the size of the elasticity of substitution. In order to provide more insights into this mitigation measure, Fig. 7 depicts energy-service demand reduction levels for six different regions under the 1.5D and the 1.5D-VES scenarios relative to the Base.
Fig. 7

Energy-service demand reduction level for six regions under the 1.5D and 1.5D-VES scenarios compared to the Base

Pursuing the limitation of warming to below 1.5 °C reduces the flexibility in mitigation measures almost completely. In fact, the urgency of putting ambitious mitigation strategies into place and limited availability of cost-effective technological options in the near future impose a “price-shock”, leading to significantly higher energy-service prices over the next 10–15 years. As a result, energy-service demands experience a notable reduction over the period 2020–2030. In both scenarios, relative energy-service demands reductions increase over time towards the end of the century. In most of the regions, the level of energy-service demands reductions in both scenarios is almost the same in the short to medium term. However, increasing energy-service prices and the higher elasticity of substitutions in the 1.5D-VES, favours higher reductions in energy-service demands in this scenario in the long-term. This is especially true for developing regions such as China and India, which experience a rapid economic growth.

It is noteworthy that although in the 1.5D-VES scenario, India has the highest elasticity of substitution in 2100, in this scenario, as well as in the 1.5D, China represents the most significant energy-service demands reductions over the whole century. This elevates the importance of the role that energy-service demands in China will play in reaching the 1.5 °C objective.

A look on the sectoral level is necessary for a more rigorous evaluation of the role of energy-service demands reductions. For this purpose, emissions from secondary energy carriers, such as electricity, are accounted for in each end-use sector. As presented in Fig. 8, energy-service demands reductions play the most critical role in decarbonising the industry, followed by the transport sector. A reasonable explanation for this issue is the relatively expensive abatement opportunities in these two sectors. Generally speaking, the challenges of decoupling expanding demands for the industry and transport sectors from CO2 emissions will require considerable changes in their current structure and processes, which may dramatically increase the prices of energy services. In contrast, the other sectors provide relatively cheap mitigation measures as their decarbonisation are mainly characterised by the decarbonisation of the electricity consumption.
Fig. 8

Contribution of energy-service demand reduction to global overall CO2 emissions reduction

5.5 Marginal Abatement Costs and GDP Losses

Analysis of marginal costs of CO2 emissions abatement indicates that reducing energy-service demands has a significant impact on the direct costs of CO2 emissions. Figure 9 shows that in the 1.5D, the marginal abatement costs rise sharply, especially in the second half of the century, reaching almost 4.5$/kg in 2100, which is beyond reasonable levels (Koljonen and Lehtilä 2012). In the 1.5D-DEM, however, marginal abatement costs increase even more rapidly through 2100 to a final price of 10.2$/Kg. It can be concluded that price-induced energy-service demands reductions play a crucial role in ensuring a more cost-effective transition to the 1.5 °C ambition.
Fig. 9

Marginal CO2 emissions abatement costs under the decarbonisation scenarios

Suffice to say that achieving an ambitious decarbonisation target cost-effectively not only requires technological options but also needs reductions in energy-service demands. However, for a more comprehensive and integrated assessment of the mitigation role of energy-service demand reduction it is of crucial importance to analyse macroeconomic implications of the relevant scenarios.

A widely-used measure in this context is GDP loss representing the difference in GDP between the Base and each of the decarbonisation scenarios. In fact, in the mitigation scenarios GDP would be lower than in the Base because of higher energy and emissions mitigation costs, as well as changes in resource allocation. Some of the losses, however, can be recovered by reducing energy-service demands.

Figure 10 depicts regional GDP losses in the 1.5D and 1.5D-VES scenarios. It can be observed that if economies become more flexible to replace energy-service demands with other production inputs, they will be less affected by ambitious global mitigation targets. This implies that besides decarbonisation of the energy mix, achieving the 1.5 °C target requires a considerable decoupling between energy consumption and economic growth. In relative terms, China suffers the most from the implemented decarbonisation target with GDP losses of 6.6% and 7.7% by 2100 in the 1.5D-VES and the 1.5D, respectively.
Fig. 10

GDP-Losses compare to the Base by region and scenario

6 Conclusion

The following key messages emerge from the proposed scenario analysis. First, the pace and scale of efforts needed to achieve carbon neutrality by 2065 and the considerable deployment of BECCS in the post-2040 period in the 1.5 °C scenario emphasize that there would be almost no room for delay. Rapid and strong policy action as well as increased effort and sustained international collaboration is needed to support aggressive deployment of a mixture of supply-side and demand-side mitigation measures in all nations in the world.

Second, decarbonising the power sector and substituting fossil fuels in less flexible end-use sectors with the decarbonised electricity is found to be a cost-effective strategy for all regions. However, a rapid and strong growth in electricity demand may bring new challenges to the power sector. In this regard, reducing energy-service demands plays a critical role in offsetting the additional pressure on the power sector.

Third, uncertainty concerning the size and function of the elasticity of substitution can have some impact on mitigation pathways and the associated costs. Nevertheless, this uncertainty does not weaken the key insight that reducing energy-service demands notably facilitates the transition towards the 1.5 °C target. Furthermore, this measure plays a vital role in recovering the negative macroeconomic impacts of the implemented mitigation policies. In other words, although reaching the 1.5 °C target without reducing energy-service demands is technically feasible, it comes with huge economic impacts. This implies that besides decarbonisation of the energy mix, achieving the 1.5 °C target requires a considerable decoupling between energy demand and economic growth. Therefore, a cost-effective mitigation strategy should benefit from policy instruments that aim at modifying consumer behaviour to further support the transition. This is especially the case for countries like China, whose economy requires deep changes under the decarbonisation policy.

Moreover, policy makers should be aware of the importance of flexibility mechanisms in the energy supply sector to support the large deployment of variable renewable sources.

Finally, due to the need for negative emissions, BECCS is found to be a necessary option to reach the 1.5 °C goal. However, deployment of large-scale BECCS technologies is limited to the availability of sustainable and sufficiently large biomass supply. Therefore, an effective system is essential to support efficient production and consumption of biomass for energy purposes.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Energy Economics and Rational Energy Use (IER)Stuttgart UniversityStuttgartGermany

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