The role of advanced demand-sector technologies and energy demand reduction in achieving ambitious carbon budgets

Applied Energy 238 (2019) 351–367

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

The role of advanced demand-sector technologies and energy demand reduction in achieving ambitious carbon budgets

T

T.A. Nappa, , S. Fewa, A. Soodb, D. Berniec, A. Hawkesd, A. Gambhira ⁎

a

Grantham Institute for Climate Change, Imperial College London, South Kensington Campus, London SW7 2AZ, UK Department of Physics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK c The Met Office Hadley Centre, Fitzroy Road, Exeter, Devon EX1 3PB, UK d Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK b

HIGHLIGHTS

< 2 °C requires a challenging transformation of the whole energy system. • Achieving details of < 2 °C consistent pathways for industry and transport are shown. • Technology residual emissions from demand-sectors is crucial for achieving < 2 °C. • Reducing technologies and energy demand reduction reduces reliance on BECCS by ∼18% • Advanced • Targeted innovation in the demand sectors is required to realize this potential. ARTICLE INFO

ABSTRACT

Keywords: Energy systems model Climate change Mitigation Energy demand reduction

Limiting cumulative carbon emissions to keep global temperature increase to well below 2 °C (and as low as 1.5 °C) is an extremely challenging task, requiring rapid reduction in the carbon intensity of all sectors of the economy and with limited leeway for residual emissions. Addressing residual emissions in ‘challenging-todecarbonise’ sectors such as the industrial and aviation sectors relies on the development and commercialization of innovative advanced technologies, currently still in their infancy. The aim of this study was to (a) explore the role of advanced technologies in achieving deep decarbonisation of the energy system and (b) provide technology-specific details of how rapid and deep carbon intensity reductions can be achieved in the energy demand sectors. This was done using TIAM-Grantham – a linear cost optimization model of the global energy system with a detailed representation of demand-side technologies. We find that the inclusion of advanced technologies in the demand sectors, together with energy demand reduction through behavioural changes, enables the model to achieve the rapid and deep decarbonisation of the energy system associated with limiting global warming to below 2 °C whilst at the same time reduces reliance on negative emissions technologies by up to ∼18% compared to the same scenario with a standard set of technologies. Realising such advanced technologies at commercial scales, as well as achieving such significant reductions in energy demand, represents a major challenge for policy makers, businesses and civil society. There is an urgent need for continued R&D efforts in the demand sectors to ensure that advanced technologies become commercially available when we need them and to avoid the gamble of overreliance on negative emissions technologies to offset residual emissions.

Abbreviations: AR5, 5th Assessment Report; BECCS, Bioenergy with Carbon Capture and Storage; CCS, Carbon Capture and Storage; EAF, Electric Arc Furnace; ECS, Equilibrium Climate Sensitivity; ETSAP, Energy Technology Systems Analysis Programme; GDP, Gross Domestic Product; GHG, Greenhouse Gas; HDVs, Heavy Duty Vehicle; IAM, Integrated Assessment Model; IPCC, International Panel on Climate Change; LDVs, Light Duty Vehicles; DRI, Direct Reduced Iron; NETs, Negative Emissions Technologies; PV, Photovoltaics; RCP, Representative Concentration Pathways; SSP, Shared Socioeconomic Pathways; TIAM, TIMES Integrated Assessment Model; TIMES, The Integrated Markal-EFOM Energy System ⁎ Corresponding author. E-mail address: [email protected] (T.A. Napp). https://doi.org/10.1016/j.apenergy.2019.01.033 Received 18 April 2018; Received in revised form 18 December 2018; Accepted 2 January 2019 0306-2619/ Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved.

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1. Introduction

Industry CO2 emissions] needs to be central to climate policy priority’ and that these emissions ‘could be reduced if innovative technologies… can be brought to market readiness swiftly’. Their analysis shows that emissions from the demand sectors (mostly industry and transport) make up more than two thirds of residual emissions even in 1.5 °C scenarios. Yet, representation of demand-side mitigation in the literature is limited. The majority of Integrated Assessment Modelling papers only provide high-level details of changes to the demand-side such as reduction in carbon emissions, energy intensity or fuels share changes [3]. There are a very limited number of papers that focus specifically on decarbonisation pathways for the demand sectors and that provide detailed uptake of technologies as part of a global decarbonisation pathway. For example, Rootzen [11] presents pathways for deep decarbonisation of carbon-intensive industry in the European Union. Van Ruijven et al. [12] presents long-term emissions pathways for the global steel and cement industries. More recently, Wang et al. [13] published global pathways for decarbonisation of the building sector. Mulholland et al. [14] provides details on decarbonisation of road freight, however, this study only goes to 2050. In summary, existing studies are either (1) regionally focussed, (2) look at the sector on its own rather than part of a full model, (3) do not consider the full time horizon to 2100 or (4) do not consider well-below 2 degrees scenarios. Thus, this paper aims to provide more detail of the specific actions and technologies, which could achieve deep decarbonisation of the industrial and transport sectors and, ultimately, reduce residual emissions from these sectors.

The ambition set by the 2015 Paris Agreement [1] to limit global temperature change to well-below 2 °C is a significant shift of the goal posts, which will require an unprecedented transition in the global energy system if we are to successfully achieve this new target. The IPCC’s analysis of published modelling scenarios which achieve 1.5 °C [2], shows that the key trends are that it would require more rapid and immediate decarbonisation, greater uptake of energy efficiency in the near term, increased shares of renewable energy technologies across all sectors and a greater role for Negative Emissions Technologies (NETs), compared to the level of effort required for 2 °C. The carbon intensity and energy intensity reductions required to meet 1.5 °C are extremely challenging to achieve in practice and require a complete shift in our energy system on both the supply and demand sides. This paper builds on other work in this area [3] by drilling down into the specific technological and demand-side changes, which would be required to achieve this level of deep decarbonisation at a sectoral level. The Grantham Institute’s TIMES Integrated Assessment Model (TIAM-Grantham) is extremely technologically rich on both the supply and demand sides. As a result, high-level carbon intensity and energy efficiency improvements can be attributed to the uptake of specific technologies at a sectoral level, making it an appropriate model to fill this important research gap. Certain sectors have long been accepted as particularly challenging to decarbonise [4–6]: these include the industrial sector, aviation and freight (both shipping and trucks). Deep decarbonisation in these sectors, of the level required for 1.5 °C, can only be achieved through the development and uptake of new and innovative processes and technologies. Technologies such as hydrogen-fuelled planes or advanced electric kilns in industry, which – owing to a combination of lack of data and a tendency towards conservatism – are often excluded from energy system models. The longer the delay in shifting to low- or zerocarbon processes and the greater the level of residual emissions from those sectors which remain carbon intensive, the more we will have to rely on NETs (in themselves a highly speculative set of technologies) to offset emissions in order to still limit global temperature rise. This is a high-risk approach since, if we are unsuccessful in deploying NETs technologies, the world will be locked in to a high temperature pathway [7]. At the same time, many modelling scenarios to date have failed to keep pace with rapidly changing technology costs, particularly for renewables such as solar PV (Photovoltaics), whose representation in future low-carbon scenarios appears to have been systematically underestimated [8]. As a result, deep mitigation to well below 2 °C risks looking costlier than it might turn out to be in reality. In this paper, we have used TIAM-Grantham to assess how low we can push emissions from the energy system and whether it is still possible to meet a 1.5 °C target. We investigate the role of advanced technologies and energy demand reduction through behavioural changes and provide technology-specific details of how deep decarbonisation could be achieved in two demand sectors, Industry and Transport.

2.1. Technology availability and uncertainty in energy modelling and longterm decarbonisation pathways Wilson et al. [15] states that ‘global integrated assessment models… are limited in their ability to analyse the emergence of novelty in energy end-use’. Wilson et al. [15] also highlights the inconsistency of the modelling field to avoid the inclusion of speculative, pre-commercial technologies such as hydrogen planes or electric trucks and limit technological availability to currently available technologies, with the exception of BECCS – arguably an equally speculative technology. Yet, the time horizon over which the energy transformation is assessed spans the next 80 years stretching out to 2100 thus it is not inconceivable that these (or indeed another) new breakthrough technologies could become commercially viable in this timeframe. The role of technological availability and its impact on decarbonisation scenarios has been explored in various ways in the literature to date: Rogelj et al. [16] investigated the impact of mitigation technology and energy demand on the probability of staying below 2 °C. They used a binary approach, i.e. turning nuclear or CCS on or off in the scenarios. As a part of the AMPERE and EMF27 model inter-comparison projects, sensitivity analyses were to assess the impact of technological availability and energy demand on the feasibility of low carbon pathways [17,18]. Availability of CCS and nuclear were explored in a binary manner, whilst the roles of intermittent renewables and bioenergy were tested by constraining power generation share and supply, respectively. Sensitivity of the demand-side was limited to aggregated energy intensity improvement and low demand case. Gambhir et al. [19] took a more nuanced approach by testing the impact of (1) late CCS (as opposed to ‘no CCS’) and (2) ‘weak electrification’ of the demand sectors. Fais et al. [20] explored all possible combinations of 5 sensitivity dimensions, generating 32 scenarios. The above studies have the following limitations: (1) focus mainly on technologies on the supply-side, (2) aggregated or high level treatment of the demand side, and (3) tend to focus on testing the impact of limiting availability of technologies in the model rather than adding technologies to the model. Thus, in our study we address these aspects by assessing the impact

2. Literature review The new IPCC Special Report on 1.5 °C [9] collates and analyses the results of 222 scenarios from different models and studies, of which 90 are scenarios consistent with 1.5 °C and 132 are scenarios consistent with 2 °C. One of the main conclusions of this report is that, ultimately, the world needs to have reached net zero emissions by around 2050. Achieving such a transformation requires fast and deep decarbonisation of all sectors of the economy. Luderer et al. [10] demonstrates that residual emissions are a significant obstacle that challenges our ability to avoid overshooting the very tight net cumulative carbon budgets associated with returning global temperatures to below 1.5 °C. The authors specifically state that ‘minimizing [Residual Fossil Fuel and 352

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353

100, 150, 200, 250, 300 Low On Revised Advanced

100, 150, 200, 250, 300 Low Off Revised Advanced

100, 150, 200, 250, 300 Central Off Revised Advanced

Central Off Revised Standard

V5

V4

V3

V2

We derive five versions of the model (named V1 to V5) based on a combination of four characteristics: (a) standard or advanced technology set, (b) original or revised costs, (c) central or low demand projections and (d) price elasticity on or off. Table 1 provides details of the model versions and the full list of scenarios in this analysis. The standard model is based on the suite of technologies in the ETSAP-TIAM model (2010 version) and consists of a mix of technologies that is typically considered in energy system models. In the advanced

Mitigation scenario with the standard technology mix, central demand projection and original cost assumptions. Price elasticity is turned off. Tested under five CO2 price scenarios ranging from 100 to 300 $/tCO2 in 2020 Mitigation scenario with the standard technology mix, central demand projection and revised cost assumptions. Price elasticity is turned off. Tested under five CO2 price scenarios ranging from 100 to 300 $/tCO2 in 2020 Mitigation scenario with the advanced technology mix, central demand projection and revised cost assumptions. Price elasticity is turned off. Tested under five CO2 price scenarios ranging from 100 to 300 $/tCO2 in 2020 Mitigation scenario with the advanced technology mix, low demand projection and revised cost assumptions. Price elasticity is turned off. Tested under five CO2 price scenarios ranging from 100 to 300 $/tCO2 in 2020 Mitigation scenario with the advanced technology mix, low demand projection and revised cost assumptions. Price elasticity is turned on. Tested under five CO2 price scenarios ranging from 100 to 300 $/tCO2 in 2020

3.3. Details of modelling approach and scenarios

V1

A key aim of this paper was to understand the contribution that advanced technologies could make at a sectoral level to achieving deep decarbonisation levels consistent with 1.5 °C scenarios. In selecting new technologies to include, we have focussed our attention on sectors that are known to be particularly challenging to decarbonise such as freight transport, aviation and industry [4–6]. Full details of the new technologies added in the advanced technology version of the model as well as their assumptions are provided in the Appendix A. The technologies included were limited to ‘known’ technologies for which cost estimations could be found. Although the new technologies that were added to the model do not necessarily represent the full suite of possible technologies that could potentially be developed in the future or that are currently in development, this study is a useful ‘what-if’ exercise that demonstrates how deep decarbonisation of the demand sectors could be achieved in the future.

Description

Table 1 Description of the model variations and scenarios in this analysis and their assumptions.

3.2. Justification for the inclusion of certain advanced technologies

Model variation

Technology set

Cost assumptions

Price elasticity

The TIAM-Grantham model is the authors’ version of the ETSAPTIAM model [21], a global energy system model with 15 regions, which was developed by the Energy Technology Systems Analysis Programme (ETSAP). TIAM is based on the TIMES (The Integrated MARKAL-EFOM System) modelling platform, details of which can be found here [21]. This energy systems model covers the full energy chain from extraction of energy resources (e.g. coal mining) through conversion (e.g. electricity generation or oil refining) and to final use to provide an ‘energy service’ to the end-user (e.g. heating or lighting in a building; mobility etc.). TIAM-Grantham is a linear programming model and the objective function is to minimise total discounted energy system cost, subject to a constraint on carbon dioxide emissions. At each step, the model can choose between different technologies to achieve this, where the attributes of these technologies are fully specified such as capital and operating cost, efficiency, fuel mix etc. There is no linkage to a macroeconomic model to observe full equilibrium impacts of changes in energy prices. Exogenous inputs of GDP, population, household size and sectoral shares of the economy are combined to project future energy service demands across the agricultural, residential, commercial, industrial, and transport sectors in each region. Energy system data such as technology costs, resource supply curves and annual resource availability are also input into the model. In solving, the model allows trade in energy commodities between regions.

Central

3.1. Description of the TIAM-Grantham model

Off

3. Methods

Original

Demand projection

(1) How can advanced technologies and energy demand reduction support decarbonisation of two challenging demand sectors and reduce residual emissions from these sectors? (2) How does this impact our ability to achieve ambitious decarbonisation targets and reducing reliance on BECCS?

Standard

Starting CO2 price in 2020 ($/tCO2)

of the inclusion of specific advanced low-carbon technologies in the demand sectors impacts our ability to reach well below 2 °C scenarios. In particular, we ask two questions:

None, 100, 150, 200, 250, 300 100, 150, 200, 250, 300

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ranging from 100 to 1000 $2005/tCO2. We note that the scenarios with no action before 2020 are those with carbon prices at the upper end of this range by 2050, which corresponds well to our numbers. Assuming that a similar increasing level of effort continues beyond 2050, their scenarios are likely to have similarly high carbon prices in 2100 to those reported here. Analysis of the AR5 database performed by Gambhir et al. [22] shows that, for scenarios achieving < 1.5 °C temperature rise, the carbon price increases from a median of ∼600 $2005/tCO2 in 2050 to ∼4400 $2005/tCO2 in 2100 with an interquartile range of 2450–5000 $2005/tCO2.

Table 2 List of new technologies represented in the advanced technology model versions. Sector

Additional technologies added in the advanced technology model version (V3 to V5)

Transport

Road – Electric heavy freight vehicles from 2030. – Electric two and three-wheelers Shipping – Hydrogen domestic/international shipping from 2035/2040. Rail – Hyperloop to replace passenger rail demand from 2020. Aviation – Hydrogen domestic/international aviation from 2040/2050. – Biofuels in aviation from 2020. – Hyperloop to replace domestic aviation demand from 2020. Iron and steel sector: Detailed technology-based (as opposed to generic energy services) representation of this sector. – Blast furnace with direct coal injection – Blast furnace with top-gas recycling – Blast furnace with top-gas recycling and CCS – Blast furnace with CCS – Corex smelting process – Corex with CCS – Direct reduced iron with CCS – Direct reduced iron with hydrogen – Onsite power generation with recycled gases – Onsite power generation with CCS Improved representation of Carbon Capture and Storage technologies for other industrial sectors: – Combined heat and power (using coal, gas or recycled gases) with CCS – Cement Precalciner with CCS – Cement whole plant with CCS – Steam Chemicals Distillate Oil New - CCS – Ethylene process in chemical sector with CCS – Hydrogen for Ammonia production with CCS – Ethylene and Propylene process with CCS – Steam generation in Pulp and Paper (Coal or gas fired) with CCS – Process Heat in Pulp and Paper (Coal or gas fired) with CCS - Process heat in other industries with CCS – No new technologies added – Tidal and wave power – Improved representation of nuclear fusion

Industry

Buildings Electricity

3.4. Simulating significant energy demand reductions from behavioural changes We have implemented energy demand reductions primarily according to assumptions deriving from meta-analyses of potential for behavioural changes in the buildings [23] and transport [24] sectors, as well as potential for reductions in manufactured material consumption leading to reduced industrial output [25]. There is a broad literature on the potential for energy demand reductions from programmes to promote behavioural changes in the use of energy. Such literature has been usefully summarised in recent analyses such as IIASA’s Global Energy Assessment [26], with specific reviews of studies focusing on such changes in the buildings [23] and transport [24] sectors, as well as the sector summaries in the IPCC’s Fifth Assessment Report WGIII on mitigation of climate change [27]. In addition, detailed analysis of potential for reduced use of materials in the manufacture of products has also been undertaken [25], forming the basis of assumptions for reduced industrial sector energy demand. Table 3 summarises the overall energy demand reductions assumed in the “low demand” scenarios in this study, as well as the primary sources used to arrive at these estimates. These changes are not endogenous mitigation options in the TIAM-Grantham model (a shortcoming common to most IAMs) so must be implemented exogenously. However, it is important to note that TIAM-Grantham does allow energy demand to be lowered in response to higher energy prices (i.e. a price-elastic response). As such, we run the “low demand” case with and without this price elasticity, in the latter case to avoid any potential double-counting of behaviourally-induced energy demand reductions [28]. The change in final energy intensity as a result of implementing the energy demand reductions is shown in Fig. 2. For the buildings sector, specific studies either highlighted in the above meta-analyses or additional to it, which identify the potential for demand reduction include:

technology version of the model, we have modified the industrial and transport sectors to include more speculative technologies such as hydrogen planes and ships as well as an improved representation of the industrial sector, including the iron and steel sub-sector and advanced CCS technologies, in particular. Table 2 details the new technologies available in the advanced technology versions of the model (V3 to V5). The impact of revised cost assumptions for technologies that have seen recent rapid cost reduction (e.g. solar PV and electric vehicles) was also investigated. Details of the new cost assumptions can be found in Tables A.1 and A.2. Each model variation was then tested under five carbon price scenarios: a starting carbon price in 2020 of 100, 150, 200, 250 and 300 $2005/tCO2 rising at 5% per annum (Fig. 1). This rate of rise is assumed in order to simulate increased effort (in real terms) to mitigate CO2 emissions over time, as the world economy grows. The carbon price levels reached at the end of the century range from 3000 $2005/tCO2 to 10,000 $2005/tCO2. These numbers may appear very high but the reasons for this are twofold: Firstly, the high carbon price required is indicative of the extreme level of effort required to limit global temperature rise to 1.5 °C. Secondly, in order to prevent unrealistic early action in the model, no action was allowed prior to 2020 (i.e. model outputs were fixed to the baseline scenario) and a CO2 emissions constraint consistent with the Cancun pledges was imposed in 2020, simulating weak action to this point. Thus, the carbon price was only effective starting in the decade 2020–2030 and beyond. In their supplementary material, Rogelj et al. [3] show carbon prices in 2050

• A simulation of building heating and cooling demand in a Los • • • • • 354

Angeles building which projected reductions of 30–60% through better operation of a heating, ventilation and air conditioning (HVAC) system to make greater use of night cooling [30]; Analysis that changes to thermostats so that higher temperatures are achieved in buildings, within comfort tolerance limits, can lead to a factor of 3 reduction in cooling demand in Zurich and a factor of more than 2 in Rome [31], and a factor of 2–3 in Hong Kong [32]; Assessment of indirect feedback in the form of bills, leading to reductions of 0–10% in energy use, and direct feedback in the form of smart meters leads to 5–15% [33]; Estimation of a potential energy demand reduction of 20% in the short-term in the USA [34] and the UK [35] and 50% or more in the longer-term, even in developed countries which already have relatively low energy consumption [36]; Estimated 25% reductions in water heating demand from shorter showers, as well as reduced electricity demand of up to 13% from reduced appliance standby times [37]; Analysis of households in urban Hangzhou, China, which used ∼10% less energy when informed by “energy-saving education” [38];

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Fig. 1. Carbon price scenarios in this analysis. Insert shows prices for the period to 2050. All prices are in 2005 USD.

• Analysis suggesting that lighting use in office buildings can be de•

in buses and trains. Aviation could be reduced by of the order 50% in the period to 2030, compared to a business-as-usual growth scenario. For the industrial manufacturing sector:

creased by up to 43% with automatic dimming controls, primarily through making better use of daylight [39]; Analysis of daylight-responsive lighting control systems which can result in a saving of 30% in Istanbul [40].

• Global metal production could be reduced by ∼30% without a reduction in the quality of final service [25]; • Analysis suggests that “Fabric formwork” (consisting of complex

These studies underpin the assumption that energy demand reductions across heating, lighting and appliances could reach of the order 10–20% in the near-term and beyond 50% in the longer-term, compared to current usage levels. For the transport sector, specific studies include:

•

• A major study undertaken for the UK Department of Transport in • • • • •

2004, which concluded that “smarter choice” measures (including personalised travel plans, public transport information, car sharing, teleshopping and teleworking) have the potential to reduce national road traffic by 11% within 10 years [41]; Analysis of the implications of lifestyle changes to travel modes and travel intensity in the UK, which see average motorised travel distances fall by over 20% between 2007 and 2050 [28]; Research across a range of regions suggesting that car clubs resulted in a net reduction in vehicle km of 28% (Belgium), 45% (Bremen), 72% (Switzerland) and 47% (Austria) [42]; Analysis that eco-driving could result in a 5–10% improvement in energy efficiency by 2030 (relative to 2010 levels) [43]. Analysis of pedestrianisation efforts resulting in a 17% reduction in trips to the city centre of Oxford, UK, without affecting overall visitor numbers [44]; A slow (“static”) growth aviation scenario which sees passenger-km increasing by approximately a factor of two and three in developed and developing countries respectively between 2015 and 2050, compared to a factor of 4 and 5–10 increase in these regions in the highest growth scenario [29]

•

lattice structures rather than solid beams) can be used to cast structures with up to 40% less concrete than those currently used [45]; Analysis of material efficiency in packaging in the Netherlands suggested that light-weighting could be responsible for a reduction in material use of 24 ± 16%, primarily affecting plastic (and therefore the chemicals industry) and paper production [46]; Further material reductions (with associated industrial energy demand reductions) are achievable in sectors through increased re-use and recycling [47] and reducing yield losses in manufacturing [48].

In summary, the industrial manufacturing sector could conceivably produce at least 30% less (for metals and cement) and at least 20% less (paper) material product, with associated energy demand reductions from these sectors. 3.5. Calculation of temperature change based on global cumulative emissions As demonstrated in recent publications [49,50], relating cumulative CO2 emissions budgets to temperature change is challenging and can result in considerable variation depending on assumptions. The different approaches presented in the literature are explained in chapter 2 of the recent IPCC Special Report on 1.5 °C [9]. In this paper, temperature change values were estimated by regressing the median 2100 warming from median temperature projections from all scenarios of the SSP-RCP matrix [51], developed by the ScenarioMIP project [52], onto cumulative total CO2 emissions. That regression is then combined with the cumulative fossil fuel and industry CO2 emissions from TIAM-Grantham and an assumption about land use emissions to estimate 2100 median temperatures. The SSP-RCP matrix

In summary these studies (though much more constrained in their geographic focus compared to the buildings sector) suggest that personal motorised transport energy demand could be reduced by the order 10–20% in the near-term, although with corresponding increases 355

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was chosen as these scenarios will be underpinning much of WG1 in the upcoming IPCC sixth assessment report (AR6), form a reference for much of WG3, and are from a more recent cohort of IAMs. Temperature projections were taken from MAGICC6, which uses an equilibrium climate sensitivity (ECS) distribution from Rogelj et al. [53] and is linked to the conclusions about ECS ranges from IPCC AR4 WG1. Land use emissions can also vary considerably depending on assumptions. For example, the SSP2-2.6 marker scenario gives cumulative 2016–2100 land use emissions of −114 GtCO2, whilst the mean of all SSP2-2.6 scenarios gives a value of −42 GtCO2. By comparison, land use emissions from the RCP 2.6 scenario were +183 GtCO2. In order to reduce uncertainty in this analysis, land use emissions were assumed as fixed across all scenarios at −42 GtCO2, the mean of all SSP2-2.6 scenarios. Previous studies of CO2-only scenarios have shown a robust nearlinear relationship between cumulative CO2 emissions and warming [54,55]. Further studies have extended this relationship to multi gas scenarios, using it to estimate cumulative carbon emissions budgets consistent with limiting warming to given target levels [56–58]. Implicit in this approach is an assumption that the contribution to warming from non-CO2 emissions also scales linearly with cumulative emissions, which can also be interpreted as assuming that mitigation effort for non-CO2 emissions is proportional to that of CO2. In this study we simply invert this relationship to estimate 2100 temperatures from the cumulative CO2 emissions our scenarios and a regression based on the SSP-RCP matrix.

4.1. Reaching ambitious carbon budgets

−20%

With the standard technology mix (V1) in TIAM-Grantham (Fig. 3a, blue data points), increasingly stringent carbon prices from 100 $/tCO2 to 250 $/tCO2, results in total global cumulative CO2 emissions from 2016 ranging from 741 to 1066 Gt CO2. This sits squarely within the range of 550 to 1200 Gt CO2 from 2016 as reported by the IPCC AR5 report [58] (Note that all ranges in the literature have been adjusted for historical emissions since their publication) that is required to have at least a 66% chance of limiting global temperature rise to 2 °C. Beyond a starting CO2 price of 250 $/tCO2, the model is unable to find a solution. A similar range of cumulative CO2 emissions, 768 to 981 Gt CO2, is obtained with the standard technology mix and revised cost estimates (V2, red data points). Following the inclusion of more speculative advanced technologies (V3) in the demand sectors (Fig. 3a, orange data points), with a starting carbon price of 250 $/tCO2, the total global cumulative emissions are 292 Gt CO2 – a reduction of 450 Gt CO2 compared to the standard technology mix. Despite this considerable reduction, with advanced technologies alone, cumulative emissions are still above the median budget of 215 Gt CO2 reported by the IPCC AR5 [58] for a 66% chance of staying below 1.5 °C, although this does bring them within the upper end of more recently estimated ranges [59]. Going beyond this, the inclusion of a range of demand reduction measures and behavioural changes in the model (Fig. 3 a, light and dark green data points, V4 and V5) further decreases the cumulative emissions (from 2016) to 168–228 Gt CO2 under the 250 $/tCO2 scenario, achieving an overall reduction of 513–573 Gt CO2 compared to the standard technology mix for the same CO2 price. This reduction brings the cumulative emissions level down below the IPCC AR5 median value [58] and also within the upper end of other ranges estimated in the literature [3,59]. In addition to reducing total cumulative emissions, the inclusion of advanced technologies and lower demands has a substantial impact on the total cost of mitigation. With the standard technology mix (V1) and starting CO2 prices from 100 to 250 $/tCO2, the total cost of mitigation (where the total cost of mitigation is the change in cumulative total discounted system cost, relative to a baseline scenario with no mitigation action, and represented as a percentage of GDP) spans 3.4–6.4% of

−6% All regions

−20%

−10% All regions

−30%

4. Results

Industry

Heating, lighting and electrical appliances Steel, cement, non-ferrous metals and other Pulp and paper Buildings

Buses Trains Aviation

−30%

Kahn Ribeiro et al. [24] (Figure 9.39). Kahn Ribeiro et al. [24] (Figure 9.39) using approximate average of “Efficiency” and “Mix” scenarios. ITF (2017) [29] (Figure 4.4) “static” demand case (approx. doubling of aviation in developed, tripling in developing regions 2015–2050). Assumes % reduction fixed 2050–2100 Near-term reductions to 2020 from information on bills and from smart meters, rising to longer term reductions of up to 50% or more through behavioural changes [23] DECC (2014) using data on light weighting of products from Allwood and Cullan [25]

−20% −25% +20% +200% −44% to −62% −30% to −38% −50% −20% −25% +20% +100% −44% to −62% −30% to −38% −20% −10% −20% +20% +33% –32% to −38% −17% to –22% −10% LDVs Transport

Developed Developing All regions All regions Developing Developed All regions

2100 2050 2030

Reduction potential Region Technology Sector

Table 3 Justification of the potential for energy demand reduction in transport, buildings and industry.

Sources

ICCT (2012) (Table 13) to 2030, Kahn Ribeiro et al. [24] (Figure 9.39) to 2050. Assumes % reduction fixed 2050–2100

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Fig. 2. Comparison of the final energy intensity of GDP (PJ/billion$, PPP) with standard technologies and central demand projections (V1) to advanced technologies with low demand (V4) and a baseline, no mitigation scenario and a carbon price scenario of 250 $/t CO2. These are shown in the context of energy intensity reduction for the SSP1 and SSP2 ranges.

GDP with cumulative emissions in the range of 783–1108 Gt CO2. With revised cost estimates (V2), a similar range of cumulative emissions can be achieved for costs ranging from 2.4 to 5.3% of GDP. With the advanced technology mix (V3), a cost range of 2.4–4.1% of GDP achieves substantially lower cumulative emissions of 334–544 Gt CO2. The reason for this is that these technologies are assumed to be cheaper solutions than BECCS, which has a substantial energy penalty. Furthermore, technologies such as electric-powered trucks can potentially offer efficiency gains compared to internal combustion engines. Under a low demand scenario (V4&5) we see a further reduction in costs (0.87–2.4% of GDP), whilst at the same time achieving even lower cumulative emissions. Note that the cost of implementation of behavioural changes to reduce energy demand has not been included. In reality there could be costs to this, such as transaction costs, ‘hassle factor’ or reduced welfare. At the same time there are likely to be benefits, such as increased physical activity, better air quality and lower energy bills. Using the approach described in the methods, indicative temperature change estimates have been determined for these scenarios (Fig. 3 b). The 250 $/t CO2 price, with the standard technology mix (V1), results in a 50% likelihood of limiting global temperature change in 2100 to below 1.7 °C. For the same carbon price, temperature change can be limited to 1.46 °C and 1.4 °C for the advanced technology mix (V3) and advanced technologies with low demand (V4), respectively. The inclusion of advanced technologies in the transport and industrial sectors combined with lower demands allows for more rapid decarbonisation in the period to 2050 compared to the standard technology mix for the same level of carbon price (Fig. 3b). Overall, the CO2 profiles show that total global CO2 emissions become net negative in the period 2050–2060, which is consistent with other studies [3]. Note that although it appears as though a greater amount of NETs is used in model versions V3 to V5, in actual fact the amount of NETs remains similar for all scenarios but the residual emissions are lower in model version V3 to V5 compared to V1 and V2, resulting in greater net negative emissions

and hence a lower cumulative carbon budget overall. This observation and its implications for reliance on BECCS is discussion in greater detail in the discussion section. 4.2. Achieving deep decarbonisation in challenging demand-side sectors In the following section, we examine the uptake of key technologies in a single scenario in order to understand the role of advanced technologies at a sectoral level. The 250 $/t CO2 price scenario with the model V3 was selected as representative of a deep decarbonisation scenario which limits temperature change to < 1.5 °C based on technical change alone and with central energy demand assumptions. This scenario has cumulative CO2 emissions of 292 Gt (from 2016) and an expected temperature change in 2100 of 1.4 °C. 4.2.1. Energy-intensive manufacturing processes Direct and process emissions from industry currently account for around 26% of global CO2 emissions [60]. The industrial sector is extremely challenging to decarbonise for a number of reasons [61,62]: firstly, it consists of very high temperature processes which currently rely largely on direct burning of fossil fuels to reach these temperatures. Secondly, the industrial sector is very heterogeneous, consisting of a wide range of sub-sectors and processes. Thirdly, some chemical processes such as the calcination of limestone in cement production, give rise to CO2 emissions that are unrelated to the combustion of fossil fuels. Thus, fuel switching away from fossil fuels does not address these emissions. Fourthly, many industrial products are traded in international markets and need to be price competitive. Energy efficiency improvements could potentially reduce industrial emissions by around 27% by 2050, but will not be enough to offset growth in demand [63]. Advanced technologies, which can result in a step-change in emissions reductions and achieve very low (or zero) emissions from industrial processes can be broadly categorised into electrification, hydrogen-based processes and Carbon Capture and 357

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Fig. 3. (a) Cost of mitigation as a function of cumulative CO2 budget from 2016 and (b) total annual CO2 emissions and associated temperature change under different model variations: V1 – Standard technologies (Blue data points), V2 – Standard technologies, revised costs and no price elasticity (Red data points), V3 – Advanced technologies and no price elasticity (Orange data points), V4 – advanced technologies and low demand without price elasticity (Green closed data points) and V5 – as for V4 but with price elasticity (Green open data points). Each model version was run under four different carbon price scenarios: a starting price in 2020 of 100 $/tCO2 (■), 150 $/tCO2 (♦), 200 $/tCO2 (●) and 250 $/tCO2 (▲), rising at 5% per annum. Note all currency is in 2005 US dollars. Cost of mitigation is represented as the change in total cumulative discounted system cost, relative to the baseline scenario with no mitigation action, as a percentage of total GDP. Temperatures represent the mean temperature change in 2100 relative to pre-industrial levels in degrees Celsius. A description of how these temperatures were calculated is provided in the methods section.

Storage (CCS) [61]. Many of these options are still in the concept phase and there is an urgent need for investment in R&D and policy support to enable breakthrough innovations and to move these technologies beyond lab-scale and early demonstration. Electrification of heat has the potential to offer complete decarbonisation in many industrial processes but it will require substantial

R&D to achieve sufficiently high temperatures, improve efficiency and to reduce costs. In the pulp and paper sector (Fig. 4c), electrification relies on a combination of different technologies including: electric boilers, novel electric drying technologies (e.g. ultrasonic, microwave or infrared drying) and pulp production using the Thermo-mechanical process [64]. In the non-metallic minerals sector (Fig. 4b), electric kilns 358

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Fig. 4. Uptake of advanced technologies in the industrial sector to achieve deep decarbonisation in the following industrial sub-sectors: (a) Steel, (b) Non-metallic minerals, (c) Pulp and paper, (d) Chemicals, (e) Total CO2 emissions captured from industrial processes. Note (a)–(e) show the results from the model V3 (i.e. with advanced technologies, no price elasticity, central demand assumptions) and a starting CO2 price of 250 $/t CO2. (f) This compares the total global CO2 emissions from the industrial sector for two model variations: the standard technology mix (blue lines) and the advanced technology mix (orange lines). Grey error bars represent the interquartile and minimum-maximum ranges for scenarios from the literature that achieve less than 1.5 °C.

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in ceramics and electric glass smelters are already proven technologies [65], but it is unlikely that cement production with electricity would ever be economical. The use of hydrogen for steelmaking could provide a breakthrough process for producing steel (Fig. 4a). Hydrogen steel making could take on a number of different forms [66,67], including novel options such as Hydrogen Plasma Smelting Reduction where molten iron ore is reduced with ionised or atomic hydrogen [68]. However, a process that is closer to realisation is direct-reduction of solid iron ore with hydrogen-rich gas. This process is not significantly different from the MIDREX-DRI (Direct Reduced Iron) process which is currently in widespread operation. Only the DRI-based hydrogen production of steel features in the TIAM-Grantham model. Uptake of hydrogen-steel production begins in the period 2020–2030 with around 13 Mt introduced by 2030. This is the equivalent of around 4–5 full-scale plants globally. By 2050, this rises to 220 Mt (or about 75 plants world-wide). Beyond 2050, the share of hydrogen-steel production rises steadily under this stringent mitigation scenario making up over 50% of steel production by 2100. This is an ambitious transformation in the steel production mix, but is not unprecedented. For example, in the US, the Open Hearth Furnace went from being the dominant process route in the 1950s (making up around 90% of crude steel production) to being completely phased out within a time period of 50 years [69]. Even with hydrogen processes and electrification, CCS is still likely to play an important role in decarbonising the industrial sector [70], particularly for the cement sector where CCS is able to address both fossil fuel and emissions arising from calcination (Fig. 4b and e). CO2 emissions captured from industrial sites increases rapidly over the next 40 years, reaching a maximum of ∼7.4 Gt/yr in 2060. However, beyond 2070, advanced processes based on hydrogen and electricity are favoured over CCS. If CCS is likely to have more of a transitional role in the industrial sector, designing sites that can be easily retrofitted with CCS will be essential. Fig. 4f shows the total global CO2 emissions from the industrial sector for two model variations – the standard technology mix (orange lines) and the advanced technology mix (teal lines) – and compares these to the ranges based on scenarios from the literature that achieve less than 1.5 °C (grey error bars). These results show that the rapid decarbonisation between now and 2050 required to meet a 1.5 °C target as observed in scenarios in the literature, will be extremely challenging to achieve without the availability of more advanced technologies. Furthermore, beyond 2050, emissions with the standard technology mix remain at ∼4 Gt/yr, well above the median values observed in the literature which decline from 3.1 to 1.4 Gt/yr over the period 2060–2100. With the advanced technology mix, however, these low CO2 emissions levels are attainable with CO2 emissions from the industrial sector of 1.8–2.6 Gt/yr in the second half of the century.

recently, the International Civil Aviation Organisation [74] specified similar targets of carbon neutral growth from 2020, and a 2% annual fuel efficiency improvement to 2050. However, this scheme partly relied on carbon-offsetting, which is harder to achieve in a net carbonzero world. In our scenario, the combined effect of uptake of advanced technologies in aviation is that CO2 emissions decline to 334 Mt CO2 by 2050, or a reduction of around 50% of emissions in 2005, which were ∼720 Mt CO2. Direct emissions fall to zero beyond 2070 as by then the aviation industry is converted entirely to biojet and hydrogen – two technologies that will be central to achieving deep decarbonisation in the aviation sector. So far, up to 50% blends of biofuels have been certified for use in aviation, and additives may be required to reach higher blend levels [75]. However, previous studies indicate that this could be achieved by 2030, and include scenarios achieving 100% biofuels in aviation between 2030 and 2050 [76]. Widespread uptake of biofuels in aviation is predominantly limited by economic rather than technical constraints [77–79]. Hydrogen planes are technically feasible and hydrogen has been theoretically considered as an aviation fuel [80] from as early as 1918. In our scenario, uptake of hydrogen planes begins in the period 2040–2050 with the share of aviation being met by hydrogen planes rising to around 67% by 2100. This is consistent with estimations from the literature [80] which indicate that the earliest hydrogen-fuelled aircraft can be expected in routine operations is around 2040–2045 (scenarios in the extensive 2003 Cryoplane technical report by Airbus [79] included a scenario in which hydrogen was introduced into commercial aviation driven by policy initiatives, however the lack of attention hydrogen has received in the intervening years have not facilitated this). The share of biodiesel consumed by conventional planes increases from zero in 2020 to 68% by 2050 and by 2080 jet kerosene is completely phased out. This is equivalent to biojet production of around 230 Mt/year in 2050. From 2040 onwards, a modal shift means that by 2070 around a third of domestic aviation demand is met by the hyperloop. Modal switching from passenger rail demand to the hyperloop could also be expected, however in our scenario only a small shift of around 5% of demand for passenger rail is observed. Full electrification of rail is observed from around 2070 onwards (Fig. 5e). Hydrogen and biofuels play a similar role in decarbonisation of shipping, with conventional ships running on fossil fuel being completely phased out by around 2070 (Fig. 5b). Fig. 5f shows the total global CO2 emissions from the transport sector for two model variations: the standard technology mix (orange lines) and the advanced technology mix (teal lines) and compares these to the ranges based on scenarios from the literature that achieve less than 1.5 °C (Grey error bars). Overall, uptake of these advanced transport technologies results in a rapid reduction in total CO2 emissions from the transport sector and complete decarbonisation of direct emissions by around 2070. As observed in the industrial sector, in the absence of more advanced technologies in the transport sector, CO2 emissions in the second half of the century remain well above the levels found in the literature indicating that these technologies are crucial for achieving the required carbon intensity reductions. Without these more advanced technologies, residual emissions from the transport sector remain at around 3–5 Gt/yr in the second half of the century, increasing the need for NETs to offset these emissions.

4.2.2. The transport sector Rapid cost reduction of Lithium ion batteries [71] and advancement in Electric Vehicle technology means that decarbonisation of light duty vehicles through full electrification is now a plausible future [8]. In our scenario, the global light duty vehicle fleet is fully electric by 2050 (Fig. 5d). Until recently, it was thought that hydrogen-fuelled trucks would be the primary solution for Heavy Duty Vehicles (HDVs), however Tesla’s recent announcement [72] that it has built a fully electric ‘semi’ prototype provides evidence that electric trucks will likely be a reality. In our scenario, electric trucks make up around 60% of the HDV fleet by 2060, with hydrogen reserved for only very heavy-duty trucks (Fig. 5c). By comparison, progress in low-carbon technologies for the remaining non-road transport sectors has been slower and these sectors remain challenging to decarbonise. In 2009, the International Air Transport Association has laid out an ambitious proposal [73] for a cap on aviation emissions from 2020, and a target of 50% reduction in net emissions from aviation by 2050, relative to the 2005 level. More

5. Discussion Fig. 6 shows that the level of cumulative CO2 captured through BECCS is ultimately driven by the carbon price. Thus, for a given carbon price scenario, the level of BECCS uptake is similar for the different model versions. However, having more mitigation solutions in the form of advanced technologies, results in lower residual emissions in ‘challenging-to-mitigate’ sectors. This allows for much lower total cumulative CO2 emissions to be achieved for the same level of BECCS uptake. The relationship between technological availability, demand assumptions and reliance on BECCS is demonstrated more clearly in Fig. 7. 360

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Fig. 5. Uptake of advanced technologies in the transport sector to achieve deep decarbonisation in the following transport sub-sectors: (a) Aviation, (b) Shipping, (c) Trucks, (d) LDVs, and (e) Rail. Note (a)–(e) show the results from the model V3 (i.e. with advanced technologies, no price elasticity, central demand assumptions) and a starting CO2 price of 250 $/t CO2. (f) This compares the total global CO2 emissions from the transport sector for two model variations: the standard technology mix (blue lines) and the advanced technology mix (orange lines). Grey error bars represent the interquartile and minimum-maximum ranges for scenarios from the literature that achieve less than 1.5 °C.

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Fig. 6. (a) Cumulative CO2 captured through BECCS as a function of cumulative CO2 budget from 2016 and under different model variations: V1 – Standard technologies (Blue data points), V2 – Standard technologies, revised costs and no price elasticity (Red data points), V3 – Advanced technologies and no price elasticity (Orange data points), V4 – advanced technologies and low demand without price elasticity (Green closed data points) and V5 – as for V4 but with price elasticity (Green open data points). Each model version was run under four different carbon price scenarios: a starting price in 2020 of 100 $/tCO2 (■), 150 $/tCO2 (♦), 200 $/tCO2 (●) and 250 $/tCO2 (▲), rising at 5% per annum. Fig. 7. Comparison of the level of CO2 removal required to achieve low cumulative CO2 budgets under different model variations. (a) V1 – Standard technologies, low costs and no price elasticity (red data points), (b) V3 – Advanced technologies and no price elasticity (orange data points), and lastly, advanced technologies and low demand d) without price elasticity (V4) (green, filled) and e) with price elasticity (V5) (green, open).

Here, instead of running the model with a carbon price, the different model variations were tested with a constraint on cumulative CO2 emissions. The cumulative constraint was decreased incrementally until the model no longer solved. Fig. 7 shows that with the standard technology mix and revised costs (red data points, V2), total cumulative CO2 captured for the period 2016–2100 hits a maximum level of 860 Gt CO2 at a cumulative CO2 budget from 2016 of 690 Gt CO2. This maximum is determined by the upper limit in the model on bioenergy supply based on land use and competition between BECCS and other uses of bioenergy. By comparison, with advanced technologies (orange

data points, V3), the same cumulative CO2 budget can be achieved with 740 Gt CO2 of cumulative CO2 removal through BECCS, a 14% reduction relative to the standard technology mix. The inclusion of energy demand reductions (green data points, V4) further decreases reliance on BECCS, allowing the same cumulative CO2 budget to be reached with 18% less CO2 removal required compared to the standard technology mix and central demand scenario. Thus, these results demonstrate that innovation and development of advanced technologies in the demand sectors combined with actions to reduce energy demand overall can significantly reduce our reliance on BECCS (and other 362

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negative emissions technologies). It is important to note that in all scenarios in this study BECCS deployment is at the upper end of what is typically observed in similar modelling scenarios. The main reason for this is owing to the fact that TIAM-Grantham focuses on the energy system alone and is limited to CO2 emissions only rather than all GHG emissions. This shifts all the burden of mitigation onto the energy sector and CO2 emissions making it harder to achieve very deep decarbonisation scenarios. In practise, reduction of non-CO2 GHGs and non-energy sector mitigation action such as in land-use or diet, could have a significant impact and could mean that a higher level of residual emissions in the energy sector can be tolerated and less BECCs is required to offset emissions. Previous modelling studies [3] have outlined the levels of CO2 emissions required at a sectoral level to limit global temperature rise to 1.5 °C. Capitalising on the technological richness of TIAM-Grantham on the demand-side, this study goes beyond this to understand the specific technologies and actions that would be required in the demand sectors to achieve these deep emissions reductions. In many Integrated Assessment Models (IAMs) the term ‘technologically rich’ refers to the supply-side [81] and there is often only limited detail on the demandside [82]. However, when assessing deep decarbonisation scenarios consistent with well-below 2 °C sufficient detail on the demand-side is essential to define specific mitigation actions. Our results for the industrial (Fig. 4f) and transport (Fig. 5f) sectors highlight that these deep emissions reductions will be extremely challenging to achieve with currently available technologies. It is crucial that policy-makers are made more aware of the need for these technologies as many are still in the very early stages of development and will still require considerable research and innovation for commercial realisation. Currently, there is a tendency for innovation efforts to focus more on supply-side technologies rather than demand-side [82]. Without a concerted and broad reaching R&D effort across the demand sectors we are in danger of putting all our bets on BECCs (and other NETs) to offset residual emissions in challenging sectors. This is a dangerous gamble as NETs are, themselves, highly speculative technologies and are not without possible negative impacts and uncertainties.

respectively. Thus, this paper demonstrates that achieving the levels of carbon and energy intensity reductions required for deep decarbonisation will be extremely challenging without (a) the development of critical advanced technologies on the demand side and (b) substantial shifts in behaviour to reduce energy demand across the end-use sectors. Together these enable very tight carbon budgets to be met, whilst reducing reliance on Bioenergy with Carbon Capture and Storage (BECCS) by around 18% compared to a standard technology mix and central demand scenario. These ambitious carbon budgets are only achieved under high carbon price scenarios, demonstrating that this level of technological innovation and demand reduction will only be possible in an environment of strong and unwavering global policy support. This study is primarily a modelling exercise of techno-economic feasibility to understand the potential for deep decarbonisation of the demand sectors. The results highlight the fundamental trade-off between (1) speculative new technologies, (2) challenging-to-implement energy demand reduction and (3) negative emissions technologies, which are also highly speculative and could potentially have adverse side effects. The results underscore the important policy agenda to undertake research and development and innovation across all sectors to ensure the development of advanced mitigation technologies rather than relying too heavily on Negative Emissions Technologies (NETs) to offset residual emissions. Given that the future remains “unwritten” [84], it is crucial for energy system and integrated assessment modellers to explore a broad suite of technologically advanced possibilities for decarbonising all the way out to 2100. Whilst this study limits these technologies to those for which cost estimates have been produced, there remains a further, critical, research agenda to more broadly explore the technological possibilities that could and should be developed over the coming 80 years, noting the progress made over recent decades. In addition, given limited assessment of the real-world feasibility of simultaneously deploying so many technologies at this pace and scale, the research agenda to further assess economic, political and social feasibility must be further developed and discussed, beyond the parameterisation of technology deployment and resource constraints presented here.

6. Conclusions This work responds to the call in the literature to provided more detail on the specific technologies that would be required at a sectoral level to achieve high levels of carbon and energy intensity reductions in the demand sectors [83]. The study focuses on the industrial and transport sectors, which (with the exception of light duty vehicles) are considered to be particularly challenging to decarbonise. The uptake of specific technologies in each sub-sector, required to achieve a scenario that is consistent with limiting global warming to 1.5 °C, is shown. Key advanced technologies in the industrial sector include hydrogen-based steel, electrification (e.g. of glass and ceramics kilns, electrification of pulp and electric boilers in chemicals) and Carbon Capture and Storage from cement production. In the transport sector, electric trucks, hydrogen ships and planes and the hyperloop present a way to achieve deep decarbonisation of this sector. In the absence of advanced low-carbon technologies, residual or unabated emissions in 2100 remain at 4 Gt CO2/yr for the transport sector and 4 Gt CO2/yr for the industrial sector. With the inclusion of advanced low-carbon technologies, these are reduced to 0 Gt CO2/yr and 2 Gt CO2/yr for the transport and industrial sectors,

Acknowledgements This work was funded by the Climate Works Foundation as part of their Global View Portfolio. Further information about the Climate Works can be found on their website: http://www.climateworks.org/. Author contributions AG conceived of the project concept and provided advice throughout. TN led the analysis and writing with contributions from AG, SF and DB. SF researched and modelled the advanced technologies in the transport sector. AH provided high level modelling support. AG and AS conducted the research on low demand options. DB ran the climate models to calculate associated temperature rise. Competing interests The authors declare no competing financial interests.

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Appendix: Modelling assumptions Revised cost assumptions for key technologies for model versions V2 to V5 (see Tables A1 and A2). Table A1 Capital cost assumptions (in M$/GW) for power generation technologies. Costs are in 2016$. Technology

2010

2020

2030

2040

2050

Biomass Coal Gas Geothermal Oil Hydro Nuclear Solar PV Solar thermal Wind onshore Wind offshore BECCS Coal CCS Gas CCS

3100–6180 3080–3390 520–5720 2380–17140 1250–2090 1160–4250 3700–4200 5350–7940 5150 1890 5390

2920–5740 2820–3342 520–2960 2290–13920 1250–2090 1150–4180 3700–4200 1120–1660 4760 1780 4090 5480 4180 1400–1570

2610–5220 2820–3342 520–2090 2240–10700 1250–2090 1130–4100 3700–4200 610–920 4070 1687 3343 4700 3660–3970 1330–2610

2610–5220 2820–3340 520–2090 2170–7480 1250–2090 1120–4030 3700–4200 430–700 3550 1640 2970 4390 3450–3760 1330–2610

2610–5220 2820–3340 520–2090 2090–4900 1250–2090 1110–3960 3700–4200 390–640 3120 1640 2970 4390 3450–3760 1330–2610

Table A2 Vehicle capital cost ($ per vehicle) assumptions used in TIAM-Grantham. Technology

2006

2010

2020

2030

2040

2050

Car

ICE BEV PHEV Alcohols LPG/gas Hydrogen

36,400 68,200 41,500 34,900 47,900 56,100

36,800 64,900 41,200 35,400 48,200 54,000

37,900 36,300 37,900 36,500 48,900 37,900

39,000 33,900 37,900 37,600 49,600 37,900

40,100 32,000 37,900 38,800 50,400 37,900

41,300 30,000 37,900 40,000 51,100 37,900

LDVs

ICE BEV PHEV Alcohols LPG/gas Hydrogen

36,400 68,200 41,500 34,400 47,900 56,100

36,800 64,900 41,250 34,850 48,200 54,000

37,850 36,250 36,250 35,900 48,900 49,050

38,950 33,900 37,850 37,050 49,600 37,850

40,100 31,950 37,850 38,200 50,350 37,850

41,300 30,050 37,850 39,400 51,150 37,850

Light truck

ICE Alcohols LPG/gas

27,700 27,400 29,700

27,700 27,400 29,700

28,050 27,200 29,450

28,700 27,700 30,000

28,700 27,700 30,000

28,700 27,700 30,000

Medium truck

ICE Alcohols LPG/gas

90,900 91,750 95,850

90,900 91,750 95,850

92,450 93,350 97,550

94,800 95,750 100,000

94,800 95,750 100,000

94,800 95,750 100,000

Heavy truck

ICE Alcohols LPG/gas Hydrogen

214,500 211,300 226,300 1,055,500

214,500 211,300 226,300 1,055,500

218,250 215,000 230,250 298,900

223,750 220,400 236,050 278,200

223,750 220,400 236,050 257,450

223,750 220,400 236,050 236,700

Bus

ICE BEV PHEV Alcohols LPG/gas Hydrogen

224,800 710,450 607,500 224,800 240,700 1,350,250

224,800 710,450 607,500 224,800 240,700 1,350,250

231,050 231,050 231,050 231,050 247,450 566,600

233,100 209,300 231,050 233,100 249,600 231,050

237,100 197,700 231,050 237,100 253,900 231,050

241,000 185,950 231,050 241,000 258,050 231,050

Details of assumptions for advanced technologies in the transport and industrial sectors (see Table A3).

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Table A3 Description of assumptions for advanced technologies. Technology

Details

Hydrogen Powered Aviation

We have included aircraft based on combustion of hydrogen, as this is a proven technology for passenger craft. However, pilot craft have run using fuel cells, and these could also play a role in the future [79,80]. Availability: Available to meet 1% of domestic aviation demand from 2040, and to meet 1% of international aviation demand from 2050. 20% annual growth rate allowed in domestic and international sectors [79,80]. Fuel Efficiency Lower than conventional planes when first introduced (by 14% and 9% for domestic and international aircraft respectively, in line with values in the literature [79]), based on the assumption that these are retrofits of existing designs. Bring efficiencies in line with conventional planes by 25 years later, based on improved design optimisation as described by [80]. Capital Cost In absence of clear evidence around costs, we take prices as indicative. We assume hydrogen-fuelled plane costs 11% above conventional costs when introduced [85,86], with costs falling in line with conventional aircraft by 25 years later following more experience in construction. We take A320 (∼150 passengers) price of $98 M and A330-200 (∼247 passengers) price of $231.5 M as new domestic and international aircraft costs in 2016 [87], and include neither cost increase or reduction in the future. Operations and Maintenance Cost: In absence of clear evidence of a long-term difference for hydrogen and conventional planes (Sefain 2000, Brewer 1982), we do not include operations and maintenance costs, as in the original ETSAP-TIAM model. Fuel Efficiency Assume continued 1% annual improvement in fuel efficiency in line with the International Council on Clean Tranport (ICCT)’s reported average annual reduction between 1980 and 2016. This continues until the a 53% reduction is achieved in 2090 (achieved in 2050 in the ICCT’s ambitious scenario with a 2.2% annual improvement [88]) Biofuels Biofuel consumption is allowed in aviation with an annual growth limit of 13.1% from 2020, enabling biofuels to meet 77% of final energy demand in aviation by 2050. This is in line with INSIGHT_E’s scenarios to meet the International Air Transport Association (IATA)’s decarbonisation targets [89,90]. For simplicity, we model aircraft as consuming biodiesel, based on an assumed similar level of refinement of diesel and jet fuel. Different rail technologies are not considered in detail in the 2012 version of the ETSAP-TIAM model, with rail modelled as one technology, consuming the same ratio of fuels (predominantly diesel, electric, and coal) throughout the century. We alter this approach to allow new trains consuming different fuels, allowing the rail sector to decarbonise. Fuel Efficiency: Whilst a range of strategies exist to improve energy efficiency in rail [91–93], trends between regions and rail types (international, intercity, metro) are not clear [94–97]. Owing to the possibility of using regenerative braking allowed by electric trains, we implement these at a 15% higher efficiency than diesel trains [91]. Otherwise, we keep all trains at the same efficiency as in the 2012 version of the ETSAP-TIAM model. Cost: Limited data is available on costs of new rail infrastructure. This is required for our study to compare with alternative modes, such as the hyperloop. New train costs for continued operation of existing lines are based on this reference [98], and cost of new tracks on this reference [99]. Availability: Electric light trucks are available from 2020 as in the original ETSAP-TIAM 2012 model. Electric medium and heavy trucks are also allowed from 2040. Cost: Costs are assumed to fall in line with conventional gasoline vehicles of the same category by 2030. This is in line with ambitious 2020 assumptions for electric cars [8]. Fuel Efficiency: Fuel efficiency ratios between gasoline and electric trucks are set to match those for gasoline and electric cars [8]. Availability: Hydrogen fuel cell options are made available for medium and commercial trucks from 2030. Costs and efficiencies for these are in the same ratio to conventional gasoline as implemented for heavy trucks in the original 2012 version of TIAM. Assumptions are identical to those for electric trucks, but these are made available from 2020 rather than 2030 based on higher current deployment smaller vehicle size. There have been very few assessments of cost or possible growth rates of hydrogen fuel cell shipping, as such, our assumptions were limited by availability of data. Availability: We allow hydrogen from 2035 for regional, 2040 for interregional shipping [100], and apply maximum growth rates allowing 15% of regional, and 6% of interregional shipping demand to be met using fuel cells by 2050. This is slightly more conservative than the former UK Department of Energy and Climate Change’s ambitious scenario, in which 10% of international shipping is hydrogen powered by 2050 [101]. Cost: We have assumed 40% higher capital cost for fuel cell than conventional ships based upon on an assessment of storage tank costs of $10 M for 20,000 tonne ship [100] and reported new build ship costs of $25 M for a 30,000 tonne ship (the closest mass for which data was available) [102]. Fuel Efficiency: Fuel efficiency ratios between conventional and fuel cell ships are assumed to match those for gasoline and fuel cell heavy trucks in the 2012 version of TIAM. Biofuels: Existing and newly built conventional ships are allowed to use biodiesel. Availability: The firm Transpod aim to develop a commercially viable system by 2020 [103]. We implement this technology allowing a starting share of 0.2% of passenger rail and domestic aviation demand in 2020, and limit annual growth rate to 20% per year (in line with hydrogen aviation). We impose a maximum share of 50% in both sectors, based on the assumption that geographical constraints will limit some routes. Cost: Cost assumptions are based upon CAPEX, route distance, and passenger capacities provided in this reference [104]. Energy Consumption: Cost estimates presented in [104] include the deployment of solar panels over the length of the hyperloop, assessed to provide more energy than is consumed by the hyperloop. We model the hyperloop as neither consuming nor generating energy. Changes to the iron and steel sector bring the TIAM-Grantham model in line with the latest version of the ETSAP-TIAM model and are based on model development carried out by ECN. A full description of the assumptions can be found in Appendix B of the paper by Morfeldt et al (2015) [105]. The revision of CCS technologies in the industrial sector bring the TIAM-Grantham model in line with the latest version of the ETSAP-TIAM model and are based on model development carried out by ECN. Assumptions for CCS costs are based on the following studies which ECN was involved in [106,107].

Conventional Aviation

Rail

Electric Trucks

Hydrogen Powered Trucks Electric Bikes and Scooters Hydrogen Powered Shipping

Conventional Shipping Hyperloop

Iron and steel sector Carbon capture and storage for industrial processes

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