Low-carbon scenarios for Russia’s energy system: A participative

backcasting approachMaria Sharmina

Tyndall Centre for Climate Change Research, University of Manchester

Suggested citation: Sharmina, M. (2017) Low-carbon scenarios for Russia’s energy system: A participative backcasting approach. Energy Policy, 104, p.303-315. doi:10.1016/j.enpol.2017.02.009.

Abstract:

Despite the high profile of climate change in scientific and policy discourse, the Russian government has thus far failed to commit to an emission reduction target based on the latest science. Given Russia is a key supplier of fossil fuels, a major greenhouse gas emitter, and climate impacts on its vast territory likely to have far-reaching consequences, this contextual research shows that the country’s current policies fall woefully short of what is required to implement the Paris Agreement. To support Russia in developing informed, internally consistent and scientifically literate energy policies, this paper presents low-carbon emission trajectories commensurate with the 2°C goal, using stakeholder-informed backcasting. The results illustrate that even if Russia’s CO2 emissions peak in 2017, a reduction rate of at least 9% per year between 2020 and 2030 is required to meet a 2°C budget constraint. These sustained rates are in excess of anything achieved globally or, indeed, deemed possible within most studies. Such emission reductions would involve unprecedented material changes to Russia’s energy system, including both rapidly cutting energy demand and building extensive low-carbon infrastructures. Nevertheless, failure to transform Russia’s existing policies will likely have global repercussions for achieving the Paris Agreement’s goals.

Keywords: emission scenarios; backcasting; Russia; climate change targets; cumulative emissions

1 IntroductionIn 2013 and 2014 the Intergovernmental Panel on Climate Change (IPCC) published its fifth report confirming a range of carbon budgets associated with the 2°C characterisation of dangerous climate change. More than 150 nations, including Russia, have recognised the 2°C target and committed to hold “the increase in the global average temperature to well below 2°C above pre-industrial levels” (UNFCCC, 2015). However, the countries’ current domestic pledges do not add up to this global commitment. There is a gap of 8–10 GtCO2e (or 18–23%) per year by 2020 between their collective pledges and a 2°C pathway (UNEP, 2014).

Russia is an example of this disconnect between national climate mitigation measures and explicit international commitments. One the one hand, Russia is a signatory to 2°C. On the other hand, it is one of the countries whose national emission reduction target of 25% by 2020/2030 compared to 1990 (Russian Government, 2015a) does not amount to a fair and science-based contribution to climate change mitigation (Kokorin and Korppoo, 2014; Sharmina et al., 2015). The low level of commitment to 2°C is further evident in the government’s attempt to sideline this target. For

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example, one of Russia’s communications to the UN Framework Convention on Climate Change specifies that 2°C “should not become the point of departure for a ‘top-down’ delineation of who pledges what” (Russian Government, 2014).

Continued global inaction and the accompanying climate change will have profound repercussions for Russia’s energy system and the wider prosperity of society. Some early climatic impacts will be beneficial, but over time most are likely to be negative. For example, increasing sleet load will lead to more breaks on overhead power transmission lines (RosHydromet, 2008b). As climate change intensifies river runoff, riverbed erosion may damage underwater parts of the national pipeline network. In the Russian Far North, substructures and foundations of both pipelines and hydrocarbon production sites are expected to become less stable due to thawing permafrost (RosHydromet, 2008b). With Russia being among the top three contributors to an observed global temperature increase (Matthews et al., 2014) and a key supplier of fossil fuels, climate impacts on its vast territory are likely to have worldwide implications.

To bridge the policy gap between global and domestic mitigation commitments, evidence-informed policy is essential. In policy-relevant research, there are two key gaps in this area. Firstly, to date, no in-depth Russia-focused scenarios for the county’s energy system have been developed constrained by carbon budgets, i.e., meaningfully linked to global temperature rises. Secondly, Russia-focused emission studies tend to cluster around highly aggregated top-down models and forecasting (e.g. Fiodorov et al., 2009; Malakhov, 2010; Novikova et al., 2009; Tchouprov, 2010). The dearth of exploratory, rather than predictive, and stakeholder-informed bottom-up tools implies that aspects of the country’s energy system remain overlooked. This paper develops scenario storylines and quantitative snapshots of Russia‘s energy system that serve as a starting point for a transition to a 2°C pathway, informed by expert interviews.

2 A review of Russia-focused emission scenario publicationsThe articles and reports were selected for this section based on one criterion: they should produce and discuss detailed emission pathways for the future energy system of Russia. Most of the studies were in Russian. Table 1 shows that the reviewed studies have several common features. For instance, forecasting dominates the modelling exercises, with elements of backcasting incorporated in a few studies. Climate change impacts, cumulative emissions and the economic crisis are rarely considered. Input-output tables appear to prevail as a basis of top-down models; however, the studies fail to acknowledge weaknesses of the input-output approach in the context of Russian economy. In particular, the volatility of prices and major economic restructuring in the 1990s may render the results of input-output modelling inadequate. Although Table 1 may not be exhaustive, it is evident that many studies have drawbacks sufficient to make them unsuitable for advising policy-makers. Therefore, there is a strong need for novel approaches, such as backcasting, to explore Russian emission pathways and targets in detail.

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Table 1. A review of Russia-focused emission scenario publications (Note: where possible the last column describes the most ambitious scenarios within each set)

Model and sourceTop-down

(TD) or bottom-up

(BU)

End year

Modelled emissions

Forecasting (F) or

backcasting (BC)

Recent economic

crisis considered

Cumulative emissions considered

Main mechanisms/incentives of changes in carbon and energy intensity

ENERGYBAL-GEM – simple simulation model (Bashmakov, 2009)

TD 2050 CO2 F In 2 out of 6 scenarios No

Energy efficiency, CCS, bio, nuclear, hydro and heavy reliance on renewables – the only scenario with slightly decreasing emissions (starting from ~2043). C price: €30-50 tCO2e.

MESAP/PlaNet –simulation model (Tchouprov, 2010; Teske and Tchouprov, 2009)

TD + BU 2050 Energy-related CO2

F with elements of BC

No No

Phasing out nuclear energy; realising full energy efficiency potential; emphasis on renewables (incl. sustainable biofuels). Global carbon trading system assumed; $50/tCO2 in 2050. CCS not included.

TIMES –optimisation model (Fiodorov et al., 2009) BU

2025 and 2030

Electricity and heat-related CO2

F In several scenarios No

Proportions of CCS, renewable and nuclear energy are unclear. C price increases from $15 to 25/tCO2 in 2013-25.

Dynamic linear optimisation model (Nekrasov and Siniak, 2007)

TD (+ BU?) 2030 CO2 F No No

Nuclear power increases from 21.7 in 2000 to 68 GW in 2030 (~40-45% of power stations). RES generate 12.5% of energy in 2030, if nuclear is not capped, and ~80% (calculated based on the text) if nuclear is capped.

MENEK-EKO –optimisation model (Malakhov, 2010; Malakhov and Dubynina, 2010)

TD + BU 2030 CO2, CH4, N2O, other GHGs

F in two scenarios and BC in one

No No

Carbon ‘charge’ is used in the third scenario, but magnitude unclear.CCS, renewables and nuclear not discussed.Prices are used instead of physical units (i.e., a ‘classic’ input-output model).

Simple simulation model (Novikova et al., 2009) TD 2020 CO2 F No Yes, in all

scenarios

The largest share of renewable energy sources is 6.6% of generated energy.GDP energy intensity is the main factor driving emissions down in the scenarios.

SRES-based scenarios (RosHydromet, 2008a) TD + BU 2100 CO2, CH4, N2O,

other GHGs F No No details provided No details provided.

World Bank’s model (Safonov, 2000) TD 2012 CO2 F No No No details provided.

WEM – World Energy Model (IEA, 2007) TD + BU 2030 Energy-related

CO2 emissions F and BC No No No details provided.

IIASA’s GAINS optimisation models (Cofala et al., 2008) TD + BU 2030 CO2, CH4, N2O,

SO2, NOx, PM F No No

“…both through structural changes in the energy system (fuel substitution, energyefficiency improvements) and through end-of-pipe measures (e.g., carbon capture).”

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3 Methods

3.1 The backcasting approachThe backcasting approach has evolved as an alternative to forecasting, with a twofold purpose (Lovins, 1976; Robinson, 1982). First, backcasting aims to break away from past and current trends, assuming they are incompatible with desirable future states. Second, it avoids relying on predictions of economic variables, for example, future costs of energy and technologies (i.e., monetised variables commonly used in optimisation and input-output models). The preference for backcasting narrows down the range of available modelling tools and offers scope for applying “explanatory models” (Börjeson et al., 2006) more suitable for generating explorative scenarios rather than predictions.

To generate low-carbon scenarios for Russia’s energy system, this paper adopts a stakeholder-informed backcasting approach. Figure 1 provides an outline of the backcasting process; its iterative nature helps to verify feasibility of a desirable objective and devise appropriate transition paths. In participative backcasting, the iteration process is aided by stakeholder engagement.

Figure 1. Stages of the backcasting approach: solid lines – the sequence of steps at first iteration, dotted lines – subsequent iterations (based on Anderson, 2001; Bows et al., 2009; Robinson, 1990)

The importance of cumulative emissions for climate change (IPCC, 2014a) suggests that they should be integrated as a pivotal constraint in low-carbon scenarios. The backcasting approach is well placed to facilitate such integration, as the first stage of backcasting requires an overarching objective placing a constraint on results of a scenario exercise. In addition to 2°C cumulative emission budgets (or ‘carbon budgets’) there are two more types of constraint placed on scenarios in this paper: the inertia in both the energy system and in the socio-economic environment, and the feasibility of implementing the scenarios. Section 4.2 expands on the second and touches on the third type of constraint, by describing and analysing past and current trends in Russia’s energy system and related aspects of the re-developing economy. This section corresponds to the second stage of backcasting. The third and fourth stages are covered in sections 4.3 and 4.4 exploring ‘desirable’ future states of Russia’s energy system in 2050 and pathways towards them. An energy system is developed iteratively to fit the pre-set carbon budget constraint. The consistency, feasibility and implications of the scenarios (the fifth and sixth stages of backcasting) have been tested through iterations, internal peer review and stakeholder input and are presented in sections 4.5 and 5.

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3.2 Stakeholder engagementStakeholder engagement is essential when researching societal issues that are complex, unstructured and long-term (Eames et al., 2013; van Asselt Marjolein and Rijkens-Klomp, 2002), such as climate change mitigation. Such issues are multi-disciplinary, multi-scale and multi-actor (Dendler et al., 2012; van Asselt Marjolein and Rijkens-Klomp, 2002) and hence difficult to tackle by a single group of actors such as researchers. To address this concern, the study has engaged expert stakeholders in the scenario development process. While all applications of the ASK model thus far have invited stakeholder input, it is the first time a participative scenario study has been done in respect to energy scenario in Russia. Stakeholders in this study helped to ground scenario assumptions and narrow the ranges of input variables, in addition to providing a reality check for the scenarios developed (see Supplementary Information for the pilot topic guide, sample interview questions and sample scenario summaries used in the expert interviews). The author then transcribed the interviews and used a simplified version of thematic analysis to interpret the findings.

Five pilot semi-structured interviews were undertaken over the telephone between March and June 2012, as Table 2 details. The interviewees’ professional background varied widely and included buildings-sector researchers, an aviation-sector entrepreneur and policy experts. Predictably, policy experts offered general and overarching insights, suitable for the contextual framing of the paper, while industry experts gave more specific interviews. The pilot interviews have informed the content and structure of the research design in this paper.

Table 2. A summary of pilot interviews with respondents from the industry and policy backgrounds

Respondent Professional backgroundRespondent’s residence

at the time of the interview

Interview date

1 Buildings-sector researcher Western Europe 09/03/20122 Aviation-sector entrepreneur Russia 26/03/20123 Buildings-sector researcher Eastern/Central Europe 17/04/20124 Policy expert (research/NGO) Northern Europe 29/05/20125 Policy expert (consulting/business) Russia 27/06/2012

The second stage of the stakeholder engagement process consisted of four face-to-face interviews and involved policy experts rather than industry representatives and governmental officials. The potential interviewees identified for the second stage recruitment focused on Russia’s fossil fuel industry and the EU-Russia energy security issues. With their expertise spanning from economic geography to technology and innovation to ‘Weak State’ environments, the breadth of the expertise was deemed sufficient for providing valuable insights to this paper, as politico-economic and governance issues cover much of the relevant context. As Table 3 shows, two experts were part of the ‘younger’ generation socialised in their discipline during Russia’s modern history, i.e., in the past 10–20 years. The other two interviewees belonged to a more ‘senior’ generation with much of their expertise developed during the existence of the Soviet Union. It was hoped that this difference would further diversify the interviewees’ responses.

Table 3. A summary of in-person interviews with policy experts

Expert Expertise Expertise developed during… Interview date

1 Technology and innovation …Russia’s modern history 23/04/20132 Political economy …Russia’s modern history 09/05/20133 Economic geography …the Soviet times 21/05/20134 Natural-gas markets …the Soviet times 13/06/2013

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Reflecting the research objective of this paper, two interview topics of different scope were identified to be discussed during in-person interviews. The first topic had a narrow focus on presentation and framing, consistency and feasibility of the scenarios. The second topic had a broad focus on both existing and potential decarbonisation triggers and Russia’s socio-political context. Although each interview started with the first topic (in particular, the presentation of scenarios), the two groups were not discussed sequentially or in a linear fashion. For example, questions about consistency and feasibility of the scenarios led to the discussion of more complex issues related to the Russian context and potential policies for decarbonisation.

3.3 The ASK-Russia scenario toolThe original scenario generator, ASK, was developed by the Tyndall Centre for Climate Change Research for constructing UK decarbonisation scenarios (Agnolucci et al., 2009; Bows et al., 2010). The approach was subsequently modified to explore emissions from China (Wang and Watson, 2008, 2009) and to develop emission pathways for the shipping sector (Bows-Larkin et al., 2014). For the purposes of this paper, the original, UK-focused, ASK tool was modified to accommodate geopolitical and national circumstances of Russia. The main peculiarities of Russia’s energy system, analysed in section 4.2 and reflected in the ASK-Russia tool (Figure 2), include Russia’s historic and current sectoral energy use and emissions, transmission and distribution losses, energy efficiency potential, and renewable energy potential.

Figure 2. A schematic representation of the ASK-Russia tool based on the original ASK

This modelling method differs from the reviewed AR5 pathways (see section 4.7) in two principal ways: (a) the ASK tool’s simplicity and (b) stringent assumptions on emission peak years (only in the future, never in the past, in order to take historical emissions into account) and on the deployment of negative emission technologies (only in one scenario out of four). The latter assumption is due to ASK’s relying on proven technologies such as biomass, nuclear, solar and wind energy, and energy efficiency, in contrast to the AR5 pathways assuming a large quantity of negative emissions (Anderson, 2015; UNEP, 2014).

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4 Results and discussion

4.1 Deriving the carbon budget constraint (backcasting stage 1)The first stage of backcasting defines the overarching objective that the backcast scenarios will aim to fulfil. In this case, such an objective is a cumulative emission constraint, also known as a ‘carbon budget’, corresponding to a low probability of exceeding the 2°C target. Despite uncertainties associated with a 2°C target (Guivarch and Hallegatte, 2013; Shaw, 2013), it is adopted in this paper as “the least unattractive course of action” (Jordan et al., 2013). This study takes a range of carbon budgets for Russia from Sharmina et al. (2015), following the method developed by Anderson and Bows (2011). The overarching objective of the backcast scenarios developed in this paper is to characterise an evolving energy system for Russia in 2011–2050 within the 5.5–7.1 GtC (20.2–26.1 GtCO2) cumulative emission budget range. It lies within the range of budgets from the AR5 emission scenario database (IPCC, 2014b).

4.2 Russia’s energy system: past and present (backcasting stage 2)Scenario exercises attempt to understand the relationship between stability and change, which makes the analysis of major developments and trends essential. This section sets out a rationale for an urgent transition to a low-carbon future and highlights the decarbonisation potential of Russia’s energy system, corresponding to the second stage of backcasting.

4.2.1 Energy infrastructure

Specific characteristics of Russia’s energy supply chain and physical geography suggest a number of potential threats to maintaining the current, conventional energy supply system. The main risks include the high depreciation and low renewal rate of capital stock, extensive power transmission lines, and climate change impacts on pipelines and other energy infrastructure.

One of the most significant problems facing Russia’s economy in general and its energy system in particular is the large proportion of ‘used up’ capital stock and equipment. Nureev (2010) estimates that, while in 1970 and 1980 about 70% and 64% of all equipment respectively was less than 10 years old, in 2000 almost 60% was older than 16 years. Although more recent data are unavailable, the trend suggests that the situation is unlikely to have improved, which may have consequences for the nation’s energy security. The age of plants and equipment determines, to a large extent, their energy efficiency. For example, the average energy efficiency of coal-fired power plants in Russia was 23.9%, as opposed to 34.7% in Canada, a country of a comparable climate and size (World Energy Council, 2016). Rezinskikh and Grin’ (2010) blame this situation on the slow-down both in commissioning new plants and in developing energy-efficient technologies in the 1990s, arising from broader socio-economic and political problems, including the lack of a business-friendly environment and limited democratic institutions (Nureev, 2010).

The length and efficiency of energy transmission and distribution (T&D) networks are important aspects of energy supply security, bearing in mind long distances and the relatively low density of end-users in Russia. There are 2.6 million km of transmission and distribution lines in the country (Ministry of Energy, 2012). For comparison, the length of the UK’s transmission and distribution lines is 825,000 km1 (Parliamentary Office of Science and Technology, 2007). In 2011, transmission and distribution losses were about 11% of total electricity supplied to Russia’s national grid (Ministry of Energy, 2012), and in absolute terms the losses increase as centralised T&D infrastructure is developed further, although they may fall in relative terms. As much as 98% of electricity in Russia is

1 Interestingly, in per-person terms the length of T&D lines in Russia and the UK are similar (about 0.018 km/cap and 0.013 km/cap respectively), despite the population densities being markedly different.

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currently supplied through a centralised delivery model. Distributed power generation is one of the ways to reduce reliance on the centralised supply and potentially cut T&D losses, depending on the share of transmission lines in the losses and on the efficiency of new capacity coming online, among other factors. Such a dramatic change in the electricity delivery model would require the country to invest significantly in local energy production infrastructure.

Inefficiencies and disruptions in the supply of Russia’s energy would affect not only domestic consumers but also those in countries dependent on energy imports from Russia. This consideration is particularly relevant for energy sources other than electricity since Russia only exports about 2.3% of its total electricity generation (Teske and Tchouprov, 2009). By contrast, fossil fuels are important for energy users both within the country and elsewhere. The bulk of hydrocarbon energy sources in Russia are transported via pipelines and by rail. By length and capacity, Russia’s pipeline system is the second largest in the world after the United States, with just under 160 thousand km for transporting natural gas and with more than 50 and 20 thousand km for transporting oil and petroleum products, respectively (Korzhubaev and Suslov, 2008). Most of the existing pipelines have been built in the past 20–35 years and have 55–70% depreciation (Korzhubaev and Suslov, 2008). The pipeline network in Eastern Siberia and the Far East is not well developed, hence oil and petroleum products (and, to a lesser extent, gas cylinders) for these regions, as well as for exports to Asia, are delivered by rail. As Russia’s energy trade with Asia is increasing, the energy flows towards this region are becoming one of the governmental priorities.

The reliability of energy supply will likely be affected by climate change impacts. With energy and other infrastructure typically designed to operate within a particular range of climatic variables, the weather and soil conditions are major factors when building and maintaining the infrastructure. For example, the sleet load is expected to increase in some Russian regions (the Volga District, the Southern District and the Far East), with more disruptions to overhead power transmission lines as a result (RosHydromet, 2008b). At the same time, in the past decades the wind force has slightly decreased across much of the country (RosHydromet, 2008a), and this trend is expected to continue (RosHydromet, 2008b). Less intensive winds would lead to fewer wire breaks; although if sleet load increases, the combined effect will be unclear. The pipeline network discussed in the previous paragraph is another climate-vulnerable element of the energy system. In particular, pipelines in the Russian Far North are highly dependent on the stability of permafrost. About 21% of the annual 35 thousand breakdowns and emergencies on the Western-Siberia pipeline network are directly or otherwise caused by faults in foundations and substructures (RosHydromet, 2008a). With climate change likely to cause at least partial thawing of the permafrost, the bearing capacity and integrity of the foundations will inevitably decline. This issue would affect not only pipelines, but also hydrocarbon production sites in the region. An additional, more extensive problem, affecting the pipeline network country-wide is riverbed erosion. As pipelines cross thousands of rivers, the river runoff intensified by climate change might exacerbate the erosion and lead to more accidents on underwater segments of the pipelines (RosHydromet, 2008b). While, currently, particular weather phenomena aggravating the wear-and-tear of and disruptions to energy infrastructure cannot be directly attributed to climate change, impacts of the changing climate are expected to be more pronounced in future. Consequently, the vulnerability of the energy sector might increase, adding to already existing inefficiencies and high depreciation.

4.2.2 The potential for energy efficiency

Although Russia is one of the world’s leaders in the development of combined heat and power technology (Popov, 2012), other energy efficiency initiatives are under-developed, which suggests its technical energy efficiency potential is relatively high. Bashmakov et al. (2008) estimate that Russia could save about 290 Mtoe by implementing energy efficiency measures and eliminating natural gas flaring (Table 4). This number is broadly in line with the estimate by McKinsey (2009) that Russia’s

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energy efficiency potential by 2030 is around 213 Mtoe. Within the final energy consumption, the most energy can be saved in the residential and manufacturing sectors, followed by transport (Bashmakov et al., 2008; McKinsey, 2009). These savings would amount to 30–50% of Russia’s total energy consumption in 2005. For the purposes of a low-carbon transition, energy efficiency and conservation measures can be implemented in the short to medium terms (which is used as an assumption in the ASK-Russia model), as opposed to most renewable and other energy supply alternatives that are longer term options.

Table 4. Energy savings through technical energy efficiency measures in Russia, used in the ASK-Russia model as assumptions, Mtoe (Bashmakov et al., 2008)

Unit: Mtoe

Coa

l

Cru

de o

il

Pet

role

um

prod

ucts

Gas

Oth

er s

olid

fu

els

Ele

ctric

ity

Hea

t

TOTA

L

Com

pare

: 20

05 e

nerg

y co

nsum

ptio

Total, including the elimination of natural gas flaring 58.34 2.50 34.65 192.09 6.92 294.49

Elimination of natural gas flaring 12.09 12.09

Total primary energy supply 58.34 2.50 34.65 180.00 6.92 282.40 653.02

Electricity generation 23.87 0.00 2.53 64.88 1.73 93.01 186.75

Heat generation 23.31 0.46 7.38 71.02 3.47 1.82 107.45 194.00

Fuel production, transformation, transmission and distribution 2.15 2.04 0.17 5.92 0.07 10.08 20.86 41.29 85.21

Total final energy consumption 9.01 0.00 24.57 38.18 1.65 19.52 60.72 153.64 422.38

Agriculture and forestry 0.02 1.53 0.08 0.04 0.73 0.50 2.90 6.21Fishery 0.04Mining 0.00 0.14 0.37 0.60 1.12 7.19

Manufacturing 8.41 1.19 9.86 1.40 7.72 12.90 41.49 109.54

Construction 0.00 0.20 0.01 0.01 0.25 0.04 0.50 1.70Transport 0.00 0.00 21.29 14.95 0.00 1.67 0.39 38.30 94.40Municipal utilities 0.00 0.01 0.00 0.00 0.36 0.34 0.72 3.61Services sector 0.01 0.02 3.12 0.01 4.60 7.44 15.20 36.31

Residential sector 0.57 0.18 10.16 0.19 3.82 38.50 53.42 108.24

Non-energy-use 45.73Note: “Numbers in italic are for total energy inputs to power and heat generation. Final energy consumption and those numbers are not additive due to the fact that both sectors have positive energy outputs - correspondingly power and heat, which are used by final consumers” (ibid.)

4.2.3 The potential for renewable energy

Even if the energy efficiency potential is fully realised, there will be a need to use a mix of existing and new energy sources to satisfy Russia’s energy demand during the transition to a low-carbon economy. The supply chain for renewable energy sources typically contains fewer elements than that for conventional fuels, which is relevant to energy security. For example, onsite renewables facilitate a de-centralised model of energy supply, reducing the need to build long-distance transmission lines or to transport fossil fuels and uranium from extraction sites to power stations. At the same time, renewable energy sources may involve other risks, for instance, the intermittency of solar and wind energy supply.

Russia’s technical potential for developing renewable energy exceeds its annual energy use by two orders of magnitude, whereas the economic potential is around half of its annual energy use (Bezrukikh et al., 2007; Fiodorov et al., 2009; Kobysheva, 2012). As Table 5 shows, non-intermittent

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renewable energy sources (geothermal, small-scale hydropower and biomass) have the highest economic potential in Russia, adding up to 250 Mtoe together. Despite some of the highest technical potentials compared to other renewables in Russia, wind and solar energy may be difficult to harvest cost-efficiently.

Table 5. Renewable energy potential for producing electricity and heat in Russia, with the technical potentials used in the ASK-Russia model as assumptions, Mtoe/year (Bezrukikh et al., 2007; Fiodorov et al., 2009; Kobysheva, 2012)Unit: Mtoe/year Technical potential Economic potentialGeothermal 11,868 114Small-scale hydropower 126 70Biomass 140 69Wind 2,216 11Solar 9,676 3Low-grade heat 194 53Bio-waste 92 54TOTAL 24,221 374

4.3 The structure of the scenarios (backcasting stages 3 and 4)Four backcasting scenarios were constructed for this paper. In addition to the backcast scenarios, a simplified reference pathway TRENDS was generated: a ‘what-if’ extrapolation of recent historical trends in energy consumption for a given period. The TRENDS pathway goes over Russia’s 21st-century carbon budget constraint in 2022/252.

Table 6 details the main characteristics of the four backcast scenarios as a range of desirable futures in 2050. The structure of the table follows the UK Climate Impacts Programme’s five key “dimensions of change” that are commonly found in socio-economic scenario studies (UKCIP, 2000) and that are broadly consistent with the more recent Shared Socioeconomic Pathways (van Vuuren et al., 2012). The categories the UKCIP identifies are demography and settlement patterns, the composition and rate of economic growth, the rate and direction of technological change, the nature of governance, and social and political values.

Table 6. The main characteristics of the backcast scenarios for Russia’s carbon dioxide emissions out to 2050 (Abbreviations: (T) 100%; (H) 60–80%; (M) 30–50%; (L) 5–20%; LG-LE – the low GDP growth, low energy demand scenario; HG-ME – the high GDP growth, medium energy demand scenario; MG-ME – the medium GDP growth, medium energy demand scenario; HG-HE – the high GDP growth, high energy demand scenario)

ScenariosLG-LE HG-ME MG-ME HG-HE

1GDP and energy dynamics

Low growthLow energy demand

High growthMedium energy demand

Medium growthMedium energy demand

High growthHigh energy demand

2Sectors with the largest GDP share in 2050

Services (H)Agriculture and Forestry (L)Construction (L)

Services (H)Highly efficient mining and processing (L)Construction (L)

Mining and processing (H)Agriculture and forestry (L)Construction (L)

Highly advanced and consumer-goods manufacturing (H)Services (M)Construction (L)

3

Main factors of economic growtha [the ‘quality’ of value added]

Human capitalLabourLand

Human capitalLabour

CapitalLabourLand

Human capitalLabour

2 The TRENDS pathway is consistent with other existing reference scenarios for Russia’s emissions. A comparison is made in the Supplementary Information file.

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4Main outputs [the ‘quantity’ of value added]

High value-added servicesLow value-added goods

High value-added servicesMedium value-added goods

Low to medium value-added goods

High value-added goods

5 Settlement patterns

De-urbanisation.Less and slower travel.Settlements spread out.Residential sector dominated by low-rise buildings.Fewer and larger households, with shared utilities.

High urbanisation.More travel.Settlements below permafrost latitudes only.Buildings mostly high-rise, with some low-rise rural buildings.More and smaller households.

Urbanisation as in baseline year.Travel as in baseline year.New housing stock of three types: high-rise buildings concentrated around industrial sites, rural low-rise houses and suburban houses.Number and size of households as in baseline year.

Urbanisation as in baseline year.More and faster travel.Settlements spread out.New housing stock predominantly low-rise.Fewer and larger households, with shared utilities.

6 Transport

Freight, but not passenger, transport used extensively.International shipping used extensively to support agricultural and forest exports.Public transport developed better than private.Main modes: shipping, rail, road.

Both passenger and freight transport used extensively.International aviation and shipping used extensively for both importing goods and travelling.Both public and private transport well developed.Main modes: shipping, aviation, rail, road.

Freight, but not passenger, transport used very extensively.Aviation and shipping dwindle compared to today and mainly support exports.Private transport developed better than public.Main modes: road, rail.

Freight and passenger transport used extensively, with the latter used for leisure rather than commutes.International aviation and shipping used extensively for trade and travelling.Private transport developed better than public.Main modes: road, aviation, shipping, rail.

7Governanceb, social and political values

Multi-level governance Type I: levels multiple but limited, multi-task, non-overlapping and stable

A centralised approach: managed democracy

A centralised approach: state-led environmentalism

Multi-level governance Type II: levels innumerable, task-specific, overlapping and flexible

8Short-term decarbonisation achieved through…

Reduction in road transport energy consumption through less and slower travel.Improved agricultural and forestry practices.Biomass replacing coal (L).

Reduction in road transport energy consumption through energy efficient vehicles.Existing nuclear and hydro-power stations used to their full capacity.Biomass replacing coal and oil (T).

Residential energy demand reduction [austere] in both old and new housing stock.Reduction in international transport energy consumption.Biomass replacing coal and oil (H).

Residential energy demand reduction [conscientious] in both old and new housing stock.Non-energy CO2 reduced through material and energy efficiency.Biomass replacing coal and oil (T).

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9

Medium- to long-term decarbonisation achieved through…

Economy restructured away from energy-intensive manufacturing.New housing stock complying with strict energy efficiency standards.Onsite renewable energy.Non-energy CO2 reduced through economic re-structuring.

New, mostly high-rise, housing stock complying with strict energy efficiency standards.Wide-spread combined heat and power (CHP).New nuclear power plants built, including CHP.

Onsite renewable energy.Non-energy CO2 reduced through the use of alternative feedstock.A small proportion of coal CCS and/or gas CCS after 2030.

Electrification of transport.Onsite renewable energy.Wide-spread combined heat and power (CHP).New nuclear power plants built.Non-energy CO2 reduced through the use of alternative feedstock.BECCS heavily used.

Notes: aFor definitions of particular factors of economic growth and of the concept ‘value added’ see, for example, Deardorff (2006)bFor more detailed definitions and examples of multi-level governance Types I and II see Hooghe and Marks (2001)

The first four substantive rows of Table 6 contain the main economic characteristics of the backcast scenarios. The ‘GDP and energy dynamics’ row qualitatively describes the mean annual growth and energy use of Russia’s economy in 2050, with ‘low’, ‘medium’ and ‘high’ indicating how these variables compare to those in the baseline year (2009). ‘Low’ is about a third lower than in 2009, ‘medium’ is similar to 2009 and ‘high’ is about a third higher than in 2009. Similarly, changes in demography-related scenario characteristics in 2050 (rows 5 and 6 of Table 6) are described in relation to the ‘baseline’; i.e., each baseline indicator refers to statistics in 2009. The final two rows of the table summarise mitigation measures that, according to the scenario storylines, take the country to the 2050 ‘futures’ described.

4.4 Scenario storylines (backcasting stages 3 and 4)

4.4.1 Scenario LG-LE

Scenario LG-LE is a low economic growth and low energy demand scenario. Russia in 2050 is a post-industrial economy built through ‘de-industrialisation’ and re-orienting economic activities towards services. The transition occurs through a series of economic crises (following the economic recession in the late 2000s) that are used as a springboard for economic and social reforms. The services industry, agriculture and forestry sectors are well developed and produce high-value-added services and low-value-added goods. The services industry focuses on information and communication technologies with ‘value’ created remotely and in a decentralised mode. Manufacturing decreases dramatically while construction rates in rural areas grow to accommodate the de-urbanising population. The construction sector innovates to support agriculture and settlements on melting permafrost. Compared to the baseline year, settlement patterns in scenario LG-LE are more spread-out, the pace of life is slower, and households become more self-sufficient. Political power is devolved to multiple but limited levels of governance, whose tasks and geographical jurisdictions are non-overlapping and relatively stable.

4.4.2 Scenario HG-ME

Scenario HG-ME is a high economic growth and medium energy demand scenario. Russia in 2050 is a post-industrial economy built through ‘de-industrialisation’ and re-orienting economic activities towards services. The transition occurs through high, ‘Baltic Tiger-type’ economic growth and partial liberalisation. As in scenario LG-LE, the economy is service-oriented but with a different flavour. The services sector heavily dominates, with industry moderately developed and little agriculture—an

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economic structure similar to today’s Russia—producing high-value-added services and medium-value-added goods. The services industry focuses on information and communication technologies with ‘value’ created in large, centralised hubs and clusters modelled after the Silicon Valley. The urbanisation rate is high and households are predominantly concentrated in high-rise buildings, with some low-rise buildings in rural areas. Melting permafrost drives settlements south. The centralisation of power is reminiscent of Russia’s current ‘managed democracy’.

4.4.3 Scenario MG-ME

Scenario MG-ME is a medium economic growth and medium energy demand scenario. Russia in 2050 is an economy based on restored and renovated light and heavy industries, including highly efficient mining and processing. A large share of products is manufactured domestically. The transition occurs through top-down (state-led) capitalist expansion, with three out of four factors of production (labour, capital and land) used intensively. The industries are situated in proximity to raw materials and/or energy sources; the share of services is low. The population is spread across the country and concentrated around industrial sites; the new housing stock is predominantly high-rise. Melting permafrost is de-watered to maintain fossil-fuel mining and agriculture. The pro-environmental government takes a centralised approach to governing the country.

4.4.4 Scenario HG-HE

Scenario HG-HE is a high economic growth and high energy demand scenario. Russia in 2050 is an economy based on restored and renovated light and heavy industries, producing a large share of products domestically. The transition occurs through a bottom-up capitalist expansion, with the economy moving away from taxing labour towards taxing capital and, hence, developing labour- and human capital-intensive, high-value-added manufacturing (including crafts, modularity and re-manufacturing). Construction rates are high to accommodate new polycentric networks of cities with sprawling suburbs. The construction sector innovates to support settlements on melting permafrost. Political power is devolved to a large number of governance levels that are task-specific, overlapping and flexible.

4.5 Short- and long-term decarbonisation options

4.5.1 The ‘hierarchy’ of decarbonisation options (backcasting stages 3 and 4)

The decarbonisation scenarios developed here rely on a qualitative ‘hierarchy’ of mitigation measures. This ‘hierarchy’, devised for the purposes of this paper, is not meant to be an in-depth account but a highly stylised ranking of available options based on their potential for short- vs. long-term deployment. In particular, the decarbonisation options in Table 6 (rows 8 and 9) are listed in the order preferred within the ‘hierarchy’, starting with energy use reductions through retrofit and reduced service demand, followed by biomass replacing coal and oil, increased renewable energy such as solar PV and onshore wind, new CHP, new nuclear, CCS, and finally biomass co-fired with fossil fuels and CCS (BECCS). The wave and tidal energy sources are not used, since more mature renewable energy technologies have technical potential sufficient for meeting Russia’s energy demand in the scenarios. The feasibility of the options is the main rationale for their hierarchy, with the key barriers analysed in the next subsection.

In the short term, the main low-carbon options are reductions in Russia’s energy use, on the demand side, and the substitution of hydrocarbons with biomass (in mainly waste and residue), on the supply side. The importance of demand-side opportunities in reducing energy consumption and emissions cannot be overestimated , particularly in the short term due to systemic carbon lock-in (Unruh, 2002; Unruh and Carrillo-Hermosilla, 2006). All of the scenarios use Russia’s technical energy efficiency potential fully, thereby saving around 290 Mtoe (Bashmakov et al., 2008) by 2050, with

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rebound issues assumed to be addressed through complementary policies. In addition, energy consumption in certain sectors decreases in line with the storylines due to lower use of particular transport modes (in the short term) and economic restructuring (in the longer term, apart from Scenario LG-LE where the economic structure is similar to that in the baseline year, i.e., 2009).

In the medium term and beyond, in addition to the existing demand-side options, scenarios LG-LE and HG-ME involve economic restructuring away from energy-intensive sectors towards services. On the supply side, many of the existing power stations start going offline, continuing the trends observed in Russia’s heavily deteriorated capital stock (Nureev, 2010; Popov, 2012; Tarasiuk and Akimova, 2010). In all scenarios the structure of energy supply is further diversified to include renewable energy sources other than biomass for electricity, heat and transport fuels, with all four scenarios relying on renewable energy as a major supply-side option in the medium to long term. The two scenarios with medium energy demand in 2050 (HG-ME and MG-ME) have to resort to either nuclear energy or carbon capture and storage (CCS) to stay within the carbon budget. The high-energy demand scenario HG-HE—with the most delayed emission peak out of the four—offers the least realistic scenario. Nevertheless, it does have theoretical merit and an analytical base, with high uptake of nuclear power, successful large-scale demonstration and deployment of negative emission technologies (BECCS) and widespread electrification of transport in order to stay within the budget constraint.

Some medium- to long-term options include ‘immature’ technologies that are currently at the R&D stage or waiting to be deployed through large-scale demonstration projects. Such options (for example, CCS and BECCS) cannot be transition technologies. Hence, in scenarios with CCS (MG-ME and HG-HE), its introduction is assumed after 2030. To accommodate such factors as a geopolitical situation, investment climate and Russia’s fossil fuel reserves, the scenarios are deliberately flexible between coal CCS and gas CCS. While two out of four scenarios use CCS, only one of them (HG-HE) relies on negative emission technologies with CCS-equipped power plants co-firing fossil fuels with biomass.

4.5.2 Barriers to implementing the decarbonisation options (backcasting stages 5 and 6)

Technical, commercial and wider environmental uncertainties associated with immature technologies render them ‘speculative’ in the foreseeable future. CCS, for example, will require additional infrastructure: from re-equipping existing power plants to building new ones to adjusting the transmission capacity of the grid and pipelines. In addition, there are still significant uncertainties over the technical performance of a full-scale power plant with CCS (Hammond et al., 2011), “water, sequestration, and pore-space competition” and regulatory challenges (Court et al., 2012), issues of risk perception and societal acceptance of the technology (Mander et al., 2011), and commercial and financial feasibility concerns (Kheshgi et al., 2012). An important but often overlooked risk is upstream emissions associated with such technologies. It is estimated that CCS is likely to reduce emissions of power stations by 70% on a life-cycle basis (Benson et al., 2012; Hammond et al., 2013), rather than the previously assumed “over 90%” (Hammond et al., 2011). Russian gas has particularly high upstream emissions due to a large amount of fugitive emissions and long transmission distances (Hammond et al., 2013). Other Russian fossil fuels are also likely to be more emission-intensive on a life-cycle basis than hydrocarbons originating in more developed countries. Although this paper does assume reduced efficiency when CCS (or BECCS) is employed in the backcast scenarios, life-cycle emissions are not factored in. It is clear, however, that it would make the carbon budget constraint yet more challenging.

While biomass can potentially mitigate upstream emissions of CCS technologies (Hammond et al., 2013), its production is associated with a number of other uncertainties and wider sustainability issues (Searle and Malins, 2014; Slade et al., 2011; Thornley et al., 2009). Besides, the four backcast

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scenarios already draw on Russia’s technical biomass potential to a significant extent for uses other than BECCS (for example, transport, onsite micro-generation and/or power plants). The barriers to widespread biomass deployment are technological (e.g. limited R&D), economic (e.g. high cost), institutional and political (e.g. the government’s low level of commitment) (Hansen et al., 2006; Larive International, 2013). While these challenges are not limited to Russia, achieving high quality of biomass across the board is a country-specific problem (Hansen et al., 2006), given inadequate energy statistics and poor regulations on biomass quality (Pristupa and Mol, 2015; Proskurina et al., 2015). In addition, an oligopolistic structure of Russia’s biomass market stymies competition that would otherwise exert a downward pressure on prices (Thiffault et al., 2016).

Among the barriers to realising Russia’s technical energy efficiency potential, there is limited investment in energy efficiency, low awareness among the population and organisations, market imperfections such as subsidised residential electricity and natural gas, poor records of energy use statistics, and patchy regulations (McKinsey, 2009; Sarkisyan and Gorbatenko, 2008). In particular, the largest potential source of energy savings in the Russian economy, the buildings sector including district heating, faces substantial challenges (IEA, 2011; Korppoo and Korobova, 2012; Lychuk et al., 2012). With most Russian families living in high-rise multi-apartment buildings and with home associations virtually non-existent, it is difficult to coordinate large-scale retrofits for the entire building (Lychuk et al., 2012). In the residential heating sector, low quality of service coupled with a prolonged cold season and an expensive heat distribution system is a systemic problem unlikely to be resolved by the current measures such as meter installations (Korppoo and Korobova, 2012). However, compared to the buildings sector, Russia’s transport and industry are even further behind other developed countries in implementing energy efficiency, with the main impediments including the absence of both funding and energy efficiency services market (Gusev, 2013).

Similar challenges pertain to implementing Russia’s technical potential for renewable energy. Rich in fossil fuels, the country has struggled to incentivise energy use from non-hydrocarbon sources that could lead to increased energy prices for consumers (IEA, 2011; IFC, 2011). Accordingly, one of the main barriers to renewable energy is subsidised consumption of natural gas in the residential sector (IFC, 2011; Øverland and Kjærnet, 2009). This problem of centrally controlled energy tariffs is exacerbated by centralised (as opposed to regional) control over approving renewable energy installations (Boute, 2013). Additionally, in common with other countries, Russia faces difficulties with grid balancing and integration related to the variability of renewables (Boute and Willems, 2012; IFC, 2011).

One of the options altogether excluded from the ‘hierarchy’ of mitigation measures, on the grounds of limited effectiveness, is carbon trading. It has failed to prove itself as an effective way of reducing emissions at a rate sufficient for a good chance of not exceeding 2°C (GCP, 2015; Laing et al., 2013). The 2°C framing is important here, indicating the urgency and scale of the problem. Carbon trading might have been a successful emission-reduction instrument if it had been launched and honed when the climate change problem was first recognised at the end of the 1980s. However, considering a rapidly shrinking carbon budget, the only option for staying below 2°C might be direct policy interventions, including incentives for renewable energy on the supply side and energy efficiency and energy conservation on the demand side.

4.6 The emission peaks and reduction ratesThe timing of the pathways’ veering away from TRENDS is determined by short-term decarbonisation options and the low/medium/high energy demand spelled out in Table 6. Sufficiently distinct and interesting futures and transitions have been selected to justify the peak years in this scenario exercise. For example, HG-HE’s emission profile is closer to Russia’s INDC commitment (Russian Government, 2015a) than are the other pathways, and is testing how a low-

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carbon transition may play out in this case. The purpose of scenario LG-LE’s peak in 2017 is to illustrate the earliest possible peak. The MG-ME scenario shows that 2020 is the most delayed peak achievable with medium energy demand, highly developed energy-intensive industry and despite deploying carbon capture and storage technology (although no negative emission technologies).

To stay within the budget constraint, a later emission peak typically results in a steeper post-peak pathway. For example, scenario HG-ME has an emission peak in 2018, compared to MG-ME’s 2020, and subsequently slopes downwards at a more gradual rate than MG-ME. Scenario HG-HE is, to some extent, an exception with emissions peaking in 2025 and an average annual reduction rate of 10.5% until 2030 being lower than HG-ME’s and MG-ME’s (Table 7). Although HG-HE’s emission reduction rate between 2030 and 2050 is positive and appears high in percentage terms, it is applied to negative emissions and to a low base. HG-HE’s largest emission reductions in absolute terms happen by 2035, as is the case in the other three scenarios. This is largely due to a high technical potential of biomass in Russia, which makes it a preferred option in the short and medium terms.

Table 7. Emission peak dates, annual emissions and emission growth/reduction rates for the low-carbon pathways in 2009 (baseline year), peak years, 2030 and 2050 (Abbreviations: LG-LE – the low GDP growth, low energy demand scenario; HG-ME – the high GDP growth, medium energy demand scenario; MG-ME – the medium GDP growth, medium energy demand scenario; HG-HE – the high GDP growth, high energy demand scenario)

2009 Peak yeara 2030 2050TRENDSAnnual emissions (MtC/yr) 418.5 - 628.1 809.7Annual emission reduction/growth rate - 2.2% 1.3%

LG-LE (peak in 2017)Annual emissions (MtC/yr) 418.5 445.9 127.5 63.1Annual emission reduction/growth rate 0.3% -8.8% -3.5%

HG-ME (peak in 2018)Annual emissions (MtC/yr) 418.5 452.1 96.6 31.3Annual emission reduction/growth rate 0.7% -12.0% -5.5%

MG-ME (peak in 2020)Annual emissions (MtC/yr) 418.5 479.8 71.8 4.1Annual emission reduction/growth rate 0.9% -16.9% -13.4%

HG-HE (peak in 2025)Annual emissions (MtC/yr) 418.5 535.4 1.2 -20.7Annual emission reduction/growth rate 1.4% -10.5% 67.0%b

Notes: a Except the TRENDS pathway that has no emission peak as such.b Scenario HG-HE’s emission reduction rate in 2050 is positive and relatively high, as it is applied to negative emissions and from a low base.

For the purposes of the cross-scenario summary3, Figure 3 illustrates the scenario emission curves between 2011 and 2050 as well as historical emissions in 1990–2010. Evidently, there is a widening gap between the backcast scenario pathways and the TRENDS trajectory post-peak. After significant reductions in fossil fuel use, three out of four backcast scenarios only just stay within the remaining carbon budget with cumulative emissions around at the upper end of the 5.5–7.1 GtC budget range. The fourth scenario, HG-HE, has the latest peak (2025) and exceeds the budget constraint at 9.5 GtC despite a dramatic reduction in energy and non-energy emissions between the peak and 2050 as

3 The figures illustrating energy systems of the individual backcast scenarios can be found in section 4 of Supplementary Information.

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well as negative emissions after 2030. This scenario allows a temporary overshoot; however, if negative emissions continue beyond the final year of the ASK-Russia model (2050), the cumulative budget of the scenario will gradually decrease to the ‘allowed’ level.

Figure 3. Highly stylised plots of Russia’s annual emissions in 1990–2050: the TRENDS pathway and four backcast scenarios, with 1990–2010 being historical emissions, MtC per year

4.7 A comparison to the IPCC AR5 emission pathwaysTo compare the emission pathways in this paper to those in existing IAM (Integrated Assessment Modelling) scenarios, their emission peak years and levels, and average emission reduction rates are compared here. The AR5 scenario database (IPCC, 2014b) contains 292 carbon emission pathways for the Economies-in-Transition (EIT) group of countries between 2005 and 2050, aiming for 450ppm or 2.6W/m2, i.e., a reasonable probability of staying below 2°C. Twenty of these pathways have historical emissions in 2005 twice as large as the majority of the pathways (1.6 GtC vs. 0.8 GtC). There are also five pathways that start low at 0.48 GtC in 2010, with no data in 2005. If they are excluded, this leaves 267 pathways that have similar starting points in 2005/2010.

For the 267 AR5 pathways, Russia is assumed to follow the same emission trajectories as the EIT group. Russia’s annual emissions are calculated in proportion to its historical share of emissions in the group. Based on historical emissions from the Global Carbon Project (GCP, 2015), Russia’s share in the EIT annual emissions is just under 50% on average between 1990 and 2013, with a range of 48.5–51.7%. Among the resulting pathways for Russia, 97 peak their carbon emissions in 2005, 64 peak in 2010, 63 peak in 2020, and 43 in 2030. The fact that these AIM pathways were completed between 2009 and 2014 might explain the early timing of most peaks compared to this study. The level of emissions in the peak year of the AR5 pathways varies from 381 to 635 MtC/yr, with an average of 487 MtC/yr. This is comparable to the 446–534 MtC/yr peak emissions, with 478 MtC/yr average, in this study.

In the AR5 sample, mean annual emission reduction rates range from the minimum rate of 0.9% in 2010–2020 to the maximum rate of 4.5% in 2030–2040. In this paper, the most dramatic emission reduction rate, averaged across the four scenarios, is 12.1% and takes place between the peak year and 2030. Possible reasons for the difference in the maximum emission reduction rates are the much later emission peak years and a limited use of negative emission technologies in this paper.

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In the sample of 267 AR5 pathways, the EIT’s 2010–2050 cumulative emissions range from 0.02 to 41.8 GtC. By halving the EIT’s 0.02–41.8 GtC range to obtain Russia’s cumulative budgets, the range for Russia is between 0.01 and 20.9 GtC. The upper end of the range is similar to the country’s cumulative emissions of the TRENDS pathway in this paper. The AR5 range is sufficiently broad to include the budget of 5.5–7.1 GtC calculated within this paper. Most of the IAM budgets cluster between 7 and 18 GtC. Therefore, although this paper’s budget range is situated towards the lower half of the AR5 range, it is not an outlier.

5 Conclusions and policy implicationsRussia has repeatedly endorsed the 2°C pledge in a range of international agreements (Group of Eight, 2012; UNFCCC, 2009; 2015). If this commitment is taken in good faith, Russia’s emissions and pathways need to be constrained by carbon budgets consistent with the global 2°C target. A 2°C temperature rise above pre-industrial levels currently represents a commonly accepted threshold for ‘dangerous climate change’. This target, along with the carbon dioxide already emitted and the continuing upwards emission trajectory, partly explains the urgency and scale of the mitigation challenge faced by Russia and the rest of the international community. However, with unique aspects of Russia’s energy sector in mind, a review of non-mitigation pathways from literature and a simple extrapolation of past trends make it clear that Russia’s continuing on its current trajectory is far removed from the global 2°C target.

The scenario exercise within this paper does not lead to an unequivocal conclusion that a particular pathway for Russia is preferable (for example, a service-oriented de-industrialisation vs. a hi-tech re-industrialisation). The purpose is to show the scale of the mitigation challenge required and its policy implications, given Russia is one the most carbon-intensive and energy-intensive economies in the world (EIA, 2012). For a reasonable chance of staying within the 2°C carbon budget constraint, the country would need to undertake immediate, all-embracing and sustained reductions in its carbon emissions. Such reductions can only be achieved through a major transformation of Russia’s energy system, which involves both rapidly cutting energy demand and initiating an unprecedented programme of building extensive low-carbon infrastructure. This would raise questions about the government’s growing investment in exploration and production of fossil fuels (Barclays, 2013) that is incompatible with a 2°C target. Nevertheless, given Russia’s resources and human capital, along with its top-down political structure, the country could yet initiate a material change to its energy system in line with its 2°C commitment.

Although these backcast scenarios, in particular, and mitigation measures, in general, may be feasible—if challenging—technically, their economic and social feasibility (and desirability) is likely to present additional constraints and policy implications, at least, within the current socio-economic paradigm. Such a dramatic transformation would require rapid technological change and, more challengingly, the dismantling of institutional and social structures that come with the current energy system. Future, more qualitative, research could elaborate on how the existing paradigm can be changed and what ‘triggers’ and opportunities exist in Russia to challenge the systemic constraints. Further research could model how the low-carbon transition pathways might affect exports of fossil fuels and hence Russia’s budget revenues; investigate how jobs might be affected in number, quality and location; and, further explore the feasibility of non-marginal emission reduction rates. In addition, as modelling in this study relies on the technical potentials for energy efficiency and renewables, future exploratory scenarios might use the economic and market potentials of these resources.

One of the common feasibility challenges across the different decarbonisation options in the backcast scenarios is financial. This challenge is caused partly by the cost of the energy efficiency and

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renewable energy technologies (given Russia’s cheap and abundant fossil fuels and subsidised energy for the residential sector) and partly by the path dependency e.g. being locked into a legacy of inefficient buildings and machinery. Many of these barriers can be overcome through targeted policies. Key policy recommendations would include phasing out energy subsidies; providing loans and grants for energy efficiency and renewable energy projects; setting environmental standards for new buildings, cars and power plants; raising awareness among the public; and collecting detailed, high quality data on energy use and emissions to ensure that the success (or otherwise) of these policies is measured (International Finance Corporation (IFC), 2011; McKinsey, 2009; Pristupa and Mol, 2015; Proskurina et al., 2015). In addition, it would be essential for the Russian government to create a domestic market for energy efficiency services (Gusev, 2013) and, similarly, encourage domestic demand for biomass (Pristupa and Mol, 2015). While Russia has introduced legislation to support both wholesale and retail renewable energy supply (Kozlova and Collan, 2016; Russian Government, 2013, 2015b), these efforts would need to be scaled up, if the country’s 2°C commitment is to be honoured. As a specific example, off-grid settlements in remotes parts of Russia, such as the Far North, could become a test bed for profitable renewable energy projects (Øverland and Kjærnet, 2009).

These recommended policies are shaped by both external and internal priorities of the Russian government. External priorities refer to the country’s independence in terms of both imports and exports, including self-sufficiency (phasing out imports of agricultural and other products) and security of demand (diversifying energy trade partners) (Stcherban, 2014; Tarasiuk and Akimova, 2010). Internal priorities, as suggested by the expert stakeholders interviewed for this study, include regional development, effective occupation and maintaining the ‘social contract’. In much simplified terms, regional development means redistributing wealth from the capital cities Moscow and St. Petersburg to other cities and towns. ‘Effective occupation’ is making sure, through subsidies and other types of preferential treatment, that Russia’s citizens settle in the country’s distant regions to maintain national presence. The ‘social contract’ is the government’s promise of relative prosperity and stability in return for the population’s political support or apathy. Part of the ‘social contract’ is to provide cheap domestic energy and other social subsidies.

If a 2°C target is to be achieved, even with the carbon capture and storage technology, about a fifth of Russia’s oil, half of its gas and almost 95% of its coal would become stranded assets, to stay within the IPCC’s 2°C budgets (McGlade and Ekins, 2015). With the phasing out of fossil fuels and with renewables being relatively expensive at least in the near term, two outcomes are likely to occur simultaneously. Russia’s federal budget revenue (Illarionov, 2012; Institut Ekonomicheskogo Analiza, 2010) would drop at the same time as domestic prices on energy increase. Reduced global demand for oil clearly has a negative impact on the Russian economy (Tuzova and Qayum, 2016). These two sides of the same coin—fossil fuels as exports and as domestic energy supply—might work to undermine the socio-economic foundations of the country’s current order, if mitigation is not pursued swiftly, systematically and in a strategic way. At the same time, continued global inaction and the accompanying climate change would have similarly profound repercussions for Russia’s energy system and the wider prosperity of society.

Lower budget revenue and higher domestic energy prices risk destabilising the existing ‘social contract’, unless the government frames the situation as becoming independent from the West, thereby justifying the need to tighten people’s belts. This framing might incentivise the nation to fulfil its self-sufficiency priority, by developing manufacturing and even becoming an exporter of renewable energy, for which Russia has a high technical potential (Bezrukikh et al., 2007; Fiodorov et al., 2009; Kobysheva, 2012). In relation to budget revenues, the storylines in this paper suggest policy options other than resource extraction among the sectors dominating the economy in each scenario, for example, high-tech or low-tech manufacturing, agriculture or services. In this sense the

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country does not have to continue relying on oil and gas exports. Such economic restructuring and modernisation would help to re-define Russia’s place in the world, since its current role is largely determined by selling fossil fuel energy.

The mitigation options suggested draw on the contemporary debate—within Russia’s political, economic and academic establishments—about the urgent need to replace decaying infrastructure, modernise the country’s economy and reduce its energy intensity. Cutting across issues of technological progress, politics and social contract, is the fundamental question unresolved for both those within and outside the country: what is Russia’s place in the world? This question has emerged, directly or otherwise, in the backcast scenarios and literature sources as well as—unprompted—in all of the interviews conducted within this research project. This paper concludes that Russia’s global leadership in delivering a low-carbon and climate-resilient economy may point to the nation’s place in a prosperous and sustainable global future.

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Acknowledgements

The author is grateful to Professors Alice Larkin and Kevin Anderson for their comments on the draft and to the expert stakeholders interviewed within this project. This research has benefitted from the supportive and collegiate atmosphere of the Tyndall Centre for Climate Change Research (Manchester).

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