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Social discounting, social costs of carbon, and

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LETTER

Social discounting, social costs of carbon, and their use in energy

system models

Konstantin Löffler1,2

1Workgroup for Infrastructure Policy, Technische Universität, Secr. H33, Straße des 17. Juni 135, Berlin 10629, Germany

2Energy, Transport, Environment, Deutsches Institut für Wirtschaftsforschung (DIW Berlin), Mohrenstraße 58, Berlin 10117, Germany

E-mail: kl@wip.tu-berlin.de

Keywords: energy system modeling, discounting, social costs of carbon, energy policy, decarbonization pathways, GENeSYS-MOD

Supplementary material for this article is available online

Abstract

Discounting plays a large role in cost-optimization models, but is nevertheless often only covered

in little detail in energy system models. The aim of this paper is to highlight the effects of varying

discount rates and social costs of carbon in energy system models with the example of the Global

Energy System (GENeSYS-MOD), propagating open debate and transparency about chosen

parameters for model applications. In doing so, this paper adds to the academic discourse on

socio-economic factors in energy system models and gives an outline to modelers in the field by

providing example results. The results show that close-to-zero discount rates that factor in

intergenerational equality, total emissions could be reduced by up to 41% until 2050 compared to

the baseline discount rate of 5%. This effect is even increased when a carbon price akin to the

actual social costs of carbon is chosen. This underlines the importance of the topic, which is, up to

now, seldom covered in cost-optimizing energy system models.

1. Introduction

Climate change is one of the most challenging topics

of the 21st century (IPCC 2014,2018, UNFCCC

2015a). While the existence of adverse effects accom-

panied with this climate change is generally agreed

upon, the concrete steps and measures, as well as their

timing, are still heavily debated. Thus, the task often

arises for modelers to provide well-founded answers

to these questions—to optimally provide a single,

least-cost, welfare-optimizing path into the future.

With the energy sector being one of the largest con-

tributors to global greenhouse gas emissions, a large

part of this burden falls to the decarbonization of the

energy system, and thus the modeling and quantitat-

ive analysis of energy systems. These cost-optimizing

energy system models try to give insights into future

energy scenarios by computing investments in a mul-

titude of technologies across various sectors, as well as

their respective dispatch and energy flows1. Since the

1While there are also other forms of energy system models, e.g.

market-oriented equilibrium models or pure dispatch models that

do not include capacity investments, I refer to bottom-up cost-

optimizing energy system models in this paper, since they are

models are cost-driven, a large importance falls onto

the valuation of costs and benefits over time, there-

fore relying on discounting to maintain comparab-

ility. While the issue of discounting is generally well

discussed in economics and other model types (such

as e.g. integrated assessment models (IAMs)), there

is little literature on the topic regarding energy sys-

tem models specifically (García-Gusano et al 2016)2.

However, since climate issues and their damages inev-

itably fall into the far-distant future, the implement-

ation and choice of discount rate(s) and costs for

negative externalities of greenhouse gases is of high

importance.

The purpose of this paper is not to find an optimal

choice of discount rate or carbon costs to be imple-

mented into energy system models. As Dasgupta

(2008) suitably put it: ‘Intergenerational welfare eco-

nomics raises more questions than it is able to

answer satisfactorily’, summarizing the Herculean

most commonly used for long-term analyses of the energy system

(Herbst et al 2012).

2In fact, García-Gusano et al (2016) and Steinbach and Staniaszek

(2015) are the only two papers published on the topic at the time

of this writing.

Environ. Res. Lett. 16 (2021) 104005 K Löffler

task that this would pose. Instead, the aim is to high-

light the effects of varying discount rates and social

costs of carbon in energy system models with the

example of the Global Energy System (GENeSYS-

MOD), propagating open debate and transparency

about chosen parameters for model applications. In

doing so, this paper adds to the academic discourse on

socio-economic factors in energy system models and

gives an outline to modelers in the field by providing

example results.

2. Discounting, social costs of carbon, and

energy system models: an overview

Discounting is a valuable tool to compare fiscal val-

ues across time. As such, the concept has been present

for thousands of years, already being used by Baby-

lonian mathematicians for the valuation of money

flows (Neugebauer 1969). Its usage and implications

have been heavily and extensively discussed in eco-

nomics over the years (Cline et al 1992, Hanley 2001,

Zhuang et al 2007, Bottero et al 2013, Pollitt and

Billington 2015). Especially in recent economic his-

tory, discounting has once again seen large attention,

specifically in the context of climate change. This cul-

minated in the 2018 Nobel Prize in economics given

to William D. Nordhaus for his work on discount-

ing and the economics of climate change. But even

there, the choice of discount rate is not quite unprob-

lematic, with the famous Stern-Nordhaus debate

focusing on the topic of finding the right discount

rate for climate damages (Nordhaus 2007,2008,

Stern 2007).

A large issue with damages related to climate

change is that they mostly fall far into the future,

likely beyond our own life expectancy. Thus, these

potential damages will mainly affect the life of com-

ing generations instead. The question of discounting

becomes a very ethical one here—while in usual busi-

ness applications, the discount rate merely represents

a tool based on market investment returns to com-

pare different financial options for both now and the

future. In the context of climate damages, one has

to weigh the damages for future generations against

the benefits of the current generation. This quickly

becomes very problematic, since unlike money flows,

where one can quite easily determine the opportun-

ity costs for each investment, putting a diminishing

value to future human life would be a quite contro-

versial point of view.

To give a better understanding of this issue, it is

best to decompose the discount rate into its differ-

ent elements to showcase how it is determined and

constructed. The famous Ramsay rule of discounting

(Ramsey 1928) portrays the discount rate as follows:

ρ=δ+ηg

where the discount rate ρ(used as the social dis-

count rate within this paper) is the sum of the the

rate of pure time preference δand the product of the

elasticity of marginal utility of consumption ηand

growth factor g. The latter part describes the inter-

temporal risk aversion of the individual or group,

with positive values for ηdescribing a preference in

consumption today in light of the uncertainty of the

future (also described as an ‘impatience factor’ in

Anthoff et al (2009b)). The main element of conten-

tion in the Stern-Nordhaus-Debate, however, is the

rate of pure time preference δ. This rate describes

the discrepancy between today’s benefits compared

to those of tomorrow. Since a sum of money could

be invested today and provide net positive returns,

the same sum of money would be worth more today

to the investor than it would be in, e.g. a year. The

rate of pure time preference would thus mimic the

gains of the investment opportunity. However, in

the case of climate damages, the concrete value is

not as easily determined, since the trade-off here lies

between money spent on climate mitigation now,

versus, among others, damages in the form of poten-

tial health and environmental issues that would arise

with unhindered global warming, some of which can

already be experienced today (although those dam-

ages occurring at a later stage might be substantially

more severe than those experienced today). Thus,

Stern (2007) proposes a rate of pure time prefer-

ence close to zero, with the only reason for a non-

zero value given in the (unlikely but not impossible)

event of total extinction of the human species, which

would thus make any benefit analysis moot (a concept

described and introduced as the so-called ‘hazard

rate’ in Yaari (1965)). Therefore, social discount

rates commonly display low discount factors and are

applied in cases where a purely monetary valuation

is not possible. Anthoff et al Anthoff et al (2009b),

however, pointed out that the risk-aversion part of

the Ramsay discounting formula is just as import-

ant as the rate of pure time preference. Commonly,

positive discount rates are applied, because economic

growth is assumed to be positive. This assumption is

not necessarily true, as climate damages might very

well lead to negative growth, especially in the Global

South, making the product of ηand g, and thus poten-

tially the entire discount rate, negative (Dasgupta

2008, Anthoff et al 2009b, Fleurbaey and Zuber

2012).

Since quantitative models aim to give insights

into possible future developments, they make use

of discounting as a tool to compare model options

(e.g. to compare investments across different time

periods). Macroeconomic models also often differ-

entiate between general and social discount rates in

order to incorporate the previously discussed ele-

ments into their analysis (García-Gusano et al 2016,

Emmerling et al 2019). However, in energy system

modeling, there is a distinct lack of such differenti-

ation, as well as a lack of literature on the topic. At

the time of this manuscript, only two papers focusing

Environ. Res. Lett. 16 (2021) 104005 K Löffler

on social discounting and energy system models were

able to be found, Steinbach and Staniaszek (2015)

and García-Gusano et al (2016), only one of which is

published in a peer-reviewed journal. Instead, most

literature that focuses on discounting in energy sys-

tem models purely analyzes the technical side, e.g.

regarding carbon-capture and storage (CCS) (Sano

et al 2013) or effects on certain technology options

(Østergaard and Andersen 2018, Rady et al 2018,

Islam et al 2019).

Steinbach and Staniaszek (2015) give an over-

view over different perspectives and use-cases for

discounting, and review applications of energy sys-

tem models with regard to their discounting. They

thereby aim to give some ‘best-practice’ advice for

modelers, coming to the conclusion that there should

be a differentiation between social and individual

(actor-specific) discount rates, since they are used

for a different forms of evaluation. While social

discount rates are used to evaluate total costs and

benefits from a societal perspective, individual dis-

count rates at a market-actor level are instead used

to model ‘investment decision making reflecting the

expected return of an investor’ (Steinbach and Stani-

aszek 2015). García-Gusano et al (2016) underline

that finding, claiming that since optimization mod-

els are cost-driven, and discount rates are used for

cost-evaluation, the choice of discount rates must

be well-founded and differentiated. They find a dis-

tinct ‘lack of sensitivity analysis and discussions con-

cerning the discount rates’ of energy system models

(García-Gusano et al 2016). Their findings showcase

a meaningful change in model results, however their

chosen discount rates were significantly higher than

those recommended in (Steinbach and Staniaszek

2015).

Another frequently asked question in both sci-

ence and policy is that of damages caused by car-

bon dioxide emissions. These damages to society

as a whole, including environmental damages and

increased healthcare costs, are summed up as social

costs of carbon. The German Environmental Agency

released a methodological convention for the assess-

ment of environmental costs (Matthey and Bünger

2019) in which they reach a social cost of carbon

of 180€ per ton of CO2emitted. This value is in

line with other studies in the field that conduc-

ted a social cost analysis on carbon (Anthoff et al

2009a, Weitzman 2013, IPCC 2014, van den Bergh

and Botzen 2014). Since the discussion about social

costs of carbon is also heavily entangled in that of

discounting (and in particular social discount rates

and the rate of pure time preference since they

heavily influence the outcomes of such cost ana-

lyses (Anthoff et al 2009a)), social costs of carbon

will be implemented in GENeSYS-MOD as a sens-

itivity to review their effect on the energy system

development.

3. Implementing social discounting in

GENeSYS-MOD

The GENeSYS-MOD is an open-source, multi-

sectoral energy system model based on the

Open-Source Energy Modeling Framework (OSe-

MOSYS) (Howells et al 2011, Löffler et al 2017).

It encompasses the sectors electricity, buildings,

industry, and transport, providing a multitude of

technologies for the model, including sector-coupling

and storage options. GENeSYS-MOD usually focuses

on long-term energy pathways in the context of deep

decarbonization until 2050, including the sectors

electricity, heat, transport, and industry. To achieve

this, the model minimizes the net present value of

total system costs across the modeled time horizon,

while also fulfilling the exogenously defined demands

and other constraints (e.g. carbon budgets, capacity

expansion limits, etc)3. In order to provide com-

parable fiscal units for the cost optimization, all

investments and other direct and indirect costs are

discounted towards the base year. The GENeSYS-

MOD/OSeMOSYS framework in its original form

only allows for one general discount rate to be set,

with the default value set to 5% per annum. For more

information on GENeSYS-MOD, its core functional-

ities, as well as source code and technical document-

ation, please refer to Löffler et al (2017), Burandt

et al (2018,2019), and the GENeSYS-MOD Git

repository.

For the purpose of this study, the European ver-

sion of GENeSYS-MOD developed in the EU Horizon

2020 project openENTRANCE (see Auer et al (2020))

has been expanded with new and improved discount-

ing functionalities. This pan-European application

includes the 27 EU member states (with the exception

of Cyprus and Malta), as well as the United Kingdom,

Switzerland, Norway, an aggregated Balkan region,

and Turkey. In total, the model covers 30 nodes for

the timeframe 2015 to 2050, computed in 5-year-

steps. To fully compare the effects of discounting on

energy pathways, there is no carbon budget imple-

mented in this study. Instead, two different carbon

price assumptions are compared: (a) a conservative

EU ETS carbon price, starting at roughly 15€ per ton

CO2in 2015 and reaching 85€ in 2050, and (b) a car-

bon price that endogenizes negative external effects of

CO2in a social-costs-of-carbon-approach. This social

cost of carbon of 180€ per ton of CO2in 2016, increas-

ing to 240€ in 2050, is taken from the ‘Methodological

Convention for the Assessment of Environmental

Costs’ of the German Environment Agency (Matthey

and Bünger 2019). Matthey and Bünger (2019) reach

this value by including various damaging aspects of

3Other, non-energy sectors are not covered by the model. There-

fore, only energy-related emissions are covered in the budget cal-

culations.

Environ. Res. Lett. 16 (2021) 104005 K Löffler

Table 1. Choice of social discount rate parameter options.

Parameter values for SocialDiscountRateranalyzed in this study

−3% −1% 0% 0.1% 1% 3% 5% 7% 9%

carbon dioxide, including air pollutants, land use

change, or noise, and using a rate of pure time pref-

erence of 1%. A variation, using a rate of time prefer-

ence of 0%, reaches a social cost value of 640€ per ton

of CO2in 2016. Also, to account for negative extern-

alities of nuclear power plants (since they would not

be included with a pure focus on CO2), an environ-

mental cost akin to von Hirschhausen (2017) has been

implemented.

To allow for a more distinct and precise use of dis-

counting in GENeSYS-MOD, multiple new paramet-

ers have been included and introduced to the math-

ematical formulation. The parameter DiscountRater

has been removed and replaced by the parameters

GeneralDiscountRater, TechnologyDiscountRater,t,

and SocialDiscountRater, where ris the model region

and tis the set of technologies. This differentiation

allows for a largely improved granularity regarding

discounting: the GeneralDiscountRaterapplies to

infrastructure investments and non-technology spe-

cific costs, the TechnologyDiscountRater,tapplies for

technology-specific costs and investments, and the

SocialDiscountRaterapplies for the costs of carbon,

or EmissionsPenalty in GENeSYS-MOD. While not

the focus of this study, the inclusion of the Techno-

logy dimension in the technology-specific discount

rate also allows for different hurdle rates for each

technology, or could be used to differentiate between

different sectors or investors (e.g. households and

industry, which might possess different discount

rates) if the user wishes to do so. All discount rates are

accounted for in the objective function of GENeSYS-

MOD, which minimizes the aggregate discounted

costs towards the base year (see equation (1) for a styl-

ized version of the updated objective function). The

detailed equations and their changes can be found in

appendix A (available online at stacks.iop.org/ERL/

16/104005/mmedia).

min z=∑CombinedGeneralCostsGeneralDiscountRater

+∑CombinedTechnologyCostsTechnologyDiscountRater,t

+∑(TotalEmissions ×EmissionsPenalty)SocialDiscountRater

Equation 1.Stylized objective function of

GENeSYS-MOD.

To properly analyze the effects of a social dis-

count rate on the outcomes of energy system models,

all other values remain unchanged (this includes the

other types of discount rates, which remain at their

Table 2. Assumptions for the different ranges of social discount

rates. o represents values close to zero, while +and – represent

positive and negative values, respectively, with the number of

symbols used highlighting their intensity.

Social dis-

count rate ρ

Pure rate of time

preference δ

Elasticity of

marginal utility and

growth factor ηg

−3% to −1% o −to −−

0% to 0.1% o o

1% to 3% o or + + or o

4% to 5% + +

7% to 9% ++ ++

base value of 5%). The chosen values for the social

discount rate comparison are shown in table 1.

The negative discount rates, −3% and −1%, rep-

resent a world where climate damages are assumed

to cause negative economic growth (Burke et al

2015, Newell et al 2021), thus leading to below-zero

discount rates (Dasgupta 2008). The rates 0% and

0.1% represent the approach to discount at a rate

of pure time preference akin to Cline et al (1992),

Stern (2007), and Weitzman (1998), placing (almost)

equal weight to future generations as to today’s pop-

ulation, while keeping the values for gand ηclose to

zero. The range from 1% to 3% represents social dis-

count rates commonly found in policies in the EU,

as well as a median range found in a survey of over

200 experts by Drupp et al (2018). Five percent is

the default value of the original version of GENeSYS-

MOD, and 7% and 9% represent a more business-

compliant discount rate, commonly found in techno-

economic models (Steinbach and Staniaszek 2015).

The fringe cases of −3% and 9% social discount

rate both cover the extremes of the imaginable range

for social discount rates. In fact, both of there rates

are quite unlikely, since e.g. climate damages lead-

ing to such high GDP losses would be avoided in a

mitigation pathway, which is a common application

for GENeSYS-MOD and other energy system mod-

els. However, both of the social discount rates fall

within the range observed in the survey by Drupp et al

(2018), promoting their theoretical use case. Since the

goal of this paper is to showcase the effects of varying

social discount rates, these extreme cases have been

selected to fully span the range of variation in pathway

results. Table 2lists the assumptions for each range of

social discount rate.

Figure 1displays the effects of the chosen discount

rates over the 35 year time-period of the GENeSYS-

MOD application.

Environ. Res. Lett. 16 (2021) 104005 K Löffler

Figure 1. Effects of chosen annual discount rates on discount factor over time.

4. Results

The results show a significant effect of both the choice

of social discount rate, as well as the carbon price

sensitivity on the model results. The inclusion of a

social discount rate of 0.1%, thus representing a very

low discount rate that assumes an intergenerationally

just rate of pure time preference, reduces cumulat-

ive emissions in 2050 by 31% compared to the base

value of 5% discounting, and even 40% compared to

the maximum 9% discount rate (see figure 2). The

European carbon budget corresponding to limiting

global warming to 2 ◦C of 51.6 GtCO2is taken from

Hainsch et al (2021). The exact calculation of the car-

bon budget is detailed in appendix B.

Interestingly enough, once the applied social

discount rate approaches intergenerational equality

(which would happen at a discount rate of 0%), the

model reduces the CO2emissions significantly, reach-

ing almost a compliance with a 2 ◦C target. The intro-

duction of negative discount rates, representing a

shrinking economy due to climate damages in the

future, lowers the cumulative emissions even more.

When considering social costs of carbon according to

Matthey and Bünger (2019), the CO2budget roughly

corresponding to limiting global warming to 2 ◦C is

actually reached for all social discount rates below

6%, showing the strong effect on emissions4. Also, the

reduction in emissions occurs sooner than in the base

case of 5% discount rate. While the choice of discount

rate mainly affects the choices that lie the furthest in

the future, the implementation of a carbon price has

a more immediate effect on decarbonization, leading

to reduced accumulated carbon emissions.

4Since the model results only encompass Europe as a region, the

results for global warming are therefore approximations assuming

similar developments in the rest of the world.

This effect also shows in the electricity mix and

the usage thereof. Figures 3and 4show the electri-

city mix in 2050 and the use of electricity per sector

in 2050, respectively. In line with previous research

(Löffler et al 2017, Hainsch et al 2018, Burandt

et al 2019), the electricity sector is the first sector

to decarbonize and thus sees no change in renew-

able share below 3% discount rate in the base case

and below 7% in the social costs of carbon sens-

itivity. This is, however, counteracted by increased

electrification in the other sectors, clearly shown in

figure 4. An increased use of electricity in other sec-

tors leads to a different electricity mix, with mostly

an increase in on- and offshore wind for lower social

discount rates. This is explained by the exhaustion of

available and commercially favorable solar potentials,

which then drive other variable renewables into the

mix.

The industrial sector, as well as hydrogen use

(shown here as renewable hydrogen generated via

electrolysis) increase significantly in electrification

when very low social discount rates are considered.

Hydrogen is especially valuable in difficult-to-

decarbonize sectors such as industry and freight

transportation, explaining the strong increase in its

usage. With the inclusion of carbon costs in line with

the actual social costs of carbon, one can observe that

the social discount rate does not change the elec-

tric sector in a meaningful way. Instead, most of it

is covered via the drastically higher CO2price. The

remaining effect of the social discount rate in combin-

ation with social costs of carbon (as seen in figure 2)

can thus be attributed to other sectors of the energy

system.

The installed capacities paint a similar picture

(shown in figure 5). In accordance with the gen-

eration of electricity, the installed capacities show

an increase in generation capacity with lower social

Environ. Res. Lett. 16 (2021) 104005 K Löffler

Figure 2. Comparison of cumulative CO2emissions from Europe over the modeled period in Mt CO2.

Figure 3. Electricity mix in Europe in the year 2050 for different choices of social discount rate, for both ETS carbon price and

social costs of carbon sensitivity.

discount rates, reaching almost double the installed

capacity in the cases below 1% compared to the base

value of 5%. While solar is steadily increasing in usage

in the ETS CO2costs sensitivity, the constantly high

electrification volumes in the social costs of carbon

model runs show a very steady capacity mix, with

solar reaching the limits of it is technical and econom-

ical potential.

For the EU ETS CO2price, the electricity

generation costs in 2050 span from roughly

31 € Megawatthour (MWh)−1for -3% social dis-

counting, to 38 € MWh−1for 9% discounting,

Environ. Res. Lett. 16 (2021) 104005 K Löffler

Figure 4. Electricity use per sector in TWh per sector for Europe in the year 2050. Displayed for each choice of social discount rate

and carbon price sensitivity. The exogenous final energy demand for electricity has been excluded from this graph, as it remains

the same across all model runs.

Figure 5. Installed capacities in the year 2050 for different choices of social discount rate and carbon price sensitivities in

GENeSYS-MOD Europe.

with 35 € MWh−1in the 5% discounting base

case. For the social costs of carbon case, the

range of electricity generation costs in 2050 spans

from 31 € MWh−1(−3% social discount rate) to

46 € MWh−1(9%), with the 5% discounting base

case reaching 31 € MWh−1. The average generation

cost of electricity is therefore very much in line with

the similarity in electricity mixes for the different

sensitivities. A full table of electricity generation costs

can be found in appendix C.

Environ. Res. Lett. 16 (2021) 104005 K Löffler

The high shares of variable renewables, espe-

cially present at lower discount rates, introduce

higher requirements for storages and other flexibility

options. The amount of electricity storage required

in the mix increases by almost 480% in the ETS CO2

price sensitivity (135 GW at 9% social discount rate

vs. 647 GW at −3% social discount rate), and by

more than 300% in the social costs of carbon case

(267 GW at 9% social discount rate vs. 831 GW at

−3% social discount rate). Especially the high share

of sector-coupling of the other sectors with the elec-

tricity sector leads to this drastic increase of electri-

city usage, mainly covered by intermittent renewable

energy sources.

5. Implications and discussion

It can be shown that by simply either including

a social discount rate that favors intergenerational

equality, or by including carbon costs that represent

actual negative externalities of carbon dioxide emis-

sions, the model runs of GENeSYS-MOD reach an

emission volume that would comply with the Paris

Agreement (UNFCCC 2015b, BP 2017), even in a

scenario featuring a conservative carbon price. Com-

bining these two measures yields even lower total

emissions, showing a further emission decrease of up

to 17% by 2050. Thus, by including social, ethical,

and environmental aspects in the scope of the optim-

ization, the model outputs drastically change, since

the model changes its valuation of investments and

damages—and thereby its recommendations. This is

in line with Emmerling et al (2019), who also determ-

ined this effect in their study, coming to the conclu-

sion that lower discount rates lead to earlier emission

reduction.

These findings heavily underline the significance

of a social discount rate when climate policy is in

focus. Since any discussion about climate is always

long-term (usually at least until 2050, regularly even

until the next century), the choice of discount rate

drastically changes the way that future damages are

evaluated (compare figure 1). It is thus paramount to

openly discuss the choice of discount rates and intern-

alization of negative external effects of carbon diox-

ide in any modeling that is aimed to inform decision

makers on future policy. The energy sector is a major

contributor to CO2emissions and thus to global

warming. Energy system models, trying to emulate

the workings of this entire sector—and often optim-

izing in the role of a social planner, should therefore

include a social discount rate to account for the dif-

ference in valuation of short- to medium-term invest-

ments and long-term damages to the environment.

That said, the choice of the applied discount

rate(s) is a challenging one, as the determination of

a properly fitting social discount rate often includes

many ethical and philosophical considerations, essen-

tially leading to an arbitrary number of possible

realizations. However, this makes an open discussion

and transparency of the choice of discount rates in the

applied models all the more important. Various dis-

count rates should at least be considered in a sensitiv-

ity analysis for any long-term energy pathway.

As only the time frame until 2050 was considered

for this study, further research should also look into

the effects until the end of the century. Since 2100 is

often a target for climate analysis, the choice of dis-

count rate is all the more relevant there. This, coupled

with an analysis of negative emission technologies,

could give additional insights on possible mitigation

pathways5.

6. Conclusion

In order to successfully avert the dangers of global cli-

mate change, an immense reduction of greenhouse

gas emissions needs to happen over the next few dec-

ades. While this fact is generally agreed upon, the dis-

tribution of the efforts involved is a point of con-

tention. While a part of the studies advocates for a

swift change towards renewable energy sources and

immediate emission reductions (Löffler et al 2017,

Ram et al 2017, Bogdanov et al 2019), others pro-

mote a slower, steady decline of fossil fuels, often

offset by negative emission technologies later in the

century, trying to remove the accrued overshoot, or

by heavy investments into nuclear energy (Paltsev

et al 2018, World Energy Council 2019, Int. Energy

Agency 2020). However, not only these technologies

themselves (von Hippel et al 2010, Hirschhausen et al

2012), but also the feasibility of such ex-post com-

pensation methods is disputed and hugely uncer-

tain (IPCC 2018). It is thus of utmost importance

for quantitative models to be properly calibrated in

order to be able to generate meaningful insights for

policy- and decision-makers. With the energy sys-

tem being a major contributor of global carbon emis-

sions, energy system models have the obligation to

take these uncertainties into account when perform-

ing their cost optimization. The results of this paper

show that the introduction and choice of a social dis-

count rate, as well as that of social costs of carbon,

have a huge impact on model results. Just the choice

of discount rate can lead to a decrease of cumulat-

ive emissions until 2050 by more than 41% compared

to the baseline of 5% discount rate. Therefore, the

issue of discounting should be more prominently dis-

cussed and evaluated in the field of energy system

analysis. Only by properly including the discussed

aspects into the application and evaluation of said

5Negative emission technologies were not considered in this study,

since their large-scale deployment and effectiveness is still unclear

(von Hippel et al 2010, Hirschhausen et al 2012). However, espe-

cially looking at longer time horizons than 2050, the inclusion of

such technologies, at least as a sensitivity, could give an outlook

for different slopes of abatement curves under varying scenario

assumptions on technology availability.

Environ. Res. Lett. 16 (2021) 104005 K Löffler

models, meaningful and robust results that properly

include socio-economic issues such as intergenera-

tional equality can be provided.

Data availability statement

The data that support the findings of this study will be

openly available on the public GENeSYS-MOD Git-

Lab repository in October 2021. The repository can be

accessed under https://git.tu-berlin.de/genesysmod/

genesys-mod-public.

Acknowledgements

The author would like to thank Anita Henschke,

Thorsten Burandt, Karlo Hainsch, Pao-Yu Oei, and

Christian von Hirschhausen for their helpful com-

ments and reviews of this manuscript.

This work has been supported by the European

Commission in the Horizon2020 project “openEN-

TRANCE” under Grant Agreement Nr. 835896.

ORCID iD

Konstantin Löffler https://orcid.org/0000-0002-

5435-1880

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