The effects of climate change on the natural rate of interest : a critical survey.
Date: 2022-11-03
Institutions: European Central Bank
Subjects: climate change, cost analysis, interest, monetary policy, price of energy, simulation

Francesco Paolo Mongelli, Wolfgang Pointner, 
Jan Willem van den End 

Working Paper Series 

The effects of climate change on 
the natural rate of interest: 
a critical survey 

No 2744 / November 2022 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract 

This survey reviews the literature about the impact of climate change on the natural rate of 

interest (r*), an important yardstick for monetary policy. Economic and financial developments 

can lower r* in scenarios with increasing climate-related damages and uncertainty that reduce 

productivity  growth  and  raise  precautionary  savings.  Instead,  in  scenarios  that  assume 

innovations  and  investments  induced  by  transition  policies,  r*  could  be  affected  positively. 

Orderly  climate  policies  have  a  pivotal  role  by  facilitating  the  transition  to  a  carbon-neutral 

economy  and  supporting  a  steady  investment  flow.  We  discuss  the  main  models  used  to 

simulate  the  effects  of  climate  change  on  r*  and  summarize  the  outcomes.  The  downward 

effects of climate change on r* can be substantial, even taking into account the high degree of 

uncertainty about the outcomes. Moreover, the downward pressure on r* will further challenge 

monetary policy in the long run, by limiting its policy space.  

JEL classification: E52, Q54 
Keywords: interest rate, monetary policy, climate change, natural rate of interest, social cost 
of carbon 

ECB Working Paper Series No 2744 / November 20221Non-technical summary 

Climate  change  increasingly  has  been  considered  an  important  factor  for  central  banks 

particularly  when  considering  monetary  policy.  Climate  change,  and  transition  policies  to 

counter it, affect the structure and dynamics of the economy and financial system, posing risks 

to both price and financial stability. The assets held on central banks’ balance sheets also face 

increased  risks  related  to  climate  change.  Moreover,  without  prejudice  to  price  stability, 

monetary policy can support other EU policies, such as environmental policies. 

In this paper, we review the literature about the impact of climate change on the natural rate 

of interest (r*). r* is a theoretical concept which serves as an important benchmark for guiding 

monetary policy decisions. It is defined as the real rate of interest which allows the economy 

to operate at its potential while simultaneously keeping inflation at its target. Estimates of r* 

show that the natural interest rate has declined continuously over the past decades, although 

a high degree of uncertainty about the level of those estimates remains. If the natural rate is 

low there is little room for manoeuvre using conventional monetary policy, i.e. changes in the 

policy rates. 

The literature identifies diverse channels at work behind the decline in r*. The most important 

channels concern shifting demographics, productivity levels, trend growth rates, risk aversion, 

fiscal  and  monetary  policy  reactions,  and  income  inequality.  The  impact  of  demographic 

changes as well as productivity trends on the natural rate have been explored extensively in 

the literature. However, so far, little attention has been paid to the possible effects of climate 

change as well as transition policies. This void can partially be explained by the different scope 

of economic models used in monetary economics versus climate economics. The central bank 

models (CBMs) usually abstract from environmental variables and their time horizon is mostly 

limited to the medium run, i.e., up to 5 years. Moreover, these models usually do not explicitly 

represent the energy sector of the economy. Instead, climate economy models (CEMs) often 

lack  a  detailed  representation  of  economic  relations,  but  they  include  important  long  run 

feedbacks  from  production  via  emissions  to  economic  damages.  We  analyse  both  types  of 

modelling approaches. 

Having identified the main channels through which climate change and transition policies could 

affect r*, we summarize some model outcomes of shocks that run through these channels in 

the euro area by a CMB and a CEM. CEMs can be seen as complements to CBMs as they 

propose an alternative perspective. Together these models provide quantitative insights about 

the effects of climate change on r*. An overarching finding in this literature is that the long-run 

ECB Working Paper Series No 2744 / November 20222 
 
 
 
 
 
effects of climate change on r* can be substantial. These effects are expected to be particularly 

strong  if    economic  trend  growth  would  drop  due  to  reduced  productivity  growth  and/or  in 

scenarios with serious climate-related physical damages.  

Uncertainty associated with adverse scenarios – as well as the transition policies to counter it 

- plays an important role in the model simulations. As a precaution, households may be more 

likely to accumulate savings and firms may postpone investments, putting additional downward 

pressure on market interest rates. On the other hand, the path of market interest rates and r* 

might depend largely on the thrust and credibility of policies governing the transition to a low-

carbon economy.  

Government  measures  are  the  most  obvious  and  effective  way  to  foster  to  transition  to  a 

climate-neutral economy. An orderly transition can mitigate the economic and financial risks of 

climate change and thereby also prevent potential downward effects on r*. In addition, active 

fiscal policy to mitigate climate change might also spur investment demand and thereby put 

upward pressure on the natural rate. This additional investment demand could offset - at least 

partially - the negative effects of climate change on r* that we found in our survey. 

Given  the  large  uncertainties  and  numerous  assumptions  needed  to simulate  the  effects  of 

climate  shocks  on  r*,  the  outcomes  must  be  interpreted  as  indicative  and  tentative,  not 

conclusive. Nonetheless, our survey shows that the possible effects of climate change on the 

natural rate of interest can be substantial and therefore should be carefully considered.  

ECB Working Paper Series No 2744 / November 20223 
 
 
 
 
  
 
 
1.  Introduction 

A  growing  body  of  literature  examines  the  effects  of  climate  change  and  the  transition  to  a 

sustainable economy on the overall economy and the financial system. Within this context, we 

focus on a narrower set of studies about the effects on interest rates, and, in particular, the 

natural rate of interest (r*). In addition to reviewing the main channels linking climate change 

and r*, we discuss the two modelling strands commonly used to analyse this link: the Central 

Bank Model (CBM) and the Climate Economy Model (CEM). 

Climate change increasingly has been considered an important factor also for central banks to 

consider, especially with regard to monetary policy (NGFS, 2021). Climate change requires 

adaptation measures to reduce its impact and transition policies to counter it. Together these 

factors affect the structure and dynamics of the economy and the financial system. Thereby it 

potentially poses risks to both price and financial stability (ECB, 2021a and Boneva el al, 2021 

and 2022). Moreover, without neglecting price stability, monetary policy can support other EU 

policies, such as environmental policies (Dikau and Volz, 2021). Climate change and transition 

policies can affect the economy and financial system through various channels. Mapping those 

channels is essential for understanding the consequences of climate change and the energy 

transition for the wider economy as well as various related financial dynamics. Understanding 

these complex dynamics is essential for central banks and governments to design effective 

policy measures. 

One  complication  is  that  r*  is  a theoretical  concept; moreover,  several  definitions  exist.  For 

example, r* might be interpreted as the real long-term interest rate where there is equilibrium 

on  capital  markets,  or  as  the  real  short-term  interest  rate  consistent  with  equilibrium  in  the 

economy. The Swedish economist Knut Wicksell was one of the originators of the concept of 

r*. According to Wicksell (1898), r* is the interest rate at which the (global) demand for and 

supply  of  capital  are  in  balance.  From  this  perspective,  r*  can  also  be  interpreted  as  the 

equilibrium interest rate that corresponds to the marginal product of capital.  

Using r* as the real short-term interest rate in the economy plays a prominent role in Central 

Bank  Models  (CBM).  According  to  Woodford  (2003),  the  natural  rate  of  interest  in  CBMs 

models is the rate at which the economy is in equilibrium while prices are fully flexible. The 

natural rate of interest r* is not necessarily constant in this equilibrium, but can fluctuate under 

the influence of several types of shocks, such as aggregated demand and productivity shocks, 

or changes in the preferences of households. Climate change and the energy transition can 

also give rise to such shocks. 

ECB Working Paper Series No 2744 / November 20224 
 
 
 
 
 
 
By  examining  the  difference  between  the  true  real  short-term  market  interest  rate  and  a 

measure of r*, or the ‘interest rate gap’, the central bank can make a judgement on its monetary 

stance, i.e. the degree to which it should ease or tighten monetary policy. If the policy rate is 

above  (below)  the  natural  rate,  monetary  policy  is  too  restrictive  (accommodative)  and  the 

central bank can alleviate excessive pressure on prices by bringing its policy rate more in line 

with r*. Thereby r* is a benchmark for assessing whether monetary policy is too tight or too 

loose (Lubik and Matthes, 2015).  

When  looking  retrospectively,  several  studies  documented  a  protracted  decline  of  real  and 

nominal  interest  rates  and  of  r*  across  most  countries,  primarily  driven  by  long-term 

demographic and economic factors, plus financial trends (e.g. Rachel and Smith, 2017, Brand, 

Goy  and  Lemke  2021,  and  Feunou  and  Fontaine,  2021).  A  low  level  of  r*  increases  the 

likelihood of hitting the effective lower bound (ELB) of the policy interest rate. This would lower 

monetary  policymakers’  room  for  manoeuvre  to  stimulate  economic  activity  when  needed. 

Under such a scenario, climate change would force central banks to rely more often on non-

standard monetary policy measures in the future, while the support from fiscal policy to reach 

the monetary policy objective becomes more important. 

Looking  ahead,  climate  change  presents  several  novel  analytical  and  policy  challenges. 

Diverse  studies  postulate  that  climate  change  might  impact  productivity  growth  and  risk 

aversion  and therefore  future  changes  in r*.  Some clues  about  this  impact  are  found  in the 

literature on natural disasters. Cantelmo (2020) simulates the impact of natural disasters on 

physical capital and productivity. Such shocks tend to reduce r* through an increase in risk 

aversion and precautionary savings. Hambel et al. (2020) show that r* decreases in a range 

of  climate  transition  scenarios,  through  the  impact  of  increasing  production  damages  and 

households  responding with  higher  precautionary  savings.  A  similar  conclusion  is  drawn  by 

Bylund and Jonsson (2020). Their model simulations indicate that weaker growth prospects, 

greater uncertainty and risk of climate change-related disasters all tend to reduce r*. These 

effects  might  become  larger  and  non-linear,  in  extreme  climate  change  scenarios.  Under 

certain conditions the impact on r* could be substantial.  

Despite the fact that  climate change is associated with predictions of a decline of r*, research 

by Benmir et al. (2020) argues that transition policies could have an upward effect on r*. This 

reversal could at least partially be achieved by a policy introducing an optimal carbon tax that 

enhances  welfare,  reduces  uncertainty,  and  lowers  risk  premia.  Such  a  policy  would  again 

raise  the  average  risk-free  real  interest  rate  by  lowering  precautionary  savings.  New 

innovations, such as productivity enhancing new emission abatement technology, might also 

ECB Working Paper Series No 2744 / November 20225 
 
 
 
 
 
prop  up  r*  by  increasing  productivity  growth  and  triggering  new  investments. Moreover, the 

replacement  of  capital  stock  destroyed  by  natural  disasters  might  give  rise  to  a  higher  real 

interest rate in equilibrium (Keen and Pakko, 2011). Pisani-Ferry (2021) underlines that green 

innovations and investments may improve potential output in the long run but divert resources 

from expenditures that drive economic growth in the short-run. Moreover, if the transition policy 

is  not  credible,  Benmir  et  al.  (2020)  find  that  climate  risk  reduces  r*  because  households 

become more risk averse when firms fail to internalize the damage caused by their emissions. 

Our survey contributes to this expanding literature by presenting an extended literature review 

of the main channels through which climate change can affect r*. Having identified the main 

channels through which climate change and transition policies could affect r*, we summarize 

the model outcomes of shocks that run through these channels in the euro area by a central 

bank model (CBM) and a climate economy model (CEM). We position CEMs as complements 

to CBMs: i.e., they provide an alternative perspective which gives some quantitative insights 

about the effects of climate change on r*. We summarize some main findings in the literature 

about the effects of climate change and transition policies on r*. One overarching finding is that 

the long-run effects of climate change on r* can be substantial, particularly if economic trend 

growth would drop due to reduced productivity growth and/or in scenarios with serious climate-

related  physical  damages.  The  effect  of  higher  risk  aversion  depends  strongly  on  habit 

consumption. Although in some simulations r* is affected positively - particularly in scenarios 

with productivity enhancing innovations -  most simulations indicate that the effects of climate 

change would reduce r*. 

The  rest  of  this  paper  is  structured  as  follows.  Section  2  compares  two  strands  of  models 

(CBMs  versus  CEMs)  with  respect  to  their  treatment  of  r*.  Section  3  discusses  channels 

through  which  climate  change  can  affect  r*.  Section  4  presents  model  simulations  of  the 

potential impact of climate change on r*. Section 5 discusses the main findings and provides 

some concluding remarks. 

2.  Modelling approaches in the literature 

In this section, we discuss the main models used in the literature to simulate the wider effects 

of climate change and transition policy, including those on interest rates. In particular we show 

how the Central Bank Model (CBM) differs from the Climate Economy Model (CEM) in the way 

it considers the possible impact of climate change on r*. We identify the main differences by 

stylized graphical overviews of the channels for this relationship.  

ECB Working Paper Series No 2744 / November 20226 
 
 
 
 
 
 
 
The New Keynesian framework - on which the CBM is based - defines r* as the equilibrium 

real  interest  rate  that  would  prevail  in  an  economy  without  nominal  rigidities  in  wages  and 

prices. A helpful starting point is the formulation of the natural rate according to the neoclassical 

growth model (Ramsey, 1928), 

𝑟∗ = 𝜌 + 𝛾 𝑔 + 𝑛  

(1) 

where  g  is  the  growth  rate  of  labour-augmenting  technological  change  (or  TFP  growth), 

reflecting  the  expected  income  growth  of  economic  agents.  Parameter  n  is  the  population 

growth  rate  and  𝛾  the  consumption  elasticity  of  marginal  utility,  describing  how  fast  utility 

changes  in  consumption.  Parameter 𝛾  also  denotes  the  smoothing  preference  of  economic 

agents,  related  to  their  aversion  to  income  and  consumption  shocks.  If  agents  have  a  high 

smoothing  preference,  they  will  adjust  their  debts  or  savings  to  smooth  consumption  in 

response  to  changes  in  g.  This  can  be  achieved  through  financial  transactions,  which 

determine  the  responsiveness  of  the  interest  rate  to  changes  in  g.  The  inverse  of  𝛾  is  the 

intertemporal elasticity of substitution in consumption, or the willingness of economic agents 

to shift consumption across time. Parameter ρ is the pure rate of time preference. It reflects 

the patience of economic agents to consume. The more patient agents are, the more they save 

and the lower becomes r*. Both CBMs and CEMs use the Ramsey equation for defining the 

long-term real interest rate. 

2.1 Central bank models (CBM) 

The natural rate of interest is a determining factor in CBMs. A CBM for r* is the semi-structural 

model of Laubach and Williams (LW, 2003; see Holston, Laubach and Williams (HLW, 2017) 

for  a  novel  specification  and  readily  available  quarterly  estimates).  It  assumes  that  r*  is  a 

function of two random walk processes, the trend growth rate of potential output, gt, and a time-

varying unobserved component, zt, 

∗ = 𝑔(cid:3047) + 𝑧(cid:3047)  
𝑟(cid:3047)

(2) 

With variable g the expected growth rate of potential output, of which TFP growth is a main 

driver. Thereby  it  reflects  expected  income  growth  of  economic  agents,  like  in  the  Ramsey 

model. Variable zt captures the effects of the other factors in the Ramsey model: i.e., the time 

preference and substitution (or smoothing) parameters. Hence variable zt includes behavioural 

factors  that  determine  borrowing  and  saving  by  economic  agents.  Through  these  financial 

ECB Working Paper Series No 2744 / November 20227 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
channels  the  changes  in  agents’  preferences  can  influence  r*.  Figure  1  presents  these 

relationships in a stylized framework of a CBM. 

Figure 1. Diagram of central bank model (CBM) 

An aspect that has become all too relevant since the global financial crisis of 2008 (GFC) is 

that  the  lower  r*,  the  tighter  the  policy  space  for  central  banks  to  provide  monetary 

accommodation  and the  higher the  risks  of  hitting  the  ELB.1  So r*  directly  affects monetary 

policy (arrow at the top of Figure 1, pointing from r* to i). Besides, monetary policy can have 

an impact on r* as well (left-hand side of Figure 1). This refers to the effect of monetary policy 

on productivity growth, via hysteresis effects and the influence on the allocation of capital. In 

Section 3.7 we explain how such channels make r* endogenous on monetary policy. 

Since r* is not directly observable, central banks rely on several types of model estimates of 

r*.2 One of the most widely used is the LW (2003) model. It is based on a semi-structural model 

which filters r* out of the (short-term) market interest rate and potential output estimates. The 

research by LW (since extended by HLW (2017)) shows that estimates of the natural rate of 

interest  are  highly  inaccurate  and  can  vary  widely  depending  on  the  model  specification. 

Another strand of CBMs are time series models, which proxy r* as the long-term trend in real 

interest rates. This trend is filtered out of the data, as in Johanssen and Mertens (2018). Other 

CBMs are general equilibrium models, in which r* is the interest rate at which the economy is 

in equilibrium while prices are fully flexible (e.g. Del Negro et al., 2017). The natural rate of 

interest  is  not  necessarily  constant  in  this  equilibrium  but  can  fluctuate  due  to  shocks  in 

aggregate demand and productivity or due to changes in preferences. Instead, shocks to the 

risk  premia  are  usually  assumed  to  be  exogenous,  which  limits  the  explanatory  power  of 

general equilibrium models for r*. 

1 This adds to the uncertain impact of climate change on output gap measures: since productivity and 
inflation trends may change, cyclical deviations become more uncertain. 
2 (Borio, 2021) even raises doubts on the usefulness of r* measure to guide monetary policy, 
“…depending on how it is employed, the concept has the potential of leading policy astray…”.  

ECB Working Paper Series No 2744 / November 20228 
 
 
 
 
 
 
 
 
Like  climate  change,  the  decline  of  r*  is  a  trend-based,  long-term,  and  global  phenomenon 

(Figure 2). Brainard (2019) suggests a (future) link between both factors. As an illustration, the 

figure below shows a global proxy for r* as modelled by LW, together with an indicator of global 

warming. 

Figure 2. Global warming and r* 

Note:  Temperature  on  earth  (smoothed  range,  deviates  from  average 
temperature  in  1951-1980  (source:  NASA).  Natural  rate  of  interest  of  US, 
UK, EA, Canada (unweighted average, Laubach & Williams) 

Another limitation is that CBMs are backward looking and do not consider the adverse impacts 

stemming from climate change (see for instance Brand et al., 2018, and Brand et al., 2021): 

however, both g and z could potentially be impacted by it. In part, this has to do with a horizon 

difference in CBMs and CEMs. The “long-term” for central banks is shorter than the “long-term” 

for climate scientists (who usually calculate in time units of five years). Moreover, CBMs have 

limitations that make them inappropriate for simulating the macro-financial effects of climate 

risks  in  a  comprehensive  manner,  in  particular the  absence  or  limited  representation  of  the 

energy sector. Recently, central banks and financial institutions have started to develop tools 

to  better  understand  the  macroeconomic  effects of  climate  change  (see ECB,  2021b  for  an 

overview). 

2.2 Climate economy models (CEM) 

CEMs, such as the leading Dynamic Integrated Climate Economy (DICE) model by Nordhaus 

(20133), consider the effects of climate damage on the discount rate, a proxy for r*. The obvious 

channel for this relationship runs via potential economic growth g, as visualized in Figure 3. In 

3  Dynamic  Integrated  model  of  Climate  and  the  Economy  (DICE)  is  a  neoclassical  and  deterministic 
growth model that also includes equations from climatology. 

ECB Working Paper Series No 2744 / November 20229 
 
 
 
 
 
 
 
 
CEMs, climate change has a negative impact  on g through physical damages in a damage 

function which negatively affects potential output (bottom arrow in Figure 3). The costs of the 

transition (abatement) to a carbon neutral economy also lower potential output, by reducing 

disposable income at least in the early stages and so reducing per capital consumption growth 

(left-hand side of Figure 3). Moreover, damage and transition costs lower the return on capital, 

which  causes  a  decline  in  investment  demand  and  reduces  output.  Because  temperatures 

increase gradually, the dampening effect on per capita consumption growth is also gradual. 

Figure 3. Diagram of climate-economy model (CEM) 

Feedback effects play an important role in the standard CEM (lhs of Figure 3). Higher output 

produces more greenhouse gas emissions, which feeds back negatively on the economy. This 

effect runs through the impact of rising cumulated emissions on temperatures, which in turn is 

the main input of the damage function. Over time, this effect decreases during the transition to 

a  carbon  neutral  economy.  It  indicates  the  intertemporal  trade-off  between  the  costs  of 

improved carbon pricing in the short run and limiting physical damage in the longer run. 

Parameter  ρ  (pure  rate  of  time  preference)  also  plays  an  important  role  in  CEMs.  In  an 

intergenerational set-up it reflects how agents assess the value of current consumption relative 

to the consumption of future generations. In CEMs, parameter ρ is used as the discount rate 

that determines the Social Cost of Carbon (SCC), i.e. the current discounted value of future 

damages (in terms of changes in wealth) of emitting one ton of carbon today, scaled by the 

marginal welfare effect of an extra unit of consumption. The lower ρ, the higher the SCC and 

in that sense ρ is also called an “ethical discount rate”. Bauer and Rudebusch (2020) find that 

the decline of the discount rate has substantially boosted the estimated economic loss from 

climate change and the SCC. Stern (2007) argues normatively that future damages should not 

be discounted at all (i.e. ρ = 0). We will elaborate on this in more detail in Section 5.  

ECB Working Paper Series No 2744 / November 202210 
 
 
 
 
 
 
 
Critics  of  current  climate  economy models  include  Weitzman (2009),  who  emphasizes their 

fundamental uncertainty, and Bolton et al. (2020), who point at unpredictable interactions and 

non-linear effects that could trigger tipping points and following from that, damages might be 

severely underestimated. Such factors are usually not considered in climate-economy models, 

which tend to be deterministic. Most IAM/DICE models do not take into account economic and 

climate  risk  and  instead  focus  on  parameter  uncertainty.  In  reality,  uncertainty  about  future 

economic  and  climate  conditions  substantially  affects  the  choice  of  policies  for  managing 

interactions  between  the  climate  and  the  economy.  Cai  and  Lontzek  (2019)  develop  a 

framework of dynamic stochastic integration of climate and economy (DSICE), showing that 

the social cost of carbon is substantially affected by both economic and climate risks and is a 

stochastic  process  with  significant  variation.  Therefore,  the  uncertainties  and  possible 

inaccuracies  in  modelling  future  climate  damage,  as  well  as  the  simplification  of  the 

representation of the climate system and the economy, call for caution about interpreting the 

outcomes of CEMs. 

3.  Literature review of main channels 

Besides  the  above  factors  influencing  r*  in  the  Ramsey  model,  the  empirical  literature  has 

identified  several  other  factors  of  interest.  The  main  drivers  of  the  decline  in  r*  include 

demographic  factors,  declining  trends  in  productivity  related  to  secular  stagnation  (see 

amongst others, Rachel and Summers (2019), Brand et al. (2018), Rachel and Smith (2017) 

and Auclert and Rognlie (2018)), and rising income and wealth inequality.4 These factors tend 

to  increase  savings  and  reduce  investments  thus  exherting  a  downward  effect  on  r*.  More 

recent research casts a new light especially on demographics and inequality. We found some 

additional contributions regarding the impacts of risk aversion -- working through the impact of 

preferences on the elasticity of intertemporal substitution -- and saving preferences, as well as 

fiscal policy.  

3.1 Demographic trends 

Most advanced economies are experiencing a demographic transition, reflecting low fertility 

rates, rising life expectancy, and changing composition of age cohorts. Brand et al. (2018) find 

that the net effects of these trends has reduced real interest rates in the euro area by around 

1  percentage  point  since  the  1980s. When  extrapolating  demographic trends,  it  is foreseen 

that real interest rates can be expected to fall by a further 0.25-0.5 percentage points by 2030.  

4 While these papers are by necessity backward looking, some extend their predictions. This section 
links these additional factors affecting r* also to climate change. 

ECB Working Paper Series No 2744 / November 202211 
 
 
 
 
 
 
 
For  the  US,  estimates  of  the  effect  of  demographics  on  interest  rates  over  the  1970–2015 

period range from a decline of less than 100 basis points (Gagnon et al. 2021) to a more severe 

decline exceeding 300 basis points (Eggertsson et al., 2019). Hence, there is substantial range 

estimating    the  magnitude  of  the  demographic  driver.  On  one  side,  Auclert  et  al.  (2021) 

foresees  that  ageing  will  be  accompanied  by  higher  wealth-to-GDP  ratios  and  capital 

deepening, lower asset returns, putting further downward pressure on real interest rates and 

widen  global  imbalances.  Such  effects  will  be  large  and  heterogeneous  across  countries. 

Hence, according to Auclert et al. (2021) demographics will continue contributing to the trend 

decline in r* (a prediction, also shared by Gagnon et al., 2020). 

On  the  other  hand,  some  studies  predict  that  future  demographic  developments  might 

contribute to lowering saving rates while lifting interest rates. One is the hypothesis of a “great 

demographic reversal” by Goodhart and Pradhan (2020). A worsening dependency ratio, with 

a  sharply  increasing  proportion  of  elderly  and retired  people  will  lower  savings  ratios  of  the 

whole economy and hence bring about rising interest rates. This follows from the assumption 

of  age-specific  savings  rates  that  decline  in  old  ages  as  retirement  thresholds  are  crossed 

(Summers  and  Carroll  1987,  Auerbach  and  Kotlikoff  1990,  Bosworth  et  al.  1991).  Thus, 

demographics and an increasing number of elderly pushes down aggregate savings, which in 

turn lifts interest rates.  

There is a growing literature on the effects of climate change on migration which was surveyed 

by Cattenao et al. (2019). They find that climate change and its negative impact on income 

variability does not automatically lead to more migration, but rather climate change often acts 

as an effective constraint to migration. Acute manifestations of physical risks like hurricanes or 

floods tend to result in temporary displacements and the affected households and firms would 

often return to their original venues. Chronic physical risks like droughts or rising sea levels 

instead cause more permanent migratory outflows, often because original locations cannot be 

rebuilt.  However,  rising  temperatures  can  also  reduce  international  migration  from  poorer 

countries, since liquidity constraints could become more binding. While Cattenao et al. (2019) 

conclude that alarmist projections of future migration levels triggered by climate change are 

unrealistic, other scenarios of large climate-related labour supply shocks which could drive r* 

are also possible.  

ECB  (2021b)  presents  some  arguments  about  how  climate  change  could  trigger  migration 

flows and thereby affect labour supply. In the case of an outflow, an adverse impact on labour 

supply leads to a higher capital / labour ratio which reduces the marginal product of capital in 

the steady state and thus r*. The impact of climate change could differ across countries, as 

ECB Working Paper Series No 2744 / November 202212 
 
 
 
 
 
labour  supply  might  rise  in  some  countries  due  to  immigration  flows.  These  effects  are 

uncertain at best and could manifest themselves over very long horizons. Summing up, the net 

impact of climate change on demographic trends is thus prima facie ambiguous: while it might 

discourage labour supply, thus exacerbating the downward effect on r*, it could also have an 

upward  effect  on  r*  by  reversing  the  current  trends  towards  higher  life  expectancy  and 

changing the age composition of the population in favour of younger cohorts.5 

3.2 Changes in productivity 

Rising  temperatures  might  impact  labour  productivity  (Tol,  2009).  “Comfort”  temperature  for 

humans lies between 18-22 degrees Celsius. Extreme temperatures above or below this level 

can  have  important  effects  on  mortality,  health  and,  in  turn,  labour  supply  and  productivity 

(Seppanen et al., 2006). Existing evidence finds that productivity levels can decrease in the 

short-term by  2%  per  degree  above  comfort  temperature  (Heal  and  Park,  2016).  Day  et  al. 

(2019) provide a survey of studies that have analysed the impact of global warming on labour 

productivity.  All  these  papers  find  substantial  reductions  in  productivity  for  temperature 

increases above certain thresholds.6 They also present a range of possible adaptation policies 

to address the productivity slowdown and weaken its impact. Sectoral shocks may also vary 

in intensity, e.g., agricultural productivity might be affected by climate change via changes in 

crop yields. 

With  regard  to  physical  risks,  the  increasing  frequency  of  extreme  weather  events  (acute 

physical  risks)  and  deterioration  of  the  environment  (chronic  physical  risks)  will  affect  the 

capital stock directly, if productive assets are damaged, or become useless, more often than 

under current climate conditions. This is comparable to an accelerated obsolescence rate of 

capital.  It  leads  to  a  shift  in  resources  from  innovative  activities  towards  reconstruction, 

adaptation and prevention of further damages as described by Batten et al. (2020) and Kahn 

et  al.  (2019).7  Dietz  and  Stern  (2015)  suggest  that  damages  to  the  capital  stock  will  also 

diminish knowledge production and knowledge spillovers hence lowering the long-term growth 

rate of the economy. Ojeda-Joya (2022) simulates the economic effects of global warming with 

a state-space semi-structural model where lower productivity growth also dampens the natural 

interest rate. 

5  In  addition,  it  cannot  be  ruled  out  that  migration  policies  will  be  adopted  to  dampen  the  impact  on 
demographics. 
6 Presumably in the absence of adequate insulations, heaters or cooling, shading, air-conditioning and 
other measures.  
7 Keen and Pakko (2011) argue that the need to replace capital stock destroyed by natural disasters 
should raise the real interest rate in equilibrium: i.e., they assume that real returns on investments in 
new technology may increase after a disaster. 

ECB Working Paper Series No 2744 / November 202213 
 
 
 
 
 
 
On the other hand, in the Ramsey model, higher productivity growth (parameter g in eq. (1)) 

increases households expected future income, reducing their need to save in order to sustain 

a  chosen  future  consumption  path.  Lower  savings  then  translates  into  a  higher  marginal 

product of capital and thus higher r*. Firms might be more willing to invest when the marginal 

product of capital increases. The consequent higher demand for investments could drive up 

r*, too. Climate change can also affect technology in several contrasting ways. It is important 

to distinguish the effects of physical risks (chronic and acute) from those of transition risks. 

With unabated climate change (e.g., in a disorderly, delayed transition scenario as presented 

in NGFS, 2021), transition risks are also increasing over time, because the demand for policies 

to  mitigate  climate  change  might  rise  or  consumers  could  eventually  decline  to  buy  carbon 

intensive goods and turn erstwhile productive investments into stranded assets. Hence, both 

physical  and  transition  risks  can  lead  to  increasing  rates  of  capital  depreciation,  thereby 

reducing the capital stock and its productivity. Over time, there might be higher R&D and rapid 

innovation (the “Porter Hypothesis”).8  

Climate  policies  will  change  the  relative  price  of  energy  inputs,  which  will  affect  the  type  of 

technologies that are developed (Acemoglu et al., 2012). Pisani-Ferry (2021) also points at an 

intertemporal  trade-off,  by  underlining  that  green innovations  and  investments  may  improve 

potential  output  in  the  long  run  but  divert  resources  from  expenditures  that  drive  economic 

growth  in  the  short-run.  While  the  transition  requires  more  investment  in  climate-neutral 

innovations, these innovations might not automatically raise productivity.9  

Instead, for additional investment to have an upward effect on productivity growth and hence 

on r*, the marginal product of capital should be higher than the marginal cost of capital. In fact, 

productivity gains of technological innovations are inherently uncertain, because they have not 

been tested and proven before. The transition towards a climate-neutral economy will require 

innovations  in  energy  generation,  in  mobility  and  in  other  core  processes  of  the  production 

sector.  In  the  gradual  process  of  establishing  new  standards  of  de-carbonized  production, 

some initial investments in innovative technologies will be lost, as competing alternatives will 

prevail, but the clearer the new standards will emerge, the more investors’uncertainty will fall.  

8 Whereas some economists postulate that stricter environmental regulations would impose additional 
costs  to  firms  and  lower  production  and  innovation,  the  so  called  “Porter  hypothesis”  (Porter,  1991) 
conjectures that environmental regulations might instead enhance competitiveness, thereby augmenting 
productivity.  Porter  asserts  that  negative  externalities  lead  to  misallocation  of  resources  therefore 
regulations  that  help  internalize  these  externalities  should  be  welfare  enhancing.  In  this  sense, 
regulation could be a driver of innovation and productivity. Empirical studies find support for an increase 
in innovative activities (more patents), but no positive impact on productivity (Calel et al., 2016). 
9 Their aim is to allow the production of goods with less carbon emissions, not with fewer inputs. 

ECB Working Paper Series No 2744 / November 202214 
 
 
 
 
 
 
Some  firms  might  not  be  able  to  afford  the  I  investments  for  new  technologies  and  will  be 

forced  out  of  the  market.  This  could  affect  productivity  either  way:  negatively  as  it  might 

diminish the productive capital stock in the economy and thereby lower the ratio of output to 

invested  capital  or  positively  as  those  firms  which  are  able  to  succeed  might  be  more 

productive than their less fortunate competitors. OECD (2006) also emphasizes the possibility 

that  climate-related  regulations  might  act  as  entry  barriers  in  certain  markets  and  thereby 

reduce firm dynamics.  

Summing  up,  the  effect  of  climate  change  on  total  factor  productivity,  and  hence  r*,  is 

ambiguous.  Physical  risks  will  accelerate  the  write-down  of  capital  stock  and  lead  to  less 

productive investments in abatement efforts to reduce damages. In addition, the increase in 

global  temperatures  will  likely  have  a  negative  impact  on  labour  productivity.  Adaptation 

policies  to  soften  these  effects  are  feasible,  they  will  likely  trigger  additional  investment 

demand, thereby raising r*. Generally, R&D and innovations may be fostered as part of the 

energy transition, also creating demand for investment and driving up the interest rate. Climate 

policy  will  be  a  main  determinant  of  these  trends.  Nevertheless,  the  transition  is  likely  to 

increase the uncertainty about productivity trends. 

3.3 Trend growth 

The  literature  suggests  a  close  relationship  between  climate  change  and  GDP  losses,  as 

higher  temperatures  adversely  affect  several  channels  of  the  economy  including  labor 

productivity (Day et al., 2019), agricultural yields (Schlenker and Roberts, 2009), and industrial 

output (Cashin, et al., 2014). Empirical studies provide rough estimations for the total potential 

impact of climate change scenarios on the trend growth rate g. A literature review by Howard 

and  Sterner (2017) shows  that the  economic  damage  of  0.7  - 4.3°C temperature  increases 

until 2100 - compared to pre-industrial levels - can range from +0.1 to -23% of GDP. This worst-

case impact is comparable to the -25% cumulative GDP impact estimated by the NGFS (2021). 

In the same NGFS scenarios, the cumulative impact of transition risk until the year 2100 ranges 

from a positive impact (net zero 2050 scenario) to around minus 2-3% GDP (delayed transition 

scenario). Alestra et al. (2020) simulate the impact of GDP damage due to climate change and 

the  GDP  impact  of  mitigating  measures  for  30  countries,  by  taking  a  long-term  supply-side 

view. They find that at the 2100 horizon, the global GDP loss ranges from 7 - 12%, depending 

on the scenario. 

ECB Working Paper Series No 2744 / November 202215 
 
 
 
 
 
These estimate ranges of damage and transition risk combined translate into an (annual) trend 

growth  effect  (g)  ranging  from  0  to  -0.45%.10  The  maximum  estimated  annual  effect  on  g 

(estimated  at  -0.45%),  may  remain  within  the  current  uncertainty  bands  of  natural  rate 

estimations for the euro area (Brand et al., 2019). However, the non-linear relation between 

climate change and the economy implies that the uncertainties about r* will compound over 

time. The uncertainty is right-skewed, with negative outcomes being more likely than positive 

outcomes (Tol, 2018). Tipping points, which are associated with temperature increases beyond 

the  3-4°C  range,  can  exacerbate  the  economic  impact  exponentially  (Howard  and  Sterner, 

2017). 

3.4 Risk aversion and savings 

Another  channel  through  which  climate  change  affects  the  natural  rate  is  an  increased  risk 

aversion  working  through  the  impact  of  preferences  on  the  elasticity  of  intertemporal 

substitution. In the Ramsey model, risk aversion is captured by the smoothing preference of 

economic agents (parameter 𝛾) which determines the sensitivity of r* to g. Hence risk aversion 

is  akin  to  the  aversion  to  income  and  consumption  shocks.  If  climate  shocks  affect  future 

income prospects (through variable g), agents that want to smooth consumption across time 

will  save  more  today.  Higher  savings  will  reduce  r*  through  an  increase  of  the  supply  of 

savings.  Climate  change  may  also  raise  uncertainties  and  lead  to  an  increase  of  the  risk 

premium. Such finance-related impacts are captured in parameter z in eq. (2) of the Laubach 

and Williams model for r*.  

Climate change, adaptation to it, and the transition to a carbon neutral economy may go in 

tandem  with  higher  economic  tail  risk  (Batten  et  al.,  2020).  Such  risks  are  associated  with 

fundamental uncertainty, as it depends on physical, social, and economic systems that involve 

complex  interactions,  non-linear  dynamics,  and  chain  reactions  (Bolton  et  al.,  2020).  Both 

physical and transition risks are inherently fat-tailed events, not reflected in past data, with a 

potentially unlimited downside exposure (Weitzman, 2009). Hence, climate change will be a 

source  of more frequent,  intense,  and  persistent  shocks  in  the  economy  that  are  difficult to 

disentangle. The related fundamental uncertainty will lead to a reduced willingness to invest 

and a greater propensity to save. These financial channels, captured by variable z in eq. (2), 

can lower r*. 

Time-varying risk aversion has been documented by several studies. The major drivers behind 

these  variations  in  the risk  attitude  of  economic agents  are  changes  in the  macroeconomic 

10  For  example,  a  23%  decline  of  GDP  volume  in  2100  (85  years  from  2015,  at  which  we  date  the 
research by approximation) equals an annual growth rate of -0.3%. 

ECB Working Paper Series No 2744 / November 202216 
 
 
 
 
 
 
environment  and  not  necessarily  idiosyncratic  negative  shocks  experienced  by  individuals. 

Dohmen et al. (2016) and Guiso et al. (2018) present evidence for increasing risk aversion in 

the  aftermath  of  the  2008  global  financial  crisis.  Cameron  and  Shah  (2015)  provide 

experimental evidence from Indonesia that individuals who have experienced natural disasters 

exhibit  a  higher  degree  of  risk  aversion  because  they  adapt  their  expectations  about  the 

occurrence  probability  of  such  events.  This  perceived  increase  in  further  natural  disasters  

reduces their willingness to accept additional separate risks. Sakha (2019) reports the results 

from repeated risk experiments in Thailand, where negative agricultural shocks like droughts 

or floods increased the risk aversion of households. She also finds that a negative assessment 

of their well-being increases individuals’ risk aversion.  

Another  contributing  factor  could  be  higher  demand  for  safe  assets,  reinforced  by  climate 

change related uncertainty. Analysing the impact of rare disasters (i.e., wars in the 20th century) 

on asset markets, Barro (2006) finds that as more frequent disasters magnify uncertainty about 

the future, households demand more and safer assets. For instance, an increase of uncertainty 

about asset valuations in the transition to a carbon neutral economy may increase the demand 

for safe assets. As a result, the premium that market participants are willing to pay for holding 

safe  assets  is  likely  to  rise,  putting  downward  pressure  on  r*.  This  refers  to  the  safe  asset 

channel, which asserts that safe assets hold a convenience yield, or scarcity premium, that 

lowers the bond yield (see e.g. Del Negro et al., 2017; Caballero and Fahri, 2019).  

An  important  consideration  is  that  climate  change  might  have  asymmetric  effects  on  risk 

aversion. Physical risks might affect risk aversion of agents in vulnerable regions and sectors, 

whereas  transition  risk  can  affect  risk  aversion  across  a  wide  spectrum,  depending  on  the 

scenario  of  policy  adjustments.  A  scenario  of  an  erratic  and  uncertain  adjustment  path  will 

likely  elevate  risk  aversion  and  market  volatility  more  than  a  clear  and  well-communicated 

climate policy path. Decarbonization policies could also lead to (temporary) disruptions in the 

supply  of  energy  and  commodities;  for  instance,  if  mining  of  certain  commodities  would  be 

curtailed by stricter regulation. This could lead to increased volatility of commodity prices and 

raise uncertainty about investment returns and the decarbonization path. 

Research shows that investors translate climate related uncertainty into a higher risk premium. 

Based on a combined asset pricing, endogenous growth model Hambel et al. (2020) find that 

the risk-free interest rate decreases due to additional precautionary savings needed to cope 

with global warming. Fernando et al. (2021) associate climate shocks with higher equity risk 

premia, which according to their simulations could lead to a loss in economic activity each year 

of  up  to  2%  of  GDP  in  the  worst-case  scenario.  Bolton  and  Kacperczyk  (2020)  find  that 

ECB Working Paper Series No 2744 / November 202217 
 
 
 
 
 
investors  in  US  stock  markets  also  charge  a  premium  for  the  risk  of  holding  the  stocks  of 

disproportionately high carbon emitters. Cevik and Jalles (2020) find that climate change also 

affects sovereign bond yields. Countries that are more resilient to climate change benefit from 

lower bond yields and credit spreads relative to countries with greater vulnerability to climate 

change.  This  suggests  that  climate  change  can  increase  the  divergence  between  safe  and 

non-safe sovereign bonds. Battiston and Monasterolo (2020) investigate the impact of climate 

policy scenarios on the valuation of corporate and sovereign bonds, considering endogeneity 

and deep uncertainty. They find that climate policy scenarios can have a substantial impact on 

bond yields, which would imply that also r* would be affected. 

Summing up, the qualitative and quantitative impact on r* through financial channels is hard to 

quantify.  Empirical  research  of  the  impact  of  climate  change  on  bond  yields  is  scarce. 

Nonetheless, the limited available research indicates that financial risks of climate change can 

put further downward pressure on r* through higher premiums. Given that financial markets 

are  forward  looking,  this  effect  through  z  likely  has  a  more  immediate  impact  on  r*  than 

changing preferences of economic agents related to changing income prospects (reflected in 

parameter g).  

3.5 Fiscal policy 

Governments have the primary responsibility and the appropriate tools to spur the transition to 

a carbon-neutral economy and to counter the economic consequences of climate change. If 

the negative externalities of climate change are insufficiently priced, this market failure can be 

addressed by taxation and regulatory measures by governments. Climate change will also lead 

to higher government spending due to the costs of physical damages stemming from extreme 

climate events and adaptation costs and mitigation investments. Higher public spending could 

also be related to social security expenditure to cover health, emergency housing, relief efforts 

and other costs stemming from natural disasters. The commensurate increase of fiscal deficits 

will likely lead to an increase of government debt and the associated higher demand for savings 

will exert an upward pressure on r*.  

The  impact  on  fiscal  sustainability  will  depend  on  the  timely  adoption  of  concerted  climate 

policies,  thus  on  the  thrust  of  transition  scenario.  An  orderly  sustainable  transition  could 

support TFP growth by utilizing  adequate carbon pricing (through a mix of carbon taxes and 

tariffs, ETS and carbon permits) with the proceeds being reallocated to support, for example, 

sustainable infrastructures and innovative projects and R&D. More transition measures would 

even contribute to higher TFP growth and so have an upward effect on r*. For instance, in the 

NGFS (2021) simulations the carbon price increase in the transition scenario has an offsetting 

ECB Working Paper Series No 2744 / November 202218 
 
 
 
 
 
growth effect owing to carbon revenue recycling. Hence, the quality and composition of fiscal 

policies in the context of climate policies matters.  

Through the safe asset channel, a higher supply of sovereign bonds, related to higher public 

debt  levels,  would  increase  r*. A  higher  bond  supply  reduces  the  scarcity  premium,  which 

keeps r* low in a situation with excess savings and a shortage of safe assets in which to invest. 

In such case, the safe asset channel may also have an upward effect on r*. 

3.6 Income inequality  

Rising  income  inequality  can  lower  r*  through  reduced  consumption  and  increased  desired 

savings  (Rachel  and  Smith,  2017; Auclert  and  Rognlie,  2016).  Climate  change  is  likely  to 

increase income inequality and so reduce r* through these channels. New evidence suggests 

that rising income inequality might be a more important factor in explaining the decline in r* 

than demographics. Based on micro data, Mian et al. (2021) weigh the relative importance of 

demographic shifts versus rising income inequality on saving behaviour in the US from 1950 

to 2019.11 These findings challenge the view that the ageing of the baby boomers explains the 

decline in r* and might taper off as baby-boomers retire. Garbinti et al. (2018) report similar 

inequality trends for France for the after-war period. Albers et al. (2022) analyse the wealth 

distribution in Germany where, despite a tax policy regime aimed at reducing inequality, the 

top 10% of households increased their share of national wealth significantly over the past 25 

years. In an attempt to quantify the contributions of different channels that lower r* Platzer and 

Peruffo  (2022)  use  a  heterogenous  agents  OLG  model  with  non-homothetic  preferences 

tcontrolling for changes in intra- and intergenerational inequality. They find that the effect of 

rising  inequality  had  dampened  the  natural  rate  as  much  as  lower  productivity  and 

demographic changes.  

The  literature  that  identifies  inequality  as  a  driver of r*  does  not  yet  include  climate  change 

related risks in the analyses. However, the impact could be substantial, disproportionately for 

poorer  countries  and  communities  more  affected  by  acute  and  chronic  effects,  thus 

exacerbating their economic vulnerabilities and increasing global income inequality. Climate 

change  will  also  exacerbate  inequality:  the  impact  of  high  temperatures  is  non-linear;  poor 

countries specialised in sectors affected by climate change have less capacity to adapt (IMF, 

2017). Some studies show that such impact could have permanent adverse economic growth 

effects  (e.g.  Dell  et  al.,2012).  Other  research  shows  that  countries  with  higher  income 

11 They find that saving rates are significantly higher for high income households within a birth cohort 
relative to middle and low income households in the same cohort. Plus, in each cohort there has been 
a large rise in income shares for high income households since the 1980s. 

ECB Working Paper Series No 2744 / November 202219 
 
 
 
 
 
 
inequality tend to have higher levels of per capita GHG emissions (Islam and Winkel, 2017). 

This hints at a feedback effect from inequality to climate change. 

3.7 Endogenous monetary policy 

New  Keynesian  models  commonly  assume  that  monetary  policy  does  not  have  long-term 

effects  on  the real  economy  and  hence  does  not  influence r*.  Monetary policy  smooths  the 

fluctuations of the economy around its long-run growth trajectory but does very little to affect 

the trend itself (Woodford, 2003). In this view, monetary policy only has temporary real effects, 

in  the  transition  to  the  new  steady  state  after  a  shock.  Even  more  so,  as  climate  change 

originates from the cumulated stock of carbon emissions monetary policy should not have a 

structural impact on climate change.12 

However,  some recent  studies  argue that  monetary  policy  can  have  very  long-term  effects, 

such as on expectations (Hansen et al. 2015; and Hansen et al. 2018)) and that r* might be 

endogenous to monetary policy (Rungcharoenkitkul et al. 2021; Jordà et al., 2020; Borio et al., 

2019;  Gopinath  et  al.,  2017).  This  refers  to  the  potential  impact  of  monetary  policy  on 

productivity  growth,  via  hysteresis  effects  and  the  impact  of  the  real  interest  rate  on  the 

allocation of capital. If monetary policy would indeed affect economic trend growth (g), it would 

also have an impact on climate change via the feedback effects of potential output on carbon 

emissions (such feedback effects are part of CEMs such as DICE, as seen in Figure 3). 

Another channel through which monetary policy could have an impact on climate change is 

the influence on capital allocation. Prolonged accommodative monetary policy can slow-down 

the energy transition, if higher polluting energy-intensive industries benefit relatively more from 

monetary  policy  stimulus  (Schoenmaker,  2021).  This  is,  for  instance,  the  case  with  asset 

purchase  programmes  of  the  ECB,  which  are  based  on  the  concept  of  market  neutrality.  It 

implies that the allocation of asset purchases follows capital market benchmarks, which are 

dominated by more polluting firms, because these are relatively capital intensive.13 This carbon 

bias  in the  purchases  of  corporate  bonds may  imply that monetary  policy  does  not  achieve 

efficient  outcomes  (Schnabel,  2020).  Ultimately  this  bias  can  reduce  productivity  in  the 

economy  and  so  (through  parameter  g)  also  contribute  to  lower  r*,  which  will  then  be 

endogenous to monetary policy via climate change in the long run. According to the Climate 

12 Ferrari and Nispi Landi (2020) show that green QE has very limited impact in reducing the stock of 
emissions and, hence, in pursuing climate objectives (a similar conclusion is reached for interest rate 
policies by Ferrari and Pagliari, 2021). 
13 This effect is probably smaller when the economy is more service-oriented. 

ECB Working Paper Series No 2744 / November 202220 
 
 
 
 
 
 
Action  Plan  the  ECB  will  adjust  the  framework  guiding  the  allocation  of  corporate  bond 

purchases to incorporate climate change criteria, in line with its mandate (ECB, 2021c). 

Monetary policy can support climate transition policies by creating accommodative financing 

conditions for sustainable firms and activities. These lower the costs of capital, by  supporting 

a reallocation of production factors from brown to green sectors and fostering the financing of 

innovative green projects. Such an allocation process will be stimulated by adequate carbon 

pricing.  This  reduces  possible  adverse  climate-related  effects  of  monetary  policy  on 

productivity and can turn the interaction between monetary policy and potential GDP growth in 

the desired direction.  

Concerted climate policies in an orderly transition scenario (NGFS, 2021) have the potential 

to  transform  the  macroeconomic  and  financial  environment.  Therefore,  central  banks  might 

need to change their response functions to meet their mandate in the “new normal”. Such a 

transition  is  unlikely  to  be  smooth  and  to  the  extent  that  it  affects  inflation  dynamics,  they 

cannot be ignored by monetary policy (Boneva el al., 2021). Such yet unseen scenarios offer 

a ground to argue that central bank should engage in “proactively” integrating climate change 

considerations  in  their  policies  (see  Ferrari  and  Nispi  Landi,  2021  and  Benmir  and  Roman, 

2021). Active fiscal policies designed to meet climate objectives might also affect the volatility 

of output and inflation which, in turn, might lead to changes in the optimal policy rule of central 

banks (Ferrari and Pagliari, 2021). 

Summing up the various effects of monetary policy on r* should not be overemphasized, as 

they may cancel out over the cycle. Nevertheless, there are various channels through which 

monetary  policy  is  not  neutral  about  climate  change.  Monetary  policy  can  influence  the 

greening of the economy through the effects on capital allocation and these effects depend on 

governments’ climate policies that determine relative prices in the economy.  

4.  Model simulations of the channels 

Having surveyed the main channels through which climate change and transition policies could 

affect  r*,  we  now  summarize  some  simulation  outcomes  of  shocks  that  run  through  these 

channels in the euro area in order to provide an overview of the possible quantitative effects.  

As a start, we follow the approach of Bylund and Jonsson (2020). They simulate the outcomes 

of  r*  with  different  values  of  the  parameters  –  calibrated  for  the  Swedish  economy  -  of  the 

ECB Working Paper Series No 2744 / November 202221 
 
 
 
 
 
 
 
 
 
Ramsey  model.  This  allows  for  a  quantification  of  the  possible  effects  of  climate  change 

through various channels, based on the key equation for r* in the CBM. The analysis is limited 

to the impact on the long-run (steady state) real interest rate. We calibrated the parameters of 

their model, focusing on Sweden, to transpose it to the euro area.  

The  results  of  the  simulations  are  summarized  in  Table  1.  As  we  have  calibrated  various 

parameter  levels  for  the  different  scenarios,  we  find  that  in  r*  reacts  most  pronounced  to 

changes in trend growth g as well as the probability of disaster p and the impact of disaster b. 

In these scenarios, climate change has a rather direct impact on the production capacity of the 

economy  relative  to  the  other  scenarios,  which  simulate  different  reaction  modes  by 

households  and  firms.  The  rising  uncertainty  often  associated  with  climate  change  would 

particularly  show  its  effect  on  r*  only  when  combined  with  habit  formation,  otherwise  the 

impacts  are  fairly  negligible.  So  even  if  individuals  perceive  a  higher  degree  of  uncertainty 

about climate risks as they react by increasing their risk aversion, this would not move r* by 

much in the simulations. 

Table 1: Effects of shock simulations on r* with a CBM model 

      scenario 

levels 
Low 

medium 

high  

lower trend 
growth 

increasing 
uncertainty 

increasing 
uncertainty with 
habit formation 

higher probability 
of disaster 

higher impact 
of disaster 

-0.5 

-1.0 

-2.0 

<0.1 

<0.1 

<0.1 

-0.1 

-0.1 

-0.4 

-1.0 

-1.9 

-2.9 

-1.3 

-1.9 

-2.7 

Notes: impacts on r* are measured as percentage point deviations from the baseline scenario. The 
scenarios are simulated with different variants of the Ramsey equation, following Bylund and Jonsson 
(2020). Lower trend growth (g) is simulated by the basic equation 𝒓∗ = 𝝆 + 𝜸 𝒈 + 𝒏, with 𝒈= 1.5% 
and 1%. Increasing uncertainty is simulated by the Ramsey equation extended with parameter 𝜸𝟐as 
measure of the risk aversion of economic agents and parameter 𝝈𝟐, representing the consumption 
𝝈𝟐), with 𝜸𝟐= 0.5, 1 and 2 and 𝝈𝟐 = 0.0002 and 0.0004. The effect of habit 
shock (𝒓∗ = 𝝆 + 𝜸 𝒈 −

𝜸𝟐
𝟐
𝟐
𝜸
formation (S) is simulated by 𝒓∗ = 𝝆 + 𝜸 𝒈 − 𝟎. 𝟓  (cid:4672)
𝝈𝟐, with 𝑺= 0.45, 0.3 and 0.15. The impact 
(cid:4673)
𝑺
of probability (p) and impact (b) of disasters is simulated by 𝒓∗ = 𝝆 + 𝜸 𝒈 − 𝒑𝒃/(𝟏 − 𝒃), with p=12% 
and p=36% and b=5, 7.5 and 10% GDP. All scenarios assume parameter 𝝆 = 0. 

Secondly,  we  use  the  CEM  model  DICE  to  conduct  dynamic  simulations  of  climate-related 

shocks  that  run  through  the  main  channels  as  identified  previously.  DICE  is  an  integrated 

assessment  model  (IAM)  developed  by  Nordhaus  (2013).14  It  is  the  workhorse  model  in 

14 We use the DICE-2016R version. 

ECB Working Paper Series No 2744 / November 202222 
 
 
 
 
  
 
 
climate-change economics. While most climate variables and relationships in the model are 

global, we include euro area data for the economic and demographical variables that are not 

common across regions. This enables us to simulate the impact of climate shocks on r* of the 

euro  area.  Therefore,  we  use  DICE  as  a  projection  model,  i.e.  an  equilibrium  model  that 

generates paths of variables following a shock (Nordhaus, 2013). As is common in IAMs the 

time interval in DICE is 5 years and variables are projected for multiple decades in the future. 

We  project  outcomes  up  to  the  year  2100,  as  this  horizon  fits  with  most  climate  policy 

objectives and is distant enough to capture long-term equilibrium effects on r*. 

DICE takes into account possible non-linear effects of climate change on economic variables 

(that  could  be  very  substantial).  For  instance,  temperature  impacts  output  via  the  damage 

function,  which  includes  an  exponential  term.  This  allows  for  simulating  non-linear  effects 

which may occur beyond certain tipping points about changes in the ecosystem. Such non-

linearities will also affect r* through their impact on economic variables. Since the uncertainty 

about the outcomes is large, due to assumed parameter values, non-linear effects amongst 

others,  we  are  more  interested  in  the  direction  of  the  effects  and  the  relative  impact  on  r*, 

rather than in the precise quantitative outcomes. 

What we present are scenario outcomes that indicate the ranges of expected changes caused 

by  global  warming.  To  do  so,  we  assume  different  values  for  key  parameters  of  the  DICE 

model. It is important to focus on the tails of the outcome distribution as the uncertainty around 

the  expectations make the  occurrence  of  tail risks more  likely.  Krogstrup  and Oman (2019) 

emphasize  that  climate  change  increases  uncertainty  to  new  levels,  because  the  tail  risks 

include catastrophic and often irreversible events like the thawing of permafrost which could 

release huge amounts of GHGs and thereby intensify climate change even further. Therefore, 

they  suggest  a  risk  management  approach  inspired  by  Value-at-Risk  (VaR)  models  which 

means  maximizing  one’s  objective  function  subject  to  the  risk  of  catastrophic  irreversible 

climate change remaining below a before agreed level. 

The simulations are conducted by shocking the parameters of DICE that determine the impact 

of  climate  change  on  the  discount  rate  (r*)  via  changes  in  TFP  growth,  risk  aversion  and 

physical  damages.  The  outcomes  display  the  sensitivity  of  r*  to  changes  in  key  model 

parameters.  While  the  outcomes  provide  partial  insights  into  the  effects  of  changes  in  TFP 

growth,  risk  aversion  and  damages,  they  are  useful  to  inform  policymakers  about  possible 

paths of r* following different sets of shocks.  

ECB Working Paper Series No 2744 / November 202223 
 
 
 
 
 
Table 2 summarizes the results of our DICE simulations for different parametrizations of the 

scenarios. The results are presented in terms of deviation from the baseline outcome in the 

year  2100.  We  find  that  even  the  most  severe  temperature  increases  as  envisaged  by  the 

IPCC’s SSP5-8.5 scenario (which predicts an increase in global temperatures of 4.4°C by the 

end of the century) would result in a relatively modest decrease of r* of less than 0.3 pp (in this 

scenario total output falls by nearly 6%). The effect  of increasing risk aversion of economic 

agents has a much greater impact on the natural rate. The low scenario in Table 2 presents 

the outcome of a benign shock (i.e., lower risk aversion), taking into account that at least in 

theory the impact of climate change on risk aversion is ambiguous. An increase in risk aversion 

due to climate change would lower r* by as much as 2.5 pp.  

The TFP shocks have been calibrated as a change in the rate of technological progress. While 

the low-level scenario presents a positive shock as we assume a slower decline than in the 

baseline,  the  medium  and  high  level  are  both  negative  shocks.  Tripling  the  decline  in  TFP 

growth in the high scenario reduces r* in 2100 by up to 1 pp. Finally, we calibrated the effects 

of changes in the damage function. These changes imply different sensitivities of the economy 

to physical risk scenarios. In contrast to other simulations, we do not include a positive shock 

scenario here. The high-level damage scenario would result in a decline of r* by 0.5 to 1 pp 

compared to the baseline, which goes in tandem with an output loss of 17% in 2100. 

Table 2: Effects of shock simulations on r* with CEM model 

      scenario 

levels 
low 

medium 

high  

increasing 
temperature  

0.0 – 0.1 

0.0 – -0.1 

0.0 – -0.5 

increasing                    

risk aversion 

increasing 
TFP shocks  

increasing     
damage function 

0.0 – 0.5 

0.0 – 0.5 

-0.5 – -1.0 

-0.5 – -1.0 

0.0 – -0.5 

0.0 – -0.5 

-2.0 – -2.5 

-0.5 – -1.0 

-0.5 – -1.0 

Notes: impacts on r* are measured as percentage point deviations from the baseline scenario in 2100; 
because  the  results  of  the  simulations  are  surrounded  by  significant  uncertainty,  we  display  only 
ranges. The low temperature scenario corresponds to the SSP1-1.9 by IPCC, the medium scenario 
to  SSP2-4.5  and  the  high  scenario  to  SSP5-8.5;  the  low  risk  aversion  scenario  to  a  risk  aversion 
parameter of = 1, medium risk aversion = 2 and high risk aversion to = 2.5 (baseline = 1.45). 
For the low TFP shock we assumed Agd = 0.0025, for the medium TFP shock 0.01 and for the high 
TFP shock 0.015. The damage function scenarios deviate from the baseline with a3 = 4 (low scenario), 
a3 = 5 (medium scenario) and a2 = 0.0044 and a3 = 5 (high scenario).  

Comparing the results of the DICE simulations in Table 2 with the steady state simulations in 

Table 1, we find quite significant differences. The increase in temperatures even to the most 

ECB Working Paper Series No 2744 / November 202224 
 
 
 
 
 
 
 
 
elevated levels under the IPCC scenarios yields a rather modest decrease in r* according to 

the  DICE  model,  whereas  the  higher  probability  of  disaster,  which  is  a  corollary  of  higher 

temperatures,  reduces r*  by  up  to  2.9  pp  in the steady  state  simulations.  This  difference  in 

outcomes is illustrative of the high uncertainty surrounding any effects of climate change on 

economic variables. Pindyck (2013) has already stressed the fact that the climate sensitivity 

of IAMs is surrounded by a high degree of uncertainty and that assumptions about the discount 

rate and risk aversion can have very decisive effects on projected outcomes. Also, the DICE 

model has been criticized for rather implausibly low effects of rather high temperatures, among 

others by Dietz and Stern (2015).  

To  illustrate  the  degree  of  uncertainty  we  present  in  Figure  4  the  effects  of  changes  in  the 

damage  function  of  DICE  on  r*.  The  damage  function  determines  the  output  losses  d  from 
higher temperatures T in the model, according to 𝑑 =   a(cid:2869) 𝑇 + a(cid:2870) 𝑇 (cid:2911)(cid:3119). Originally, the model uses 
a  quadratic  term  to  link  damages  to  temperatures  (a3  =  2),  but  since  the  effects  are  highly 

uncertain,  we  use  different  specifications.  If  we  assume  a3  =  5  the  damages  increase 

significantly by the end of the century, but the resulting impact on r* is rather negligible. 

Figure 4. Different specifications of damage functions in DICE 

Notes:  the  left-hand  panel  shows  the  deviation  of  projected  r*  from  its  original  level  in  the  baseline 
scenario and under a different specification of the damage function. The right-hand panel depicts the 
effects of the different specifications of the damage function on expected output up to the year 2105. 
Despite very significant  damages of more  than  8%  of annual output by the end of the century in the 
damage shock specification, the natural rate is only 0.4 pp. lower than in the baseline specification. 

Increasing risk aversion on the other hand leads to a very strong reaction of r* in the DICE 

model, whereas the steady state simulations in Section 5 yield a mere decline of r* by 0.4 pp 

if  habit  formation  is  included.  The  TFP  shock  which  corresponds  to  lower  trend  growth 

produces significant effects in both models. Increasing the sensitivity of the damage function 

in DICE has comparable effects on r* as intensifying the impact of disaster in the steady state 

simulations. Both simulations yield economically significant results. 

ECB Working Paper Series No 2744 / November 202225 
 
 
 
  
 
 
5.  Discussion 

This survey is the first to systematically review the possible effects of climate change on the 

natural rate of interest. While r* is a theoretical concept, it is used as a benchmark by central 

banks  to  assess  the  stance  of  their  monetary  policy  and  the  room  for  policy  manoeuvre. 

Starting from the Ramsey equation we reviewed the channels through which climate change 

or policies to mitigate it might affect the determinants of r*, e.g., demography, productivity and 

risk  aversion  (working  through  the  impact  of  preferences  on  the  elasticity  of  intertemporal 

substitution). In most cases, we find that climate change would have a rather dampening effect 

on r*, which implies a narrower room for manoeuvre for central banks. Because climate change 

is likely to have wider repercussions on economic policies, we also assess the impact of fiscal 

policies and inequality on r* and the possibility endogenous effects of monetary policy.  

We  illustrate  how  the  uncertainty  associated  with  climate  change  impacts  –  as  well  as  the 

transition policies to counter it - plays an important role in several simulations. This uncertainty 

might raise households’ precautionary motive to accumulate savings and may induce firms to 

postpone investment, putting additional downward pressure on market interest rates. On the 

other hand,  the  path  of market  interest rates  and  r*  might  depend  largely  on the  thrust  and 

credibility of policies governing the transition to a low-carbon economy. Along this mitigation 

path,  the  reallocation  of  capital  across  sectors  and  the  impact  on  firm  valuations  and  risk 

premia  can  lead  to  complex  dynamics  affecting  aggregate  variables  and  ultimately  r* 

(Donadelli et al., 2018). 

An  additional  challenge  for the central  banks  is  that the  effects  of  climate  change  on  these 

drivers may differ across euro area countries both in terms of potency and timing. This opens 

complicating scenarios for future monetary policy, rendering it more difficult to reach precisely 

narrow  objectives  over  common  policy  horizons  (e.g.  price  stability  in  the  medium  term). 

Hence, the uncertain impact of climate change on main r* may call for an increasing flexibility 

in the monetary policy strategy, both in terms of objectives and time horizon. 

Lower  levels  of  the  natural  rate  of  interest  reduce  a  central  bank’s  policy  space  since  it 

increases the likelihood that the ELB becomes binding. This adds to the uncertain impact of 

climate  change  on  the  output  gap,  which  would  also  imply  that  guidance  from  Taylor  rules 

weakens.  The  reduced  policy  space  for  conventional  monetary  policy  might  only  partly  be 

compensated  for  by  unconventional  policy  tools  like  QE.  Therefore,  it  will  be  important  to 

conduct  policies  that  can  help  to  avoid  hitting  the  ELB,  including  fiscal  policy.  Government 

measures are the most obvious and effective way to foster to transition to a climate-neutral 

ECB Working Paper Series No 2744 / November 202226 
 
 
 
 
 
 
economy. An orderly transition will mitigate the economic and financial risks of climate change 

and thereby also prevent potential downward effects on r*. In addition, active fiscal policies to 

mitigate climate change might also spur investment demand and thereby put upward pressure 

on  the  natural  rate.  This  additional  investment  demand  could  offset  -  at  least  partially  -  the 

negative  effects  of  climate  change  on  r*  that  we  found.  Future  research  could  assess  the 

consequences of different fiscal policy strategies in reaction to climate change on r*. 

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Acknowledgements 

The authors wish to thank Martin Admiraal and Sofia Gori for data assistance and Jesus Crespo Cuaresma, Livio Stracca, Oscar Arce, 
Magnus Jonsson, Warwick McKibbin, Ralph Setzer, Pinelopi Tsalaporta, Ariana Gilbert, and participants to various seminars for useful 
comments. 
Views expressed are those of the authors and do not necessarily reflect official positions of the European Central Bank, 
Oesterreichische Nationalbank or De Nederlandsche Bank. 

Francesco Paolo Mongelli 
European Central Bank, Frankfurt am Main, Germany; email: francesco.mongelli@ecb.europa.eu 

Wolfgang Pointner 
Oesterreichische Nationalbank, Vienna, Austria; email: Wolfgang.Pointner@oenb.at 

Jan Willem van den End 
De Nederlandsche Bank, Amsterdam, The Netherlands; Vrije Universiteit, Amsterdam, The Netherlands; email: w.a.van.den.end@dnb.nl 

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