The impact of global warming on inflation : averages, seasonality and extremes.
Date: 2023-05-26
Institutions: European Central Bank
Subjects: consumer price, economic consequence, economic indicator, economic stabilisation, global warming, inflation, macroeconomics

Maximilian Kotz, Friderike Kuik, Eliza Lis, 
Christiane Nickel 

Working Paper Series 

The impact of global warming on 
inflation: averages, seasonality and 
extremes 

No 2821 

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 

Understanding of the macroeconomic effects of climate change is developing rapidly, but the 

implications for past and future inflation remain less well understood. Here we exploit a global 

dataset of monthly consumer price indices to identify the causal impacts of changes in climate 

on  inflation,  and  to  assess  their  implications  under  future  warming.  Flexibly  accounting  for 

heterogenous impacts across seasons and baseline climatic and socio-economic conditions, we 

find that increased average temperatures cause non-linear upwards inflationary pressures which 

persist over 12 months in both higher- and lower-income countries. Projections from state-of-

the-art climate models show that in the absence of historically un-precedented adaptation, future 

warming  will  cause  global  increases  in  annual  food  and  headline  inflation  of  0.92-3.23  and 

0.32-1.18 percentage-points per year respectively, under 2035 projected climate (uncertainty 

range  across  emission  scenarios,  climate  models  and  empirical  specifications),  as  well  as 

altering the seasonal dynamics of inflation. Moreover, we estimate that the 2022 summer heat 

extreme  increased  food  inflation  in  Europe  by  0.67  (0.43-0.93)  percentage-points  and  that 

future warming projected for 2035 would amplify the impacts of such extremes by 50%. These 

results suggest that climate change poses risks to price stability by having an upward impact on 

inflation, altering its seasonality and amplifying the impacts caused by extremes. 

JEL: Q54, E31, C33 

Keywords: climate change, climate physical risk, inflation dynamics 

ECB Working Paper Series No 28211Non-technical summary 

As one of the major challenges of the 21st Century, researchers have illuminated the many ways 

in which climate change threatens human welfare, as well as the large societal transformations 

required to prevent these risks from intensifying further. Central banks are paying increasing 

attention to the importance of these physical and transition risks, including for price stability. 

Yet, research on the role of physical climate risks for inflation is still in its infancy. In particular, 

estimates  of  the  impact  on  future  inflation  from  projected  climate  change  are  lacking.  This 

constitutes a barrier to effective and balanced assessments of both physical and transition risks 

related to climate change. This study addresses these issues by providing empirical estimates 

of  historical  climate  impacts  on  inflation  and  combining  them  with  state-of-the-art  physical 

climate models to estimate the implications for inflation from projected future warming. 

We draw on and expand the growing empirical literature on climate-inflation impacts, using a 

dataset of monthly consumer price indices across 121 countries, to flexibly identify the impacts 

of  a  variety  of  climate  indices  across  different  seasons  and  regions.  We  find  non-linear, 

persistent  impacts  of  increasing  average  temperatures  in  both  higher-  and  lower-income 

countries,  as  well  as  impacts  from  other  climate  variables  with  differing  magnitudes  and 

persistence dependent on regional socio-economic and climatic conditions. These results are 

robust to a number of different empirical specifications.  

Combining these empirical estimates with projections from state-of-the-art climate models, we 

find that future warming will cause global increases in annual food and headline inflation of 

0.92-3.23  and  0.32-1.18  percentage-points  per-year  by  2035  respectively  (uncertainty  range 

across emission scenarios, climate models and empirical specifications). These impacts vary 

across regions with generally larger impacts in the global south. At high latitudes impacts show 

a strong seasonality, with considerably larger impacts in the summer leading to alterations of 

the seasonal inflation cycle.  Finally, we estimate the impacts of the 2022 summer (June-July-

August) extreme heat in Europe, finding that it cumulatively increased food inflation by 0.67 

(0.43-0.93) percentage-points. The impacts from an equivalent extreme would be amplified by 

50% under 2035 projected warming. 

ECB Working Paper Series No 282121 Introduction 

The  effects  of  climate  change  on  the  economy  are  becoming  increasingly  well  understood. 

Empirical analyses have demonstrated impacts on labour productivity (Dasgupta et al., 2021), 

agricultural  output  (de  Lima  et  al.,  2021;  Moore  et  al.,  2017a,  2017b),  energy  demand 

(Auffhammer et al., 2017; Wenz et al., 2017), and human health (Guo et al., 2016; Song et al., 

2017)  from  changes  in  historical  climate.  The  resulting  consequences  for  macroeconomic 

production have also been elucidated, with non-linear impacts of average temperature (Burke 

et al., 2015; Dell et al., 2012; Kalkuhl and Wenz, 2020), temperature variability (Kotz et al., 

2021), and various aspects of precipitation (Kotz et al., 2022) identified on aggregate economic 

output in historical data. The future climate change expected due to greenhouse gas emissions 

imply considerable welfare losses when evaluated through both these micro- (Bressler, 2021; 

Moore et  al.,  2017a; Rode  et  al., 2021)  and macro-economic impact  channels (Burke  et al., 

2018, 2015; Kalkuhl and Wenz, 2020; Moore and Diaz, 2015).  

Despite  these  advances,  the  implications  of  these  climate  impacts  for  inflation  remain 

understudied.  Advancing  this  understanding  is  crucial  to  a  comprehensive  assessment  of 

climatic risks because rising or unstable prices threaten economic (Chu and Lai, 2013; Dotsey 

and Ireland, 1996) and human welfare (Gazdar and Mallah, 2013; Nord et al., 2014) as well as 

political stability (Abaidoo and Agyapong, 2022) . The 2022 cost of living crisis provides an 

example  of  such  implications,  with  UN  estimates  suggesting  that  an  additional  71  million 

people may fall into poverty due to rapidly rising prices (Molina et al., 2022; Lagarde, 2022). 

Moreover, the potential for climate change to impact inflation dynamics is of increasingly high-

relevance for the conduct of monetary policy and for central banks’ ability to deliver on their 

price stability mandate in the future (Bolton et al., 2020; Boneva et al., 2021; Drudi et al., 2021). 

A comprehensive assessment of climatic risks on inflation is therefore an important element in 

guiding  the  mitigation  and  adaptation  efforts  of  governments,  as  well  informing  monetary 

policy concerning the risks posed by climate change.  

Recent work has begun to shed some light on the links between climate and inflation, typically 

exploiting the panel structure of national inflation data and using weather changes as exogenous 

ECB Working Paper Series No 28213shocks. Studying the potential effects of various disasters on consumer price inflation, Parker, 

2018 found historical impacts of storms and floods on inflation. Focusing on large temperature 

deviations as exogenous shocks, Faccia et al., 2021 identified short-term inflationary impacts 

from  hot  summers  with  stronger  effects  in  emerging  market  economies.  Mukherjee  and 

Ouattara, 2021 study the effects of changing annual temperatures, finding impacts which persist 

for a number of years.  Ciccarelli et  al., 2023,  took a VAR approach to study the effects of 

different  temperature  shocks  in  the  four  largest  euro  area  economies  on  prices.  They  found 

upward impacts on inflation from increasing average temperatures in summer, which are more 

expressed in warmer countries, and upward impacts on inflation from increasing temperature 

variability.  Regarding  other  variables  asides  from  temperature,  Moessner,  2022  found  non-

linear impacts of annual precipitation changes. Here, we extend this emerging literature along 

two perspectives. First, we provide an assessment of the global historical inflationary impacts 

arising  from  a  wide  range  of  shocks  to  different  climate  variables.  We  focus  on  month-on-

month  price  changes,  allowing  us  to  flexibly  account  for  the  heterogeneity  of  impacts  from 

weather  shocks  across  seasons  and  regions  given  different  baseline  climatic  and  socio-

economic  conditions.  Moreover,  the  second  key  contribution  of  our  assessment  is  the 

combination of our empirical results with projections from physical climate models to estimate 

the inflationary impacts caused by future warming.  

We  combine  measures  of  national  exposure  to  different  climate  conditions  based  on  high-

resolution climate data, with a global dataset on monthly price indices for different aggregates 

of goods and services across 121 countries of the developed and developing world (Osbat and 

Parker, forthcoming). The availability of monthly price indices allows a detailed assessment of 

the temporal dynamics of inflationary responses to climate shocks, as well as the heterogeneity 

of such effects across seasons. National-level economic exposure to climate shocks is estimated 

based on the proportion of population exposed to different climatic indices in a given month. 

Deviations of monthly mean temperature and daily temperature variability, as well as excess 

wet and drought conditions and extreme daily rainfall (see section 2.3 for full definitions) are 

the main impact channels for which we identify statistically significant impacts.  

ECB Working Paper Series No 28214 
 
Our empirical framework assesses the causal effects of these climatic measures on national, 

month-on-month inflation rates (measured as the change in the logarithm of price indices) by 

exploiting within-country variation using fixed-effects panel regression models. Country fixed 

effects  account  for  unobserved  differences  between  regions  such  as  baseline  climate  and 

inflation rates, while the use of year fixed effects accounts for contemporaneous global shocks 

to both variables such as El Nino events or global recessions. We further include country-month 

fixed  effects  to  account  for  country  specific  seasonality  –  a  crucial  step  given  the  strong 

seasonal  cycle  in  both  monthly  inflation  and  climate  data.  Furthermore,  our  baseline 

specification accounts  for country specific time trends to avoid spurious correlations arising 

from common trends.  Consequently, our framework accounts for a wide variety of un-observed 

confounders, and our results stem from the deviations of climate conditions from their national 

and  seasonal  patterns  which  cannot  be  accounted  for  by  global  shocks  or  country-specific 

trends. Combined with the exogenous nature of the realisation of monthly climate variables, 

these  methodological  choices  strengthen  confidence  in  a  causal  interpretation  of  our  results 

(Auffhammer et al., 2020).  

The rest of this paper is structured as follows. Section 2 provides a more detailed description of 

our data and methods; section 3 describes the empirical results for the response of inflation to 

historical climate shocks; section 4 introduces estimates of the average effect on inflation from 

projected future warming while section 5 assesses the implications for inflation seasonality; 

section 6 estimates the impacts of the 2022 European extreme summer heat on inflation and 

assesses how these impacts would be amplified under future warming. Finally, we conclude 

with a discussion of the implications and limitations of our results in section 7. 

ECB Working Paper Series No 28215 
 
 
 
2 Data and Methods 

2.1 Inflation data 

Data on national-level inflation of different price aggregates is obtained from a dataset compiled 

by  Osbat  and  Parker,  forthcoming.  The  data  used  here  constitute  monthly,  non-seasonally 

adjusted  prices  at  different  levels  of  aggregation.  Data  are  available  for  121  countries  with 

varying  temporal  coverage  from  1991-2020.  The  countries  included  cover  most  of  the 

developed world (minus Australia and New Zealand where monthly data are not available), as 

well as large parts of the developing world. Coverage across South America and Africa is good, 

but large gaps exist in South-East Asia where detailed information on price aggregates are not 

available. Month-on-month inflation rates are used as the main dependent variable, estimated 

as the first difference in the logarithm of the consumer price index (CPI). 

In  a  robustness  test  we  alternatively  use  monthly  inflation  data  from  the  World  Bank. 

Differences  in  the  aggregation  procedures  exist  and  are  documented  in  Osbat  and  Parker, 

forthcoming. Two important differences are the inclusion of imputed rents in some headline 

inflation indices in the World Bank data and differences in the aggregation of food. We use the 

data compiled by Osbat and Parker, forthcoming as our main specification because the inclusion 

of imputed rents may bias estimates away from the impacts on widely consumed goods and 

services. In those countries where imputed rents are incorporated, they typically have a large 

weight, but there are many indices that do not incorporate them, notably including all European 

countries using the Harmonised Index of Consumer Prices. We find that the impacts on food 

inflation  are  qualitatively  and  quantitatively  consistent  when  using  World  Bank  data.  The 

response of headline inflation differs considerably, likely due to the inconsistent inclusion of 

imputed rents in headline inflation in the World Bank data.  

2.2 Climate data 

The  primary  source  of  climate  data  for  this  study  is  the  ERA-5  reanalysis  of  historical 

observations (Hersbach et al., 2018, 2020). ERA-5 combines satellite and in-situ observations 

ECB Working Paper Series No 28216 
 
 
 
 
with  state-of-the-art  assimilation  and  modelling  techniques  to  provide  estimates  of  climate 

variables with global coverage and at 6-hourly resolution. Daily 2m air temperature and surface 

precipitation rates for the years 1990-2020 are used as well as monthly average temperature for 

the  recent months  of June, July and August  in 2022.  All data from ERA-5 is obtained on a 

regular 0.25-by-0.25-degree grid for the years 1990-2020. For the estimates of SPEI, we follow 

the literature (Vicente-Serrano et al., 2010) in using monthly mean temperature and monthly 

precipitation totals from the CRU TS v4.05 for the years 1901-2020. This data is obtained at 

the same resolution and on the same grid as ERA-5.    

2.3 Climate variables 

Monthly, m, averages, 

, and standard deviations, 

,  of daily ERA-5 temperatures are 

calculated  at  the  grid  cell, 

𝑇𝑇�𝑥𝑥,𝑚𝑚

,  level.  Moreover,  the  relative  exceedance  of  certain  high 

𝑇𝑇�𝑥𝑥,𝑚𝑚

precipitation thresholds, 

,  are calculated according to 

𝑥𝑥

𝑃𝑃�𝑥𝑥,𝑚𝑚   =   ∑
where  

𝑃𝑃𝑥𝑥,𝑑𝑑−𝑇𝑇𝑥𝑥

𝐷𝐷𝑚𝑚
𝑇𝑇𝑥𝑥 𝐻𝐻(𝑃𝑃𝑥𝑥,𝑑𝑑 − 𝑇𝑇𝑥𝑥)
𝑑𝑑=1
 are daily precipitation totals, 

 the Heavide step function and 

 the number of 

𝑇𝑇𝑥𝑥

(1) 

days in a given month. Following Kotz et al., 2022, precipitation thresholds are estimated as 

𝑃𝑃𝑥𝑥,𝑑𝑑

𝐷𝐷𝑚𝑚

𝐻𝐻

different percentiles of the local distribution of daily rainfall (1990-2020).  

Standardised  Precipitation  Evapotranspiration  Indices  (SPEI)  are  calculated  following  the 

methods of reference (Vicente-Serrano et al., 2010), applying their publicly available code to 

monthly temperature and precipitation data from CRU TS v4.05. The SPEI calculation is based 

on a physical model of moisture balance and considers contributions to dry or wet conditions 

from both temperature and precipitation. It is a widely used tool to flexibly compare dry and 

wet  conditions  across  countries.  Moreover,  its  flexible  estimation  over  different  timescales 

allows  exploration  of  different  impact-relevant  timescales.  We  estimate  SPEI  at  one,  two-, 

three-, six- and twelve-month timescales to flexibly assess the impacts of shocks across these 

timescales.  

ECB Working Paper Series No 28217 
 
  
 
 
 
 
 
 
 
 
 
 
2.4 Spatial aggregation 

We  use  gridded  population  estimates  from  the  History  database  of  the  Global  Environment 

(HYDE)(Klein Goldewijk et al., 2011) to estimate national-level exposure to changes in these 

climate variables. The data are provided at 0.25-by-0.25-degree resolution by the Inter-Sectoral 

Impact Model Intercomparison Project (ISIMIP). Monthly average temperature, temperature 

variability and the measure of daily precipitation extremes are aggregated to the national level 

by a simple population weighted average. In this weighting we also account for the proportion 

of grid-cells falling within a given administrative units, estimated by evenly distributing 100 

points  within  each  grid-cell  and  estimating  the  proportion  which  fall  within  the  given 

administrative unit. Given these weightings, 

 for all 

 grid-cells falling at least partially 

within the administrative boundary of a country, 
𝑤𝑤𝑥𝑥,𝑛𝑛,

, this weighted average reads: 

𝑵𝑵𝒙𝒙

𝑐𝑐

(2) 

𝑇𝑇�𝑐𝑐,𝑚𝑚 =

1
𝑁𝑁𝑥𝑥 ∑

𝑁𝑁𝑥𝑥
𝑥𝑥=1

𝑇𝑇�𝑥𝑥,𝑚𝑚𝑤𝑤𝑥𝑥,𝑐𝑐

for  monthly  average  temperature  for  example.  The  equivalent  procedure  is  applied  to 

temperature variability and the measure of daily precipitation extremes.  

For  SPEI,  we  first  estimate  binary  variables  indicating  whether  a  grid  cell  is  experiencing 

conditions  which  exceed  a  certain  level  of  dry  or  wet  (indicated  as 

, 

respectively in equation 3). We choose thresholds of one, one-point-five, two and two-point-
𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆 <  𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆 >

five deviations to flexibly assess the exceedance of different thresholds. We then apply the same 

spatial aggregation procedure as outlined in equation 2 to estimate the proportion of population 

exposed to these excessively wet or dry conditions.  

The  magnitude  of  the  temperature  seasonal  cycle, 

  is  estimated  from  national  monthly 

temperatures which are then averaged over the historical period (1990-2020) before use in the 

𝑇𝑇�𝑐𝑐
regression  models.  Deviations  of  average  temperature, 

,  and  temperature  variability, 

d

,  from  their  historical  average  (1990-2020)  are  also  calculated  for  use  as  dependent 

𝑎𝑎𝑻𝑻�𝒄𝒄,𝒎𝒎

variables.  
𝑇𝑇�𝑐𝑐,𝑚𝑚

ECB Working Paper Series No 28218 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.5 Empirical framework for estimation of causal effects 

We  apply  fixed-effects  panel  regression  models  to  identify  the  causal  effects  of  changes  in 

climate  variables  on  national  level  inflation.  Month-on-month  national  level  inflation  rates, 

 are the dependent variable. Deviations of average temperature are included with an 

interaction with the average temperature level, whereas deviations of temperature variability 
𝑎𝑎𝑑𝑑𝑑𝑑𝑃𝑃𝑆𝑆𝑐𝑐,𝑚𝑚
are included with an interaction with the magnitude of the seasonal temperature cycle following 

previous work (Kotz et al., 2021). Daily precipitation extremes are included with an interaction 

with the monthly average temperature level (having also tested alternative interactions with the 

monthly share of annual precipitation). Both positive (excess wet) and negative (excess dry) 

SPEI threshold exceedance are used. For the former we find no significant effect of interactions 

with the monthly average temperature level or the monthly share of annual precipitation and as 

a result include no interactions in our main specification. For the latter we find a significant 

effect of an interaction with monthly average temperature level and therefore include this in our 

main specification. We include all climate variables simultaneously to ensure that any effects 

we identify occur independently of one another and are therefore additive (Auffhammer et al., 

2020;  Kotz  et  al.,  2021).  Each  climate  variable  is  included  with  11  lags  in  addition  to  the 

contemporaneous term, to assess the delayed effects of monthly climate shocks over the course 

of the following year.  

Our  baseline  specification  includes  country, 

,  year, 

  and  country-month, 

  fixed 

effects, in addition to country specific linear time-trends, 

𝜇𝜇𝑐𝑐
unobserved differences between regions such as baseline climate and inflation rates, while the 

. Country fixed effects account for 

𝜋𝜋𝑐𝑐,𝑚𝑚,

𝜂𝜂𝑦𝑦,

𝛾𝛾𝑐𝑐𝑦𝑦

use of year fixed effects accounts for contemporaneous global shocks to both variables such as 

El Nino events or global recessions. The inclusion of country-month fixed effects accounts for 

country specific seasonality – a crucial step given the strong seasonal cycle in both monthly 

inflation  and  climate  data.  This  constitutes  an  additionally  conservative  step  by  ignoring 

inflation impacts which could repeatedly occur seasonally due to seasonal changes in climate. 

This ensures that our results only estimate the impacts of deviations from normal conditions, 

both in terms of annual and seasonal conditions. Finally, our baseline specification accounts for 

ECB Working Paper Series No 28219 
 
 
country specific time trends to avoid spurious correlations arising from common trends. This is 

important  given  the  presence  of  strong  warming  trends  in  the  historical  period  which  could 

cause spurious correlations to inflation changes. Interestingly, we find that accounting for these 

linear trends enhances the magnitude of estimated effects, suggesting that it indeed assists in 

removing estimation biases. Estimates without linear time trends are qualitatively similar. The 

regression model of the baseline specification then reads: 

11
𝑎𝑎𝑑𝑑𝑑𝑑𝑃𝑃𝑆𝑆𝑐𝑐,𝑡𝑡   =   ∑ (𝑎𝑎𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿 +
𝐿𝐿=0
11
𝐿𝐿=0

11
𝐿𝐿=0

  ∑ (𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆

>𝑐𝑐,𝑡𝑡−𝐿𝐿)   +   ∑ (𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆

𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿 . 𝑎𝑎𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿)   + ∑ (𝑎𝑎𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿 +

𝑇𝑇�𝑐𝑐. 𝑎𝑎𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿)   +

11
𝐿𝐿=0

<𝑐𝑐,𝑡𝑡−𝐿𝐿 +   𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿. 𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆 <𝑐𝑐,𝑡𝑡−𝐿𝐿)   +   ∑ (𝑃𝑃�𝑐𝑐,𝑡𝑡−𝐿𝐿 +

11
𝐿𝐿=0

(3) 

𝑇𝑇�𝑐𝑐,𝑡𝑡−𝐿𝐿. 𝑃𝑃�𝑥𝑥,𝑡𝑡−𝐿𝐿)  + 𝜇𝜇𝑐𝑐   +   𝜂𝜂𝑦𝑦   +   𝜋𝜋𝑐𝑐,𝑚𝑚   +   𝛾𝛾𝑐𝑐𝑦𝑦  +   𝜀𝜀𝑐𝑐,𝑡𝑡  
where 

 is the date in terms of a given year’s month and 

is the country-date residual error. 

Note that here m refers to the general date i.e., the month of a specific year, except in the fixed 

𝜀𝜀𝑐𝑐,𝑡𝑡 

𝑡𝑡

effects terms where m refers to all general occurrences of a particular month, and y refers to the 

particular year. In our baseline specification, errors are clustered by country. 

In alternative robustness tests we estimate a dynamic model in which we also include 11 lags 

of the inflation rates, 

 (Table 1 Column 2), account for cross-sectionally correlated 

errors using Driscoll Kraay errors (Driscoll and Kraay, 1998) (Table 1 Column 3), use World 

𝑎𝑎𝑑𝑑𝑑𝑑𝑃𝑃𝑆𝑆𝑐𝑐,𝑚𝑚

Bank data on monthly inflation rates (Table 1 Column 6), and alternatively normalise monthly 

inflation rates by their interannual standard deviation to account for differing baseline inflation 

volatilities. Moreover, we also estimate models in which we include additional interactions of 

each climate variable with a binary term indicating whether a given country has above or below 

median national income per capita (based on world bank estimates of GDP and population). 

2.6 Climate model data 

Daily 2-m temperature and precipitation totals are taken from 21 climate models participating 

in CMIP-6 under the most pessimistic (RCP8.5) and most optimistic (RCP2.6) greenhouse gas 

emission  scenario  from  2015-2100.  The  data  have  been  bias-adjusted  and  statistically 

ECB Working Paper Series No 282110 
   
 
 
 
 
 
 
 
 
downscaled  to  a  common  half-degree  grid  to  reflect  the  historical  distribution  of  daily 

temperature  and  precipitation  of  the  W5E5  (Cucchi  et  al.,  2020)  dataset  using  the  trend-

preserving  method  developed  by  ISIMIP  (Lange,  2019).  Primary  data  on  grid-cell  level  are 

publicly available for 10 models from the ISIMIP database.  

2.7 Estimating impacts from projected future warming 

To  assess  the  impact  on  inflation  from  projected  future  warming,  we  first  estimate  future 

warming at the country level from each climate model. To do so, we take the difference between 

30-year  averages  of  country-level  monthly  temperatures  centered  on  the  future  period  in 

question (2035 or 2060) and 30-averages over the historical period of 1991-2020. The impacts 

on inflation arising from these future monthly temperatures are then calculated by evaluating 

the first terms of equation 3 which pertain to monthly average temperatures. This produces 12 

months of lagged impacts from each month under future warming. These lagged impacts are 

then aggregated, avoiding double counting impacts which fall in the proceeding calendar year.  

We also assess the impacts of the 2022 extreme summer heat in Europe using ERA-5 estimates 

of monthly temperatures in June, July and August. The same procedure as described above is 

applied to these monthly temperatures to estimate the historical inflationary impacts of these 

three  months.  To  then  assess  the  amplification  of  impacts  from  such  extremes  under  future 

warming, we evaluate the future warming occurring between 2022 and 2035 or 2060 (using the 

difference between 30-year averages of temperature centred on 2022 and 2035 or 2060 in each 

climate model and scenario). This additional month-specific warming is then added on to the 

historically  observed  2022  summer  temperatures,  and  the  impacts  on  inflation  evaluated  as 

before. This approach assumes that future warming will shift the mean of the distribution of 

possible  summer  temperatures  and  does  not  account  for  the  potential  role  of  changing 

temperature variability in altering future temperature extreme intensity. However, evidence for 

a  role  of  temperature  variability  in  enhancing  extremes  at  monthly  time-scales  is  limited 

(Lenton et al., 2017; van der Wiel and Bintanja, 2021).   

ECB Working Paper Series No 282111 
 
 
Uncertainty in estimated impacts under projected climate arises from a combination of factors, 

including the choice of empirical specification, the range of climate model projections, as well 

as future emission scenarios if not explicitly comparing their differences. We report estimates 

with  an  uncertainty  range  accounting  for  these  contributing  factors.  This  uncertainty  range 

spans  the  lowest  estimates  across  empirical  specifications  (and  emission  scenario  unless 

explicitly  comparing  their  differences)  combined  with  the  lower  range  of  climate  model 

projections (the mean minus one standard deviation of impacts across climate models), and the 

largest projection across empirical specifications combined with the higher range of climate 

model projections (the mean plus one standard deviation of impacts across climate models). 

This framework provides a transparent assessment of uncertainty across a range of factors.  

3 The response of inflation to historical changes in climate 

We  find  a  rich  response  of  inflation  in  different  price  aggregates  to  a  variety  of  climatic 

measures (Fig. 1). Concerning headline inflation, the response to average monthly temperature 

increases  is  non-linear,  such  that  temperature  increases  in  hotter  months  and  regions  cause 

larger inflationary impacts. This finding is consistent with previous results (Ciccarelli et al., 

2023 ; Faccia et al., 2021; Mukherjee and Ouattara, 2021), but by using interaction terms our 

framework allows a flexible and unifying description of this heterogeneity across both seasons 

and  regions.  This  non-linearity  is  such  that  increases  in  average  temperatures  in  the  hottest 

month of the year cause upwards inflationary pressures in all regions of the world as well as in 

the coldest months at lower latitudes. At higher latitudes, the effects of a temperature increase 

in the coldest months is mostly negligible and can even be negative at the highest latitudes (Fig. 

S1). By using lagged climate measures, we find that the impacts of a 1C increase in monthly 

temperature persist across the entire twelve months following the initial shock (Fig. 1), causing 

a  cumulative  effect  of  0.17  percentage-points  over  the  following  year  (under  our  central 

specification shown in column 1 of Table 1). 

ECB Working Paper Series No 282112 
 
 
 
 
 
 
Figure 1. The response of inflation in three price aggregates to monthly climate shocks. 

Plots show the cumulative causal effect of a climatic change on month-on-month inflation rates in headline (a-e), 

food (f-j) and electricity (k-o) price aggregates at different times after the initial shock, as estimated from fixed-

effects panel regression models (see section 2.5). The effects on inflation due to deviations of monthly mean 

temperatures from their historical (1990-2020) average, deviations of the variability of daily temperatures from 

ECB Working Paper Series No 282113 
their historical (1990-2020) average, national exposure to excess wet (measured as the proportion of national 

population exposed to a standardised precipitation-evapotranspiration index (SPEI) of greater than 1-std), to 

excess dry (SPEI less than1std) and to extreme daily rainfall (defined to account for both extreme frequency and 

intensity based on regional percentiles, see section 2.3), are shown from top to bottom, having accounted for all 

measures simultaneously in the regression models. Effects are shown per 1C increase or per 1-std increase for 

the temperature and precipitation metrics respectively. The effects of mean temperature, excess dry and extreme 

precipitation are moderated by interactions with the monthly average temperature, and as such the effects are 

shown separately at the upper and lower quartiles and the median of observed temperatures, the values of which 

are shown in the legend of each panel. The effect of temperature variability is moderated by an interaction with 

the magnitude of the seasonal temperature cycle, and effects are shown at the corresponding upper and lower 

quartiles and the median of observed seasonal magnitudes. No significant interactions are found for impacts 

from exposure to excess wet. Points and lines show central estimates while error bars show 95% confidence 
intervals when clustering standard errors by country. 

In addition to the impacts arising from average temperatures, we also assess impacts from daily 

temperature variability (the standard deviation of daily temperatures within each month, see 

section 2.3). We find upwards pressures on inflation which depend on the magnitude of the 

seasonal  cycle,  with  larger  impacts  at  lower  latitudes  where  the  seasonal  cycle  is  less 

pronounced, reflecting the same patterns of vulnerability identified from the impacts of daily 

variability on economic growth (Fig. 2c) (Kotz et al., 2021). These impacts also persist over 

twelve months, although with increasingly large errors.  

To assess impacts from exposure to abnormal precipitation conditions, we use the Standardised 

Precipitation  Evapotranspiration  Index  (SPEI)  (Vicente-Serrano  et  al.,  2010).  This  measure 

accounts for contributions to moisture balance from both precipitation and temperature changes 

flexibly  across  different  timescales.  By  calculating  the  grid-cell  level  exceedance  of  certain 

SPEI thresholds and aggregating the proportion of national population exposed to these excess 

wet or dry conditions, we aim to limit the issue of spatially averaging over opposing effects. 

This is particularly relevant for precipitation given its larger spatial variability (Auffhammer et 

al.,  2020).  Using  a  time  scale  of  two  months  and  an  exposure-threshold  of  one  standard 

deviation, we find that excess wet conditions cause upwards impacts on inflation which persist 

over twelve months, independent of baseline climate conditions (Fig. 1). Excess dry conditions 

have some significant upwards impacts when occurring in hot months or regions, but these are 

ECB Working Paper Series No 282114 
 
generally less persistent or significant (Fig. 1). These results are qualitatively consistent under 

different SPEI timescales and thresholds (see SI Fig. S2). We further consider the impacts of 

daily  precipitation  extremes  (defined  as  population  exposure  to  the  grid-cell  level  relative 

exceedance  of  the  99th  percentile,  see  section  2.3)  to  assess  potential  heavy-precipitation 

impacts  arising  over  shorter  timescales  such  as  flooding  (Kotz  et  al.,  2022).  Statistically 

significant inflationary pressures can be identified in hot months in the first month following 

the  shock,  but  these  impacts  appear  not  to  persist  with  insignificant  cumulative  impacts  at 

further time-horizons. 

Assessing  different  price  aggregates,  we  find  that  the  effects  on  headline  inflation 

predominantly arise through food inflation (Fig. 1f-j, see Fig. S3 for other price aggregates). 

Impacts in food inflation are approximately twice as large, likely reflecting, besides a generally 

greater volatility in food inflation, the greater vulnerability of the agricultural sector to climatic 

shocks (Fig. S4 shows that the differential impacts on food and headline inflation persist when 

assessing normalised CPI data). Impacts on food inflation are likely to arise through supply-

side mechanisms given the considerable evidence for impacts on agricultural production from 

changes in mean temperature (de Lima et al., 2021; Moore et al., 2017b), temperature variability 

(Semenov  and  Porter,  1995;  Wheeler  et  al.,  2000)  as  well  as  precipitation  characteristics 

(Damania, 2020; Liang et al., 2017). Further work would however be required to elucidate these 

mechanisms  explicitly.  We  also  find  some  impacts  of  average  temperature  and  temperature 

variability  on  electricity  inflation,  although  with  larger  errors.  This  may  reflect  the  fewer 

observations  in  this  more  detailed  price  aggregate  as  well  as  a  relatively  weaker  signal. 

Nevertheless,  the  response  of  electricity  prices  to  temperature  is  consistent  with  micro-

economic  evidence  of  effects  of  temperature  on  the  demand  for  heating  and  cooling 

(Auffhammer et al., 2017; Wenz et al., 2017).  

ECB Working Paper Series No 282115 
 
 
 
Mean 
temperature 
impact on 
food 
inflation 

1 
Central 

2 
Dynamic 
panel 

3 
Driscoll-
Kray 
errors 

4 
Higher-lower- income 
split 

5 
Higher-lower- 
income split + 
normalised 

Higher-
income 

Lower-
income 

Higher-
income 

Lower-
income 

Instantaneous monthly impact 

at 21C 

at 25C 

0.09% 
(0.05, 
0.14) 
0.12% 
(0.07, 
0.18) 

Cumulative 12-month impact 

at 21C 

at 25C 

0.13% 
(0.06, 
0.20) 
0.17% 
(0.08, 
0.26) 

0.08% 
(0.03, 
0.13) 
0.11% 
(0.05, 
0.17) 

0.18% 
(0.03, 
0.33) 
0.23% 
(0.04, 
0.42) 

Impact under projected climates 

0.08% 
(0.04, 
0.12) 
0.11% 
(0.06, 
0.16) 

0.18% 
(0.02, 
0.34) 
0.23% 
(0.03, 
0.43) 

0.07% 
(0.003, 
0.051) 
0.09% 
(0.04, 
0.14) 

0.17% 
(0.09, 
0.25) 
0.22% 
(0.13, 
0.32) 

0.14% 
(0.07, 
0.21) 
0.19% 
(0.11, 
0.28) 

0.12% 
(0.004, 
0.24) 
0.11% 
(-0.04, 
0.27) 

0.06 stds 
(0.03, 
0.09) 
0.09 stds 
(0.04, 
0.12) 

0.16 stds 
(0.09,0. 
23) 
0.21 stds 
(0.12, 
0.30) 

0.10 stds 
(0.70, 
0.14) 
0.14 stds 
(0.10, 
0.17) 

0.11 stds 
(0.03, 
0.19) 
0.11 stds 
(0.002, 
0.21) 

Projected 
global 
average 
annual 
impact under 
SSP585 by 
2035 
Difference 
SSP585-
SSP126 at 
2060 

1.79% 
(1.24, 
2.33) 

2.48% 
(1.72, 
3.24) 

2.48% 
(1.72, 
3.24) 

2.14% 
(1.56, 
2.73) 

3.01% 
(2.17, 
3.84) 

1.87% 
(1.38, 
2.36) 

No 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Details of the empirical specification, accounting for… 
Country, year 
and country-
month fixed-
effects 
Country-
specific time 
trends 
Dynamic 
lags 
 Cross-
sectionally 
correlated 
errors 
Different 
economic 
development 
Different 
baseline 
inflation 
volatilities 

Yes 

Yes 

Yes 

No 

No 

No 

No 

No 

No 

No 

No 

1.65% 
(1.16,  
2.14) 

1.87% 
(1.38,  
2.36) 

Yes 

Yes 

No 

No 

Yes 

No 

6 
World 
bank 
data 

0.11% 
(0.06, 
0.15) 
0.13% 
(0.08, 
0.18) 

0.09% 
(0.01, 
0.17) 
0.12% 
(0.02, 
0.22) 

1.10% 
(0.63, 
1.20) 

1.42% 
(1.00, 
1.85) 

1.60% 
(1.16,  
2.04) 

1.87% 
(1.39,  
2.35) 

Yes 

Yes 

Yes 

No 

No 

Yes 

Yes 

Yes 

Yes 

No 

No 

No 

Inflation data 

Osbat 
and 
Parker 

Osbat 
and 
Parker 

Osbat 
and 
Parker 

Osbat and Parker 

Osbat and Parker 

WB 

Table 1: Robustness of the impact of mean temperature on food inflation (see next page) 

ECB Working Paper Series No 282116 
Table 1 (cont.). Robustness of the impact of mean temperature on food inflation to the 

empirical specification. The table provides an overview of alternative econometric specifications 

conducted as robustness tests which include different methodological techniques and datasets. Results of the 

historical monthly impacts identified empirically are shown in the first four rows. The fifth row shows the 

projected annual impacts under each specification by 2035 under a high emission scenario (SSP585), and the 

sixth row shows the difference between the high (SSP585) and low (SSP126) emission scenarios by 2060. Point 

estimates are shown with the 95% confidence intervals in the first four rows, whereas in the fifth row point 

estimates plus or minus the standard deviation of climate model spread are shown. Column 4 shows results 

having estimated effects separately for countries dependent on above- or below-median national incomes based 

on WB estimates (denoted higher- or lower- income), and column 5 having normalised month-on-month 

inflation rates by their within-country historical standard deviation. Results are qualitatively consistent across 

specifications, in terms of significant instantaneous and cumulative impacts of monthly average temperatures on 

food inflation which are stronger at higher temperatures and which cause considerable upward pressures on 
average inflation under future warming.  

These  historical  impacts  are  robust  to  a  number  of  tests  and  alternative  specifications,  an 

overview of which is shown in Table 1 concerning mean temperature impacts (the results for 

other variables can be found in Figs. S5-7). These tests include accounting for auto-correlations 

in inflation using a dynamic panel specification and for cross-sectionally correlated errors using 

Driscoll Kraay errors (Driscoll and Kraay, 1998). Moreover, we conduct tests in which we split 

our estimates based on above- or below-median national incomes (estimated from World Bank 

GDP and population data), as well as when normalising inflation data by its historical volatility. 

Doing  so  we  find  the  effects  of  average  temperature  increases  to  be  consistent  across  both 

higher-  and  lower-income  countries  and  when  accounting  for  different  baseline  historical 

volatility. The impacts of temperature variability are more heterogenous across income classes 

and price aggregates, and the effects from excess wet conditions are considerably stronger in 

higher-income countries (Fig. S6). The independence of the impacts of average temperature on 

baseline  socioeconomic  conditions  suggests  that  historical  adaptation  to  warming  has  been 

limited insofar as that adaptation is related to socioeconomic development.  

ECB Working Paper Series No 282117 
4. Implications under future warming – persistent upwards pressures on inflation 

This  empirical  evidence  for  historical  impacts  of  climate  shocks  on  inflation  suggests  that  the 

ongoing warming  and  intensification of  weather  extremes  and variability  due to anthropogenic 

greenhouse gas emissions (Arias et al., 2021) may have consequences for both current and future 

inflation. To assess these consequences, we combine the empirical responses identified above with 

projections of future climate change from an ensemble of 21 bias-adjusted climate models from 

the Coupled Model Intercomparison Project Phase 6. We focus on the role of average temperature 

due to the persistence of its impacts across income groups and price aggregates (Fig. 1), as well as 

due  to  the  stronger  response  of  warming  to  greenhouse  gas  forcing  compared  to  other  climate 

variables. We do not introduce explicit models of future adaptation which could mitigate these 

impacts but note that our results do account for historical adaptations which may have evolved 

through  prolonged  exposure  to  different  climate  conditions  and  through  differing  levels  of 

economic development. Indeed, the fact that we find larger impacts from increasing temperatures 

in hot regions and comparable impacts across both higher- and lower-income countries suggests 

that historical adaptation to temperature increases through these mechanisms has been limited. As 

such, we consider our estimated impacts to represent the exogenous effects of future warming on 

inflation which would occur in the absence of historically unprecedented adaptation such as the 

development of new technologies and other factors.  

Estimating the net annual impacts from the average warming occurring in the next 15 to 40 years 

with  our  central  empirical  specification  (see  section  2.7)  shows  upwards  inflationary  impacts 

across all of the world. In a projected 2035 climate, future warming is estimated to cause persistent 

increases  in  food  inflation  of  1.49±0.45  or  1.79±0.54  percentage-points  per-year  (p.p.p.y.) 

respectively  in  an  optimistic  or  pessimistic  emission  scenario  on  average  globally  (errors 

indicating the standard deviation across climate model projections). Impacts on headline inflation 

follow similar patterns and are approximately half as large, 0.76±0.23 or 0.91±0.28 p.p.p.y. under 

a best- or worst-case emission scenario (Fig. 3a, c). Moreover, these impacts vary quantitatively 

dependent on both the future emission pathways (shown in Fig. 3) and on the choice of empirical 

specification  (shown  in  Table  1  and  Figs.  S8-10),  as  well  as  on  the  range  of  climate  model 

projections. Uncertainty across all of these factors (see section 2.7) results in a range of potential 

ECB Working Paper Series No 282118 
 
global  average  annual  impacts  on  food  inflation  of  0.92-3.23  p.p.p.y.  by  2035,  and  0.32-1.18 

p.p.p.y. for headline inflation. These results therefore provide robust evidence of persistent upward 

pressures on inflation from projected global warming of considerable magnitudes already during 

the next decade, independent of future emission trajectories.  

Beyond  2035  the  magnitude  of  estimated  inflation  impacts  diverges  strongly  across  emission 

scenarios,  suggesting  that  decisive  mitigation  of  greenhouse  gases  could  substantially  reduce 

impacts.  The  difference  in  global  impacts  on  food  inflation  between  the  highest  and  lowest 

emission scenarios is 2.1 p.p.p.y. by 2060 in our central specification, with a full uncertainty range 

across empirical specifications and climate models of 1.6-3.8 p.p.p.y. For headline inflation this 

difference is 1.1 percentage-point under our central specification, with a full uncertainty range of 

0.49-1.38 p.p.p.y.  (Fig. 2, Figs. S8-10, Table 1 row 6). Under an optimistic emission scenario, 

impacts are only marginally larger in 2060 than in 2035, but a pessimistic emission scenario would 

cause impacts exceeding 4 p.p.p.y. across large parts of the world across empirical specifications 

(Fig. 2, Figs. S8-10). 

The  spatial  heterogeneity  of  inflationary  impacts  from  projected  warming  is  less  robust  across 

empirical specifications. Most empirical specifications estimate larger impacts across the global 

south  compared  to the global north,  and the largest impacts are found across Africa and South 
America  robustly  across  specifications  (Figs.  2  &  S8-10).1  However,  when  accounting  for  the 

different response between higher- and lower-income countries (Fig. S6, column 4 of Table 1), 

equally large impacts are estimated for North America and Europe as in Africa and South America 

(Fig. S9). The extent of projected future warming is larger at higher latitudes than at low latitudes 

due to polar amplification (Previdi et al., 2021), appearing to counter-balance the fact that higher 

latitudes currently experience lower temperatures and therefore a weaker response to temperatures 

in  this  specification.  However,  when  also  accounting  for  different  baseline  historical  inflation 

volatility (column 5 of Table 1), impacts are again larger across Africa and South America than 

North America or Europe (Fig. S10). 

1 Country-level impacts are aggregated to larger spatial regions using weighted by population (using World Bank 
estimates of national level population in 2017) to reflect the human exposure to future inflationary pressures. 

ECB Working Paper Series No 282119 
 
 
Figure 2. Estimated impacts on annual inflation rates from projected future warming. Maps 

of the impacts on annual national inflation from the warming expected by 2035 (assessed via 30-year averages 

centred on 2035) under a high-emission scenario (SSP585), as estimated from the projections of CMIP-6 climate 

models for headline (a) and food (b) price aggregates. Impacts are estimated accounting only for increasing average 

temperatures using the central specification shown in column 1 of Table 1. The annual impacts on inflation 

aggregated across world regions (population weighted), at different time periods (30-year averages centred on the 

point in question) and under both a low (SSP126) and high (SSP585) emission scenario for headline (a) and food (b) 

price aggregates. Point estimates show the average, and error bars the standard deviation, of impacts from the 

warming projected across the ensemble of 21 CMIP-6 climate models, in the absence of historically unprecedented 
adaptation.  

ECB Working Paper Series No 282120 
 
 
5. Implications under future warming – altered seasonality 

The use of monthly CPI data allows us to further assess how the estimated impacts from projected 

future  warming  are  distributed  across  the  year.  Concerning  food  inflation,  the  impacts  from 

average warming are fairly constant throughout the year across the tropics but vary considerably 

across seasons in Northern mid-latitudes (20-40N) where they can be twice as large in summer 

compared to winter (Fig. 3a).2 At the highest latitudes (>40N) upwards impacts in summer even 

contrast downwards impacts in winter. This seasonal and spatial heterogeneity to the inflationary 

impacts  of  future  warming  is  robust  across  empirical  specifications  (Figs.  S11-13),  although 

accounting for different historical baseline inflation volatilities (column 5 of Table 1) introduces 

some additional noise (Fig. S13). These seasonally heterogenous impacts would cause alterations 

to the  usual seasonality  of inflation, resulting in an amplification  of seasonal variability across 

most of the global south and the USA, and reductions in seasonal variability across most of Europe 

(excluding Spain) and the higher northern latitudes (Figs. 3b & S11-13b). A reduction in variability 

occurs  when  higher  temperatures  increase  month-on-month  inflation  rates  in  months  with 

historically lower rates, compared to other months (Fig. 3d). Figs. 3c-f show examples of such 

impacts for a selection of countries (see Fig. S14 for further European countries). The pattern of 

impacts across seasons and regions is similar for headline inflation, with smaller estimated impacts 

(Figs.  S15).  These  alterations  to  the  seasonal  dynamics  of  inflation  may  pose  challenges  to 

inflation forecasting, which could however be mitigated by introducing of weather variables in 

inflation forecast models (Vidal-Quadras Costa et al., forthcoming). 

2 To evaluate differences across different latitudinal zones we take binned averages across latitudinal zones to 
convey the relationship between latitude and the seasonality of inflation response and impacts. Countries are 
considered part of a latitudinal zone if their centroid falls within the zone’s boundaries, and in this context, we use a 
raw average without population weighting to reflect the nature of this relationship between latitude and impacts 
rather than to reflect the average human exposure to impacts. 

ECB Working Paper Series No 282121 
 
 
Figure 3. The estimated impacts of projected future warming on the seasonality of food 

inflation. (a) The average monthly impacts on food inflation across latitudinal bands estimated from the warming 

expected by 2035 (assessed via 30-year averages centred on 2035) under a high-emission scenario, as projected on 

average across the ensemble of CMIP-6 climate models. Impacts are estimated accounting only for increasing 

average temperatures. (b) The relative change in the seasonal variability of food inflation under future warming, 

estimated as the ratio of the standard deviations of future and historical average seasonal inflation cycles. (c-f) 

Country-specific examples of the impact on the seasonal cycle of food inflation. Black curves show the historical 

average month-on-month percentage change in the food consumer price index (CPI) with blue error bars showing 

the standard deviation across years. Red curves show this historical average cycle plus the impacts estimated from 
projected future warming with the error bars showing the standard deviation of projections across climate models. 

ECB Working Paper Series No 282122 
 
 
6. Implications under future warming – amplified extremes 

In addition to average warming trends, climate change is also altering the intensity and frequency 

of hot extremes which may pose additional risks compared to changes in average conditions. The 

heat extremes of the 2022 summer in Europe is a prominent example in which combined heat and 

drought had wide-spread impacts on agricultural and economic activity. These effects have likely 

added to recent inflationary pressures, but their exact magnitude is difficult to assess in the context 

of  other  pressures  from  the  Russian  invasion  of  Ukraine  and  the  aftermath  of  the  Covid-19 

pandemic. Using ERA-5 estimates of monthly temperatures, in June, July and August we estimate 

that  the  anomalous  heat  over  these  three  months  alone  caused  a  cumulative  impact  of  0.67 

percentage-points  (0.43-0.93 across empirical specifications)  on annual food inflation and 0.34 

percentage-points (0.18-0.41) on annual headline inflation in Europe, with larger impacts across 

Southern Europe (Fig. 4b shows maps of these impacts under our central empirical specification, 

see Figs. S16-18 for other specifications). For the euro area, we find aggregate impacts on food 

inflation of 0.78 p.p. in our central specification (using HICP country weights for the aggregation). 

Given that we find impacts from drought over the short-term but find their cumulative impacts 

over 12-months to be mostly negligible (Fig. 1), these figures may under-estimate the impacts of 

the  2022  summer  combined  heat  and  drought  extreme  in  the  short-term  but  are  likely  to  be 

representative of the cumulative long-term impacts. 

Future climate change will amplify the magnitude of such heat extremes, thereby also amplifying 

their potential impacts on inflation. To assess such effects, we assume that climate change will 

alter the distribution of future summer temperatures by shifting the mean. We therefore add the 

future  summer  warming  occurring  from  2022  onwards  in  CMIP-6  projections  to  the  historical 

temperatures  of  2022  and  re-evaluate  their  impact  using  our  empirical  response  functions  (see 

section 2.7). This approach estimates that an equivalent extreme summer (i.e., in the upper tail of 

the temperature distribution, shifted for future climates) would have impacts on food inflation in 

Europe of 1.0 percentage-points (0.6-1.6, uncertainty range across climate models and empirical 

specifications, see also Figs. S15-17) were it to occur in 2035 under a high-emission scenario, or 

of  0.9  percentage-points  (0.5-1.4)  under  a  low-emission  scenario  (Fig.  4d),  in  the  absence  of 

unprecedented adaptation. In the later part of the century, the amplification of such extremes would 

ECB Working Paper Series No 282123 
 
diverge under different emission scenarios, remaining at 1.1 percentage-points (0.6-1.8) by 2060 

under the  most optimistic  scenario compared  to  1.8 percentage-points (1.0-3.2) under the most 

pessimistic scenario of emission mitigation. Impacts on headline inflation follow similar patterns 

with  approximately  half  the  magnitude  (Fig.  4c).  This  suggests  that  in  addition  to  causing 

persistent  upward  pressures  on  inflation,  future  warming  will  also  amplify  the  impacts  from 

exceptionally hot summers such as that experienced in Europe in 2022. Extremely hot summers 

would  therefore  pose  increasingly  important  upside  risks  to  inflation,  causing  challenges  for 

inflation forecasting and, particularly under pessimistic emission scenarios, posing risks to price 

stability. 

Figure 4. Estimated impacts of 2022 summer extreme heat in Europe on inflation, and the 

amplification of impacts from such extremes under projected future warming. (a) The 

cumulative impacts on headline (a) and food (b) inflation from the observed temperatures of June, July and August 

of 2022 across Europe. Regionally aggregated (using a population weighting) impacts on headline (a) and food (b) 

inflation from the historical 2022 summer temperatures, as well as those impacts which would result from such a 

summer when amplified by future warming (see section 2.7). Future warming by 2035 and 2060 are estimated as the 

difference between 30-year averages centred on the respective future time-points and 2022, using climate models of 

the CMIP-6 ensemble under both low (SSP126) and high (SSP585) emission scenarios. Point estimates and error 
bars show the mean and standard deviation of impacts across climate models. 

ECB Working Paper Series No 282124 
 
7. Discussion  

While this work has identified a number of climatic measures with significant historical impacts 

on headline and food inflation globally, some limitations persist in providing a comprehensive and 

general  relationship  between  climate  and  inflation.  Firstly,  a  comprehensive  assessment  is 

challenging due to the wide range of potential climatic sources of impacts. For example, tropical 

cyclones  and  sea  level  rise  cause  damage  to  infrastructure  and  production  and  are  therefore 

potential sources of inflationary impacts of importance under both historical and future climate 

change.  Our  assessment  does  not  explicitly  account  for  their  effects  and  therefore  likely 

underestimates the possible future inflationary impacts of climate change in this regard. Future 

work assessing such climatic phenomena would be valuable but may face challenges due to limited 

extent  of  historical  sea  level  rise  and  the  smaller  spatial  extent  of  tropical  cyclones.  The 

development of datasets of consumer prices at higher spatial resolutions such as for sub-national 

regions may assist in the latter, as well as also improving the accuracy with which the impacts of 

temperature and precipitation can be identified. Indeed, precipitation changes have a higher spatial 

variability than temperature (Auffhammer et al., 2020) which may be a source of the large errors 

and  lack  of  persistence  in  the  precipitation  impacts  we  identify,  despite  using  a  thresholding 

approach to mitigate this issue (see section 2.4). High-resolution economic data has assisted in the 

identification of precipitation impacts on economic output (Damania, 2020; Kotz et al., 2022) and 

may also do so for inflation. 

Second, our results are identified predominantly for food and headline inflation, while we find a 

limited response of other price aggregates to the climate measures we utilise. Electricity prices 

show  some  response  to  temperature  with  larger  errors.  Lesser  data  availability  for  this  more 

detailed price aggregate as well as complex and heterogeneous electricity price-setting practices 

may  contribute  to  these  errors,  but  part  of  the  obscurity  of  impacts  here  may  also  result  from 

empirical  misspecification.  Indeed,  the  strong  response  of  electricity  demand  to  temperature 

(Auffhammer  et  al.,  2017;  Wenz  et  al.,  2017)  suggests  that  impacts  on  electricity  prices  are 

certainly plausible and that their identification may require more detailed analysis of either the 

heterogeneity  across  countries  and  seasons  or  the  design  of  alternative  climate  measures. 

Moreover, as electricity supply is - at least in parts of the world – increasingly met with renewable 

ECB Working Paper Series No 282125 
 
sources, the price sensitivity to temperature may change, warranting further research. A detailed 

analysis  of  other  price  aggregates  may  be  a  fruitful  avenue  of  future  work,  as  well  as  the 

investigation of other important mechanisms such as the spill-over of climate impacts on inflation 

via  trade (Kuhla  et al.,  2021) which may have important implications for commodity-intensive 

components in particular.    

The estimated inflationary impacts on food and headline price aggregates from projected future 

warming  have  considerable  implications  for  both  price  stability  and  wider  societal  welfare. 

Persistent additional upward pressures on annual food inflation from a changing climate across the 

world of magnitudes of 1-3 p.p.p.y. by 2035 (as demonstrated in Fig. 2 and Table 1) would place 

global incomes under pressure from rising prices and could impact inflation expectations, thereby 

requiring  monetary  policy  to  react.  These  impacts  will  be  heterogenous  across  regions,  with 

generally  larger  impacts  in  the  global  south.  Heterogeneous  impacts  may  be  particularly 

challenging  for  economic  union  areas  such  as  the  EU  where  larger  inflationary  impacts  from 

climate  change  in  southern  Europe  may  increase  inflation  differentials  with  implications  for 

monetary policy. Moreover, this may exacerbate pre-existing welfare discrepancies which can fuel 

anti-EU  sentiment  (Kuhn  et  al.,  2016).  In  addition  to  the  impacts  arising  from  average  future 

warming, the chaotic nature of temperature anomalies implies short-term, more rapid prices rises 

from exceptionally hot summers such as in Europe in 2022 (Fig. 4). The increasing intensity of 

such hot extremes with climate change will amplify their impact. Such additional shocks to prices 

–  happening  at  unpredictable  intervals  but  with  increasing  intensity  –  would  pose  additional 

challenges to monetary policy, especially when occurring on top of persistently higher inflationary 

pressures  from  higher  average  temperatures.  Importantly,  these  impacts  from  both  average 

warming  and  extremes  would  increase  considerably  by  2060  (approximately  doubling  in 

comparison to 2035) if future emissions are not mitigated, thereby providing strong incentive for 

rapid mitigation of greenhouse gases to limit future warming and its inflationary impacts. 

These estimated inflationary impacts from projected future warming do not explicitly model the 

potential  role  of  adaptation,  such  as  through  technological  progress,  in  limiting  their  extent. 

However, by accounting for the heterogenous response of economies to climate shocks dependent 

on baseline climatic conditions and levels of socio-economic development, these results implicitly 

ECB Working Paper Series No 282126 
 
account for adaptation that has actually occurred historically via either prolonged exposure to (and 

presumably normalisation of) certain climatic conditions, or technological adaptations which are 

related  to  socio-economic  development.  The  evidence  suggests  that  both  forms  of  historical 

adaptation have been very limited, with larger impacts in hotter regions and equivalent impacts 

across higher- and lower-income countries (Fig. 1, Table 1). As such, we interpret our results as 

the  impacts  which  are  likely  to  occur  in  the  absence  of  historically  un-precedented  future 

adaptation. Such future adaptation offers an opportunity to limit these impacts and its occurrence 

may increase in likelihood as awareness of climate change and its impact increase. For example, 

planned adoption of space cooling could limit heat stress impacts on labour productivity and crop 

switching could limit agricultural productivity losses, two major channels of impacts with potential 

relevance  to  inflation.  The  efficacy  and  opportunity  costs  of  such  practices  remain  largely 

unknown and therefore present an important avenue for further research on the scope to limit the 

inflationary impacts of a warming climate and intensifying heat extremes. 

ECB Working Paper Series No 282127 
 
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Annex 

A.1 Supplementary figures 

Figure S1. The historically observed non-linear response of inflation to temperature across 

regions and seasons. The cumulative response over 12 months of food inflation to a 1C increase 

in monthly mean temperature when occurring in either the hottest month of that countries year 

(a) or the coldest month of that countries year (b). The response to mean temperature increases in 

each separate month of the year, aggregated across latitudinal zones is shown in (d). The 

equivalent response of inflation to a 1C increase in daily temperature variability in any month of 

a given country (c). Estimates are extrapolated to regions without data on consumer price indices 

by evaluating the marginal effects estimated in Fig. 1 at the values of the moderating variable 

(either mean temperature or the magnitude of the seasonal temperature cycle) for each country. 

Countries in which these marginal effects have been extrapolated are shown hatched.  

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Figure S2. Precipitation impacts on inflation using SPEI at different timescales and impact 

thresholds. As Fig. 1 in the main manuscript but with alternative measures of excess wet and dry 

using SPEI at different timescales and impact thresholds. “spei0X_gt_Y” refers to a measure of 

national population exposure to SPEI values at a timescale of X months of greater than Y 

standard deviations, whereas “spei0X_ltm_Y” refers to the equivalent exposure at timescale of X 

months of less than minus Y standard deviations.  

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Figure S3. Historical impacts of climate variables on inflation in different price aggregates. 

As Fig. 1 but showing results for special price aggregates (goods, energy and services). Note that 

food constitutes a considerable proportion of the goods price aggregate. 

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Figure S4. Historical impacts of climate variables on normalized inflation in headline and 

food price aggregates. As Fig. 1 but showing results having normalized monthly inflation rates 

by their within-country standard deviation. Note the y-axis units of within-region standard 

deviations. 

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Figure S5. Historical impacts of climate variables on inflation using a dynamic lag 

specification and accounting for cross-sectionally correlated errors. As Fig. 1 but results for 

this alternative specification. 

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Figure S6. Historical impacts of climate variables on inflation accounting for heterogenous 

impacts across socio-economic conditions. As Fig. 1 but having estimated separately for above 

and below-median income countries.  

ECB Working Paper Series No 282136 
Figure S7. Historical impacts of climate variables on inflation using World Bank data on 

price aggregates. As Fig. 1 but using World Bank data, note the different methodological 

choices in constructing headline inflation in the World Bank and European Central Bank data 

used in our central specification (see methods).  

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Figure S8. Estimated inflationary impacts from projected future warming using dynamic 

panel model. As Fig. 2 of the main manuscript but using the empirical specification shown in 

column 2 of Table 1. 

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Figure S9. Estimated inflationary impacts from projected future warming accounting for 

the different responses between higher- and lower-income countries. As Fig. 2 of the main 

manuscript but using the empirical specification shown in column 4 of Table 1. 

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Figure S10. Estimated inflationary impacts from projected future warming accounting for 

the different responses of higher- and lower-income countries as well as baseline historical 

inflation volatility. As Fig. 2 of the main manuscript but using the empirical specification shown 

in column 5 of Table 1.  

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Figure S11. Seasonality of estimated impacts on food inflation from projected future 
warming using a dynamic panel specification. As Fig. 3 of the main manuscript but using 
the empirical specification shown in column 2 of Table 1. 

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Figure S12. Seasonality of estimated impacts on food inflation from projected future 
warming accounting for the different response between higher- and lower-income 
countries. As Fig. 3 of the main manuscript but using the empirical specification shown in 
column 4 of Table 1. 

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Figure S13. Seasonality of estimated impacts on food inflation from projected future 
warming accounting for the different response between higher- and lower-income 
countries and different historical baseline inflation volatilities. As Fig. 3 of the main 
manuscript but using the empirical specification shown in column 5 of Table 1. 

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Figure S14. Seasonality of estimated impacts on food inflation from projected future 
warming for major European economies by 2035 under a high-emission scenario. As 
Fig. 3c-f but for a broader range of countries. 

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Figure S15. Seasonality of estimated impacts on headline inflation from projected 
future warming. As Fig. 3 of the main manuscript but for headline inflation. 

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Figure S16. Estimated impacts of 2022 summer extreme heat in Europe on inflation, 
and the amplification of such impacts under projected future warming, using a dynamic 
panel specification. As Fig. 4 of the main manuscript but using the empirical specification 
shown in column 2 of Table 1. 

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Figure S17. Estimated impacts of 2022 summer extreme heat in Europe on inflation, 
and the amplification of such impacts under projected future warming, accounting for 
the different response between higher- and lower-income countries. As Fig. 4 of the main 
manuscript but using the empirical specification shown in column 4 of Table 1. 

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Figure S18. Estimated impacts of 2022 summer extreme heat in Europe on inflation, 
and the amplification of such impacts under projected future warming, accounting for 
the different response between higher- and lower-income countries and for different 
historical inflation volatilities. As Fig. 4 of the main manuscript but using the empirical 
specification shown in column 5 of Table 1. 

ECB Working Paper Series No 282148 
 
Acknowledgements 

We thank Chiara Osbat, Miles Parker, Mario Porqueddu and participants of an ECB-internal seminar for helpful comments and 
discussions. We acknowledge Potsdam Institute for Climate Impact Research for use of their high-performance computing facilities and 
data management services. 
The views expressed in this paper are those of the authors and should not be reported as representing the views of the European 
Central Bank (ECB) or the Eurosystem. 

Maximilian Kotz 
Potsdam Institute for Climate Impact Research, Potsdam, Germany; Institute of Physics, Potsdam University, Potsdam, Germany; 
email: maxkotz@pik-potdsam.de 

Friderike Kuik 
European Central Bank, Frankfurt am Main, Germany; email: friderike.kuik@ecb.europa.eu 

Eliza Lis 
European Central Bank, Frankfurt am Main, Germany; email: eliza.lis@ecb.europa.eu 

Christiane Nickel 
European Central Bank, Frankfurt am Main, Germany; email: christiane.nickel@ecb.europa.eu 

© European Central Bank, 2023 

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