Audition MCF

Université Paris 1 et CES

Jérôme Deyris
Chercheur post-doctorant - Sciences Po, Centre d’Études Européennes

Plan de la présentation

Mario Draghi
01
Profil de recherche
Intérêts de recherche, travaux passés et projets en cours
Job market paper
02
Job market paper
Warning words in a warming world,
EER (2025)
CES Université Paris 1 Panthéon-Sorbonne
03
Projet d'intégration
Au CES et à l'Université Paris 1 Panthéon-Sorbonne

Présentation du profil

Résumé du profil

Parcours

  • Thèse à l’Université Paris Nanterre (2019-2023)

    • avec visiting à l’Università di Bologna, au sein du projet ERC SMOOTH

  • Post-doc à Sciences Po (2023-2026), au sein du projet ANR-DFG KnowLegPo

Thématiques

  • Banques centrales, finance et changement climatique
  • Politique monétaire et dynamiques politiques

Méthodes

  • Données textuelles massives
  • Analyse textuelle computationnelle
  • Régressions économétriques

Banques centrales et changement climatique

Comment expliquer l’essor différencié du climat au sein des banques centrales ?

  • Au-delà des déterminants économiques et climatiques, le rôle des facteurs institutionnels (Deyris et al. 2024) et des pressions politiques (Deyris 2023)

Quelles conséquences économiques et financières ?

  • Une diversité de réformes des instruments, de la politique monétaire à la supervision (Baer et al. 2021)
  • Au-delà des interventions directes, les effets performatifs de la communication climatique (Campiglio et al. 2025)

Politique monétaire et dynamiques politiques

Quelles interactions entre banques centrales et politiciens ?

Quel impact des dynamiques électorales sur les banques centrales ?

  • Partis au pouvoir et diagnostic de l’inflation (Braun et al. 2026)
  • Cycles électoraux et ingérences fiscales (travail en cours)

Comment les conflits internes influencent-ils la politique monétaire ?

Job market paper

Central banks’ climate communication

In 2015, Mark Carney framed climate change as a financial stability issue.

This speech triggered a widespread interest among central banks.

Ten years later, we still know little about the causes and consequences of central banks’ climate communication.

A new dataset of CB speeches

Most papers on central bank communication rely on the BIS database.

  • >18,000 speeches from 100+ central banks

Yet, the BIS source suffers from important data gaps, which we address through

  • Systematic web-scraping of all CB websites
  • Targeted archival work for offline resources

CBS dataset: 35,487 central bank speeches (+89%), with new unique metadata (Gender, Position) - now available open access

Identifying climate speeches

How to identify climate communication in that corpus?

  • Dictionary-based approach with ~200 climate-related expressions [see appendix]

  • Only 2,968 climate speeches with at least one mention of a climate-related expression

A spectacular increase of climate-related communication

  • Extensive margin: More relevant speeches
  • Intensive margin: More climate-oriented

Identifying climate narratives

We implement a structural topic model, and uncover two main climate narratives

  • Green finance, a promotional narrative centered around opportunities in the low-carbon transition
  • Climate-related risks, a prudential narrative centered around the costs of climate change

These narratives are leveraged in a differentiated fashion across time and space

More in the appendix

What drives climate communication?

Dependent variable

  • Climate frequency (# of climate words)
  • Climate salience (% of climate words)
  • Climate-related risks (topic salience)
  • Green finance (topic salience)

Independent variables and controls

  • Climate drivers: physical exposure, carbon intensity
  • Institutional drivers: supervision, objectives, NGFS membership
  • Macro-financial controls: inflation, output gap, GDP per capita, insurance company assets to GDP, private credit to GDP


Main results: Robust to variable exclusion, shorter timeline, alternative topic model

  • Climate drivers are not significantly associated with more climate communication of any sort
  • Institutional variables matter (esp. involvement in supervision on CRR and NGFS membership)

More in the appendix

Climate communication impacts

Climate communication should affect firms differently depending on their greenness

3 measures of firms’ greenness (LSEG)

  • Environment score: emissions, innovation, resource use
  • Emission score: abatement commitment and effectiveness
  • Emission intensity: GHG/net revenue (scope 2)

Two research designs

  1. A portfolio analysis in the US market (building sectoral Green-minus-dirty portfolios)
  2. A firm level analysis in 41 countries (explaining individual stocks’ performance)

Main results:

  1. Portfolio analysis: On days with Fed communication, climate content is associated with higher GMD performance - with a bigger effect for emission intensity, CRR, and 10-90 percentiles portfolios

  2. Firm level analysis: With firm-FE or date-country-industry-FE, interaction between Climate Focus and Greenness is always significant and in the expected direction

More in the appendix

Projet d’intégration

Intégration dans le GR Finance

Politique monétaire & banques
Poursuivre mes travaux sur les déterminants des décisions de politique monétaire

Projets futurs

  • Le ciblage de l’inflation face aux chocs énergétiques, géopolitiques et climatiques
Marchés financiers & finance responsable

Nouer des collaborations avec des chercheurs en économie financière et finance quantitative

Projets futurs

  • Prolonger mes travaux sur les effets financiers de la communication climatique des banques centrales

Insertion au CES

Économie & Société

Intérêt commun pour les interactions entre sphères économique et politique

Projets futurs

  • CBParl : Essor du populisme, parlements nationaux et politique monétaire européenne
CES & AI Lab

Mettre mes compétences méthodologiques au service d’autres projets de recherche

Projets futurs

  • Participer aux réflexions sur l’ouverture, la reproductibilité et la soutenabilité des usages de l’IA générative en SHS

Expérience d’enseignement

J’ai enseigné à l’université (Paris Nanterre), en IEP (Sciences Po Paris), et en business school (NEOMA), à des étudiants de licence et de master

510h

équivalent TD, en français et en anglais

Doctorant contractuel ATER Vacataire

Matière Niveau Type Heures
Travaux dirigés
Macroéconomie A L1 TD 48h
Microéconomie C L2 TD 64h
Économie Industrielle L3 TD 112h
Finance d’entreprise M1 TD 32h
Gestion de portefeuilles M1 TD 96h
Cours magistraux
Marchés, Organisations, Institutions L3 CM 24h
The political economy of the transition M1 CM 12h
Central banks, finance & the climate crisis M1 CM 48h

Enseignements à Paris 1

Fondamentaux
Licence d’économie

Macroéconomie (L1-L3) Monnaie Banque Finance (L2) Mécanismes financiers (L3)

Magistère Finance et Master MBFA

Théorie économique et politique monétaire (I et II) Economics of banking (M1) Corporate finance (M1)

Ouverture
Économie, AES/SES et MEEF

Questions économiques contemporaines (L2) Économie européenne (L2) Politiques économiques (M1)

Master MBFA (parcours FRIC)

Mutations financières & finance durable (M1) Finance climat (M2) Green banking (M2)

Méthodes
Sciences sociales computationnelles
Web scraping, numérisation, OCR, structuration de données textuelles, Shiny apps
Modèles de topics, embeddings, classifieurs, grands modèles de langage

Merci !

Appendix JMP

Literature and research questions

3 distinct strands of literature

The political economy of green central banking, and its heterogenous nature

(Baer et al. 2021; Dikau and Volz 2021; Kedward et al. 2024; DiLeo 2023; Deyris 2023; D’Orazio and Popoyan 2019)

Central bank communication and its performative impacts on financial markets

(Blinder et al. 2008, 2024; Swanson 2021; Gorodnichenko et al. 2023)

The imperfect but improving financial market pricing of climate- and transition-related risks

(Bolton and Kacperczyk 2021; Ardia et al. 2023; Bauer et al. 2024; Bauer et al. 2023)

Few papers investigate central banks’ climate communication, leaving important questions unanswered (Arseneau and Osada 2023; Feldkircher et al. 2024)

A new dataset

Four main steps

  1. Data collection to complement the BIS central bankers’ speeches dataset

    • Systematic webscraping (143 CBs)
    • Targeted archival work (4 CBs)

  2. Data cleaning and de-duplication

  3. Translation of 5,347 speeches to English

  4. New meta-data coded (Position, Gender)

In total, from 18,802 to 35,487 central bank speeches (+89%), open access

Shiny app

Country heterogeneity in climate speeches

Climate dictionary

Keyword #
abrupt transition 19
brown penalising factors 3
carbon emission 68
carbon emissions 265
carbon price 62
carbon prices 56
carbon pricing 92
carbon tax 72
carbon taxes 62
climate action 177
climate actions 19
climate adaptation 29
climate aligned 8
climate change 2007
climate changes 34
climate crisis 122
climate damage 2
climate data 47
climate economics 5
climate event 4
climate events 60
climate exposure 1
climate exposures 7
climate extremes 6
climate finance 75
climate friendly 56
Keyword #
climate goals 67
climate harm 1
climate hazard 2
climate hazards 7
climate impact 44
climate impacts 19
climate metrics 3
climate minsky moment 15
climate policies 108
climate policy 151
climate protection 57
climate related 745
climate relevant 6
climate risk 432
climate risks 480
climate scenario 50
climate scenarios 110
climate science 20
climate sensitivity 3
climate shock 1
climate shocks 25
climate stability 9
climate stress test 63
climate stress tests 51
climatologist 2
climatologists 7
Keyword #
climatology 1
cotwo 174
decarbonise 43
decarbonised 10
decarbonising 23
decarbonization 59
decarbonize 9
decarbonized 9
decarbonizing 6
disorderly transition 51
disorderly transitions 4
environment risk 4
environment risks 3
environmental risk 122
environmental risks 276
global warming 341
green bond 240
green bonds 300
green economy 115
green finance 458
green finances 1
green investment 114
green investments 118
green monetary 6
green policies 10
green policy 13
Keyword #
green qe 6
green quantitative easing 8
green supporting factor 10
green supporting factors 5
green swan 27
green swans 8
green technologies 82
green technology 48
green transition 254
green transitions 6
greener 325
greenhouse 378
greening 529
low carbon 432
ngfs 357
paris agreement 274
physical risk 91
physical risks 237
stranded asset 4
stranded assets 68
sustainable finance 607
sustainable finances 12
sustainable investing 50
tcfd 139
transition risk 117
transition risks 310

Note: This table reports the list of keywords used in our dictionary. Speeches # indicates the number of speeches using each n-gram at least once. Red entries: 10 expressions with the highest number of hits.

STM topic selection

Choice of K

  • STM estimated over multiple values of \(K\)
  • Trade-off between semantic coherence and exclusivity
  • Baseline: 10-topic STM
  • Robustness: alternative 13-topic STM

Back to the climate topics

STM topic labels

Topic Label Most frequent words
1 European economy policy, european, euro, economic, monetary, country
2 Financial markets financial, market, finance, global, asia, industry
3 Social economy people, economic, time, country, social, economy
4 Financial stability bank, financial, risk, market, asset, regulation
5 Economic outlook price, economy, growth, economic, percent, increase
6 Climate-related risks risk, climate, financial, change, insurance, impact
7 Inflation and monetary policy inflation, policy, rate, monetary, price, target
8 Debt and crisis increase, economic, crisis, economy, debt, sector
9 Financial inclusion and development bank, financial, development, economic, sector, country
10 Green finance green, climate, finance, sustainable, investment, transition

Back to the climate topics

Climate topics in time

Back to the climate topics

Climate topics by central bank

Back to the climate topics

Dependent variables at the CB-year level

Distributions of dependent variables at the central-bank-year level

Diagnostic plot on the available speech panel, 2005-2021. Non-climate speeches are coded as zero for the STM climate topics before CB-year aggregation, so the figure visualizes why the outcomes are non-negative, zero-heavy and right-skewed.

Back to climate communication drivers

Correlations between dependent variables

Pairwise correlations between dependent variables at the central-bank-year level

Pairwise diagnostic plot on the same CB-year panel. Axes use log(1 + value); labels report Pearson correlations on the transformed variables.

Back to climate communication drivers

Why PPML?

Why PPML?

  • Dependent variables are either count variables (frequency), or share(-like) variables: non-negative intensity measures
  • The distributions are highly skewed, with many country-years observations at zero (72%)
  • A log-linear OLS setup would handle zeros awkwardly and can be sensitive to retransformation issues
  • PPML estimates a multiplicative conditional mean while keeping coefficient interpretation in proportional terms

Why year FE and country-clustered SE?

  • Year fixed effects absorb common global shocks: Paris, NGFS diffusion, global climate salience, and macro-financial shocks
  • The identifying variation is therefore mostly cross-country within the same year
  • Errors are likely serially correlated within country over time
  • Country clustering gives more conservative inference than iid standard errors in this panel setting

Back to climate communication drivers

Detailed model estimated with PPML

\[ \begin{aligned} \mathbb{E}\!\left[ClimateFocus_{c,t}\right] = \exp\Big(& \beta_0 + \beta_1 PhysicalExposure_{c,t} + \beta_2 CarbonIntensity_{c,t} + \beta_3 CarbonIntensity^2_{c,t} \\ &+ \beta_4 CBSupervision_{c,t} + \beta_5 CBObjectives_{c,t} + \beta_6 NGFSMembership_{c,t} \\ &+ \beta_7 Inflation_{c,t} + \beta_8 OutputGap_{c,t} + \beta_9 GDPpc_{c,t} \\ &+ \beta_{10} InsuranceAssets/GDP_{c,t} + \beta_{11} PrivateCredit/GDP_{c,t} + \mu_t \Big) \end{aligned} \]

Element Variable(s) Data source / construction
Dependent variable \(ClimateFocus_{c,t}\) Estimated separately for climate frequency, climate salience, climate-related risks, and green finance Central bank speeches dataset; climate dictionary for frequency/salience; STM topic prevalence for risks/green finance; aggregated at CB-year level
Physical exposure Monetary damages from climate-related disasters / GDP EM-DAT disaster losses; World Bank GDP
Transition exposure CO\(_2\) emissions / GDP; squared term Our World in Data / Global Carbon Project emissions; World Bank GDP
Central bank institutions CB supervision (0, .2, .4, .6, .8, 1)
CB objectives (0, .25, .5, .75, 1)		 CBIS: none; shared banking; banking; banking + insurance; banking + securities; full responsibility.
CBIE objectives: no price stability; growth/development; conflicting objectives; non-conflicting objectives; price stability primary/sole
Peer network NGFS membership dummy NGFS annual reports and press releases; coded 0 before 2017
Macro controls Inflation; output gap; GDP per capita IMF / World Bank macroeconomic series
Financial structure controls Insurance company assets / GDP; private credit / GDP World Bank financial development indicators
Fixed effects and inference Year fixed effects \(\mu_t\); country-clustered SE Year FE absorb common global shocks; clustered SE allow within-country serial correlation

Back to climate communication drivers

Drivers: main results

PPML estimates, year fixed effects, country-clustered standard errors. 59 countries, 1140 country-years observations:

Variable Green finance Climate risks Frequency Salience
Physical exposure -0.420 -0.141 -0.466 -0.475
Carbon intensity -4.087 10.282 0.055 1.054
Carbon intensity² 1.199 -23.755 -4.299 -6.762
CB supervision 0.170 1.875*** 0.651** 0.734*
CB objectives -0.062 -0.537 0.320 0.040
NGFS membership 2.309*** 2.196*** 1.944*** 2.075***
Inflation -0.011 -0.015 -0.021 -0.025
Output gap -6.107** -5.686 -1.919 -5.044
GDP per capita 0.005 -0.010 0.005 -0.008
Insurance assets / GDP -0.006 0.005 0.001 0.001
Private credit / GDP 0.004 0.007* 0.003 0.004

In a PPML model, coefficients are interpreted proportionally. Example: a one-standard-deviation increase in CB supervision (\(0.29\)) is associated with about 21% more expected climate keywords (\(e^{0.651 \times 0.29}-1\)), roughly +0.08 keywords per speech at the sample mean.

Back to climate communication drivers

Drivers: robustness checks

What is tested?

  • Excluding the NGFS dummy
  • Shortening the timeline to post-2005
  • Using an alternative 13-topic STM
  • Combining 13-topic STM with NGFS exclusion / shortened timeline
  • Entering explanatory variables separately to probe collinearity
Robustness check What it addresses Main takeaway
NGFS excluded NGFS may absorb institutional effects CB supervision remains positive and significant
Shorter timeline Early zero-heavy years may drive results NGFS remains strongly associated with climate focus
13-topic STM Topic measurement may drive results Institutional variables remain the strongest predictors
Separate variables Multicollinearity among regressors CB supervision and NGFS remain significant

Overall, climate exposure variables remain weak and unstable, while institutional variables are the most robust predictors of climate communication.

Back to climate communication drivers

Drivers: robustness on frequency and risks

Climate frequency

Specification Physical exposure Carbon intensity CB supervision NGFS membership N
Baseline -0.466 0.055 0.651** 1.944*** 1140
NGFS excluded -0.574* -0.597 0.885** - 1140
Shorter timeline -0.022 1.788 0.328 1.519*** 1536
13-topic STM -0.466 0.055 0.651** 1.944*** 1140
13-topic STM, NGFS excluded -0.574* -0.597 0.885** - 1140
13-topic STM, shorter timeline -0.022 1.788 0.328 1.519*** 1536

Climate-related risks

Specification Physical exposure Carbon intensity CB supervision NGFS membership N
Baseline -0.141 10.282 1.875*** 2.196*** 1140
NGFS excluded -0.207 7.754 2.184*** - 1140
Shorter timeline -0.016 6.225 1.148*** 1.662*** 1536
13-topic STM -0.296 7.869 0.957 2.485*** 1140
13-topic STM, NGFS excluded -0.428 6.010 1.216* - 1140
13-topic STM, shorter timeline -0.052 6.265 0.749* 1.933*** 1536
Variables entered separately -0.314 6.131 1.635*** 2.703*** 1140

PPML estimates with year fixed effects and country-clustered standard errors. Stars denote significance at 10%, 5%, and 1%. Separate-variable robustness is reported in the paper for climate-related risks, not climate frequency.

Back to climate communication drivers

Portfolio analysis: specification

We estimate the following linear regression with OLS:

\[ r^{GMD}_{s,t} = \beta_0 + {\color{#ff7f0e}{\beta_1}} ClimateFocus_t + \beta_2 Mkt_t + \beta_3 Value_t + \beta_4 Size_t + \beta_5 \Delta Oil_t + \mu_m + \varepsilon_{s,t} \]

Portfolio construction and sample

  • US industry-level green-minus-dirty portfolios
  • Green/dirty firms selected within industry at 25-75 or 10-90 percentiles
  • Three greenness measures: Environmental score, Emission score, Emission intensity
  • Sample includes all Fed communication days, so identification comes from higher vs lower climate focus

Coefficient of interest and controls

  • \(\beta_1\): association between Fed climate focus and GMD returns, conditional on market factors
  • \(Mkt_t\): excess market return
  • \(Value_t\): high-minus-low factor proxy
  • \(Size_t\): small-minus-big factor proxy
  • \(\Delta Oil_t\): daily crude oil returns
  • Monthly fixed effects \(\mu_m\)
  • Standard errors clustered at sector level

Back to the main text

Portfolio analysis: full table

Impact of climate-related communication measures on green-minus-dirty portfolio returns. Standard errors clustered at sector level. Stars denote significance at 10%, 5%, and 1%.

Green-minus-dirty portfolio returns Green finance Climate risks Frequency Salience
Panel A: 25-75th percentiles
Environmental score 2.066 0.114 0.002* 0.085**
(2.621) (0.080) (0.001) (0.036)
Emission score 2.472 0.121 0.002** 0.082**
(2.514) (0.078) (0.001) (0.035)
Emission intensity 4.516* 0.277** 0.003*** 0.123**
(2.356) (0.100) (0.001) (0.046)
Panel B: 10-90th percentiles
Environmental score 1.949 0.171 0.003** 0.124**
(4.518) (0.147) (0.001) (0.053)
Emission score 5.991* 0.220** 0.003*** 0.137***
(3.230) (0.094) (0.001) (0.033)
Emission intensity 6.117 0.575*** 0.006*** 0.226***
(3.934) (0.174) (0.002) (0.072)
Controls Yes Yes Yes Yes

Positive coefficients: conditional on market factors, higher Fed climate focus is associated with higher green-minus-dirty portfolio returns. The effect is stronger for more polarised portfolios (10-90)

Back to the main text

Firm-level analysis (with firm FE)

We estimate the following linear panel regression with OLS:

\[ r_{i,c,t} = \beta_0 + \beta_1 ClimateFocus_{c,t} + \beta_2 Greenness_{i,y} + {\color{#ff7f0e}{\beta_3}} \left(ClimateFocus_{c,t} \times Greenness_{i,y}\right) + \theta'X_{i,c,t} + \alpha_i + \mu_y + \varepsilon_{i,c,t} \]

Variables

  • \(r_{i,c,t}\): Daily stock returns for firms
  • Climate focus: country-day central bank communication
  • Greenness: Environmental score, Emission score, Emission intensity
  • \(X_{i,c,t}\): firm controls and market factors
    • firm controls: size, cash flow/sales, leverage, revenue growth, profitability
    • market factors: market, size and value factors

Coefficient of interest and estimation

  • \(\beta_3\): do greener firms react differently when communication is more climate-focused?
    • Expected sign: positive for scores, negative for emission intensity
  • Firm and year fixed effects
  • SE clustered at country-industry level

Back to the main text

Firm-level FE: Environmental score

Daily returns Green finance Climate risks Frequency Salience
Climate focus -0.109* -0.077** -0.002*** -0.037***
(0.058) (0.032) (0.000) (0.012)
Environmental score -0.018 -0.031** -0.031** -0.033***
(0.012) (0.012) (0.013) (0.013)
Climate focus x Environmental score -0.012 0.102*** 0.001*** 0.035***
(0.038) (0.022) (0.000) (0.007)
Observations 381 092 381 092 381 092 381 092
R-Squared 0.149 0.150 0.150 0.150
Firm FE / Year FE / Controls Yes Yes Yes Yes

Effects of climate-related communication with firm and year fixed effects. Controls included. Standard errors clustered at country-industry level.

Back to the main text

Firm-level FE: Emission score

Daily returns Green finance Climate risks Frequency Salience
Climate focus -0.113* -0.077** -0.002*** -0.037***
(0.058) (0.031) (0.000) (0.012)
Emission score -0.015 -0.029*** -0.030** -0.032***
(0.012) (0.011) (0.012) (0.011)
Climate focus x Emission score 0.003 0.104*** 0.001*** 0.039***
(0.045) (0.022) (0.000) (0.007)
Observations 381 092 381 092 381 092 381 092
R-Squared 0.149 0.150 0.150 0.150
Firm FE / Year FE / Controls Yes Yes Yes Yes

Effects of climate-related communication with firm and year fixed effects. Controls included. Standard errors clustered at country-industry level.

Back to the main text

Firm-level FE: Emission intensity

Daily returns Green finance Climate risks Frequency Salience
Climate focus -0.144*** -0.073** -0.002*** -0.027***
(0.052) (0.030) (0.000) (0.010)
Emission intensity 0.001 0.001 0.002* 0.002
(0.001) (0.001) (0.001) (0.002)
Climate focus x Emission intensity -0.039*** -0.016*** -0.000*** -0.005***
(0.009) (0.002) (0.000) (0.002)
Observations 254 975 254 975 254 975 254 975
R-Squared 0.163 0.163 0.164 0.163
Firm FE / Year FE / Controls Yes Yes Yes Yes

Effects of climate-related communication with firm and year fixed effects. Controls included. Standard errors clustered at country-industry level.

Back to the main text

Firm-level analysis (saturated FE)

We estimate a stricter linear panel regression with OLS:

\[ r_{i,c,t} = \beta_0 + \beta_2 Greenness_{i,y} + {\color{#ff7f0e}{\beta_3}} \left(ClimateFocus_{c,t} \times Greenness_{i,y}\right) + \theta'X_{i,c,t} + \lambda_{c \times industry \times date} + \varepsilon_{i,c,t} \]

Sample and variables

  • Same firm-level return and greenness measures
  • \(X_{i,c,t}\): firm controls and market factors
    • firm controls: size, cash flow/sales, leverage, revenue growth, profitability
    • market factors: market, size and value factors
  • Compares greener and dirtier firms in the same country-industry-date
  • Absorbs all shocks common to that country, industry and day

Coefficient of interest and implication

  • \(\beta_3\): within-cell heterogeneous response by greenness
  • The standalone \(ClimateFocus_{c,t}\) coefficient is absorbed by the fixed effect
  • The interaction remains identified through firm greenness variation

Back to the main text

Saturated FE: Environmental score

Daily returns Green finance Climate risks Frequency Salience
Environmental score 0.002 -0.002 -0.008 -0.009
(0.007) (0.008) (0.009) (0.009)
Climate focus x Environmental score 0.085** 0.055*** 0.001*** 0.031***
(0.042) (0.020) (0.000) (0.008)
Observations 372 581 372 581 372 581 372 581
R-Squared 0.219 0.219 0.219 0.219
Country x Industry x Date FE / Controls Yes Yes Yes Yes

Identification comes from variation between green and dirty firms in the same country-industry on the same date. Standard errors clustered at country-industry level.

Back to the main text

Saturated FE: Emission score

Daily returns Green finance Climate risks Frequency Salience
Emission score -0.005 -0.011 -0.016* -0.018**
(0.007) (0.009) (0.010) (0.009)
Climate focus x Emission score 0.074* 0.065*** 0.001*** 0.034***
(0.042) (0.022) (0.000) (0.009)
Observations 372 581 372 581 372 581 372 581
R-Squared 0.219 0.219 0.219 0.219
Country x Industry x Date FE / Controls Yes Yes Yes Yes

Identification comes from variation between green and dirty firms in the same country-industry on the same date. Standard errors clustered at country-industry level.

Back to the main text

Saturated FE: Emission intensity

Daily returns Green finance Climate risks Frequency Salience
Emission intensity -0.001 -0.000 0.000 0.000
(0.001) (0.001) (0.001) (0.001)
Climate focus x Emission intensity -0.051*** -0.022*** -0.000*** -0.007***
(0.006) (0.002) (0.000) (0.001)
Observations 245 424 245 424 245 424 245 424
R-Squared 0.257 0.257 0.257 0.257
Country x Industry x Date FE / Controls Yes Yes Yes Yes

Identification comes from variation between green and dirty firms in the same country-industry on the same date. Standard errors clustered at country-industry level.

Back to the main text

References

Ardia, David, Keven Bluteau, Kris Boudt, and Koen Inghelbrecht. 2023. “Climate Change Concerns and the Performance of Green Vs. Brown Stocks.” Management Science 69 (12): 7607–32. https://doi.org/10.1287/mnsc.2022.4636.
Arseneau, David M, and Mitsuhiro Osada. 2023. Central Bank Mandates and Communication about Climate Change. Bank of Japan Working Paper Series No.23-E-14. Bank of Japan.
Baer, Moritz, Emanuele Campiglio, and Jérôme Deyris. 2021. “It Takes Two to Dance: Institutional Dynamics and Climate-Related Financial Policies.” Ecological Economics 190 (December): 107210. https://doi.org/10.1016/j.ecolecon.2021.107210.
Bauer, Michael D., Eric A. Offner, and Glenn D. Rudebusch. 2024. Green Stocks and Monetary Policy Shocks: Evidence from Europe. Hutchins Center Working Paper No. 100. Hutchins Center on Fiscal; Monetary Policy at Brookings. https://www.brookings.edu/articles/green-stocks-and-monetary-policy-shocks-evidence-from-europe/.
Bauer, Michael D, Eric A Offner, and Glenn D Rudebusch. 2023. The Effect of u.s. Climate Policy on Financial Markets: Hutchins Center Working Paper No. 89. Brookings Institution. https://www.brookings.edu/articles/the-effect-of-u-s-climate-policy-on-financial-markets-an-event-study-of-the-inflation-reduction-act/.
Blinder, Alan S., Michael Ehrmann, Jakob De Haan, and David-Jan Jansen. 2024. “Central Bank Communication with the General Public: Promise or False Hope?” Journal of Economic Literature 62 (2): 425–57. https://doi.org/10.1257/jel.20231683.
Blinder, Alan S., Michael Ehrmann, Marcel Fratzscher, Jakob De Haan, and David-Jan Jansen. 2008. “Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence.” Journal of Economic Literature 46 (4): 910–45. https://doi.org/10.1257/jel.46.4.910.
Bolton, Patrick, and Marcin Kacperczyk. 2021. “Do Investors Care about Carbon Risk?” Journal of Financial Economics 142 (2): 517–49. https://doi.org/10.1016/j.jfineco.2021.05.008.
Braun, Benjamin, Jérôme Deyris, and Monica DiLeo. 2026. “Fear of Full Unemployment: Labor and Inflation at the Federal Reserve.” Submitted to AJPS.
Campiglio, Emanuele, Jérôme Deyris, Davide Romelli, and Ginevra Scalisi. 2025. “Warning Words in a Warming World: Central Bank Communication and Climate Change.” European Economic Review 178 (September): 105101. https://doi.org/10.1016/j.euroecorev.2025.105101.
D’Orazio, Paola, and Lilit Popoyan. 2019. “Fostering Green Investments and Tackling Climate-Related Financial Risks: Which Role for Macroprudential Policies?” Ecological Economics 160 (June): 25–37. https://doi.org/10.1016/j.ecolecon.2019.01.029.
Deyris, Jérôme. 2023. “Too Green to Be True? Forging a Climate Consensus at the European Central Bank.” New Political Economy 28 (5): 713–30. https://doi.org/10.1080/13563467.2022.2162869.
Deyris, Jérôme. 2026. “Accountability for What? The Parliamentary Hearings of Banque de France.” Submitted to EJPE.
Deyris, Jérôme, Gaëtan Le Quang, and Laurence Scialom. 2024. “Un passé dépassé ? L’indépendance des banques centrales au XXIe siècle.” Revue Française de Socio-Économie 33 (2): 127–47. https://doi.org/10.3917/rfse.033.0127.
Deyris, Jérôme, Bart Stellinga, and Matthias Thiemann. 2026. “Central Bankers as Migrating Birds: How Inflation Shapes the Rhetorical Strategies of Doves and Hawks.” JCMS: Journal of Common Market Studies 64 (2): 787–810. https://doi.org/10.1111/jcms.70017.
Dikau, Simon, and Ulrich Volz. 2021. “Out of the Window? Green Monetary Policy in China: Window Guidance and the Promotion of Sustainable Lending and Investment.” Climate Policy 0 (0): 1–16. https://doi.org/10.1080/14693062.2021.2012122.
DiLeo, Monica. 2023. “Climate Policy at the Bank of England: The Possibilities and Limits of Green Central Banking.” Climate Policy 23 (6): 671–88. https://doi.org/10.1080/14693062.2023.2245790.
Feldkircher, Martin, Paul Hofmarcher, and Pierre L. Siklos. 2024. “One Money, One Voice? Evaluating Ideological Positions of Euro Area Central Banks.” European Journal of Political Economy 85 (December): 102582. https://doi.org/10.1016/j.ejpoleco.2024.102582.
Gorodnichenko, Yuriy, Tho Pham, and Oleksandr Talavera. 2023. “The Voice of Monetary Policy.” American Economic Review 113 (2): 548–84. https://doi.org/10.1257/aer.20220129.
Kedward, Katie, Daniela Gabor, and Josh Ryan-Collins. 2024. “Carrots with(out) Sticks: Credit Policy and the Limits of Green Central Banking.” Review of International Political Economy 0 (0): 1–25. https://doi.org/10.1080/09692290.2024.2351838.
Stellinga, Bart, Matthias Thiemann, and Jérôme Deyris. 2026. “Acknowledged but Ignored? Financial Stability in US Monetary Policy After the Crisis.” Submitted to RIPE.
Swanson, Eric T. 2021. “Measuring the Effects of Federal Reserve Forward Guidance and Asset Purchases on Financial Markets.” Journal of Monetary Economics 118 (March): 32–53. https://doi.org/10.1016/j.jmoneco.2020.09.003.
Vale, Adriano do, Jérôme Deyris, Thibault Laurentjoye, and Léo Malherbe. 2026. “Presumed Independent - European Central Bankers’ Interactions and Political Capitalism.” In The Handbook of Political Capitalism, Cambridge University Press. Cambridge University Press.