IMES DISCUSSION PAPER SERIESInternational Comparison of Climate Change News
Index with an Application to Monetary Policy
Takuji Fueki, Takeshi Shinohara, and Mototsugu Shintani
Discussion Paper No. 2024-E-3

INSTITUTE FOR MONETARY AND ECONOMIC STUDIESBANK OF JAPAN
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IMES Discussion Paper Series 2024-E-3
April 2024

International Comparison of Climate Change News
Index with an Application to Monetary Policy

Takuji Fueki*, Takeshi Shinohara**, and Mototsugu Shintani***
Abstract
We construct a Climate Change News (CCN) index which measures attention toclimate change risk for Japan, based on text information from newspaper articles.
We compare our index with the original WSJ Climate Change News index of Engle
et al. (2020) for the U.S. (WSJ-CCN index), as well as other measures ofmacroeconomic uncertainty. We find that the correlation between the CCN indexesof the U.S. and Japan is much higher than the correlation between the CCN index
and other uncertainty measures in either of those countries. We also find that shocks
to the CCN indexes have significantly negative effects on economic sentiment, buthave ambiguous effects on industrial production. This contrasts with the fact that,for both the U.S. and Japan, other uncertainty shocks have negative effects on both
economic sentiment and industrial production. As an application of the CCN indexes,
we investigate if the effectiveness of monetary policy depends on the degree ofattention to climate change risks.

Keywords: Climate Change; Text Analysis; Monetary Policy; Nonlinear Local
Projection
JEL classification: E32, E52, Q54* Hitotsubashi University (E-mail: takuji.fueki@r.hit-u.ac.jp)** Deputy Director and Economist, Institute for Monetary and Economic Studies, Bank of Japan(E-mail: takeshi.shinohara@boj.or.jp)*** The University of Tokyo (E-mail: shintani@e.u-tokyo.ac.jp)
The authors would like to thank Jouchi Nakajima, Etsuro Shioji, Markus Heckel, Ulrich Volz,participants of the CEBRA Annual Meeting 2022, the 2022 JEA Autumn Meeting, the
CMStatistics 2023, Sixth Annual Conference of the Japan Economy Network and the staff of the
Bank of Japan for their valuable comments. The views expressed in this paper are those of theauthors and do not necessarily reflect the official views of the Bank of Japan.

1Introduction

Climate change is at the forefront of academic and policy debates. A growing body ofliterature has explored the negative impacts of climate change on the real economy, suchas rising temperatures or sea levels and the increasing frequency of extreme weather events(Burke et al. (2015), Colacito et al. (2019), and Kim et al. (2022)). Additionally, climatechange negatively affects financial markets through the damage to asset values associatedwith disasters (Ortega and Tas.pınar (2018), Alok et al. (2020), and Addoum et al. (2023)).
Recently, the impact of climate change uncertainty and risk on financial markets has been
discussed by Engle et al. (2020), Krueger et al. (2020), and Bua et al. (2022). They pointout that climate change is affecting financial markets as investors begin to hedge against
growing climate change-related risks. However, few empirical studies have examined theimpact of climate change uncertainty and risk on the real economy.
Recent studies, pioneered by Bloom (2009), have found that various kinds of uncertainty have significant impacts on the real economy.1 Some studies argue that increases in
uncertainty related to economic policies (i.e., Economic Policy Uncertainty [EPU]) negatively affect the real economy (Baker et al. (2016), Caldara et al. (2020), and Husted et al.
(2020)). Jurado et al. (2015), Scotti (2016), and Redl (2020) show that macroeconomic
uncertainty (MU) has a detrimental effect on the real economy. Moreover, financial market
volatility (VI) adversely influences the real economy (Bloom (2009), Bonciani and Ricci(2020), and Ludvigson et al. (2021)). However, there is a lack of understanding of the
impact of climate change uncertainty and risk on the real economy.
This study aims to fill these gaps in the literature. We examine how increasing uncertainties and concerns related to climate change affect the real economy. To this end, we
follow Engle et al. (2020) and construct a climate change news (CCN) index that measuresattention to climate change risks in Japan based on text information from newspaper articles. Engle et al. (2020) extract a climate news series using textual analysis based on The
Wall Street Journal and build an index of attention to climate change for the U.S. (WSJCCN index). We apply this method to Japan and measure the extent to which attention ispaid to climate change risks. Based on these indexes, we statistically investigate the link
between climate change uncertainty and risk and the real economy.
1
Bloom (2014) stresses that uncertainty is “a broad concept, including uncertainty over the path of macro
phenomena ..., micro phenomena ..., and noneconomic events like war and climate change.”1This study’s main contributions are twofold. First, we investigate the statistical properties of the CCN indexes. Focusing on the U.S. and Japan, we compare the indexes withthree uncertainty-related indexes that are extensively used in the literature on uncertainty:
EPU, MU, and VI.2 Second, we examine the impact of changes in the CCN indexes on thereal economy, focusing on economic sentiment and industrial production. Some theoretical studies focus on the expectation channel through which expectations about climatechange impact the real economy (e.g., Dietrich et al. (2023) and European Central Bank
(2021)). The intuition is straightforward. An increase in the probability of disasters related
to climate change is bad news for people; thus, it depresses current economic activity. We
empirically test the theoretical implications of this channel.
Furthermore, as an application of the CCN indexes, we examine how attention to climate change risks influences the transmission of monetary policy shocks. We apply the
local projection method developed by Jordà (2005). Departing from Jordà (2005), the key
feature of our approach is the introduction of smooth regime switching between regimes
with high and low attention to climate change risks. In this model, the transmission mechanism of monetary policy shocks can change depending on the level of attention to climatechange risks. Hence, we estimate the regime-dependent impact of monetary policy shocks.
The main findings of this study are as follows:• The correlation between the CCN indexes of the U.S. and Japan is much higher thanthe correlation between the CCN index and other uncertainty measures in either of
those countries.
• Shocks to the CCN indexes have significantly negative effects on economic sentiment.
However, they have ambiguous effects on industrial production. More specifically,shocks to the WSJ-CCN index have no significant impact on industrial production
for the U.S., while the response of industrial production to the CCN shock is significantly negative for Japan. This contrasts with the fact that, for both the U.S. andJapan, other uncertainty shocks have negative effects on both economic sentimentand industrial production.
• The transmission of monetary policy weakens as climate change risks increase. In
2

It should be noted that the CCN index may be better considered as a measure of public attention to climatechange risk rather than a direct measure of climate change risk, such as the probability of natural disasters. In
this respect, the CCN index differs from the other uncertainty measures we compare it with.

2

other words, responses of economic activity and inflation become significantly weakeras climate change risks increase for both the U.S. and Japan.
This study is closely connected to four strands of the literature. First, many empiricalstudies have found that climate change affects economies. Burke et al. (2015) use panel
data for 166 countries and show that a nonlinear relationship exists between annual temperature and productivity growth. Colacito et al. (2019) conduct a panel analysis using
U.S. state- and sector-level data and find a statistically significant negative relationship between summer temperature and GDP growth. Dell et al. (2014) review empirical studies
that examine how climate variables such as temperature, precipitation, and windstorms
influence economic outcomes. This study contributes to the literature by examining theimpact of climate change uncertainty and risk on the real economy.
Second, this study contributes to the literature on the impact of uncertainty on thereal economy. Several studies have focused on Economic Policy Uncertainty (Baker et al.
(2016), Caldara et al. (2020), and Husted et al. (2020)), macroeconomic uncertainty (Jurado et al. (2015), Scotti (2016), and Redl (2020)), and financial market volatility (Bloom(2009), Bonciani and Ricci (2020), and Ludvigson et al. (2021)). What sets this study
apart from the literature is its examination of the impact of uncertainty or risk associatedwith climate change on the real economy.
Third, several studies have focused on the impact of rare disasters, including natural
disasters, on expectation formation. Barro (2006) and Gourio (2012) show that raredisaster expectations could be an important driver of asset prices and the business cycle.3
Isoré and Szczerbowicz (2017) estimate a New Keynesian model with a small time-varying
probability of disaster. They show that even without the occurrence of a disaster, an increase in its probability decreases consumption and wages. They argue that this effect canbe interpreted as a shift in the agents’ degree of patience.
Expectations about natural disasters due to climate change are examples of rare-disaster
expectations. Dietrich et al. (2023) conduct a survey in the U.S. to measure expectations
about the economic impact of climate change. They find that respondents change their
expectations owing to various factors, including media consumption, and tend to assign
high probabilities to natural disasters. They also calibrate a New Keynesian model for rare
3
They define a “rare-disaster” as an infrequent and large macroeconomic shock, including not only natural
disasters but also wars or financial crises.

3disasters and show that disaster expectations could lower the natural rate of interest. We
empirically confirm this mechanism and quantify how it affects monetary policy transmission.
Fourth, this study contributes to the growing empirical literature on the state-dependent
effects of fiscal and monetary policy shocks. Auerbach and Gorodnichenko (2012) combine the local projection method of Jordà (2005) with a smooth regime-switching model
to estimate the effects of fiscal policy during booms and recessions. Tenreyro and Thwaites(2016) employ a similar local projection method to study the efficacy of monetary policy
shocks during booms and recessions. They find that monetary policy is less powerful during recessions. Weise (1999) estimates a smooth transition VAR model and demonstrated
that money supply shocks have a greater impact on output but a lesser impact on inflation
when the growth rate of output is lower. Miyao and Okimoto (2020) examine the effects
of unconventional monetary policy in Japan using a smooth transition VAR model, withthe level of unconventional assets held by the Bank of Japan as a transition variable. Theirfindings suggest that the effects of monetary policy become more pronounced following
the introduction of quantitative and qualitative monetary easing. Aastveit et al. (2017),
Castelnuovo and Pellegrino (2018) and Pellegrino (2021) empirically explore if economic
uncertainty alters the effectiveness of monetary policy. They employ non-linear structural
VAR models and show that monetary policy shocks are less powerful when uncertainty
is high. Aastveit et al. (2017) argue that the results are consistent with the hypothesisthat agents gather more information and postpone decisions under high uncertainty, andthat this “wait-and-see” behavior makes them less responsive to changes in the economic
environment such as interest rates. Furthermore, Falck et al. (2021) estimate the effects
of monetary policy under high and low disagreement about inflation expectations. Theyshow that a contractionary U.S. monetary policy shock leads to a statistically significant
increase in inflation and inflation expectations under high disagreement, whereas it leads
to a significant decline in these variables under low disagreement. Based on these studies,
we focus on climate change risk.
The remainder of this paper is organized as follows. Section 2 presents a method formeasuring attention to climate change risks. In Section 3, we present a statistical analysisof the CCN indexes. As an application of the CCN indexes, Section 4 investigates whether
monetary policy effectiveness depends on the degree of attention to climate change risks.

4Finally, Section 5 concludes the paper with suggestions for future research.

2

Climate Change News Index

This section describes how to construct the CCN index. First of all, we introduce themethodology developed in Engle et al. (2020). They propose indexes to measure thedegree of attention to climate change for the U.S. Next, we discuss how we apply their
method to Japan.

2.1

WSJ-CCN Index of Engle et al. (2020)

Engle et al. (2020) propose an index (WSJ-CCN index) measuring innovations in news on
climate risk. They construct the index to extract news on climate change using textual
analysis from The Wall Street Journal (WSJ).
The construction of the WSJ-CCN index follows three steps. First, they make a corpus of climate change-related risks that appear in official reports. Official white papers
on climate change include those of the Intergovernmental Panel on Climate Change, theEnvironmental Protection Agency, and the U.S. Global Change Research Program. Basedon these white papers, they construct a “Climate Change Vocabulary (CCV).” They createa list of unique terms and define the CCV as the frequency at which each term is foundin the corpus of all white papers. The list includes extreme weather events (e.g., floods,
hurricanes, droughts, wildfires, and extreme temperatures), physical changes to the planet(e.g., sea level changes, glacial melting, and ocean temperatures), regulatory discussions,
technical progress in alternative fuel delivery, and fossil fuel prices.
Second, they create term frequency–inverse document frequency scores for the CCV,
called tf -idf scores. They also generate a daily count of terms used in the WSJ. They countthe term separately for every issue of the WSJ. These term counts are then converted into
tf -idf scores. The term “tf ” refers to term frequency, which represents the number oftimes a term appears in a specific document. When a term j is not commonly found in adocument i, it does not accurately represent the document; therefore, tfi,j is small. The
term “idf ” refers to inverse document frequency, which is calculated as the logarithm of one
divided by the proportion of documents containing a specific word. tf -idfi,j is derived bymultiplying tfi,j and idfj for every term j and document i. Consequently, tf -idfi,j identifies

5the most important term in a document by assessing its frequency within that document
and its rarity across all documents.
Finally, using the tf -idf scores of the CCV and daily WSJ editions, they create a daily
climate change index based on the “cosine similarity” between the two. An index valueof one is assigned to days when the WSJ and CCV use identical terms in the same ratio.
However, if the WSJ does not use words from the CCV, the index value is zero. The
WSJ-CCN index provides an approximate representation of the amount of climate change
coverage in the WSJ on a daily basis, as determined by the underlying texts in the CCV. The
index is multiplied by 10,000 to make the innovation magnitudes easier to understand.
Figure 1 shows the WSJ-CCN index for the U.S. constructed in Engle et al. (2020). The
WSJ-CCN index spikes when domestic or international climate events occur, such as the
Copenhagen Accord of December 2009 and the publication of the Third National Climate
Assessment in May 2014. It also indicates that the climate news coverage intensity in the
U.S. has steadily increased and remained high since the mid-2000s. We use this WSJ-CCN
index to capture attention to climate change risks in the U.S.4

2.2

CCN Index for Japan

We apply the approach of Engle et al. (2020) to extract the CCN index for Japan. We use
textual news coverage in The Mainichi Shimbun newspaper, one of the major newspapers
in Japan, from 1994 onward, covering a wide range of Japanese and world news.5
We also collect data from the Annual Report on the Environment, the Sound MaterialCycle Society, and Biodiversity in Japan issued by the Ministry of the Environment from1997 to 2021. These white papers cover various topics, including industrial waste andbiodiversity. We extract the chapters on climate change. Appendix Table A.1 presents a
4
Engle et al. (2020) note that this index is constructed under the assumption that CCN is bad news or
related to risks. To justify this premise, they construct another index designed to focus on negative news
about climate change. They confirm that both indexes spike around salient climate events and indicate a highcorrelation across these measures. In conclusion, they argue that both indexes capture common elements ofclimate change risks.
5

We also use data from The Nikkei Shimbun to create a climate change attention index to determine
robustness. Like The Mainichi Shimbun, The Nikkei Shimbun also covers Japanese and world news, but has arelatively large number of articles related to financial markets and corporations. While The Mainichi Shimbun
is a media source that is mainly consumed by households, The Nikkei Shimbun is consumed more by financial
market participants. As discussed in more detail below, there are no major differences between the indexes
using The Mainichi Shimbun and The Nikkei Shimbun in Japan. Therefore, for the empirical analysis, we use
the index based on The Mainichi Shimbun because it has a longer sample.

6

full list of these authoritative texts. The collected documents consist of statements aboutclimate change, such as an increase in natural disasters, and actions to mitigate climatechange.
We then follow the same steps as in Engle et al. (2020) to construct our CCV for Japan.
Figure 2 illustrates the CCV for Japan in the form of a word cloud. The font sizes of theterms are proportional to their frequency. The CCV is mainly composed of words suchas “environment” and “emissions,” which is consistent with Engle et al. (2020). Thesewords imply a physical risk, such as an increase in extreme weather events. Furthermore,
the CCV includes other aspects of climate risk, such as transition risk. Terms such as“countermeasure,” “reduction,” and “implementation” are representative of the transition
risk topic.6
Following the same steps as in Engle et al. (2020), we obtain tf -idf scores for the CCV
and construct our daily index of attention to CCN based on the cosine similarity between
the tf -idf vector for the CCV and for each daily issue of The Mainichi Shimbun. Figure
3 presents the CCN index for Japan from January 1994 to December 2021. The index
increased steeply when global climate treaties and major global conferences were implemented to prevent climate change. As the previous subsection shows, this is consistent
with the indexes observed for the U.S.
As a robustness check, we use another major Japanese newspaper, The Nikkei Shimbun, to construct a CCN index for Japan. Appendix Figure B.1 illustrates the developmentof the CCN index from the Nikkei. The figure indicates that the CCN indexes from both
The Mainichi Shimbun and The Nikkei Shimbun increased during the same period. Furthermore, the correlation between the two CCN indexes is 0.72; therefore, our CCN index
for Japan is robust in terms of the data source.

3

Statistical Analysis on Climate Change News Index

To understand the statistical features of the CCN indexes, we investigate how the CCN
indexes are correlated with three other measures of economic uncertainty. We also evaluate
the impact of changes in the CCN index on economic sentiment and industrial production,
6

Both Engle et al. (2020) and our study focus on climate change risks from a broad perspective and do
not identify them in detail. Bua et al. (2022) use a methodology similar to ours to extract the physical andtransition risks for the euro area.

7

as well as that in the three uncertainty measures.

3.1

Three Alternative Measures on Economic Uncertainty

We compare the CCN indexes with three measures that are commonly used in empiricalstudies on uncertainty in macroeconomics. The first measure is the EPU indexes for the
U.S. and Japan. The EPU index for the U.S. is calculated by Baker et al. (2016), and Arbatli
et al. (2022) calculate the index for Japan. These indexes reflect the frequency of articles
in major newspapers that contain certain terms relevant to “economy,” “policies,” and “uncertainty.” 7 EPU is computed as the ratio of the number of articles that include at least
one word listed in all three categories, namely, “economy,” “policies,” and “uncertainty,” tothe total number of articles. It tends to spike during events that are ex-ante likely to cause
increases in perceived policy uncertainty, such as debates over the stimulus package, debt
ceiling disputes, wars, and financial crises.
The second is the MU index, which measures uncertainty by computing the common
factor of the time-varying volatility of forecast errors from a large number of economic timeseries. This implies that it captures the uncertainties caused by various macroeconomic
factors simply and comprehensively. For example, the MU index for Japan rose sharply
during economic downturns, such as the Global Financial Crisis, and during other events,
such as the Great East Japan Earthquake in March 2011. This index is developed by Jurado
et al. (2015) for the U.S., and the index for Japan is calculated by Shinohara et al. (2020).
Finally, we utilize the stock market volatility index used by Bloom (2009) as an indicator of uncertainty. This represents the degree of real-time implied volatility quantified by
the financial markets. This implies that it mainly captures uncertainty in financial conditions, as perceived by market participants. We use the VIX for the U.S. and the Nikkei VI
for Japan.
We select the three uncertainty-related indexes discussed above because there is extensive empirical literature on the statistical properties of these measures; thus, their properties are well understood. Therefore, by comparing the CCN indexes with these three
7

The number of newspapers used for EPU is ten in the U.S. and four in Japan. The index for the U.S.
comprises USA Today, The Miami Herald, The Chicago Tribune, The Washington Post, The Los Angeles Times,
The Boston Globe, The San Francisco Chronicle, The Dallas Morning News, The Houston Chronicle, and The
Wall Street Journal. For Japan, The Asahi Shimbun, The Nikkei Shimbun, The Mainichi Shimbun, and The
Yomiuri Shimbun are used for the calculation.

8

measures, we can improve our understanding of how different the climate change-related
issues are from economic uncertainty.

3.2

Correlations among Uncertainty IndexesFirst, we compute the correlations among the indexes based on monthly measures.8 Table
1 summarizes the correlation coefficients for each pair. Panel (a) presents the results for
the U.S., and three findings are worth noting. First, the first column of Panel (a) showsthat the correlation between the CCN index and the other economic uncertainty measures
is significantly positive or not statistically significant. Specifically, the correlation of the
WSJ-CCN index with EPU is 0.06, and its correlations with MU and VI are 0.18 and -0.07,
respectively.
Second, the correlation between the CCN for the U.S. (WSJ-CCN) and the CCN index
for Japan is 0.45. Therefore, the WSJ-CCN index is more correlated with the CCN index
for Japan than with other MU measures for the U.S., which suggests that concerns aboutclimate change are driven by common factors in the U.S. and Japan, such as salient global
climate events or global discussion trends on climate change risks.
Third, the correlations among other economic uncertainty measures are significantly
positive and higher than those of other economic uncertainty measures with the WSJ-CCN
index, except for the correlation between EPU and MU. The correlation coefficient between
EPU and MU is 0.27, which is comparable to the correlation between the CCN index andother uncertainties. However, the correlation between EPU and the volatility index is 0.45,
and that between MU and the volatility index is 0.58. Both are higher than the correlationof the CCN index with other uncertainty measures. This indicates that the development ofattention toward climate change risks differs from other economic uncertainty measures.
Examining the results for Japan (Panel [b]), we find the same implications as for the
U.S. The CCN index for Japan exhibits the highest correlation with the CCN index for the
U.S. (WSJ-CCN). On the other hand, the correlations between the CCN index and othereconomic uncertainty measures are all significantly positive.
To determine whether there is a prior lagged relationship between CCN for the U.S.
(WSJ-CCN) and CCN for Japan, we calculate their cross correlations. We find that there
is no prior lagged relationship between the attention to climate change risks for the U.S.
8

The results are robust when using quarterly data in both the U.S. and Japan.

9and Japan. Figure 4 shows the results of calculating the cross correlations between them.
Notably, the correlation coefficients are largest when there is no time lag, and the crosscorrelations are symmetric around lag zero. Therefore, the CCN indexes for both countries
move similarly in response to climate change-related events.9
The results discussed above suggest that climate change concerns are driven by common factors in the U.S. and Japan, such as salient global climate events and global trends
in discussions on climate change risks. Moreover, the development of attention toward
climate change risks differs from other economic uncertainties.

3.3

Uncertainty, Sentiment and Economic Activity

We evaluate the impact of the CCN indexes on the real economy. Specifically, we focus
on economic sentiment and industrial production. Economic sentiment in the U.S. is estimated by Shapiro et al. (2022), and UTEcon assesses the sentiment for Japan.10 Both aredeveloped by extracting sentiment from economic and financial newspaper articles using
textual analysis. Shapiro et al. (2022) note that news sentiment has a predictive power
regarding the movements of survey-based consumer sentiment; its development can affectreal variables such as consumption and output.
3.3.1

Correlations with Sentiment and Industrial Production

Panel (a) of Table 2 shows the correlation coefficients for the U.S. What is worth noting isthat, as with other economic uncertainty measures, the correlation between the CCN index
and economic sentiment or industrial production is significantly negative. The correlation
coefficients between news sentiment and CCN, EPU, MU, and VI are -0.17, -0.61, -0.50,
and -0.53, respectively. Furthermore, the correlation coefficients of industrial production
with CCN, EPU, MU, and VI are -0.25, -0.32, -0.74, and -0.34, respectively.
Panel (b) of Table 2 shows that the finding discussed above is also true for Japan.
Therefore, it can be concluded that the CCN index, as well as other economic uncertainty
measures, are negatively correlated with economic sentiment and industrial production, atleast in simple correlations.
9

There is still room for further scrutiny, including comparisons based on higher frequency data such as
weekly or daily rather than monthly data. Furthermore, trends in other countries, such as those in Europe,
could influence the attention to climate change risks in the U.S. and Japan.
10

See Goshima et al. (2022) and the UTEcon website for the details.

10

To wrap up, several descriptive statistics show that the relationship between the CCN
indexes and the real economy is negative. We formally examine this relationship in thefollowing section.
3.3.2

Impulse Responses of Sentiment and Industrial Production to Uncertainty
ShocksHere, we investigate how shocks to the CCN index and other uncertainty measures arepropagated to news sentiment and the real economy in a structural vector autoregressive(VAR) framework using data from the U.S. and Japan. To identify uncertainty shocks,
we follow Bachmann et al. (2013) and Baker et al. (2016), among others, and employ
the recursive identification scheme with an uncertainty measure being ordered first in the
VAR(p) model given by
Xt =

p
X

Bk Xt−k + Cεt ,

(1)

k=1

where Xt is an n × 1 vector of variables, Bk ’s and C are n × n coefficient matrices, εt
is an n × 1 vector of structural shocks with E(εt ) = 0 and E(εt ε′t ) is a diagonal matrix.
The C matrix is a nonsingular matrix with all the elements in the first row being zero
except for the first one. The uncertainty shock corresponds to the first element of εt . In
other words, an uncertainty measure is assumed to respond contemporaneously only tothe uncertainty shock but not to other structural shocks. Since we are only interested in
the impulse responses to the uncertainty shock, we do not identify the remaining structural
shocks.
The first specification we consider is a simple bivariate VAR model with n = 2 and
Xt = [unt , yt ]′ , where unt is a measure of uncertainty and yt is either industrial production
or economic sentiment. We refer to this bivariate specification as “VAR-2.” The second
specification we consider is an eight-variable VAR model. In particular, we set n = 8 and
let Xt = [unt , yt , x1t , ..., x6t ]′ where unt and yt are the same as the VAR-2 specification. The
additional six variables x1t , ..., x6t are: (i) log of the stock prices; (ii) short-term interest
rate; (iii) log of nominal wages; (iv) log of the consumer price index; (v) hours worked, (vi)log of employment.11 We refer to this second specification as “VAR-8.” For the stock price11

Note that Bloom (2009) used the stock volatility index as a proxy for uncertainty and estimated the eightvariable VAR model by placing the stock price first and the stock volatility index second. We also employ suchan alternative identification scheme but results remain unchanged.

11

variable, we use the S&P 500 for the U.S. and the TOPIX index for Japan. For the shortterm interest rate, we use the Federal Funds Rate for the U.S. and the Uncollateralized
Overnight Call Rate for Japan.
Figure 5 lays out the responses of news sentiment or industrial production to a one
standard deviation shock to the CCN index or other uncertainty measures for the U.S.
Based on the BIC, the lag length p was set to two in the U.S. for both specifications. As
is shown in the figures, news sentiment decreases significantly in response to shocks touncertainty measures and the CCN index, except for the case of VAR-8 with the CCN index. On the other hand, for the industrial production, the shocks to uncertainty measures
have significantly negative impacts on industrial production, consistent with the findings ofBloom (2009), while the shocks to the CCN index have no significant impact on industrial
production in either the VAR-2 and VAR-8 specifications.
Figure 6 shows the results for Japan. Based on the BIC, the lag length p was set totwo for VAR-2 concerning economic sentiment and one for other specifications in Japan.
For both specifications, the results suggest that news sentiment is significantly reduced
by an uncertainty shock in the CCN index and other uncertainty measures. Shocks in thethree uncertainty measures also have significant negative impacts on industrial production.
On the other hand, the response of industrial production to shocks in the CCN index issomewhat ambiguous. In the U.S., shocks to the CCN index have no significant effect on
industrial production regardless of the VAR specification, while in Japan, the CCN shocks
significantly reduce industrial production in the VAR-8 specification. Therefore, the results
for both the U.S. and Japan show that there is a clear difference between the CCN index
and other uncertainty indexes in terms of their impact on industrial production, but not
on economic sentiment. These results imply that, while attention to climate change risks
can exacerbate economic sentiment, it does not necessarily lead to an immediate stallingof economic activity.12
To check the robustness of our findings based on the VAR model, we also estimate theimpulse responses to uncertainty shocks using the local projection method developed by
Jordà (2005). Let yt be the variables of interest, namely, economic sentiment or industrial
production. The impulse response βh of yt to an uncertainty shock at horizon h(≥ 0) can
12This contrast becomes more apparent in the local projection methods described below.

12be obtained by running a regression of the form:
yt+h = αh + βh unt + γh′ xt + ut+h(2)

where unt is one of the uncertainty measures, including the CCN index, xt is a vector of control variables and ut+h is the regression error term. As in the VAR analysis,
we consider two specifications. The first one is a bivariate specification where we set
xt = [unt−1 , yt−1 , ..., unt−p+1 , yt−p+1 ]′ . The second one is an eight-variable specification
where we set xt = [unt−1 , yt−1 , x1,t−1 , ..., x6,t−1 , ..., unt−p+1 , yt−p+1 , x1,t−p+1 , ..., x6,t−p+1 ]′ .
In what follows, we denote the first specification as “LP-2” and the second specification as“LP-8.”The estimation results are shown in Figure 7 for the U.S. and Figure 8 for Japan. The
impulse responses from the local projection are consistent with the VAR results with respect
to uncertainty measures. An increase in economic policy uncertainty, macroeconomic uncertainty or stock market volatility reduces both sentiment and industrial production in the
U.S. and Japan. In addition, for both the U.S. and Japan, the CCN index has significantly
negative effects on economic sentiment. However, the impulse responses of industrial production to CCN shocks have different implications in the U.S. and Japan. Shocks to the
CCN index have no significant impact on industrial production for the U.S. in the LP-2
specification, while the response of industrial production to the CCN shock is significantly
negative regardless of the model for Japan. These results from both VAR models and local
projections indicate that the effect of CCN shocks on industrial production is ambiguous
and can be different between the U.S. and Japan.
To wrap up this section, two points are worth noting. First, our CCN index for Japanis more correlated with the WSJ-CCN index than the other macroeconomic uncertainty
measures. Second, we find that shocks to the CCN index have significantly negative effects
on economic sentiment, but ambiguous effects on industrial production. This contrasts
with the fact that, for both the U.S. and Japan, shocks to other uncertainty measures havenegative effects on both economic sentiment and industrial production.

13

4

Application of the CCN Indexes to Monetary Policy

This section presents our empirical approach to estimate how attention to climate changerisks influences the transmission of monetary policy shocks. To quantify this, we apply the
local projection method developed by Jordà (2005). Departing from Jordà (2005), ourapproach allows smooth regime switches, following Auerbach and Gorodnichenko (2012)
and Tenreyro and Thwaites (2016). In our estimation, the regimes are identified by focusing on climate change risks. Based on this model, the transmission mechanism of monetary
policy shocks can potentially change depending on the extent of the attention to climatechange risks. The following section describes our econometric model and data sources.

4.1

Local Projection with a Regime Shift

To examine how attention to climate change risks affects the transmission of monetary
policy, we extend the local projection model of Jordà (2005) to introduce a smooth regimeswitching mechanism as follows:
yt+h = τh t + (αhH + βhH ϵt + γhH xt )F (zt ) + (αhL + βhL ϵt + γhL xt )(1 − F (zt )) + ut+h(3)

where h(≥ 0) indicates the number of periods after a shock hits the economy and superscripts H and L on coefficients refer to the high (H) and low (L) attention regimes, respectively. A time trend is denoted as τh t. We control for the regime-specific constants αhH,L ,regime-dependent effects of the monetary policy shock βhH,L , and a set of regime-specificcoefficients γhH,L . Control variables xt includes industrial production, consumption, firms’
capital investment, consumer prices, and corporate bond spreads. We use corporate bondspreads as a control variable because they could be a source of business cycle fluctuations(Gilchrist and Zakrajšek (2012)).13 A measure of monetary policy shock is denoted as ϵtand ut+h refers to the regression residual.
The regimes are identified using the transition variable zt . For zt , we use the six-month
backward-moving average of the CCN index, reflecting the recent level of attention toclimate change risks. For the smooth transition function F (zt ), we use the logistic function
13

Bu et al. (2021) add excess bond premiums to their VAR model for the same reason.

14

given by:zt − c
exp θ
σ
 z

F (zt ) =zt − c1 + exp θ
σz


(4)

where c is the mean and σz is the standard deviation of zt . The function increases in zt .
The parameter θ determines the curvature of F (zt ), and hence, how strongly the probability reacts to changes in attention to climate change risks. Because of the difficulty inestimating θ with good precision, previous studies often used calibrated values (Auerbachand Gorodnichenko (2012), Tenreyro and Thwaites (2016), and Falck et al. (2021)). We
follow the literature and set the value at θ = 5. However, our results are robust to a wide
range of values, as mentioned in Appendix C.
To estimate the impulse responses, we use local projections that provide a direct estimate of the response of the dependent variable h periods after shock ϵt , depending onwhether the economy is in a high- or low-attention regime when the shock occurs. The
estimation of Eq.(3) is repeated for each horizon h, and the set of βhH,L reflects the impulse
response function for yt in period h.
By introducing F (zt ), we allow for the possibility of two regimes with respect to attention to climate change risks, which are characterized by potentially different macroeconomic dynamics. The response of the endogenous variable, yt+h , to a monetary policy
shock ϵt depends on the probability of being in a high- or low-attention regime, F (zt ).
Hence, the effects of monetary policy shocks are potentially conditioned by the probabilityof being in a high- or low-attention regime.
Furthermore, our approach captures potential regime switches after a shock. The empirical model controls for the probability of being in a high-attention regime when theshock occurs but makes no assumptions about the state of the economy in subsequent periods. If attention to climate change risks responds to shocks or economic conditions, thiswould be implicitly captured in the estimated coefficients.
We estimate econometric models for the U.S. and Japan separately. By comparing theestimation results in the U.S. with those in Japan, we can examine if the effects of monetary
policy differs between the two countries.
In our benchmark specification, the control variables are twelve lags in the U.S. and15

seven lags in Japan, as determined by the Akaike information criterion. The results remainunchanged when the lag lengths of the control variables change, as mentioned in Appendix
C.

4.2

Data

We use monthly data from the U.S. and Japan. As mentioned above, the data include
industrial production, consumption, firms’ capital investment, consumer prices, and corporate bond spreads. The estimation period covers October 1997 to December 2019 becauseof the availability of data for corporate bond spreads. We exclude 2020 because COVID-19
dramatically affected economies, which might distort our estimation.
The price index for the U.S. is the PCE deflator excluding food and energy, whereas
we use the CPI excluding fresh food and energy, for Japan. We use manufacturers’ valueof shipments (nondefense capital goods excluding aircraft) for the U.S. and domestic shipments and imports of capital goods for Japan as firms’ capital investment. The consumption
index is the PCE for the U.S. and the retail sales value for Japan. The indexes for industrial
production, consumer prices, firms’ capital investment, and consumption are three-monthmoving averages for smoothing. The corporate bond spreads are the ICE BofA US High
Yield Index (option-adjusted spread) for the U.S. and BBB-rated corporate bond spreads
for Japan. We take the logarithms of indexes other than corporate bond spreads.
Regarding monthly monetary shocks, we use exogenous monetary policy shocks, as in
the literature. We follow Bu et al. (2021) for the U.S. and Kubota and Shintani (2022)
for Japan. Both studies develop shock series that stably bridge the periods of conventional
and unconventional monetary policies. These series are largely unpredictable from theavailable information on the economy and contain no significant central bank information
effect. This allows for a cleaner inference of the transmission of an exogenous monetary
policy shock.

4.3

Results

4.3.1

Identifying Periods of High and Low Attention to Climate Change Risks

Figures 9 and 10 show F (zt ), the probability of being in a regime with high attention toclimate change risks in the U.S. and Japan, respectively. It should be noted that the regimes

16repeatedly switched over the sample period. This indicates that a model that preserves thedegree of freedom of regime switching is more suitable than one in which regime changes
are not permitted.
Specifically, the probability of being in a low-attention regime is relatively high throughout the first half of the sample period in both the U.S. and Japan. However, the probabilityof being in a high-attention regime increased after the mid-2000s in both countries. Thisimplies that more people have become concerned about climate change risks in recent
years. Furthermore, unlike in the U.S., the probability of being in a high-attention regime
was high between 1998 and 2002 in Japan. This is because Japan held the Kyoto Protocol
meetings and more Japanese people were paying attention to climate change risks duringthat period.
4.3.2

The Regime-dependent Transmission of Monetary Policy Shocks

Figures 11 and 12 show the impulse responses of industrial production, consumer prices,
firms’ capital investment, and consumption to an expansionary 100 basis points monetary
policy shock in the U.S. and Japan, respectively. The upper rows in Figures 11 and 12 show
the results of the linear model estimated without assuming regime changes, implying thatstate-dependent effects are not considered. Meanwhile, the lower panels in both figures
show the results of our regime-switching approach. The responses in regimes with highand low attention to climate change risks are presented. The red solid lines and blue dottedlines represent the impulse responses in the high- and low-attention regimes, respectively.
The upper panels in Figures 11 and 12 indicate that all variables significantly increase
with an expansionary monetary policy shock. This finding is consistent with the implications of standard New Keynesian models and related empirical studies.
Regarding the lower panels in these figures, two points are worth noting. First, the responses in a high-CCN regime are significantly different from those in a low-CCN regime.
This implies that the transmission of monetary policy shocks is regime-dependent and thatthe extent to which climate change risks are considered plays an important role in propagating monetary policy shocks.
Second, the transmission of monetary policy becomes significantly weaker in a regime
with high attention to climate change risks. Specifically, the responses to monetary policy
shocks in a low-CCN regime (dashed blue lines) are almost the same as those of the linear

17

model. Thus, all variables increase significantly in a low-CCN regime in response to expansionary monetary policy shocks. Figure 11 shows that, after twenty months, industrial
production significantly increases by approximately one percentage points and the inflation
rate also rises by approximately 0.1 percentage point in the U.S. Meanwhile, the responsesof all variables to expansionary monetary policy shocks in a high-attention regime (solid
red lines) are not significant, which implies that the transmission of monetary policy becomes weaker.14
This is also true for Japan. The lower panels in Figure 12 show the responses to an
expansionary monetary policy shock in regimes with high and low levels of attention toclimate change risks in Japan. The first column shows that the response of industrial
production significantly increases by approximately three percentage points after twenty
periods in the low-CCN regime. By contrast, the response is insignificant in the high-CCN
regime. In the third and fourth columns, the responses of firms’ capital investment andconsumption in the low-CCN regime are statistically significant. However, the responses in
the high-CCN regime are small and statistically insignificant.
The lower panels in Figures 11 and 12 also indicate that the responses of almost all
variables are statistically different in regimes with high and low attention to climate changerisks in both the U.S. and Japan.15

5Conclusion

This study statistically investigates the impact of attention to climate change risks in the
U.S. and Japan. We find that for both the U.S. and Japan, the CCN index has significantly
negative effects on economic sentiment but has ambiguous effects on industrial production,
which is inconsistent with the fact that other uncertainty measures have negative effects
on both economic sentiment and industrial production.
We apply the CCN index to investigate how changes in attention to climate changerisks alter the transmission of monetary policy. We find that monetary policy effectiveness
14

Some empirical studies explore whether economic uncertainty alters monetary policy effectiveness(Aastveit et al. (2017), Castelnuovo and Pellegrino (2018), and Pellegrino (2021)). They employ nonlinear structural VAR models and show that monetary policy shocks are less powerful when uncertainty is high.
Aastveit et al. (2017) argue that the results are consistent with the hypothesis that agents gather more information and postpone decisions under high uncertainty, and this “wait-and-see” behavior makes them lessresponsive to changes in the economic environment such as interest rates.
15

We present the robustness check in the Appendix C.

18

depends on the degree of attention to climate change risks.
One additional direction for future research is to construct a model to interpret ourempirical findings and provide some policy analysis. For example, we can include a mechanism for the transmission of transition risk in addition to physical risk. This would allow
us to consider more channels of climate change risks. Another possible approach is to
apply a smooth-transition VAR. This would allow us to investigate how the transmissionof structural shocks, such as demand and supply shocks, depends on the state of climatechange risks.

19

References
Aastveit, K. A., Natvik, G. J., and Sola, S. (2017). “Economic Uncertainty and the Influenceof Monetary Policy.” Journal of International Money and Finance, 76, pp. 50–67.
Addoum, J. M., Ng, D. T., and Ortiz-Bobea, A. (2023). “Temperature Shocks and Industry
Earnings News.” Journal of Financial Economics, 150(1), pp. 1–45.
Alok, S., Kumar, N., and Wermers, R. (2020). “Do Fund Managers Misestimate Climatic
Disaster Risk.” The Review of Financial Studies, 33(3), pp. 1146–1183.
Arbatli, E. C., Davis, S. J., Ito, A., and Miake, N. (2022). “Policy Uncertainty in Japan.”
Journal of the Japanese and International Economies, 64, 101192.
Auerbach, A. J., and Gorodnichenko, Y. (2012). “Fiscal Multipliers in Recession and Expansion.” Fiscal Policy after the Financial Crisis, pp. 63–98.
Bachmann, R., Elstner, S., and Sims, E. R. (2013). “Uncertainty and Economic Activity: Evidence from Business Survey Data.” American Economic Journal: Macroeconomics, 5(2),pp. 217–249.
Baker, S. R., Bloom, N., and Davis, S. J. (2016). “Measuring Economic Policy Uncertainty.”
The Quarterly Journal of Economics, 131(4), pp. 1593–1636.
Barro, R. J. (2006). “Rare Disasters and Asset Markets in the Twentieth Century.” The
Quarterly Journal of Economics, 121(3), pp. 823–866.
Bloom, N. (2009). “The Impact of Uncertainty Shocks.” Econometrica, 77(3), pp. 623–685.
Bloom, N. (2014). “Fluctuations in Uncertainty.” Journal of Economic Perspectives, 28(2),pp. 153–176.
Bonciani, D., and Ricci, M. (2020). “The International Effects of Global Financial Uncertainty Shocks.” Journal of International Money and Finance, 109, 102236.
Bu, C., Rogers, J., and Wu, W. (2021). “A Unified Measure of Fed Monetary Policy Shocks.”
Journal of Monetary Economics, 118, pp. 331–349.

20

Bua, G., Kapp, D., Ramella, F., and Rognone, L. (2022). “Transition Versus Physical Climate
Risk Pricing in European Financial Markets: A Text-based Approach.” ECB Working Paper
Series No. 2677, European Central Bank.
Burke, M., Hsiang, S. M., and Miguel, E. (2015). “Global Non-linear Effect of Temperature
on Economic Production.” Nature, 527(7577), pp. 235–239.
Caldara, D., Iacoviello, M., Molligo, P., Prestipino, A., and Raffo, A. (2020). “The Economic
Effects of Trade Policy Uncertainty.” Journal of Monetary Economics, 109, pp. 38–59.
Castelnuovo, E., and Pellegrino, G. (2018). “Uncertainty-dependent Effects of Monetary
Policy Shocks: A New-Keynesian Interpretation.” Journal of Economic Dynamics and Control, 93, pp. 277–296.
Colacito, R., Hoffmann, B., and Phan, T. (2019). “Temperature and Growth: A Panel Analysis of the United States.” Journal of Money, Credit and Banking, 51(2-3), pp. 313–368.
Dell, M., Jones, B. F., and Olken, B. A. (2014). “What Do We Learn from the Weather? The
New Climate-economy Literature.” Journal of Economic Literature, 52(3), pp. 740–98.
Dietrich, A., Müller, G. J., and Schoenle, R. (2023). “Big News: Climate Change and theBusiness Cycle.” University of Tübingen Working Papers in Business and Economics No.
158.
Engle, R. F., Giglio, S., Kelly, B., Lee, H., and Stroebel, J. (2020). “Hedging Climate Change
News.” The Review of Financial Studies, 33(3), pp. 1184–1216.
European Central Bank (2021). “Climate Change and Monetary Policy in the Euro Area.”
Occasional Paper Series No 271.
Falck, E., Hoffmann, M., and Hürtgen, P. (2021). “Disagreement about Inflation Expectations and Monetary Policy Transmission.” Journal of Monetary Economics, 118, pp.
15–31.
Gilchrist, S., and Zakrajšek, E. (2012). “Credit Spreads and Business Cycle Fluctuations.”
American Economic Review, 102(4), pp. 1692–1720.

21

Goshima, K., Shintani, M., and Takamura, H. (2022). “Sentiment Dictionary for Business
Cycle Analysis and its Applications (in Japansese).” Journal of Natural Language Processing, 29(4), pp. 1233–1253.
Gourio, F. (2012). “Disaster Risk and Business Cycles.” American Economic Review, 102(6),pp. 2734–2766.
Husted, L., Rogers, J., and Sun, B. (2020). “Monetary Policy Uncertainty.” Journal of Monetary Economics, 115, pp. 20–36.
Isoré, M., and Szczerbowicz, U. (2017). “Disaster Risk and Preference Shifts in a New
Keynesian Model.” Journal of Economic Dynamics and Control, 79, pp. 97–125.
Jordà, Ò. (2005). “Estimation and Inference of Impulse Responses by Local Projections.”
American Economic Review, 95(1), pp. 161–182.
Jurado, K., Ludvigson, S. C., and Ng, S. (2015). “Measuring Uncertainty.” American Economic Review, 105(3), pp. 1177–1216.
Kim, H. S., Matthes, C., and Phan, T. (2022). “Severe Weather and the Macroeconomy.”
Available at SSRN 3918533.
Krueger, P., Sautner, Z., and Starks, L. T. (2020). “The Importance of Climate Risks for
Institutional Investors.” The Review of Financial Studies, 33(3), pp. 1067–1111.
Kubota, H., and Shintani, M. (2022). “High-frequency Identification of Monetary Policy
Shocks in Japan.” Japanese Economic Review, 73, pp. 483–513.
Ludvigson, S. C., Ma, S., and Ng, S. (2021). “Uncertainty and Business Cycles: Exogenous
Impulse or Endogenous Response?” American Economic Journal: Macroeconomics, 13(4),pp. 369–410.
Miyao, R., and Okimoto, T. (2020). “Regime shifts in the effects of Japan’s unconventionalmonetary policies.” The Manchester School, 88(6), pp. 749–772.
Newey, W. K., and West, K. D. (1987). “A Simple, Positive Semi-Definite, Heteroskedasticity
and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55(3), pp. 703–708.

22

Ortega, F., and Tas.pınar, S. (2018). “Rising Sea Levels and Sinking Property Values: Hurricane Sandy and New York’s Housing Market.” Journal of Urban Economics, 106, pp.
81–100.
Pellegrino, G. (2021). “Uncertainty and Monetary Policy in the US: a Journey into Nonlinear Territory.” Economic Inquiry, 59(3), pp. 1106–1128.
Redl, C. (2020). “Uncertainty Matters: Evidence from Close Elections.” Journal of International Economics, 124, 103296.
Scotti, C. (2016). “Surprise and Uncertainty Indexes: Real-time Aggregation of Realactivity Macro-surprises.” Journal of Monetary Economics, 82, pp. 1–19.
Shapiro, A. H., Sudhof, M., and Wilson, D. J. (2022). “Measuring News Sentiment.” Journalof Econometrics, 228(2), pp. 221–243.
Shinohara, T., Okuda, T., and Nakajima, J. (2020). “Characteristics of Uncertainty Indicesin the Macroeconomy.” Bank of Japan Working Paper Series No.20-E-6, Bank of Japan.
Tenreyro, S., and Thwaites, G. (2016). “Pushing on a String: US Monetary Policy is Less
Powerful in Recessions.” American Economic Journal: Macroeconomics, 8(4), pp. 43–74.
Weise, C. L. (1999). “The asymmetric effects of monetary policy: A nonlinear vector autoregression approach.” Journal of Money, Credit and Banking, 31(1), pp. 85–108.

23

Table 1: Correlation coefficients between uncertainty measures(a) USClimate Change
News
(CCN)

Economic Policy
Uncertainty(EPU)

Macroeconomic
Uncertainty(MU)

Stock Market
Volatility
(VI)

CCN

1.00
–

EPU

0.06
(0.06)

1.00–

MU

0.18***(0.06)

0.27***(0.06)

1.00
–

VI

-0.07
(0.06)

0.45***(0.05)

0.58***(0.06)

1.00–

CCN(Japan)

0.45***(0.05)

Macroeconomic
Uncertainty(MU)

Stock Market
Volatility(VI)(b) Japan
Climate Change
News
(CCN)

Economic Policy
Uncertainty
(EPU)

CCN

1.00
–

EPU

0.23***(0.06)

1.00–

MU

0.30***(0.06)

0.17***(0.06)

1.00–

VI

0.19***(0.06)

0.53***(0.05)

0.55***(0.05)CCN(US)

0.45***(0.05)

1.00–

Notes: Correlation coefficients with standard errors in parentheses. *** denotes
statistical significance at the one percent level. Estimation period is from January
1994 to June 2017.

24

Table 2: Correlation with news sentiment and industrial production(a) USClimate Change
News
(CCN)

Economic Policy
Uncertainty(EPU)

Macroeconomic
Uncertainty(MU)

Stock Market
Volatility
(VI)

News
Sentiment-0.17***(0.06)-0.61***(0.05)-0.50***(0.05)-0.53***(0.05)

Industrial
Production-0.25**(0.06)-0.32***(0.06)-0.74***(0.04)-0.34***(0.06)(b) Japan
Climate Change
News
(CCN)

Economic Policy
Uncertainty(EPU)

Macroeconomic
Uncertainty(MU)

Stock Market
Volatility
(VI)

News
Sentiment-0.20***(0.06)-0.59***(0.05)-0.34***(0.06)-0.63***(0.05)

Industrial
Production-0.17***(0.06)-0.28***(0.06)-0.49***(0.05)-0.41***(0.05)

Notes: Correlation coefficients with standard errors in parentheses. *** and ** denote statistical significance at the one percent and five percent levels, respectively.
Estimation period is from January 1994 to June 2017. Industrial Production is converted to year-over-year changes.

25Figure 1: Climate Change News index in the U.S. (WSJ-CCN index)
Notes: This figure shows the U.S. Climate Change News index extracted from the WSJ,
constructed by Engle et al. (2020). The unit of the vertical axis is basis points.

26

Figure 2: Climate change vocabulary in Japan
Notes: Each word is originally in Japanese and is translated into English by Google Translate. Term sizes are proportional to their frequency in the corpus.

27

Figure 3: Climate Change News index in Japan
Notes: This figure shows the Japanese Climate Change News index extracted from The
Mainichi Shimbun. The unit of the vertical axis is basis points.

28

Figure 4: Cross correlation of Climate Change News index in the U.S. and Japan
Notes: The figure indicates cross correlation coefficients of the Climate Change News indexes in the U.S. and Japan at different lags. Sample period is from January 1994 to June
2017.

29

Figure 5: VAR impulse response in the U.S.
Notes: These panels show the impulse responses to a one standard deviation shock. Solidlines are the responses estimated by the bivariate VAR (VAR-2) and dotted lines are thosefrom the eight-variable VAR (VAR-8). The sample period is from January 1994 to June
2017. The shaded area and thin dotted lines indicate the 90 percent confidence bands of
VAR-2 and VAR-8 estimates, respectively.

30

Figure 6: VAR impulse response in Japan
Notes: These panels show the impulse responses to a one standard deviation shock. Solidlines are the responses estimated by the bivariate VAR (VAR-2) and dotted lines are thosefrom the eight-variable VAR (VAR-8). The sample period is from January 1994 to June
2017. The shaded area and thin dotted lines indicate the 90 percent confidence bands of
VAR-2 and VAR-8 estimates, respectively.

31

Figure 7: Local Projection impulse response in the U.S.
Notes: These panels show the impulse responses to a one standard deviation shock fromlocal projections. The sample period is from January 1994 to June 2017. The lag length
used in local projections (LP-2 and LP-8) is the same as that of the VAR models (VAR-2 andVAR-8). The shaded area and thin dotted lines indicate the 90 percent confidence bands of
the LP-2 and LP-8 estimates, respectively.

32

Figure 8: Local Projection impulse response in Japan
Notes: These panels show the impulse responses to a one standard deviation shock fromlocal projections. The sample period is from January 1994 to June 2017. The lag length
used in local projections (LP-2 and LP-8) is the same as that of the VAR models (VAR-2 andVAR-8). The shaded area and thin dotted lines indicate the 90 percent confidence bands of
the LP-2 and LP-8 estimates, respectively.

33

Figure 9: Probability of being in the high attention to climate change regime in the U.S.
Notes: The thin line represents the six-month backward moving average of the CCN Index(right axis) and the thick line indicates the probability of being in the high attention toclimate change regime (left axis) in the U.S.

34

Figure 10: Probability of being in the high attention to climate change regime in Japan
Notes: The thin line represents the six-month backward moving average of the CCN Index(right axis) and the thick line indicates the probability of being in the high attention toclimate change regime (left axis) in Japan.

35

Figure 11: Responses to an expansionary monetary policy shock in the U.S.
Notes: This figure shows the impulse responses to an expansionary monetary policy shockin the U.S. The coefficients reflect the response to a 100 bps monetary policy shock. The
shaded areas display the 90 percent confidence bands based on Newey and West (1987)
standard errors. The upper panel shows the state independent responses (linear local projection model). In the lower panel, the solid red (dashed blue) lines denote the responses
during high attention to climate change (low attention to climate change) regimes.

36Figure 12: Responses to an expansionary monetary policy shock in Japan
Notes: This figure shows the impulse responses to an expansionary monetary policy shockin Japan. The coefficients reflect the response to a 100 bps monetary policy shock. The
shaded areas display the 90 percent confidence bands based on Newey and West (1987)
standard errors. The upper panel shows the state independent responses (linear local projection model). In the lower panel, the solid red (dashed blue) lines denote the responses
during high attention to climate change (low attention to climate change) regimes.

37

A

Source of Japanese Climate Change Vocabulary

To create the Climate Change Vocabulary (CCV) list in Japan, we collect Japanese climatechange white papers issued by the Ministry of the Environment from 1997 to 2021. We
extract the chapter on climate change from these white papers as shown below.
Table A.1: List of climate change white papers
Year
1997
1998
1999
2000
2001
2001
2002
2002
2003
2004
2004
2005
2005
2005
2006
2006
2007
2007
2007
2008
2008
2008
2009
2009
2009
2010
2010
2010
2011
2011

Part
1
1
1
1
1
3
2
3
2
2
3
1
2
3
2
3
1
3
4
1
2
3
1
2
3
1
2
3
1
2

Chapter(Section)
1(1,2,3)0(1), 3(1), 4(1)
4(1)
0(1)
2(2)
1(1)
1(1)
1(1)
1(1)
1(1,3), 7(3)1(2)
1, 2, 31(1,3), 7(3)1(2)
1(1,3), 7(3)1(2)
1, 2(3), 3(1,2,3,4,5)
1(2), 7(8)1(1)
1(1,2,3)
1(2), 7(8)1(1)
3(1,2,3)
1(1,2,3), 6(8)1(1,2)
2(1,2,3,4), 5(1,2,3,4)
1(1,2,3), 6(8)1(1,2)
4(3)1(1,2,3), 6(8)

Year
2011
2012
2012
2012
2013
2013
2013
2014
2014
2014
2015
2015
2016
2016
2016
2017
2017
2017
2018
2018
2018
2019
2019
2019
2020
2020
2020
2021
2021
2021

38

Part
3
1
2
3
1
2
3
1
2
3
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

Chapter (Section)
1(1,2)
4(1,2)
1(1,2,3), 6(8)1(1,2)2(3)1(1,2,3), 6(2)1(1,2)
1(1), 3(2,3,4)
1(1,2,3), 6(2)1(1,2)
1(1,2,3), 6(2)1(1,2)
1(1,2)
1(1,2,3), 6(2)1(1,2)
2(1,2,3)
1(1,2,3), 6(2)1(1,2)
1(1,2)
1(1,2,3), 6(2)1(1,2)
2(1,2,3,4,5,6,7)
1(1,2), 6(2)1(1,2)
1(1,2,4), 2(1,2), 3(1,2)
1(1,2), 6(2)1(1,2)
1(2,4), 2(1)1(1,2), 6(2)1(1,2)

B

Japanese CCN index using another source

We use another major newspaper in Japan, The Nikkei Shimbun, to construct the CCN index. Figure B.1 illustrates the development of the CCN index based on The Nikkei Shimbun
which is also one of the major newspapers in Japan. As shown in the figure, our baseline
CCN index based on The Mainichi Shimbun is basically consistent with the Nikkei-based
CCN index. Also, the correlation between the two indexes is 0.72, and it rises to 0.79
when they are converted to six-months backward moving averages. The high correlation
between two indexes indicates that our CCN index is robust in terms of the data source.16

Figure B.1: Japanese CCN index from The Nikkei Shimbun

16

There are some differences in the development of the indexes. In particular, they differ after 2021, whichsuggests that the newspapers may have different decision-making processes for how much they report on
climate change.

39

C

Robustness check: The regime-dependent transmission of monetary policy shocks

The robustness of our methodology is assessed along several dimensions. We perform
three types of exercises to check if our results are robust or not: alternative choices of theintensity of regime-switching θ, lags of control variables, and smoothness of the regime
variable.
The first exercise is to show that our results are robust with respect to the choices of
θ. To this end, we re-estimate the regressions with different values of θ. θ determines theintensity of regime-switching and we set θ = 5 in the baseline specification. Figure C.1 forthe U.S. and Figure C.2 for Japan show how the results change when we set θ to three oreight. In both figures, the responses to an expansionary monetary policy shock are wellwithin the confidence bands of the baseline estimates, and regardless of the value of θ,
the responses of all variables to monetary policy shocks tend to be weaker in high-CCNattention regimes compared to those in low-CCN-attention regimes. This indicates that thebaseline results are robust with respect to the intensity of regime-switching.
The second exercise is to examine if the results are robust when we change the lag
length of control variables. The baseline regression Eq.(3) contains twelve lags of control
variables for the U.S. and seven lags for Japan. As is discussed before, this lag structure is
optimal as indicated by the AIC. We re-estimate our model to check whether our results arerobust or not when we change the lag length. Figure C.3 and Figure C.4 show the impulse
responses estimated in a regression model with different lag structures for the U.S. andJapan, respectively. Figure C.3 indicates that in the U.S., the impulse response is broadly
in line with the baseline impulse response even when the length of the lag is changed,
except when the lag of the control variable is short, such as when the lag length is three.
The estimation results using Japanese data in Figure C.4 show that when the number oflags is twelve, the response of inflation in the high-CCN-attention regime deviates from thebaseline. Otherwise, the results of the empirical analysis are robust with respect to the lag
length of control variables in Japan.
The third and the last exercise is to check whether the results vary if we change thelength of time for calculating the CCN index moving-average. Figure C.5 shows the results
for the U.S. and Figure C.6 shows the results for Japan. They show that the qualitative40

message of the earlier analysis is unchanged in both the U.S. and Japan.

41

Figure C.1: Robustness to the values of θ in the U.S.
Notes: The upper panel shows the responses during the high attention to climate change
regime in the U.S. and the lower panel shows the responses during the low attention toclimate change regime. The solid line denotes the responses when θ = 5(baseline), thedashed line denotes the case of θ = 2 and the dot-dash line denotes the case of θ = 8,
respectively. The shaded areas display the 90 percent confidence bands based on Newey
and West (1987) standard errors around the baseline responses.

42

Figure C.2: Robustness to the values of θ in Japan
Notes: The upper panel shows the responses during the high attention to climate change
regime in Japan and the lower panel shows the responses during the low attention toclimate change regime. The solid line denotes the responses when θ = 5(baseline), thedashed line denotes the case of θ = 2 and the dot-dash line denotes the case of θ = 8,
respectively. The shaded areas display the 90 percent confidence bands based on Newey
and West (1987) standard errors around the baseline responses.

43

Figure C.3: Robustness to the length of lags of control variables in the U.S.
Notes:The upper panel shows the responses during the high attention to climate change
regime in the U.S. and the lower panel shows the responses during the low attention toclimate change regime. The solid line denotes the responses when the lag length is twelve(baseline), the dashed line denotes the responses when the lag length is three, the dot-dashline denotes the responses when the lag length is six and x denotes the responses when thelag length is nine, respectively. The shaded areas display the 90 percent confidence bands
based on Newey and West (1987) standard errors around the baseline responses.

44

Figure C.4: Robustness to the length of lags of control variables in Japan
Notes:The upper panel shows the responses during the high attention to climate change
regime in Japan and the lower panel shows the responses during the low attention toclimate change regime. The solid line denotes the responses when the lag length is twelve(baseline), the dashed line denotes the responses when the lag length is three, the dot-dashline denotes the responses when the lag length is six and x denotes the responses when thelag length is nine, respectively. The shaded areas display the 90 percent confidence bands
based on Newey and West (1987) standard errors around the baseline responses.

45

Figure C.5: Robustness to smoothness of the CCN index in the U.S.
Notes: The upper panel shows the responses during the high attention to climate change
regime in the U.S. and the lower panel shows the responses during the low attention toclimate change regime. The solid line denotes the responses when using a six-month moving average of the CCN index (baseline), the dashed line denotes the responses whenusing a three-month moving average, the dot-dash line denotes the responses when using
a five-month moving average, x denotes the responses when using a seven-month moving
average, and the square marker denotes the responses when using a nine-month moving
average, respectively. The shaded areas display the 90 percent confidence bands based on
Newey and West (1987) standard errors around the baseline responses.

46

Figure C.6: Robustness to smoothness of the CCN index in Japan
Notes: The upper panel shows the responses during the high attention to climate change
regime in Japan and the lower panel shows the responses during the low attention to climate change regime. The solid line denotes the responses when using a six-month moving
average of the CCN index (baseline), the dashed line denotes the responses when usinga three-month moving average, the dot-dash line denotes the responses when using afive-month moving average, x denotes the responses when using a seven-month moving
average, and the square marker denotes the responses when using a nine-month moving
average, respectively. The shaded areas display the 90 percent confidence bands based on
Newey and West (1987) standard errors around the baseline responses.

47