(AGENPARL) - Roma, 22 Settembre 2023 - (AGENPARL) – ven 22 settembre 2023 Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
Assessing credit risk sensitivity to climate and energy shocks
Number
September 2023
by Stefano Di Virgilio, Ivan Faiella, Alessandro Mistretta
and Simone Narizzano
Mercati, infrastrutture, sistemi di pagamento
(Markets, Infrastructures, Payment Systems)
Assessing credit risk sensitivity to climate and energy shocks
by Stefano Di Virgilio, Ivan Faiella, Alessandro Mistretta
and Simone Narizzano
Number 41 – September 2023
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Assessing credit risk sensitivity to climate and energy shocks
by Stefano Di Virgilio,* Ivan Faiella,** Alessandro Mistretta,**
and Simone Narizzano*
Abstract
We apply a novel method to estimate the impact of a change in energy expenditure on Italian
firms’ credit risk, measured as the 12-month default probability (PD). We examine a shock to
energy expenditure originating from different carbon tax levels and then 1) shock energy prices;
2) re?compute the firm-level energy mix; 3) assess the impact on the firm PD via the re?calculation
of its financial statement. The flexibility of this approach, which includes scope 2 emissions,
enables us to assess the transmission channels of energy shocks and firm exposure in detail.
Our results show that the introduction of carbon taxation would have a limited impact on credit
risk: a carbon tax of EUR 40, EUR 90 and EUR 140 per tonne of CO2 would raise the average
PD by 0.6, 2.3 and 4.1 basis points, respectively. The effect is slightly larger for the Agriculture
and Services sectors, while there is no clear pattern relating to firm size.
JEL Classification: Q41, Q54, Q58.
Keywords: climate change, carbon tax, credit risk.
Sintesi
Il lavoro applica una nuova metodologia per stimare gli effetti di un aumento della spesa
energetica sul rischio di credito delle imprese italiane, misurato dalla probabilità di insolvenza
a 12 mesi (PD). Nel dettaglio, ipotizziamo l’introduzione di differenti livelli di carbon tax
e, conseguentemente: 1) stressiamo i prezzi energetici; 2) ristimiamo la domanda di energia
delle imprese; 3) valutiamo gli effetti sulla PD delle imprese attraverso il ricalcolo del bilancio
aziendale. La flessibilità dell’approccio, che include l’analisi delle emissioni scope 2, ci
consente di valutare in dettaglio il canale di trasmissione degli shock energetici e la contestuale
esposizione delle imprese. I nostri risultati mostrano che l’introduzione di una carbon tax
avrebbe effetti mediamente contenuti sul rischio di credito: una carbon tax di EUR 40, EUR
90 e EUR 140 per tonnellata di CO2 aumenterebbe la PD media di 0,6, 2,3 e 4,1 punti base,
rispettivamente. L’effetto è lievemente maggiore per il settore dell’agricoltura e dei servizi
mentre non si rilevano effetti chiari considerando la dimensione aziendale.
Bank of Italy, Financial Risk Management Directorate.
Bank of Italy, Secretariat to the Governing Board.
CONTENTS
1. Introduction
2. The relationship between climate change and credit risk
3. Data
4. The BI-ICAS model
5. Simulation
6. Results
7. Conclusions
References
Appendix A
Appendix B
1. Introduction1
This paper applies a novel method to estimate the impact of a change in energy expenditure
on Italian firms’ credit risk, measured as the 12-month probability of default (PD). In particular, we
examine a shock to energy cost originating from different levels of a carbon tax and then 1) shock
energy prices; 2) re-compute the firm-level energy mix; 3) assess the impact on the firm PD via the
re-calculation of its financial statement, which is the key input for credit risk assessment. This study
is part of a broader effort within the Bank of Italy to define the impact of transition policies on
corporate performance. The impact of climate policies on credit risk has some similarity with that
of the energy market disruptions that firms and households have experienced since the second
half of 2021.
Climate change poses a threat to economic activity via several channels, e.g. owing to reduced
labour and energy productivity; some extreme weather events have also drove down short-term
economic growth (IPCC, 2022). The biggest threat is for the most climate-exposed sectors such as
agriculture, forestry, fishery, energy, and tourism. Climate change has an impact on financial
institutions as well (e.g. de Guindos, 2021). The risks for the stability of the financial system will
also depend on the climate policies and on the exposure of the different sectors of the economy
to such policies.
Climate-change effects may spread to the financial sector in several ways. Natural disasters,
by disrupting the activities of companies and households, raise their financial vulnerability and
lower the value of the collateral pledged for loans. Repaying loans may become more complex due
to the diversion of resources for restoring damaged property. Environmental shocks may increase
the number of non-performing loans in the portfolio of those banks that are particularly exposed
to households and businesses in the areas most at risk. This could induce banks to restrict the
supply of credit, which would potentially affect the effectiveness of the credit channel of monetary
policy. If these effects occurred on a large scale, they might threaten the stability of the financial
system as a whole (Faiella and Malvolti, 2020).
In the future, climate-related credit risk management will increasingly be a key element for
successful banks, that will enable them to assess the impact of climate on the borrowers’ ability to
repay their loans. The estimation of these risks, however, is a challenging task because the impacts
of future climate manifestations will be highly uncertain, non-linear and largely endogenous
(Monasterolo, 2020). The evaluation of these risks at individual firm level is made even more
challenging due to the lack of detailed and comparable data on the exact geographical location
and on the carbon intensity of individual activities (FSB, 2021).
Public and private institutions, central banks and supervisors are promoting joint efforts to
assess the possible implications of climate policies on the economy and the financial sector.
Central banks are well positioned to assess the threat posed by climate change on the financial
system. At the end of 2017 the growing awareness of these issues led to the creation of the
We would like to thank the participants to the Workshop on Climate change risk and credit assessment. We are also
indebted to Antonio Scalia and Alessandra Iannamorelli for the useful comments and suggestions. The opinions
expressed and conclusions drawn are those of the authors and do not necessarily reflect the views of the Bank of Italy;
all errors remain under our responsibility.
Network for Greening the Financial System (NGFS), a global network of central banks and
supervisory authorities that promotes the sharing of experiences and best practices concerning
the management of environmental risks in the financial sector, focusing specifically on climate
risks. The NGFS found that ‘climate- or environmental-related criteria are not yet sufficiently
accounted for in internal credit assessments or in […] credit agencies’ models’ (NGFS, 2018). More
recently, the NGFS has published two documents to help central banks and financial regulators in
assessing climate risks. The first (NGFS, 2022a) provides an update on existing analyses and
practices on the methodologies used by financial institutions, credit rating agencies (CRAs) and
supervisors to assess and quantify climate-relate financial risk. The second document (NGFS,
2022b) examines how CRAs integrate climate-related risks in their assessments.
There have been several attempts to assess how climate risks may affect credit risk
differentials. Climate policies, either indirectly through regulation or directly via carbon pricing,
increase firms’ energy costs – at least in the short and medium-term – possibly impairing the ability
to repay their debt. The flexibility of our approach, which includes scope 2 emissions, enables us
to assess the transmission channels of energy shocks and the firm exposure in detail.
The paper is organized as follows. Section 2 quickly reviews the literature on the relationship
between climate change and credit risk and introduces the contribution of our approach. Section
3 describes the dataset for the empirical analysis. Section 4 introduces the in-house credit
assessment system of the Bank of Italy (BI-ICAS). Section 5 presents the main elements of our
simulation and Section 6 gives the results. Section 7 concludes.
2. The relationship between climate change and credit risk
The literature on the impact of climate change on credit risk obtains mixed results. Capasso et
al. (2020) find that the distance-to-default is negatively associated with firm’s carbon emissions
and carbon intensity. Balasirishwaron et al. (2022), using an Ordered Probit approach for credit
analysis which includes the issuer ESG status and credit ratings, find a low correlation but some
common factors driving credit and ESG ratings. They suggest that, in a diversified bank portfolio,
ESG and credit factors could jointly boost overall risk through their positive correlation.
More recently, a series of studies at the Bank of Italy have analyzed the possible impact of
climate transition risk on financial fragility and sectoral PDs. Faiella et al. (2022) use granular data
at the counterparty level to estimate the impact of alternative carbon tax levels on energy demand
and costs, thus identifying the most financially vulnerable sectors. Aiello and Angelico (2022),
building on the previous work, estimate the impact in terms of default rates at the sector level;
they find that credit risk stemming from the introduction of a carbon tax raises the one-year PD of
firms but it remains below the historical average.2
In this study, we extend the above analyses to firm-level PD information using the in-house
credit assessment system of the Bank of Italy (henceforth BI-ICAS). This model has been developed
They use data from March 2006 to December 2019.
since 2013 to enable Italian banks to use the loans granted to non-financial firms as collateral in
Eurosystem monetary policy operations.3 The model has two components: i) the statistical
component, with an extremely wide coverage of Italian non-financial companies; ii) the expert
component, which applies to a subsample of companies. The statistical component is a highly
automated system which produces an estimate of the probability of default (Statistical PD) for
Italian non-financial corporations on a monthly basis.
Using the methodology developed in Faiella et al. (2022), we simulate the detailed energy
expenditure of each firm and employ an estimate of the additional cost due to the effect of
different levels of carbon tax on operating costs in the financial statement of each firm. These
values are then used as an input for BI-ICAS to calculate the stressed 12-month PD. The flexibility
of this approach enables us to assess the channels and the exposure with greater detail compared
to the existing studies. We also cover the exposure of the firm in terms of scope 2 emissions, that
we implicitly consider because we have a complete picture of each firm’s energy mix. This
approach is not limited to climate policy shocks, but it can be used to assess how other types of
climate and energy shocks might affect credit risk.
3. Data
Our approach integrates a new dataset on firm-level costs with the information routinely
used to estimate the PD in the BI-ICAS model. Following the approach of Faiella et al. (2022), the
new dataset includes the sector-level fuel-specific use per employee together with data on
employees and other financial information at firm-level.
Information on the PD – Statistical PDs produced with the BI-ICAS model mainly rely on two
types of information: 1) credit behaviour data, collected by the Bank of Italy in the National Credit
Register (NCR); 2) financial statements collected by Cerved Group.
The NCR is a national archive of banks’ and financial intermediaries’ debtors. On a monthly
basis, all banks and other financial intermediaries are required to send to the NCR a wide set of
information about the financial liabilities and payment behaviour of individual entities (companies
and households). In return, banks have access to information on the debt exposure of their
borrowers towards the whole banking system. The NCR is extremely accurate, as it collects
information about all credit relationships with a minimum size of EUR 30,000.
The model relies on the Bank of Italy’s financial statement archive (Sistema informativo
economico-finanziario, SIEF) based on data collected from the Cerved Group. Such data are
available in two separate datasets:4 Cebi (from Centrale dei Bilanci) and Cerved. The Cebi dataset
contains around 80,000 financial statements per year covering nearly all the medium and large
firms and about half of the small ones. These data are collected partly through banks participating
The definition of default used by BI-ICAS was approved by the Eurosystem and is consistent with the definition given
in the Basel Accords.
Financial statement data are available according to the Cebi reclassification accounting scheme, which can be applied
to both the national GAAP and IFRS financial statements.
in the Cebi program and for the rest through the Official Business Register. Cerved is a more
comprehensive dataset of the Italian corporate sector, including also nearly all the small and micro
limited-liability companies. Data are provided by the National Official Business Register.
The analysis covers more than 200,000 limited liability firms, representing about 60 per cent
of all joint-stock company revenues. From an industry perspective, more than half of the
companies belong to the Services sector, while the second largest sector is Manufacturing.5
Construction and Agriculture have the highest average PD, with a right-skewed distribution (Table
Table 1 – Baseline PDs by sector (percentage points)
Sector
Agriculture
Construction
Manufacturing
Services
Entire sample
percentile
percentile
Number
of firms
2,084
25,856
61,872
116,065
205,877
The PD pattern by size6 reflects the large number of micro and small firms operating in Italy.
The micro companies represent more than half of the total number of firms and exhibit slightly
higher PDs (Table 2).
Table 2 – Baseline PDs by size (percentage points)
Micro
Small
Medium
Large
Entire sample
percentile
percentile
Number
of firms
117,900
66,603
17,151
4,223
205,877
Energy expenditure – Energy costs are a relevant, although sometimes neglected, driver of firm
performance. According to Faiella and Mistretta (2015), the energy expenditure of the Italian
manufacturing sector is equivalent to about 2.4 per cent of total sales and energy plays a key role
in business decisions, such as the propensity to export. Faiella and Mistretta (2022) find that the
purchase of energy is a key factor also at the European level: over the last decades the energy cost
Firms operating in the Mining and Utilities sectors have been excluded from the analysis considering the limited
number of observations in our sample.
The size is based on total assets, turnover and number of employees (according to the European recommendation
2003/361/EC).
has steadily increased, becoming equivalent to about one third of total labor cost. The boldest
decarbonization targets will involve a further increase in energy prices (e.g. for the extra-costs of
a full-fledged EU ETS or the introduction of an EU-wide carbon tax of non-ETS emissions). These
added costs may have a detrimental effect on industry competitiveness and on the individual
firm’s ability to honor its debt, thus providing a material example of transition risk. Despite the
relevance of this issue, European statistics on energy costs are scant, irregular and with a very
limited level of disaggregation. To close this information gap we impute energy expenditure
integrating balance sheets information from Cerved Group with information on employment at
the firm level using data from the National retirement planning agency (INPS) and the industrylevel statistics provided through the Physical energy flow accounts (PEFAs). We use the
information on per-worker energy consumption (using total workers data provided in the National
Accounts) and on different energy sources at sector-level; 7 in addition we use firm-level employee
data to obtain the firm-level estimate of energy use. This method implies that within-sector
variability depends only on the number of workers. A check of this approach with external sources
shows that our estimates provide a fair picture of firms’ energy consumption (for further details
see Appendix B in Faiella et al., 2022). The energy use in physical terms is then multiplied by fuelspecific energy prices (from Eurostat and the Ministry of economic development) to obtain the
energy expenditure for each firm in the sample.
4. The BI-ICAS model
Since 2013 the Bank of Italy has developed the BI-ICAS model, for assessing the
creditworthiness of Italian non-financial corporations, to be used in the context of the Eurosystem
Credit Assessment Framework (ECAF).8 ECAF defines the minimum credit quality requirements as
well as the rules and procedures which ensure that the Eurosystem only accepts adequate
collateral, as required by article 18.1 of the Protocol on the Statute of the European System of
Central Banks and of the European Central Bank.
BI-ICAS plays a key role in monetary policy operations since it allows all Italian counterparties
to pledge credit claims to non-financial corporations as collateral. In particular, ICAS can be used
by small and medium-sized banks that do not have an internal rating system and are not in a
position to easily fund themselves through structured finance operations, such as asset-backed
securities or covered bonds. The development of BI-ICAS has contributed to increase collateral
availability for a wide range of Italian counterparties with different business models, thus paving
the way for a smooth implementation of monetary policy.9
The BI-ICAS rating process is based on a two-stage procedure to assign a rating (Giovannelli et
al., 2020). The first stage involves the use of a statistical model, is based on hard data and is largely
automated; the second stage is an expert assessment made by at least two financial analysts using
We use PEFA information for 6 fuels (gas, power, renewables, coal, oil products and heat) and 74 sectors for 2019.
https://www.ecb.europa.eu/paym/coll/risk/ecaf/html/index.en.html
The BI-ICAS statistical model produces, every month with a fully automated procedure, a PD for around 350,000 Italian
limited-liability non-financial companies.
also qualitative information. At the end of the second stage, analysts can either confirm or modify
the rating derived from the statistical model. Overall, the expert assessment performs a review of
the output of the statistical model based on a larger set of key risk drivers. However, the expert
assessment is only performed for a subsample of firms for which a statistical assessment is
available, in the order of 4,000 companies every year.10
The statistical model consists of a system of logistic regressions and produces a one-year PD
for all Italian limited liability non-financial companies having both an available financial statement
and an exposure towards the financial system of at least 30,000 euro, as reported in the National
Credit Register (NCR).
The statistical model, consisting of multiple statistical sub-models, has two independent
components providing distinct credit scores, each representing partial measures of credit risk:
? the credit behaviour sub-model employs a logit regression with data from the NCR and
models the credit relationship among the companies and the banking system. Three
different sub-models are set up for different firm classes according to their financial
debt exposure (small companies with exposure below EUR 300,000; medium
companies with exposure between EUR 300,000 and 20 million; and large companies
with exposures exceeding EUR 20 million);
? the financial sub-model employs a logit regression based on yearly financial statement
data, such as the debt-sustainability ratio, financial structure and liquidity ratios. The
financial component consists of sub-models separately estimated for each of six
different sectors (Manufacturing, Trade, Construction, Services, Real estate and
Holdings11).
The results of these two exercises are then merged into the final model through an additional
logistic regression that provides the final score, which is then transformed into a PD via the inverse
logit function.
5. Simulation
In this section we present the assumptions underlying our exercise to assess the impact of a
climate policy shock on firm PDs. We use 2019 fiscal year financial statement data12 for
approximately 200,000 Italian non-financial limited-liability companies. We simulate the effects of
three possible carbon taxes that are consistent with the trajectories of the following three NGFS
scenarios: Below 2°C (EUR 40 per tonne of CO213), Net Zero 2050 (EUR 90) and Delayed Transition14
This subsample covers a large share of the credit claims pledged as collateral by banks using BI-ICAS.
Real estate and Holding companies are not included in our sample.
The 2020 fiscal year financial statement data were not considered in the analysis because of the Covid-19 pandemic
and the related extraordinary measures (e.g. government firms’ support).
To put things into perspective, consider that Introducing a carbon tax of EUR 50 per tonne is equivalent to increase
energy costs as follows (values in EUR 2015): +EUR 0.014 per kWh for electricity, + EUR 2.8 per GJ for gas and +EUR 0.12
per litre for liquid fuels (see Faiella and Lavecchia, 2021).
We assume that the carbon tax implied in different scenarios is implemented immediately.
(EUR 140). We assume a one-off introduction of the taxation on final energy use on top of existing
taxes on energy (and costs levied as part of the EU-ETS). A carbon tax is often used as a climate
policy shocks: due to its effectiveness in adjusting the (mis)pricing of climate risks and in providing
an incentive to move away from fossil-fuels; but its simplicity is counterbalanced by its low social
acceptability (Jagers et al., 2021). However, a change in the relative price among different energy
fuels might arise from other drivers, such as a ban on a specific type of carbon-intensive technology
(e.g. Internal combustion engine cars) or the support for low-carbon energy sources.
The climate shock in all these different scenarios can be modelled as an increase in the price
of fossil-based energy services, whose extent changes with the carbon content of different energy
fuels. We use the approach of Faiella and Lavecchia (2021) and Faiella et al. (2022), who translate
the impact of each carbon tax on final energy prices applying the specific carbon emission factors
for each fuel source (Table 3).
Table 3 – Carbon tax effect on different energy prices (€ 2015 and p.p.)
Carbon tax
Power
Gasoil
€/ton
€/toe
€/toe
€/toe
124.6
110.9
280.4
203.9
249.5
436.2
317.2
388.2
* Percentage changes compared with 2019 average prices; toe stands for ton of oil equivalent, i.e.
the quantity of energy contained in a ton of crude oil.
We assess the effects of these changes on energy expenditure and firms’ EBITDA computing
the price increase caused by the introduction of a carbon tax on the firms’ energy use and
expenditure, using the estimates of Faiella et al. (2022).15 We thus compute the additional cost
that each firm in our sample should bear and we can recalculate all the impacted income
statement variables, such as value added, EBITDA, EBIT and net income (NI). For instance, the new
value of EBITDA for firm i with a carbon tax c is calculated as follows:
???????,? = ???????,2019 ? ???,?,
where ???????,2019 is the initial value and ???,? is the additional cost that should be borne
by firm i with a carbon taxation equal to c.
When assessing the impact on the net income variable, we must take into account the
reduction in taxation:
The dependent variable is firm energy demand and the explanatory variable are the energy prices and a set of
additional controls, such as information regarding firms’ energy efficiency, its market power, its value added, and the
share of renewable sources in its energy mix.
???,? = ???,2019 ? [???,? ? (1 ? ?)],
where ? is the corporate tax rate.16
After the recalculation of the income statement, it is possible to re-compute the balance sheet
variables. For the accounting principle, the impact on equity (E) is equal to the difference
estimated for net income:
??,? = ??,2019 ? [???,? ? (1 ? ?)]
We also take into account a reduction in liquidity due to the carbon tax. If the impact of the
carbon tax cannot be entirely absorbed by the initial value of liquidity, the remaining part is
covered by an increase in the firm’s short-term financial debt.17 Recalculations of assets and
liabilities follow directly.
The results of the financial statement recalculations are used as input to the statistical BI-ICAS
model to assess the effect of the carbon tax on the PD of Italian non-financial companies.
6. Results
In this section we report the main results in terms of the impact of the three possible levels of
carbon tax on the one-year statistical PDs of the firms in our sample. The results are presented in
terms of deviation from the current baseline without carbon tax.
On average, the effect of a carbon tax on the credit-worthiness of Italian non-financial
corporations would be contained and diversified across industrial sectors. When considering the
total economy, the average PD in the three different scenarios (carbon tax of EUR 40, EUR 90 and
EUR 140) would increase respectively by 0.6, 2.3 and 4.1 basis points. On the one hand, the higher
the increase in energy costs the higher is the relative impact on companies’ operating margin and
leverage and, consequently, the effect on PDs is increasing and non-linear. On the other hand,
firms’ energy demand elasticity compensates this effect but not entirely. As in Aiello and Angelico
(2022), the average increase on the PDs is heterogeneous across different economic sectors, with
Agriculture and Services being the most exposed considering both the energy consumption and
the low reactivity to change in energy prices18 (Faiella et al., 2022) (Table 4).
In Italy, the corporate tax rate 24% (IRES, imposta sul reddito delle persone fisiche). There is also an additional 3.9%
regional tax on productive activities (IRAP, imposta regionale sulle attività produttive).
We assume a maximum reduction of liquidity equal to 90 per cent of the initial value. This hypothesis derives from
the fact that firms prefer, whenever possible, to keep a minimum amount of liquidity for operating needs (De Socio et
al., 2020).
Firms’ energy demand elasticity depends on their capacity to adjust their energy mix towards less expensive and low
carbon energy sources.
Table 4 – Average PD increments by sector (basis points)
Sector
Agriculture
Construction
Manufacturing
Services
Entire sample
Carbon tax
€ 40
€ 90
Number of firms
€ 140
2,084
25,856
61,872
116,065
205,877
A more granular analysis gives some further insight on the heterogeneity of exposure to
transition risks of the industries classified within the same sector. Looking at the detailed effects
of the increase in carbon taxation for each NACE 2-digit division (Figure 1), we find that all sectors,
excluding Construction, show a high dispersion of average PD increments (see Appendix A for
further details). The high level of within-sector variability suggests, as already pointed out by other
studies (such as Allen et al., 2020), that more granular analyses at sector level would provide a
more accurate assessment of environmental risk.
Figure 1 – Average PD increments by NACE division (basis points)
According to our results, the five most exposed industries are (Figure 2):
– Water transport (NACE 50);
– Air transport (NACE 51);
– Petrochemical: manufacture of coke and refined petroleum products (NACE 19);
– Land transport and transport via pipelines (NACE 49);
– Fishing and aquaculture (NACE 3).
Unsurprisingly, among the most exposed industries, we find sectors with a very high
dependence on fossil fuels such as the Petrochemical sector and/or sectors with an inelastic
energy demand (a proxy of their possibility to switch to a greener energy mix), such as Water
transport and Air transport.
For these sectors, the introduction of a EUR 40 carbon tax increases the PDs in the range
between 5.2 (Land transport) and 28.7 basis points (Water transport) and the highest carbon tax
would worsen the credit worthiness by between 39.8 (Fishing) and 94.4 basis points (Water
transport).
Figure 2 – Most impacted NACE divisions, average PD increments (basis points)
As a robustness check, we replicate our estimates on 2018 data and we do not find any
significant difference (the results are available in the Appendix A).
Our findings are broadly consistent with the results of Vermeulen et al. (2018) and Allen et al.
(2020), who find that the Transportation and Petrochemical sectors are among the high-risk
sectors.
Considering the relevant increase of the PDs compared to the baseline, with a EUR 140 carbon
tax, Air and Water transports would become the sectors with the largest average PD (Figure 3).
Figure 3 – Average PD by sector (percentage points)
Looking at different firm size, we do not detect any clear pattern in terms of average PD
increase (Table 5).
Table 5 – Average PD increments by dimensional class (basis points)
Carbon tax
Micro
Small
Medium
Large
Entire sample
€ 40
€ 90
€ 140
Number of
firms
117,900
66,603
17,151
4,223
205,877
7. Conclusions
In this paper we propose a novel method to correlate the change in energy expenditure with
the individual firm probability of default, drawing from the methodology developed in Faiella et
al. (2022) and employing the internal credit assessment model of the Bank of Italy (BI-ICAS).
In particular, we hypothesize the introduction of a carbon tax at different levels, used as a
possible innovation in climate policy in Italy, and simulate the energy expenditure of each firm.
These values are then used as input for the BI-ICAS model to calculate the stressed 12-month PDs.
This approach also covers the firm exposure in terms of scope 2 emissions, since we have a
complete picture of each firm’s energy mix. This approach may have further uses, since it allows
us to assess how the developments of climate change and energy prices may affect credit risk.
The results show that the impact of the introduction of carbon taxation on average credit risk
would be contained: the three different carbon tax levels (EUR 40, EUR 90 and EUR 140 per tonne
of CO2) would increase the average PD by 0.6, 2.3 and 4.1 basis points, respectively. The effect is
slightly larger for the Agriculture and Service sectors, while there is no clear pattern relating to
firm size.
The strength of our approach lies in its very accurate representation of firm heterogeneity.
We directly model energy demand considering the different fuel mix of each firm. We then
translate the carbon tax into final energy prices. Using microdata, we can assess what type of firm
is more exposed (sector, size, export propensity, position in the income distribution, etc.).
Our approach has some limitations. It is focused on the short term and it is static. We consider
only the short-term effect of carbon taxation: there is no translation, no adaptation by the firms
and no appraisal of the possible use of the revenues (for example using the proceedings of the
carbon tax to reduce other corporate taxes). Our analysis is based on partial equilibrium only: we
do not consider the interdependencies between companies and the effects on firms’
competitiveness and trade.
On the positive side this approach is rather flexible; it provides a realistic representation of
the firms’ energy mix and expenditure; it can be used also to assess the impact on credit worthiness
of a different type of energy shock, like the one Italian and European firms are experiencing
following the disruption in the gas and power markets (see Appendix B).
References
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Appendix A
Table A.1 – Fiscal year 2019 – Average PD increments by NACE division (basis points)
division
NACE division description
Crop and animal production, hunting and related
service activities
Forestry and logging
Fishing and aquaculture
Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel
Manufacture of leather and related products
Manufacture of wood and cork; manufacture of
articles of straw and plaiting materials
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum
products
Manufacture of chemicals and chemical products
Manufacture of pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral
products
Manufacture of basic metals
Manufacture of fabricated metal products, except
machinery and equipment
Manufacture of computer, electronic and optical
products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles and trailers
Manufacture of other transport equipment
Manufacture of furniture
Other manufacturing
Repair and installation of machinery and
equipment
Construction of buildings
Civil engineering
Specialised construction activities
Wholesale and retail trade and repair of motor
vehicles and motorcycles
Wholesale trade, except of motor vehicles and
motorcycles
Retail trade, except of motor vehicles and
motorcycles
Number
of firms
€ 40
€ 90
€ 140
1,832
5,396
2,132
2,387
2,205
1,896
1,244
2,025
1,736
3,448
2,945
1,023
14,620
1,597
2,068
7,083
2,611
2,015
2,766
11,480
1,984
12,392
7,515
28,776
16,513
NACE
division
Number
of firms
NACE division description
€ 40
€ 90
€ 140
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for
transportation
Postal and courier activities
Accommodation
Food and beverage service activities
Publishing activities
Video and television programme production,
sound recording and music publishing activities
Programming and broadcasting activities
Telecommunications
Computer programming, consultancy and related
activities
Information service activities
Activities auxiliary to financial services and
insurance activities
Real estate activities
Legal and accounting activities
Activities of head offices; management consultancy
activities
Architectural and engineering activities; technical
testing and analysis
Scientific research and development
Advertising and market research
Other professional, scientific and technical
activities
Veterinary activities
Rental and leasing activities
Employment activities
Travel agency, tour operator and other reservation
service and related activities
Security and investigation activities
Services to buildings and landscape activities
Office administrative, office support and other
business support activities
Education
Human health activities
Residential care activities
Social work activities without accommodation
Creative, arts and entertainment activities
Libraries, archives, museums and other cultural
activities
Gambling and betting activities
Sports activities and amusement and recreation
activities
7,870
2,835
5,785
10,241
3,384
2,338
1,078
1,999
2,708
1,288
1,654
1,268
1,068
2,293
1,707
1,136
3,334
1,062
1,291
1,902
NACE
division
NACE division description
Repair of computers and personal and household
goods
Other personal service activities
Number
of firms
€ 40
€ 90
€ 140
1,813
Table A.2 – Fiscal year 2018 – Average PD increments by sector (basis points)
Sector
Agriculture
Construction
Manufacturing
Services
Entire sample
Carbon tax
€ 40
€ 90
Number of firms
€ 140
1,971
22,630
58,680
101,043
184,324
Table A.3 – Fiscal year 2018 – Average PD increments by dimensional class (basis points)
Carbon tax
Micro
Small
Medium
Large
Entire sample
€ 40
€ 90
€ 140
Number of
firms
101,071
62,830
16,308
4,115
184,324
Figure A.1 – Fiscal year 2018 – Average PD increments by NACE division (basis points)
Appendix B
In the period from mid 2021 to the end of 2022, COVID-related disruptions to the global supply
chain and the Russian invasion of Ukraine have pushed energy prices to record levels. This shock
in energy costs constitutes a significant risk for Italian firms, thus increasing their vulnerability. We
use our approach to translate these higher costs into a notional carbon tax of EUR 420 and assess
its impact on the PDs in order to give a preliminary insight on the potential effects of these energy
cost spikes. This preliminary analysis will be updated when more recent financial data will become
available, together with information on the evolution of energy prices and demand.
Considering the Italian economy, the short-term effect on the average PD of a EUR 420 carbon
tax would be relevant but still moderate, involving an increase by 13.4 basis points.
Table B1 – Average PD increments by sector (basis points)
Sector
Agriculture
Construction
Manufacturing
Services
Entire sample
Carbon tax
Number of firms
€ 420
2,084
25,856
61,872
116,065
205,877
An analysis at NACE 2-digit division level confirms the high heterogeneity of the effects on
average PDs (Figure B1). The most exposed industries are the same reported in Figure 2, with the
three Transport sub-sectors (Water, Air and Land) showing the highest effect on PDs. For these
sectors, the rise in the energy prices due to a EUR 420 carbon tax would raise the average PD in
the range between 80.7 (Fishing) and 161.8 basis points (Water transport).
Comparing the results reported in Figure B1 with the ones in Figure 1, we notice that the spike
in the energy cost causes a relevant effect on the credit worthiness of a larger number of sectors.
With a EUR 140 carbon tax only five sectors show an average PD increase above 30 basis points. In
the stress scenario also other manufacturing sectors (such as the manufacture of chemical
products, non-metallic mineral products and basic metals) and services sectors (food and
beverage, sports and recreation activities, and other personal services) would be affected by more
than 30 basis points on average. Consequently, the increase in production costs due to the increase
in electricity and gas prices experienced in Italy during 2022 has been a major risk source for
several economic sectors. The targeted measures adopted by the Italian Government for energyintensive firms have clearly helped mitigate the burden of the price shock.
Figure B1 – Average PD increments by sector (basis points)
Average PD increments (basis points)
Agriculture
Manufacture
Construction
Services
Papers published in the ‘Markets, Infrastructures, Payment Systems’ series
n. 1
TIPS – TARGET Instant Payment Settlement – The Pan-European Infrastructure for the
Settlement of Instant Paymentsi, by Massimiliano Renzetti, Serena Bernardini, Giuseppe
Marino, Luca Mibelli, Laura Ricciardi and Giovanni M. Sabelli (Institutional Issues)
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by Mauro Arcese, Domenico Di Giulio and Vitangelo Lasorella (Research Papers)
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Stefano Antonelli (Research Papers)
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T2S – TARGET2-Securities – The pan-European platform for the settlement of securities in
central bank money, by Cristina Mastropasqua, Alessandro Intonti, Michael Jennings, Clara
Mandolini, Massimo Maniero, Stefano Vespucci and Diego Toma (Institutional Issues)
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Valentini (Institutional Issues) (in Italian)
n. 10
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Auria, Markus Bingmer, Carlos Mateo Caicedo Graciano, Clémence Charavel, Sergio Gavilá,
Alessandra Iannamorelli, Aviram Levy, Alfredo Maldonado, Florian Resch, Anna Maria Rossi
and Stephan Sauer (Institutional Issues)
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Marco Fanari, Simone Letta, Tommaso Perez and Giovanni Secondin (Research Papers) (in
Italian)
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Enrico Bernardini, Johnny Di Giampaolo, Marco Fruzzetti, Simone Letta, Raffaele Loffredo
and Davide Nasti (Research Papers)
n. 16
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n. 18
Cyber resilience per la continuità di servizio del sistema finanziario, by Boris Giannetto
and Antonino Fazio (Institutional Issues) (in Italian)
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Cross-Currency Settlement of Instant Payments in a Cross-Platform Context: a Proof of
Concept, by Massimiliano Renzetti, Andrea Dimartina, Riccardo Mancini, Giovanni Sabelli,
Francesco Di Stasio, Carlo Palmers, Faisal Alhijawi, Erol Kaya, Christophe Piccarelle, Stuart
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Haferkorn, Gaetano Marseglia and Onofrio Panzarino (Research Papers)
n. 21 Report on the payment attitudes of consumers in Italy: results from ECB surveys,
by Gabriele Coletti, Alberto Di Iorio, Emanuele Pimpini and Giorgia Rocco (Institutional
Issues)
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When financial innovation and sustainable finance meet: Sustainability-Linked Bonds,
by Paola Antilici, Gianluca Mosconi and Luigi Russo (Institutional Issues) (in Italian)
n. 23
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The bonfire of banknotes, by Michele Manna (Research Papers)
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Integrating DLTs with market infrastructures: analysis and proof-of-concept for secure DvP
between TIPS and DLT platforms, by Rosario La Rocca, Riccardo Mancini, Marco Benedetti,
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Bank of Italy and Istat, by Guerino Ardizzi and Alessandra Righi (Institutional Issues) (in
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Capotosto, Silvio Orsini and Pietro Tiberi (Research Papers)
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n. 30
The security of retail payment instruments: evidence from supervisory data, by Massimiliano
Cologgi (Research Papers)
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supervisory and oversight practices, by Roberto Pellitteri, Ravenio Parrini, Carlo Cafarotti
and Benedetto Andrea De Vendictis (Institutional Issues) (in Italian)
n. 32
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Quantum safe payment systems, by Elena Bucciol and Pietro Tiberi
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Letta and Pasquale Mirante
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Ceri and Eleonora Laurenza
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Capasso, Johnny Di Giampaolo and Nicola Pellegrini (in Italian)
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case by Claudio Doria, Gianluca Maddaloni, Giuseppina Marocchi, Ferdinando Sasso, Luca
Serrai and Simonetta Zappa (in Italian)