Corporate cost of debt in the low-carbon transition

CORPORATE COST OF DEBT IN THE LOW-CARBON TRANSITION:

THE EFFECT OF CLIMATE POLICIES ON FIRM FINANCING AND INVESTMENT THROUGH THE BANKING CHANNEL

Filippo Maria D’Arcangelo, Tobias Kruse, Mauro Pisu, Marco Tomasi

OECD – ECB Workshop on “Navigating the Path to Net Zero – The Interplay of Climate, Monetary and Financial Policies”, 19 April 2024​, Paris

Motivation

• Meeting net-zero targets requires largescale investments in low-carbon technologies

Figure 1: Required clean energy investment in IEA net zero pathway (2015-2030)

• Financial markets can play a key role in funding private clean investment – account for about half of all climate finance – is expected to mobilise additional financing needs (CPI, 2021)

• Being unable to price transition risk is a source of financial instability

Source: IEA (2023)

Research questions

• Do banks reward good environmental performance with lower cost of debt?

• What is the role of mitigation policy in mediating this channel?

• What is the effect on investment?

Literature

1. Firm environmental performance and financing costs

Firms with bad environmental performance pay higher cost of capital

- Stock returns (Chava, 2014; Bolton and Kacperczyk, 2021, Cheng et al., 2024); Loan spreads (Kleimeier and Viehs, 2016; Ehlers, Packer and de Greiff, 2022)

2. Isolating transition risk: Effects of policy on financing costs

– Bolton and Kacperczyk (2020) interpret the higher cost of capital as transition risk

– More carbon-intensive firms paid a higher cost of capital after the PA or observed lower stock returns (Delis et al., 2021; Degryse et al., 2021; Kruse, Mohnen and Sato 2024)

3. ESG and environmental performance

– ESG score has low correlation with environmental performance (Boffo and Patalano, 2020; OECD, 2022; Berg, Kölbel and Rigobon, 2022)

– But ESG affects investment decisions (Matsumura, Prakash and Vera-Munoz, 2014; Pástor, Stambaugh and Taylor, 2022)

This paper

Uses micro-data across countries and time linking syndicated loans with firm-level data

• Assesses empirically the relationship between loan spreads, firms’ environmental performance and mitigation policy stringency

– Three measures of environmental performance: ‘green’ patents, emission intensity, ESG score

– Two types of policy, measured with a composite index: technology support and CO2 taxes

– Identification: controlling for observables + exposure to policy argument

• Estimates in-sample elasticity of investment to cost of debt and performs back-of-theenvelope calculation of investment effect of mitigation policies

– Identification: IV strategy (debt structure + monetary policy)

• Provides policy recommendations on financially material information about environmental performance

Preview of results

• Firms with good environmental performance benefit from a lower cost of debt as climate change mitigation policies become more stringent

– These effects are economically significant when mitigation policy is stringent but disappear when it is not

• Climate change mitigation policies promote investment in green firms and reduce it in brown firms via the change in the cost of debt

• Firms with an ESG score benefit from lower loan spreads and this advantage increases with better scores, but the effect of ESG scores on loan spreads is disconnected from mitigation policy stringency

Data

Type of data

Deal-level data on the universe of syndicated loans

• All in spread drawn (AISD), deal amount, maturity, etc.

Firm-level financial data

• Assets, return on assets, debt etc.

Country-level environmental- policy indicators

Datasets

LPC Loan Connector Dealscan

BvD Orbis

OECD EPS indicators

• The sample consists of 6000 observations across 1400 firms for 2002-2018, across countries and sectors

Firm-level exposure variables

• Green innovativeness

• Emission intensity

• ESG scores (ESG and Environmental Pillar)

Green innovativeness: Climate change mitigation patents (PATSTAT Y02 classification)

ESG scores, CO2 emission (Refinitiv EIKON)

• The firms in the estimation sample are similar on observables to firms that are excluded because of missing data

Data: Syndicated loans

• Characteristics of syndicated loans:

– Syndicated loans have large volumes and several (large) lenders

• (avg. loan volume: 770 mUSD; median 360 mUSD)

– Borrowers tend to be large firms

– In-depth monitoring of borrowers due to large loan volume

• Dealscan provides precise information on deals (reported by banks for league table computations)

– Loan spreads (in bps)

– No need to back out cost of debt from balance sheet

Conceptual Framework

Firm environmental performance

CO2 tax

Market-pull effect

Policies

Green technology support policy

Technology-push effect

Innovation in green technologies

Ability to innovate to abate CO2 or sell abatement technology is priced-in

Ability to capitalize on technology support policies is priced-in

Expected sign of effect on loan spreads (-)

Expected sign of effect on loan spreads (-)

Emission intensity

Additional cost from CO2 tax is priced-in

Expected sign of effect on loan spreads (+)

Opportunity cost and ability to capitalise on technology support policies is priced-in

Expected sign of effect on loan spreads (+/-)

Baseline empirical model

Where:

�� deal, �� firm, �� country, �� year, �� industry

• ����������������: All-In-Spread-Drawn, log basis points

, Policies:

➢ CO2 Tax, index (1 to 6)

➢ Technological Support, index (1 to 6) ������, Environmental performance:

➢ Green innovativeness, stock (n.) of climate patents (log)

➢ Emission intensity, tCO2 per total assets (standardized)

��������, Controls:

➢ Deal-level: tranche amount, tenor or maturity, secured deal, number of lenders, provisions, covenant

➢ Firm-level: Total assets, ROA, Debt-to-Total-Assets, overall patenting

• ������: country × year fixed effects

• ������: industry × year fixed effects

Results: Baseline

Var.: (log) All-in-spread-drawn

Interpretation:

• Unit increase in CO2 tax index (+10 EUR/tCO2) is associated with -17% in loan spread for firms with green patenting one standard deviation above the mean (84pct)

• For the median deal: 21bps and EUR 0.7M per year

Robustness checks:

• Firm fixed effects

• Time invariant exposure

• Other definitions of emission intensity

• More fixed effects

• Excluding USA

Green innovators enjoy lower loans’ interest rates due to stricter climate policies

• With a high carbon tax (above EUR 50/t CO2), loans’ interest rates for top green innovators is more than 30% lower than that for bottom green innovators

• At the highest level of the technology support, top green innovators benefit from a 15% lower spread than the bottom green innovators

Emitting firms face higher loans’ interest rates due to higher carbon pricing

• With a high carbon tax (above EUR 50/t CO2), loans interest rate for the most emission intensive firms is almost 15% higher than that for least emission intensive firms

• No significant effects of policies when distinguishing firms by their ESG (or E pillar) score

ESG

scores relate only weakly with firms’

environmental performance

• Weak correlation of green innovativeness with ESG scores

• No correlation (or counterintuitive correlation) of emission intensities with ESG scores

The effect of ESG scores on the loan spread

Dep. Var.: (log) All-in-spread-drawn

• Firms that disclose an ESG enjoy lower loan spreads

• A higher ESG reduces loan spreads

• The policy interaction is not significant or points in the opposite direction

➢ Banks may have other motives for ESG investments rather than pricing exposure to climate policy

The effects vary within- and across industries

• The majority of firms in each sector will experience an increase in loan spreads following a one unit increase in the carbon tax index (EUR 10/tCO2)

Predicted % change on loan spreads

• But there is large within-sector heterogeneity

– Detailed firm-specific information is needed to identify exposure to transition risks.

Note: The figure shows the distribution of percent predicted changes in loan spreads (using the baseline regression results shown in Table 3, column 5) from increasing the carbon tax index by one unit (around 10 EUR/tCO2). The blue dots represent the median predicted change in firms’ loan spreads within each industry. Differences across and within industries are driven by the variation in deal- and firm-level characteristics (including environmental performance).

The effect of loan spreads on investments

Construct an IV to estimate the effect of loan spreads on emissions:

• (Debt maturity) × (Fluctuations in monetary policy)

• Investors adapt portfolios maturity in response to changes in interest rates. Firms with loans close to expiration have lower bargaining power. (Jeenas, 2019; Ottonello and Winberry, 2020)

• Exclusion restriction: monetary policy transmits to investments only through loans spreads and debt-structure is predetermined

• Instrument is relevant (F-Statistics, Hansen J) and the first stage looks acceptable

• → Elasticity estimates are in line with existing literature (~-1)

Results suggest large effects of climate policies on investments among green innovators and high emitters

• A typical (1 std. dev.) increase in the carbon tax score is associated with a

– 7-12% increase in investment for top green innovators

– 6-11% decline in investment for the top emitters

• A typical increase in the green technology support score is associated with a

– 4-7% increase in investment for top green innovators

– 2- 4% decline in investment for top emitters

Note: Darker shades: upper bound estimates from this paper (-1.38); medium-light shades: lower bound estimates from this paper (-0.99); light shades: estimates from Gilchrist and Zakrajsek (2007) (-0.75). Green innovators’ are firms with (log) green innovativeness 1 standard deviation above the median (84th percentile), ‘Top emitters’ are firms with emission intensity 1 standard deviation above the mean (88th percentile).

Conclusions and policy implications

• The paper provides evidence that sophisticated investors (banks) reward good environmental performance if climate policy is sufficiently stringent

• Policy recommendations to help capital reallocation towards the green transition

– Mandatory emission disclosure

– Standardise ESG ratings and data collection

– Encourage firms to produce transition plans

• Some open questions:

– Are banks pricing all transition risk, and for all types (volumes) of loans?

– Are less sophisticated investors as good in monitoring environmental performance?

– What reasons other than transition risk motivate ESG investments?

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SUPPLEMENTARY MATERIAL

Descriptive statistics