---
title: "Air Pollution and Bank Loan Pricing: Li et al. (2026)"
description: >-
  Distilled: Using proprietary loan data from a Chinese state-owned commercial bank
  linked to firm-level ESR emissions, Li et al. find that higher air pollutant
  intensity significantly raises bank loan spreads via labor risk and environmental
  transition risk channels, confirmed causal by a PSM-DID design around China's 2013
  Air Pollution Control Action Plan. Journal of Banking and Finance 185 (2026),
  paywalled. Eight core results with source locators, datasets, and estimating
  specifications.
sidebar:
  label: Li et al. 2026
  order: 1
tags: [paper-summary, environmental-finance, bank-lending, air-pollution, credit-pricing,
       china, panel-regression, difference-in-differences, peer-reviewed, unreplicated,
       data:esr-china, data:csmar, data:wustl-pm25]
paper:
  authors: Donghui Li, Jian Sun, Rui Xu, Chun Yuan, Liyi Zhu
  authorList:
    - { family: Li, given: Donghui, affiliation: Shenzhen University }
    - { family: Sun, given: Jian, orcid: "0000-0002-8049-3487", affiliation: Central University of Finance and Economics }
    - { family: Xu, given: Rui, orcid: "0000-0002-5574-0028", affiliation: Guangdong University of Foreign Studies }
    - { family: Yuan, given: Chun, affiliation: Central University of Finance and Economics }
    - { family: Zhu, given: Liyi, orcid: "0009-0006-9257-087X", affiliation: Capital University of Economics and Business }
  year: 2026
  venue: Journal of Banking and Finance 185 (2026) 107655
  venueShort: J. Banking Finance 2026
  tier: field
  doi: 10.1016/j.jbankfin.2026.107655
  jel:
    codes: [G21, G32, Q53]
    assignedBy: claude-sonnet-4-6
    date: 2026-06-25
  topics: ["Energy, Environment, Economic Growth", "Corporate Social Responsibility Reporting", "Sustainable Finance and Green Bonds"]
  dataAccess: proprietary-confidential
  outcome:
    - bank loan spread (actual rate minus PBOC benchmark)
    - total loan volume extended to borrowing firm
    - mortgage-related loan contract terms
  outcomeClass: [firm-financing, credit-supply]
  license: "© 2026 Elsevier B.V. All rights reserved, including those for text and data mining, AI training, and similar technologies. No open-access or CC licence found in Crossref metadata; licence blocks are tdm and stm-asf only."
  licenseShort: paywalled
  access: paywalled
  machineAccess: "blocked-paywall (Elsevier ScienceDirect, 2026-06-25)"
  redistribution: extract-only
  resultsCount: 8
  citedByCount: 0
  methods:
    role: applies-method
    family: reduced-form-causal
    buildsFrom: [panel-regression, difference-in-differences, matching]
    identification: natural-experiment
  contributionType: [new-fact]
  mechanisms: [externality]
  introducesData: true
  scope:
    region: China
    assetClass: corporate loans (Chinese listed firms)
    period: 2007-01..2019-12
    frequency: annual
    dataType: [accounting, administrative, other]
    granularity: [firm, transaction]
    n: "55,483 loan-level observations, 745 firms, 175 cities, 2007-2019"
  findings:
    - { ref: R1, outcome: "bank loan spread", metric: coefficient, value: "0.047*** (t=3.20); 1-SD increase in emissions ~ 17.85% of mean loan spread", direction: positive }
    - { ref: R2, outcome: "bank loan spread", metric: coefficient, value: "0.062*** (t=4.58) in PSM-OLS (N=42,530)", direction: positive, vsBenchmark: "confirms baseline after PSM selection correction" }
    - { ref: R3, outcome: "bank loan spread", metric: coefficient, value: "Treatpost = -0.124*** (t=-3.07) in PSM-DID", direction: negative, vsBenchmark: "Air Pollution Control Plan reduces spreads for treated high-pollution firms" }
    - { ref: R4, outcome: "bank loan spread", metric: coefficient, value: "Airpollution x Bankruptcy_dummy = 0.039*** (t=3.34); x Educated_dummy = 0.031** (t=2.33); x Technical_dummy = 0.028** (t=2.00)", direction: positive, vsBenchmark: "labor risk channel: premium concentrated in firms with high bankruptcy risk or low-skill workforce" }
    - { ref: R5, outcome: "bank loan spread", metric: coefficient, value: "Airpollution x Violation_dummy = 0.065** (t=2.40); x Gsubsidy_dummy = 0.021** (t=2.00); x Greeninno_dummy = 0.024** (t=1.97)", direction: positive, vsBenchmark: "transition risk channel: concentrated in firms with environmental violations or weak green support" }
    - { ref: R6, outcome: "bank loan spread", metric: coefficient, value: "Political=0: 0.052*** (t=3.16); Political=1: 0.018 (t=0.97)", direction: mixed, vsBenchmark: "premium significant only for politically unconnected firms; subsample difference p=0.000" }
    - { ref: R7, outcome: "bank loan spread", metric: coefficient, value: "Weak envir. governance: 0.079*** (t=4.67); strong governance: -0.002 (t=-0.11)", direction: mixed, vsBenchmark: "premium significant only in provinces with weak environmental governance; difference p=0.000" }
    - { ref: R8, outcome: "total loan volume and mortgage contract terms", metric: coefficient, value: "Sum_volume: -0.017* (t=-1.67); Mortgage_terms: 0.009** (t=2.08)", direction: mixed, vsBenchmark: "air pollution tightens non-price terms: less credit extended, more mortgage requirements" }
  resultType: new-finding
  relatesTo:
    - { cite: "Bolton and Kacperczyk (2021)", relation: extends, note: "extends evidence on pollution premiums from equity returns to bank loan markets" }
    - { cite: "Hsu et al. (2023)", doi: '10.1111/jofi.13217', relation: extends, note: "extends environmental transition risk evidence to bank loans using firm-level ESR data" }
    - { cite: "Graham et al. (2008)", relation: builds-on, note: "builds on the determinants-of-bank-loan-pricing framework (firm characteristics, loan attributes)" }
    - { cite: "Barwick et al. (2024)", doi: '10.1257/aer.20200956', relation: tests, note: "PSM-DID result partially supports their finding that environmental regulation can ease bank creditors' concerns" }
    - { cite: "Houston et al. (2014)", doi: '10.1111/1475-679x.12038', relation: builds-on, note: "builds on political connections and loan pricing to motivate the political connection cross-section test" }
  replicationCode:
    status: none
  extraction:
    - { by: "paper-distiller (claude-sonnet-4-6)", date: 2026-06-25, role: extracted, note: "Full text read (pp. 1-16 including all tables and appendices); eight results extracted from the paywalled PDF. Not human-verified. Not reproduced." }
    - { by: paper-verifier (claude-sonnet-4-6), date: 2026-06-25, role: verified, note: "Locators and reported magnitudes re-checked against the source PDF; all 8 Core results rows confirmed against Tables 2-9 and A3; Eq. (1) and Eq. (2) verified term-by-term; outcome classes, metric slugs, and relatesTo cites all valid; no errors found." }
  licenceVerification:
    - { source: "Crossref REST API works/10.1016/j.jbankfin.2026.107655", checked: 2026-06-25, by: "paper-distiller (claude-sonnet-4-6)", found: "license[] contains only tdm (elsevier.com/tdm/userlicense/1.0/) and stm-asf (doi.org/10.15223/policy-*) blocks; no CC or open-access licence present; paper is paywalled" }
  rightsSignalConflict: false
---

**What this is.** The paper's core results, identification strategy, estimating equations, and key datasets: enough to understand what it found and how, without reading the full paper. To replicate or extend, read the original at [https://doi.org/10.1016/j.jbankfin.2026.107655](https://doi.org/10.1016/j.jbankfin.2026.107655).

## TL;DR

Using a proprietary loan dataset from a nationwide Chinese state-owned commercial bank (55,483 loan observations, 745 borrowing firms in 175 cities, 2007-2019) linked to firm-level air pollutant emissions from China's Environmental Survey and Reporting (ESR) database, this paper documents that firms with higher air pollution intensity pay significantly higher bank loan spreads. In the baseline specification, a one-standard-deviation increase in air pollutant emissions is associated with a 17.85% increase relative to the mean loan spread (Table 2, Col. 4, p. 5). The effect is confirmed causal via propensity-score matching combined with a difference-in-differences design around China's 2013 Air Pollution Prevention and Control Action Plan: after the plan, treated high-pollution firms saw significantly lower loan pricing (Table 3). Two economic channels explain the premium: (1) a labor risk channel, where pollution undermines retention of skilled employees and raises default probability; and (2) an environmental transition risk channel, where high-pollution firms face elevated regulatory compliance costs. Both channels are confirmed through interaction tests and survive horse-racing together. The premium is concentrated in politically unconnected firms, in provinces with weak environmental governance, and for non-headquarter borrowers, and extends to non-price terms: pollution also reduces total credit volume extended to borrowers and increases mortgage-related contract requirements.

## Core results

Magnitudes as reported; `\*`/`\*\*`/`\*\*\*` = 10%/5%/1%. Locators point to the source PDF.

| # | Result | Locator | Magnitude |
|---|---|---|---|
| R1 | Air pollutant emissions positively predict bank loan spread in the full baseline specification with firm, year, borrower-category, and risk-type fixed effects | Table 2, Col. (4), p. 5 | Airpollution = 0.047\*\*\* (t=3.20); 1-SD increase associated with 17.85% of mean loan spread; N=55,483 |
| R2 | PSM-OLS confirms the baseline: positive and significant after controlling for self-selection of high-polluting firms | Table 3, Panel B, Col. (1), p. 6 | Airpollution = 0.062\*\*\* (t=4.58); N=42,530 |
| R3 | PSM-DID: the 2013 Air Pollution Control Action Plan significantly reduces loan pricing for treated high-pollution firms; parallel trends confirmed pre-shock | Table 3, Panel B, Col. (2), p. 6; Table 4, p. 7 | Treatpost = -0.124\*\*\* (t=-3.07); pre-period interactions Pre3\*Treat and Pre2\*Treat insignificant |
| R4 | Labor risk channel: pollution premium concentrated in firms lacking high-skill workers or with higher bankruptcy risk | Table 5, Cols. (1)-(3), p. 8 | Airpollution × Educated\_dummy = 0.031\*\* (t=2.33); × Technical\_dummy = 0.028\*\* (t=2.00); × Bankruptcy\_dummy = 0.039\*\*\* (t=3.34) |
| R5 | Transition risk channel: premium concentrated in firms with weak green innovation, rich environmental violation records, or low green subsidy support | Table 6, Cols. (1)-(3), p. 8 | Airpollution × Greeninno\_dummy = 0.024\*\* (t=1.97); × Violation\_dummy = 0.065\*\* (t=2.40); × Gsubsidy\_dummy = 0.021\*\* (t=2.00) |
| R6 | Cross-section: premium significant only for politically unconnected firms; banks discount pollution risk for firms with implicit political insurance | Table 8, Cols. (1)-(2), p. 10 | Political=0: 0.052\*\*\* (t=3.16); Political=1: 0.018 (t=0.97); subsample difference p=0.000 |
| R7 | Cross-section: premium significant only in provinces with weak environmental governance; strong governance regions show no effect | Table 8, Cols. (3)-(4), p. 10 | Weak governance: 0.079\*\*\* (t=4.67); strong governance: -0.002 (t=-0.11); difference p=0.000 |
| R8 | Air pollution tightens non-price contract terms: reduces total credit volume and increases mortgage requirements | Table 9, Cols. (1)-(2), p. 11 | Sum\_volume: Airpollution = -0.017\* (t=-1.67); Mortgage\_terms: 0.009\*\* (t=2.08) |

**Overall (paper's conclusion).** Firm-level air pollution intensity, measured by annual ESR emissions, is a significant and causal predictor of bank loan spreads in China. The effect operates through two complementary channels (labor risk and transition risk) that are both statistically independent and economically distinct. The premium is not uniform: it is largely absent for politically connected firms and in provinces with stringent environmental enforcement, consistent with banks pricing the residual uninsured risk. Beyond price, pollution also constrains the non-price terms of credit, reducing total loan availability and tightening collateral requirements. The findings extend to multiple emission types (SO2, NOx, smoke dust), with smoke emissions having the strongest effect (Table A1, p. 14). Bolton and Kacperczyk (2021) established pollution premiums in equity markets; this paper establishes the same channel in bank debt markets using firm-level Chinese data, extending the evidence beyond the US equity context of Hsu et al. (2023).

## Theory / model

This paper has no formal theoretical model. The two tested hypotheses are derived from prior literature and stated as verbal mechanisms:

**Hypothesis 1 (main).** *Ceteris paribus*, firms with higher air pollutant emissions intensity are charged higher bank loan prices than those with lower emissions intensity (pp. 2-3).

**Channel 1: Labor risk.** Air pollutant emissions have an adverse impact on employee health and retention, especially for skilled workers with higher education or technical background. This results in lower operational efficiency and greater default risk for the borrower. Following Liu and Yu (2020) and Xue et al. (2021), employees at high-pollution firms "vote with their feet," depressing productivity and raising expected credit losses for the bank lender. The mechanism is tested by interacting emissions with borrower-level proxies for the proportion of educated staff (Educated\_dummy), technical staff (Technical\_dummy), and bankruptcy risk (Bankruptcy\_dummy).

**Channel 2: Transition risk.** Schneider (2011) and Hsu et al. (2023) document that environmental transition risk, the cost of shifting toward a low-carbon economy, is elevated for high-pollution firms. Funding green production, meeting regulatory obligations, and managing the risk of factory shutdowns or contract termination raises the cash-flow concerns that lenders must price. The mechanism is tested via interactions with green innovation experience (Greeninno\_dummy), environmental violation records (Violation\_dummy), and government green subsidies (Gsubsidy\_dummy).

**Identification logic.** Because air pollution and loan pricing may share unobserved determinants, the paper exploits the Air Pollution Prevention and Control Action Plan promulgated by China's State Council in 2013 as an exogenous shock. Firms above the industry-year median emission intensity are designated as treated; propensity-score matching (PSM) balances pre-treatment covariates. The DID design compares treated and control firms before and after 2013, isolating the causal effect of the regulatory reduction in pollution on loan pricing. The parallel trend assumption is validated by insignificant pre-period coefficients in Table 4 (p. 7).

## Method

The paper applies standard OLS panel regression for the baseline, and a two-step PSM-DID for causal identification. It builds on `panel-regression` (multi-way fixed effects), `difference-in-differences` (pre/post policy shock), and `matching` (propensity-score matching on firm characteristics).

**Baseline OLS.** The estimating equation (Eq. 1, p. 3) is:

$$
\text{Loan\_spread}_{ijt} = \alpha + \beta \, \text{Airpollution}_{it-1} + \delta \, \text{Controls}_{t-1} + \text{FirmFE} + \text{LoanAttributesFE} + \text{YearFE} + \varepsilon_{ijt}
$$

where $$\text{Loan\_spread}_{ijt}$$ is the actual loan rate on loan $$j$$ of firm $$i$$ in year $$t$$ minus the PBOC benchmark rate, $$\text{Airpollution}_{it-1}$$ is the year-specific air pollutant emission volume (tons) lagged one year to mitigate endogeneity, and $$\text{Controls}_{t-1}$$ includes loan attributes (Volume, Maturity, Collateral) and firm attributes (Firmsize, Lev, Soe, Roa, Growth, Tangible, Age, BM, Cash\_holding). Fixed effects span firm (FirmFE), year (YearFE), borrower category (Borrower\_category\_FE), and loan risk type (Risk\_type\_FE). Standard errors are clustered at the firm level (p. 3-4).

**PSM-DID.** After PSM matching on 2012 (pre-policy) firm characteristics, the DID equation (Eq. 2, p. 4) is:

$$
\text{Loan\_spread}_{ijt} = \alpha + \beta \, \text{Treatpost}_{it-1} + \delta \, \text{Controls} + \text{FirmFE} + \text{LoanAttributesFE} + \text{YearFE} + \varepsilon_{ijt}
$$

where $$\text{Treatpost}$$ equals one for firms above the industry-year median emission intensity in the post-2013 period. The coefficient $$\beta$$ identifies the policy-induced change in loan pricing for treated firms relative to matched controls. Matching variables are Firmsize, Lev, Growth, Roa, Age, BM, and Cash\_holding; 1:1 nearest-neighbor matching without replacement is used. Balance tests are in Table 3, Panel A (p. 6).

## Empirical specifications

**Baseline (R1).** Table 2 (p. 5) reports four progressively richer columns: (1) air pollution and explained variable only, no FE; (2) firm and year FE; (3) adds firm-level controls; (4) adds loan-level controls plus borrower-category and risk-type FE. The headline result from Col. (4): Airpollution = 0.047\*\*\* (t=3.20), implying a 17.85% of mean loan spread increase per standard deviation of emissions (calculation: 0.047 × 4.783 / 1.259, where 4.783 = SD of Airpollution and 1.259 = mean of Loan\_spread, from Table 1).

**PSM-DID (R2, R3).** Table 3, Panel B (p. 6), Col. (1) is PSM-OLS on the matched sample: Airpollution = 0.062\*\*\* (t=4.58). Col. (2) is PSM-DID: Treatpost = -0.124\*\*\* (t=-3.07), confirming that the 2013 regulation causally reduces spreads for high-pollution firms. All columns include firm, year, borrower-category, and risk-type FE. The Graham et al. (2008) framework for loan-pricing determinants motivates the choice of control variables.

**Parallel trend test (R3 supplement).** Table 4 (p. 7) replaces Treatpost with interactions of Treat with year dummies. Pre-period interactions (Pre3\*Treat, Pre2\*Treat) are insignificant (t=0.71, -0.57). Post-period interactions (Post1\*Treat, Post2\*Treat) are significantly negative (t=-3.58, -1.92), consistent with causal post-policy reduction. Figure 1 (p. 7) plots the coefficient path with 95% confidence intervals.

**Mechanism tests (R4, R5).** Tables 5-6 (pp. 8) add triple-interaction terms Airpollution × mechanism\_dummy to Eq. (1). Subgroup dummies (Educated\_dummy, Technical\_dummy, Bankruptcy\_dummy for labor risk; Greeninno\_dummy, Violation\_dummy, Gsubsidy\_dummy for transition risk) take the value one for firms in the riskier half of each proxy distribution. All interaction coefficients are positive and significant at 5-10%, confirming both channels. Table A3 (p. 15) horse-races all six interactions jointly; Bankruptcy\_dummy (t=3.58) and Violation\_dummy (t=2.24) and Gsubsidy\_dummy (t=2.85) remain significant, supporting channel independence.

**Cross-sectional heterogeneity (R6, R7).** Table 8 (p. 10) splits the sample by political connection (Political = 0 vs. 1), regional environmental governance (Envir\_governance = 0 vs. 1 based on province-level pollution treatment cost above/below median), and borrower headquarters status. Bootstrap p-values test coefficient equality across subsamples. For Houston et al. (2014), politically connected borrowers face an implicit government guarantee that attenuates the pollution premium; the cross-section confirms this by showing zero effect for Political=1 firms. Barwick et al. (2024) find environmental regulation eases creditor concerns; this is consistent with the PSM-DID result and the strong-governance subsample showing no pollution premium.

**Non-price terms (R8).** Table 9 (p. 11) re-estimates Eq. (1) with two non-price outcomes: Sum\_volume (total annual loan volume from the bank to the borrowing firm) and Mortgage\_terms (change in mortgage-related contract terms). The regression is at the firm-year level. Both Airpollution coefficients have the expected sign and are statistically significant, indicating that pollution tightens credit quantity and collateral requirements, not only the interest rate.

**Robustness.** Alternative explanatory variables (Airpollution\_revenue: emission intensity scaled by revenue; separate SO2, NOx, smoke-dust regressions in Table A1), alternative outcome variables (Actual\_rate, Loan\_spread2, Loan\_spread3 in Table A4), province-year and industry-year FE replacing year FE (Tables A5), branch-bank FE (Table A5, Col. 4), and exclusion of the 2008 financial crisis period and non-SOE borrowers (Table A6) all confirm positive and significant Airpollution coefficients.

## Datasets used

| Dataset | Role in paper | Wiki page |
|---|---|---|
| Environmental Survey and Reporting (ESR) database, China | Main explanatory variable: firm-year air pollutant emissions (tons); jointly managed by Ministry of Environmental Protection and National Bureau of Statistics; self-reported and government-audited | No page yet |
| China Stock Market Accounting Research (CSMAR) | Firm-level financial and governance controls (size, leverage, ROA, BM, tangibility, state ownership, etc.); lagged one year | [CSMAR](/wiki/commercial/csmar/) (licensed) |
| Proprietary state-owned commercial bank loan data | Main dataset: 61,718 raw loan observations (55,483 after exclusions) from 745 firms in 175 cities; annual credit exceeding RMB 50 million; nationwide deposit and loan network; covers 2008-2019 | No page yet (proprietary) |
| Washington University satellite PM2.5 data | City-level PM2.5 concentrations (ug/m³) from the Atmospheric Composition Analysis Group; used for city-level robustness analysis (not the main explanatory variable) | No page yet |

Sample period: air pollution controls 2007-2018 (lagged to match loan data 2008-2019); final loan-level sample 55,483 observations, annual frequency.

## When to read the full paper

Read the [original](https://doi.org/10.1016/j.jbankfin.2026.107655) if you are: studying how environmental risk gets priced into bank loan markets (Tables 2-4 contain the headline regressions); designing a study that uses China's ESR emission database or proprietary bank loan data (Sections 3.1 and Appendix B contain the variable construction); working on the mechanisms linking pollution to firm credit risk (Tables 5-7 and Appendix C break down emission types and loan types); or extending this evidence to other countries, private banks, or post-2019 periods (the paper's own institutional scope is limited to one Chinese state-owned bank, 2007-2019).

## Attribution and rights

Source: peer-reviewed, *Journal of Banking and Finance* 185 (2026), article 107655. © 2026 Elsevier B.V. All rights reserved. This paper is paywalled; no open-access or CC licence was found in Crossref metadata as of 2026-06-25. This distillation was extracted by an LLM on 2026-06-25 and is **not human-verified or independently reproduced**. Extract-only; the verbatim PDF is not hosted here.

> Li, Donghui, Jian Sun, Rui Xu, Chun Yuan, and Liyi Zhu. "Air pollution and bank loan pricing."
> *Journal of Banking and Finance* 185 (2026): 107655.
> DOI: [10.1016/j.jbankfin.2026.107655](https://doi.org/10.1016/j.jbankfin.2026.107655)