Sustainable supply chain management operations: does sustainable environmental disclosure matter for banks’ financial performance in Nigeria?

The study investigates whether sustainable supply chain management (SSCM) environmental disclosure has a dynamic effect on the financial performance of Nigerian deposit money banks (DMBs). Motivated by increasing stakeholder demand for non-financial information and the banking sector’s exposure to sustainability risks via lending to environmentally sensitive industries, the paper addresses a gap in empirical research that has largely overlooked banks in SSCM-performance analyses. Grounded in stakeholder and signaling theories, the authors posit that SSCM disclosures can enhance corporate value by improving transparency, ethics, and governance, thereby influencing investor perceptions and mitigating information asymmetry. Using annual data (2005–2023) for seven Nigerian banks, the study evaluates short- and long-run effects of SSCM environmental disclosure on profitability (ROA) and market value (Tobin’s Q).

The literature highlights regulatory and normative pressures (e.g., GRI G4 guidelines) encouraging banks to disclose environmental, social, and economic impacts. Although banks are service-based, they indirectly contribute to environmental risk by financing high-impact sectors (oil and gas, extractives, manufacturing). Stakeholder theory suggests accountability to diverse interest groups can drive adoption of sustainable practices; signaling theory posits that high-quality SSCM disclosures reduce information asymmetry, influencing market perceptions. Empirical research on SSCM’s effects on performance is more prevalent in developed economies and non-financial sectors, showing mixed results: many studies find positive links between green/SSCM practices and environmental/financial performance, while some report negative or contingent effects. In emerging markets, evidence is sparse for the banking sector. The review underscores the need to examine how environmental dimensions of SSCM and disclosure influence banks’ profitability and market value in Nigeria.

Design and data: Ex-post facto research design using retrospective annual data from LSEG (Refinitiv Eikon DataStream) Workspace. Sample comprises seven Nigerian DMBs meeting criteria: continuous operation 2005–2023; national/regional/international authorization; NSE listing; availability of annual reports and sustainability disclosures. Banks: Access Bank Plc, Fidelity Bank Plc, First Bank Nigeria Plc, First City Monument Bank Plc, United Bank for Africa Plc, Guaranty Trust Bank Plc, Zenith Bank Plc. Period: 2005–2023. Variables: Dependent variables—Financial performance measured by (i) Return on Assets (ROA: Average Net Income/Total Assets ×100) and (ii) Tobin’s Q (TQ) using Chung and Pruitt (1994) approximation: TQ = (MV + OPS + DEBT)/TA. Key independent variable—SSCM Environmental Disclosure Index (0–100%), capturing ecological impact, emissions, waste management, decommissioning. Controls—Bank Size (BS: natural log of total revenue), Bank Capital Structure (BCS: financial leverage), Corporate Social Responsibility (CSR: Central Bank of Nigeria CSR score, %). Pre-estimation diagnostics: Cross-sectional dependence (Breusch-Pagan LM; Pesaran scaled LM; Pesaran CD). Second-generation panel unit root tests (CADF; CIPS) to allow for cross-sectional dependence; mixed integration orders I(0)/I(1). Panel cointegration tests (Pedroni within- and between-dimension; Kao ADF) to assess long-run relationships. Slope homogeneity test (Pesaran and Yamagata) to check heterogeneity across banks. Estimation strategy: Cross-sectional Dependence ARDL (CS-ARDL) model (Chudik & Pesaran, 2015) to estimate short-run and long-run effects while accommodating cross-sectional dependence, heterogeneity, and mixed integration orders. Error correction terms (ECT) capture speed of adjustment to long-run equilibrium. Separate CS-ARDL models estimated for ROA and TQ. Data characteristics: Descriptive statistics (N=133 bank-year observations) show moderate SSCM mean ~51.8%, non-normal distributions typical for financial data, and correlations below multicollinearity thresholds.

• Cross-sectional dependence: Significant across banks (Breusch-Pagan LM=419.862, p<0.001; Pesaran scaled LM=27.371, p<0.001; Pesaran CD=2.521, p=0.0117).
• Stationarity: Mixed orders; some variables I(0) and others I(1) per CADF and CIPS, justifying CS-ARDL.
• Cointegration: Strong evidence of long-run relationships (Pedroni several statistics significant up to 1%; Kao ADF t=-9.1521, p<0.001).
• Slope heterogeneity: Significant heterogeneity across banks (Delta=8.732, p<0.001; Adjusted Delta=10.831, p<0.001).
• CS-ARDL results for ROA (profitability): • SSCM Environmental Disclosure positively and significantly affects profitability in both long run and short run (Long-run β=0.1795, p<0.05; Short-run β=0.2025, p<0.01). • Bank Capital Structure (BCS) positive and significant (Long-run β=0.7633, p<0.01; Short-run β=0.1724, p<0.05). • Bank Size (LBS) and CSR are not statistically significant in reported estimates for ROA. • Error correction term ECT(-1) = -0.4645 (p<0.05), indicating about 46% annual speed of adjustment toward long-run equilibrium.
• CS-ARDL results for TQ (market value): • SSCM Environmental Disclosure positive and significant (Long-run β=0.3031, p<0.001; Short-run β=0.1001, p<0.05). • BCS positive and significant (Long-run β=1.5397, p<0.05; Short-run β=0.6620, p<0.05). • CSR: Short-run negative and significant (β=-0.0237, p<0.05); Long-run positive and significant (β=0.0047, p<0.05). • Bank Size not significant. • ECT(-1) = -0.7877 (p<0.001), implying roughly 78% of disequilibrium is corrected in the subsequent period.
• Overall: SSCM environmental disclosure is associated with improved bank profitability and market valuation, with stronger short-run impacts; optimized capital structure enhances performance; CSR may depress short-run outcomes but supports long-run market value.

Findings indicate that enhanced SSCM environmental disclosure improves both profitability (ROA) and market valuation (Tobin’s Q) of Nigerian banks, aligning with stakeholder theory (value creation through accountability to diverse stakeholders) and signaling theory (credible disclosures reduce information asymmetry and enhance investor perceptions). The coexistence of significant cointegration and meaningful error correction terms suggests that SSCM practices are embedded in banks’ long-run value-creation processes while also yielding immediate benefits, possibly through operational efficiencies, risk mitigation in lending portfolios, and reputational gains. The positive role of capital structure underscores financial robustness as a complementary driver of performance. The short-run negative effect of CSR on market value, contrasted with its positive long-run effect, suggests near-term resource allocation costs with longer-term reputational and legitimacy benefits. For practitioners and policymakers, the results support integrating SSCM into strategic decision-making, refining disclosure quality, and encouraging selective financing that favors environmentally responsible sectors to reduce sustainability-related credit risk.

The study concludes that SSCM environmental disclosure exerts positive and significant effects on Nigerian banks’ financial performance in both the short and long run. By adopting SSCM principles and strengthening disclosure practices, banks can enhance profitability and market value while mitigating sustainability-related risks. Policy recommendations include integrating SSCM into regulatory frameworks, improving disclosure standards to address information asymmetry, and promoting profile/selective financing to channel funds toward environmentally friendly investments aligned with SDGs. The research extends SSCM-performance evidence to the banking sector of an emerging economy and provides actionable insights for managers, investors, and regulators.

The study employs non-probability (availability) sampling and focuses on seven Nigerian banks due to data availability, limiting generalizability. It concentrates on the environmental dimension of SSCM, excluding social and governance aspects. Results rely on proprietary LSEG data and disclosed indices, which may entail measurement constraints. Future research should expand samples across institutions and countries, incorporate broader ESG dimensions of SSCM and sustainability reporting, and explore alternative performance metrics and identification strategies.