Abstract

This study evaluates the capital structure efficiency (CSE) of green micro, small, and medium enterprises (MSMEs) in India, employing a hybrid approach combining Data Envelopment Analysis (DEA) and Gene Expression Programming (GEP). The research aims to assess the efficiency levels across different MSME categories, identify significant determinants of CSE, and develop predictive models to enhance financial decision-making. The study addresses key questions on the current efficiency levels, influential factors, and the effectiveness of various predictive models in forecasting CSE. Utilizing data from the CMIE Prowess database, the analysis reveals that micro-sized green MSMEs exhibit higher capital structure efficiency compared to medium and small enterprises due to lower operational costs and greater flexibility in decision-making. Significant factors influencing CSE include the Debt-to-EBITDA ratio, Debt-to-Asset Ratio, and Return on Equity. Comparative analysis shows that the DEA-GEP model consistently outperforms other models, particularly in predictive accuracy and reliability, as validated by Monte Carlo simulations. Key findings suggest that efficient debt management and profitability enhancement are crucial for improving CSE in green MSMEs. This research contributes to the theoretical understanding of capital structure in sustainable enterprises and offers practical implications for managers and policymakers to foster financial and environmental sustainability.

Keywords: Capital structure efficiency (CSE), Data envelopment analysis (DEA), Gene expression programming (GEP), Micro small and medium enterprises (MSMEs)

How to Cite:

Tripathi, D. K., Chadha, S. & Tripathi, A., (2025) “Mapping and Predicting Capital Structure Efficiency of Sustainable Green MSMEs: Evidence from an emerging country”, Australasian Accounting, Business and Finance Journal 19(5): 3, 28–56. doi: https://doi.org/10.14453/aabfj.v19i5.04

Rights:

In Copyright