Prediction of Green Sukuk Investment Interest Drivers in Nigeria Using Machine Learning Models

Mukail Akinde, Olasunkanmi Olapeju (), Olusegun Olaiju, Timothy Ogunseye, Adebayo Emmanuel, Sekinat Olagoke-Salami, Foluke Oduwole, Ibironke Olapeju, Doyinsola Ibikunle and Kehinde Aladelusi
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Mukail Akinde: Department of Taxation, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Olasunkanmi Olapeju: Department of Urban and Regional Planning, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Olusegun Olaiju: Department of Mathematics and Statistics, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Timothy Ogunseye: Department of Banking and Finance, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Adebayo Emmanuel: Department of Urban and Regional Planning, Federal University of Technology Akure, Akure City PMB 704, Nigeria
Sekinat Olagoke-Salami: Department of Estate Management and Valuation, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Foluke Oduwole: Department of Accountancy, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Ibironke Olapeju: Department of Urban and Regional Planning, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Doyinsola Ibikunle: Department of General Studies, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria
Kehinde Aladelusi: Department of Banking and Finance, Federal Polytechnic Ilaro, Ilaro City PMB 50, Nigeria

JRFM, 2025, vol. 18, issue 2, 1-22

Abstract: This study developed and evaluated machine learning models (MLMs) for predicting the drivers of green sukuk investment interest (GSII) in Nigeria, adopting the planks of hypothesised determinants adapted from variants of the planned behavioural model and behavioural finance theory. Of the seven models leveraged in the prediction, random forest, which had the highest level of accuracy (82.35% for testing and 90.37% for training datasets), with a good R 2 value (0.774), afforded the optimal choice for prediction. The random forest model ultimately classified 10 of the hypothesised predictors of GSII, which underpinned constructs such as risk, perceived behavioural control, information availability, and growth, as highly important; 21, which were inclusive of all of the hypothesised constructs in measurement, as moderately important; and the remaining 15 as low in importance. The feature importance determined by the random forest model afforded an indicator-specific value, which can help green sukuk (GS) issuers to prioritise the most important drivers of investment interest, suggest important contexts for ethical investment policy enhancement, and inform insights about optimal resource allocation and pragmatic recommendations for stakeholders with respect to the funding of climate change mitigation projects in Nigeria.

Keywords: climate change; drivers; green sukuk; interest; investment; MLMs; pollution mitigation (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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