Profitability and SDG 13-Climate Action: The Moderating Role of Environmental Management Training: A Machine Learning Approach
Volume 03 Issue 1
Authors
Hassan Raza, Sándor Kovács, Mushtaq Hussain Khan
Keywords
SDG 13, corporate profitability, environmental management training, machine learning, sustainability, climate action
Citation in APA style
Raza, H., Kovacs, S., & Khan, M. H. (2025). Profitability and SDG 13-Climate Action: The Moderating Role of Environmental Management Training: A Machine Learning Approach. Journal of Business Sectors, 3(1), 1–12. https://doi.org/10.62222/BVCH4721
DOI
Abstract
Research background:
This study examined the critical intersection between corporate profitability and adherence to Sustainable Development Goal (SDG) 13 - Climate Action, highlighting the importance of environmental management training within organizations. As the global business landscape evolves, it is essential to understand how internal sustainability practices influence financial outcomes. This research addressed the gap in understanding the dynamic interplay between corporate financial performance and strategic commitment to environmental sustainability.
Purpose of the article:
The primary objective of this research was to analyze the moderating role of environmental management training on the relationship between firm profitability and support for SDG 13. It attempted to elucidate how structured training initiatives can improve corporate financial performance while promoting robust climate action strategies.
Methods:
This study used a comprehensive panel dataset of 31,346 firms in 67 countries from 2013 to 2022. Advanced machine learning algorithms, including decision tree, random forest, gradient boosting, and logistic regression, were employed to analyze the impact of environmental management training on corporate profitability and sustainability initiatives. These methods allow for a nuanced understanding of the complex, non-linear interactions among the variables studied, providing deep insights into the dynamics at play.
Findings & Value added:
The results indicate that certain predictive models, such as Decision Tree and Random Forest, initially faced challenges like overfitting. However, incorporating environmental management training variables improved their robustness, highlighting the importance of variable selection in sustainability analytics. Among the models tested, Gradient Boosting demonstrated a strong balance between precision and recall, making it particularly effective for predicting corporate engagement in climate initiatives. The incorporation of machine learning provides a novel methodological perspective that deepens our understanding of how profitability metrics can influence and enhance corporate sustainability efforts. This research adds value to the discourse on sustainable business practices by providing robust empirical evidence and methodological innovations that can guide policymakers and business leaders in crafting strategies that promote sustainable development. Moreover, this study aligns with the journal’s focus by offering innovative approaches to economic policy and enhancing understanding of the intersections between corporate strategy and sustainable business practices.