From Waste to Wealth: AI-Enabled Financial Insights into Briquette and Pellet Markets

Abstract

Purpose: The growing global emphasis on sustainable energy has accelerated the demand for biomass-based alternatives such as briquettes and pellets, which transform agricultural and industrial waste into renewable energy sources. Despite their potential to reduce carbon emissions and promote circular economy practices, market adoption of briquettes and pellets faces significant challenges including fluctuating raw material prices, fragmented supply chains, and inconsistent demand forecasting. This paper explores how artificial intelligence (AI) can be leveraged to generate actionable financial insights that enhance decision-making for stakeholders across the value chain.

Design/Methodology/Approach: By integrating predictive analytics, machine learning models, and real-time market intelligence, the study examines AI-driven approaches to price forecasting, cost optimization, investment evaluation, and risk management. The research highlights how AI tools can assist producers in identifying profitable sourcing strategies, enable investors to assess return potential with greater precision, and support policymakers in formulating evidence-based incentives for renewable energy markets. Case-based simulations demonstrate how AI can process large volumes of heterogeneous data, from agricultural outputs and energy prices to government subsidies and consumer demand patterns, thereby providing a comprehensive framework for sustainable business strategies.

Findings: The findings suggest that AI-enabled financial insights not only improve profitability and operational efficiency but also accelerate the transition from waste management to wealth creation through clean energy adoption.

Originality: Ultimately, this study showcases the transformative potential of AI in unlocking the economic value of biomass markets, fostering innovation, and aligning financial incentives with environmental sustainability goals.

Paper Type: Theme Based Paper