Exploring Predictive Analytics Frameworks for Sustainable Pricing and Revenue Growth in the U.S. Chemical Industry
DOI:
https://doi.org/10.65150/EP-jmrr/V2E6/2026-06Keywords:
predictive analytics, chemical industry, lifecycle assessment, AI, value-based pricing.Abstract
The American chemical sector is facing growing challenges in price instability, sustainability, and financial stability. Predictive analytics has become a transformative capability, enabling firms to integrate operational, market, and environmental data into strategic pricing decisions. This narrative review summarizes the literature on predictive analytics frameworks, sustainable pricing models, and mechanisms for improving revenue growth, and examines their interrelations and implications for industrial practice. We discuss traditional, value-based, and dynamic pricing models, sustainability measures such as lifecycle costs, carbon footprint, and resource efficiency, and how they affect competitive advantage and long-term revenue stability. The review draws on empirical research and theoretical models that present a unified predictive analytics framework for the U.S. chemical industry, covering data sources, predictive engines, pricing decision layers, sustainability and financial performance, and governance. Major implications for managers, policies, and research are presented, including the need for interpretable AI, real-time sustainability analytics, and a policy-aligned pricing architecture. The review also highlights severe issues regarding data quality, model transparency, organizational acceptance, and ethical governance, and identifies future research directions to implement responsible and efficient applications of predictive analytics. The result is that predictive analytics is introduced as a benchmark between profitability and sustainability in this study, as it provides a roadmap for developing data-driven, environmentally responsible, and resilient pricing models that align with financial bottom lines.
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Copyright (c) 2026 Ishmael Adams (Author)
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