Artificial Intelligence and Sustainable Economic Development: Opportunities and Challenges

Dr.Siny Philip Dr. Sreeja K · Economic Sciences. · 17 Jun 2026

Where this sits in the debate

Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

A broad review of AI's role in sustainable economic development, cataloguing benefits across manufacturing, agriculture, healthcare, finance, and climate mitigation (resource optimization, energy efficiency, financial inclusion) against costs including labor disruption, algorithmic bias, digital inequality, and the rising energy footprint of large-scale compute. Its value to the debate is framing AI-growth tradeoffs through a sustainability lens—arguing gains are contingent on governance, ethics, and inclusive policy—though it is a qualitative synthesis without new empirical estimates.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

How does AI relate to sustainable economic development, and what opportunities and challenges does it bring?

The approach

A review of theoretical perspectives and empirical evidence on AI across sectors including manufacturing, agriculture, healthcare, education, finance, transport and environmental management, with attention to developing economies.

The findings

The article lists benefits such as better resource allocation, energy efficiency and financial inclusion, and challenges such as labour market disruption, algorithmic bias, digital inequality and rising energy use. It proposes a framework combining innovation, governance, sustainability and social inclusion.

How to read it: review. A synthesis of existing studies — breadth over novelty; quality depends on what it includes and excludes.

Limitations and counterpoints AI-written

The abstract describes a broad review without new data or quantified estimates.

Abstract

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping economies, industries, governance, and social systems. Its ability to process vast amounts of data, automate decision-making, enhance productivity, and facilitate innovation has positioned AI as a strategic driver of sustainable economic development. Sustainable economic development seeks to balance economic growth with environmental protection and social equity, ensuring that present development does not compromise the needs of future generations. AI contributes significantly to achieving these objectives by optimizing resource allocation, improving energy efficiency, enhancing healthcare delivery, promoting smart agriculture, supporting climate change mitigation, and fostering financial inclusion. Simultaneously, AI presents considerable challenges, including labor market disruptions, ethical concerns, algorithmic bias, cybersecurity threats, privacy violations, digital inequality, and increasing energy consumption associated with large-scale computational infrastructures. These challenges necessitate robust governance frameworks, ethical standards, and inclusive policy interventions. This article critically examines the relationship between artificial intelligence and sustainable economic development by reviewing contemporary theoretical perspectives and empirical evidence. It explores AI's role across multiple sectors including manufacturing, agriculture, healthcare, education, finance, transportation, and environmental management. Furthermore, the study investigates the policy implications of AI adoption and identifies barriers that may hinder equitable and sustainable implementation, particularly in developing economies. The article concludes by proposing a comprehensive framework integrating technological innovation, institutional governance, environmental sustainability, and social inclusion. Such an integrated approach is essential

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (4 months ago)
Citations: 0 (OpenAlex)