AI and robotics slash supply chain emissions but face validation and cost barriers

Artificial intelligence (AI) and robotics are at the forefront of efforts to decarbonize global supply chains, yet gaps in validation, regulatory frameworks, and scalability continue to slow their impact, says a new study published in Logistics.

The study titled "A Comprehensive Survey of Artificial Intelligence and Robotics for Reducing Carbon Emissions in Supply Chain Management", the research provides the first systematic scoping review of how AI and robotics contribute to carbon reduction efforts in supply chain operations. By examining 23 studies published between 2013 and 2024, the authors map current applications, benefits, challenges, and future directions for technology-driven sustainability.

How are AI and robotics being applied to cut emissions?

The study identifies four key supply chain domains where AI and robotics deliver measurable carbon savings: transportation, warehousing, manufacturing, and waste management. AI-driven optimization algorithms play a critical role in reducing emissions by improving route planning, forecasting demand, and managing energy consumption. These algorithms can cut transportation emissions by up to 15%, primarily through dynamic routing and real-time adjustments to logistics flows.

In warehousing, robotics automation has demonstrated the potential to reduce energy use by approximately 20%. Robots streamline order fulfillment, minimize idle energy consumption, and reduce the inefficiencies of manual handling. Combined with IoT sensors, these systems enable smarter energy distribution, real-time monitoring, and predictive maintenance to avoid unnecessary emissions.

The research also highlights the growing integration of AI with blockchain and digital twin technologies. These combinations allow companies to trace carbon footprints across the entire supply chain, enhancing transparency and supporting compliance with environmental regulations. As global markets push for greener operations, these tools offer an effective way to track and minimize emissions while improving operational efficiency.

What barriers limit the adoption of AI and robotics in supply chains?

While these technologies are intended to reduce emissions, their development and deployment carry their own environmental and economic costs. The energy consumption required for AI computation is substantial, raising concerns about whether the net emissions benefits outweigh the resource demands.

Implementation costs present another barrier, particularly for small and medium-sized enterprises that lack the capital to invest in advanced technologies. Data integration also remains a persistent challenge, as supply chains often operate with fragmented systems that make it difficult to aggregate and analyze emissions data effectively. Moreover, the presence of algorithmic biases in AI models could distort outcomes, leading to inefficient or inequitable decisions.

The absence of standardized benchmarks for evaluating emissions reductions further complicates the landscape. Without common metrics, companies struggle to assess whether AI and robotic solutions deliver the promised sustainability benefits. This gap also hinders cross-industry comparisons and slows regulatory progress.

The review reveals that many studies rely on simulations or theoretical modeling rather than real-world testing. Only 8.7% of the reviewed studies included empirical experimentation, and none provided replication packages or open datasets. This lack of reproducibility undermines confidence in the findings and slows the translation of research into industry practice.

What steps are needed to maximize impact on supply chain sustainability?

Technology alone is not a silver bullet for decarbonizing supply chains. Instead, a coordinated approach involving standardization, experimentation, and policy support is required. The study proposes a roadmap to address current gaps and ensure that AI and robotics deliver measurable emissions reductions.

First, the researchers call for the development of standardized validation benchmarks to measure and compare the effectiveness of different technologies. These benchmarks would allow businesses to assess their investments more reliably and help regulators establish clear guidelines for sustainable practices.

Second, the authors urge more pilot studies and experimental deployments to test AI and robotic systems in real-world conditions. These trials would provide crucial data on operational performance, scalability, and unintended environmental consequences, ensuring that solutions deliver practical benefits rather than theoretical gains.

Next up, the study advocates for open-science practices. By sharing datasets, algorithms, and replication packages, researchers and companies can accelerate innovation while maintaining transparency. This openness would also enable regulators and industry stakeholders to assess technologies objectively.

While AI and robotics can lower emissions, they may also lead to job displacement, ethical dilemmas, and the transfer of carbon burdens to other parts of the supply chain. Policymakers, as the authors stress, must weigh these factors to ensure that decarbonization efforts are sustainable and socially responsible.