The Sustainability Imperative

Europe has an urgent need to decarbonize, but its progress lags other regions, including the US, Australia, and Japan. This Advisor examines the current state of carbon capture, utilization, and storage (CCUS) technologies in Europe and explains how some countries are increasing their carbon-capture capacities.

To illustrate the benefits of our dynamic pricing approach over other benchmarks, we carried out several computational studies and a case study using Share Now data from the city of Vienna, Austria. As this Advisor explores, that approach outperforms all benchmarks, saves providers operational costs, and improves sustainability via clear decarbonization benefits.

Companies are applying AI, computer vision, machine learning, and robotics to develop intelligent recycling platforms that enhance the efficiency and profitability of the recycling process. This Advisor examines some of these emerging technologies.

This Advisor takes a closer look at a new concept called “smart farming.” This concept refers to the use of advanced technologies and data-driven approaches to improve agricultural productivity, efficiency, and sustainability. Specifically, it involves integrating modern technologies into traditional farming practices to monitor, automate, and optimize agricultural operations

This Advisor examines the relationship between corporate environmental disclosure and environmental innovation (known as “ecovation”). It suggests that firms avoid “greenwashing” and “brownwashing,” as both are associated with lower innovation than “green-highlighting” strategies.

Cutter Expert San Murugesan provides a comprehensive overview of how AI is transforming agriculture on a global scale. His piece delves into a wide range of AI applications, from precision agriculture and automated irrigation to crop monitoring, robotics, and market forecasting. It showcases the potential of AI to not only increase yields and optimize resource use but to reduce waste, minimize environmental impact, and enhance farmers’ livelihoods. Murugesan emphasizes the need for a multi-stakeholder approach, calling for increased investment in R&D, the creation of supportive policies, and targeted efforts to address the barriers to AI adoption, including high implementation costs, data privacy concerns, and the digital divide.

Kasuni Vidanagamachchi, Dilupa Nakandala, and Athula Ginige examine the vulnerabilities of agri-food supply chains (ASCs), drawing on lessons learned from adaptations made during the pandemic. They posit that long-term viability, rather than short-term resilience, is essential for these systems to withstand prolonged crises. The article highlights the importance of diversifying food supply methods, incorporating local production, community-based sharing, and digital technologies to enhance adaptability and responsiveness to disruptions. Through a case study from Sri Lanka, they demonstrate how a combination of government support, community engagement, and digital innovation enabled effective adaptation during the pandemic.

Successfully integrating AI into agriculture requires a nuanced understanding of the social, cultural, and ecological contexts in which it is deployed. Vijaya Lakshmi and Jacqueline Corbett explore this concept, arguing that a conjoint-learning approach (one that combines the precision of AI with the rich tapestry of traditional agricultural knowledge) holds the key to unlocking truly sustainable solutions. Their article presents three case studies from India, each showcasing how farmers are blending generations-old practices with AI-powered tools to enhance decision-making, optimize resource use, and adapt to changing conditions.