SPEAKERS
| Jerome Silla UNU-IAS | Jonghwi Park UNU-IAS | Doug Specht University of Westminster | Masahise Koda Okayama University |
| Naruhiko Shiratori Tokyo City University |
DESCRIPTION
As climate change accelerates, developing countries face growing challenges in maintaining and expanding infrastructure systems that can withstand increasingly severe weather events, rising sea levels, and other environmental stressors. Traditional approaches to infrastructure planning and development are often constrained by limited financial resources, outdated data, and a lack of technical expertise. In this context, artificial intelligence (AI) presents a transformative opportunity to enable smarter, more resilient, and cost-effective infrastructure solutions. This session explores the potential of AI-driven research and development (R&D) to revolutionize the design, implementation, and maintenance of climate-resilient infrastructure in developing nations.
AI technologies, including machine learning, computer vision, and geospatial analytics, can process large volumes of environmental, social, and economic data to generate predictive models and actionable insights. These tools can be employed to assess climate risks, optimize material use, predict infrastructure failure, and prioritize investments based on vulnerability and resilience metrics. For example, AI can help identify flood-prone zones by analyzing satellite imagery and historical weather data, enabling the development of adaptive drainage systems and early warning mechanisms. Similarly, machine learning algorithms can support predictive maintenance by monitoring the structural health of bridges, roads, and buildings, minimizing downtime and repair costs.
The integration of AI into infrastructure planning also enhances participatory decision-making by incorporating data from diverse sources, including local communities and mobile technologies. This inclusivity is especially vital in developing countries, where informal settlements and unplanned urban growth often exacerbate vulnerabilities. Furthermore, AI can support rapid scenario modeling and climate impact simulations, aiding governments and development agencies in designing policies and allocating resources more efficiently.
Despite its promise, the adoption of AI in climate-resilient infrastructure R&D is not without challenges. Developing countries often lack the digital infrastructure, skilled workforce, and governance frameworks required to implement AI at scale. Data quality and availability are persistent concerns, particularly in remote or conflict-affected areas. Additionally, ethical considerations such as algorithmic bias, data privacy, and the digital divide must be addressed to ensure equitable and sustainable outcomes.
This session highlights several case studies where AI has been successfully piloted or implemented in infrastructure resilience projects in regions such as Sub-Saharan Africa, South Asia, and Latin America. It also outlines key policy recommendations, including capacity-building initiatives, public-private partnerships, and the creation of open data ecosystems to support AI deployment. By leveraging global advancements in AI and fostering local innovation ecosystems, developing countries can leapfrog traditional barriers and build infrastructure systems that are both resilient to climate change and inclusive in their development.
In conclusion, AI-driven R&D holds immense potential to transform the landscape of infrastructure resilience in developing countries. It offers a pathway toward proactive, data-informed decision-making that aligns with sustainable development goals (SDGs) and climate adaptation strategies. As global collaboration and investment in this field grow, AI will increasingly become a cornerstone of infrastructure resilience in the face of a rapidly changing climate.
