Search Climate Insights Directory

This report by Ember examines the dual challenge facing Asia-Pacific power grids: managing the variability introduced by rapid renewable energy expansion and adapting to increasing climate volatility. It argues for a shift from reactive asset-level hardening to systemic adaptation, proposing that artificial intelligence (AI) can serve as a strategic coordination layer to integrate fragmented data, link siloed analytical models, and support complex decision-making to ensure grid reliability in a warming climate.

This report by Ember examines the risks associated with integrating artificial intelligence (AI) into the power sector, specifically within the ASEAN region. It details technical, operational, and governance challenges—including data limitations, cybersecurity vulnerabilities, and regulatory gaps—while providing policy recommendations to ensure a safe and efficient energy transition.

This report by Ember examines how artificial intelligence (AI) can accelerate the integration of variable renewable energy (VRE) in ASEAN power systems. It identifies five commercially proven AI applications—forecasting, predictive maintenance, dispatch optimisation, real-time control, and dynamic line rating—that address the technical challenges of wind and solar variability to improve grid stability, reduce operational costs, and defer expensive transmission investments.

This report by Ember examines how artificial intelligence (AI) can facilitate the integration of variable renewable energy (VRE) in ASEAN power systems. It identifies five key AI applications—forecasting, predictive maintenance, dispatch optimisation, real-time control, and dynamic line rating—that can reduce operational costs and emissions. While the region shows strong AI readiness, adoption is currently fragmented and limited to pilot projects. The report estimates that widespread AI adoption could save up to $67 billion and reduce CO2 emissions by nearly 400 million tonnes by 2035.

This report examines the impact of rapid data centre growth in ASEAN on power sector emissions, highlighting that while these facilities could account for 2% to 30% of national power demand by 2030, approximately 30% of this demand could be met by solar and wind power without battery storage. The authors argue for urgent policy interventions, energy efficiency improvements, and expanded renewable procurement options to prevent the digital boom from derailing regional energy transition goals.

This briefing document, prepared by the IAI Directorate for the 33rd meeting of the Conference of the Parties, outlines recommendations from the Advisory Board of the IAI Science Diplomacy Center (SDC). The recommendations focus on expanding capacity building, redesigning fellowship programs, addressing emerging issues like artificial intelligence, and launching a digital Knowledge Hub to integrate science, policy, and diplomacy across the Americas.

This briefing document presents recommendations from the Advisory Board of the IAI's Science Diplomacy Center (SDC) to be discussed at the 33rd meeting of the Conference of the Parties (CoP) in Asunción, Paraguay. It outlines strategic goals across four pillars: capacity building, the STeP fellowship program, emerging themes, and a knowledge center, while emphasizing the need for inclusive, multi-sectoral partnerships to advance science diplomacy in the Americas.

This report examines the rising energy demands of data centers driven by the growth of generative AI and outlines the challenges and pathways for decoupling this growth from carbon emissions.

This perspective article explores the potential of combining artificial intelligence (AI) and Earth observation technologies to enhance forest management and sustainability, while also addressing the associated risks and the need for governance mechanisms.

This research paper examines the intersection of global supply chain governance, digitalization, and environmental sustainability, emphasizing the need for international cooperation and the integration of decent work into green transitions.

This research paper examines the deployment of AI in farming, forestry, and marine resource extraction, highlighting a gap in systemic risk research and identifying four primary risks: algorithmic bias, unequal access, cascading failures, and efficiency-resilience trade-offs.

This briefing summarizes testimony and discussions from the 116th Congress regarding the necessity of up-to-date geophysical, hydrologic, and planetary-scale data, as well as the role of artificial intelligence, in understanding Earth processes and informing climate adaptation and disaster response.

The Global Resilience Partnership (GRP) is launching the Technology for Evolving Challenges in Humanitarian Contexts (TECH) 4 Resilience Challenge. This initiative seeks AI-driven technological solutions to mitigate the effects of extreme climate events and improve Anticipatory Action (AA) frameworks in emergency contexts within low- and middle-income countries.

This case study examines Dragonfly, a Cape Town-based start-up that provides AI-driven camera hardware and a data management platform to optimize material recovery facilities (MRFs). By integrating machine learning to analyze real-time waste sorting, the solution aims to increase recovery rates, reduce landfill tailings, and enhance job security for workers in developing economies like South Africa.

This briefing by the Stockholm Environment Institute (SEI) details a pilot project evaluating the use of the SEI AI Reader, a tool based on large language models, to conduct systematic policy analysis. The project tested the tool's ability to extract data from policy evaluations and audits using a framework of 12 independent variables and 46 questions, focusing on climate policy implementation. The results indicate that with iterative prompt calibration, the AI can achieve high levels of accuracy and consistency, making it a viable method for processing large volumes of grey literature to identify drivers of successful policy outcomes.