Tracking financial investments in climate adaptation is a complex and expertise-intensive task, particularly for Early Warning Systems (EWS), which lack standardized financial reporting across multilateral development banks (MDBs) and funds. To address this challenge, we introduce an LLM-based agentic AI system that integrates contextual retrieval, fine-tuning, and multi-step reasoning to extract relevant financial data, classify investments, and ensure compliance with funding guidelines. Our study focuses on a real-world application: tracking EWS investments in the Climate Risk and Early Warning Systems (CREWS) Fund. We analyze 25 MDB project documents and evaluate multiple AI-driven classification methods, including zero-shot and few-shot learning, fine-tuned transformer-based classifiers, chain-of-thought (CoT) prompting, and an agent-based retrieval-augmented generation (RAG) approach. Our results show that the agent-based RAG approach significantly outperforms other methods, achieving 87\% accuracy, 89\% precision, and 83\% recall. Additionally, we contribute a benchmark dataset and expert-annotated corpus, providing a valuable resource for future research in AI-driven financial tracking and climate finance transparency.

The Deep Learning Indaba 2024 was not just another event for us--it was a reunion. For AfriClimate AI, the Indaba represents our birthplace. It was at the Indaba 2023 in Accra, Ghana, that a pivotal conversation ignited a movement, sparking the creation of AfriClimate AI. "Last year, I was invited to give a talk about my work on Uncertainty, AI, and Climate Science at the Deep Learning Indaba in Accra, Ghana. As is usual with invited talks, one tends to focus on the successful parts of the work. But for some reason, that morning, I decided to add a slide about the challenges of working in AI and sustainability in Africa, primarily driven by the pervasive data scarcity issues. It turned out that almost everyone in the room identified with these issues. This was the birthplace of AfriClimate AI, a grassroots research community dedicated to tackling these issues head-on through capacity building, open datasets, representative benchmarks, and state-of-the-art weather forecasting models for Africa."

As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent's unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This position paper explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.

Two key problems in India's water sector are the estimation of dry-spell vulnerability during kharif, the monsoon season, and the design of water and energy-planning inputs to help villages undertake demand-side management during rabi, the post-monsoon season. In this article, we report our joint work with the Government of Maharashtra's Department of Agriculture on a World Bank-assisted program called the Project on Climate Resilient Agriculture, or PoCRA. The project is spread over 5,000 villages in 15 districts of Maharashtra (see Figure 1). Its main objective is to make smallholder farmers resilient to climate variability through targeted interventions. A key strategy is to promote water and energy budgeting in these villages and to supplement the community infrastructure and the capabilities of individual farmers.

"We just know it's the right thing to do for our customers and--I say this from years of doing risk management-- it's good, basic risk management," says Shannon Carroll, director of global environmental sustainability at AT&T. "If all indications are that something is going to happen in the future, it's our responsibility to be prepared for that." Globally, leaders from government, business, and academia see the urgency. When citing risks with the highest impact, those surveyed listed climate action failure and other environmental risks second only to infectious diseases. AT&T is taking action with its Climate Resilience Project, using spatial data analysis and location information to tackle the complex problem of how increasingly powerful storms could affect infrastructure such as cell towers and the telecom's ability to deliver service to its customers. "Spatial analysis is this way of going beyond what we visually see," explains Lauren Bennett, head of spatial analysis and data science at Esri, a geographic information systems (GIS) company.

Ask not what the government can do for Silicon Valley; ask what Silicon Valley can do for the government. He presented WIRED with six challenges he feels the tech industry needs to address--just a few earthshaking problems the country could use some help with, that's all. We reached out to six of the biggest names in the WIRED world, and we gave each of them a challenge from the president's list. Then we asked: To get this done, what's the industry's best play? Silicon Valley runs on stories. So does the economy in general. We create what we believe in. If we believe we can use technology to identify and solve big problems, then that's what we'll do.