Artificial intelligence, machine learning, and technology driving environmental and industrial innovation.
21 articles
A data-rich guide to carbon capture technology costs: what’s included, sector-by-sector ranges, key drivers, and how incentives and innovation will bend the curve.
Design, size, and operate a rainwater harvesting system: components, costs, ROI, safety, and smart controls—backed by EPA, CDC, WHO, and utility data.
Fashion drives 2–4% of global emissions. See the data, technologies, and shopping strategies that make sustainable fashion real — not just marketing.
A practical, data-rich guide to AI for environmental monitoring—sensors, models, datasets, case studies, costs, and deployment tips you can use now.
A data-backed, tech-forward guide to sustainable home improvements: audits, high-impact upgrades, real costs, ROI, incentives, and product picks that pay off.
A data-rich guide to energy storage—technologies, costs, metrics, use cases, and policies—showing how storage unlocks renewables and grid reliability.
Training GPT-4 consumed an estimated 50 GWh of electricity. Running inference for billions of daily queries consumes even more. As AI becomes ubiquitous, its environmental footprint demands honest accounting.
From solid-state batteries to direct air capture, these ten technologies are moving from lab to market — and they could reshape energy, food, materials, and industry within a decade.
From predicting extreme weather events to discovering new materials for carbon capture, artificial intelligence is becoming an essential tool in climate science. Here is how researchers are using machine learning to accelerate our understanding of a changing planet.
