Countries have fallen behind on emissions goals, but Hannah Ritchie looks at the numbers and sees real gains. Get your news from a source that's not owned and controlled by oligarchs. It has been 10 years since countries signed on to the Paris Agreement, and emissions and temperatures continue to reach new highs, fueling unprecedented weather disasters around the globe. Meanwhile, the shift to clean energy is facing powerful headwinds in the United States, where climate policies are being reversed and support for clean energy is withdrawn. Yet, while the headlines paint a dismal picture of efforts to rein in climate change, the numbers often tell a different story. That is the assessment of data scientist Hannah Ritchie, a researcher at the University of Oxford and deputy editor of the publication .

In 2021, Google set a lofty goal of achieving net-zero carbon emissions by 2030. Yet in the years since then, the company has moved in the opposite direction as it invests in energy-intensive artificial intelligence. In its latest sustainability report, Google said its carbon emissions had increased 51% between 2019 and 2024. New research aims to debunk even that enormous figure and provide context to Google's sustainability reports, painting a bleaker picture. A report authored by non-profit advocacy group Kairos Fellowship found that, between 2019 and 2024, Google's carbon emissions actually went up by 65%.

After more than a day of continuous debate, the US Senate passed its version of the budget megabill Tuesday afternoon--with potentially disastrous implications for the future of renewable energy in the country. The bill ends credits for projects placed in service--a term meaning, essentially, that a project is ready to provide power to the grid--after 2027, putting hundreds of planned projects around the country in jeopardy. "This is a bill to punish renewables," says Costa Samaras, a professor of civil and environmental engineering at Carnegie Mellon University. "There is a real need to add clean energy supply to the grid--electrifying our cars, electrifying our homes, electrifying our buildings, electrifying our factories, and the demands from AI are all going to require new clean energy. What this bill does is make it harder and more expensive."

Google's carbon emissions have soared by 51% since 2019 as artificial intelligence hampers the tech company's efforts to go green. While the corporation has invested in renewable energy and carbon removal technology, it has failed to curb its scope 3 emissions, which are those further down the supply chain, and are in large part influenced by a growth in datacentre capacity required to power artificial intelligence. The company reported a 27% increase in year-on-year electricity consumption as it struggles to decarbonise as quickly as its energy needs increase. Datacentres play a crucial role in training and operating the models that underpin AI models such as Google's Gemini and OpenAI's GPT-4, which powers the ChatGPT chatbot. The International Energy Agency estimates that datacentres' total electricity consumption could double from 2022 levels to 1,000TWh (terawatt hours) in 2026, approximately Japan's level of electricity demand.

As President Donald Trump has argued, America's economic security is our national security, whether it's reshoring jobs in critical industries or promoting robust commerce to create the jobs and technologies that will drive the world economy during this new Golden Age and into the future. America is already the leader in AI, finance, healthcare, manufacturing, agriculture, energy and more – but that position can still be threatened not just by China, our greatest economic adversary, but also by some of our oldest allies in the European Union. As China seeks to weaken the U.S. and expand its global influence, it's eyeing the 27-nation EU as a partner against American industry. In critical sectors – auto manufacturing, clean energy, technology, aerospace and defense – China is tightening its grip on European markets. In critical sectors – auto manufacturing, clean energy, technology, aerospace and defense – China is tightening its grip on European markets.

This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.

U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the Congress Committee Hearing Dataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee's stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.

When the Three Mile Island power plant in Pennsylvania was decommissioned in 2019, it heralded the symbolic end of America's nuclear industry. In 1979, the facility was the site of the worst nuclear disaster in the nation's history: a partial reactor meltdown that didn't release enough radiation to cause detectable harm to people nearby, but still turned Americans against nuclear power and prompted a host of regulations that functionally killed most nuclear build-out for decades. Many existing plants stayed online, but 40 years later, Three Mile Island joined a wave of facilities that shut down because of financial hurdles and competition from cheap natural gas, closures that cast doubt over the future of nuclear power in the United States. Now Three Mile Island is coming back, this time as part of efforts to meet the enormous electricity demands of generative AI. The plant's owner, Constellation Energy, announced yesterday that it is reopening the facility.

In this paper, we conduct sentiment analysis on social media data to study mass opinion about offshore wind energy. We adapt three machine learning models, namely, TextBlob, VADER, and SentiWordNet because different functions are provided by each model. TextBlob provides subjectivity analysis as well as polarity classification. VADER offers cumulative sentiment scores. SentiWordNet considers sentiments with reference to context and performs classification accordingly. Techniques in NLP are harnessed to gather meaning from the textual data in social media. Data visualization tools are suitably deployed to display the overall results. This work is much in line with citizen science and smart governance via involvement of mass opinion to guide decision support. It exemplifies the role of Machine Learning and NLP here.

Three years ago, Google set an ambitious plan to address climate change by going "net zero", meaning it would release no more climate-changing gases into the air than it removes, by 2030. But a report from the company on Tuesday showed it is nowhere near meeting that goal. Rather than declining, its emissions grew 13 percent in 2023 over the year before. Compared with its baseline year of 2019, emissions have soared 48 percent. Google cited artificial intelligence and the demand it puts on data centres, which require massive amounts of electricity, for last year's growth.