2025 is ‘a pivotal year for methane mitigation' in EU thanks to world-first rules. What's changing?
The region is the first to officially constrain this major source of pollution after adopting a Methane Regulation last year. Coal mine methane emissions decreased by 8 per cent in 2024 compared to 2023.
But the greenhouse gas - which is responsible for around 30 per cent of the rise in global temperatures since the Industrial Revolution - remains a huge problem in Europe and around the world.
Record production of oil, gas and coal has kept emissions above 120 million tonnes (Mt) annually, according to the IEA's 2025 Global Methane Tracker. The analysts included abandoned wells and mines for the first time - finding that these sources contributed around 8 Mt to emissions in 2024.
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The EU Methane Regulation was the world's first regulation to set a threshold on how much active underground mines, as well as abandoned and closed underground mines, can emit.
It forces the fossil fuel industry to follow measurement, reporting and verification requirements; bans routine flaring and venting; sets leak detection and repair (LDAR) mandates for all oil and gas facilities; and limits venting in thermal coal mines.
'2025 marks a pivotal year for methane mitigation, with coal mine emissions decreasing for the first time as the EU Methane Regulation for the energy sector takes effect,' says Dr Sabina Assan, methane analyst at global energy think tank Ember.
The new regulation also stipulates that by 2027, importers must demonstrate that imported fossil energy meets the same requirements.
'By ensuring that all fossil fuels meet the same methane standards, the regulation will create a level playing field between importers and domestic producers, extending the regulations' impact far beyond European coal mines,' adds Dr Assan.
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This is significant, as most of the methane emissions from fossil fuels used in Europe are tied to imports.
In 2024, according to IEA's tracker, methane emissions from the supply chain for oil, gas and coal imports were around 6 Mt - nearly four times what Europe emits within its own fossil fuel sector.
Around 55 per cent of the fossil fuel methane emissions that occur within Europe come from the oil and gas sector, mostly from downstream operations. 45 per cent come from coal mines, mainly in Poland and Ukraine.
Upstream oil and gas operations are responsible for the majority of emissions in Romania and the UK. Norway and the Netherlands have the lowest upstream intensities in the world, it says, while most other countries in the region perform near the global average.
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Abandoned underground coal mines constitute a significant and overlooked source of methane emissions.
Methane gas is generated when organic matter turns to coal and is buried underground in these coal seams. When mining creates a route to the surface, much of the methane escapes. If it is not plugged, these emissions can continue for decades after a mine is abandoned.
Globally, the IEA estimates that abandoned coal mines emitted nearly 5 Mt of methane in 2024, and abandoned oil and gas wells released just over 3 Mt. Combined, these sources would be the world's fourth-largest emitter of fossil fuel methane - after China, the US and Russia, and ahead of Iran, Turkmenistan and India.
Since most emissions result from mines and wells that have recently been abandoned, timely action is critical, the IEA urges. Options include plugging and monitoring wells that are no longer in use, sealing abandoned coal mines, and directing methane flows for energy use.
In total, the energy sector – including oil, natural gas, coal and bioenergy – accounts for more than 35 per cent of methane emissions from human activity.
The agriculture and waste sectors are also major sources of methane emissions, but fossil fuel supply offers the greatest potential for immediate reductions in methane emissions, the IEA notes.
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Wall Street Journal
42 minutes ago
- Wall Street Journal
Measuring Generative AI's Economic Impacts
I am often asked about the potential impact of generative AI on the economy. The reality is that investment in generative AI is already having an impact on economic activity, but not necessarily on productivity which affects longer-term growth. In the first half of 2025, real (inflation-adjusted) investment in information technology equipment accounted for 59% of real GDP growth.1 This category largely involves building out the infrastructure of generative AI, suggesting that investment in this technology drove the economy in the first half of this year. Moreover, in the first half of 2025, there was a sharp decline in real non-residential investment in structures,2 including factories, warehouses, office buildings, and shopping centers. Yet it is likely that there was a significant increase in investment in data warehouses, which would be included in the larger category of structures. If so, it implies that other investments in structures amounted to even less, making investment in generative AI even more impactful. The weakness of investment in structures may be attributed to businesses postponing decisions about the location of facilities due to tariff uncertainty. If investment in generative AI is so massive, this might explain the frothiness of equity prices3 at a time when there is reason to expect a weakening of the overall economy. In fact, the so-called 'Magnificent Seven' tech-related companies accounted for about half of the increase in the S&P 500 index of U.S. shares in 2024. Some believe this is a bubble that will inevitably unwind. A quarter of a century ago, there was the so-called 'dotcom bubble,' in which shares of tech-related companies surged dramatically, ultimately leading to a market correction as the profitability of investments in dotcoms came into question. Moreover, that correction contributed to a mild recession. In today's case, a market correction is a potential scenario worth considering. Tariffs and immigration policy may slow the U.S. economy, and when that slowdown becomes apparent, it will likely result in lower prices of non-tech equities. Tech prices could also come down if investors liquidate positions to cover other losses. Another major area of concern related to generative AI is electricity consumption.4 The International Energy Agency (IEA) predicts that in the U.S. 'power consumption by data centers is on course to account for almost half of the growth in electricity demand between now and 2030. Driven by AI use, the U.S. economy is set to consume more electricity in 2030 for processing data than for manufacturing all energy-intensive goods combined, including aluminum, steel, cement and chemicals.' In addition, the IEA said that 'global electricity demand from data centers is set to more than double over the next five years, consuming as much electricity by 2030 as the whole of Japan does today.' Moreover, about half of planned increases in electricity capacity in the U.S. involve renewable energy sources. With cuts to subsidies for such investments, this capacity will involve higher energy costs for consumers. In fact, the Deloitte Center for Energy & Industrials predicts that these trends will result in a significant increase in electricity costs for U.S. households. That, in turn, could have a negative impact on consumer demand. While investment in generative AI is playing a big role in driving economic growth and asset valuations in the U.S., there remains uncertainty as to when this investment will pay off in terms of productivity gains, which affects longer-term growth. Theoretically, generative AI should eventually have a big positive impact on labor productivity, thereby generating faster economic growth and improvements in living standards. On the other hand, it will significantly disrupt labor markets. Yet what we know from history is that there is a lag between the introduction of new technologies and their impact on productivity. It takes time for new technologies to become fully embedded in an economy. In part this is because it takes time to figure out the best ways to utilize new technologies. Although generative AI is already having an impact in some industries and some processes, it is not yet fully integrated into the economy in a way that drives overall productivity. Influences on U.S. Government Debt Following passage of the so-called One Big Beautiful Bill by the U.S. Congress in July, the non-partisan Congressional Budget Office (CBO) published its baseline forecasts for US government deficits and debt.5 The baseline makes reasonable assumptions about future trends in economic growth and other factors. Under that baseline scenario, the deficit remains high and the level of debt continues to grow sharply. However, the CBO also published alternative scenarios based on different assumptions. One assumption that is very important is the predicted rate of growth of total factor productivity (TFP), a measure of the increase in output growth that comes after taking account of increased supplies of labor and capital. It reflects the impact of technological innovation and process improvements. If the introduction of generative AI is successful in the years ahead, it will likely boost the growth of TFP. Although the CBO does not explicitly discuss the impact of generative AI on productivity, it does offer an alternative scenario in which TFP grows 0.5 percentage points faster than the baseline scenario for the foreseeable future. Under this scenario, federal debt held by the public would be 113% of GDP by 2055 versus a baseline scenario of 156%. That is because a stronger economy would generate faster growth of revenue. In fact, under the faster productivity growth scenario, real GDP per person would be 17% higher than under the baseline scenario by 2055. If government borrowing turns out to be less than currently anticipated that could mean lower bond yields. On the other hand, if the economy grows faster (all other things being equal), that implies higher bond yields. Meanwhile, faster productivity growth would likely mean lower inflation than otherwise. Should productivity grows even faster than the CBO's alternative scenario suggests, then theoretically the budget deficit could go away. We simply don't know. We know that, historically, productivity growth has been uneven and unpredictable.6 We also know that there has generally been a large lag between the introduction of radically new technologies and their impact on productivity. This was true of computers which were widely introduced in the 1980s but where productivity acceleration did not take place until the late 1990s. Of course, no one knows how much faster productivity will grow due to the introduction of generative AI, or even if it will grow faster at all. ——by Ira Kalish, chief global economist, Deloitte Touche Tohmatsu Limited
Time Business News
2 hours ago
- Time Business News
EU AI Office Issues Next Guidance on Foundation Models, Downstream Compliance Strategies
Vancouver, Canada — The European Union's AI Office has published its most detailed guidance yet on the regulatory expectations for foundation models under the EU Artificial Intelligence Act (AI Act), marking a pivotal moment in the staged rollout of the bloc's sweeping AI framework. The guidance, aimed at both upstream developers and downstream deployers, clarifies that compliance responsibilities extend through the entire AI value chain, with an emphasis on high-risk applications such as identity verification, Know Your Customer (KYC) processes, fraud detection, and biometric authentication. While foundation models have been widely celebrated for their adaptability and efficiency, the EU AI Office has made it clear that their general-purpose nature is no excuse for regulatory gaps. Whether these models are developed by a major U.S. tech firm, an EU-based AI lab, or an open-source consortium, any deployment in high-risk contexts within the EU will be subject to strict performance, transparency, and governance obligations. The AI Office's latest guidance is particularly significant for regulated industries, where downstream services integrate foundation models into decision-making processes that affect individuals' legal rights, financial access, or physical security. In these scenarios, compliance is not just a matter of upstream assurances; it requires active oversight and testing by downstream deployers. Understanding the EU's Regulatory Position on Foundation Models Foundation models are large-scale, pre-trained AI systems that can be adapted for a wide range of applications. They form the backbone of many downstream services, from automated loan assessments to biometric border controls. Under the AI Act, the developers of these models must meet transparency and documentation requirements. Still, the deployers who adapt them for specific purposes, particularly in high-risk sectors, must conduct their risk assessments, conformity checks, and monitoring. The EU AI Office has now formally stated that compliance is a shared responsibility: upstream developers cannot 'wash their hands' of downstream risks, and downstream deployers cannot rely solely on vendor claims of compliance. This shared responsibility framework is intended to close loopholes where responsibility could otherwise be passed between parties, leading to gaps in oversight. It mirrors principles in other EU regulatory frameworks, such as GDPR's joint controller obligations. It is expected to fundamentally change how AI model procurement, integration, and lifecycle management are approached in the EU market. Key Elements of the New Guidance 1. Mandatory Technical Documentation Transfer Developers must provide downstream deployers with detailed information about a foundation model's architecture, training methodology, dataset sources, risk profiles, and performance metrics across relevant demographic groups. Downstream deployers must keep these records, adapt them to their operational context, and include them in their conformity assessment filings. 2. No Liability Laundering Through Contracts While contracts may allocate operational responsibilities, they cannot eliminate legal obligations under the AI Act. Both parties remain directly accountable to regulators. 3. Context-Specific Testing Requirements Even if a foundation model has been tested by its developer, downstream deployers must test it under real-world conditions relevant to their application. For example, a model used for verifying ID documents must be tested with authentic local document types, lighting conditions, and demographic variations. 4. Continuous Monitoring and Drift Detection Deployers must monitor for model drift (changes in performance over time), especially when models are updated or retrained by the upstream developer. 5. Public AI Database Registration High-risk deployments of foundation models must be listed in the EU's public AI database, including details on both upstream and downstream entities. Sector-Specific Compliance Implications Financial Services Banks using AI-driven fraud detection or credit scoring models must integrate AI governance checks into their vendor risk management processes. Procurement teams will need to request complete compliance documentation and ensure that models are tested for fairness, explainability, and reliability under operational conditions. Identity and KYC Providers These providers are in the direct path of enforcement, as identity verification is a designated high-risk use case. A KYC platform adapting a foundation model for biometric face matching will need to run localized accuracy tests, integrate human-in-the-loop reviews for borderline cases, and ensure that demographic bias is eliminated or mitigated. E-Commerce Platforms using AI to verify seller identities, detect counterfeit goods, or flag fraudulent transactions must confirm that the models they use meet the AI Act's transparency and testing requirements. Border and Travel Security Government agencies and airlines using foundation models for passenger verification must confirm that systems work reliably across all demographic groups, avoid over-reliance on a single vendor's performance claims, and maintain independent audit logs. Case Study 1: Cross-Border Banking and Shared Liability A large EU-based bank uses a biometric verification service that incorporates a U.S.-developed foundation model. The bank's vendor provides a compliance statement. Still, under the new guidance, the bank must independently validate the model's accuracy and fairness in its operational environment, including for customers in rural EU regions whose identity documents may be older or less machine-readable. Case Study 2: E-Commerce Fraud Detection A central e-commerce platform integrates a foundation language model to scan communications between buyers and sellers for scam patterns. While the upstream developer provides a list of known biases and error rates, the platform must conduct its testing to ensure that cultural and linguistic differences across EU member states do not lead to false positives that unfairly penalize legitimate sellers. Strategic Recommendations from Amicus International Consulting For Downstream Deployers Maintain a Model Registry — Track all foundation models in use, their origins, versions, and compliance documentation. Integrate AI Governance into Procurement — Require AI Act compliance proof as part of vendor onboarding. Test Locally, Not Just Globally — Conduct independent testing tailored to your operational jurisdiction and demographic profile. Create Feedback Loops — Develop processes that enable customers and end users to challenge or appeal AI-driven decisions. For Upstream Developers Standardize Documentation — Provide a compliance packet for downstream partners containing all required technical and risk information. Support Downstream Testing — Offer tools and datasets to help deployers run localized performance checks. Communicate Updates Proactively — Notify downstream clients when retraining or model updates could alter compliance status. Geopolitical and Competitive Context The EU's foundation model guidance is part of a broader trend in global AI regulation. The U.S. and UK are focusing on voluntary frameworks, while Singapore and Canada have begun shaping mandatory compliance rules. However, none currently match the AI Act's enforceable obligations for foundation models. This creates a competitive advantage for companies that meet EU standards early, as they will be prepared for similar frameworks elsewhere. Conversely, vendors who cannot meet the EU's documentation and testing requirements risk losing access to one of the world's largest markets. Long-Term Outlook Foundation models are likely to remain at the center of both innovation and regulatory scrutiny. As the AI Act moves toward full enforcement in 2026, the EU AI Office is expected to issue additional guidance refining the shared responsibility model and possibly expanding obligations for models with systemic impact. For identity verification, KYC, and financial services, the guidance means compliance work must start now, not in 2026. The ability to demonstrate early adoption of AI Act principles could serve as both a regulatory shield and a market differentiator. Amicus International Consulting advises all affected businesses to treat the AI Office's guidance as a baseline for global AI governance strategy. The most resilient organizations will integrate upstream and downstream compliance into a single operational framework, ensuring that no part of the AI lifecycle is left without oversight. Contact Information Phone: +1 (604) 200-5402 Email: info@ Website: TIME BUSINESS NEWS
USA Today
2 hours ago
- USA Today
When is Labor Day 2025? What to know about holiday's history and why it is celebrated
With the sun setting earlier and pumpkin spice lattes returning to menus, summer is wrapping up. It also means Labor Day is approaching, which marks the traditional end of summer as schools reopen, and vacationers return from trips. During the three-day Labor Day weekend, many Americans will travel, shop for deals online and in-store and maybe sneak in one final visit to the beach or neighborhood pool. However, the federal holiday is much more than just the summer's last hurrah. Observed each year on the first Monday of September, Labor Day is also a celebration of the hard-won achievements of America's labor movement and a recognition of what workers have contributed to the nation's prosperity. Here's what to know about the Labor Day holiday, including when it is in 2025 and how it started. What's happening in August 2025? Back-to-school, Pumpkin Spice Latte, college football, more When is Labor Day in 2025? This year, Labor Day falls on Monday, Sept. 1. Why is Labor Day celebrated? Rooted in the labor movement of the 19th century, the Labor Day holiday originated during a dismal time for America's workers, who faced long hours, low wages and unsafe conditions. As labor unions and activists advocated and fought for better treatment for workers at the height of the Industrial Revolution, the idea arose to establish a day dedicated to celebrating the members of trade and labor unions, according to How did Labor Day begin? Two workers can make a solid claim to the title of Labor Day's official founder, according to the U.S. Department of Labor. Some records show that it was Peter J. McGuire, the co-founder of the American Federation of Labor, who in 1882 first suggested the idea for the holiday. However, recent research supports the contention that machinist Matthew Maguire proposed the holiday in 1882, while serving as secretary of the Central Labor Union in New York. Regardless of who started the holiday, Labor Day soon became recognized by labor activists and individual states long before it became a federal holiday. Organized by the Central Labor Union, the Labor Day holiday was first celebrated in New York City in 1882, according to the labor department. On that day, 10,000 workers took unpaid time off to march from City Hall to Union Square, according to New York was also the first state to introduce a bill recognizing Labor Day, but Oregon was the first to pass such a law in 1887, the labor department states. By 1894, 32 states had adopted the holiday. When did Labor Day first become federally recognized? Labor Day became a national holiday in 1894 when President Grover Cleveland signed a law passed by Congress designating the first Monday in September a holiday for workers. The federal recognition was hard-won, having come after a wave of unrest among workers and labor activists brought the issue of workers' rights into public view. In May that year, employees of the Pullman Palace Car Company in Chicago went on strike to protest wage cuts and the firing of union representatives, according to A month later, the government dispatched troops to Chicago to break up a boycott of the Pullman railway cars initiated by labor activist Eugene V. Debs, unleashing a wave of fatal riots. Congress quickly passed an act making Labor Day a legal holiday in the District of Columbia and the territories. By June 28, Cleveland signed it into law. Gabe Hauari is a national trending news reporter at USA TODAY. You can follow him on X @GabeHauari or email him at Gdhauari@ Saman Shafiq is a trending news reporter for USA TODAY. Reach her at sshafiq@ and follow her on X and Instagram @saman_shafiq7.
