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News articleYahoo Finance· December 12, 2025

Energy Companies from Around the World Win Honors at S&P Global Energy's 27th Annual Platts Global Energy Awards

View original at finance.yahoo.com
Energy Companies from Around the World Win Honors at S&P Global Energy's 27th Annual Platts Global Energy Awards CNBC's Brian Sullivan Emceed as Winners from Australia, Brazil, China, Denmark, India, Singapore, Spain, UAE, UK, and US Were Announced NEW YORK, Dec…
Opening lines of the source · Yahoo Finance · short snapshot — read the full document at the original

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  • Armstrong is a leader with deep personal integrity

    80% confidence
  • The company provides vital stability to markets in Europe and Asia and across the global stage

    80% confidence
  • Not only do we congratulate tonight's winners but all who were finalists, as well

    80% confidence
  • As the world charts a path toward a more equitable and sustainable future, ensuring access to modern and expanded sources of energy is critical

    80% confidence
  • Fusco is a true trailblazer with impressive metrics ranging from robust financials, to operational discipline, to commitment to and demonstrated community engagement and an exemplary safety record

    80% confidence
  • Li is a pioneer in China and throughout the world

    80% confidence
  • Energy is everything, everywhere, and the lifeblood of the world's economy and we're pleased to be recognizing those individuals and companies that are embracing change, utilizing artificial intelligence (AI), innovating and helping steer a course for a sustainable future for generations to come

    80% confidence

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The observation date (2025-12-27) precedes Q1 2026, making it logically impossible to have actual Q1 2026 cash data at that point. Q1 2026 would not end until March 31, 2026. Additionally, the magnitude of the difference ($45.3B vs $132.42) is implausibly large even as a normal quarterly change for Apple. While different fiscal periods can show different values, the timing relationship here suggests a data integrity issue rather than legitimate period-over-period variation.
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