Reuters Article Rating

Comment: How companies can lead to make the energy transition work for people and planet

Dec 22, 2022 View Original Article
  • Bias Rating

    -6% Center

  • Reliability

    N/AN/A

  • Policy Leaning

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  • Politician Portrayal

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Bias Score Analysis

The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.

Sentiments

Overall Sentiment

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Bias Meter

Contributing sentiments towards policy:

61% : United Nations' analysis suggests transitioning to a more sustainable economy, including investments in renewable energy and sustainable infrastructure projects, could directly produce $26 trillion in direct economic gains through 2030.
57% : For example, ACEN, the listed energy company of Ayala Corporation in the Philippines, has committed to divesting all thermal plants and its remaining coal plants by 2025, working with the Asian Development Bank's Energy Transition Mechanism to leverage low-cost, long-term funding designed toward early coal retirement and reinvestment in renewable energy.
48% : But government action alone will not be enough to avoid the most catastrophic socioeconomic and environmental effects of climate change, some of which we are already experiencing.
47% : The renewable energy sector alone is expected to create as many as 18 million net jobs by 2030, loss of fossil fuel jobs included.
36% : The bad news is that, if we transition to clean energy without concurrently acting to address the transition's negative impact on society and economy - including high prices for consumers, a loss of carbon-intensive jobs and investments in communities ‒ the energy transition itself will be at risk.

*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.

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