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Rappler Article Rating

What is climate finance and why does it matter?

  • Bias Rating
  • Reliability

    25% ReliableLimited

  • Policy Leaning

    6% Center

  • Politician Portrayal

    N/A

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

-2% Negative

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  •   Conservative
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Bias Meter

Contributing sentiments towards policy:

56% : BELEM, Brazil - Countries trying to shift to clean energy while also preparing for extreme weather and other impacts in a warmer world will need money -- a lot of it.
56% : Here's what you need to know: This describes all funding from governments, development banks, private investors and philanthropies aimed at helping countries cut greenhouse gas emissions or adapt to climate impacts, for example through projects in renewable energy or for flood defenses.
56% : This is shifting focus toward ways to attract more private money, through changes in financial regulations, credit ratings and multilateral bank lending practices and the creation of new financial instruments.
48% : Beijing insists it should continue to be treated as a developing nation under the UN climate treaty.
47% : Between now and 2035, the target is assumed to remain at the previous $100 billion level, though this is not spelled out in UN documentation.
43% : Because these countries contributed the least in emissions, the 1992 UN climate treaty determines they should carry less of the burden of addressing the problem today.
42% : (COP30: UN Climate Change Conference agenda, latest news) Countries today disagree about who should pay climate finance, how much is needed and how it should be distributed.

*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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