The War in Ukraine Upended Energy Markets. What Does That Mean for the Climate?
- Bias Rating
-72% Very Liberal
- Reliability
65% ReliableFair
- Policy Leaning
-84% Very Liberal
- Politician Portrayal
1% Positive
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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.
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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Contributing sentiments towards policy:
63% : The vast majority of climate scientists say that in order to limit the extent of the warming, humans should transition to renewable energy as fast as possible.63% :More important, the war -- and the sudden unreliability of Russia as an energy exporter -- has prompted many countries to accelerate their development of renewable energy.
63% : From England to Spain to Albania, countries across the European continent are rushing to deploy wind and solar power at record rates.
62% : Even as European countries embrace renewable power, it will be years before those sources can fully replace fossil fuels.
56% : And in the United States, the past year was also a story of short-term energy shocks and a longer term investment in renewable power.
52% : And coal has had a resurgence, subduing hopes for meeting goals to rein in greenhouse gas emissions.
50% :The European Union is working to streamline permitting for renewable projects, countries are racing to build wind and solar farms, and some countries, including Germany, are slowing plans to phase out nuclear energy.
49% : And while that is a dire scenario, it only reinforces what many experts say is one of the key lessons of the war so far: that renewable energy is not just good for the climate, but good for national security, too.
44% : That had the effect of pushing some countries to turn to coal.
*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.