Wood Mackenzie: Global Crude Oil Demand To Peak In 2032
- Bias Rating
- Reliability
30% ReliableAverage
- Policy Leaning
-4% Center
- Politician Portrayal
N/A
Continue For Free
Create your free account to see the in-depth bias analytics and more.
By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates.
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
7% Positive
- Liberal
- Conservative
| Sentence | Sentiment | Bias |
|---|---|---|
Unlock this feature by upgrading to the Pro plan. | ||
Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
Extremely
Liberal
Very
Liberal
Moderately
Liberal
Somewhat Liberal
Center
Somewhat Conservative
Moderately
Conservative
Very
Conservative
Extremely
Conservative
-100%
Liberal
100%
Conservative
Contributing sentiments towards policy:
61% : Displacing fossil fuels with renewable power is a pillar of the energy transition that seeks to meet the Paris Agreement target to limit global temperature rises to 1.5 C (2.7 F) above preindustrial levels.58% : The report comes ahead of the COP30 meeting in Brazil this month where countries are due to present updated national climate commitments and assess progress on renewable energy targets.
50% : Rising dependence on fossil fuels due to increased power demand from artificial intelligence and to geopolitical tensions have led to 2050 net zero goals becoming unattainable, Wood Mackenzie said in its Energy Transition Outlook report.
44% : "Fossil fuels are no longer uncontested; they are being squeezed into narrower roles, but their decline is proving more gradual than expected," the Wood Mackenzie report said.
*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.
