Editorial: Cleaning up California's oilfields may cost $21.5 billion. Taxpayers shouldn't get the bill
- Bias Rating
-30% Somewhat Liberal
- Reliability
70% ReliableGood
- Policy Leaning
-38% Somewhat Liberal
- Politician Portrayal
2% Positive
Continue For Free
Create your free account to see the in-depth bias analytics and more.
Continue
Continue
By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates. Already a member: Log inBias 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
N/A
- Liberal
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:
53% :California's Department of Conservation Geologic Energy Management Division, or CalGEM, said that it shares the concerns raised in Carbon Tracker's report, but that its data show more bond money has been set aside than stated in the report and that it has been making progress plugging idle wells using $100 million in new state funding.47% : Oil companies caused this problem, but it's public officials' job to make sure they can't wash their hands of it and stick us with the bill.
36% : The California Independent Petroleum Assn., an industry trade group, criticized the Carbon Tracker report, saying it overestimated the costs by calculating what it would cost the state to plug wells, which is higher than what it costs the oil industry.
*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.