Navigating the Sustainability Commitment Paradox: A roadmap for corporate treasurers
- Bias Rating
-58% Medium Liberal
- Reliability
55% ReliableFair
- Policy Leaning
-58% Medium Liberal
- Politician Portrayal
N/A
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:
63% : The Sustainability Commitment Paradox report highlights the disconnect between interest and action.62% : "As consumer and regulator demands for sustainability increase, corporate treasurers have an opportunity to bridge the gap between intention and action, turning the ambition of a greener and more equitable future into today's business practices.
57% : However, several factors hinder the transition from intent to action, including uncertainties about the financial implications to complexities in implementing better practices.
52% : "It's clear that while action is underway, the step to formalise these efforts through concrete targets remains a significant hurdle for corporates," explains Pradeep Nair, Global Head of Structured Solutions and Development, Trade at Standard Chartered.
*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.