Eco-Friendly Delivery Robots Gain Consumer Favor
- Bias Rating
- Reliability
50% ReliableAverage
- Policy Leaning
46% Medium Right
- Politician Portrayal
N/A
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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
18% Positive
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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Contributing sentiments towards policy:
59% : Many automatic delivery robots, which can travel on sidewalks and roads, are electric, and some rely on renewable energy sources like solar power.58% : In this study, participants who had more environmental awareness and knowledge about carbon emissions were more likely to choose the robots as a delivery method.
55% : The researchers found a strong correlation between high ranked statements related to carbon emissions and the willingness to use the automatic delivery robots or ADRs.
54% : The participants watched short videos about automatic delivery robots and answered questions about carbon emissions as well as the robots themselves.
54% : ADRs may appeal to businesses simply because they help meet the growing demand for delivery services, but as this study indicates, their ability to curb carbon emissions may also prove a powerful motivator for consumers.
53% : The suitcase-sized, self-driving electric vehicles are much greener than many traditional food delivery methods because they have low, or even zero, carbon emissions.
43% : " The pandemic pushed an increase in online food ordering by 63%, according to Statista, which has in turn resulted in increased congestion and carbon emissions as more gas-powered vehicles hit the road to deliver the food.
*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.