The argument is about whether banning red cars saves lives, not about whether insurance companies have the right to charge higher premiums. Even if the author had questioned the insurance companies' pricing practices, that wouldn't affect whether banning red cars would save lives.

Repair costs have nothing to do with saving lives. The conclusion is about whether fewer people would die if red cars were banned, not about whether accidents involving red cars are more expensive to fix.

This identifies the core flaw. If drivers who drive recklessly prefer red cars, then the higher accident rate for red cars is explained by the behavior of the people driving them, not the color. The color is just along for the ride. And if reckless driving is the actual cause of the accidents, banning red cars wouldn't change anything. Those same drivers would switch to another color and keep driving the same way. In the language of the phenomenon-hypothesis framework: (C) supplies a competing hypothesis that the author failed to consider.

The argument doesn't need an exact percentage. We're told red cars are involved in a greater percentage of accidents than cars of any other color. The problem isn't that this claim is imprecise. The problem is that the argument treats the correlation as evidence for a specific causal hypothesis without ruling out alternatives. Even with exact figures, the same logical gap would exist.

You might be drawn to this because you notice the jump from "involved in accidents" to "lives could be saved." That jump does seem to assume some connection between accidents and loss of life. But the argument says lives could be saved, not that every accident is fatal. It only needs some car accidents to result in death, not every single one of them.