As a Project for His Business Statistics Class, a Student

As a project for his business statistics class, a student examined the factors that determined parking meter rates throughout the campus area. Data were collected for the price per hour of parking, blocks to the quadrangle, and one of the three jurisdictions: on campus, in downtown and off campus, or outside of downtown and off campus. The population regression model hypothesized is Y_i = α + β₀ + β₁X_1i + β₂X_2i + β₃X_3i + ε
Where
Y is the meter price
X₁ is the number of blocks to the quad
X₂ is a dummy variable that takes the value 1 if the meter is located in downtown and off campus and the value 0 otherwise
X₃ is a dummy variable that takes the value 1 if the meter is located outside of downtown and off campus, and the value 0 otherwise
Suppose that whether the meter is located on campus is an important explanatory factor. Why should the variable that depicts this attribute not be included in the model?

A) Its inclusion will introduce autocorrelation.
B) Its inclusion will introduce collinearity.
C) Its inclusion will inflate the standard errors of the estimated coefficients.
D) Both B and C.