How to interpret regression coefficients in a log-log model [duplicate]

This question already has an answer here:

I'm using a linear model to analyse some data,

y~N(mu, sigma) where
mu[y] <- Intercept + Beta1X + Beta2X1 + Beta3X2
and Beta2 = Beta1^2
Beta[n] ~ N(mu.b[n], sigma.b[n])

but I have had to log-transform both the predicted and all the predictor variables, because I'm using BUGS, just for efficiency. Gelman alludes to this being called "elasticity" and says the coefficients can be directly interpreted as " a Beta% increase in X is associated with a 1% increase in y".

However, my results :

Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.1135924  0.1495142   0.760    0.448
B1           1.4934436  0.0580981  25.706   <2e-16 ***
I(B1^2)     -0.1477196  0.0062205 -23.747   <2e-16 ***
B2           0.0003612  0.0515368   0.007    0.994

suggest that there is a 150% increase in y, with each 1% increase in Beta1 which would be bonkers. Also, how do interpret the "non-linear" or self-interaction term? My suggestion is : A 15% decrease in the effect of B1 on y occurs with each 1% increase in Beta1.

However If I don't transform either the predicted or the predictors I get:

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  5.382e+01  3.410e+00  15.782  < 2e-16 ***
B1          -3.026e-02  8.775e-03  -3.449 0.000591 ***
I(B1^2)      8.654e-06  7.828e-06   1.106 0.269264
B2           2.363e+00  2.490e+00   0.949 0.342789

In which the effect of X1 seems to be reversed, and the effect sizes are miniscule ( certainly not in the order of 150% and 15% respectively)

Hoping for correction!!

Thanks