  This paper explores some sufficient conditions for the enhanced solvability
of strong vector equilibrium problems, which can be established via a
variational approach. Enhanced solvability here means existence of solutions,
which are strong with respect to the partial ordering, complemented with
inequalities estimating the distance from the solution set (namely, error
bounds). This kind of estimates plays a crucial role in the tangential
(first-order) approximation of the solution set as well as in formulating
optimality conditions for mathematical programming with equilibrium constraints
(MPEC).
  The approach here followed characterizes solutions as zeros (or global
minimizers) of some merit functions associated to the original problem. Thus,
to achieve the main results the traditional employment of the KKM theory is
replaced by proper conditions on the slope of the merit functions. In turn, to
make such conditions verifiable, some tools of nonsmooth analysis are
exploited. As a result, several conditions for the enhanced solvability of
strong equilibrium problems are derived, which are expressed in terms of
generalized (Bouligand) derivatives, convex normals and various (Fenchel and
Mordukhovich) subdifferentials.
