Abstract: This paper presents a simplified version of Data Envelopment Analysis (DEA) - a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object - the one having greatest outputs and smallest inputs. It allows for obtaining an explicit analytical solution and making a step to an absolute efficiency. This paper develops this approach further and introduces a DEA model with Partially Perfect Objects. DEA PPO consecutively eliminates the smallest relative inputs or greatest relative outputs, and applies DEA PO to the reduced collections of indicators. The partial efficiency scores are combined to get the weighted efficiency score. The computational scheme remains simple, like that of DEA PO, but the advantage of the DEA PPO is taking into account all of the inputs and outputs for each actual object. Firm evaluation is considered as an example.
Abstract: It is suggested to evaluate environmental performance
of energy sector using Data Envelopment Analysis with nondiscretionary
factors (DEA-ND) with relative indicators as inputs and
outputs. The latter allows for comparison of the objects essentially
different in size. Inclusion of non-discretionary factors serves
separation of the indicators that are beyond the control of the objects.
A virtual perfect object comprised of maximal outputs and minimal
inputs was added to the group of actual ones. In this setting, explicit
solution of the DEA-ND problem was obtained. Energy sector of the
United States was analyzed using suggested approach for the period
of 1980 – 2006 with expected values of economic indicators for 2030
used for forming the perfect object. It was obtained that
environmental performance has been increasing steadily for the
period from 7.7% through 50.0% but still remains well below the
prospected level