This study contributes to the literature by proposing a new method of complex evaluation of multiple life cycle environmental impacts of different PV technologies based on the Data Envelopment Analysis (DEA). The main advantage of DEA as a non-parametric technique is that it does not require prior knowledge of underlying production functions. An empirical production technology frontier is estimated based on best-practice boundary of the input-output relationship. DEA evaluates comparative or relative efficiency, which means the measurement with reference to some set of units we are comparing with each other. The proposed approach allows to aggregate disparate quantitative estimates of individual negative environmental effects from the literature and special databases in a transparent and easily understandable index or coefficient of ecology efficiency. The evaluation of environmental effects is performed on data from the EcoInvent Database. The results of this study clearly show that from an environmental point of view it is more practical to prefer technologies, which are less resource and energy intensive in manufacturing and upstream activities. As of right now, this requirement is met by thin-film technologies: amorphous silicon (a-Si), cadmium telluride (CdTe), and copper-indium-diselenide (CIS); however, their ecologic efficiency evaluation may change as we obtain more data on the final stages of the lifecycle for PV modules of various types. Our computational results show that 5 out of 19 PV technologies were identified as efficient compared to the other technologies against which they were assessed. CdTe, a-Si, and ribbon silicon laminated panels installed slanted-roof, CIS panel mounted slanted-roof, and open ground installation m-Si are demonstrated highest efficiency scores from an ecological point of view throughout the life cycle. Based on results of this study a number of opportunities for improving existing government incentives and rationalizing the design of state programs under elaboration can be identified.