The researchers will develop a procedure for measuring success in pollution prevention using a technique called Data Envelopment Analysis (DEA). They will than apply this procedure to data from a set of chemical plants to identify those that have made substantial progress in pollution prevention and those that have not. The validity of these measures of success in pollution prevention will be evaluated by reviewing public information on the historical environmental behavior of the plants. A more in-depth study of a selected set of these plants then will be used to validate the data and refine the efficiency measure for each plant, to identify attributes that separate industry leaders from laggers, and to develop a context in which information and technology may be transferred from the leaders to the laggers. The DEA technique, which operationalizes concepts of production efficiency developed in the economics literature, calculates the relative efficiency of a plant, defined as the relative amount of input the plant uses to produce its output. Data on inputs and outputs can be measured in their natural units, for example measures of weight, volume, toxicity, and dollars may be combined in the analysis. A plant's relative efficiency is obtained by calculating the weighted sum of its outputs divided by the weighted sum of its inputs. The researchers will describe how the DEA "efficiency" of a plant reflects the amount of pollution generated in that plant's use of chemical inputs. The DEA-based measure permits simultaneous comparisons across a number of plants having a mix of pollutants and plant "output" measures. It also provides a performance measure for plants based on the success achieved by efficient plants (leaders) and groups other plants (laggers) into sectors that are defined by one or more of the efficient plants. The results of the DEA procedure can be used to evaluate the total amount of pollution that can be prevented if all lagger plants were to become as efficient as the leaders.