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Abstract
Advances in the productivity with which food is produced around the world have been made possible through the intensive
use of industrial inputs that have important environmental impacts. Like standard measures of macroeconomic performance,
however, commonly used measures of agricultural efficiency and productivity account only for marketed commodities and
inputs, but ignore the environmental effects of these production processes. A more complete analysis of trends in the sector's
productivity requires the use of models that incorporate these environmental effects to provide better measures of the contributions
of the sector from the social point of view. This paper compares the conceptual merits and empirical performance of
alternative approaches that can be employed for this purpose: input distance functions, output distance functions, nonparametric
methods, and index number approaches. Each of the methods has relative strengths and weaknesses. The methods are
empirically illustrated using data from the Canadian pulp and paper industry.© 2001 Elsevier Science B.V. All rights reserved.