This chapter proposes environmental efficiency measures for agricultural production where nitrogen and phosphorus effluents and greenhouse gas emissions are notable environmental stresses. These environmental efficiency measures are based on the principle of materials balance and are used to construct environmental Malmquist total factor productivity (TFP) indices. The chapter illustrates one application using panel data from 32 OECD economies covering 17 years from 1992 to 2008.
Browse by title
Viet-Ngu Hoang and Clevo Wilson
Edited by Thijs ten Raa
H. K. Edmonds, J. E. Lovell and C. A. K. Lovell
We analyse stream health and some of its likely influences for a sample of 30 sites in 16 urban sub-catchments located in the Lower Brisbane River catchment and surrounding coastal catchments in southeast Queensland, Australia. We measure stream health with macroinvertebrate and fish diversity and abundance indicators. We specify three influences on stream health based on metrics generated using geographical information system techniques: an ecological connectivity index is created by aggregating two indicators of in-stream longitudinal connectivity; a land cover index is created by aggregating two indicators of land cover; and upstream sub-catchment drainage area. We use data envelopment analysis to create the indices, and stochastic frontier analysis to explain variations in the two stream health indicators. We rely heavily on dominance analysis, which is independent of the concept of a frontier and which provides the foundation for our ultimate evaluation of the ability of each site to convert ecological connectivity, land cover and upstream drainage area to stream health.
Neal Hughes and Kenton Lawson
This chapter presents a method of controlling for climate effects on firm-level productivity measures. The approach involves applying non-parametric (machine learning) regression methods to large spatio-temporal data sets where firm productivity is observed for many time periods and locations. This method is applied to the Australian broadacre cropping industry, using Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) farm survey data for the period 1977–78 to 2014–15 and climate data from the Australian Water Availability Project (AWAP). The study demonstrates the importance of controlling for climate variability and change when measuring productivity in agriculture, particularly on cropping farms. The results show a significant deterioration in climate conditions for cropping over the last 15 to 20 years, particularly in southern Australia. After the effects of climate have been removed a clear picture of underlying productivity trends emerges, with a slowdown in productivity growth after 1993–94 and a rebound since 2007–08.
José M. Rueda-Cantuche
It is not easy to transform the input and output tables "produced" by statistical offices into matrices of input-output coefficients. There are commodity-by-commodity and industry-by-industry input-output matrices and each of them can be constructed using different models. This chapter provides a unifying framework for all these alternatives and discusses the theoretical and practical pros and cons of the alternatives in a way that consolidates the vast literature. The chapter is authored by an expert who combines statistical office experience and academic contributions to the interface of input-output statistics and economic-environmental modeling.
In dynamic input-output analysis investment meets the capital requirements of output growth. The model is linear and the proportionality between type i capital requirements and output j is represented by a capital coefficient. This chapter presents the dynamic input-output model, its solution, and two main issues, namely singularity of the matrix of capital coefficients and causal indeterminacy. Singularity is a mathematical problem that has been solved. Causal indeterminacy is the incompatibility between non-negative output solutions and arbitrary initial conditions, an issue related to the instability of the model. Alternative modifications of the model address the issue. The dynamic input-output model revives in three areas. Human capital formation is modeled to explain endogenous growth. Environmental accounts are added to analyze the depletion of nonrenewable resources. And lagged production and expenditure models are employed in disaster impact analysis.
Douglas S. Meade
Input-output analysis was invented by Wassily Leontief, who continued to be closely involved with its development, mostly in the United States. This chapter organizes the history in a nice, concrete way, by tracing the input-output tables of the USA from 1939 until 2007, released in 2014. The history is peppered by observations of Leontief's close collaborators Anne Carter and Clopper Almon, shared with the author. The chapter concludes with a clear discussion of the myths of input-output tables, such as consistency and purity.
Kevin J. Fox and Lisa Y.T. Lee
In operating environments where there is a great deal of uncertainty, there can be great diversity in terms of outputs of firms even if inputs are similar. Some firms may achieve relatively high levels of output due to environmental effects that are unrelated to their efficiency. These firms may be treated as outliers. This chapter implements an innovative approach to outlier detection in two separate case studies, using data for a fishery and an irrigated agriculture industry. The approach recognizes that a firm may be an outlier either in terms of the mix or scale of its input-output vector and provides distinct measures of dissimilarity relative to its peers. This distinction is particularly relevant to non-stochastic frontier methods such as data envelopment analysis (DEA), since it identifies the firms that exert the most influence on the resulting efficiency scores. Such identification allows either for an explicit adjustment of the scores for environmental factors, or the exclusion of the outliers – and hence an implicit adjustment of the scores that would otherwise have resulted.
Kim Swales and Karen Turner
The basic input-output model is first extended by differentiating industry outputs by region. The consequent interregional input-output matrix accounts for pollution footprints of final consumption, possibly even including household income effects, which further boost output and pollution. Another extension is the internalization of cleansing activities, to account for the social cost of emissions. Attempts at full integration of production and environmental accounting, following the "materials balance principle," are critically examined. Other environmental analyses follow. Water satellite accounts facilitate the analysis of water trade. Waste input-output models integrate waste creation and management options so that waste can be tracked. Energy efficiency improvements reduce costs, which in turn boosts demand for energy: the rebound effect. The rebound effect is related to the input-output multipliers that include the household consumption effects. The extension to general equilibrium analysis is introduced.
Direct input-output coefficients reflect the effects of the delivery of goods and services in terms of produced and non-produced inputs and environmental impacts (emissions). Evaluation of the further effects of the produced inputs yields the total input-output coefficients, which thus incorporate the multiplier effects of the final delivery of goods and services. The most concrete examples of these are footprints, which trace the environmental impacts of final consumption through the direct and indirect production requirements. After presenting a short history of environmental accounting in input-output analysis, this chapter discusses five types of footprints: ecological, carbon, material, water and land footprints. The methodology of footprint analysis is Leontief inversion of the matrix of input-output coefficients, where products are differentiated by their locations. The dimension of such a matrix is the number of products times the number of regions and this analysis is called multiregional input-output analysis. Multiregional input-output analysis traces the indirect requirements of final consumption in terms of national and international outputs. Application of environmental pressure coefficients yields the footprints. This combination of multiregional input-output and environmental analyses is also called life-cycle assessment and accounts for the environmental impacts embodied in trade.