Conventional measures of gross domestic product (GDP) fail to account for the effect of economic activity on the environment. The primary objectives of the project are (1) to develop a methodology for computing green GDP and to apply the method to international data and (2) to compute a green Malmquist productivity index (MPI). Related objectives include (3) examining the effect of R&D spending on green productivity and (4) examining the convergence or divergence of green total factor productivity (TFP) growth rates across countries over time. Throughout, environmental depletion/degradation variables are treated as inputs. Examples of these variables include air pollution, deforestation, and energy and land use. Green GDP will be calculated using a data-envelopment-analysis (DEA) framework. For this purpose a dynamic cross-country technology will be specified and estimated. Exploiting the duality properties of the underlying technology, the shadow prices of these variables will be calculated. Deriving green GDP, then, involves subtracting the inner product of the vectors of shadow prices and degradation variables from conventional GDP. Green MPI will be calculated using a nonparametric output distance function, from which an output-oriented Malmquist index will be derived. This index incorporates the effects of environmental depletion/degradation. The effects of R&D on green productivity growth will be examined using regression analysis, in which lagged values of the R&D variable also appear as explanatory variables. Lagged terms are introduced to reflect the fact that R&D spending may have lagged effects as well as current effects. The convergence or divergence of green TFP growth rates will be tested using time-series analysis. An auto-regressive model of green TFP growth will be specified and a unit-root test will be employed to determine whether green TFP growth rates are converging or diverging over time.
The U.N. System of National Accounts has been criticized because GDP, the most widely used measure of aggregate economic activity, fails to account for environmental effects. Economists and others have suggested that GDP accounts should be adjusted for the value of environmental damages, an adjustment that would lead to so-called "green GDP" accounts. It is thought that green GDP is a more accurate measure of social welfare than is GDP itself, because it captures the disutility due to environmental degradation and not only reflects the "true" social welfare at the national level but also provides an informational background for evaluating social policy. Adjusting GDP to account for environmental effects is difficult, though, because it requires measuring the monetary value of environmental depletion and degradation. The study will develop a method for making this adjustment, and will go on to compare green GDP across countries and over time. Productivity growth, which stems from improvements in the fundamental productive capacity of an economy, is the engine of GDP growth and of increases in welfare over time. As with GDP, though, it is important to account for environmental factors in measuring productivity growth. In addition to measuring green GDP, the study also will develop a related method for measuring green productivity growth and apply the method to international data over several years. Using the green productivity measures across countries, two statistical models are employed. The first will explore the effects of R&D activities and technological spillovers on productivity advancement. The second will determine whether productivity converges or diverges across countries over time. The study will thus produce a number of extensions to the academic literature. It will also yield important insights for policymaking. It seems clear that environmental policy should be based on integrated environmental-economic analyses. The measures of green GDP and green productivity growth developed in the study will provide a useful input to the formation of policies regarding environmental management and economic development.
