Integrated Environmental-Economic Analysis of Gross Domestic Product (GDP) and Productivity

传统的国内生产总值（GDP）衡量标准未能考虑到经济活动对环境的影响。该项目的主要目标是：(1)开发一种计算绿色GDP的方法，并将该方法应用于国际数据；(2)计算绿色Malmquist生产力指数（MPI）。相关目标包括：(3)检验研发支出对绿色生产率的影响；(4)检验各国绿色全要素生产率（TFP）增长率随时间的趋同或差异。在整个过程中，环境损耗/退化变量被视为投入。这些变量的例子包括空气污染、森林砍伐、能源和土地利用。绿色GDP将使用数据包络分析（DEA）框架进行计算。为此目的，将规定和估计一种动态的越野技术。利用底层技术的对偶性，计算这些变量的影子价格。因此，推导绿色GDP需要从传统GDP中减去影子价格和退化变量向量的内积。绿色MPI将使用非参数输出距离函数来计算，从中将推导出面向输出的Malmquist指数。这个指数包括了环境耗竭/退化的影响。R&amp；D对绿色生产力增长的影响将使用回归分析进行检验，其中R&amp；D变量的滞后值也作为解释变量出现。滞后术语的引入是为了反映研发支出可能具有滞后效应和当前效应这一事实。绿色全要素生产率增长率的趋同或发散将采用时间序列分析进行检验。将指定绿色TFP增长的自回归模型，并采用单位根检验来确定绿色TFP增长率是随时间收敛还是发散。联合国国民经济核算体系（un System of National Accounts）一直受到批评，因为国内生产总值（GDP）没有考虑到环境影响。GDP是衡量总体经济活动最广泛使用的指标。经济学家和其他人建议，GDP核算应该根据环境损害的价值进行调整，这种调整将导致所谓的“绿色GDP”核算。绿色GDP被认为是一个比GDP本身更准确的衡量社会福利的指标，因为它捕捉了由于环境退化造成的负效用，不仅反映了国家层面的“真实”社会福利，而且还为评估社会政策提供了信息背景。然而，调整GDP以计入环境影响是困难的，因为这需要衡量环境损耗和退化的货币价值。这项研究将开发出一种进行这种调整的方法，并将继续比较不同国家和不同时期的绿色GDP。生产率增长源于一个经济体基本生产能力的提高，是GDP增长和长期福利增加的引擎。不过，与GDP一样，在衡量生产率增长时考虑环境因素也很重要。除了测量绿色GDP外，本研究还将开发一种测量绿色生产力增长的相关方法，并将该方法应用于几年来的国际数据。本文采用了两种统计模型来衡量各国的绿色生产率。第一部分将探讨研发活动和技术溢出对生产率进步的影响。第二个因素将决定各国生产率随着时间的推移是趋同还是分化。因此，这项研究将产生一些学术文献的延伸。它还将为政策制定提供重要见解。显然，环境政策应以综合环境-经济分析为基础。研究中制定的绿色国内生产总值和绿色生产力增长措施，将为制定有关环境管理和经济发展的政策提供有用的投入。

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.