Production Of Good Outputs Research Articles

Measuring environmental inefficiency through machine learning: An approach based on efficiency analysis trees and by-production technology

The main objective of this study is to introduce machine learning-type extensions for the measurement of environmental inefficiency based on regression trees under shape constraints. The new methods developed are implemented using a by-production approach that distinguishes two technologies, one related to the generation of pollution and the other to the production of good outputs. In particular, we define two alternative approaches to measuring environmental inefficiency: by-production Efficiency Analysis Trees (by-production EAT) and by-production Convexified Efficiency Analysis Trees (by-production CEAT). The main advantage of the methods developed is that they do not suffer from the typical statistical problem of overfitting connected to Free Disposal Hull (FDH) and Data Envelopment Analysis (DEA). The performance of the new models is evaluated through a simulation study which shows that the new approaches outperform FDH and DEA in terms of mean squared error and bias. We also illustrate the practical usefulness of the new techniques through empirical application to 43 developing and developed countries over a fifteen-year period - from 2000 to 2014. Our empirical findings using real data clearly indicate the higher discriminating power of the by-production EAT and CEAT models as compared respectively to FDH and DEA.

Endogenous Bad Outputs and Technical Inefficiency in U.S. Electric Utilities

In this paper, we consider a simultaneous modeling of good and bad outputs. We use an input distance function (IDF) with endogenous inputs as well as endogenous bad outputs, which is novel in the literature. Moreover, we model input efficiency to depend on the production of bad outputs which allows us to investigate whether emissions of pollutants (bad outputs) are related to technological performance (technical efficiency). We also model production of each bad output with a spatial structure separately, each depending on production of good outputs, inputs and other exogenous variables. These bad output production functions allow us to estimate both direct and indirect effects of good output on the production of bad outputs, which may be of special interest because they show the cost (to the society) in terms of releasing pollutants to the environment in order to increase production of good outputs. We apply the new technique to a data set on U.S. electric utilities with four bad outputs, three inputs and two good outputs. We used a Bayesian technique to estimate the model which is a system consisting of the input distance function, reduced form equations for each input, dynamics of inefficiency and bad output production technology—separately for each. Empirically, bad outputs are found to affect inefficiency positively. Percentage increases in inefficiency due to a percentage increase in each bad output are found to vary from 0.225% to 0.42%. Energy prices are found to be positively related to inefficiency. From the spatial specifications of bad outputs, we find that the spillover effects of increasing production of good outputs account for the majority of the total effect, indicating that neighborhood effects are more important than own effects. This means, the neighboring utilities played a crucial role indicating “contagion” of practices.

A panel frontier system model with good and bad outputs and endogenous treatment decision

In this paper we consider a simultaneous panel stochastic frontier system for the production of good and bad outputs when inefficiency is present in both. In addition, we consider treatment of bad output when the treatment decision is endogenous. Inclusion of a treatment decision is important because treatment affects production of bad output which, in turn, affects production of good output. Because of simultaneity, inefficiency from the production of good (bad) output is transmitted to the production of bad (good) output. We derive the likelihood function and formulas for predicting inefficiency (efficiency) for both good and bad outputs as well as overall inefficiency.

Decomposing banking performance into economic and credit risk efficiencies

This paper proposes a non-parametric approach of a banking production technology that decomposes performance into economic and credit risk efficiencies. The basis of our approach is to separate the production technology into two sub-technologies. The former is the production of non-interest income and loans from a set of traditional inputs. The latter is attached to the production of interest income from loans where an explicit distinction between good and non-performing loans is introduced. Economic efficiency comes from the production of good outputs, namely interest and non-interest income, while credit risk management efficiency is related to the minimization of the non-performing loans that can be considered as an unintended or bad output. The model is applied to Chinese financial data covering 30 banks from 2005 to 2012 and different scenarios are considered. The results indicate that income could be increased by an average rate of 16% while non-performing loans could be decreased by an average rate of 33%. According to our results, banking managers could strike a balance between economic performance and credit risk management and make more appropriate decisions in line with their preferences.

Pollution-Adjusted Productivity Changes: Extending the Färe–Primont Index with an Illustration with French Suckler Cow Farms

Several approaches have been proposed in the literature to compute technical efficiency indices that account for bad outputs. The literature is, however, less rich when it comes to incorporating bad outputs in productivity indices. In this context, this article extends the multiplicatively complete Fare–Primont productivity index to a generalized version that considers pollution. This novel pollution-adjusted total factor productivity (TFP) is the ratio of an aggregated measure of good outputs to an aggregated measure of bad outputs and non-polluting inputs, in such a way that the materials balance principle is not distorted. A decomposition of this new pollution-adjusted TFP is proposed using the by-production. The latter implies considering a technology that lies at the intersection of two sub-technologies: one for the production of good outputs and the other for the generation of pollution. The pollution-adjusted TFP is further decomposed into pollution-adjusted technical change and efficiency change. The latter is further broken down into technical, scale, mix, and residual efficiency change components. A crucial advantage of the Fare–Primont index is the verification of the transitivity property that allows multi-temporal and multilateral comparisons. The generalized (pollution-adjusted) Fare–Primont TFP proposed here is illustrated with a sample of French suckler cow farms surveyed over the period of 1990 to 2013. The bad outputs considered are greenhouse gas emissions, namely methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). The results reveal a decrease in pollution-adjusted TFP by 5.57% during the period, due to technological regress of 2.23%, and to technical efficiency decrease of almost 3.34%.

Environmental production and productivity growth: evidence from european paper and pulp manufacturing

The production and manufacturing sector is one of the primary factors that affects the environment, which has been a very important topic of recent studies. Many approaches are employed to reduce the impact of production on the environment. More recently, carbon-abatement technology and activities have been introduced into the production processes to reduce carbon emissions, such as the implementation of emission trading programs in many industrial sectors, including the paper and pulp sector. Nevertheless, the costs of abatement activities will result in a certain level of sacrifice in productivity growth, when the inputs are reallocated from good output production to abatement activities to maintain bad output under the regulatory limit. However, how and the extent to which such technology will affect productivity remain unclear. Therefore, it is worth investigating the opportunity cost of introducing such technology. In this paper, we offer new empirical evidence by studying panel data on 17 EU member states from 1995 to 2006. Productivity changes are calculated using a data envelopment directional distance function with and without adapting the carbon-abatement technology in the paper and pulp production. The results support our concern about the potential opportunity cost of introducing carbon-abatement technology, which leads to a decline in productivity growth. In addition, industrial production is not operating efficiently; on average it moves further away from the efficient production frontier over time.

The social dimension of firm performance: a data envelopment approach

A data envelopment model is proposed to quantify the social dimension of firm performance. Corporate social responsibility activities are treated as netputs to production. Production of good outputs and unintended by-products are combined in the overall measurement of firm performance. Our model is illustrated using a sample of global firms and suggests strikingly low social performance levels.

Pollution Abatement and Productivity Growth: Evidence from Germany, Japan, the Netherlands, and the United States

The passage of environmental legislation was accompanied by concerns about its potential detrimental effect on productivity. We assume inputs can be assigned to either abatement activities or good output production. This allows us to specify regulated and unregulated production frontiers to determine the association between pollution abatement and productivity growth. We then employ our “assigned input” model to determine the association between productivity and abatement activities for manufacturing industries in Germany, Japan, the Netherlands and the United States.

Environmental production functions and environmental directional distance functions

This study derives the relationship between environmental production functions and environmental directional distance functions. These two approaches make different assumptions when modeling the joint production of good and bad outputs. The environmental production function credits a producer solely for expanding good output production, while the directional environmental distance function credits a producer for simultaneously increasing production of the good output and reducing production of bad outputs. Estimates of technical efficiency and pollution abatement costs are calculated using data from coal-fired power plants. These results provide the empirical basis for comparing the environmental production function to the environmental directional distance function.

Environmental Production Functions and Environmental Directional Distance Functions: A Joint Production Comparison

This study derives the relationship between environmental production functions and environmental directional distance functions. These two approaches make different assumptions when modeling the joint production of good and bad outputs. The environmental production function credits a producer solely for expanding good output production, while the directional environmental distance function credits a producer for simultaneously increasing production of the good output and reducing production of bad outputs. Estimates of technical efficiency and pollution abatement costs are calculated using data from coal-fired power plants. These results provide the empirical basis for comparing the environmental production function to the environmental directional distance function.