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2021 | OriginalPaper | Chapter

Modelling Production of Bad Outputs: Theory and Empirics

Author: Surender Kumar

Published in: Sustainable Development Insights from India

Publisher: Springer Singapore

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Abstract

Generally, unintended outputs are generated while producing desired outputs. Standard production theory ignores the production of unintended outputs, especially when the market for these outputs is missing or absent and produces unreliable estimates of the production processes and fails to credit the producer for their efforts invested in reducing bad outputs. This chapter provides an overview of different approaches to modelling bad outputs. Bad outputs can be modelled either following a joint production approach or by-production approach. The joint production framework is based on the axioms of null jointness in the production of good and bad outputs and weak disposability of bad outputs. The by-production approach assumes costly disposability of bad outputs rather than weak disposability. This approach treats good and dirty inputs asymmetrically. It postulates that the dirty inputs cannot be disposed off in free. However, in the empirical applications, the joint production approach is more popular, and the later approach is yet in its infancy.
Footnotes
1
The words environmental pollution, unintended output, bad output and undesired output are used interchangeably throughout the chapter.
 
2
For recent review of the literature on environmental pollution-generating production technologies, please refer to Dakpo et al. (2017) and Depko and Ang (2019). Murty and Russell (2017) provide an excellent detailed overview of these technologies.
 
3
Directional output distance is defined and discussed in the Sect. 3.
 
4
Another option for reducing bad outputs without reducing polluting inputs is abatement activities conducted as end-of-pipe treatment. In the end-of-pipe treatment, non-polluting inputs are diverted from main production process to abatement activities to reduce bad outputs that lead to a reduction in marketed output.
 
5
For details on deterministic parametric estimation of distance functions, please refer to Murty and Kumar (2002), Kumar and Rao (2003), Murty et al. (2006) and Jain and Kumar (2018). Murty and Kumar (2002) and Kumar and Rao (2003) estimate output distance function, Murty (2006) estimates input distance function, and Jain and Kumar (2018) estimate DODF.
 
6
Translation property is used while estimating DODF using stochastic frontier analysis (SFA) (Kumar et al., 2015).
 
7
However, the stochastic methods have their own disadvantages such as distributional assumptions for the inefficiency and error terms, and the problem of imposing nonlinear monotonicity constraints in the estimation process.
 
8
For detailed application using the stochastic estimation of output distance function, please refer to Murty and Kumar (2003) and Kumar and Kumar (2015), and stochastic estimation of directional output distance function, please read Murty et al. (2007), Kumar and Managi (2011), Kumar et al. (2015) and Kumar and Jain (2019).
 
9
Details on the non-parametric estimation of environmental production function using DEA can be obtained in Kumar et al. (2020) and Kumar and Jain (2020). Kumar (2006), Kumar and Managi (2009), Khanna and Kumar (2011), Kumar and Managi (2012), Kumar and Managi (2016) and Shetty and Kumar (2017) applied DEA technique for estimation of DODF.
 
10
The four studies are: Jain and Kumar (2018), Kumar and Jain (2019), Kumar and Jain (2020) and Kumar et al. (2020). Jain and Kumar (2018) apply deterministic parametric approach for estimating DODF, whereas Kumar and Jain (2019) use SFA for recovering parameters of a quadratic DODF. Kumar and Jain (2020) and Kumar et al. (2020) estimate environmental production functions using non-parametric DEA approach.
 
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Metadata
Title
Modelling Production of Bad Outputs: Theory and Empirics
Author
Surender Kumar
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-4830-1_22

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