Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling

Ioannis E Tsolas

doi:10.3390/mining1010007

Abstract

This paper proposes a stochastic frontier model for measuring both technical and environmental performance at the mine level by using a translog production function. The Kardia Field opencast lignite mine of the Greek Public Power Corporation (PPC), S.A. is the topic of the case study. Efficiency ratings are derived over a long period of time using annual operating data, and in addition, the determinants of inefficiency are established by means of the technical inefficiency effects model. In the light of the results, there is a strong correlation between technical and environmental efficiency; the results are validated by those produced by data envelopment analysis (DEA). In addition, the stripping ratio is identified as the statistically significant determinant of performance. The proposed framework could be used as an instrument to measure the efficiency of lignite mining operations and to identify the drivers of performance.

Highlights

Performance measurement in mining is carried out by deriving metrics such as productivity and efficiency [1]
Four types of models are listed in the related literature that deals with integrating environmental effects into traditional frontier-based performance analysis [12,13]: “Environmentally adjusted production efficiency”(EAPE) models, “frontier eco-efficiency” (FEE)
Dependent variable: Logarithm of lignite production; L: labor; O: overburden; E: electrical energy consumed; SR: stripping ratio; capital to labor ratio (CLR): capital-labor ratio; AGE: age of the mine; σ2 = σu2 + σv2 ; γ = σu2 /σ2 ; σu, σv are the standard deviation of u and v, respectively; Number of obs. = 23; maximum likelihood (ML) estimates and their standard errors, z- and p-values have been produced by the R package Frontier [34]

Summary

Introduction

Performance measurement in mining is carried out by deriving metrics such as productivity and efficiency [1]. Environmentally-adjusted efficiency models have emerged in a new research area. These new models come from the above frontier approaches (i.e., SFA and DEA) by introducing an additional pollution variable into the analysis, either as an input or as a weakly disposable bad output [6]. Within the context of SFA, environmental efficiency can be calculated as the ratio of minimum to actual environmentally detrimental (bad) input using the input minimization perspective. In this view, environmental performance refers to a bad input that has detrimental effects on the environment [7]. Two approaches [8] can be used to identify the drivers (i.e., environmental factors outside of the production function) of inefficiency, namely: (i) the one-step approach whereas the environmental factors are integrated into SFA; and (ii) the two-step approach, in which SFA is combined with Tobit model that is used to regress the estimated inefficiency against the environmental factors

Objectives

Methods

Results

Conclusion