Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Review
2.2. Methods-Stochastic Frontier Analysis
2.2.1. The Measurement of Technical Efficiency
2.2.2. The Measurement of Environmental Efficiency
2.3. Data Set
3. Results
3.1. SFA Results
3.2. Validation of Results
4. Conclusions
4.1. Contribution of the Study
4.2. Key Conclusions
4.3. Implications
4.4. Future Research
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Abbreviation | Definition | Units |
---|---|---|---|
Input and output variables | |||
Labor | L | Annual total man-hours | man-hours |
Electrical energy | E | Electrical energy consumed by bucket wheel excavators | kWhs |
Overburden | O | Uneconomical material removed to reach the underlying ore body | bank m3 |
Output | Q | Annual lignite production | tonnes |
Explanatory variables | |||
Stripping ratio | SR | Ratio of overburden extracted per ton of lignite mined | bank m3 of overburden per tonne of lignite |
Energy productivity | EP | Ratio of total materials conveyed to conveyor energy consumed | bank m3/kWh |
Capital-labor ratio | CLR | Ratio of operating hours of bucket-wheel excavators to total man-hours | hours of bucket-wheel excavators/man-hours |
Age of mine | AGE | Age of mine, measured in years since it first began operations | years |
Variable | Parameter | Coefficient | Standard Error | z-Value | Pr (>|z|) |
---|---|---|---|---|---|
Constant | 53.74 | 0.98 | 54.59 | 0.000 | |
lnL | −10.89 | 0.98 | −11.07 | 0.000 | |
lnE | 39.34 | 0.91 | 43.32 | 0.000 | |
lnO | −79.01 | 0.92 | −85.98 | 0.000 | |
0.5 ln2L | 14.31 | 1.00 | 14.36 | 0.000 | |
0.5 ln2E | −12.50 | 0.48 | −25.97 | 0.000 | |
lnE lnL | −21.64 | 0.64 | −33.92 | 0.000 | |
0.5 ln2O | 5.12 | 0.75 | 6.79 | 0.000 | |
lnL lnO | 29.53 | 0.80 | 36.72 | 0.000 | |
lnE lnO | 9.14 | 0.59 | 15.48 | 0.000 | |
Inefficiency model | |||||
Constant1 | −0.17 | 0.66 | −0.26 | 0.796 | |
SR | 0.21 | 0.10 | 2.11 | 0.035 | |
CLR | −0.04 | 1.00 | −0.04 | 0.964 | |
AGE | −0.01 | 0.03 | −0.22 | 0.825 | |
Variance parameters | |||||
0.05 | 0.04 | 1.33 | 0.183 | ||
0.97 | 0.28 | 3.46 | 0.001 | ||
Log Likelihood function = 12.79 |
Descriptive Statistics | TE | EE |
---|---|---|
Min. | 0.4668 | 0.9939 |
Max. | 0.9720 | 0.9998 |
Mean | 0.7763 | 0.9978 |
Standard deviation | 0.1477 | 0.0017 |
TE-SFA | EE-SFA | UO-DEA | IUO-DEA | |
---|---|---|---|---|
TE-SFA | 1.000 | |||
EE-SFA | 0.973 | 1.000 | ||
UO-DEA | 0.767 | 0.758 | 1.000 | |
IUO-DEA | 0.765 | 0.761 | 0.823 | 1.000 |
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Tsolas, I.E. Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling. Mining 2021, 1, 100-111. https://doi.org/10.3390/mining1010007
Tsolas IE. Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling. Mining. 2021; 1(1):100-111. https://doi.org/10.3390/mining1010007
Chicago/Turabian StyleTsolas, Ioannis E. 2021. "Efficiency Analysis of Lignite Mining Operations Using Production Stochastic Frontier Modeling" Mining 1, no. 1: 100-111. https://doi.org/10.3390/mining1010007