Efficiency Scores Analysis of Coal Mines using IRS, DRS and Cross Efficiency Models

G. Reddy
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Abstract

Economic growth world over is driven by energy, whether in the form of finite resources such as coal, oil and gas or in renewable forms such as hydroelectric, wind, solar and bio-mass or its converted form, electricity(power). Increased energy consumption (especially of electricity) is inevitable with higher GDP growth. Coal was created by the fossilised remains of plants and has high carbon content. DEA is a multi-factor productivity analysis model for measuring the relative efficiency of a homogenous set of coal mines (DMU’s). For every inefficient coal mine, DEA identifies a set of corresponding efficient coal mines that can be utilized as benchmarks for improvement of performance and productivity. Benchmarking and ranking of coal mines based on efficiency scores using advanced DEA models like, Increasing Returns to Scale (IRS), Decreasing Returns to Scale (DRS), Cross Efficiency (CE) Models.
基于IRS、DRS和交叉效率模型的煤矿效率评分分析
世界各地的经济增长都是由能源驱动的,无论是以煤炭、石油和天然气等有限资源的形式,还是以水力、风能、太阳能和生物质能等可再生形式或其转化形式——电力(动力)的形式。随着GDP的增长,能源消耗(尤其是电力)的增加是不可避免的。煤是由植物的化石残骸产生的,碳含量很高。DEA是一种多因素生产率分析模型,用于衡量一组同质煤矿的相对效率。对于每一个效率低下的煤矿,DEA确定了一组相应的效率较高的煤矿,这些煤矿可以作为提高绩效和生产率的基准。利用先进的DEA模型,如规模收益递增模型(IRS)、规模收益递减模型(DRS)、交叉效率模型(CE),对煤矿的效率评分进行基准测试和排名。
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