Benchmarking energy use of iron and steel industry: a data envelopment analysis

Abstract

Purpose The purpose of this paper is to benchmark the energy use of Indian iron and steel industry. For this purpose, the authors have estimated a production frontier to know the best performing states. Further, the energy-saving targets are estimated to lie below the benchmark level for those states. Panel data for this purpose are extracted from the Annual Survey of Industry (an official database from the government of India) for 19 major steel-producing states over the period from 2004–2005 to 2013–2014. Design/methodology/approach The authors employed a radial and non-radial (slack-based measure) variant of the data envelopment analysis (DEA) to estimate the production frontier. Particularly, slack-based measures (SBMs) developed by Tone (2001) are used to get a more comprehensive measure of energy efficiency along with technical efficiency. Variable returns to scale technology is specified to accommodate market imperfection and heterogeneity across states. Four inputs (capital, labour, energy and material) and a single output are conceptualised for the production process to accommodate input substitution. The relative position of each state in terms of the level of energy efficiency is then identified. Findings The authors started by examining energy-output ratio. The average level of energy intensity shows declining trends over the period of time. States like Bihar, Jharkhand, Gujarat and Uttarakhand remain stagnant in the energy intensity level. SBM of energy efficiency shows an overall average energy saving potential of 8 per cent without reducing average output level. Considerable heterogeneity exists among states in terms of the energy efficiency scores. Further, the authors calculated scale efficiency (SE) which shows the overall average level of SE is 0.91; hence, the scale of operation is not optimal and needs to adjusted to enhance energy efficiency. Originality/value The authors demonstrate the empirical application of DEA with SBM to energy use performance. This is the first study that benchmarks Indian states in terms of the consumption of energy input to produce iron and steel by applying DEA.

Determinants of material footprint in BRICS countries: an empirical analysis

“…However, a study by Menegaki (2011) in EU countries using panel regression analysis found that renewable energy does not significantly affect economic growth in the study period. Lean and Smyth (2013) The researchers also investigate the role of energy efficiency for mitigating environmental quality in firm-level and country-level data case and suggest that with improved technologies and cross-border spending in performance improvement programs, materials, and energy efficiency should be enhanced (Haider and Ganaie, 2017;Haider and Bhat, 2018;Haider and Bhat, 2019;Haider and Mishra, 2019). The renewable energy sector transition is increasing nowadays to protect the environment.…”

Determinants of Material Footprint in BRICS Countries: An Empirical Analysis

“…The researchers also investigate the role of energy efficiency for mitigating environmental quality in firm-level and country-level data case and suggest that with improved technologies and cross-border spending in performance improvement programs, materials, and energy efficiency should be enhanced (Haider and Ganaie, 2017;Haider and Bhat, 2018;Haider and Bhat, 2019;Haider and Mishra, 2019). The renewable energy sector transition is increasing nowadays to protect the environment.…”

Measuring performance of government-supported drug warehouses using DEA to improve quality of drug distribution

“…DEA is the most prevalent and popular quantitative approach for performance measurement because of accuracy (Shirouyehzad et al , 2017 and Ho et al , 2010). In last few years, numerous studies have evaluated the performance and efficiency of various organization by using DEA approach such as insurance companies (Jaloudi, 2019), port (Saragiotis, 2019), educational institutional and universities (Tho, 2019), banking industry (Adeabah et al , 2019), motorcycle assembly company (Restrepo and Villegas, 2019), wine firms (Bashiri Behmiri et al , 2019), gas company and petrochemicals (Kaviani et al , 2019), energy (Jalali Sepehr et al , 2019), retail services (Cruz et al , 2019), PPP model (Bao et al , 2019), leather industry (Gupta et al , 2019), hotels (Rahman et al , 2019), iron and steel industry (Haider and Mishra, 2019), construction sectors (Gao et al , 2019), airport (Keskin and Köksal, 2019), dairy (Mareth et al , 2019), health care, hospital and pharmaceutical Mahajan et al , 2014) and so on. Each study differs in its scope, decision-making units (DMUs) and variables.…”