Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico's iron and steel industry

2010 ◽  
Vol 32 (6) ◽  
pp. 1337-1344 ◽  
Author(s):  
Claudia Sheinbaum ◽  
Leticia Ozawa ◽  
Daniel Castillo
Keyword(s):  
Carbon Dioxide ◽  
Steel Industry ◽  
Carbon Dioxide Emissions ◽  
Energy Use ◽  
Iron And Steel Industry ◽  
Logarithmic Mean ◽  
Divisia Index ◽  
Iron And Steel

scholarly journals Multi-Perspective Analysis of Household Carbon Dioxide Emissions from Direct Energy Consumption by the Methods of Logarithmic Mean Divisia Index and σ Convergence in Central China

2021 ◽  
Vol 13 (16) ◽  
pp. 9285
Author(s):  
Yueyue Rong ◽  
Junsong Jia ◽  
Min Ju ◽  
Chundi Chen ◽  
Yangming Zhou ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Carbon Dioxide Emissions ◽  
Central China ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Growth Trend ◽  
Divisia Index ◽  
Direct Energy ◽  
Urban Rural

Currently, household carbon dioxide (CO2) emissions (HCEs) as one of the leading sources of greenhouse gas (GHG) have drawn notable scholarly concern. Thus, here, taking six provinces in the period of 2000–2017 of Central China as a case, we analyzed the characteristics and the driving factors of HCEs from direct energy consumption and three perspectives: Central China as a whole, urban-rural differences, and inter-provincial comparison. The drivers of direct HCEs were analyzed by the Logarithmic Mean Divisia Index (LMDI). The σ convergence was adopted for analyzing the trend of inter-provincial differences on the HCEs. The key findings are as follows. First, all the direct HCEs from three perspectives had an obvious growth trend. The total direct HCEs grew from 9596.20 × 104 tonnes in 2000 to 30,318.35 × 104 tonnes in 2017, with an increase of 2.16 times. Electricity and coal use were the primary sources. The urban and rural increases of direct HCEs were up 2.57 times and 1.77 times, respectively. The urban-rural gap of direct HCEs narrowed first and then widened. The direct HCEs in the six provinces varied significantly, but the gap was narrowing. Second, as a whole the per capita consumption expenditure and energy demand were the main drivers to the increment of HCEs, with cumulative contribution rates of 118.19% and 59.90%. The energy price effect was mainly responsible for the mitigation of HCEs. Third, the similar drivers’ trend can also be seen from the perspective of inter-provincial comparison. However, from the perspective of urban and rural difference, the population urban-rural structure effect played a reverse influence on both urban and rural areas. Thus, raising the energy prices appropriately, upgrading the residents’ consumption to a sustainable pattern, controlling the growth of population size reasonably, and optimizing the household energy structure might effectively mitigate the growth of HCEs in Central China.

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

2019 ◽  
Vol 26 (4) ◽  
pp. 1314-1335 ◽  
Author(s):  
Salman Haider ◽  
Prajna Paramita Mishra
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Steel Industry ◽  
Energy Use ◽  
Energy Intensity ◽  
Iron And Steel Industry ◽  
Data Envelopment ◽  
Production Frontier ◽  
Content Type ◽  
Iron And Steel

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.

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