Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis

Haider, Salman; Danish, Mohd Shadab; Sharma, Ruchi

doi:10.1016/j.eneco.2019.04.027

Cited by 70 publications

(47 citation statements)

References 53 publications

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“…The size of the mill positively affects energy efficiency in both groups showing that an increase in size induces energy efficiency. This is consistent with the previous study of Irawan, Hartono, and Achsani (2010), Mandal andMadheswaran (2011), andHaider et al (2019). Among the ownership type, the percentage of capital owned by the government, private and foreign have a significant effect on both groups.…”

Section: Resultssupporting

confidence: 92%

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Energy Efficiency of Indonesia’s Sugar Industry Based on Economic Framework: Stochastic Metafrontier Analysis

2021

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This study investigates the efficiency of energy use and technology gap in the Indonesian sugar industry and the factors influencing energy efficiency. Using the firm-level data of sugar mills in 42 regencies in Indonesia from 2010 to 2014, this study applies the meta stochastic frontier based on the input distance function. The metafrontier analysis is applied in sugar mills in the East Java province and other provinces in Indonesia. All the data used in this study are the secondary data taken from the Indonesian Central Board of Statistics. The results reveal that there is a large room to save energy consumption in this industry. The mills in East Java provinces have higher energy efficiency, technology gap ratio, and metafrontier energy efficiency compared to the mills in other provinces. According to the metafrontier energy efficiency, energy inefficiencies in both groups come from operational inefficiency and technology gap. The size of the mills and age of the mills have a positive relationship with the energy efficiency of sugar mills and the size of the mills is positively related to the technology gap ratio. Meanwhile, the productivity of labor and the types of ownership do not affect the energy efficiency and technology gap.

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Section: Resultssupporting

confidence: 92%

“…The positive sign implies that the older the age of the mill, the more efficient energy consumption. This result is the opposite of Haider, Danish, and Sharma (2019) in which younger firms are more efficient in energy use. Based on the literature, the older mills have lower performance in energy efficiency.…”

Section: Resultsmentioning

confidence: 79%

Energy Efficiency of Indonesia’s Sugar Industry Based on Economic Framework: Stochastic Metafrontier Analysis

2021

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“…Hu and Wang [20] (1) Capital; (2) Labor; (3) Energy consumption; (4) Total sown area of farm crops GDP Hu and Kao [23] (1) Capital; (2) Labor; (3) Energy consumption GDP Zhang et al [36] (1) Capital;(2) Labor;(3) Energy consumption GDP Song et al [37] (1) Capital formulation; (2) Labor; (3) Energy consumption GDP Lin and Du [38] (1) Capital; (2) Labor; (3) Energy consumption GDP Bian et al [39] (1) Capital; (2) Labor; (3) Energy consumption GDP Borozan [40] (1) Capital; (2) Employment; (3) Energy consumption GDP Jebali et al [41] (1) Capital; (2) Labor; (3) Energy consumption GDP Eguchi et al [42] (1) Capital; (2) Coal consumption (1) Net electricity production; (2) sample size Haider et al [43] (1) Capital; (2) Labor; (3) Energy consumption; (4) Material output Table A2. Cont.…”

Section: Tfee Without Pollutionmentioning

confidence: 99%

Energy Efficiency Measurement: A VO TFEE Approach and Its Application

Diao

Wang

et al. 2021

Sustainability

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Energy efficiency is crucial to the 2030 UN Sustainable Development Goals (SDGs), but its widely measured indicator, energy intensity, is still insufficient. For this reason, in 2006, total factor energy efficiency (TFEE) was proposed with capital, labor, and energy as inputs and GDP as the desirable output. The later TFEE approach further incorporated pollution as the undesirable output. However, it is problematic to regard GDP (the total value of final products) as the desirable output, because GDP does not include the intermediate consumption, which accounts for a large part of the production activities and may even be larger than the value of GDP. GDP is more suitable for measuring distribution, while VO (value of output) is more appropriate for sustainable production analysis. Therefore, we propose a VO TFEE approach that takes VO as the desirable output instead and correspondingly incorporates the other intermediate materials and services except energy into inputs. Finally, the empirical analysis of the textile industry of EU member states during 2011–2017 indicates that the VO TFEE approach is more stable and convergent in measuring energy efficiency, and is more suitable for helping policymakers achieve the SDGs of energy saving, emissions reduction, and sustainable economic development.

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“…Elsewhere, there is a growing body of theoretical and empirical literature on energy efficiency that applies the DEA for several countries or regions; these include the US [45]; China [46][47][48][49]; India [50,51]; Japan [52]; and OECD countries [53,54]. In the African context, there exists a lacuna of energy efficiency studies using the data envelopment analysis approach, although there are some important energy efficiency studies in Africa [2,3,[55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Improvement Assessment in Africa: An Integrated Dynamic DEA Approach

Amowine

et al. 2019

Energies

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In Africa, energy plays an important role in the processes of economic and sustainable development. However, inefficiency such as mismanagement of resources constrains productivity. Prior energy efficiency studies in Africa have failed to provide the paths through which energy efficiency improvement can be achieved. The current study aims to assess energy efficiency improvement among 25 selected countries in Africa. First, the dynamic slack-based measure (DSBM) data envelopment analysis (DEA) model is applied to gauge the efficiency measurement. Further, the Malmquist productivity index (MPI) is employed to investigate the energy efficiency improvement during 2006–2014. Empirically, the results from the dynamic slack-based measure (DSBM) model show that energy efficiency in Africa is generally low. Also, the findings from the MPI suggest there is no significant improvement in energy efficiency in Africa. Based on the estimated results, some energy efficiency improvement strategies are further proposed for sample countries in Africa.

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Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis

Cited by 70 publications

References 53 publications

Energy Efficiency of Indonesia’s Sugar Industry Based on Economic Framework: Stochastic Metafrontier Analysis

Energy Efficiency of Indonesia’s Sugar Industry Based on Economic Framework: Stochastic Metafrontier Analysis

Energy Efficiency Measurement: A VO TFEE Approach and Its Application

Energy Efficiency Improvement Assessment in Africa: An Integrated Dynamic DEA Approach

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