Energy Efficiency Improvements in Asia: Macroeconomic Impacts

Sharma, Deepak; Sandu, Suwin; Misra, Suchi

doi:10.2139/ssrn.2511098

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…It indicates that with a speed of 11%, the shocks in independent variables are adjusted for the co-movements in employment. This finding is consistent with past studies suggesting that ENE improvements influence employment in the short run (Sharma et al , 2014; Mikulic et al , 2016). Related results have been observed in the studies of Tuominen et al (2013), Sharma et al (2014) and Mikulic et al (2016), where it is evident that changes in ENE improvements influence employment in many countries.…”

Section: Empirical Findingssupporting

confidence: 93%

“…This finding is consistent with past studies suggesting that ENE improvements influence employment in the short run (Sharma et al , 2014; Mikulic et al , 2016). Related results have been observed in the studies of Tuominen et al (2013), Sharma et al (2014) and Mikulic et al (2016), where it is evident that changes in ENE improvements influence employment in many countries. Such relationships have been evident in previous studies as well showing that both in the short and long run, an increase in ENE generates employment through direct, indirect and induced effects (Kuckshinrichs et al , 2010; Rosenow et al , 2014; Mikulic et al , 2016; Brown et al , 2020).…”

Section: Empirical Findingssupporting

confidence: 93%

“…The findings area investigated in earlier studies. MEE (2013) and Sharma et al (2014) argue that the adoption of ENE policies results in a shift in employment from the energy and mining sectors to manufacturing and services. Hence, the number of job losses in the energy and mining sectors would only be partially offset by new jobs in the non-energy sectors.…”

Section: Short-and Long-run Asymmetric Impact Resultsmentioning

confidence: 99%

“…Likewise, another study on the employment impact of ENE programmes in Tunisia found a positive effect of ENE on employment generation (Lehr et al , 2016). In the Indian context, Sharma et al (2014), using data for 2010–2050, find that improvement in ENE inversely affects the employment scenario. Their study suggests that the adoption of ENE policies will decrease employment opportunities in the energy and mining sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

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Estimating energy efficiency using panel stochastic frontier approach: investigating its asymmetric impacts on employment in India

Mahapatra

Irfan

2022

IJESM

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Purpose This study aims to examine the asymmetric effects of energy efficiency on employment in India. Instead of relying on partial factor energy efficiency measures, this study uses a total factor energy efficiency (TFE) measure to estimate sector-specific energy efficiency for empirical investigation. Design/methodology/approach Multi-sectoral panel data for India from 2000 to 2014 are considered for empirical estimation. The sector-specific energy efficiency estimates (using the TFE measure) are estimated in the initial stage using the stochastic frontier approach (SFA). Then the asymmetric effect of energy efficiency on employment is investigated by using a non-linear panel autoregressive distributed lag model. Findings The estimates of energy efficiency display that there is not much significant change in the trend of average energy efficiency over the period. The negative and statistically significant value of the error-correction term confirms the existence of asymmetric cointegrating relationship between energy efficiency and employment in India. Moreover, in the empirical findings, the positive and negative shocks in energy efficiency provide a long-run asymmetric and short-run symmetric effect on employment in India. Originality/value Rather than depending on the absolute measure of energy efficiency (energy to output ratio), this study estimates the sector-specific energy efficiency for India using panel SFA, which provides a relative measure of energy efficiency. Moreover, to the best of the authors’ knowledge, it is the first empirical study investigating the asymmetric impact of energy efficiency on employment at an aggregate level in developing countries like India. By contrast, previous studies have either concentrated on the symmetric effect of energy efficiency on employment or primarily restricted to developed countries.

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Section: Empirical Findingssupporting

confidence: 93%

Section: Empirical Findingssupporting

confidence: 93%

Section: Short-and Long-run Asymmetric Impact Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Estimating energy efficiency using panel stochastic frontier approach: investigating its asymmetric impacts on employment in India

Mahapatra

Irfan

2022

IJESM

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“…Improving energy efficiency is required to achieve sustainable economic development while the pollutant emissions are reduced. The development of the energy industry in China is not only affected by the firm-specific determinants (Costa-Campi et al, 2015;Sineviciene et al, 2017) 2 , but more importantly, the performance of the energy industry is significantly influenced by the energy industry development (Craig, 2016), and the financial system (Chang, 2015) as well as the macroeconomic environment (Sharma et al, 2014) in China 3 .…”

Section: Energy Efficiency and Business Environment Drivers-hypotheses Developmentmentioning

confidence: 99%

Business environment drivers and technical efficiency in the Chinese energy industry: A robust Bayesian stochastic frontier analysis

Tan

Antunes

Hadi-Vencheh

2020

Computers & Industrial Engineering

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Improving the technical efficiency of the energy industry is a fundamental way to ensure energy security and sustainable development and is also a requirement of the supply-side structural reform of China's energy. Although the business environment plays an important role in the energy industry, this relationship between efficiency and business environment has been scarcely studied. In addition, there has been no attempt made to link the financial sector and energy industry together. To this end, a novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed here, where DIC (Deviance Information Criteria) is minimized by the variance/covariance optimization of three classical distributional assumptions for the inefficiency term (u): Gamma, Exponential, and Half-Normal. The goal is to develop a RBSFA model to relate the technical efficiency of the energy industry in China with major business environment performance variables (financial sector variables, energy industry variables and macroeconomic variables). Results indicate that the efficiency level of the Chinese energy industry is quite high over the examined period, although there is a large variance between different companies. Our findings further show that increases in the efficiency of the Chinese energy industry can be achieved by increasing the level of inventories and fixed assets, as well as research and development expenses. Finally, we find that the efficiency level in the Chinese energy industry is affected by the business environment. In particular, we notice that financial sector development and competition are helpful to improve the efficiency of Chinese energy companies.

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