Estimating the Technical Improvement of Energy Efficiency  in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches

Park, Kyu Tae; Hildreth, Alfred J.

doi:10.3390/en7096196

Cited by 37 publications

(16 citation statements)

References 21 publications

(24 reference statements)

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“…They use DEA to account for the Bstructural factors^of energy use, such as input and product mix to create energy efficiency rankings but apply it to countrylevel OECD data rather than plant-level data. Oh and Hildreth (2014) apply both statistical and DEA methods to auto industry data.…”

Section: Relationship To Other Benchmarking Approachesmentioning

confidence: 99%

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Boyd

2016

Energy Efficiency

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There is growing interest among policy makers and others regarding the role that industrial energy efficiency can play in mitigation of climate change. For over 10 years, the US Environmental Protection Agency (EPA) has supported the development of sector-specific industrial energy efficiency case studies using statistical analysis of plant level on energy use, controlling for a variety of plant production activities and characteristics. These case studies are the basis for the ENERGY STAR ® Energy Performance Indicators (EPIs). These case studies fill an important gap by estimating the distribution of efficiency using detailed, sector-specific, plant-level data. These estimated distributions allow Energy Star to create Benergy-efficient plant benchmarks^in a variety of industries using the upper quartile of the estimated efficiency distribution. Case studies have been conducted for 14 broad industries, 2 dozen sectors, and many more detailed product types. This paper is a meta-analysis of the approach that has been used in this research and the general findings regarding the estimated distribution of performance within and across industries. We find that there are few sectors that are well represented by a simple Benergy per widget^benchmark that less energy-intensive sectors tend to exhibit a wider range of within-industry efficiency than energy-intensive sectors, but changes over time in the level and range of energy efficiency do not reveal any single pattern.

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Section: Relationship To Other Benchmarking Approachesmentioning

confidence: 99%

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Boyd

2016

Energy Efficiency

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“…Although different models have been applied for the definition of a benchmarking frontier aiming, for instance, to measure energy efficiency (e.g., Oh & Hildreth, 2014;Otsuka & Goto, 2015) or to compare countries and regions in terms of performance (e.g., Afonso, Schuknecht, & Tanzi, 2005;Suzuki, Nijkamp, & Rietveld, 2011), the specific use of the DEA technique for the assessment of QoL in cities is scarce. To the authors' knowledge the few exceptions that used the DEA technique to assess QoL in cities are Morais and Camanho (2011) and Morais et al (2013), covering, respectively, 206 and 246 European cities.…”

Section: The Concept and Measurement Of Quality Of Life In Citiesmentioning

confidence: 99%

Comparing the quality of life of cities that gained and lost population: An assessment with DEA and the Malmquist index

Amado

Barreira

Santos

et al. 2019

Papers in Regional Science

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This study compares the quality of life (QoL) of cities that lost population with that of cities that gained population. A unique dataset composed of observations for 11 dimensions of QoL for all mainland Portuguese cities is used. By employing a non‐parametric approach (data envelopment analysis), and by using a Malmquist‐type index, this study identifies differences in QoL between the group of cities that lost population and the group of cities that gained population, as well as differences within each group. Despite the heterogeneity in cities that shrunk, this group presents, on average, higher QoL than cities that have grown.

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“…Furthermore, the relatively high value of the correlation among the three key inputs of our analysis may lead to multicollinearity problems that can severely affect the results of stochastic production frontiers (SPFs). Another reason is that SFA cannot successfully distinguish between technical improvements and efficiency improvements [57]; when using panel data, SPF estimators are often biased [58] and the independency of the inefficiency terms over the different time periods is difficult to hold [57]. Although DEA is not completely free of disadvantages, given the characteristics of the available data and the above reasons, this type of analysis was chosen in this particular study.…”

Section: Methodsmentioning

confidence: 99%

Capturing Firms’ Heterogeneity through Marketing and IT Capabilities in SMEs

Ramón-Jerónimo

Herrero

2017

Sustainability

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To achieve sustainability, firms capable of surviving economic recessions is of key relevance; the capabilities that firms need to face dynamic environments remain an open question. In this work, a new procedure is proposed to capture firms' heterogeneity with regard to the capabilities they possess in operating efficiently in dynamic environments. This approach enables the identification of the classes of firms that develop efficiency with a specific integration of resources. While the literature has most often measured firm capabilities using subjective measures, this study suggests the use of Data Envelopment Analysis to capture the ability to transform resources into outcomes and of Latent Class Regression to capture differences across firms that explain firms' heterogeneity in the way they perform. By combining these two techniques, this work presents a way to identify those firms that need to invest in and develop certain capabilities. This work analyses a large dataset of manufacturing Small Medium Enterprises (SMEs) extracted from the Business and Strategy survey provided by Fundación de la Sociedad Estatal de Participaciones Industriales( SEPI) in Spain. The dataset used enfolds 10,960 observations from 2048 firms during the period 1994-2011. The complete dataset has been employed to calculate manufacturing firms' efficiency. In a second step, data were cleaned to eliminate outliers, and to identify SMEs and observations with records of IT capabilities. As a result, 329 manufacturing SMEs were analysed to capture their heterogeneity. The results contribute to the current literature by explaining how manufacturing SMEs show a different need in their development of capabilities to be efficient and adapt to environmental changes. While approximately 20% of firms analysed really take advantage of recessions through their investment in R&D, the remaining 80% need to adjust their size or invest in IT capabilities to become competitive. The assumption shown in previous studies regarding the performance of SMEs to better confront recessions is discussed in this paper.

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Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches

Cited by 37 publications

References 21 publications

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Comparing the quality of life of cities that gained and lost population: An assessment with DEA and the Malmquist index

Capturing Firms’ Heterogeneity through Marketing and IT Capabilities in SMEs

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