Industrial energy demand and energy efficiency – Evidence from Sweden

Lundgren, Tommy; Marklund, Per-Olov; Zhang, Shanshan

doi:10.1016/j.reseneeco.2016.01.003

Cited by 63 publications

(35 citation statements)

References 32 publications

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“…12 If there is no empirical evidence of skewness of the error term, which can be tested via a likelihood ratio test, then the ML estimate of σ v 2 will be close to zero and the estimates are equivalent to ordinary least squares (OLS) regression. The SF approach is also used by Lundgren et al (2016) to measure energy efficiency in the Swedish pulp and paper industry, by Filippini and Hunt (2012) on US residential energy data and by Filippini and Hunt (2011) on aggregate OECD country energy data.…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…The third expands the notion of efficiency to include both allocative and technical efficiency (Filippini and Hunt 2011). This paper also expands the concept of efficiency to include allocative efficiency via introducing energy prices in an ad-hoc energy demand equation as in Hunt 2011, Filippini andHunt 2012) and (Lundgren, Marklund et al 2016). In addition, (Adetutu, Glass et al 2016) apply SFA to OECD level data to estimate rebound effects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Energy prices provide the basis for the frontier cost (minimizing) level of energy demand, given output and quasi-fixed inputs. The approach used by (Lundgren, Marklund et al 2016) is the most similar to our analysis.…”

Section: Price Effectsmentioning

confidence: 99%

Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis

Boyd

Lee

2019

Energy Economics

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The research program of the Center for Economic Studies (CES) produces a wide range of economic analyses to improve the statistical programs of the U.S. Census Bureau. Many of these analyses take the form of CES research papers. The papers have not undergone the review accorded Census Bureau publications and no endorsement should be inferred. Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. Republication in whole or part must be cleared with the authors. To obtain information about the series, see www.census.gov/ces or contact J.

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“…Other studies that employed an EDF include Filippini and Hunt [15], Filippini et al [16], Alberini and Filippini [17], Filippini and Hunt [18], Lundgren [19], and Otsuka [20] for the US residential sector, the European Union (EU) residential sector, the US residential sector using householdlevel data, the whole economy of 49 US states, Swedish multi-sectors, and the Japanese residential electricity sector, respectively; whereas, Zhou et al [21], Adetutu et al [22], Otsuka and Goto [23], and Marin and Palma [24] are examples of studies that employed the SEDF for the whole economies of the OECD countries, whole economies of OECD and non-OECD countries, regional jurisdictions of Japan, and EU countries, respectively. Kipouros [25] also employed an EDF (and compared the results with an input distance function approach) to measure underlying energy efficiency for a panel of 39 developing countries.…”

Section: Introductionmentioning

confidence: 99%

Measuring underlying energy efficiency in the GCC countries using a newly constructed dataset

et al. 2019

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Energy consumption accounts for most of global anthropogenic greenhouse gas emissions. Managing the growth in energy demand is therefore a key part of climate change mitigation. In the Gulf Cooperation Council (GCC) countries, energy consumption has been growing rapidly. Between 2004 and 2014, final energy consumption grew at an average annual rate of 6.8% compared to a global average of 1.8%. Energy efficiency can help GCC countries manage their energy demand growth, but it is difficult to measure energy efficiency, so analysts often rely on indirect indicators such as energy intensity. Frontier analysis can be used to measure energy efficiency, but is data intensive. To undertake frontier analysis for the GCC countries it was therefore necessary to construct a new dataset, which was used for corrected ordinary least squares to estimate underlying energy efficiency between 2004 and 2014 for two key sectors in the GCC countries: residential electricity and road transport gasoline. The results suggest that underlying energy efficiency generally improved in the GCC region, in contrast to the trend of rising energy intensity that the region has been witnessing. The energy efficiency improvements may have been driven by global technical progress and tighter global fuel economy standards, of which the GCC countries were beneficiaries. With the provision of high quality, recent data with shorter lag times, frontier analysis could be used to provide prompt feedback on the impact of energy efficiency policies and programmes, leading to better outcomes.

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Industrial energy demand and energy efficiency – Evidence from Sweden

Cited by 63 publications

References 32 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)

Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis

Measuring underlying energy efficiency in the GCC countries using a newly constructed dataset

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