Analysis of electricity consumption: a study in the wood products industry

This paper evaluates the effect of industry segment, year, and US region on electricity consumption per employee, per dollar sales, and per square foot of plant area for wood products industries. Data was extracted from the Industrial Assessment Center (IAC) database and imported into MS Excel. The extracted dataset was examined for outliers and abnormalities with outliers outside the quantile range 0.5–99.5 dropped from the analysis. A logarithmic transformation was applied to eliminate the skewness of the original data distributions. Correlation measurements indicated a moderate association between the response variables; therefore, a multivariate analysis of variance test was performed to measure the impact of the three factors: industry type, year, and region, simultaneously on all response variables. The results indicated some effect associated with all three factors on the three measures of electricity consumption. Subsequently, univariate ANOVA tests were conducted to determine the levels of the factors that were different. Most levels of industry type were associated with significantly different energy consumption, an expected result since some of the industries are more energy intensive than others. The industries in Standard Industry Code (SIC) 2493 (reconstituted wood products) are the groups with the highest electricity consumption with means of 38,096.28 kWh/employee, 0.86 kWh/sales, and 154.14 kWh/plant area while industries grouped in SIC 2451 (mobile homes) have the smallest consumption with means of 6811.01 kWh/employee, 0.05 kWh/sales, and 9.45 kWh/plant area. Interestingly, differences in regional consumption were found to be linked to the proportion of industry types by region. Data analysis also indicated differences in electricity consumption per employee for the factor year, but for the other response variables, no differences were found. These main results indicate that industries in the wood products sector have different electricity consumption rates depending on the type of manufacturing processes they use. Therefore, industries in this sector can use these comparisons and metrics to benchmark their electricity consumption as well to understand better how electricity costs might vary depending on the region they are located.     

The impact of ICT investment and energy price on industrial electricity demand: Dynamic growth model approach

The authors investigate the effects of information and communications technology (ICT) investment, electricity price, and oil price on the consumption of electricity in South Korea's industries using a logistic growth model. The concept electricity intensity is used to explain electricity consumption patterns. An empirical analysis implies that ICT investment in manufacturing industries that normally consume relatively large amounts of electricity promotes input factor substitution away from the labor intensive to the electricity intensive. Moreover, results also suggest that ICT investment in some specific manufacturing sectors is conducive to the reduction of electricity consumption, whereas ICT investment in the service sector and most manufacturing sectors increases electricity consumption. It is concluded that electricity prices critically affect electricity consumption in half of South Korea's industrial sectors, but not in the other half, a finding that differs somewhat from previous research results. Reasons are suggested to explain why the South Korean case is so different. Policymakers may find this study useful, as it answers the question of whether ICT investment can ultimately reduce energy consumption and may aid in planning the capacity of South Korea's national electric power.     

Forecasting the electricity consumption of the Mexican border states maquiladoras

The consumption of electricity by maquiladora industries in the Mexican border states is an important driver for determining future powerplant needs in that area. An industrial electricity forecasting model is developed for the border states' maquiladoras, and the outputs are compared with a reference forecasting model developed for the US industrial sector, for which considerably more data are available. This model enables the prediction of the effect of implementing various energy efficiency measures in the industrial sector. As an illustration, here the impact of implementing energy‐efficient lighting and motors in the Mexican border states' maquiladoras was determined to be substantial. Without such energy efficiency measures, electricity consumption for these industries is predicted to rise by 64% from 2001 to 2010, but if these measures are implemented on a gradual basis over the same time period, electricity consumption is forecast to rise by only 36%. Copyright © 2004 John Wiley & Sons, Ltd.     

Energy assessment of the Portuguese meat industry

The meat product industries play a major role in many global economies. In several countries, it is the industry with a higher economic weight within the food industries. As refrigeration systems are indispensable within the production processes, it is important to improve the overall energy efficiency in order to reduce the electricity consumption in these industries. The electrical energy consumption of the Portuguese meat industries is evaluated in this paper. The field study shows that electrical energy accounts for 66.5 % of the overall energy consumption in slaughterhouses, 85 % in sausage houses and 92.5 % in ham industries. Relatively to the refrigeration systems of these industries, results show that the average compressor nominal power per unit of cold room volume is comprised between 0.072 and 0.043 kW/m³. The average value of the specific electrical energy consumption for the slaughterhouses was 149 kWh/ton_HSCW, 660 kWh/ton_RM for sausage houses and 1208 kWh/ton_RM for ham production industries. Finally, a potential reduction of the electricity consumption based on simple energy efficiency measures was estimated in 24 % for the slaughterhouses, 13 % for the sausage houses and 8 % for the ham industry.     

The savings of energy saving: interactions between energy supply and demand-side options—quantification for Portugal

Reducing demand by increasing end-use energy efficiency on the demand side of energy systems may also have advantages in reducing fossil dependency and greenhouse gas (GHG) emissions on the supply side. This paper addresses interactions between energy supply- and demand-side policies, by estimating the impact of measures addressing end-use energy efficiency and small-scale renewables uses in terms of (1) avoided large-scale electricity generation capacity, (2) final energy consumption, (3) share of renewables in final energy and (4) reduction of GHG emissions. The Portuguese energy system is used as a case study. The TIMES_PT bottom-up model was used to generate four scenarios covering the period up to 2020, corresponding to different levels of efficiency of equipment in buildings, transport and industry. In the current policy scenario, the deployment of end-use equipment follows the 2000–2005 trends and the National Energy Efficiency Action Plan targets. In the efficient scenarios, all types of equipment can be replaced by more efficient ones. Results show that aggressive demand-side options for the industry and buildings sector and the small-scale use of renewables can remove the need for the increase in large-scale renewable electricity capacity by 4.7 GW currently discussed by policy makers. Although these measures reduce total final energy by only 0–2 %, this represents reductions of 11–14 % in the commercial sector, with savings in total energy system costs of approximately 3,000 million euros₂₀₀₀—roughly equivalent to 2 % of the 2010 Portuguese GDP. The cost-effectiveness of policy measures should guide choices between supply shifts and demand reduction. Such balanced policy development can lead to substantial cost reductions in climate and energy policy.     

Energy efficiency and CO2 emissions in Swedish manufacturing industries

This paper analyses the trends in energy consumption and CO₂ emissions as a result of energy efficiency improvements in Swedish manufacturing industries between 1993 and 2008. Using data at the two-digit level, the performance of this sector is studied in terms of CO₂ emissions, energy consumption, energy efficiency measured as energy intensity, value of production, fuel sources, energy prices and energy taxes. It was found that energy consumption, energy intensity and CO₂ emission intensity, measured as production values, have decreased significantly in the Swedish manufacturing industries during the period studied. The results of the decomposition analysis show that output growth has not required higher energy consumption, leading to a reduction in both energy and CO₂ emission intensities. The role of structural changes has been minor, and the trends of energy efficiency and CO₂ emissions have been similar during the sample period. A stochastic frontier model was used to determine possible factors that may have influenced these trends. The results demonstrate that high energy prices, energy taxes, investments and electricity consumption have influenced the reduction of energy and CO₂ emission intensities, indicating that Sweden has applied an adequate and effective energy policy. The study confirms that it is possible to achieve economic growth and sustainable development whilst also reducing the pressure on resources and energy consumption and promoting the shift towards a low-carbon economy.     

The relationship between electricity consumption,electricity prices and GDP in Pakistan

This study analyzes the relationship among electricity consumption, its price and real GDP at the aggregate and sectoral level in Pakistan. Using annual data for the period 1960–2008, the study finds the presence of unidirectional causality from real economic activity to electricity consumption. In particular, growth in output in commercial, manufacturing and agricultural sectors tend to increase electricity consumption, while in residential sector, growth in private expenditures is the cause of rising electricity consumption. The study concludes that electricity production and management needs to be better integrated with overall economic planning exercises. This is essential to avoid electricity shortfalls and unplanned load shedding.     

Policy drivers for improving electricity end-use efficiency in the USA: an economic–engineering analysis

This paper estimates the economically achievable potential for improving electricity end-use efficiency in the USA from a sample of policies. The approach involves identifying a series of energy efficiency policies tackling market failures and then examining their impacts and cost-effectiveness using Georgia Institute of Technology's version of the National Energy Modeling System. By estimating the policy-driven electricity savings and the associated levelized costs, a policy supply curve for electricity efficiency is produced. Each policy is evaluated individually and in an integrated policy scenario to examine policy dynamics. The integrated policy scenario demonstrates significant achievable potential: 261 TWh (6.5 %) of electricity savings in 2020 and 457 TWh (10.2 %) in 2035. All 11 policies examined were estimated to have lower levelized costs than the average electricity retail price. Levelized costs range from 0.5 to 8.1 cents/kWh, with the regulatory and information policies tending to be most cost-effective. Policy impacts on the power sector, carbon dioxide emissions, and energy intensity are also estimated to be significant.     

Changes in industrial electricity consumption in china from 1998 to 2007

Electricity consumption in the industrial sector experienced a dramatic increase between 1998 and 2007, accounting for approximately 75% of China’s total electricity consumption. This study analyzes the potential factors influencing the growth of electricity consumption in China’s industrial sector over the past decade using a logarithmic mean Divisia index I decomposition method. Results show that activity effect and shift effect (caused by the change in the electricity’s share of industrial energy use) are the major factors responsible for the rise in electricity consumption between 1998 and 2007. It is found that structural change also contributed to the increase in electricity consumption, it had only a small effect. In contrast, the technological effect is responsible for a decrease in electricity consumption during this period. The influences of technological effects and shift effects followed approximately an inverse-U-shaped and U-shaped curve, respectively. Furthermore, the results show that the main contributors to incremental electricity consumption among industrial subsectors were manufacturing of raw chemical material and products, manufacturing of non-metal mineral products, smelting and pressing of ferrous and non-ferrous metals, and production and supply of electric power and heat power. These sectors should take priority for industrial restructuring in order to implement policies for energy and electricity savings.     

A harmonized calculation model for transforming EU bottom-up energy efficiency indicators into empirical estimates of policy impacts

This study is an impact analysis of European Union (EU) energy efficiency policy that employs both top-down energy consumption data and bottom-up energy efficiency statistics or indicators. As such, it may be considered a contribution to the effort called for in the EU's 2006 Energy Services Directive (ESD) to develop a harmonized calculation model. Although this study does not estimate the realized savings from individual policy measures, it does provide estimates of realized energy savings for energy efficiency policy measures in aggregate. Using fixed effects panel models, the annual cumulative savings in 2011 of combined household and manufacturing sector electricity and natural gas usage attributed to EU energy efficiency policies since 2000 is estimated to be 1136 PJ; the savings attributed to energy efficiency policies since 2006 is estimated to be 807 PJ, or the equivalent of 5.6% of 2011 EU energy consumption. As well as its contribution to energy efficiency policy analysis, this study adds to the development of methods that can improve the quality of information provided by standardized energy efficiency and sustainable resource indexes.     

Made in Mexico: Energy reform and manufacturing growth

We assess the real effects of a recent opening of the energy sector in Mexico to private investment. We look at one particular channel, which operates through the change in the structure of electricity generation in favor of cheaper sources of energy, such as natural gas. We look at the potential impact of this structural change on electricity prices and ultimately on manufacturing output using subsector and state-level manufacturing output data. We first document that electricity prices—relative to oil and gas—are more important to the manufacturing sector, with a one-standard deviation reduction in those prices leading to a 2.8% increase in manufacturing output. This elasticity, together with estimated decreases in electricity prices on the back of the reform, could increase manufacturing output by up to 3.6%, and overall real GDP by 0.6%. Larger effects are possible in the long run if increased efficiency in the electricity sector leads to further decreases in electricity prices. There can also be larger effects stemming from output in the services sector which we find to also respond statistically significantly to electricity prices; and from the endogenous response of unit labor costs, which decrease with lower electricity prices.     

Energy policy formulation for Pakistan: An optimization approach

Pakistan is a low income, low energy consumption country. In view of the close interdependence between economic growth and energy consumption, she will need increasing energy supplies in order to maintain her economic growth. This paper develops an energy sector optimization model for the Pakistan economy, which consists of production models for five energy industries, ie oil, gas, coal, electricity and non-commercial fuels. The model is first used to forecast energy balances for the period 1975–2006. The model is then employed to formulate a long-term comprehensive energy policy for Pakistan. Finally the suggested policy is compared with the current official energy programme.     

Determining the responsibility of manufacturing sectors regarding electricity consumption. The Spanish case

The aim of this paper is to develop a methodology based on the input-output approach which allows us to identify the structural and technological responsibility of manufacturing sectors in electricity consumption. Sectors with a high structural responsibility are those whose products are strongly demanded by other sectors and this leads to a high electricity consumption/production. Sectors with a high technological responsibility are those whose technologies use inputs which directly or indirectly require much electricity independently of the composition of final demand in the economy. This methodology is applied to the manufacturing sectors in Spain. It is found out that the chemical sector and industries manufacturing metal products have a large structural responsibility, but their technological responsibility is low. In contrast, the mineral (non-metallic) sectors form a cluster of industries with a high technological responsibility.     

Environmental metabolism of educational services. Case study of nursery schools in the city of Barcelona

The environmental analysis of public nursery schools is of great interest since they are crucial in the early education of children and are expected to show high environmental standards. This paper aims to analyse the environmental profile (energy, water and transport flows) of this sector. A sample of 12 public nursery schools belonging to the Scholar Agenda 21 (SA21) of the city of Barcelona were selected given their data quality (eight centres applied to all analysis) to determine their energy and water consumption, as well as the greenhouse gas emissions resulting from energy consumption and transport use. For each centre, energy and water consumption were obtained from bills and surveys were conducted to get data regarding the transport associated with the centre. Results show that, on average, a child consumes 966 kWh of energy (electricity and gas) and 12.9 m³ of potable water every year. Nursery schools with more energy-efficient devices hold lower energy consumption, a trend which could not be found in the case of water and water-efficient devices. Regarding transport, car usage was the flow with highest impact, since it accounts for 69 % of CO₂eq emissions, although only 19 % of the children commute by car.     

Forecasting electricity consumption in Pakistan: the way forward

Growing shortfall of electricity in Pakistan affects almost all sectors of its economy. For proper policy formulation, it is imperative to have reliable forecasts of electricity consumption. This paper applies Holt-Winter and Autoregressive Integrated Moving Average (ARIMA) models on time series secondary data from 1980 to 2011 to forecast total and component wise electricity consumption in Pakistan. Results reveal that Holt-Winter is the appropriate model for forecasting electricity consumption in Pakistan. It also suggests that electricity consumption would continue to increase throughout the projected period and widen the consumption-production gap in case of failure to respond the issue appropriately. It further reveals that demand would be highest in the household sector as compared to all other sectors and the increase in the energy generation would be less than the increase in total electricity consumption throughout the projected period. The study discuss various options to reduce the demand-supply gap and provide reliable electricity to different sectors of the economy.     

A proposal to combine classical and hypothetical extraction input–output methods to identify key sectors for the production and distribution of electricity

This analysis explores the possibility of merging into a ‘combined’ proposal two standard I-O methods identifying key sectors, the Classical Multiplier and Hypothetical Extraction. In the context of the latest revision of the European Union Energy Efficiency Plan, we use this proposal to single out key sectors that boost potential energy savings in the economic system—specifically, in the production and distribution of electricity. Using the main distinctions and complementarities of the two traditional I-O key-sector approaches, the combined formulation allows us to disaggregate the backward stimuli of the electricity sector into three indicators: total, internal and external backward indicators. This combined proposal provides additional insights into the structure of the industrial linkages that participate in the production and distribution of electricity. Our results reveal that the explanation for the intensity of the backward effects of the electricity sector depends not only on other energy sectors but also on some manufacturing industries. We put forward that these findings may be important for developing a more balanced and cost-effective design for energy efficiency policies.     

U.S. manufacturing aggregate energy intensity decomposition: The application of multivariate regression analysis

This paper introduces the use of a multivariate regression analysis to explain factors that impact aggregate energy intensity. This kind of study is useful to evaluate the past and predicts the future trends for energy‐policy evaluation. Historical aggregate fuel and electricity intensities of the entire U.S. manufacturing sector (Standard Industrial Classification, SIC, codes of 20–39) over the 1977–1998 period are used to develop the proposed multivariate regression model. The proposed model allows identifying the structural effect of aggregate energy intensity changes without relying on detailed disaggregated energy data. Its results are validated by comparison with those from conventional decomposition techniques based on economic index numbers. For illustration, the historical aggregate fuel intensity of the U.S. primary metal industry (SIC 33) is used as an example of a situation for which economic index numbers fail to decompose the historical aggregate energy intensity since the disaggregated energy data are unavailable, while the multivariate regression analysis can still be applied. Empirical results show that a structural shift contributes to decreases of about 28, 41 and 19% of total declines of U.S. manufacturing aggregate fuel, U.S. manufacturing aggregate electricity, and U.S. primary metal industry aggregate fuel intensities, respectively, for the 1977–1998 period. The method based on multivariate regression models estimates the time series structural effects within deviation averages of 8.5 and 7.0% of the time series structural effect estimates based on the economic index numbers for the U.S. manufacturing aggregate fuel and electricity intensities, respectively, even though the multivariate regression model does not require disaggregated energy data. Copyright © 2007 John Wiley & Sons, Ltd.     

Assessing the technological responsibility of productive structures in electricity consumption

A methodology is developed which allows us to measure the responsibility of the productive structure of an economic system with respect to the consumption and generation of electricity within an input–output framework. We propose a technical indicator of technology responsibility in electricity consumption based on the assessment of technical coefficients. Technological responsibility refers to the capacity of a sector or economic transaction between sectors to induce electricity consumption regardless of the final demand vector. Sectors with a high technological responsibility are those whose technologies use inputs which either directly or indirectly require much electricity independently of the composition of the final demand in the economy. This methodology is applied to the productive sectors of the Spanish economy. It is found out that a few transactions between sectors are highly technologically responsible regarding electricity consumption. The results show that, although the service sectors are the ones with the greatest share in electricity consumption, the industrial sectors (particularly, the extractive, heavy and energy industries), the electricity generation sector and construction are the ones with the greatest technological responsibility, i.e., they have technology mixes with a large propensity to consume electricity, propagating to the other sectors. The sectors with the highest technological responsibility are clustered around three broad sectors: energy, metal manufacturing and transport.     

Estimation of the energy efficiency in Chinese provinces

China is one of the largest energy consumers and CO₂ emitters globally. The growth rate of energy consumption in China is about 6 % per year, and it consumed 21 % of the world’s total energy in 2012. In recent years, the Chinese government decided to introduce several energy policy instruments to promote energy efficiency. For instance, the reduction targets for the level of energy intensity have been defined for provinces in China. However, energy intensity is not an accurate proxy for energy efficiency because changes in energy intensity are a function of changes in several socioeconomic factors. In this paper, we present an empirical analysis on the estimation of the persistent and transient energy efficiency of Chinese provinces by employing a log-log aggregate energy demand frontier model. The model is estimated by using data on 29 provinces observed over the period 2003 to 2012. Several econometric model specifications for panel data are used: the random effects model and the true random effects model along with other versions of these models. Our analysis shows that energy intensity cannot measure accurately the level of efficiency in the use of energy in Chinese provinces. Further, our empirical analysis shows that the average value of the persistent energy efficiency is around 0.81 whereas the average value of the transient energy efficiency is relatively high and shows a value of approximately 0.97. By improving the level of efficiency in the use of energy to 100 %, the total energy consumption in China would decrease by approximately 1000 Mtce, which corresponds to 25 % of total energy consumption in 2012.     

交通运输业能耗现状及未来走势分析

低碳经济要求交通运输有效、合理地使用能源,优化配置各种交通工具,降低能耗。近年来,我国交通运输业能耗增长率总体上高于全社会能耗增长率,占全社会能耗比重基本维持在7.5%左右。各种运输方式的能耗主要集中在油耗上,2007年交通运输业汽煤柴3种油耗叠加在一起,占全社会油耗比重近70%。交通运输中电能利用效率较高,节电效果好于全社会,电耗占全社会电耗比重从2002年的2.07%降至2007年的1.63%,但占全国交通运输能耗比重仅10%左右,能耗结构不合理现象并未得到改善。2008年国家铁路单位运输工作量综合能耗比上年降低3.1%,2009年我国铁路电气化率达到41.9%,铁路能耗结构出现根本性改善和优化,开始转变为以电耗为主。公路运输油耗总量呈快速增长趋势,百吨公里油耗指标呈稳中略升态势,节能空间和潜能较大。水运(含港口)能耗2004年之前呈上升趋势,之后下降趋势明显,约占交通运输业总能耗的15%。民航每吨公里油耗从2002年的0.364kg降至2007年的0.309kg,航油消耗增长率基本维持在12%上下,有较为明显的减弱趋势。未来10年,我国交通运输能源消耗总量将进一步攀升,虽然能耗结构将得到一定程度优化,电耗比重会迅速增长,但由于公路能耗在交通运输能耗中占有绝对比重,故难以从根本上改善交通运输以油耗为主的结构特点。我国交通运输业应逐步调整到以铁路为主导的各种交通方式协调发展的模式上来,最大限度地降低运输业油耗在整个交通运输行业中的比重,"以电代油"。