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.     

Technical and cost assessment of energy efficiency improvement and greenhouse gas emission reduction potentials in Thai cement industry

The cement industry is one of the most energy-consuming industries in Thailand, with high associated carbon dioxide (CO₂) emissions. The cement sector accounted for about 20.6 million tonnes of CO₂ emissions in 2005. The fuel intensity of the Thai cement industry was about 3.11 gigajoules (GJ)/tonne cement; the electricity intensity was about 94.3 kWh/tonne cement, and the total primary energy intensity was about 4.09 GJ/tonne cement in 2005 with the clinker to cement ratio of around 82%. In this study, the potential application of 47 energy-efficiency measures is assessed for the Thai cement industry. Using a bottom-up electricity conservation supply curve model, the cost-effective electricity efficiency improvement potential for the Thai cement industry is estimated to be about 265 gigawatt hours (GWh), which accounts for 8% of total electricity use in the cement industry in 2005. Total technical electricity-saving potential is 1,697 GWh, which accounts for 51% of total electricity use in the cement industry in 2005. The CO₂ emission reduction potential associated with the cost-effective electricity savings is 159 kilotonne (kt) CO₂, while the total technical potential for CO₂ emission reductions is 902 ktonne CO₂. The fuel conservation supply curve model shows a cost-effective fuel-efficiency improvement potential of 17,214 terajoules (TJ) and a total technical fuel efficiency improvement potential equal to 21,202 TJ, accounting for 16% and 19% of the total fuel use in the cement industry in 2005, respectively. CO₂ emission reduction potentials associated with cost-effective and technical fuel-saving measures are 2,229 ktonne and 2,603 ktonne, respectively. Sensitivity analyses were conducted for discount rate, electricity and fuel prices, and exchange rate that showed the significant influence of these parameters on the results. Hence, the results of the study should be interpreted with caution.     

Analysis of Jordan's industrial energy intensity and potential mitigations of energy and GHGs emissions

This paper aims to identify the main drivers behind energy intensity changes of the Jordanian industrial sector and to introduce the impact of energy efficient measures within the Jordanian industrial sector. To achieve these objectives, two empirical models were developed for electricity and fuel intensities, respectively of the Jordanian industrial sector based on multivariate linear regression. It was found that the structural effect, electricity prices, capacity utilizations and number of employees are the most important variables that affect changes of electricity intensity while fuel prices, capacity utilizations and number of employees factors are the most important variables that affect fuel intensity. The results show that multivariate linear regression model can be used adequately to simulate industrial energy intensity with very high coefficient of determination. Also, the impact of implementing energy saving technologies, such as use of high efficiency motors (HEMs), optimize motor size, variable speed drives (VSDs), bare steam pipes insulations, steam leak prevention, steam traps repair, and adjustment of boiler air/fuel ratio were investigated and found to be significant. Without such basic energy conservation and management programs, energy consumptions and associated GHG emissions for the industrial sector are predicted to rise by 25% and 23%, respectively in the year 2021. If these measures are implemented on a gradual basis, over the next decade, industrial energy consumption is predicted to rise at a lower rate, reaching 11.9% for same period with low/no cost actions. This would yield an estimated annual emission reductions of 570×10³ t. In addition, the total installed capacity cost savings is estimated to be around 81.9 million US$ by year 2021.     

Electricity consumption,electricity intensity and industrial structure

This paper describes an analysis of recent trends in industrial output and electricity consumption carried out at the Electricity Council. The analysis examines the significance of changes in industrial structure and in the intensity of electricity use within major industries using a simple arithmetical procedure. The conclusion reached is that such factors have, in recent years, had a major influence on trends in industrial electricity consumption. The importance of these factors explains why simple econometric models which describe industrial electricity sales as a function of total industrial output have proved unsatisfactory for forecasting purposes. This conclusion underlines the need to use a disaggregated approach to electricity forecasting, an approach which the electricity industry has used increasingly.     

Analysing energy intensity trends and decoupling of growth from energy use in Indian manufacturing industries during 1973–1974 to 2011–2012

This paper has two research objectives: first, it derives and analyses energy intensity trends for seven energy intensive manufacturing industries and the aggregate manufacturing sector in India for the period 1973–1974 to 2011–2012 and compares the same with best practice benchmarks. Second, based on Index Decomposition Analysis, it studies the extent to which the energy efficiency has contributed in the decoupling of industrial activity growth from growth in energy use. The study finds faster decline in energy intensity in all the seven industries during the recent years (1998–1999 to 2011–2012). Aluminium, cement and fertilizer industries are found to operate close to the global best-practice energy intensities with transformational changes in process technology. Iron and steel and pulp and paper are found to be lagging behind with only incremental transformation in technology in place. The decomposition results show that activity growth is the major driver of growth in energy demand with marginal impact coming from structural change. However, declining energy intensity has been able to neutralize a major portion of the growing energy demand resulting in decoupling trends, especially in recent years, with more energy efficiency-related voluntary and mandatory policies in place).     

Temporal carbon intensity analysis: renewable versus fossil fuel dominated electricity systems

In order to overcome the negative effects of climate change and ensure a global low-carbon future, decarbonizing the electricity sector has been recognized as an important focus area. Internationally, policymakers use average carbon intensity (in gCO₂-e/kWh) in calculating greenhouse gas (GHG) emissions from the electricity system. However, average carbon intensity is a single rate and a fixed quantity; thus, it does not provide any information about the time-varying nature of carbon intensity. The focus of this paper is to show the usefulness of time-varying carbon intensity estimation, which can provide detailed insights into GHG emissions, and help in identifying potential emission cut opportunities from the electricity sector in order to lessen atmospheric pollution. Time-varying carbon intensity estimation (i) reveals temporal variability of carbon intensity, (ii) explores the interplay between generations and emissions, (iii) identifies peak carbon-intensive hours, and (iv) provides evidence for designing appropriate demand-side management strategies with respect to GHG emission reduction.     

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.     

Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection

In this paper, an empirical model is developed for electricity consumption of the Jordanian industrial sector based on multivariate linear regression to identify the main drivers behind electricity consumption. In addition, projection of electricity consumption for the industrial sector based on time series forecasting is presented. It was found that industrial production outputs and capacity utilization are the two most important variables that affect demand on electrical power and the multivariate linear regression model can be used adequately to simulate industrial electricity consumption with very high coefficient of determination. To illustrate the importance of integrating energy efficiency within national energy plans, the impact of implementing high-efficiency motors was investigated and found to be significant. Without such basic energy conservation and management programs, electricity consumption and associated GHG emissions for the industrial sector are predicted to rise by 63% in the year 2019. However, if these measures are implemented on a gradual basis, over the same period, electricity consumption and GHG emissions are forecasted to ascend at a lower rate with low/no cost actions.     

Assessment of China's climate commitment and non-fossil energy plan towards 2020 using hybrid AIM/CGE model

China made a commitment in Copenhagen to reduce its carbon dioxide emissions per unit of GDP from 40% to 45% compared with the 2005 level by 2020, and is determined to vigorously develop non-fossil fuels. This study analyzes the effects and impacts of policies that could help to achieve China's Copenhagen commitments with a hybrid static CGE model in which the electricity sector is disaggregated into 12 generation technologies. Four scenarios are developed, including the reference scenario A, the reference scenario B and two carbon constraint scenarios. The results show that carbon intensity in terms of GDP will fall by 30.97% between 2005 and 2020 in the reference scenario A, and will be reduced further by 7.97% if China's targeted non-fossil energy development plans can be achieved in the reference scenario B. However, the rest of the 40–45% target must be realized by other measures such as carbon constraint. It is also observed that due to carbon intensity constraints, GDP loss would be from 0.032% to 0.24% compared to the reference scenario B, and CO₂ emission reductions are due mainly to decreases in coal consumption in the electricity sector and manufacturing sector.     

Decomposition of aggregate energy intensity changes in two measures: ratio and difference

Decomposition analysis has been popular in energy demand analysis and has been found useful in policy-related studies. Past studies include decomposition of changes of an aggregate indicator, measured in terms of either ratios or differences, into several pre-defined contributing factors. Aggregate indicators that are often studied include total national energy demand, energy demand in specific consuming sectors, aggregate energy intensity and energy-related carbon dioxide emissions. However, the possible linkages between the ratio measure and the difference measure, including their decomposition results, have seldom been analysed. This paper examines this issue using the Divisia decomposition technique and a unique pair of decomposition formulae. Numerical examples based on Singapore and Taiwan industrial electricity demand data are presented.     

我国经济发展对电耗的影响及电力的需求浅析

能/电耗的变化与经济发展所处的时期有关。针对我国经济快速发展,从电力角度分析我国工业化进程需经历的三个时期:1949～1979年高度工业重型化时期(工业化初期);1980～2000年高度工业轻型化时期(工业化中期);2001～202X年工业重轻基本协调时期(工业化后期)。由于各时期的特点不同,其能耗电耗也不同。现阶段只有能耗下降超过3%时,电耗才会下降。我国完成工业化进程对电力的需求为:人均用电量达到4500kWh左右,人均发电装机容量达到1kW左右;第二产业用电比重在60%左右,第三产业用电比重高于17%,居民生活用电比重20%左右。     

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.     

Provincial energy intensity in China: The role of urbanization

Chinese policymakers have attached great importance to energy intensity reduction. However, the unprecedented urbanization process exercises additional pressure on the realization of energy intensity reduction targets. A better understanding of the impacts of urbanization is necessary for designing effective policies aimed at reaching the next energy intensity reduction targets. This paper empirically investigates the impacts of urbanization on China's aggregate and disaggregated energy intensities using a balanced panel dataset of 30 provinces covering the period from 2000 to 2012 and panel estimation techniques. The results show that urbanization significantly increases aggregate energy intensity, electricity intensity and coal intensity.     

Embedded energy and total greenhouse gas emissions in final consumptions within Thailand

In order to quantify the total Greenhouse Gas (GHG) emissions from different commodities, the contribution of emissions in all subprocess chains has to be considered. In embedded energy analysis, the higher order production processes are usually truncated due to a lack of data. To fill the truncated subprocesses up to infinite process chains, energy intensities and GHG emission factors of various final consumptions in the economy evaluated by the Input–Output Analysis (IOA) must be applied. The direct GHG emissions in final consumptions in Thailand are evaluated by imitating the approach in the energy sector of the revised 1996 Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories. The indirect energy and indirect emissions are evaluated by using the 1998 Input–Output (I–O) table. Results are presented of emissions in the main process, indirect processes, and on each subprocess chain order. The trend of energy intensity and emission factors of all final consumptions for 1995, 1998, 2001 and 2006 are also presented. Results show that the highest energy intensive sector is the electricity sector where fossil fuel is primarily used, but the highest total GHG emitter is the cement industry where the major sources of the emissions are industrial processes and the combustion of fossil fuels. Implication of the emission factors on electricity generating technologies shows that various cleaner electricity generating technologies, including renewable energy technology, could help in global GHG mitigation.     

Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis

Based on time series decomposition of the Log-Mean Divisia Index (LMDI), this paper analyzes the change of industrial carbon emissions from 36 industrial sectors in China over the period 1998–2005. The changes of industrial CO₂ emission are decomposed into carbon emissions coefficients of heat and electricity, energy intensity, industrial structural shift, industrial activity and final fuel shift. Our results clearly show that raw chemical materials and chemical products, nonmetal mineral products and smelting and pressing of ferrous metals account for 59.31% of total increased industrial CO₂ emissions. The overwhelming contributors to the change of China's industrial sectors’ carbon emissions in the period 1998–2005 were the industrial activity and energy intensity; the impact of emission coefficients of heat and electricity, fuel shift and structural shift was relatively small. Over the year 1998–2002, the energy intensity change in some energy-intensive sectors decreased industrial emissions, but increased emissions over the period 2002–2005. The impact of structural shift on emissions have varied considerably over the years without showing any clear trend, and the final fuel shift increased industrial emissions because of the increase of electricity share and higher emissions coefficient. Therefore, raw chemical materials and chemical products, nonmetal mineral products and smelting and pressing of ferrous metals should be among the top priorities for enhancing energy efficiency and driving their energy intensity close to the international advanced level. To some degree, we should reduce the products waste of these sectors, mitigate the growth of demand for their products through avoiding the excessive investment highly related to these sectors, increasing imports or decreasing the export in order to avoid expanding their share in total industrial value added. However, all these should integrate economic growth to harmonize industrial development and CO₂ emission reduction.     

Changes in energy intensities of Thai industry between 1981 and 2000: a decomposition analysis

Industrial demand accounts for about 30% of total final energy demand in Thailand, which experienced rapid increases in energy demand. This paper analyzes the changes in industrial energy intensities over a period of 20 years (1981–2000) and identifies the factors affecting the energy consumption using logarithmic mean Divisia decomposition technique. It is found that Thai industry has passed through four different phases of growth and energy consumption has closely followed the industrial growth pattern. Energy intensity of Thai industry decreased from 17.6 toe/million baht (constant 1988 prices) in 1981 to 15.8 toe/million baht (1988 prices) in 2000. Non-metallic mineral industry is the most intensive industry followed by basic metal, food and beverage, chemical and paper industries. The factor analysis indicates that both the structural effect and intensity effect contributed to a decline of aggregate intensity by 8% during 1981–1986 but in the rest of the periods, the two effects acted in opposite directions and thereby reducing the overall effect on aggregate intensity. Food and beverages, non-metallic mineral and chemical industries had significantly influenced the changes in aggregate intensity at sectoral level.     

Comparative assessment of CO2 capture technologies for carbon-intensive industrial processes

This article presents a consistent techno-economic assessment and comparison of CO₂ capture technologies for key industrial sectors (iron and steel, cement, petroleum refineries and petrochemicals). The assessment is based on an extensive literature review, covering studies from both industries and academia. Key parameters, e.g., capacity factor (91-97%), energy prices (natural gas: 8 €₂₀₀₇/GJ, coal: 2.5 €₂₀₀₇/GJ, grid electricity: 55 €/MWh), interest rate (10%), economic plant lifetime (20 years), CO₂ compression pressure (110 bar), and grid electricity CO₂ intensity (400 g/kWh), were standardized to enable a fair comparison of technologies. The analysis focuses on the changes in energy, CO₂ emissions and material flows, due to the deployment of CO₂ capture technologies. CO₂ capture technologies are categorized into short-mid term (ST/MT) and long term (LT) technologies. The findings of this study identified a large number of technologies under development, but it is too soon to identify which technologies would become dominant in the future. Moreover, a good integration of industrial plants and power plants is essential for cost-effective CO₂ capture because CO₂ capture may increase the industrial onsite electricity production significantly.For the iron and steel sector, 40-65 €/tCO₂ avoided may be achieved in the ST/MT, depending on the ironmaking process and the CO₂ capture technique. Advanced LT CO₂ capture technologies for the blast furnace based process may not offer significant advantages over conventional ones (30-55 €/tCO₂ avoided). Rather than the performance of CO₂ capture technique itself, low-cost CO₂ emissions reduction comes from good integration of CO₂ capture to the ironmaking process. Advanced smelting reduction with integrated CO₂ capture may enable lower steel production cost and lower CO₂ emissions than the blast furnace based process, i.e., negative CO₂ mitigation cost. For the cement sector, post-combustion capture appears to be the only commercial technology in the ST/MT and the costs are above 65 €/tCO₂ avoided. In the LT, a number of technologies may enable 25-55 €/tCO₂ avoided. The findings also indicate that, in some cases, partial CO₂ capture may have comparative advantages. For the refining and petrochemical sectors, oxyfuel capture was found to be more economical than others at 50-60 €/tCO₂ avoided in ST/MT and about 30 €/tCO₂ avoided in the LT. However, oxyfuel retrofit of furnaces and heaters may be more complicated than that of boilers.Crude estimates of technical potentials for global CO₂ emissions reduction for 2030 were made for the industrial processes investigated with the ST/MT technologies. They amount up to about 4 Gt/yr: 1 Gt/yr for the iron and steel sector, about 2 Gt/yr for the cement sector, and 1 Gt/yr for petroleum refineries. The actual deployment level would be much lower due to various constraints, about 0.8 Gt/yr, in a stringent emissions reduction scenario.     

Energy and exergy analyses of energy consumptions in the industrial sector in South Africa

The energy-utilization over a 10-year period (1994–2003) has been analysed for the South African industrial sector, which consumes more primary energy than any other sector of the economy. Four principal sub-sectors, namely iron and steel, chemical and petrochemical, mining and quarrying, and non-ferrous metals/non-metallic minerals were considered in this study. Primary-energy utilization data were used to calculate the weighted mean energy and exergy efficiencies for the sub-sectors and then overall values for the industrial sector were obtained. The results indicate that exergy efficiency is considerably lower than energy efficiency in all the sub-sectors, particularly in mining and quarrying processes, for which the values were approximately 83% and 16%, respectively. The performance of exergy utilization in the industrial sector can be improved by introducing various conservation strategies. Results from this study were compared with those for other countries.     

Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis

The industrial sector accounts for 70% of the total energy-related CO₂ emissions in China. To gain a better understanding of the changes in carbon intensity in China's industrial sector, this study first utilized logarithmic mean Divisia index (LMDI) decomposition analysis to disentangle the carbon intensity into three influencing factors, including the emission coefficient effect, the energy intensity effect, and the structure effect. Then, the analysis was furthered to explore the contributions of individual industrial sub-sectors to each factor by using an extension of the decomposition method proposed in Choi and Ang (2012). The results indicate that from 1996 to 2012, the energy intensity effect was the dominant factor in reducing carbon intensity, of which chemicals, iron and steel, metal and machinery, and cement and ceramics were the most representative sub-sectors. The structure effect did not show a strong impact on carbon intensity. The emission coefficient effect gradually increased the carbon intensity, mainly due to the expansion of electricity consumption, particularly in the metal and machinery and chemicals sub-sectors. The findings suggest that differentiated policies and measures should be considered for various industrial sub-sectors to maximize the energy efficiency potential. Moreover, readjusting the industrial structure and promoting clean and renewable energy is also urgently required to further reduce carbon intensity in China's industrial sector.     

Grey relation performance correlations among economics,energy use and carbon dioxide emission in Taiwan

This study explores the inter-relationships among economy, energy and CO₂ emissions of 37 industrial sectors in Taiwan in order to provide insight regarding sustainable development policy making. Grey relation analysis was used to analyse the productivity, aggregate energy consumption, and the use of fuel mix (electricity, coal, oil and gas) in relation to CO₂ emission changes. An innovative evaluative index system was devised to explore grey relation grades among economics, energy and environmental quality. Results indicate that a rapid increase in electricity generation during the past 10 years is the main reason for CO₂ emission increase in Taiwan. The largest CO₂ emitting sectors include iron and steel, transportation, petrochemical materials, commerce and other services. Therefore, it is important to reduce the energy intensity of these sectors by energy conservation, efficiency improvement and adjustment of industrial structure towards high value-added products and services. Economic growth for all industries has a more significant influence, than does total energy consumption, on CO₂ emission increase in Taiwan. It is also important to decouple the energy consumption and production to reduce the impacts of CO₂ on economic growth. Furthermore, most of the sectors examined had increased CO₂ emissions, except for machinery and road transportation. For high energy intensive and CO₂ intensive industries, governmental policies for CO₂ mitigation should be directed towards low carbon fuels as well as towards enhancement of the demand side management mechanism, without loss of the nation's competitiveness.