Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing

Journal of Cleaner Production xxx (2014) 1e14

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Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing Yan Zhang*, Hongmei Zheng, Zhifeng Yang, Jinjian Li, Xinan Yin, Gengyuan Liu, Meirong Su State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Xinjiekouwai Street No. 19, Beijing 100875, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 April 2014 Received in revised form 30 June 2014 Accepted 26 July 2014 Available online xxx

Based on urban metabolism theory, urban energy consumption and carbon emission can be analyzed and the urban energy structure and carbon metabolic processes can be specified. By combining inputeoutput analysis with ecological network analysis, the embodied energy and carbon footprint implied in urban products and services can be quantified. On this basis, we introduced energy structure indices based on the embodied energy per unit carbon emitted (i.e., the emission efficiency), which we used to evaluate the energy structure attributes of the metabolic actors. This approach provides a scientific basis for energy conservation and carbon emission reduction. Beijing is trying to control pollution (especially the smog produced by coal combustion), and this task is complicated by its large metabolic fluxes and strong metabolic influence. In this study, we analyzed the energy consumption structure of 28 sectors in Beijing from the perspective of their carbon footprint, and divided the sectors into four categories based on the relationship between their embodied energy consumption and the emission efficiency. We found that most sectors had high energy consumption and low emission efficiency, and that from 2000 to 2010, Beijing's overall consumption structure alternated between high and low embodied energy per unit of carbon emission, but the overall trend of emission efficiency changed toward higher. The insights provided by our analysis reveal ways to reduce carbon emission. Based on the carbon footprints of the 28 sectors, we propose how managers could adjust its energy consumption structure to decrease energy consumption and carbon emission. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Urban metabolism Carbon footprint Energy structure Network analysis Beijing

1. Introduction Energy is crucial for urban development. During rapid urbanization, many environmental problems arise from the heavy dependence of this process on energy; these include energy shortages and excessive carbon emission (Fu, 2008; Liu and Diamond, 2008). These problems can be analyzed from the perspective of urban metabolism theory, which focuses on the inputs and outputs of energy that occur during a city's metabolic processes. Researchers can consider the inputs of energy, which is one of the key resources required to sustain the city's metabolic processes for the benefit of humans; the outputs are the wastes produced by these processes, and carbon is one of the most important wastes given its adverse effects on the ecological

* Corresponding author. Tel./fax: þ86 10 5880 7596. E-mail address: [email protected] (Y. Zhang).

environment. These environmental problems arise from inefficient energy utilization, an irrational energy consumption structure, pollutants contained in the fuels, and a lack of explicit responsibility for carbon dioxide emission (Brunner, 2007; Kennedy et al., 2010). Beijing provides a good example of this problem, since the city's economic activity is highly concentrated, and its total energy consumption has been increasing rapidly for more than a decade. In 2000, 54.3% of the energy consumption was provided by coal (Beijing Municipal Commission of Development and Reform, 2012). As a result of continuous efforts to adjust the city's energy structure, this proportion fell to 30% by 2010, and this level was lower than that for high-quality energy sources such as natural gas and electricity, which accounted for about 70% of total energy consumption (Beijing Municipal Commission of Development and Reform, 2012). This adjustment mitigated the smog problem, but did not solve it because total carbon emission remained high. In 2011, Beijing emitted 5037 t more carbon than in 2002, of which

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Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075

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93.8% was from energy consumption (People's Tribune, 2013). As the energy consumption and carbon emission are still high, the city's managers have begun to ask whether a better understanding of the energy consumption structure could improve their management of energy conservation and carbon emission reduction activities. To provide the required information, it is necessary to determine how to trace the city's energy metabolic processes, how to evaluate the attributes of the energy consumption structure, and how to determine the required carbon emission reduction by each sector. To solve these problems, it is necessary to analyze the energy consumption structure based on the carbon footprint of each sector. The results of such an analysis can improve efforts to conserve energy and reduce carbon emission. The analysis of urban energy metabolism can be divided into two perspectives. First, some researchers have analyzed the physical processes involved in the urban energy metabolism. Based on energy consumption processes, they quantified energy exploitation, transformation, consumption, and recycling processes, and established models of urban energy metabolic processes (e.g., Zhang et al., 2010b, 2011a, b). Unfortunately, these researchers only studied the sectors related to energy consumption. Second, other researchers have considered the direct and embodied energy consumption and used this to calculate the total consumption implied in all utilization processes related to a given product. This calculation includes both the direct energy consumption to produce a product and the indirect energy consumption during the utilization and exchange of intermediate products (non-energy products and materials) that support this production. The resulting embodied energy is the sum of all the energy required to produce goods or services (IFIAS, 1974). Inputeoutput analysis has been widely used to account for embodied energy (Casler and Wilbur, 1984; Herendeen, 1978; Limmeechokchai and Suksuntornsiri, 2007; Liu et al., 2012; Wright, 1974). Some researchers have combined systems ecology with economic inputeoutput models to develop equilibrium equations that account for the flows of embodied energy from a macro-scale perspective (Chen and Chen, 2012; Chen et al., 2010; Xu, 2010). This also supports research on the sectoral energy distribution and its impacts for climate change (Proops et al., 1993; Wier et al., 2001). Although inputeoutput analysis can effectively assess the embodied energy of a product through an integral consumption coefficient, this process cannot specify the energy consumption implied in the production of intermediate products. Combining inputeoutput analysis with ecological network analysis can solve this problem. This is because ecological network analysis can describe the network structure that arises from the direct and indirect flows among industries or sectors. This approach can analyze the indirect paths implied in the direct flows among sectors, and the sum of the direct and indirect flows represents the integrated flows within the ecological network, thereby revealing the system's structure and functioning (Fath and Killian, 2007; Levine, 1980; Patten, 1982; Szyrmer and Ulanowicz, 1987). This approach has been widely used to study energy systems (Zhang et al., 2010b, 2011a, b), societal systems (Zhang et al., 2012), and water systems (Zhang et al., 2010a). Based on inputeoutput analysis, a traditional monetary inputeoutput table can be converted into a physical one, and the energy exchanges of intermediate products can be obtained. These flows comprise the direct paths between pairs of nodes in ecological network analysis. Then, using ecological network analysis, the indirect paths implied in the direct paths can be accounted for, and the energy consumption during the production of intermediate products (i.e., indirect paths) can be traced. Combining these two methods can quantify energy metabolic processes and calculate metabolic fluxes, thereby supporting an analysis of the carbon

footprint of each industry or sector. Carbon footprints are an effective way to evaluate the pressure imposed by human activities on the environment because they account for the direct and indirect carbon emission from or carbon accumulation in products (Pandey et al., 2011; Wiedmann and Minx, 2007, 2011). Because fuel combustion results in the emission of greenhouse gases, inputeoutput analysis has also been extended to analyze carbon emission for areas around the world. Lenzen (1998) used inputeoutput tables for Australia in 1992/1993 to account for carbon dioxide emission. Limmeechokchai and Suksuntornsiri (2007) analyzed the full energy chain to estimate greenhouse gas emission from energy-related activities in all production chains for 130 economic sectors in Thailand. Fan et al. (2012) used an inputeoutput model to analyze the embodied carbon footprint of Chinese urban households from 2003 to 2009. However, few researchers have studied the sectoral distribution of carbon footprints by accounting for both direct and indirect flows. For example, Sun et al. (2010) used inputeoutput model to analyze the carbon footprint in China, and found that manufacturing, electric and heat power and agriculture were sectors with a large carbon footprint. Although energy fluxes are obviously important, they are not the only elements that influence a city's carbon footprint. The energy consumption structure is also important (Geng et al., 2013b; Guo et al., 2012). In analyzing the relationships between a city's energy consumption structure and its carbon footprint, some researchers focused mostly on the carbon emission related to consumption of fossil energy. For example, Gingrich et al. (2011) analyzed carbon emission emitted by primary energy consumption (including coal, crude oil, and natural gas) in Austria and Czechoslovakia from 1830 to 2000. To further specify the types of fossil energy, Guo et al. (2012) considered gasoline, kerosene, and diesel in their study of carbon emission by the construction sector in Beijing. Geng et al. (2013b) examined the different types of coal consumed by the heavy industry of Liaoning, a province in China. Shao et al. (2014a, b) added information on high-quality energy sources, such as coal gas, to calculate the energy-related carbon emission in Beijing's EconomiceTechnological Development Area. In addition to the carbon emission from fossil energy, some nonfossil energy has also been considered. For example, Diakoulaki et al. (2006) analyzed how the energy consumption structure influenced carbon emission in Greece from 1990 to 2002, and divided energy into renewable and nonrenewable sources. They found that although renewable and high-quality energy had already been used on a large scale, these forms of energy did not greatly influence overall carbon emission. Furthermore, because different regions have different development characteristics, these characteristics also affect efforts to adjust the region's energy consumption structure. In China's rural areas, living conditions have improved, but this has caused the energy consumption structure to change from biomass energy to fossil fuel energy (Wang and Feng, 2002). As a result, nonrenewable energy from commercial sources dominated the energy consumption structure (Liang et al., 2013; Wang and Feng, 2002; Zeng et al., 2007). Some scholars have used decomposition analysis to reveal that the energy consumption structure strongly influences carbon emission (Jiang et al., 2014; Tian et al., 2013a; Wang et al., 2013). This is particularly true for most developing countries, where fossil fuels with high CO2 emissions are the main energy source Shao et al. (2014a, b). To solve this problem, increased use of renewable forms of energy with a lower carbon footprint will be needed (Luis Mundaca et al., 2013). Agnolucci et al. (2009) examined whether the proportions of fossil and non-fossil energy affected carbon emission; the found that unless the supplies of fossil energy constrained, the target of CO2 emission reduction tended to fail. Lise

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(2006) divided fossil energy into primary and secondary energy, and combined them with non-fossil energy in a decomposition analysis for Turkey from 1980 to 2003. Andreoni and Galmarini (2012) found that in Italy, changing the energy source from oil and hard coal to renewable sources reduced carbon emission. To reduce carbon emission reduction on an urban scale, some cities have adjusted the proportion of different types of energy or introduced new types to optimize the energy structure. For example, Geng et al. (2013b) evaluated carbon emission in Liaoning Province and found that the energy consumption structure could be adjusted to use more renewable and high-quality sources, and that reducing the consumption of fossil energy reduced carbon emission (Geng et al., 2013a; Lindner et al., 2013). Scenario analysis can also be used to determine whether it is possible to improve a city's energy structure. For example, researchers can assume that the total energy consumption is constant, then adjust the proportions of different types of energy to determine how this influences carbon emission. Das and Paul (2014) replaced coal by petroleum and found that this decreased carbon emission in India. Alternatively, researchers can introduce new types of energy. For example, Chen (2011) studied energy policies in Taiwan, and found that nuclear power and wind power had considerable potential to reduce carbon emission from future development, although they were competing technologies. San et al. (2012) analyzed energy consumption in Cambodia's Sammeakki Meanchey district, and proposed four suggestions to reduce carbon emission, of which biogas appeared to produce the best results. Based on this review of the literature, the energy consumption structure can affect carbon emission at both local (e.g., urban) and regional scales. However, there has been insufficient research that evaluated an energy consumption structure from the perspective of carbon footprints for multiple sectors to support planning of energy conservation and carbon emission reduction measures. In the present study, we attempted to provide such information for an urban metabolism (using Beijing as a case study) by tracing the overall energy utilization processes through an urban metabolic system. Using inputeoutput analysis and ecological network analysis, we were able to calculate the direct, indirect, and embodied energy consumed during the production, utilization, and disposal of a product, and by using carbon-emission coefficients for each energy source, we calculated the corresponding direct, indirect, and embodied carbon footprints. Based on a comparison of energy consumption with the associated carbon footprint, we proposed indices of the embodied energy per unit carbon emission that provided insights into the energy structure attributes of each sector. This approach provides a strong basis for energy conservation and carbon emission reduction at an urban scale, and lets us determine the responsibility for carbon reduction by each sector. 2. Methods 2.1. Energy, carbon metabolic processes, and metabolic actors We used the concepts of embodied energy and the associated embodied carbon footprint to analyze the energy consumption and carbon footprint implied in the production, use, and disposal of goods, including products and services. Based on the energy utilization processes in an industrial chain, it is possible to trace the energy and carbon metabolic processes performed by the various actors (e.g., industries or sectors) in the urban metabolic system (Fig. 1). In this figure, the number assigned to each sector is based on their order in the inputeoutput table for Beijing. An urban energy and carbon metabolic process begins with a producer, which comprises the farming and mining sectors. In the present study, we defined the first four sectors in Fig. 1 (one agricultural and three

3

Fig. 1. Actors in the urban energy and carbon metabolic system, and the associated flows between these actors. Numbers refer to the following sectors: 1, farming, forestry, animal husbandry, fisheries, and water conservation; 2, energy extraction; 3, mining and processing of metal ores; 4, mining and processing of nonmetal ores and other ores; 5, processing of food from agricultural products, and manufacture of foods, beverages, and tobacco; 6, manufacture of textiles; 7, manufacture of textile apparel, footwear, caps, leather, fur, feathers, and related products; 8, processing of timber, and manufacture of wood, bamboo, rattan, palm, straw products, and furniture; 9, manufacture of paper and paper products, printing, reproduction of recording media, and manufacture of articles for culture, education, and sports activities; 10, processing of petroleum, coking, and processing of nuclear fuel; 11, manufacture of raw chemical materials and chemical products, medicines, chemical fibers, rubber, and plastics; 12, manufacture of non-metallic mineral products; 13, smelting and pressing of metals; 14, manufacture of metal products; 15, manufacture of general- and special-purpose machinery; 16, manufacture of transportation equipment; 17, manufacture of electrical machinery and equipment; 18, manufacture of communication equipment, computers, and other electronic equipment; 19, manufacture of measuring instruments and machinery for cultural activities and office work; 20, manufacture of artwork and other manufacturing; 21, recycling and disposal of wastes; 22, production and distribution of electrical power and heat power; 23, production and distribution of gas; 24, production and distribution of water; 25, construction; 26, transportation, storage, and postal services; 27, other social services; 28, domestic consumption.

mining-related) as producers. All four sectors extract resources from the Earth, such as (respectively) agricultural products and minerals and crude oil. The process continues with primary consumers, which include primary manufacturing and processing sectors and energy and resource transformation sectors. These sectors use raw materials provided by the producers to produce primary products, such as by transforming bauxite ore into aluminum or using crude oil to produce plastics. In the present study, the primary consumers included sectors 5 to 9 and sectors 22 to 24. The products of primary consumers are then used as inputs by secondary consumers (sectors 10e20), which comprise advanced manufacturing and processing sectors that process the primary products into final products. These products then flow into tertiary consumption sectors, including service sectors that utilize infrastructure or products from the manufacturing and processing sectors to provide services to consumers, including the public (sectors 26 and 27). These actors are higher in the metabolic hierarchy than the advanced manufacturing and processing sectors, but lower than the top consumer sectors. The next level in the hierarchy comprises the top consumers, which include the construction sector and the domestic consumption sector (sectors 25 and 28). The construction sector plays a unique role because it transforms resources into permanent stock. The final actor serves as the equivalent of the decomposers in a natural ecosystem. The position of decomposers within the metabolic system is problematic. On the

Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075

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one hand, they exist at the top of the hierarchy because they are the final level in an industrial chain that reuses or recycles byproducts or wastes from lower levels in the hierarchy. On the other hand, they decompose these resources into raw materials that can be reused by actors at lower levels in the hierarchy, as in the case of the recycling and disposal of wastes sector. From this perspective, they occupy the bottom position in the hierarchy. In this study, we treated the recycling and disposal of wastes sector (21) as the decomposer; because Fig. 1 shows the cyclical flow through the urban metabolism rather than a vertical hierarchy, decomposers follow the top consumers in this diagram. Although direct measurements of the flows of energy and materials through a system would be the ideal data for our analysis, such data are rarely available, an alternative approach is required. In this study, we started our analysis based on monetary inputeoutput tables for Beijing in 2000, 2002, 2005, 2007, and 2010 and energy consumption data for each industry (a total of i ¼ 28 sectors based on the available data for Beijing). We obtained this data from the Beijing Statistical Yearbook (BMBS and NBS, 2001, 2003, 2006, 2008, 2011) and the China Energy Statistical Yearbook (NBSC, 2000e2002, 2006, 2008, 2011). We began our analysis with inputeoutput tables for Beijing that included 42 sectors. To allow us to combine this data with the energy data, we combined the original 42 sectors into 28 sectors. The inputeoutput tables for Beijing and at the national scale are mostly similar, and the name and scope of the sectors are the same in both tables. The main difference between the city-level and national-level tables is that the national tables focus on the imports and exports of resources from other countries, whereas the city tables focus on the inputs and outputs due to exchanges with other cities. The raw data for the direct energy consumption in each sector in each year, and relevant conversion factors, are presented in Supplementary Tables S1 to S6 and S8. Using appropriate estimates of the energy embodied in these monetary flows, it is possible to estimate the corresponding energy flows. Based on the theory of food chains, the 28 sectors can be divided into actors at the six levels of an urban energy and carbon metabolic system that are shown in Fig. 1. 2.2. Urban energy and carbon metabolic flux analysis Urban energy and carbon metabolic processes need energy inputs to sustain their operation, and this operation produces metabolites (here, carbon compounds) that influence the ecological environment. In this study, we combined inputeoutput analysis and ecological network analysis to calculate the embodied energy consumption and the associated embodied carbon footprint. Based on inputeoutput analysis, the direct paths among sectors can be defined, and the associated data can be treated as the direct flows in the ecological network analysis. Using ecological network analysis, we can also calculate the indirect energy consumption that occurs through the exchanges of intermediate products. In the present study, we based our analysis on traditional monetary inputeoutput tables, as described above, and introduced an embodied energy consumption coefficient (Equation (1)) to convert the monetary table into a physical one that describes the energy flows among the sectors. Zhang et al. (2014) provide details of these calculations, which we will summarize here:

ε ¼ E½U  H1

monetary value flow matrix, where H ¼ [hij]nn, hji ¼ xji. If k represents an energy form whose consumption coefficient is embodied in the products of sector i, then εki represents the consumption coefficient of the k-th energy form embodied in the products produced by sector i. The m energy forms that we analyzed were coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gas (LPG), natural gas, heat, and electricity (i.e., m ¼ 11). Based on the direct energy consumption, we can calculate the direct carbon footprint. We accounted for the carbon emission of each sector of the urban energy metabolism system based on the methods and indicators proposed by the 2006 Guidelines for National Greenhouse Gas Inventories (IPCC, 2006). The IPCC (2007) equation for the carbon footprint is:

C¼

m X k¼1

Ck ¼

m X ½ECk  efk  ð1  csk Þ  ok 

(2)

k¼1

where C is the carbon flow during energy consumption; k is the energy type (from 1 to m); Ck is the carbon flow for energy type k; ECk is the energy consumption for energy type k; efk is the carbon content of energy type k; csk is the proportion of energy type k that is not oxidized and that instead serves as raw material for downstream products; and ok is the carbon's oxidation rate for energy type k. The carbon emission coefficients for heat and electricity were assumed to be 0 because consumption of both forms of energy generates no carbon emission, although carbon may be generated in the production of these forms of energy. By accounting for both the direct and the indirect energy consumption, the carbon footprint therefore also includes the associated direct and indirect carbon footprints. We did not account for renewable energy sources because the China Energy Statistical Yearbook (NBSC, 2000e2002, 2006, 2008, 2011) totals the data for renewable resources by region rather than by sector. Thus, it is not possible to integrate these data with the other data in our study. In addition, China's 12th 5-year plan predicts that renewable resources will only account for 11.3% of total energy consumption by 2015, which represents a small increase from the value of 9.6% in 2010. Thus, although the contribution of renewable resources should not be ignored, their omission will not greatly influence our results. This is particularly true because renewable resources typically have a small carbon footprint. Nonetheless, finding a way to account for these resources represents an important challenge for future research, and these energy sources should be included in the analysis when they become available with suitably high resolution. Using the direct energy flows among sectors, ecological network analysis can calculate the indirect energy consumption that accompanies the flows among sectors. We can calculate the nondimensional energy metabolic flux matrix N' (n'ij) and the G′ matrices for flows along pathways of each possible metabolic length (l). Zhang et al. (2014) provide details of these calculations, which can be summarized as follows:

 0   0 0  0 1  0 2  0 l   0 1 0 N ¼ n ij ¼ G þ G þ G þ…þ G ¼ IG (3)

(1)

where ε represents the embodied energy coefficient matrix, and ε ¼ [εki]mn for m forms of energy and n sectors; E represents a matrix for the energy forms that enter or flow out of a sector; U ¼ [uji]nn when i ¼ j and uji ¼ Xi, where Xi is the economic output of sector i, and when i s j, uji ¼ 0; and H represents the

where (G0 )0 is a self-feedback matrix that reflects flows that occur within each node. (G')1 is the energy metabolic flux matrix when the metabolic length follows a single path (i.e., l ¼ 1). (G')l reflects metabolic flows of path length l (l  2), and I represents the identity matrix. N' indicates the nondimensional energy metabolic flux between nodes, and we can multiply N' by the diagonal of the

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energy input matrix from the local environment, diag(T), to obtain the indirect energy transfer flow matrix (Y):

Y ¼ diagðTÞN0

To characterize the system's structure, we defined three indices of the embodied energy that is consumed per unit of carbon emission. First, RE represents the relative emission efficiency, which equals the ratio of embodied energy consumption to the associated carbon footprint. We also defined similar indices for the relative efficiency of direct (RD) and indirect (RI) consumption of embodied energy. For all three indices, a high ratio means a lower carbon emission per unit of energy consumption, which means that the sector for which the index was calculated has high emission efficiency. Conversely, a low ratio indicates high carbon emission per unit of energy consumption, which indicates low emission efficiency. The equations for these indices are as follows:

(5)

where RE represents the overall embodied energy emission efficiency, EE represents the embodied energy consumption, and EC represents the embodied carbon footprint. This term can be divided into two additional terms:

RD ¼

DE DC

RI ¼

IE IC

(7)

where RI represents the indirect embodied energy emission efficiency, IE represents the indirect energy consumption, and IC represents the indirect carbon footprint. 3. Results 3.1. Metabolic fluxes and carbon footprints

2.3. Energy structural attributes index

EE EC

where RD represents the direct embodied energy emission efficiency, DE represents the direct energy consumption, and DC represents the direct carbon footprint.

(4)

The row vector yi ¼ (yi1, yi2, yi3, …, yin) then reflects the indirect energy that sectors 1 to n transfer to node i. By combining these values with the direct energy consumption, we can calculate the embodied energy consumption. Using the same method, the indirect carbon footprint can be obtained. Combining them with the direct energy consumption and the direct carbon footprint, the urban energy and carbon metabolic processes flux can be calculated.

RE ¼

5

(6)

Fig. 2 (based on the data in Supplementary Table S7) shows that in all five years during our study period, the embodied energy consumption was highest for the energy production and processing sector, the service sector, and the consumer sectors (sectors 10, 13, 22, 26, 27, 28). Their consumption ranged from 1000  104 tce to 4000  104 tce. However, their temporal trends differed. The consumption by the production and distribution of electrical power and heat power sector (22) increased fastest. In 2010, the embodied energy consumption was 23.1 times that in 2000. This was because the direct and indirect energy consumption both grew rapidly. Thus, during the study period, the demand for electricity was increasing rapidly both in terms of direct energy consumption and in terms of the demand for non-energy products. Some sectors had relatively low embodied energy consumption at the start of the study period but grew rapidly, such as the mining and processing of metal ores sector (3) and the production and distribution of gas sector (23). The increase for the mining and processing of metal ores sector (3) resulted primarily from increased indirect consumption. In contrast, the change for the production and distribution of gas sector (23) resulted from similar increases in direct and indirect energy consumption. The embodied energy consumption of the transportation, storage, and postal services sector (26), other social services sector (27), and domestic consumption

Fig. 2. Direct, indirect, and total (direct þ indirect) embodied energy consumption from 2000 to 2010. Sector numbers are defined in the caption of Fig. 1. Bars for a sector represent (from left to right) the values in 2000, 2002, 2005, 2007, and 2010.

Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075

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sector (28) were all high, and they also increased relatively rapidly. For all three sectors, both the direct and indirect energy consumption increased by roughly equal amounts, although indirect consumption remained larger than direct consumption throughout the study period. These increases resulted from the expansion of China's tertiary industries under economic development planning that emphasized these industries. The energy extraction sector (2), which had a low embodied energy consumption, also showed a rapid increase, with consumption in 2010 reaching 4.3 times the 2000 value. On the first four dates, embodied energy consumption by the smelting and pressing of metals sector (13) was stable, but high, ranging from 1300  104 tce to 1700  104 tce. But in 2010, it decreased dramatically, with embodied consumption only 0.04 times the 2007 value. The direct and indirect consumption both decreased greatly. This was caused by national economic policy, which relocated many of the plants in this sector to western China to promote regional development. Among the 28 sectors, indirect energy consumption was generally higher than direct consumption, with indirect consumption accounting for about 60% of the total consumption. This meant that their energy consumption through utilization and exchanges of intermediate products was higher than direct consumption. Sectors with a relatively low indirect consumption were mostly positioned at the beginning of an industrial chain; for example, indirect consumption of sectors 3 and 4 were about 50e57% of the total. In contrast, sectors with high indirect consumption were at the end of an industrial chain; for example, indirect consumption in sectors 23 and 24 accounted for 65e70% of the total. However, some manufacturing and processing sectors had high direct consumption that accounted for a high proportion of total consumption; for example, the direct consumption of sectors 10, 13, and 22 ranged from 600  104 tce to 1800  104 tce, and direct consumption accounted for nearly 45% of the total consumption. Fig. 3 (based on the data in Supplementary Table S9) indicates that the carbon footprint trends were similar to the energy consumption trends. The embodied carbon footprints of the energy production and processing, metal manufacturing and processing,

service, and domestic consumption sectors were largest (sectors 10, 13, 22, 26, 27, 28), with values ranging from 500  106 t to 2500  106 t. However, the carbon footprint of these sectors increased at different rates. The carbon footprint of the production and distribution of electrical power and heat power sector (22), the transportation, storage, and postal services sector (26), and the domestic consumption sector (28) increased rapidly. These increases resulted from large increases in both the direct and the indirect carbon footprints. As was the case for embodied energy consumption, the embodied carbon footprint of sector 10 alternated between increases and decreases. The embodied carbon footprint of the smelting and pressing of metals sector (13) and other social services sector (27) in 2010 were less than those in 2007. This indicated that in 2010, changes in both sectors permitted significant reductions in carbon emission. The footprint of sector 13 decreased dramatically in 2010, reaching only 0.02 times the 2007 value. The footprint of sector 27 in 2010 decreased to 0.94 times its 2007 value. The embodied carbon footprint of the chemical processing sector and the nonmetal manufacturing and processing sector (sectors 11 and 12) were also large, ranging from 100  106 t to 500  106 t. These sectors showed alternation between increases and decreases, or stability. Similar to the relationships for direct and indirect energy consumption, the indirect carbon footprints were all higher than the direct footprints. This meant that for all sectors, the carbon footprint produced by indirect energy consumption was larger than the direct footprint produced by direct energy consumption. The general increase in the carbon footprint during the study period also means that, overall, all sectors still depended heavily on fossil fuels rather than on alternative energy sources with lower carbon footprints. The direct carbon footprints as a proportion of the total footprint for sectors 3 and 4 were highest, with values between 40 and 50% in all years, but their direct carbon footprints were lower, with values lower than 600  106 t. The sectors with a high indirect carbon footprint were also distributed at the end of an industrial chain, as in the case of sectors 14 to 20, for which the indirect carbon footprint accounted for more than 60% of the total. The indirect carbon footprints of sectors 10, 13, and 22 were high, but the corresponding proportion of the total was low, at only 50e60%.

Fig. 3. Direct, indirect, and total (direct þ indirect) embodied carbon footprints from 2000 to 2010. Sector numbers are defined in the caption of Fig. 1. Bars for a sector represent (from left to right) the values in 2000, 2002, 2005, 2007, and 2010.

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Fig. 4. The average total embodied energy consumption (direct þ indirect), direct energy consumption, and indirect energy consumption of the six actors in Fig. 1. P, producers; PC, primary consumers; SC, secondary consumers; TC, tertiary consumers; Top, top consumers; D, decomposers.

Figs. 4 and 5 show the mean proportions of embodied, direct, and indirect energy consumption (Fig. 4) and of the corresponding carbon footprints (Fig. 5) by the six metabolic actors shown in Fig. 1. The proportions of energy consumption for each actor were similar to the corresponding proportions for the carbon footprint. The embodied energy consumption and carbon footprint of the secondary consumers (i.e., the advanced manufacturing and processing sectors) were both the largest, but the carbon footprint of these sectors had a larger proportion of the total than the energy consumption, with values of 44.0 and 41.0%, respectively. This indicated that the advanced manufacturing and processing sectors had low emission efficiency. The third and fourth largest embodied consumption and embodied carbon footprint were for primary consumers (i.e., the primary manufacturing and processing sectors) and top consumers (i.e., the construction sector and the domestic consumption sector), respectively. Their embodied energy consumption accounted for 15.2 and 14.7% of the total, respectively,

versus embodied carbon footprints of 18.3 and 13.0%, respectively. These results showed that the primary consumers had low emission efficiency, whereas the top consumers had high emission efficiency. The proportions of total direct energy consumption by the six actors were similar to their proportions of the total embodied energy consumption. The direct consumption proportion was largest for secondary consumers (44.3% of the total). This was slightly higher than their proportion (44.0%) of the total embodied energy consumption, which reflects the fact that the advanced manufacturing and processing sectors relied greatly on direct energy inputs, and less on the energy consumed by the exchanges of intermediate products; their indirect consumption accounted for only 38.8% of the total. The direct carbon footprint of this actor accounted for 47.3% of the total, which was larger than the proportion (41.0%) for the embodied carbon footprint. This suggests that these sectors had low emission efficiency due to high direct

Fig. 5. The total (direct þ indirect) embodied, direct, and indirect carbon footprints of the six actors in Fig. 1. P, producers; PC, primary consumers; SC, secondary consumers; TC, tertiary consumers; Top, top consumers; D, decomposers.

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consumption. Primary consumers had similar characteristics. The direct energy consumption of primary consumers accounted for 15.8% of the total, which was larger than the proportion (15.2%) for the total embodied consumption. Their direct carbon footprint accounted for 19.0% of the total, and this was also larger than the proportion of total embodied consumption (18.3%). The direct energy consumption accounted for a smaller proportion of the total than the direct carbon footprint, which suggests that the direct energy consumption had low emission efficiency. In contrast, the direct energy consumption of the next two actors (tertiary and top consumers) accounted for a smaller proportion of the total embodied energy consumption than their indirect consumption. Using the tertiary consumers as an example, indirect consumption accounted for 28.1% of the total, which was larger than the proportion (26.4%) for total embodied consumption, and their indirect carbon footprint accounted for 23.3% of the total, which was also larger than the proportion of total embodied consumption. The proportion of the total accounted for by its indirect carbon footprint was less than its proportion of total energy consumption, indicating that the indirect energy consumption by this actor had high emission efficiency. 3.2. Energy structural attributes analysis of the urban energy and carbon metabolic processes Fig. 6 (based on the data in Supplementary Table S10) presents the changes in the three efficiency indices (RD, RE, and RI) during the study period. From 2000 to 2002, the values of all three indices generally increased, suggesting that most sectors had improved their energy consumption structure and were tending to use more high-quality energy. Thereafter, the values for some sectors remained stable (e.g., sectors 7, 10, 12, 26, and 28), whereas others showed either a decrease followed by an increase (e.g., sectors 8, 11, and 20) or an increase followed by a decrease (e.g., sector 23). During the first four time points, the emission efficiency of the production and distribution of water sector (24) were the highest for all three indices. This reflected the fact that this sector emitted the least carbon per unit of energy consumption. However, by 2010, the values of the three indices had decreased from their peak values, and the gap between this sector and the other sectors decreased. The RE index of sector 18 was increasing rapidly. In 2000, it ranked third, and in 2002, second. In 2010, it surpassed sector 24 and ranked first. This means that the energy consumption structure of this sector improved greatly in these 10 years, and that the sector used more high-quality, low-carbon energy. The emission efficiency for the production and distribution of electrical power and heat power sector (22) fluctuated. In 2000, it ranked second, and was about 3.67  102 tce/t. But from 2002 to 2010, it ranked last among all the sectors, with a value of only 1.30  102 tce/t to 1.60  102 tce/t. This showed that the energy structure of this sector changed to have low emission efficiency. This may be because during its “Tenth Five-Year Plan”, China promoted the development of electricity generation, and although the energy structure of this industry improved to some extent through the use of high-quality energy, the demand for electricity by Beijing was also increasing; as a result, the increased utilization of highquality renewable energy was not sufficient to improve the overall energy structure, which relied on low-quality fuel such as coal to cope with increasing demand. The sectors with low emission efficiency were the nonmetal manufacturing and processing sector (12) and the manufacture of textiles sector (6), which had values ranging from 1.00  102 tce/t to 2.00  102 tce/t and only accounted for about 7 and 15% of the values for the sector with the highest value (sector 24). The indices for the manufacture of textiles sector (6), the manufacture of textile

apparel, footwear, caps, leather, fur, feathers, and related products sector (7), and the manufacture of non-metallic mineral products sector (12) were lowest, with low emission efficiency. These are therefore very important sectors in which to promote energy utilization efficiency. Comparing the direct and indirect emission efficiencies for all the sectors, we found that RD was highest, followed by RE and then RI. This reflected the fact that direct energy was used more efficiently and produced less carbon emission than indirect utilization processes. RD and RI for most sectors were stable during the study period, but some changed from low to high emission efficiency (sectors 13, 14, 15, 16, and 19). For these sectors, the direct and indirect energy consumption both tended to use more efficient forms of energy. Some sectors alternated between low and high emission efficiency, such as sectors 23 and 24. For example, their direct and indirect emission efficiencies increased in 2002, to about 3.6 and 4.2 times the 2000 value, but in 2005, they began to decrease, reaching only 1 to 2 times the 2000 value. 3.3. Attributes analysis for the metabolic actors We calculated the median values for embodied energy consumption (100 t) and RE (0.03 tce/t), and used these to distinguish low and high values of the two parameters. Fig. 7 shows the distribution of the sectors among the four possible combinations: high or low consumption combined with high or low RE. Of these combinations, most of the sectors combined high consumption with low RE (quadrant B in the figure). For these sectors, it will be important to both control their energy consumption and improve their energy utilization efficiency. To understand how this could be done, it is first necessary to understand the relative contributions of direct and indirect energy consumption. For a sector with high direct consumption, managers should improve reuse or multiple use of this energy; this will directly decrease the amount of new direct energy that must be consumed. For sectors with high indirect consumption, managers should trace the manufacturing processes backward to identify all steps in their supply chain, as this will let them look for energy savings during each step. There were no sectors that combined high consumption with high RE (quadrant A in the figure). Although this combination is better than some of the other combinations, any sector that evolves to have this combination should still seek ways to reduce its energy consumption (through increased efficiency), but also to reduce its carbon footprint (by using more energy sources with a low carbon footprint, such as solar or wind power). Many sectors combined low consumption with low RE (quadrant C in the figure). Although the low consumption implies a low total carbon footprint despite the low RE, these sectors should nonetheless try to improve their energy utilization efficiency, thereby reducing the total footprint, but should also seek ways to adopt high-quality energy sources with a lower carbon footprint to improve RE. The four sectors with low consumption and high RE (quadrant D in the figure) are the most sustainable sectors, and the techniques they use to achieve this combination should be investigated and promoted to other sectors that could benefit from these approaches. Fig. 8 shows the energy structures for the six actors shown in Fig. 1, divided among the three high-priority categories (i.e., the ones that require the most improvement based on Fig. 7). Fig. 8 shows that 53.6% of the 28 sectors (i.e., 15 sectors) combined high embodied energy consumption with low emission efficiency; these sectors were therefore the dominant form within the industrial chain, and almost all of the categories of actor (except decomposers) included this kind of sector. An additional 32.1% of the sectors (i.e., 9 sectors) combined low energy consumption with low emission efficiency; most of these belonged to the primary

Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075

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Fig. 6. Changes in the emission efficiency indices during the study period: RE, the ratio of total embodied energy consumption to the associated total carbon footprint; RD and RI, the relative efficiencies of direct and indirect consumption of embodied energy, respectively. Sector numbers are defined in the caption of Fig. 1.

consumer category, but some belonged to the producer and secondary consumer categories. Only 14.3% of the sectors (i.e., 4 sectors) combined low energy consumption with high emission efficiency; these belonged to the primary and secondary consumer categories, and to the decomposer category. No sectors combined high consumption with high emission efficiency. Therefore, improving the energy structure is a key priority to reduce the carbon footprint created by Beijing's rapid development. Fig. 8 shows that 75% of all the producer sectors (i.e., 3 sectors) had high consumption and low emission efficiency, and that one

sector (sector 2) had low consumption and low emission efficiency. All of the sectors that belonged to this category of actor had low emission efficiency, and their metabolic fluxes and carbon footprints remained largely stable during the study period. Therefore, they should first optimize their energy consumption structure by introducing more high-quality energy to reduce their carbon emission. For the primary consumers, more than 60% of the sectors (i.e., 5 sectors) had low energy consumption and low emission efficiency, 25% (i.e., 2 sectors) had high consumption and low emission efficiency, and only 10% (1 sector) had low consumption and

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Fig. 7. Four categories of energy and carbon metabolic structure: (A) high consumption and high emission efficiency (RE), (B) high consumption and low emission efficiency, (C) low consumption and low emission efficiency, and (D) low consumption and high emission efficiency. Sector numbers are defined in the caption of Fig. 1.

high emission efficiency. Except for production and distribution of gas sector (23), primary consumers were dominated by sectors with low emission efficiency. These sectors are similar to the producers in needing to improve their energy structure. The energy structure of secondary consumers (the advanced manufacturing and processing sectors) was complex. Nearly 55% of the sectors (i.e., 6 sectors) had high energy consumption and low emission efficiency, indicating that large amounts of energy are consumed by the advanced manufacturing and processing sectors. Sectors that belong to this actor category can use various measures to save energy and decrease carbon emission. For example, for the processing of petroleum, coking, and processing of nuclear fuel sector (10), some outdated technologies and equipment used for petroleum exploitation should be replaced with newer ones that consume less energy. The manufacture of raw chemical materials and chemical products, medicines, chemical fibers, rubber, and

plastics sector (11) should increase its recovery of waste heat, or promote the adoption of high-temperature air combustion technology. The metal and non-metallic mineral products manufacturing sectors (manufacture of non-metallic mineral products (12) and smelting and pressing of metals (13)) should strengthen their reuse and recycling of metal and nonmetal resources, and outdated equipment such as the equipment used for electrolytic smelting of aluminum or ironmaking, should be replaced with newer technologies using low temperature and low voltage. For the tertiary consumers, both sectors had high energy consumption and low emission efficiency. This is because these sectors are at the end of an industrial chain, and require more products from upstream sectors to support their production activities, leading to high indirect energy consumption. The energy structure of these sectors should also be improved, especially for the transportation, storage, and postal services sector (26). In recent years, China has proposed strengthening the development of low-carbon fuel for motor vehicles, and has recommended that the number of vehicles in China and the characteristics of the fuel oil should both be controlled to reduce carbon emission. In 2013, China issued its “Plan for Controlling Air Pollution”, which promoted reductions of the number of motor vehicles in big cities such as Beijing, encouraged the use of low-speed cars (which are more fuel-efficient), and advocated the use of “new energy” vehicles (e.g., electric cars). The recycling sector (decomposers) had low energy consumption and high emission efficiency, so the development of this sector appears to be following the concept of a “low-carbon city”, and this sector's energy structure should be encouraged. 4. Discussion In this study, we combined analyses of the embodied energy consumption and the carbon emission efficiency of the 28 economic sectors in Beijing. Based on the results of this analysis, we divided the sectors into four categories according to the magnitude of their energy consumption (low or high) and their carbon emission efficiency (low or high). This approach revealed which sectors were most important for energy conservation and carbon emission reduction. Most previous research only analyzed urban sectors

Fig. 8. Energy consumption structures for the sectors within each of the six metabolic actor categories defined in Fig. 1. Structures are described based on the four categories shown in Fig. 7. Light red ¼ low consumption/high RE, yellow ¼ low consumption/low RE, and blue ¼ high consumption/low RE. Sector numbers are defined in the caption of Fig. 1. P, producers; PC, primary consumers; SC, secondary consumers; TC, tertiary consumers; Top, top consumers; D, decomposers.

Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075

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from a single perspective, either energy consumption or carbon footprints, and proposed energy conservation suggestions based on one of the two perspectives (Chung et al., 2013; Hasanbeigi et al., 2014; Oikonomou et al., 2009; Shahiduzzaman and Alam, 2013). These analyses based on a single aspect cannot account for how a sector's energy consumption structure influences the carbon footprint. Others have assessed energy consumption and carbon emission of a city's sectors and developed recommendations on how to control both consumption and emission based on the characteristics of the different sectors (Chen, 2011; Chen and Chen, 2010; Chen et al., 2010). To account for the influence of the energy structure, decomposition analysis has been used to identify the factors that influence the carbon footprint. These results showed that the energy efficiency and energy consumption structure both strongly influence the carbon footprint (Geng et al., 2013b; Tian et al., 2013b). However, this analysis cannot propose specific energy structure adjustment suggestions for different sectors. Other research, such as Guo et al. (2012), has demonstrated that for some sectors with high energy consumption, it is reasonable to introduce more high-quality or renewable energy, but they did not propose a quantitative indicator to evaluate the energy consumption structure of different sectors, so they could not identify the emission efficiency of the sectors. Our approach solves most of these problems. In this study, we found that for the sectors with high energy consumption, we could define two categories: sectors with high and low emission efficiency. That is, even though the energy consumption by a sector is high, this does not mean that the sector automatically has low emission efficiency. In contrast, for a sector with low energy consumption, the emission efficiency may be very low. For example, in our analysis, 75% of the sectors that could be classified as producers had high energy consumption, but their carbon emission per unit energy consumed was low. Although this is a desirable combination, these sectors should still seek ways to conserve energy and reduce carbon emission, thereby further improving their environmental performance. Combining an analysis of energy consumption with an analysis of the energy structure and the associated carbon emission therefore provides a more holistic perspective that better reveals which sectors to prioritize to conserve energy and reduce carbon emission. To assess energy consumption, we combined inputeoutput analysis with ecological network analysis. This let us trace both the direct and indirect flows of embodied energy consumption and the associated carbon footprint. In the traditional analysis of energy consumption, most researchers used only the input energy, but this approach could not describe the outputs. In actual production processes, energy alone cannot produce outputs until it has been combined with other inputs such as capital and labor (Patterson, 1996). Relying solely on the direct energy consumption will neglect the indirect consumption that is embodied in these inputs; in a traditional inputeoutput table, the “final” energy use refers to the direct consumption. For example, Saboori and Sulaiman (2013) studied the relationships between energy consumption and carbon emission in Malaysia, but only used final energy use to propose carbon emission reduction suggestions. Akinlo (2008) also used the final energy use to analyze the relationships between energy consumption and economic development in countries in Sub-Saharan Africa. Other researchers have analyzed the final energy use structure, and used this consumption to trace the energy utilization ~ zen processes and provide suggestions for energy conservation (So et al., 2007; Wang et al., 2011). All of these studies neglected the influence of indirect energy consumption. As our results show, indirect consumption is too large a component of total consumption to ignore.

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Leung (2011) analyzed the final energy consumption in China and found that the transportation sector accounted for about 10.4% of the overall energy consumption by all industrial sectors. However, this analysis was one-sided, since it only considered direct energy consumption and neglected the indirect consumption; again, our analysis showed that indirect consumption is usually higher than direct consumption, and cannot be ignored. Therefore, proposing energy conservation measures based only on direct consumption is likely to result in inefficient or ineffective recommendations. Chen and Chen (2010) and Zhou et al. (2010) accounted for inputs from the external environment and paths of length 1 between any two sectors in their calculations of energy consumption in Beijing and in China, but neglected the effects of the indirect flows (paths with a length of 2 or more) among sectors. Zhou et al. (2010) found that the energy consumption of the metal smelting and pressing sector (13) and the construction sector (25) were highest. However, our analysis ranked the embodied energy consumption by these two sectors third and ninth, respectively, in 2002. In our analysis, the processing of petroleum, coking, and processing of nuclear fuel sector (10) and the other social services sector (27) ranked first and second, versus seventh or lower in their research. The high embodied consumption of the other social services sector (27) resulted from high indirect energy consumption, which outweighed the relatively low direct energy consumption. The industries in this sector include services, education, and management industries, all of which consume little direct energy for their operating processes, but require intermediate or final products produced by upstream sectors; for example, they utilize equipment such as computers that are produced by advanced manufacturing sectors, and require office space in buildings produced by the construction sector. These products have consumed a large quantity of energy in their production before they can be utilized by these service industries, and this means they include a high quantity of embodied energy that represents indirect consumption. This difference suggests that Chen and Chen and Zhou et al. ignored the importance of sectors with low direct consumption but high indirect consumption. For these sectors, the energy efficiency of exchanges via intermediate processes should be improved to lower the sector's total energy consumption. These results have similar consequences for strategies to reduce carbon emission. Chen et al. (2013) analyzed the carbon emission of 42 sectors in Beijing in 2007. The sector with the highest carbon emission was the electricity sector, followed by the construction sector. In our analysis, the sector with the highest emission was the processing of petroleum, coking, and processing of nuclear fuel sector (10), followed by the production and distribution of electrical power and heat power sector (22). These different results are also a consequence of neglecting the effects of the indirect flows (paths with a length of 2 or more) among sectors by Chen et al. (2013). When calculating the energy consumption, Chen et al. analyzed whether direct exchanges existed between any two sectors, and only accounted for the energy consumption via these direct paths. However, as we have demonstrated in the present study, such direct paths also imply the existence of indirect paths. For example, consider a system in which sector 1 provides direct flows to sector 2, and sector 2 provides direct flows to sector 3, but there are no direct flows from sector 1 to sector 3. Even though sector 1 and sector 3 are not directly connected, resources flow indirectly from 1 to 3 in the products provided by sector 2. In contrast, Tian et al. (2013b) accounted for the direct, indirect, and embodied carbon emission in their study of carbon emission by the iron and steel industries. They found that only embodied carbon emission can fully reflect the energy metabolic processes. Therefore, it is necessary to analyze the energy consumption and carbon footprint from

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the perspective of the overall utilization processes for a product, as this can provide more comprehensive suggestions for energy conservation. Through the three indices we developed to describe the carbon emission efficiency, we found that many of the sectors in Beijing alternated between periods with low and high emission efficiency. The values of the indices (RE, RD, or RI) in 2000 were lower than those in 2010, indicating that energy consumption in Beijing was evolving towards the use of more high-quality and renewable energy. This result was similar to the findings of Chen et al. (2013), who found that from 2000 to 2005, Beijing's final energy consumption structure had improved, and that some solid fuels that caused serious pollution, such as coal, had been replaced by electricity or oil. From 2005 to 2010, Beijing continued to improve its energy consumption structure, and used more high-quality energy. Shao et al. (2014a, b) used an inputeoutput model to account for the embodied energy consumption and its structure for the construction sector of the Beijing EconomiceTechnological Development Area, and found that some forms of fossil energy, such as coal and crude oil, were the fundamental energies used by the construction sector. These carbon-based energy forms contributed to high carbon emission. In our analysis, we also found that although the embodied energy consumption by the construction sector was low, it nonetheless had a low emission efficiency that must be improved to sustain its development. Guo et al. (2012) analyzed Beijing's direct carbon emission in 2007, and found that the electricity, water, metal, and nonmetal manufacturing and processing sectors mainly used coal as their energy source. Coal has a high carbon-emission coefficient, and will produce more carbon emission than other fuels during combustion. In our research, we found that the RD values in 2007 showed that sectors 12, 13, and 22 had low emission efficiency, with values of less than 3.0  102 tce/t. This is in line with the results of Guo et al. (2012). These comparisons confirm that the emission efficiency can be used to evaluate the energy structure of a sector. Therefore, the method described in the present study represents an effective way to analyze the direct, indirect, and embodied energy consumption structures at urban or regional scales. Beijing's industrial chain was dominated by sectors with low emission efficiency. To further improve the city's energy consumption structure, it will be necessary to adjust and optimize specific sectors, and this improvement can be based on comparisons with similar efforts for other regions or cities. For example, Geng et al. (2013b) analyzed the carbon emission in Liaoning Province, and found that with rapid urbanization, the demand for the construction sector increased, and this resulted in decreased carbon emission efficiency. In our analysis, we also found that the construction sector had low emission efficiency, which is problematic because Beijing is also witnessing rapid urbanization. The city's energy structure is similar to that in Liaoning Province. Based on Geng et al.'s research, it is clear that the sectors with low emission efficiency should be prioritized for improvements. As the indirect consumption of Beijing's construction sector (25) accounted for about 70% of the embodied consumption, it will be necessary to decrease this sector's energy consumption by focusing on indirect consumption. For example, designers should adopt energysaving raw materials, technologies, and equipment when they design new buildings. It would also be worthwhile decreasing the direct energy consumption, and this could be achieved by increasing the use of renewable resources such as solar energy to replace some of fossil energy that is used when constructing the building. Chen (2011) also noted that renewable energy is a sustainable resource and can be used to reduce greenhouse gas emission based on his analysis of the energy structure of Taiwan. Based on this analysis, Taiwan has formulated policies to increase

the use of renewable energy as a replacement for fossil energy; for example, solar energy could be used for the electricity sectors. Beijing should examine Taiwan's policies to determine whether any lessons can be learned. 5. Conclusions In this study, we analyzed the urban energy and carbon metabolic processes of Beijing from 2000 to 2010 from the perspectives of embodied energy consumption and carbon footprints. We considered both direct energy consumption and the indirect energy consumption implied in the utilization and exchanges of intermediate products. The sum of the direct and indirect consumption represents the total embodied energy consumption. This analysis fully accounted for the energy consumption and carbon footprint. By combining the embodied energy consumption with the associated emission efficiency, we provided deeper insights into the energy structure attributes of the city's different sectors, and these insights provide clues to more effective suggestions for energy conservation and carbon footprint reduction through adjustments of the energy consumption structure. We found that from 2000 to 2010, the emission efficiency of Beijing's energy consumption structure fluctuated, but with an overall trend toward higher emission efficiency. More than 54% of the sectors had high consumption and low emission efficiency, versus 32% with low consumption and low emission efficiency, and the remaining 14% had low consumption and high emission efficiency. Of the different metabolic actors, the producer sectors all had low emission efficiency, whereas most consumers had high energy consumption and low emission efficiency, especially for the tertiary consumers. In this study, there was only one decomposer sector, and it had low energy consumption and high emission efficiency. Because of this favorable balance between consumption and emission, this sector is more sustainable than the others, and its development should be promoted. Our results demonstrate that it is necessary to consider both energy consumption and the associated emission efficiency to propose effective suggestions for adjusting a sector's energy structure. Nonetheless, our results also demonstrate that the production of more high-quality and renewable energy should be prioritized during Beijing's long-term development. In this study, we only considered five points in time from 2000 to 2010, and did not examine the policy changes that may have been responsible for the observed changes. In the future, we should expand the time span, and examine the energy policies that were proposed and the effects of their implementation on the adjustment of Beijing's energy consumption structure. Another issue is that we only obtained data for 28 sectors, which provides insufficiently fine resolution to focus on individual industries within these sectors. In future research, it would be helpful to obtain finerresolution data that will allow us to propose some more detailed and effective suggestions to improve energy conservation and reduce carbon emission by specific industries. Acknowledgments This work was supported by the National Science Foundation for Innovative Research Group (no. 51121003), by the Program for New Century Excellent Talents in University (no. NCET-12-0059), and by the National Natural Science Foundation of China (no. 41171068). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2014.07.075.

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Please cite this article in press as: Zhang, Y., et al., Analysis of urban energy consumption in carbon metabolic processes and its structural attributes: a case study for Beijing, Journal of Cleaner Production (2014), http://dx.doi.org/10.1016/j.jclepro.2014.07.075