Energy use and CO2 emissions of China's industrial sector from a global perspective

Energy Policy 58 (2013) 284–294

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Energy use and CO2 emissions of China's industrial sector from a global perspective Sheng Zhou a,n, G. Page Kyle b, Sha Yu b, Leon E. Clarke b, Jiyong Eom b, Patrick Luckow b, Vaibhav Chaturvedi b, Xiliang Zhang a, James A. Edmonds b a b

Institute of Energy, Environment, and Economy, Tsinghua University, Beijing 100084, China Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA

H I G H L I G H T S







Eleven industrial subsectors in China are detail analyzed from a global perspective. Industrial energy use and CO2 emissions will approach a plateau between 2030 and 2040. Industrial CHP and CCS are truly encouraged by carbon tax. Some degree of industrial sector electrification are observed by carbon tax.

art ic l e i nf o

a b s t r a c t

Article history: Received 9 June 2012 Accepted 11 March 2013 Available online 10 April 2013

The industrial sector has accounted for more than 50% of China's final energy consumption in the past 30 years. Understanding the future emissions and emissions mitigation opportunities depends on proper characterization of the present-day industrial energy use, as well as industrial demand drivers and technological opportunities in the future. Traditionally, however, integrated assessment research has handled the industrial sector of China in a highly aggregate form. In this study, we develop a technologically detailed, service-oriented representation of 11 industrial subsectors in China, and analyze a suite of scenarios of future industrial demand growth. We find that, due to anticipated saturation of China's per-capita demands of basic industrial goods, industrial energy demand and CO2 emissions approach a plateau between 2030 and 2040, then decrease gradually. Still, without emissions mitigation policies, the industrial sector remains heavily reliant on coal, and therefore emissions-intensive. With carbon prices, we observe some degree of industrial sector electrification, deployment of CCS at large industrial point sources of CO2 emissions at low carbon prices, an increase in the share of CHP systems at industrial facilities. These technological responses amount to reductions of industrial emissions (including indirect emission from electricity) are of 24% in 2050 and 66% in 2095. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Industry energy CO2 emission Saturation effect

1. Introduction In the past 30 years, China's energy use has increased dramatically: from 1980 to 2009, the total primary energy consumption (TPE) increased from 16.4 EJ/yr to 89.9 EJ/yr. This increase of 5.5 times was much higher than the world average increase of 70% (NBS, 2010). The primary driver of this growth has been China's development as a whole: gross domestic product (GDP) increased about 16 times during this period. Still, TPE per capita was 67 GJ in 2008, only 36% of the OECD countries' level (WB, 2010). Also in contrast to OECD economies, all throughout the

n

Corresponding author. Tel.: þ86 10 62795352; fax: þ 86 10 62792759. E-mail address: [email protected] (S. Zhou).

0301-4215/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2013.03.014

observed industrialization process in the past 30 years, the industrial sector has accounted for more than 50% of China's total final energy consumption (excluding traditional biomass), which is much higher than that of about 30% of OECD countries (WB, 2010). Note that, according to the China energy statistics year book, the industry energy share is about 70% (NBS, 2010), which also shows the important role of industry sector energy use. Within China's industrial sector, 73% of the energy use currently takes place in five high energy intensity (HEI) industrial subsectors: iron and steel, nonmetallic minerals (including cement), chemicals, nonferrous metals, and pulp and paper (NBS, 2010). These same HEI subsectors account for 62% of industrial sector energy consumption in the OECD (IEA, 2011b). In the near term, China's industrial energy demand will likely continue to grow, driven by strong GDP growth, and the energy-intensive

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Nomenclature CCSP CCTP CCS CO2 GDP EIA EJ EMF GHG IEA

Climate Change Science Program Climate Change Technology Program carbon dioxide capture carbon dioxide gross domestic production Energy Information Administration 1018 J Energy Modeling Forum greenhouse gas International Energy Agency

structure of the industrial sector. However, taken together, the above points imply that if the structure of China's economy develops towards patterns observed in modern-day developed economies, one can expect continued increase in energy demand, but with a lower share of energy used by industrial sector, and within the industrial sector, reduced shares of energy used by the five HEI subsectors identified above. In fact, demand of cement and steel are already departing from the observed historical trends, driven in large part by trends in construction of buildings and infrastructure in China. According to the historical trends observed in other countries, the growth of the buildings sector will slow down when per-capita residential building floor space reaches about 35 m2 (ERI, 2010). In fact, percapita residential building floorspace has already reached 31.6 m2 and 34.1 m2 in urban and rural areas in 2010, respectively (NBS, 2011a, 2011b), and approached that of most developed countries (though not the USA). As such, the growth in new building area is expected to slow down in the near term, which has direct and near-term implications for the demands of Chinese cement and steel, as only a small portion of these commodities are exported. On this note, the role of government actions and policies will likely be important for the future evolution of the industrial sector, and industrial energy use. As the world's most populated country and the largest carbon dioxide (CO2) emitter, China has become a focus of international attention regarding global energy supply security and climate change. In response to these issues, the Chinese government has taken several measures in recent years. In 2005, the government set a national energy efficiency target: to reduce economy-wide energy intensity (energy consumption per unit of GDP) by 20% between 2005 and 2010. This target was in fact realized (NBS, 2011a, 2011b). For the upcoming decade, the Chinese government has set up two ambitious autonomous domestic emissions mitigation targets. One is that the carbon intensity (CO2 emissions per unit of GDP) in 2020 should be reduced by 40–45% compared with the 2005 level; the other is that the share of nonfossil fuel energy in primary energy consumption should be increased to around 15% by 2020 (NDRC, 2010). Given its prominence in national energy use and CO2 emissions, the industrial sector will likely be a key player in meeting these targets. There have been a number of studies relevant for assessing China's future industrial energy consumption and carbon emissions. These studies can broadly be classified into two groups. The first group assesses China's national energy use in energyeconomic models, and includes studies such as Jang et al. (2006, 2008) IPAC-AIM model, Zhidong (2010) 3E Model and Chai and Zhang (2010) LCEM model. These studies made comprehensive analyses of China's energy consumption and CO2 emissions, but as is typical for integrated assessment studies, the industrial sector is characterized in aggregate form. Although industrial sector in MARKAL model are also disaggregated into subsectors of steel,

INC IPCC LBNL MIIT NBS NDRC OECD PNNL WB

285

Initial National Communication on Climate Change Intergovernmental Panel on Climate Change Lawrence Berkeley National Laboratory Ministry of Industry and Information Technology of China National Bureau of Statistics of China National Development & Reform Commission of China Organization for Economic Co-operation and Development Pacific Northwest National Laboratory World Bank

cement, aluminum, paper, etc., the energy service demand for most sectors are based on historical data 1978–2000 (Chen et al., 2007), and such do not consider the rapid development that took place in subsequent years. In addition, such work is based on a national model, not considering the interaction impact among the different regions. The annual World Energy Outlook by IEA (2011a, 2011b) and International Energy Outlook by EIA (2011), which focus on the next decade or two, are similarly vague in their characterizations of industrial energy use. The second group of studies provides more detailed analysis about industrial energy consumption, but either with a focus on developed countries (Kyle et al., 2011; Murphy et al., 2007; Katja and Sands, 2007; Agnolucci, 2009), specific industrial subsectors or production technologies (Olsen et al., 2010; Akashi et al., 2011), or specific fuel types (Andersen et al., 2011). Miranda-da-Cruz (2007) explored the evolution of industrial energy demand in China using a simple threephase model. Yuan et al. (2010) analyze the relationship between Chinese economic development and energy consumption over time. Nevertheless, there are few technologically detailed assessments of future industrial energy consumption and CO2 emissions in China. The question is important; given the magnitude of industrial energy use in China, different development and technological trajectories may have important implications for future global energy use and emissions. Moreover, we note that in the case of China, studies prior to 2005 consistently underestimated future energy consumption and demand for industrial goods (e.g. Nakicenovic and Swart, 2000; Humphreys and Mahasenan, 2002). Nowadays, in similar fashion, there is likely a tendency to overestimate future energy consumption by extrapolating the recent historical trends, without considering saturation effects of different industrial products. In China, these saturation effects are expected to result in a decoupling between the growth in income and the increase in demand for various energy-intensive industrial goods over the next few decades (Zhou et al., 2011). In summary, this study sets out to enhance understanding of the role of China's industrial sector in future CO2 emissions, and in particular, technological strategies for emissions mitigation in this sector in the context of China's future development. How can we expect China's industrial sector to evolve, and what are the implications for energy demand? What are the means by which China's industrial sector will adapt to large-scale emissions mitigation, as per achieving an aggressive mitigation target? What will be the importance of low-carbon technologies, such as combined heat and power (CHP) and carbon capture and storage (CCS) in the industrial sector?

2. Methods In this section, we present the structure of the detailed, servicebased industrial energy module that was developed and incorporated

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into the Global Change Assessment Model (GCAM), a long-term, global integrated assessment model of human Earth systems. The integrated assessment framework is used in order to consistently represent the interactions between the sector that is the focus of this study – industry in China – and other components of the global economy.

2.1. GCAM GCAM was developed at the Pacific Northwest National Laboratory (PNNL), and was previously called MiniCAM (Clarke et al., 2007; Kim et al., 2006; Brenkert et al., 2003). GCAM is a technologically detailed integrated assessment model of energy, agriculture and land use, greenhouse gas emissions, and climate change. It has been used extensively in assessment and modeling activities such as the Energy Modeling Forum (EMF), the U.S. Climate Change Technology Program (CCTP), the U.S. Climate Change Science Program (CCSP), and IPCC assessment reports, including the recent 4.5 W/m2 scenario in the Representative Concentration Pathways (Thomson et al., 2011). GCAM has a modular structure, allowing enhanced detail to be developed for individual components or sectors while preserving consistency in all markets modeled. Therefore, in addition to global research, it has also been used for detailed regional analysis—for instance, on the U.S. industry, building and transport sectors (Wise et al., 2007, 2010; Kyle et al., 2010, 2011). GCAM version 2.0 includes a detailed U.S. industrial sector, composed of 11 industrial subsectors, six intermediate industrial services, allowing examination of the industrial sector's technological and behavioral response to climate policies (Wise et al., 2007). However, for all other regions, industrial energy use is

treated in highly aggregate form, with little technology-level detail, and only one subsector broken out (cement).

2.2. Modeling China's industrial sector In addition to the cement subsector, this study further partitions China's industrial sector into 10 subsectors, including: (1) iron and steel, (2) chemicals, (3) aluminum and nonferrous metals, (4) other nonmetallic minerals, (5) pulp paper and wood, (6) food processing, (7) other manufacturing, (8) mining, (9) agriculture, and (10) construction. Each manufacturing sector (numbers 1–7 in the preceding list) is represented with a generic production technology that produces output by consuming up to 6 types of energy services: (1) boilers, (2) process heat, (3) machine drive, (4) electrochemical processes, (5) other energy services, and (6) feedstocks. In the iron and steel and cement industries, output is represented in physical terms (Mt); for all other manufacturing subsectors, due to the heterogeneity of the goods produced, output is indexed, with the index corresponding to value of shipments. The other manufacturing sectors include the rest of the manufacturing sectors defined in the China Energy Statistics Yearbook, such as textiles, medicines, metal products, equipment, electronics, and many others. These subsectors are generally less energy-intensive than the subsectors that we have disaggregated, but may become larger energy consumers in the future due to shifts in the composition of the industrial sector. Energy consumed by the non-manufacturing sectors – agriculture, construction, and mining – is treated without any service-level detail due to the lack of data on the specific uses of energy in these sectors. The links between demand drivers, industrial output, industrial energy service demand, and final energy demand are illustrated in Figs. 1 and 2.

Income elasticity Leontief production function Price elasticity GDP, Prices

Logit choice Technology efficiency

Industrial service demand

Industrial output

Final energy demand

Fig. 1. Industry model flow chart.

GDP

Industrial subsectors

End uses

industry

boiler steam

process heat

machine drive

electro chemical

Competing fuels

coal

refined liquids

gas

Competing technologies

coal conv

coal cogen

coal ccs

feedstock

electricty

Fig. 2. End-use and technology competing in a sector.

other enduse

biomass

S. Zhou et al. / Energy Policy 58 (2013) 284–294

80%

60%

40%

20%

boilers

process heat other end use

g

n

in

io

in

co

ns

m

re

machine drive energy use

tru ct

tu

pe r ag

ric ul

pa

in

um lp pu

al

um

he r ot

en t m

el

ce

ste

d

s

0% fo o

Energy consumption by fuel in the industrial sector in China in GCAM is from the IEA (2010) Energy Balances; this is partitioned to the different industrial subsectors using Chinese government statistics (NBS, 2011a, 2011b). These statistics, however, do not allow partitioning of energy to services or technologies; in fact, no such estimates exist for China. Gielen and Taylor (2007) provide general estimates for the entire industrial sector at the global

100%

ic al

3.1. Base year energy by industry, fuel, and end use

As with allocations of energy to industrial services, detailed end-use efficiency data in China are not available. According to Wang (2009), generally, the end-use efficiency of China's industrial sector in 2007 is about 8% lower that of Japan level, or similar to that of the EU 1990s level and Japan's 1975 level, but no further detail information available. For this reason, we use efficiencies

m

In the model, factors driving industrial energy demand include GDP, energy prices, income and price elasticities, and technology characteristics, among others. Most of the data required for building the base-year (2005) model inputs are available in Chinese statistics and other authoritative reports. Regarding other information, such as the partitioning of energy to services, the technological energy efficiency, or the income and price elasticities, we made a reasoned judgment based on China's current situation relative to OECD countries' historical experience. Data and key assumptions used for this study are detailed in the following sections.

3.2. Base year energy efficiency by technology

ch e

3. Data and key assumptions

scale, but these provide no guidance for how the different industries use energy, or what fuels tend to be used for what purposes. The Manufacturing Energy Consumption Survey (MECS, 2011) provides detailed data on energy use by service and fuel for about 80 industrial subsectors in the U.S., but these data cannot be used directly for the Chinese industrial sector due to the differences in the fuel blends between the U.S. and China. Where natural gas is the dominant industrial fuel in the U.S., coal is the dominant fuel in China (IEA, 2010). In this study, we assume the same end-use partitioning of total final energy consumption by each industry in China as the corresponding industry in the U.S.A, according to the MECS (Fig. 3). We use total final energy in order to minimize distortions stemming from the different fuel blends between the two countries. Given that the MECS only addresses the manufacturing industries, the non-manufacturing industries (agriculture, construction, and mining) are still modeled in aggregate form, though asphalt consumption of the construction industry is indicated as a feedstock use. Once the total final energy quantities by each industrial subsector in China have been assigned to services, the final step in building up the base-year dataset is to assign fuels to the services. This is done using scaled allocations from the USA data, preserving to some extent that within any industrial subsector, individual fuels tend to be preferred for certain end uses. For example, the electrochemical services (e.g. electrolysis) are only provided by electricity, and machine drive is almost entirely provided by electricity, whereas this fuel is rarely used to produce steam, and cannot be used as a feedstock. This approach assumes that within each industrial subsector disaggregated above, the production technologies for the various goods produced generally use energy for similar purposes regardless of the location of the facilities (U.S. or China). Therefore, in spite of the different fuel mixes in the industrial sectors between the U.S. and China, our base-year dataset of China's industrial energy use by subsector, fuel, and end-use in this study is generally consistent with that of the U.S. industrial sector.

inIndustrial enduse and feedstock share(%)

Starting from the left side of Fig. 1, the GDP, derived from exogenous population and labor productivity growth assumptions, drives the output from each industrial subsector, according to the income elasticity. As will be discussed later, the income elasticity for industrial output in this study is industry-specific, and changes over time in order to return expected levels of output from each industry. The output is also modified by model-derived costs of manufactured goods (influenced by fuel prices), and an exogenous price elasticity. Each unit of output is then tied to fixed quantities of industrial service requirements, using a Leontief production function derived from analysis of base-year data in China and in the USA. For example, to produce a unit of output from the chemicals subsector requires x units of steam, y units of process heat, z units of machine drive, and so on. The use of fixed ratios means that the service inputs are non-substitutable; in a scenario with high fossil energy prices, there is no capacity to substitute machine drive for steam or process heat. While these ratios are exogenous, they do evolve over time, reflecting anticipated improvements in industrial production technologies that reduce the amounts of each service input required to produce each unit of output. Moreover, there are multiple technologies available for providing each service, allowing energy and emissions prices to affect the energy and emissions intensities of manufacturing. Fuel demands are determined from this technology choice, which is modeled as a logit competition between different fuel options (Clarke, 1993; Edmonds et al., 1997), with a second (nested) level of competition in the boilers and process heat services between cogeneration, steam/heat-only systems, and (in future periods, and for selected industries) systems with CCS. The energy efficiencies of these specific technologies are assumed to improve in the future, according to exogenous improvement rates. Finally, industrial CO2 emissions are derived by multiplying the fuel consumption by the respective fuel carbon contents, minus any carbon sequestered by CCS. For purchased electricity, in this study we generally report upstream emissions using the average emissions intensity of the central electric grid in the given time period. No such upstream emissions adjustments are considered in the reporting for any other fuels, but note that in scenarios with emissions pricing, the prices of any secondary fuels consumed by the industrial sector will include any costs for upstream CO2 emissions.

287

electrochemical

feedstock

Fig. 3. End-use and feedstock allocation by sectors.

S. Zhou et al. / Energy Policy 58 (2013) 284–294

Boilers Process heat Machine drive Other end uses Cogeneration boilers Cogeneration process heat

Biomass Coal Electricity Gas

Refined liquids

0.60 0.64 0.29 0.90 0.71 0.76

0.68 0.72 0.38 0.90 0.90 0.90

0.68 0.72 0.29 0.90 0.81 0.86

0.85 0.85 0.95 0.90 0.81 0.86

0.68 0.72 0.33 0.90 0.81 0.86

from the U.S. industrial module, but adjusted downward (Kyle et al., 2011; see Table 1). Compared with GCAM's USA industrial sector, in 2005, efficiencies of boilers and process heat efficiency are reduced by 10%, machine drive and cogeneration by 5%, and all other end uses are generally small share and assumed to be the same as the USA for simply (Table 1). Note that cogeneration efficiencies are indicated here in terms of whole-system efficiency, with electricity output added to steam or heat output.

3.3. Development of future scenarios Nine scenarios are analyzed in this paper, consisting of a factorial of three output levels for China's industrial sector (Low, Middle, and High), and two emissions policy regimes (Reference and Carbon Tax). The output levels are documented in detail below. The Reference scenarios (RES) have no greenhouse gas emissions constraints or taxes, and the Carbon Tax (Ctax) scenarios have an emissions price on greenhouse gas emissions in all time periods starting in 2015. The emissions price pathway, used for all scenarios with carbon pricing, is set to limit radiative forcing not to exceed 4.5 W/m2 (650 ppm CO2-equivalent, with maximum CO2 levels of approximately 550 ppm CO2), and consistent with the radiative forcing target limit set for Representative Concentration Pathway 4.5 (RCP 4.5 Thomson et al., 2011). The emissions price starts relatively low in the early periods ($53.40 tC−1 in 2015 or $14.56 tCO2−1, 2005 US$ price), and increases at 3% per year through the end of the century. RCP2.6 target (van Vuuren et al., 2011) are also explored in this paper through much higher carbon prices imposed, which is set to limit end-of-century radiative forcing to 2.6 W/m2 (550 ppm CO2-equivalent, with maximum CO2 levels of approximately 450 ppm CO2 to reach 2 degree target). All scenarios use the same social and macroeconomic drivers including population and labor productivity, from Eom et al. (2012), and all scenarios also incorporate the peak output and saturation effects as described above.

3.3.1. Population and GDP As a macroeconomic driver of the model, the medium-fertility population in UN's 2011 long-range population prospect was employed. In this demographic projection, Chinese population peaks in 2035 and declines thereafter due to a low fertility rate, reflecting the Chinese government's “one child” policy and population aging (Fig. 4). We assume that per capita GDP in China grows rapidly in the near term, consistent with the recent historical trend, but the rate of growth continues to approach the level of currently developed economies (Eom et al., 2012). Although Chinese per capita GDP continues to grow faster than the average per capita GDP of developed economies in GCAM, the income gap between the two remain pronounced for most of the century.

2000

120

1600 90 1200 60 800 30 400

0 2005

China Population (million)

Table 1 Technology efficiencies assumed for China's industrial sector in 2005.

GDP per capita MER(thousand 2005US$)

288

0 2020 Developed

2035

2050

Developing

2065 China

2080

2095

China population

Fig. 4. China population and GDP per capita MER assumed in all scenarios.

3.3.2. Future demands of industrial output Industrial energy demand in China is currently dominated by a few energy-intensive sectors, especially the main construction inputs—steel and cement. In this section, we examine the trends of these industries in China, and compare with the trends in other developed economies, in order to ensure that our major modeling assumptions relevant for the future growth of these subsectors are consistent with other countries' experiences. Production of these goods in China has increased very fast in the past 30 years (LBNL, 2008; NBS, 2010, 2011a, 2011b). In 2010, China steel reached 627 million tons, 16 times of that of the 1980 level, accounting for 44% of total world steel production. Similarly, China cement reached 1880 million tons in 2010, 24 times of that of the 1980 level and 61% of the world total (Indexmundi, 2011; World steel, 2011). However, recent research suggests that because of saturation effects in residential building and infrastructure construction, demand for steel and cement will decline in the future (Chen, 2010a, 2010b; Ke et al., 2012; Hong, 2008; Zeng, 2003; Zuo, 2010; Yu et al., 2011; Zhou et al., 2011). Overall, the rapid increase of energy-intensive industrial sectors in recent years is not expected to continue in the future (Hong, 2008; Zeng, 2003; Zuo, 2010), instead peaking by about 2015 (Zhou et al., 2011). Consistent with this estimate, a second study considers the historical trends of developed countries – taking into account the industrialization process, urbanization rate, consumption peak and accumulated consumption per capita and GDP per capita – and predicts that China's peak output of intensive energy products will come around 2015 for cement, 2020 for steel, and 2030 for aluminum and copper (Chen, 2010a, 2010b). Domestic policies also play a role in slowing down industrial energy use in China. The Chinese government has released several measures to rein in capacity expansion in the cement and steel sectors in the next five to ten years (State Council of China, 2010; MIIT of China, 2012), which will likely lead to slow growth in China's energy-intensive industrial sectors. In any case, the future of these industries is quite uncertain; for example, projections of future cement production in 2020 vary from 1.2 to 2.1 billion metric tons (Yu, et al., 2011; Ke et al., 2012). Considering that steel and cement constitute basic requirements to building and maintaining a modern, industrialized society, we posit in this study that China will experience similar per capita consumption of these goods in the long term to what is observed in modern-day industrialized economies (Francois, 2011). Historically, as developed countries reached peak consumption, cement consumption per capita typically decreased sharply from 0.60–1.35 t/yr to 0.51–0.73 t in 2005 (Indexmundi, 2011), but steel consumption changed little (World steel, 2011). Fig. 5 shows historical per capita consumption of steel and cement in China together with the following industrialized economies: WEU (Western Europe; average of Belgium, France, Germany, Italy, Spain, and United Kingdom), Canada, USA, Japan and Korea. The chart also

S. Zhou et al. / Energy Policy 58 (2013) 284–294

Cement consumption per capita(t)

steel production per capita(t)

1.5 1.2 0.9 0.6 0.3 0.0 0

10

20

30

40

50

GDP per capita(thousand 2005US$)

289

2.0 1.6

WEU CAN USA

1.2

CHN JPN

0.8

KOR RES_Low

0.4

RES_High RES_Middle

0.0 0

10

20

30

40

50

GDP per capita(thousand 2005US$)

Fig. 5. Steel and cement pathway in China.

Estimated Peak year

Steel

2015

Cement 2015

Peak consumption Mt per year

Peak consumption t per captia

650–820

0.47–0.54

2200–2400

1.60–1.75

2050 level t per captia

Low:0.42(WEU 2005 ) High: 0.65(Japan 2005 ) Middle: 0.52 Low: 0.51(WEU 2005) High: 0.73(Japan 1995) Middle: 0.62

Industry enegy use per capita(toe)

3.5

Table 2 China steel and cement peak output and 2050 level.

3.0 WEU 2.5

CAN USA

2.0

CHN 1.5

JPN KOR

1.0

RES_Low 0.5

RES_High RES_Middle

0.0 0

10

20

30

40

50

GDP per capita(thousand 2005US$)

overlays three future scenarios of demand of these commodities in China used in this study. In order to capture these anticipated peak demand and saturation effects, these two energy intensive sectors in this study are parameterized so as not to continue the recent historical trends through the end of the century. However, in order to address the uncertainty around future demand levels for these goods in China, we do use three levels of demand that have been observed in the industrialized economies. The scenarios used in this study are detailed in Table 2. For steel and cement, the low scenario (RES_Low in Fig. 5) follows the WEU pathway, which in 2050 reaches WEU's 2005 per-capita levels for both steel and cement (0.42 t of steel and 0.51 t of cement). The high scenario (RES_High) converges to Japan's historical per-capita consumption, reaching 0.65 t of steel and 0.73 t of cement. The middle scenario (RES_Middle) is the average of the above two scenarios. In general, even the high-demand scenarios in this study do not assume a large portion of either of these goods is exported. Cement production is a low value-added industry. It is unlikely that China will export cement in large scale due to the high cost of transportation. Limited by resources, the steel sector is also not export-oriented. As China steel consumption already accounts about half the world total, China steel per capita cannot follow Korea pathway, where most steel is produced for steel product export. Historically, these industries have not been export-oriented (Yu et al., 2011), and this is unlikely to change in the future, particularly given the government's focus on reducing energy intensity of manufacturing and reducing energy use and CO2 emissions (State Council of China, 2009). As such, none of our scenarios explore the implications of China following the pathway of Korea for steel, for example. Due to the heterogeneity of the goods produced by all remaining industries, these industries' future growth rates are set based on analysis of historical per-capita industrial sector energy consumption in modern-day industrialized economies. In other words, for these countries, industrial energy consumption is used

Fig. 6. Industry energy pathways in China.

as a proxy of industrial output. As shown in Fig. 6, simply extrapolating China's recent historical trend would result in greater per-capita industrial energy use than has been observed for other nations, so all scenarios in this study do assume a break from the recent historical growth rates. Similar to the description above, the low-growth scenario follows WEU and the high scenario follows Japan, with the middle scenario being the average of these scenarios (Fig. 6). These demand scenarios are implemented in GCAM by adjusting the income elasticity of industrial output in each industrial subsector and in each time period. These elasticities are calculated in each period, using the assumed GDP path, in order to return the desired per-capita consumption of steel, cement, and all other industrial goods. Note that for the other industrial goods whose outputs are not in physical units, we use total final energy to define the scenarios, instead of output. Note also that the trajectories shown in Fig. 6 are for reference scenarios; changes in energy prices (as in policy scenarios) will modify the paths shown. Finally, in order to accurately reflect the latest data available for China's economic growth and industrial energy use, these income elasticities in the first future GCAM time period (2010) are calculated to return observed industrial energy use in 2010. Historical estimates of industrial fuel price elasticities in China are not available, because as Hang et al. (2007) points out, the price of energy in China has been state-influenced to varying degrees for the past 30 years. This means that regression analysis on historical data will not provide useful information for future estimation. In this study, we assign a price elasticity of −0.7 to each industry, where the elasticity applies to the total production costs of goods (Edmonds and Reilly, 1985). Therefore, the

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industries where fuel costs account for the largest portion of total costs will have the greatest energy price elasticities. 3.3.3. Industrial technology development In all scenarios in this study, we assume that the Chinese government's targets for energy efficiency for several industrial subsectors are met. For projected periods, compared with 2005, the end-use efficiency target (steel, chemical, cement, aluminum, etc.) in 2020 entail improvement of 18–27% (ERI, 2010), which means annual energy efficiency improvement of 0.75%/year to 1.67%/year. Thereafter, these improvement rates are interpolated to a long-term autonomous improvement rate of 0.40% assumed for all industrial subsectors (Wise et al., 2007), as shown in Fig. 7. These improvements to the input–output coefficients are assumed for all inputs to the industrial production functions in each industrial subsector (e.g. boilers, process heat, machine drive, etc.). 3.3.4. Industrial CCS CO2 capture and storage (CCS) is a technology option that would not be expected to deploy in the absence of a carbon emissions mitigation regime. However, when CO2 emissions mitigation policies are implemented, it provides a potential mechanism by which fossil fuels could continue to be used. Several industrial applications of carbon capture (e.g. ammonia and ethylene oxide manufacturing) have lower costs than carbon capture in the electricity sector, and may make important contributions to economy-wide mitigation. While about 95% of capturable CO2 (i.e. at large point sources) is in the energy sector in the USA, in China about 25% of capturable CO2 is in the industrial sector (Dahowski et al., 2011). Within China's industrial sector, there are about 1000 Mt of CO2 produced per year at large point sources that may be candidates for CCS; the relevant industrial subsectors are cement (55%), iron and steel (28%), and chemicals (17%) (Dahowski et al., 2011). In comparison, IEA (2011a, 2011b) provided a roadmap for capture of about 600 Mt of CO2 from China's industrial sector in 2050, with 63% from iron and steel, 13% from cement, and 25% from chemicals. In this study, we model CCS in industry among alternative GHG mitigation options in an integrated framework; in the industrial sector, CCS-equipped technologies compete with conventional technologies and in some cases cogeneration technologies. The relative competitiveness of CCS in industry is determined not only by the carbon emissions price as compared with the capture and storage costs, but also by other emissions mitigation options, such as fuel-switching or CHP, whose relative competitiveness is in turn influenced by the availability of low-carbon electricity generation and liquid fuel production technologies. In this paper, to be conservative and to remain consistent with the existing assessments of industrial sector industrial applications of CCS in China,

4. Results 4.1. Total final energy by scenario In the reference scenarios (RES), industrial energy use continues to grow rapidly to 2020, but growth rates consistently decline for the next 20 years. Energy demand peaks in about 2040 due to the assumed peak output and saturation effects, and decreases after 2050 (Fig. 8). Note that the peak in industrial energy use is between 70 EJ and 90 EJ, which is about 65% higher than 2010 level. The reason for the decline in energy demand after 2050 is that the per-capita final demands of industrial goods generally remain constant once the saturation levels have been reached, but during this time interval total population is assumed to decrease gradually. In contrast to these trends, however, cement production decreases sharply from 2020 due to the assumed structural shifts in the economy (Fig. 5). The policy scenarios (Ctax) are characterized by reduced industrial sector final energy consumption, generally by about 5% in 2050 and 10% in 2095 from the corresponding reference scenarios to reach RCP45 target, by about 12% in 2050 and 17% in 2095 to reach RCP26 target. The impact of the carbon tax on total industrial final energy is generally less than the effect of the different demand levels, up to the end of the century. 100

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we only consider cement, iron and steel, and chemicals sectors, consistent with IEA (2011a, 2011b) and Dahowski et al. (2011). The CCS costs assumed are $30 tCO2−1 in steel, $40 tCO2−1 in cement, and $9 tCO2−1 in chemicals (Climatelab, 2011). In all technologies with CCS, we assume a capture efficiency rate of 90% (Dahowski et al., 2011). Note that this does not mean that 90% of the entire industry's CO2 emissions are captured, as the CCS technologies in this study are parameterized to only include the large point sources within the industrial facilities (e.g. process heat and feedstock conversion in the cement and iron and steel industries, but not “other uses”). As a final note on CCS, some of the CCS technologies considered in this study are in the “feedstocks” end use (Freed et al., 2005); this application of CCS is relevant for the manufacture of products such as ammonia or cement that have a lower carbon content (by mass) than the material from which they are manufactured (coal, gas, oil, or limestone). Such transformations typically involve large volumes of by-product CO2 of higher concentrations than postcombustion flue gases. As such, these feedstock conversion applications of CCS are often lower-cost than energy-related CO2 capture.

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Fig. 8. Industrial total final energy consumption for two climate policy futures (RES and Ctax for RCP45 and RCP26 target) and three demand levels (Low, Middle, and High).

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the other side to the decreasing shares of HEI industries described above. The subsectors that are most affected by the climate policy are cement and other nonmetallic minerals; in these sectors, energy consumption in 2050 is reduced by the policy by 22% and 12%, respectively. This reflects that in these industries, the production technologies have especially high carbon intensity relative to the value of the goods produced. The high carbon intensities are due to both the heat-intensive nature of the production technologies, as well as the calcination of limestone feedstocks for several of the goods produced (e.g. cement, lime, and soda ash). Note that GCAM does not model the inter-linkages between the different industrial subsectors, and as such in this study we do not assess any effects stemming from high prices and reduced output in one industrial subsector on any other subsectors (e.g. the effect of high cement prices on the construction industry).

Fig. 9. Industrial energy use and HEI sectors contribution.

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In the reference scenario, coal remains the dominant fuel through the end of the century; as with all of the fuels, its peak consumption is between 2030 and 2040 (Fig. 11). Compared with the reference scenario, when the carbon tax is imposed, the fuel mix switches towards lower carbon fuels, shown in Fig. 11, though coal does remain the dominant fuel until near the end of the century. In 2050, the policy leads to a 23% reduction in coal, offset by increases in all other fuels; at the end of the century, electricity accounts for the majority of industrial energy use. Fig. 12 further explores the fuel-switching capacity of the industrial sector, indicating the degree to which the different energy services are supplied by different fuel blends, and the degree to which these

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In order to focus on the trends underlying the total industrial energy use shown in Fig. 8, the remainder of the analysis focuses on the Middle demand scenarios to reach RCP45 target. Industrial energy use in the reference (RES_Middle) and carbon tax (Ctax_ Middle) scenarios are shown in Fig. 9, along with the share of industrial energy that is used by the HEI industries. As shown, the contribution of HEI sectors, which currently account for 73% of energy use, decrease in these scenarios to 58% in 2050, and then 55% in 2095. This underscores a general shift in the composition of China's industrial sector in the scenarios, from energy-intensive raw materials manufacturing and towards finished goods production. This shift is consistent with the historical patterns of energy consumption observed in the industrial sectors of the OECD (IEA, 2010). Nevertheless, the HEI subsectors account for the majority of China's industrial energy use in all periods, and their relative shares of industrial energy use are similar between the reference and carbon tax scenarios. Therefore, these HEI subsectors that have accounted for China's rapid increase in industrial energy in recent years still play an important role in the future in these scenarios, even with carbon taxes generally consistent with aggressive climate stabilization.

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Fig. 12. Energy consumption by end use and fuel.

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4.5. CCS in industry

fuel blends may evolve in response to changes in energy prices (electrochemical services are only provided by electricity and not listed). Because most industrial energy use in China is for boilers, process heat, and feedstocks, this study indicates that industrial sector electrification may play a smaller role than has been suggested by prior assessments without the level of detail of industrial energy use used in this study (Clarke et al., 2007; Riahi and Roerhl, 2000). We note however that further fuel-switching than is borne out in our results could nevertheless be achieved through the deployment of fundamentally different production technologies for industrial goods, not considered in this study.

As shown in Fig. 14, the CO2 emissions reduction from CCS in industry in the Carbon Tax scenario is 446 MtCO2 in 2050 and 937 MtCO2 in 2095. This study indicates that industrial CCS in China has a huge potential to reduce emissions, and that this strategy is actually cost-competitive in a climate-constrained world. The amounts observed in our scenarios are similar in magnitude to the existing assessments, though our estimates are somewhat lower, particularly for cement (Dahowski et al., 2011; IEA, 2011a, 2011b; see Fig. 14). There are a number of possible reasons for these discrepancies. First, cement production to 2050 in our study is likely less than the other two studies, due to our sharp decrease cement production from 2015 to 2050. Dahowski et al. (2011) assessment was based on existing production levels, whereas the IEA (2011a, 2011b) assessment was based on a scenario that likely had a greater amount of cement production in China in 2050 than ours (though the output was not reported). As well, in GCAM the deployment of CCS depends on the relative economics as compared with other options whose costs are also evolving; the deployment of industrial CCS could be increased by higher electricity or biomass prices, for instance. Finally, our coverage of the emissions that could be captured in each industrial subsector was intentionally conservative; this is a new area of research, and we wanted to ensure that our scenarios only applied CO2 capture technologies to large point sources of CO2.

4.4. Industrial CHP Generally, industrial cogeneration (CHP) is thought to be more energy efficient from a whole-system perspective than separate heat and power systems. However, CHP systems also typically incur higher capital and operations and maintenance costs, and reduced output of heat or steam as compared with systems designed to only produce heat or steam. As such, the relative cost competitiveness of CHP will depend on the net value of reduced electricity purchases, reduced efficiencies for producing steam or heat, and additional capital and O&M costs. Nevertheless, it is typically thought that by increasing fuel prices, a carbon tax will encourage the deployment of CHP. Our results bear out this dynamic, to some extent. Shown in Fig. 13, when carbon prices are at low to moderate levels, they tend to increase the deployment of CHP as compared with steamor heat-only systems. As a result, the CHP-generated electricity increases faster than in the reference scenario, and takes a greater share of total industrial energy use. In the long run, however, the increasing price of fossil fuels plus the increasing carbon costs results in that CHP electricity is less favorable relative to electricity from grid dominated by low carbon generations. 3.5

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Industrial CO2 emissions (including indirect emission from electricity) under the reference scenario approach a peak of 9.3 GtCO2 in 2035—this is nearly two times the 2005 level (Fig. 15). Emissions then decrease gradually, by 5% in 2050 and 34% in 2095. From 2005 to 2050, indirect (upstream) emissions from electricity account for about 30% of the industrial sector's emissions, gradually decreasing to 23% in 2095. From 2015, when

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the carbon tax is imposed, the CO2 emissions are decreased by 24% (14% from direct emission reduction and 10% from indirect emission reduction) in 2050, and further decreased by 66%( 44% from direct emission reduction and 22% from indirect emission reduction) in 2095 as shown in Fig. 15. Generally, the industrial emissions reductions are from reduced consumption of coal and from reductions in the average carbon intensity of the electric sector. Note that, in this study electricity is indirect emission and the emission factor is the average of electricity sector in a given period. 4.7. Results compared with other studies Due to data availability, this comparison focuses on the time interval from 2005 to 2030, shown in Fig. 16. Note that even in the base year there are differences in the energy consumption estimates, presumably due to different primary data sources and different definitions of the industrial sector. For instance, energy consumed by coke ovens and blast furnaces is considered with the industrial sector in GCAM, whereas in the IEA (2010) Energy Balances it is part of the energy sector. In any case, in the reference scenarios, the total industrial final energy growth rates from 2005 to 2030 are reasonably similar. All models show an upward trend in both time periods, with higher growth rates from 2005 to 2020 than from 2020 to 2030. Policy scenarios are even more difficult to compare. Nonetheless this study appears to indicate less responsiveness of industrial final energy to carbon prices than earlier studies, though we note that our policy scenario was different than the mitigation targets used in the other studies. The IEA (2009) study sought to limit global average surface temperature change to no more than 2-degree centigrade, roughly consistent with 2.6 W/m2 of radiative forcing (450 ppm CO2-equivalient), which is similar to RCP26 target in this study. Similarly, the ERI scenario was a national mitigation target of 8.6 GtCO2 in 2030, whereas our policy scenarios for RCP26 target had about 9.1 GtCO2 from the entire country in 2030. As well, our study included options for industrial CCS, which allows industrial sector mitigation without consequent decreases in energy consumption. So our policy results are some higher than other studies as the right of Fig. 16.

5. Conclusions This paper explores China's long term industrial energy use and CO2 emissions by incorporating a technologically detailed representation of the sector into a global integrated assessment model. The scenarios analyzed incorporate anticipated demand saturation effects, changes in the structure of the industrial sector, and the availability of low-carbon technologies such CCS and CHP. The key results are as follows. First, in our study, the industrial sector's peak energy consumption and emissions are reached between 2030 and 2040, due to demand saturation combined with low population growth in the

long term. However, the saturation levels of demand of industrial goods will be quite important for future energy use and CO2 emissions. In our scenarios, whether China's final demand levels of industrial goods more closely resemble Japan (high) as opposed to Western Europe (low) had a greater effect on future energy demand than did an imposition of carbon taxes generally consistent with stabilization of climate forcing at 4.5 W/m2 or 2.6 W/m2. Second, our study incorporates a compositional shift in the industrial sector, driven in part by the demand saturation. Where the five high energy intensity (HEI) subsectors currently account for 73% of industrial energy use, their relative shares of total final energy decrease gradually following 2020, reaching about 55% at the end of the century. This underscores the movement of China's industry toward subsectors with lower energy intensity and higher value added; still, however, the HEI industries account for the majority of energy use in all periods, and their share of total industrial energy is not noticeably affected by a carbon tax. The exceptions are the cement and other nonmetallic minerals industries, which are the industries most affected by the carbon taxes, due to the high emissions intensities relative to the value of the manufactured products. Without any carbon tax (i.e. reference scenarios), coal continues to dominate the industrial fuel demands through the end of the century. Carbon taxes encourage some degree of fuel switching towards low-carbon fuels – coal is reduced about by 23% in 2050 as compared with corresponding no-policy scenarios – though our study finds a lesser role for industrial electrification in climate change mitigation than past assessments using more aggregate representations of the fuel choices in industry (Clarke et al., 2007; Riahi and Roerhl, 2000). This is because a large portion of the services supplied by coal are steam, high-temperature process heat, and feedstocks, for which electricity is not likely to displace hydrocarbon fuels even at high carbon prices. We note however that this analysis did not include the option of fundamentally different production technologies wherein thermal processes are replaced with mechanical processes or electrolysis. As well, while GCAM does include a hydrogen economy, we did not assess scenarios where this was actually a cost-competitive option for providing industrial steam and heat. Industrial cogeneration (CHP) is a promising technological option for reducing the use of fossil fuels and CO2 emissions. In the near term, low to moderate carbon taxes will favor the deployment of this technology as compared with separate heat and power systems. However, after carbon prices exceed a certain level, and the emissions intensity of grid-produced electricity has declined through the use of CCS and renewable energy sources, industrial CHP becomes less cost-competitive. Our results indicate that CCS in industry is a cost-competitive means of reducing greenhouse gas emissions from the industrial sector. Moreover, we note that in its initial applications, industrial CCS is more economically attractive than in the electricity sector, presenting early opportunities for emissions mitigation. With the carbon tax used in this study, CCS in industry captured and

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sequestered 446 MtCO2 in 2050 and 937 MtCO2 in 2095, all in the cement, iron and steel, and chemicals industries. In total, CO2 emissions (including indirect emission from electricity) under our reference scenario approached a peak of 9.3 GtCO2 in 2035 and two times of 2005 level and then decreased gradually by 5% in 2050 and 34% in 2095. The imposition of carbon taxes reduced industrial sector CO2 emissions by 24% in 2050 and 66% in 2095 to reach climate forcing target at 4.5 W/m2. Taken together, these results highlight the importance of future demand growth in determining China's future industrial energy demand, but also that the sector can play an important role in technological mitigation of greenhouse gas emissions.

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