- Email: [email protected]
Prospect of the upper limit of the energy demand in China from regional aspects
Energy 35 (2010) 5320e5327
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Prospect of the upper limit of the energy demand in China from regional aspects Toshihide Ito a, *, Youqing Chen b, Shoichi Ito c, Kaoru Yamaguchi d a
Faculty of Informatics, Kansai University, Add: 2-1-1 Ryozenji Takatsuki-City 569-1095, Japan Graduate School of Energy Science, Kyoto University, Japan c School of International Studies, Kwansei Gakuin University, Japan d Senior economist, The Institute Energy Economics, Japan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 November 2009 Received in revised form 18 July 2010 Accepted 22 July 2010 Available online 15 September 2010
The aim of this article is to indicate the upper limit of the annual energy demand in China, taking into consideration regional demand trends and projecting these trends into the distant future. The upper limit of energy consumption is not strictly the maximum amount of consumption. It means that the actual consumption will possibly exceed this level but not by much. Consumption was calculated using the current energy consumption in the US and Japan as a reference, whose energy demands have already almost reached their upper limits. Scenario analysis was conducted for both semiquantitative and numerical models. Scenarios were developed taking into account the situation in rural regions. The prospect of regional population growth was also taken into consideration. The results revealed large differences between the estimates in this study (2810e14,450 Mtoe), which means that if the energy consumption per capita in low-consumption areas increases, the total consumption in China will also increase significantly. According to the OECD prospect rates, our estimated upper limit will be surpassed in China around 2032e2073. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: China Energy demand Long-term prospects Regional analysis
1. Introduction China’s recent economic growth has been quite rapid, and China’s economy has recovered quickly after the financial crisis of 2008. The growth rate of the gross domestic product (GDP) in China has been approximately 10% over the last decade [1]. In addition, the present population of China is over 1.3 billion, and this number will also clearly continue to increase for an extended period of time. The United Nations reports that population growth in China is expected to continue at least until 2030 [2]. Because such dynamic expansions of the economy and the population are expected to increase energy consumption, China’s energy demand has a strong impact on the global energy balance [3,4]. At the end of the 1990s, around the time Hong Kong was returned to China, energy consumption began to increase rapidly [5]. However, regardless of future improvements in the standard of living or future increases in the level of industrialization, the upper limit of energy consumption will follow the historical experience of many industrially advanced countries such as the US and Japan [6]. Although a number of studies have calculated the future energy demand of China, many of these studies analyzed the time-series
* Corresponding author. Tel./fax: þ81 72 690 2409. E-mail address: [email protected] (T. Ito). 0360-5442/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2010.07.028
shift in energy demand for the near or relatively distant future. These analyses examined the past trends in energy consumption, extracted efficient parameters, and then predicted the future transition of annual demand by mathematical modeling [3,7]. Some studies discussed the energy demand in China as a whole, but in recent years, analyses have been conducted by separating the huge market of China into several factors. This separation shows great variety, e.g., industrial sectors [7,8], regional aspects [9,10], primary energy resources [11,12], and final consumption patterns [13]. Furthermore, analytical models become more complex when they interconnect individual analyses. The credibility of these analyses has improved along with the development of model precision and computer technologies. These studies indicate that many people are interested in the energy consumption of China and its impact to the world. However, the methodology for establishing the estimated value in this study is not completely consistent with that of many other studies. The starting viewpoint looks at what kind of influence such a huge country will ultimately have on the world. The upper limit of the energy demand in China is predicted, taking into consideration regional demand trends. The upper limit of energy consumption is not strictly the maximum amount of consumption. This level means that actual consumption will possibly exceed this level but not by much. As a regional consideration, the 31 province-level divisions, including 22 provinces, 5 autonomous regions, and 4 municipalities,
T. Ito et al. / Energy 35 (2010) 5320e5327
were analyzed [1]. Some of these administrative districts have a larger area and larger populations than other Asian countries. The populations, climates, environments, resources, industrial structures, and economic situations of these administrative districts vary considerably. Therefore, it is difficult to predict China’s energy consumption by viewing the country as a whole. For a more accurate prediction, energy consumption can be analyzed based on regional characteristics or trends. The estimate of China’s energy consumption upper limit was calculated using the current energy consumption in the US and Japan as references, which have already reached their upper limits. Certain scenarios were assumed in both the qualitative and quantitative cases. It is imperative to study the future energy scenario in China because of the large scale of this country’s energy consumption. 2. Estimate of the upper limit using consumption trends 2.1. Methodology In general, the total energy consumption within an area E is described as
E ¼
X
ei pi
i
where, ei and pi are energy consumption per capita and population in subarea i, respectively. The energy consumption per capita is a good parameter of living standards. It is possible to quickly estimate future energy consumption when the prospects of these two parameters are individually given. Therefore, to estimate the upper limit of energy consumption, the energy consumption tendency of each province is classified into several patterns according to geographical features or industrial structures, etc. and then the
5321
energy consumption per capita is determined for each pattern. However, some scenarios must be assumed for areas where energy consumption per capita is difficult to determine. The upper limit of energy consumption Es, when only one classified area is assumed by a scenario, is calculated by
Es ¼
X c
ec
X i
pi þ es
X
pj
j
where ec is the energy consumption per capita in a province that is classified as pattern c, pi is the population in a province that is classified as pattern c, es is the estimated energy consumption per capita for scenario s, and pj is the population in a province where scenario s is assumed. The maximum population in each province is estimated using the UN prediction. 2.2. Classification of provinces by energy consumption patterns 2.2.1. Current energy consumption trend in each province The energy consumption in each region was analyzed by observing the energy consumption trends in all provinces, except the Tibet Autonomous Region because the energy data in this province have not been made public [1,14]. In 2005, Tibet had a population of approximately 2.7 million (0.2% of the China’s population) [1]. In other provinces, many statistical data such as indices of economic conditions, energy production, and energy consumption have been made available since 1991. First, the economic conditions and energy consumption trends in each province were considered based on the gross regional product (GRP) per capita and the energy consumption per capita [1,14]. Fig. 1 shows the ranking of energy consumption per capita in each province. After sorting the provinces in descending order based on energy consumption, a set of five consecutive provinces are represented in this map by a single color, with six colors used in
Fig. 1. Ranking of energy consumption per capita by province (2005). Thirty provinces are classified into six categories by rank. The green line is the hypothetical border of the northwest lower-consumption region and the southeast higher-consumption region.
5322
T. Ito et al. / Energy 35 (2010) 5320e5327
total. Similarly, a ranking of the provinces based on their GRP per capita is shown in Fig. 2. A comparison of these two maps reveals that the GRP per capita is not always the main factor in determining the energy consumption in each province. The five provinces in the highest and lowest ranked groups with respect to GRP are not the same provinces as those in the highest and lowest ranked groups with respect to consumption. However, upon division into two groups, the provinces in the higher GRP group and the higher energy consumption group are almost identical. The industrial provinces in the eastern coastal region, which are characterized by both high GRP and high-energy consumption, are expected to get better at saving energy as industrialization advances. On the other hand, the energy consumption of inland provinces, which have both low GRP and low-energy consumption, will not increase unless their standard of living improves. This also indicates that an increase in the standard of living or automobile use will result in much higher energy consumption. In Fig. 3, the color of each province shows the average temperature of its capital in January [1]. A comparison of Fig. 3 with Fig. 1 reveals that the low temperature region in the north has highenergy consumption, and the line from the northeast to the southwest (like the green line in Figs. 1 and 3) divides the northern high-energy consumption area and the southern low-energy consumption area. Because this energy consumption trend in the cold provinces is basically climate-dependent, it is not expected to change in the future. Because it is generally considered that more energy is consumed in industry than in agriculture, the provinces that have a high percentage of primary industry in their GRP consume less energy. Fig. 4 shows the percentage of primary industry of GRP using six colors, as shown in Fig. 1. As can be seen in Fig. 4, in the Beijing, Tianjin, and Shanghai municipalities, the percentage is less than 3%. In the provinces of the eastern coastal region, the percentage is between 3 and 15%. In almost all other provinces the percentage is more than 15%. The percentage of primary industry in the GRP also influences the region’s energy consumption. The relationships among the GRP, temperature, industrial structure and energy consumption mentioned above are different
in the Shanxi Province and Ningxia Autonomous Region because the conditions in these provinces are exceptional compared with other regions. First, Shanxi is the main producing province that contributes to 68% of the coal energy consumption in China. This province is rich in coal resources. However, advancements in the iron industry in this province have resulted in an increase in energy consumption. Additionally, coal used in household sectors accounts for 90% of the total energy consumption and this is inefficient use of energy [15]. Although Ningxia is thinly populated, it has many coal mines (similar to Shanxi) and there are many chemical industries that consume a significant amount of energy. As a result, consumption per capita remains at an all-time high [16]. 2.2.2. Five categories of energy consumption patterns First, five patterns of future energy consumption trends were hypothesized and then the provinces were categorized according to their geomorphology, resources, economies or past energy consumption trends. The five patterns corresponding to the five categories are named “urban area”, “coastal industrialized area”, “northern area”, “agricultural area”, and “exceptional area” (Fig. 5). The urban area includes the Beijing, Tianjin, and Shanghai municipalities, which are the provinces with the highest GRP per capita and highest energy consumption per capita (Figs. 1 and 2). The five provinces in the coastal regions (Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong) belong to the coastal industrialized area. These five provinces and the provinces in the urban area have the eighth highest energy consumption per GRP in nonstate-owned industrial enterprises above a designated size (this category is defined in Chinese statistical data). Moreover, the percentages of primary industries in GRP are less than 15% and secondary industries in the GRP are more than 30% in these five provinces. Apart from Shanxi and Ningxia, the provinces where the average temperature in January is less than 5 C are Inner Mongolia, Liaoning, Jilin, Heilongjiang, Gansu, Qinghai, and Xinjiang. These seven provinces and Hebei, where the average temperature in January is below zero, belong to the northern area. In these cold provinces, a considerable amount of energy is consumed for heating in winter.
Fig. 2. Ranking of GRP per capita by province (2005). Thirty provinces are classified into six categories by rank.
T. Ito et al. / Energy 35 (2010) 5320e5327
5323
Fig. 3. Average temperatures in January of provincial capitals (2005). The northern areas above the green line correspond to cold areas.
Exceptional areas include Shanxi and Ningxia, which are unique with respect to energy consumption, as mentioned earlier. The other 14 provinces belong to agricultural areas where the primary industry forms a relatively high percentage of the GRP
(Fig. 4), and the percentage of the workforce is lower in the secondary industry than in the coastal industrialized area. The present situations of the five classified categories are tabulated in Table 1.
Fig. 4. Distribution map of the share of primary industry in GRP by province (2005).
5324
T. Ito et al. / Energy 35 (2010) 5320e5327
Fig. 5. Geographical setting of five types of classified provinces for semiquantitative predictions.
2.3. Estimates using several scenarios in the agricultural areas The upper limits of energy consumption could be approximately estimated in urban areas, coastal industrial areas, northern areas and exceptional areas, following the current tendencies of the US and Japan. However, it is difficult to determine the energy demand in agricultural areas in the future because these provinces have many uncertain factors. Therefore, the upper limits of energy consumption were estimated by assuming some scenarios in agricultural area. (1) Urban areas The energy demand in urban areas is assumed to be 2.67 toe per capita, which was the energy consumption of Tokyo in 2004. (2) Coastal industrialized areas Because these provinces are expected to get better at saving energy with greater industrialization, the demand level is taken to be the current energy consumption of Japan, where energy saving is almost the highest in the world. The demand level in coastal industrialized areas is taken as 4.18 toe per capita, which was the average consumption of all prefectures in Japan in 2004.
Table 1 Classification of provinces by energy consumption characteristics. Type
Provinces
Urban area Coastal industrialized area Northern area
Beijing, Tianjin, Shanghai Shandong, Jiangsu, Zhejiang, Fujian, Guangdong
Exceptional area Agricultural area (scenario area)
Inner Mongolia, Liaoning, Jilin, Heilongjiang, Gansu, Qinghai, Xinjiang, Hebei Shanxi, Ningxia Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi
(3) Northern areas In northern areas, the energy consumption in 2004 is assumed to be the future demand level in each province because climate is the main factor that determines energy consumption. (4) Exceptional areas The demand in exceptional areas is assumed to be the current level in the US, 8.6 toe per capita, which is the highest in the world. (5) Agricultural areas Given the low population density and the vast stretch of land, this area is similar to the US, where a significant amount of energy is consumed by mass transportation. Therefore, on the basis of the energy consumption in the US, three scenarios are assumed in this area; that is, 50% of US consumption (scenario C50), 75% of US consumption (scenario C75), and 100% of US consumption (scenario C100). With regard to future population levels, China’s peak population estimated by the UN (1,455,000,000) was distributed to each province according to the proportions in 2005 (Table 2). Finally, using these assumptions, the upper limits of the energy demand were estimated, and the results are listed in Table 3.
3. Estimate of the upper limit by quantitative comparison 3.1. Quantitative analysis of regional energy consumption Fig. 6 shows the shift in regional energy consumption balance per capita in Japan (47 prefectures) [17,18] and China (30 provinces) between 1990e2005. Each polygonal curve in the figure shows energy consumption per capita in each district in descending order.
T. Ito et al. / Energy 35 (2010) 5320e5327
Population (1000)
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang Total
17,439 11,826 77,681 38,041 27,054 47,860 30,796 43,313 20,160 84,756 55,536 69,392 40,082 48,881 104,859 106,356 64,743 71,728 104,247 52,838 9388 31,725 93,112 42,293 50,457 3141 42,180 29,412 6157 6758 22,791 1,455,000
9 2005
8
2000
7 6 5 4 3 2
1995 1990
1 0
b 3.5 Energy consumption per capita (toe)
District
Energy consumption per capita (toe)
a
Table 2 Assumption of maximum population.
5325
3 2005
2.5 2000
2 1.5 1 0.5
1995 1990
0
Starting from the bottom, the curves correspond to the years 1990, 1995, 2000 and 2005, respectively. In this paper, we call this type of curve a “DOC curve (Descending Order Consumption curve)”. On the basis of past trends in Japan, the rate of increase is expected to decline when the consumption reaches a certain level. There is no marked change in the regional gap. On the other hand, in China, the DOC curve started increasing markedly in 2000, and the rates of increase in high-consumption areas were higher than those in low-consumption areas. It is predicted that the DOC curve in Fig. 6(b) will ascend, and the overall consumption in the country will get closer to the upper limit. Moreover, in high-consumption areas, the consumption level will reach the level of developed countries, and the rates of increase in low-consumption areas will be slow. As a result, the upper limit demand in high-consumption areas can be easily predicted by simply observing the patterns obtained for developed countries. However, the limit in low-consumption areas is difficult to estimate. In such areas, energy consumption needs to be estimated by assuming certain scenarios, because there are many variable factors. 3.2. Methodology The process of assessing the upper limits of the DOC curve from the current DOC curve in China is shown by the large gray arrow in Table 3 Predictions by categorizing each administrative district. Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
C50 C75 C100
5625 7089 8556
3.86 4.87 5.88
Fig. 6. The shift in regional energy consumption per capita between 1990e2005: (a) Japan, (b) China. A polygonal line is drawn for each prefecture or province in descending order (DOC curve). The horizontal axis corresponds to 47 prefectures (a) and 30 provinces (b).
Fig. 7. If the same energy consumption per capita is consumed in all provinces, the DOC curve will reach line A in Fig. 7. However, such equal consumption per capita is unrealistic because of the many differences in each province. Consequently, the DOC curve in China will actually rise up the curves B, C, and D shown in Fig. 7. In this paper, to predict the future demand for energy, the pattern of the current DOC curves in the US and Japan was assumed as China’s DOC curve at the upper limit. Moreover, it was assumed that the top three large consumption districts (Shanghai, Beijing, and Tianjin) will consume the same level as the top three districts in the US and Japan, and in other provinces some scenarios were supposed. 3.3. Estimates by several scenarios in lower-consumption provinces To begin with, two cases were proposed to predict the consumption pattern: (1) an increase to the current demand level in the US, where considerable energy is consumed by mass transportation over vast stretches of land, and (2) an increase to the current demand level in Japan, where energy saving is among the most successful in the world. The former assumption is the socalled “continental type” consumption model, and the latter an “energy saving” consumption model. In both cases, the top three provinces (the Shanghai, Beijing, and Tianjin municipalities), which together consume 10% of the country’s energy, are assumed to consume energy at the same level as the US and Japan. The graphs in Fig. 8 are curves that approximate the energy consumption by using logarithms [18e20]. Because the horizontal
5326
T. Ito et al. / Energy 35 (2010) 5320e5327 35
B C D
USpopulation
30 Energy consumption per capita (toe)
Energy consumption per capita
A
expanded population in China
25 20 15 10 5 0 0
200
400
600
800
1,000
1,200
1,400
Population (millions)
current DOC curve in China Accumulative population Fig. 7. Schematic illustration of the improvement process to the upper limit of energy consumption.
axis corresponds to the population, the integrated sum of this curve is nearly equal to the total energy consumption. In these figures, the curves represent the present situation of energy consumption in China, the US, and Japan and are drawn in the following manner: (1) Provinces are arranged in descending order according to their energy consumption per capita. (2) The horizontal axis is formed by determining the cumulative population. (3) In the case of the US and Japan, the population is linearly expanded to the population of China. Fig. 9 shows this drawing process in the US case. In this graph, the circled dots are actual energy consumption per capita, and asterisks show energy consumption per capita in an expanded population in China. The equations of regression become
China : 0:54ln p þ 7:07 R2 ¼ 0:98 Japan : 1:15ln p þ 15:83 R2 ¼ 0:98 US : 3:45ln p þ 45:92 R2 ¼ 0:96 where p is the accumulated population (horizontal axis of the graph) and R2 is the coefficient of determination.
Energy consumption per capita (toe)
20
US.
15
10
Fig. 9. Drawing of the US case. The circled dots are actual energy consumption per capita, and asterisks show energy consumption per capita in an expanded Chinese population.
Aside from this assumption, the key point is the extent of the increase in energy consumption in the other provinces. For this purpose, the following three scenarios are considered in these provinces: Scenario US50 and JP50: The upper limit of demand is 50% of the current demand level in the US and Japan, respectively. Scenario US75 and JP75: The upper limit of demand is 75% of the current demand level in the US and Japan, respectively. Scenario US100 and US100: The upper limit of demand is the same as the current demand level in the US and Japan, respectively. Based on these graphs, the upper limit of the energy demand in China is predicted, and the results are summarized in Tables 4 and 5. 4. Discussion 4.1. Review of the predictions and scenarios Based on semiquantitative analysis that the energy consumption level determined for individual provinces is categorized into five characteristic regions, the predictions of upper limit energy demand in China, using the mentioned scenarios, range from 5625 to 8556 Mtoe (Table 3). The predictions range from 8552 to 14,450 Mtoe based on the scenario of the current US consumption per capita (Table 4), and 2810 to 4898 Mtoe based on that of the current Japanese consumption level (Table 5). These significant gaps indicate that the future energy consumption in China could vary greatly depending on factors such as economic and population changes. One of the key factors in future consumption is how energy demands will rise in low-consumption provinces. From such a viewpoint, the energy consumption will increase if the economic gaps between the provinces decrease because a lot of low-energy consuming provinces correspond to the lower GRP regions. However, recent GRP trends reveal that the gap between the highest and lowest provinces was a factor of 9.7 in 2005, and has gradually widened compared to 15 years ago. If these gaps increase
Japan
5
Table 4 Prediction using US consumption.
China
0 0
300
600
900
1,200
Accumulative population (millions)
Fig. 8. Comparison of energy consumption per capita.
1,500
Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
US50 US75 US100
8552 11,505 14,450
5.88 7.91 9.93
T. Ito et al. / Energy 35 (2010) 5320e5327 Table 5 Prediction using Japan’s consumption. Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
JP50 JP75 JP100
2810 3854 4898
1.93 2.65 3.37
or are maintained, energy consumption would not increase like scenario US100 or US75 in Table 4. 4.2. Point of the upper limit According to the statistical data published by each province, the energy consumption in China in 2005 was 1836 Mtoe. A lot of research and reports used this data, as does our study. However, the energy consumption in China during the same year was 1742 Mtoe in the International Energy Agency (IEA) statistical data [4]. The difference between the two sets of data is 94 Mtoe (5.4% of the IEA statistical data). Although this difference is not so large, it could be caused by different statistical methods, e.g., a different range of energy resources such as renewable energy. The energy demand in China as predicted by the IEA is 3819 Mtoe [4]. This predicted amount is approximately 2.2 times the consumption in 2005 and indicates an annual increase rate of approximately 3.2%. This 3819 Mtoe already exceeds the minimum estimate of our study (2810 Mtoe, scenario JP50). That is, China’s energy consumption will reach the upper limit in 2021 in this scenario using the predicted annual increase rate of the IEA. On the other hand, in the other scenarios, China’s consumption will reach the upper limit at the earliest in 2032 (3854 Mtoe, scenario JP75) and the latest in 2073 (14,450 Mtoe, scenario US100) under the same annual increase rate. 4.3. Aspects from the supply side It is also important to talk about the energy supply structure when the upper limit is reached. Although there is much uncertainty when discussing the possibility of energy supply sources in the distant future. It is well-known that a marked characteristic of energy supply in China is its considerably high dependence on coal compared to other developed countries [14]. In recent years, primary energy has been supplied by coal (68% of the calorie base), oil (22%), natural gas (3%), and other sources comprising both hydroelectric and nuclear power (7%) [1]. In addition, it is inferred that many traditional sources such as fuel wood and livestock manure are used in rural areas. Almost all the coal is produced domestically and production is increasing due to the recent increase in energy consumption. Meanwhile, China has been a net importer of oil since the mid1990s. Natural gas imports are also increasing. The percentage of hydroelectric power, however, is still low and there are uncertainties in the case of nuclear power [21]. If the present supply situation does not change, the very-highconsumption scenarios in Table 4 are not realistic because it is impossible to consume most of the world’s fossil energy in one country, China. 5. Conclusions The upper limit of annual energy consumption in China was predicted by analyzing the regional economic situations, industrial structures, and past consumption trends in each province. The prospects are calculated by using three methods from the energy consumption observed in the current US and Japan. Moreover, in
5327
each method, three scenarios were assumed by prospecting future energy consumption levels in low-consumption areas in China. The predicted range is from 5625 to 8556 Mtoe using semiquantitative analysis of regional aspects, 8552 to 14,450 Mtoe using the current US consumption levels, and 2810 to 4898 Mtoe using current Japanese consumption levels. According to the OECD prospect rates, our estimated upper limit would be consumed in China by around 2032e2073. The results revealed large differences between the estimates, implying that future energy consumption in China could vary quite significantly depending on industrial changes, economic trends, and population growth. If the energy consumption per capita in low-consumption areas increases, the total consumption in China will also increase significantly. Such increases point to an improvement in the standard of living in these areas. However, the difference in GRP per capita between the various provinces in China is large, and this difference has continued to grow in recent years. As a result, the energy consumption cannot increase rapidly. However, if the standard of living in low GRP areas improves in the future, huge energy demands will arise. Although the manner in which consumption increases depends on the economic gaps between the provinces, the upper limit of demand will occur between the second half and the end of this century. Until then, the present supply structure, which is heavily dependent on coal or fossil resources, should be improved. References [1] National Bureau of Statistics of China, China Statistical Yearbook (Zhongguo tongji nianjian). Beijing: China Statistics Press [each year edition]. [2] Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. World population prospects (the 2006 revision). New York: United Nations; 2007. [3] OECD/IEA. World energy outlook 2005. Paris: OECD/IEA; 2006. [4] OECD/IEA. World energy outlook 2007. Paris: OECD/IEA; 2008. [5] Department of Comprehensive Statistics of National Bureau of Statistics of China. In: Proceedings of China compendium of statistics 1949e2004. Beijing: China Statistics Press; 2005. [6] Department of Economic and Social Affairs, UN. Energy statistics yearbook. New York: United Nations [each year edition]. [7] Zhang M, Mu H, Li G, Ning Y. Forecasting the transport energy demand based on PLSR method in China. Energy 2009;34:1396e400. [8] Zhao X, Ma C, Hong D. Why did China’s energy intensity increase during 1998e2006: decomposition and policy analysis. Energy Policy 2010;38:1379e88. [9] Yang M, Yu X. China’s rural electricity market e a quantitative analysis. Energy 2004;29:961e77. [10] Wang JJ, Zhang CF, Jing YY. Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China. Applied Energy 2010; 87:1247e59. [11] Liao H, Wei YM. China’s energy consumption: a perspective from Divisia aggregation approach. Energy 2010;35:28e34. [12] Zhu L, Fan Y. Optimization of China’s generating portfolio and policy implications based on portfolio theory. Energy 2010;35:1391e402. [13] Tao Z. Scenarios of China’s oil consumption per capita (OCPC) using a hybrid factor decomposition-system dynamics (SD) simulation. Energy 2010;35:168e80. [14] National Bureau of Statistics of China, China energy statistical yearbook (Zhongguo nengyuan tongji nianjian). Beijing: China Statistics Press [each year edition]. [15] Bureau of Statistics, Shanxi Province. Shanxi statistical yearbook 2006 (Shanxi tongji nianjian 2006). Beijing: China Statistics Press; 2006 [in Chinese]. [16] Bureau of Statistics, Ningxia Autonomous Region. Ningxia statistical yearbook 2006 (Ningxia tongji nianjian 2006). Beijing: China Statistics Press; 2006 [in Chinese]. [17] Ministry of Internal Affairs and Communications, Japan. Population by prefecture. Available from: http://www.stat.go.jp/english/data/nenkan/143102.htm. [18] Research Institute of Economy, Trade & Industry, Japan. Energy consumption by prefecture 1990e2006 (Todo-huken bestu energy shohi 1990e2006). Available from: http://www.rieti.go.jp/users/kainou-kazunari/energy/index. html [in Japanese]. [19] Population estimates. U.S. Census Bureau . Available from: http://www. census.gov/popest/states/NST-ann-est.html; 2000e2007. [20] EIA, Consumption price and expenditure estimates. Available from: http:// www.eia.doe.gov/emeu/states/_seds.html. [21] Xing H. China nuclear power industry development and its prospect. Energy and Resources 2006;27(3):26e30 [in Japanese].
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Prospect of the upper limit of the energy demand in China from regional aspects Toshihide Ito a, *, Youqing Chen b, Shoichi Ito c, Kaoru Yamaguchi d a
Faculty of Informatics, Kansai University, Add: 2-1-1 Ryozenji Takatsuki-City 569-1095, Japan Graduate School of Energy Science, Kyoto University, Japan c School of International Studies, Kwansei Gakuin University, Japan d Senior economist, The Institute Energy Economics, Japan b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 November 2009 Received in revised form 18 July 2010 Accepted 22 July 2010 Available online 15 September 2010
The aim of this article is to indicate the upper limit of the annual energy demand in China, taking into consideration regional demand trends and projecting these trends into the distant future. The upper limit of energy consumption is not strictly the maximum amount of consumption. It means that the actual consumption will possibly exceed this level but not by much. Consumption was calculated using the current energy consumption in the US and Japan as a reference, whose energy demands have already almost reached their upper limits. Scenario analysis was conducted for both semiquantitative and numerical models. Scenarios were developed taking into account the situation in rural regions. The prospect of regional population growth was also taken into consideration. The results revealed large differences between the estimates in this study (2810e14,450 Mtoe), which means that if the energy consumption per capita in low-consumption areas increases, the total consumption in China will also increase significantly. According to the OECD prospect rates, our estimated upper limit will be surpassed in China around 2032e2073. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: China Energy demand Long-term prospects Regional analysis
1. Introduction China’s recent economic growth has been quite rapid, and China’s economy has recovered quickly after the financial crisis of 2008. The growth rate of the gross domestic product (GDP) in China has been approximately 10% over the last decade [1]. In addition, the present population of China is over 1.3 billion, and this number will also clearly continue to increase for an extended period of time. The United Nations reports that population growth in China is expected to continue at least until 2030 [2]. Because such dynamic expansions of the economy and the population are expected to increase energy consumption, China’s energy demand has a strong impact on the global energy balance [3,4]. At the end of the 1990s, around the time Hong Kong was returned to China, energy consumption began to increase rapidly [5]. However, regardless of future improvements in the standard of living or future increases in the level of industrialization, the upper limit of energy consumption will follow the historical experience of many industrially advanced countries such as the US and Japan [6]. Although a number of studies have calculated the future energy demand of China, many of these studies analyzed the time-series
* Corresponding author. Tel./fax: þ81 72 690 2409. E-mail address: [email protected] (T. Ito). 0360-5442/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2010.07.028
shift in energy demand for the near or relatively distant future. These analyses examined the past trends in energy consumption, extracted efficient parameters, and then predicted the future transition of annual demand by mathematical modeling [3,7]. Some studies discussed the energy demand in China as a whole, but in recent years, analyses have been conducted by separating the huge market of China into several factors. This separation shows great variety, e.g., industrial sectors [7,8], regional aspects [9,10], primary energy resources [11,12], and final consumption patterns [13]. Furthermore, analytical models become more complex when they interconnect individual analyses. The credibility of these analyses has improved along with the development of model precision and computer technologies. These studies indicate that many people are interested in the energy consumption of China and its impact to the world. However, the methodology for establishing the estimated value in this study is not completely consistent with that of many other studies. The starting viewpoint looks at what kind of influence such a huge country will ultimately have on the world. The upper limit of the energy demand in China is predicted, taking into consideration regional demand trends. The upper limit of energy consumption is not strictly the maximum amount of consumption. This level means that actual consumption will possibly exceed this level but not by much. As a regional consideration, the 31 province-level divisions, including 22 provinces, 5 autonomous regions, and 4 municipalities,
T. Ito et al. / Energy 35 (2010) 5320e5327
were analyzed [1]. Some of these administrative districts have a larger area and larger populations than other Asian countries. The populations, climates, environments, resources, industrial structures, and economic situations of these administrative districts vary considerably. Therefore, it is difficult to predict China’s energy consumption by viewing the country as a whole. For a more accurate prediction, energy consumption can be analyzed based on regional characteristics or trends. The estimate of China’s energy consumption upper limit was calculated using the current energy consumption in the US and Japan as references, which have already reached their upper limits. Certain scenarios were assumed in both the qualitative and quantitative cases. It is imperative to study the future energy scenario in China because of the large scale of this country’s energy consumption. 2. Estimate of the upper limit using consumption trends 2.1. Methodology In general, the total energy consumption within an area E is described as
E ¼
X
ei pi
i
where, ei and pi are energy consumption per capita and population in subarea i, respectively. The energy consumption per capita is a good parameter of living standards. It is possible to quickly estimate future energy consumption when the prospects of these two parameters are individually given. Therefore, to estimate the upper limit of energy consumption, the energy consumption tendency of each province is classified into several patterns according to geographical features or industrial structures, etc. and then the
5321
energy consumption per capita is determined for each pattern. However, some scenarios must be assumed for areas where energy consumption per capita is difficult to determine. The upper limit of energy consumption Es, when only one classified area is assumed by a scenario, is calculated by
Es ¼
X c
ec
X i
pi þ es
X
pj
j
where ec is the energy consumption per capita in a province that is classified as pattern c, pi is the population in a province that is classified as pattern c, es is the estimated energy consumption per capita for scenario s, and pj is the population in a province where scenario s is assumed. The maximum population in each province is estimated using the UN prediction. 2.2. Classification of provinces by energy consumption patterns 2.2.1. Current energy consumption trend in each province The energy consumption in each region was analyzed by observing the energy consumption trends in all provinces, except the Tibet Autonomous Region because the energy data in this province have not been made public [1,14]. In 2005, Tibet had a population of approximately 2.7 million (0.2% of the China’s population) [1]. In other provinces, many statistical data such as indices of economic conditions, energy production, and energy consumption have been made available since 1991. First, the economic conditions and energy consumption trends in each province were considered based on the gross regional product (GRP) per capita and the energy consumption per capita [1,14]. Fig. 1 shows the ranking of energy consumption per capita in each province. After sorting the provinces in descending order based on energy consumption, a set of five consecutive provinces are represented in this map by a single color, with six colors used in
Fig. 1. Ranking of energy consumption per capita by province (2005). Thirty provinces are classified into six categories by rank. The green line is the hypothetical border of the northwest lower-consumption region and the southeast higher-consumption region.
5322
T. Ito et al. / Energy 35 (2010) 5320e5327
total. Similarly, a ranking of the provinces based on their GRP per capita is shown in Fig. 2. A comparison of these two maps reveals that the GRP per capita is not always the main factor in determining the energy consumption in each province. The five provinces in the highest and lowest ranked groups with respect to GRP are not the same provinces as those in the highest and lowest ranked groups with respect to consumption. However, upon division into two groups, the provinces in the higher GRP group and the higher energy consumption group are almost identical. The industrial provinces in the eastern coastal region, which are characterized by both high GRP and high-energy consumption, are expected to get better at saving energy as industrialization advances. On the other hand, the energy consumption of inland provinces, which have both low GRP and low-energy consumption, will not increase unless their standard of living improves. This also indicates that an increase in the standard of living or automobile use will result in much higher energy consumption. In Fig. 3, the color of each province shows the average temperature of its capital in January [1]. A comparison of Fig. 3 with Fig. 1 reveals that the low temperature region in the north has highenergy consumption, and the line from the northeast to the southwest (like the green line in Figs. 1 and 3) divides the northern high-energy consumption area and the southern low-energy consumption area. Because this energy consumption trend in the cold provinces is basically climate-dependent, it is not expected to change in the future. Because it is generally considered that more energy is consumed in industry than in agriculture, the provinces that have a high percentage of primary industry in their GRP consume less energy. Fig. 4 shows the percentage of primary industry of GRP using six colors, as shown in Fig. 1. As can be seen in Fig. 4, in the Beijing, Tianjin, and Shanghai municipalities, the percentage is less than 3%. In the provinces of the eastern coastal region, the percentage is between 3 and 15%. In almost all other provinces the percentage is more than 15%. The percentage of primary industry in the GRP also influences the region’s energy consumption. The relationships among the GRP, temperature, industrial structure and energy consumption mentioned above are different
in the Shanxi Province and Ningxia Autonomous Region because the conditions in these provinces are exceptional compared with other regions. First, Shanxi is the main producing province that contributes to 68% of the coal energy consumption in China. This province is rich in coal resources. However, advancements in the iron industry in this province have resulted in an increase in energy consumption. Additionally, coal used in household sectors accounts for 90% of the total energy consumption and this is inefficient use of energy [15]. Although Ningxia is thinly populated, it has many coal mines (similar to Shanxi) and there are many chemical industries that consume a significant amount of energy. As a result, consumption per capita remains at an all-time high [16]. 2.2.2. Five categories of energy consumption patterns First, five patterns of future energy consumption trends were hypothesized and then the provinces were categorized according to their geomorphology, resources, economies or past energy consumption trends. The five patterns corresponding to the five categories are named “urban area”, “coastal industrialized area”, “northern area”, “agricultural area”, and “exceptional area” (Fig. 5). The urban area includes the Beijing, Tianjin, and Shanghai municipalities, which are the provinces with the highest GRP per capita and highest energy consumption per capita (Figs. 1 and 2). The five provinces in the coastal regions (Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong) belong to the coastal industrialized area. These five provinces and the provinces in the urban area have the eighth highest energy consumption per GRP in nonstate-owned industrial enterprises above a designated size (this category is defined in Chinese statistical data). Moreover, the percentages of primary industries in GRP are less than 15% and secondary industries in the GRP are more than 30% in these five provinces. Apart from Shanxi and Ningxia, the provinces where the average temperature in January is less than 5 C are Inner Mongolia, Liaoning, Jilin, Heilongjiang, Gansu, Qinghai, and Xinjiang. These seven provinces and Hebei, where the average temperature in January is below zero, belong to the northern area. In these cold provinces, a considerable amount of energy is consumed for heating in winter.
Fig. 2. Ranking of GRP per capita by province (2005). Thirty provinces are classified into six categories by rank.
T. Ito et al. / Energy 35 (2010) 5320e5327
5323
Fig. 3. Average temperatures in January of provincial capitals (2005). The northern areas above the green line correspond to cold areas.
Exceptional areas include Shanxi and Ningxia, which are unique with respect to energy consumption, as mentioned earlier. The other 14 provinces belong to agricultural areas where the primary industry forms a relatively high percentage of the GRP
(Fig. 4), and the percentage of the workforce is lower in the secondary industry than in the coastal industrialized area. The present situations of the five classified categories are tabulated in Table 1.
Fig. 4. Distribution map of the share of primary industry in GRP by province (2005).
5324
T. Ito et al. / Energy 35 (2010) 5320e5327
Fig. 5. Geographical setting of five types of classified provinces for semiquantitative predictions.
2.3. Estimates using several scenarios in the agricultural areas The upper limits of energy consumption could be approximately estimated in urban areas, coastal industrial areas, northern areas and exceptional areas, following the current tendencies of the US and Japan. However, it is difficult to determine the energy demand in agricultural areas in the future because these provinces have many uncertain factors. Therefore, the upper limits of energy consumption were estimated by assuming some scenarios in agricultural area. (1) Urban areas The energy demand in urban areas is assumed to be 2.67 toe per capita, which was the energy consumption of Tokyo in 2004. (2) Coastal industrialized areas Because these provinces are expected to get better at saving energy with greater industrialization, the demand level is taken to be the current energy consumption of Japan, where energy saving is almost the highest in the world. The demand level in coastal industrialized areas is taken as 4.18 toe per capita, which was the average consumption of all prefectures in Japan in 2004.
Table 1 Classification of provinces by energy consumption characteristics. Type
Provinces
Urban area Coastal industrialized area Northern area
Beijing, Tianjin, Shanghai Shandong, Jiangsu, Zhejiang, Fujian, Guangdong
Exceptional area Agricultural area (scenario area)
Inner Mongolia, Liaoning, Jilin, Heilongjiang, Gansu, Qinghai, Xinjiang, Hebei Shanxi, Ningxia Anhui, Jiangxi, Henan, Hubei, Hunan, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi
(3) Northern areas In northern areas, the energy consumption in 2004 is assumed to be the future demand level in each province because climate is the main factor that determines energy consumption. (4) Exceptional areas The demand in exceptional areas is assumed to be the current level in the US, 8.6 toe per capita, which is the highest in the world. (5) Agricultural areas Given the low population density and the vast stretch of land, this area is similar to the US, where a significant amount of energy is consumed by mass transportation. Therefore, on the basis of the energy consumption in the US, three scenarios are assumed in this area; that is, 50% of US consumption (scenario C50), 75% of US consumption (scenario C75), and 100% of US consumption (scenario C100). With regard to future population levels, China’s peak population estimated by the UN (1,455,000,000) was distributed to each province according to the proportions in 2005 (Table 2). Finally, using these assumptions, the upper limits of the energy demand were estimated, and the results are listed in Table 3.
3. Estimate of the upper limit by quantitative comparison 3.1. Quantitative analysis of regional energy consumption Fig. 6 shows the shift in regional energy consumption balance per capita in Japan (47 prefectures) [17,18] and China (30 provinces) between 1990e2005. Each polygonal curve in the figure shows energy consumption per capita in each district in descending order.
T. Ito et al. / Energy 35 (2010) 5320e5327
Population (1000)
Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang Total
17,439 11,826 77,681 38,041 27,054 47,860 30,796 43,313 20,160 84,756 55,536 69,392 40,082 48,881 104,859 106,356 64,743 71,728 104,247 52,838 9388 31,725 93,112 42,293 50,457 3141 42,180 29,412 6157 6758 22,791 1,455,000
9 2005
8
2000
7 6 5 4 3 2
1995 1990
1 0
b 3.5 Energy consumption per capita (toe)
District
Energy consumption per capita (toe)
a
Table 2 Assumption of maximum population.
5325
3 2005
2.5 2000
2 1.5 1 0.5
1995 1990
0
Starting from the bottom, the curves correspond to the years 1990, 1995, 2000 and 2005, respectively. In this paper, we call this type of curve a “DOC curve (Descending Order Consumption curve)”. On the basis of past trends in Japan, the rate of increase is expected to decline when the consumption reaches a certain level. There is no marked change in the regional gap. On the other hand, in China, the DOC curve started increasing markedly in 2000, and the rates of increase in high-consumption areas were higher than those in low-consumption areas. It is predicted that the DOC curve in Fig. 6(b) will ascend, and the overall consumption in the country will get closer to the upper limit. Moreover, in high-consumption areas, the consumption level will reach the level of developed countries, and the rates of increase in low-consumption areas will be slow. As a result, the upper limit demand in high-consumption areas can be easily predicted by simply observing the patterns obtained for developed countries. However, the limit in low-consumption areas is difficult to estimate. In such areas, energy consumption needs to be estimated by assuming certain scenarios, because there are many variable factors. 3.2. Methodology The process of assessing the upper limits of the DOC curve from the current DOC curve in China is shown by the large gray arrow in Table 3 Predictions by categorizing each administrative district. Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
C50 C75 C100
5625 7089 8556
3.86 4.87 5.88
Fig. 6. The shift in regional energy consumption per capita between 1990e2005: (a) Japan, (b) China. A polygonal line is drawn for each prefecture or province in descending order (DOC curve). The horizontal axis corresponds to 47 prefectures (a) and 30 provinces (b).
Fig. 7. If the same energy consumption per capita is consumed in all provinces, the DOC curve will reach line A in Fig. 7. However, such equal consumption per capita is unrealistic because of the many differences in each province. Consequently, the DOC curve in China will actually rise up the curves B, C, and D shown in Fig. 7. In this paper, to predict the future demand for energy, the pattern of the current DOC curves in the US and Japan was assumed as China’s DOC curve at the upper limit. Moreover, it was assumed that the top three large consumption districts (Shanghai, Beijing, and Tianjin) will consume the same level as the top three districts in the US and Japan, and in other provinces some scenarios were supposed. 3.3. Estimates by several scenarios in lower-consumption provinces To begin with, two cases were proposed to predict the consumption pattern: (1) an increase to the current demand level in the US, where considerable energy is consumed by mass transportation over vast stretches of land, and (2) an increase to the current demand level in Japan, where energy saving is among the most successful in the world. The former assumption is the socalled “continental type” consumption model, and the latter an “energy saving” consumption model. In both cases, the top three provinces (the Shanghai, Beijing, and Tianjin municipalities), which together consume 10% of the country’s energy, are assumed to consume energy at the same level as the US and Japan. The graphs in Fig. 8 are curves that approximate the energy consumption by using logarithms [18e20]. Because the horizontal
5326
T. Ito et al. / Energy 35 (2010) 5320e5327 35
B C D
USpopulation
30 Energy consumption per capita (toe)
Energy consumption per capita
A
expanded population in China
25 20 15 10 5 0 0
200
400
600
800
1,000
1,200
1,400
Population (millions)
current DOC curve in China Accumulative population Fig. 7. Schematic illustration of the improvement process to the upper limit of energy consumption.
axis corresponds to the population, the integrated sum of this curve is nearly equal to the total energy consumption. In these figures, the curves represent the present situation of energy consumption in China, the US, and Japan and are drawn in the following manner: (1) Provinces are arranged in descending order according to their energy consumption per capita. (2) The horizontal axis is formed by determining the cumulative population. (3) In the case of the US and Japan, the population is linearly expanded to the population of China. Fig. 9 shows this drawing process in the US case. In this graph, the circled dots are actual energy consumption per capita, and asterisks show energy consumption per capita in an expanded population in China. The equations of regression become
China : 0:54ln p þ 7:07 R2 ¼ 0:98 Japan : 1:15ln p þ 15:83 R2 ¼ 0:98 US : 3:45ln p þ 45:92 R2 ¼ 0:96 where p is the accumulated population (horizontal axis of the graph) and R2 is the coefficient of determination.
Energy consumption per capita (toe)
20
US.
15
10
Fig. 9. Drawing of the US case. The circled dots are actual energy consumption per capita, and asterisks show energy consumption per capita in an expanded Chinese population.
Aside from this assumption, the key point is the extent of the increase in energy consumption in the other provinces. For this purpose, the following three scenarios are considered in these provinces: Scenario US50 and JP50: The upper limit of demand is 50% of the current demand level in the US and Japan, respectively. Scenario US75 and JP75: The upper limit of demand is 75% of the current demand level in the US and Japan, respectively. Scenario US100 and US100: The upper limit of demand is the same as the current demand level in the US and Japan, respectively. Based on these graphs, the upper limit of the energy demand in China is predicted, and the results are summarized in Tables 4 and 5. 4. Discussion 4.1. Review of the predictions and scenarios Based on semiquantitative analysis that the energy consumption level determined for individual provinces is categorized into five characteristic regions, the predictions of upper limit energy demand in China, using the mentioned scenarios, range from 5625 to 8556 Mtoe (Table 3). The predictions range from 8552 to 14,450 Mtoe based on the scenario of the current US consumption per capita (Table 4), and 2810 to 4898 Mtoe based on that of the current Japanese consumption level (Table 5). These significant gaps indicate that the future energy consumption in China could vary greatly depending on factors such as economic and population changes. One of the key factors in future consumption is how energy demands will rise in low-consumption provinces. From such a viewpoint, the energy consumption will increase if the economic gaps between the provinces decrease because a lot of low-energy consuming provinces correspond to the lower GRP regions. However, recent GRP trends reveal that the gap between the highest and lowest provinces was a factor of 9.7 in 2005, and has gradually widened compared to 15 years ago. If these gaps increase
Japan
5
Table 4 Prediction using US consumption.
China
0 0
300
600
900
1,200
Accumulative population (millions)
Fig. 8. Comparison of energy consumption per capita.
1,500
Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
US50 US75 US100
8552 11,505 14,450
5.88 7.91 9.93
T. Ito et al. / Energy 35 (2010) 5320e5327 Table 5 Prediction using Japan’s consumption. Scenario
Annual consumption (Mtoe)
Consumption per capita (toe)
JP50 JP75 JP100
2810 3854 4898
1.93 2.65 3.37
or are maintained, energy consumption would not increase like scenario US100 or US75 in Table 4. 4.2. Point of the upper limit According to the statistical data published by each province, the energy consumption in China in 2005 was 1836 Mtoe. A lot of research and reports used this data, as does our study. However, the energy consumption in China during the same year was 1742 Mtoe in the International Energy Agency (IEA) statistical data [4]. The difference between the two sets of data is 94 Mtoe (5.4% of the IEA statistical data). Although this difference is not so large, it could be caused by different statistical methods, e.g., a different range of energy resources such as renewable energy. The energy demand in China as predicted by the IEA is 3819 Mtoe [4]. This predicted amount is approximately 2.2 times the consumption in 2005 and indicates an annual increase rate of approximately 3.2%. This 3819 Mtoe already exceeds the minimum estimate of our study (2810 Mtoe, scenario JP50). That is, China’s energy consumption will reach the upper limit in 2021 in this scenario using the predicted annual increase rate of the IEA. On the other hand, in the other scenarios, China’s consumption will reach the upper limit at the earliest in 2032 (3854 Mtoe, scenario JP75) and the latest in 2073 (14,450 Mtoe, scenario US100) under the same annual increase rate. 4.3. Aspects from the supply side It is also important to talk about the energy supply structure when the upper limit is reached. Although there is much uncertainty when discussing the possibility of energy supply sources in the distant future. It is well-known that a marked characteristic of energy supply in China is its considerably high dependence on coal compared to other developed countries [14]. In recent years, primary energy has been supplied by coal (68% of the calorie base), oil (22%), natural gas (3%), and other sources comprising both hydroelectric and nuclear power (7%) [1]. In addition, it is inferred that many traditional sources such as fuel wood and livestock manure are used in rural areas. Almost all the coal is produced domestically and production is increasing due to the recent increase in energy consumption. Meanwhile, China has been a net importer of oil since the mid1990s. Natural gas imports are also increasing. The percentage of hydroelectric power, however, is still low and there are uncertainties in the case of nuclear power [21]. If the present supply situation does not change, the very-highconsumption scenarios in Table 4 are not realistic because it is impossible to consume most of the world’s fossil energy in one country, China. 5. Conclusions The upper limit of annual energy consumption in China was predicted by analyzing the regional economic situations, industrial structures, and past consumption trends in each province. The prospects are calculated by using three methods from the energy consumption observed in the current US and Japan. Moreover, in
5327
each method, three scenarios were assumed by prospecting future energy consumption levels in low-consumption areas in China. The predicted range is from 5625 to 8556 Mtoe using semiquantitative analysis of regional aspects, 8552 to 14,450 Mtoe using the current US consumption levels, and 2810 to 4898 Mtoe using current Japanese consumption levels. According to the OECD prospect rates, our estimated upper limit would be consumed in China by around 2032e2073. The results revealed large differences between the estimates, implying that future energy consumption in China could vary quite significantly depending on industrial changes, economic trends, and population growth. If the energy consumption per capita in low-consumption areas increases, the total consumption in China will also increase significantly. Such increases point to an improvement in the standard of living in these areas. However, the difference in GRP per capita between the various provinces in China is large, and this difference has continued to grow in recent years. As a result, the energy consumption cannot increase rapidly. However, if the standard of living in low GRP areas improves in the future, huge energy demands will arise. Although the manner in which consumption increases depends on the economic gaps between the provinces, the upper limit of demand will occur between the second half and the end of this century. Until then, the present supply structure, which is heavily dependent on coal or fossil resources, should be improved. References [1] National Bureau of Statistics of China, China Statistical Yearbook (Zhongguo tongji nianjian). Beijing: China Statistics Press [each year edition]. [2] Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. World population prospects (the 2006 revision). New York: United Nations; 2007. [3] OECD/IEA. World energy outlook 2005. Paris: OECD/IEA; 2006. [4] OECD/IEA. World energy outlook 2007. Paris: OECD/IEA; 2008. [5] Department of Comprehensive Statistics of National Bureau of Statistics of China. In: Proceedings of China compendium of statistics 1949e2004. Beijing: China Statistics Press; 2005. [6] Department of Economic and Social Affairs, UN. Energy statistics yearbook. New York: United Nations [each year edition]. [7] Zhang M, Mu H, Li G, Ning Y. Forecasting the transport energy demand based on PLSR method in China. Energy 2009;34:1396e400. [8] Zhao X, Ma C, Hong D. Why did China’s energy intensity increase during 1998e2006: decomposition and policy analysis. Energy Policy 2010;38:1379e88. [9] Yang M, Yu X. China’s rural electricity market e a quantitative analysis. Energy 2004;29:961e77. [10] Wang JJ, Zhang CF, Jing YY. Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China. Applied Energy 2010; 87:1247e59. [11] Liao H, Wei YM. China’s energy consumption: a perspective from Divisia aggregation approach. Energy 2010;35:28e34. [12] Zhu L, Fan Y. Optimization of China’s generating portfolio and policy implications based on portfolio theory. Energy 2010;35:1391e402. [13] Tao Z. Scenarios of China’s oil consumption per capita (OCPC) using a hybrid factor decomposition-system dynamics (SD) simulation. Energy 2010;35:168e80. [14] National Bureau of Statistics of China, China energy statistical yearbook (Zhongguo nengyuan tongji nianjian). Beijing: China Statistics Press [each year edition]. [15] Bureau of Statistics, Shanxi Province. Shanxi statistical yearbook 2006 (Shanxi tongji nianjian 2006). Beijing: China Statistics Press; 2006 [in Chinese]. [16] Bureau of Statistics, Ningxia Autonomous Region. Ningxia statistical yearbook 2006 (Ningxia tongji nianjian 2006). Beijing: China Statistics Press; 2006 [in Chinese]. [17] Ministry of Internal Affairs and Communications, Japan. Population by prefecture. Available from: http://www.stat.go.jp/english/data/nenkan/143102.htm. [18] Research Institute of Economy, Trade & Industry, Japan. Energy consumption by prefecture 1990e2006 (Todo-huken bestu energy shohi 1990e2006). Available from: http://www.rieti.go.jp/users/kainou-kazunari/energy/index. html [in Japanese]. [19] Population estimates. U.S. Census Bureau . Available from: http://www. census.gov/popest/states/NST-ann-est.html; 2000e2007. [20] EIA, Consumption price and expenditure estimates. Available from: http:// www.eia.doe.gov/emeu/states/_seds.html. [21] Xing H. China nuclear power industry development and its prospect. Energy and Resources 2006;27(3):26e30 [in Japanese].