Abstract

Based on the detailed origins of each province’s electricity consumption, a new method for calculating CO2 emissions from the power sector at the provincial level in China is proposed. With this so-called consumer responsibility method, the emissions embodied in imported electricity are calculated with source-specific emission factors. Using the new method, we estimate CO2 emissions in 2005 and 2010. Compared with those derived from the producer responsibility method, the power exporters’ emissions decreased sharply. The emissions from the power sector in Inner Mongolia, the largest power exporter of China, decreased by 109 Mt in 2010. The value is equivalent to those from Shaanxi’s power production and Canada’s power and heat production. In contrast, the importers’ emissions increased substantially. The emissions from the power sector in Hebei, the largest power importer of China, increased by 74 Mt. Emissions of Beijing, increased by 60 Mt (320%), in 2010. Thus, we suggest that the Chinese government should take the emissions, as calculated from the consumption perspective, into account when formulating and assessing local CO2 emission reduction targets.

Keywords

power sector ; CO2 emissions ; consumer responsibility method ; producer responsibility method

1. Introduction

The power sector is the largest source of CO2 emissions in China, accounting for approximately 38% of the total CO2 emissions in 2005 (NDRC, 2012 ). In accordance with the relevant power development plan (CEC, 2010 ), China’s power generating capacity will reach 1.4 billion kW in 2015 and 1.9 billion kW in 2020, and fossil fuel will remain the dominant energy source of power plants. Thus, the power sector will still be a major CO2 emission source in future. China is a vast country with substantial regional variations in primary energy resource endowment (Zhao, 2004 ). There exists a spatial mismatch between the electricity demand and supply, therefore the trade in power among the provinces is considerable (Ma and Ge, 2014 ). Except Tibet, all other 30 provinces had net power imports or exports in 2010. Inner Mongolia was the largest net exporter, exporting 95.2 billion kW h accounting for 38% of its power generation. In contrast, the largest importing province of Guangdong, imported 82.3 billion kW h, accounting for 20% of its electricity consumption. Moreover, the proportion of the imported electricity in Beijing took up 68% of its total power consumption, which is the highest ratio among all provinces (Table 1 ). Thus, the calculating method for the CO2 emissions of the power sector strongly affects a province’s estimated emissions. Our study is helpful for estimating the emissions and evaluating emission reduction progress at the provincial level.

Table 1. Amounts of net power imports and exports by region in 2010
Net exporter Net importer
Region Exports (109 kW h) Exports as percent of generation (%) Region Imports (109 kW h) Imports as percent of consumption (%)
Inner Mongolia 95.2 38 Guangdong 82.3 20
Shanxi 69.1 32 Hebei 69.8 26
Hubei 62.5 31 Beijing 56.2 68
Guizhou 55.0 40 Jiangsu 50.5 13
Anhui 36.6 25 Shanghai 42.0 32
Yunnan 36.1 26 Liaoning 42.0 24
Shaanxi 25.3 23 Henan 27.2 11
Sichuan 24.6 14 Shandong 25.6 8
Fujian 4.1 3 Zhejiang 25.3 9
Ningxia 4.0 7 Hunan 12.7 9
Guangxi 3.9 4 Chongqing 12.1 19
Jilin 2.8 5 Tianjin 8.6 13
Xinjiang 1.7 3 Jiangxi 3.6 5
Heilongjiang 1.5 2 Gansu 1.3 2
Qinghai 0.3 1 Hainan 0.6 3

Note: There were no power trades between Tibet and other provinces in 2010, and the power data of Hong Kong, Macau and Taiwan were not available

Source: Table 3–9 and Table 4–23 of the China Energy Statistical Yearbook 2011 (NBSC, 2011 )

The consumer responsibility method (Ma et al., 2013 ) is recommended to estimate the CO2 emissions of the power sector at sub-country level in various international protocols and standards (ICLEI, 2009 ; UNEP, 2010  ;  WRI et al ., 2012 ). Under this method, power consumers are responsible for CO2 emissions, regardless of whether the power is produced inside or outside the province. However, the producer responsibility method by the IPCC, 1996  ;  IPCC, 2006 was generally used in emissions study of China’s power sector (Jin, 2011 ; An and Jiang, 2011  ;  Zhou et al ., 2011 ). Under this method, all CO2 emissions from the power generation are accounted for the power producers. Thus power exporters, such as the provinces of Inner Mongolia and Shanxi, are responsible for the emissions embodied in their exported power. However, power importers such as Guangdong are not responsible for the emissions from imported electricity. Obviously, this is unfair for power producers. Furthermore, the producer responsibility method does not encourage consumers to reduce electricity consumption; therefore, this method is not efficient for emission reduction at the national level. Two studies dealt with the consumer’s perspective (Tang, 2013  ;  Zhou et al ., 2012 ), but they only adopted a simplified assumption that the power’s emission factors in East China or nationwide were identical. In fact, the power sources of the provinces are disparate. For example, both Shanghai and Zhejiang belong to the East China power grid, but Shanghai imports power mainly from Hubei and Sichuan, for which the proportion of hydropower is much higher than the national average; Zhejiang imports power mainly from Anhui which relies on thermal power. If the emission factors of the East China power grid or of the national average were applied, the emissions embodied in the imported power of Shanghai would be overestimated while those of Zhejiang would be underestimated.

To better reflect the power sector’s CO2 emissions from each province, this study proposes a new method by tracking the detailed origins of each province’s power consumption. Emissions from each province in 2005 and 2010 are calculated, and the results are compared with those calculated by the producer responsibility method. This study offers a different process for formulating CO2 emission reduction policies.

2. Methodology and data sources

2.1. Methodology

The power consumed in a province may have many sources, including own productions and imports from other regions. The province may also export power to other provinces. Similar to power consumption, emissions from the consumer perspective are equal to emissions from power production plus emissions embodied in the imported power and minus emissions embodied in the exported power. This can be determined as:

 ${\displaystyle C_{c}=C_{g}+C_{imp}-E_{\mbox{exp}}{\mbox{,}}}$
( 1)

where Cc  refers to CO2 emissions from power consumption in one province, Cg represents emissions from in-province power production, i.e., emissions calculated with the producer responsibility method, Cimp is emissions embodied in imported electricity (Eq. 2 ), and Cexp is emissions embodied in exported electricity (Eq. 3 ).

 ${\displaystyle C_{imp}=\sum _{i}\left(E_{imp,i}\times F_{i}\right)+\sum _{j}\left(E_{imp,j}\times F_{j}\right)+}$${\displaystyle E_{imp,k}\times F_{k{\mbox{,}}}}$
( 2)

where Eimp,i , Eimp,j  and Eimp,k  represent power imported from other provinces, other countries or regions, and the regional power grid which the province belongs to, respectively. Fi  refers to emissions per unit power production. Fj  and Fk  are emission factors of other countries or regions and the regional power grid (Northeast, North China, Northwest, Central China, East China or South China power grid), respectively.

 ${\displaystyle C_{\mbox{exp}}=\left(\sum _{i}E_{exp,i}+\sum _{j}E_{exp,j}+\right.}$${\displaystyle \left.E_{exp,k}\right)\times F_{g}{\mbox{,}}}$
( 3)

where Eexp,i , Eexp,j  and Eexp,k  represent power exported to other provinces, other countries or regions and the regional power grid respectively, Fg  is the emissions per unit power production in the province.

In addition, the power imported from or exported to the regional power grid is calculated with (4)  ;  (5) :

 ${\displaystyle E_{imp,k}=max\left(\left(E_{c}-E_{g}-\sum _{i}E_{imp,i}-\sum _{j}E_{imp,j}\right),0\right){\mbox{,}}}$
( 4)

 ${\displaystyle E_{exp,k}=-min\left(\left(E_{c}-E_{g}-\sum _{i}E_{imp,i}-\right.\right.}$${\displaystyle \left.\left.\sum _{j}E_{imp,j}\right),0\right){\mbox{.}}}$
( 5)

2.2. Data sources

The data of fuel consumption from thermal power generation, of the average net calorific values by fuel type, and of power generation and consumption by regions are taken from the China Energy Statistical Yearbook (NBSC (National Bureau of Statistics of China), 2007  ;  NBSC (National Bureau of Statistics of China), 2011 ). According to the released national greenhouse gas inventories of 1994 and 2005 (NCGCC (National Coordination Group on Climate Change), ERI (Energy Research Institute, National Development and Reform Commission), 2007  ;  DCC (Department of Climate Change, National Development and Reform Commission), 2014 ), China’s net calorific values by fuels, especially coal, are not totally equal to the default values in the China Energy Statistical Yearbook. Due to the fact that coals are not classified in anthracite, bituminous coal, lignite etc. in official statistics, they are not applicable for a detailed calculation of provincial CO2 emissions using the calorific values in the national greenhouse gas inventories (NCGCC (National Coordination Group on Climate Change), ERI (Energy Research Institute, National Development and Reform Commission), 2007  ;  DCC (Department of Climate Change, National Development and Reform Commission), 2014 ). Furthermore, this study mainly focuses on the methodology, thus applying the default net calorific values should be feasible. The data of power generation and consumption can be obtained from the China Electric Power Yearbook (EBCEPY (The Editorial Board of China Electric Power Yearbook), 2006  ;  EBCEPY (The Editorial Board of China Electric Power Yearbook), 2011 ) and the China Energy Statistical Yearbook (NBSC (National Bureau of Statistics of China), 2007  ;  NBSC (National Bureau of Statistics of China), 2011 ), which show small differences in comparison. As the fuel consumptions are only published in the China Energy Statistical Yearbook, for consistency purposes the data of power generation and consumption are also obtained from the China Energy Statistical Yearbook. The Power Industry Statistical Data Compilation (CEC, 2007  ;  CEC (China Electricity Council), 2011 ) provides data on the interprovincial power exchanges, including power imports and exports.

Carbon emission factors by fuel type and carbon oxidation factors are obtained from the Provincial Guidelines for Greenhouse Gas Inventories (Trial) (NDRC, 2011 ). CO2 emissions per unit electricity production of other countries or regions are available by IEA (2013) . CO2 emission factors of regional power grids are obtained from Song et al. (2013) . The data of 2005 are lacking, so those of 2006 substitute them, as the regional grid emission factors generally remain relatively stable between adjacent years.

3. Results

3.1. Regional CO2 emissions per unit eletricity production

CO2 emissions per unit electricity production at the provincial level in 2010 vary greatly (Fig. 1 ). An increase in the amounts per unit can be seen from Southwest China, through central, eastern and northwestern China, to Northeast China. Provinces with emission factors higher than 1 kg CO2  (kW h)–1  were Heilongjiang, Jilin, Liaoning, Shandong, Ningxia, Hebei, and Inner Mongolia. In contrast, provinces with low emission factors were Qinghai, Sichuan, Yunnan, Hubei, Hunan, Fujian, and Guangxi. The results are closely correlated to the energy structure of power generation. Provinces of Heilongjiang, Hebei, etc., are dominated by thermal power generation, especially coal power. Moreover, the carbon content per unit of energy released from coal is the highest in all fuels. Therefore, CO2 emission factors of electricity production in these provinces were unsurprisingly much higher than in the other provinces. Qinghai, Sichuan, Hubei, etc., mostly rely on non-fossil fuels, especially hydropower. Generally hydro, wind, solar, and nuclear are thought to release zero emissions. Thus, CO2 emission factors of electricity production in these provinces were much lower.

 Figure 1. CO2 emission factors of electricity generation by regions in 2010

3.2. CO2 emissions from power sector by region

The new consumer responsibility method and the producer responsibility method have been used to calculate each province’s CO2 emissions from the power sector in 2010 (Table 2 ). The calculated results of the consumer responsibility method greatly differ from the results using the producer responsibility method. In general, the emissions derived from the consumer responsibility method are larger for the net electricity importing provinces. Nine provinces (Hebei, Beijing, Liaoning, Guangdong, Jiangsu, Shandong, Shanghai, Zhejiang, and Henan) show higher amounts in their CO2 emissions of more than 10 Mt. The emissions of Beijing rose by 320%, ranking first among the provinces with higher emissions. In contrast, the emissions calculated by using the consumer responsibility method are lower for the net electricity exporting provinces. Here, the provinces of Inner Mongolia, Shanxi, Guizhou, Anhui, Shaanxi, Hubei, and Yunnan, show less amounts in CO2 emissions of lower than 10 Mt compared to the producer responsibility method. The emissions of Inner Mongolia decreased by 109 Mt (38%), it was approximately equal to the annual CO2 emissions from electricity production in Shaanxi or Canada (IEA, 2013 ). Though Qinghai is a net power exporter, it shows higher emissions with the consumer responsibility method. The reason is that the CO2 emissions per unit electricity production of imported electricity from Gansu province is higher than its in-province electricity production (Fig. 1 and Table 2 ), leading to larger imported emissions than exported emissions. In summary, using the consumer responsibility method, emissions from central and eastern China show higher amounts, while western China show lower amounts. This indicates a transfer of emissions from the central and eastern regions to the western regions due to the power trade when the producer responsibility method is applied.

Table 2. CO2 emissions from the power sector by region in 2010 using the consumer responsibility method and the producer responsibility method
Region Producer responsibility method Consumer responsibility method Difference (Mt CO2 ) Difference (%)
Emissions (Mt CO2 ) Emission factors of power production (g CO2  (kW h)–1 ) Emissions (Mt CO2 ) Emission factors of power consumption (g CO2  (kW h)–1 )
Hebei 211 1,059 285 1,060 74 35
Beijing 19 697 79 950 60 321
Liaoning 137 1,054 180 1,051 44 32
Guangdong 215 665 259 637 44 20
Jiangsu 270 803 309 800 39 15
Shandong 306 1,004 333 1,010 28 9
Shanghai 83 944 105 809 22 27
Zhejiang 170 664 191 677 21 12
Henan 219 999 237 962 18 8
Tianjin 52 883 61 909 9 18
Hunan 71 582 77 570 6 8
Chongqing 35 687 38 605 3 9
Gansu 56 704 59 731 3 6
Qinghai 10 216 13 269 2 24
Hainan 11 720 11 717 0 3
Jiangxi 58 866 58 823 0 0
Guangxi 51 495 50 502 –1 –3
Xinjiang 52 771 51 771 –1 –3
Jilin 61 1,016 60 1,033 –2 –3
Heilongjiang 82 1,057 80 1,052 –2 –2
Fujian 73 540 71 540 –2 –3
Ningxia 60 1,015 55 1,012 –4 –7
Sichuan 57 315 52 334 –5 –9
Yunnan 72 530 53 525 –20 –27
Hubei 82 403 60 424 –22 –27
Shaanxi 101 909 77 891 –25 –24
Anhui 126 872 94 870 –32 –26
Guizhou 98 708 59 707 –39 –40
Shanxi 215 998 146 998 –69 –32
Inner Mongolia 285 1,147 176 1,146 –109 –38

Notes: Difference in Mt CO2 is defined as emissions from consumer responsibility method minus emissions from producer responsibility method; difference in % is difference in Mt CO2 divided by emissions from producer responsibility method; totals may not sum exactly due to independent rounding

As the CO2 emissions per unit electricity production among provinces vary greatly, for a given amount of imported power, the emission amounts are very different. As shown in Table 1 , both Shanghai and Liaoning imported 42 billion kW h respectively in 2010, the imported emissions of Liaoning were 43.74 Mt CO2 while those of Shanghai were only 22.15 Mt CO2 . This phenomenon can be explained as the imported power of Shanghai was mainly generated from hydropower plants of Hubei and Sichuan, whose CO2 emissions per unit electricity production were low compared to the imported electricity power of Liaoning, which was mainly thermal-power from Jilin, with relatively high emission factors. Similarly, the imported emissions of Guangdong were less than those of Hebei and Shandong. Guangdong mainly imported hydropower from Yunnan, Hubei, and Hunan, while Hebei and Beijing imported power via the North China grid, which is dominated by thermal power generation. Compared with the study by Zhou et al. (2012) , who assumed identical emission factors of the imported electricity, this study tracked the detailed source of the imported electricity, and used source-specific emission factors. Therefore, the results of this study more objectively reflect the diversity of imported power.

By using two different methods, the changes in the CO2 emissions from the power sector during the 11th Five-Year Plan (2005–2010) are shown in Figure 2 . Similar in both methods, 28 provinces’ emissions increased substantially. Inner Mongolia ranked the first with doubled emissions from 2005 to 2010, which is consistent with the assumption that CO2 emissions increase as the economy develops. For any given province, emissions calculated by the consumer responsibility method were different from those with the producer responsibility method. For example, Beijing’s emissions by the producer method decreased by 5.3% while those calculated with the consumer method increased by 38.1%. This demonstrates the achievements of emission reduction in Beijing from the production perspective during the 11th Five -Year Plan. By optimizing the electricity generation structure, by phasing out small coal-fired units, by setting up new gas burning and renewable energy power plants, and by increasing the import of power, Beijing’s emissions decreased from the producer perspective. However, its power consumption increased continuously, and as the power increment is mainly based on the power import its emissions increased strongly from the consumer perspective. Another example of differing changes due to the use of different methods is Shaanxi, whose emissions increased by 102% based on the producer method, while they only increased by 62% based on the consumer method from 2005 to 2010. This phenomenon can be explained by the fact that during the 11th Five-Year Plan, the power generation increased faster than power consumption, and the majority of power increment was exported to other regions.

 Figure 2. Difference of CO2 emissions from the power sector by region between 2005 and 2010 using the consumer responsibility method and the producer responsibility method (the first 15 provinces from Shanxi to Qinghai export power, while those from Beijing to Gansu import power)

4. Conclusions and suggestions

The concept that production originates from consumption and consumption is the substantial source of CO2 emissions was introduced by Ma et al. (2013) . Based on this concept, this study analyzed the profile of CO2 emissions from the power sector in China by using the consumer responsibility method. Our data show that the CO2 emissions of ten regions, including Zhejiang, Jiangsu, Shanghai, Guangdong, Fujian, Shandong, Hebei, Tianjin, Beijing, and Liaoning, which account for 46% of the country’s total emissions according to the method recommended by the IPCC, account for 55% based on the new consumer responsibility method. In contrast, the CO2 emissions of seven regions, including Inner Mongolia, Shanxi, Shaanxi, Anhui, Hubei, Guizhou, and Yunnan, account for 29% with the IPCC method, but only for 20% based on the consumer method. It means that approximately 9% of the national emissions shift from the coal and water-rich less developed regions to economically more developed regions. Furthermore, compared with other studies (Tang, 2013  ;  Zhou et al ., 2012 ) in which a unique nationwide emission factor is supposed, our approach uses detailed sources of electricity transfer, and reflects the actual situation more objectively.

One of the most important aims of analyzing the provincial CO2 emission is to set references for emission reduction. The producer responsibility method would encourage emission reduction of the electricity production, including the optimization of the structure of the electricity production, the increase in the proportion of non-fossil energy power, and the improvement in the power generation efficiency among others. By acknowledging the consumer responsibility method, the CO2 emission reduction focuses on the process of power consumption, such as the conservation of electricity, improvement of the energy efficiency, and other related mitigation measures. So the consumer responsibility method is objectively more valuable for the emission reduction policy making. Up to date, only the production responsibility method has been applied. Our results show that in order to achieve advances in CO2 emission reduction when formulating CO2 emission reduction targets, not only emissions from electricity production should be taken into account, but also the influence of power imports and exports, and the emissions from electricity consumption.

As the production method is simple and easy to operate, with easy access to data, as well as providing data for other basic statistics, we suggest that this method should still be the main method for calculating regional power sector emissions. Although the consumer method is comprehensive and effective, the complex calculation process drives us to propose this method as an important supplement to the production method. When compiling a regional CO2 emission inventory, the results calculated using the consumer method might be listed as additional information. When evaluating the regional emission reduction targets, the emissions calculated by the consumer method should take a certain weighting for effective enhancements in the policy making.

Acknowledgements

This study was sponsored by the China Clean Development Mechanism Fund (No. 1213006).

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