Method for Calculating CO2 Emissions from the Power Sector at the Provincial Level in China

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.

Citation

Ma, C.-M., Ge, Q.-S., 2014. Method for calculating CO₂ emissions from the power sector at the provincial level in China. Adv. Clim. Change Res. 5(2), doi: 10.3724/SP.J.1248.2014.092.

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:

$C_{c}=C_{g}+C_{imp}-E_{\mbox{exp}}{\mbox{,}}$

( 1)

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

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

( 2)

where E_imp,i , E_imp,j  and E_imp,k  represent power imported from other provinces, other countries or regions, and the regional power grid which the province belongs to, respectively. F_i  refers to emissions per unit power production. F_j  and F_k  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.

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

( 3)

where E_exp,i , E_exp,j  and E_exp,k  represent power exported to other provinces, other countries or regions and the regional power grid respectively, F_g  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) :

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

( 4)

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

References

1. An and Jiang, 2011 X.-H. An, J. Jiang; Status quo and some suggestions on control of CO₂ emissions from the thermal power industry of China  ; Energy Conservation and Environment Protection (in Chinese), 37 (1) (2011), pp. 108–110
2. CEC, 2007 CEC (China Electricity Council), 2007. Power industry statistical data abstract 2006 (in Chinese).
3. CEC, 2010 CEC (China Electricity Council), 2010 [2014-06-25]. The study report of the power sector’s twelfth five year plan (in Chinese). Accessed http://www.cec.org.cn/yaowenkuaidi/2012-03-09/81451.html .
4. CEC (China Electricity Council), 2011 CEC (China Electricity Council); Power industry statistical data compilation 2010 (in Chinese) (2011)
5. DCC (Department of Climate Change, National Development and Reform Commission), 2014 DCC (Department of Climate Change, National Development and Reform Commission); The People’s Republic of China National Greenhouse Gas Inventory 2005 (in Chinese); China Environmental Science Press, Beijing (2014)
6. EBCEPY (The Editorial Board of China Electric Power Yearbook), 2006 EBCEPY (The Editorial Board of China Electric Power Yearbook); 2006 China Electric Power Yearbook (in Chinese); China Electric Power Press, Beijing (2006)
7. EBCEPY (The Editorial Board of China Electric Power Yearbook), 2011 EBCEPY (The Editorial Board of China Electric Power Yearbook); 2011 China Electric Power Yearbook (in Chinese); China Electric Power Press, Beijing (2011)
8. ICLEI, 2009 ICLEI (Local Governments for Sustainability), 2009 [2014-05-21]. International local government GHG emissions analysis protocol (IEAP) version 1.0. Accessedhttp://carbonn.org/fileadmin/user_upload/carbonn/Standards/IEAP_October2010_color.pdf .
9. IEA (International Energy Agency), 2013 IEA (International Energy Agency); CO₂ Emissions from Fuel Combustion Highlights 2012 Edition  ; Publication, OECD/IEA (2013)
10. IPCC, 1996 IPCC; Workbook: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories; J.T. Houghton (Ed.), et al. , National Greenhouse Gas Inventories Programme, IGES, Japan (1996)
11. IPCC, 2006 IPCC; 2006 IPCC Guidelines for National Greenhouse Gas Inventories; H.S. Eggleston (Ed.), et al. , National Greenhouse Gas Inventories Programme, IGES, Japan (2006)
12. Jin, 2011 Y.-M. Jin; Status quo and trends of carbon emissions of China’s provincial power industries; Energy Technology and Economics (in Chinese), 23 (10) (2011), pp. 56–60
13. Ma and Ge, 2014 C.-M. Ma, Q.-S. Ge; Greenhouse gas emission factors for grid electricity for Chinese provinces; Resources Science (in Chinese), 36 (5) (2014), pp. 1005–1012
14. Ma et al., 2013 C.-M. Ma, H.-Q. Xu, M.-S. Su; The research progress on greenhouse gas inventory methods; Progress in Geography (in Chinese), 32 (3) (2013), pp. 400–407
15. NBSC (National Bureau of Statistics of China), 2007 NBSC (National Bureau of Statistics of China); China Energy Statistical Yearbook 2006 (in Chinese); China Statistics Press, Beijing (2007)
16. NBSC (National Bureau of Statistics of China), 2011 NBSC (National Bureau of Statistics of China); China Energy Statistical Yearbook 2011 (in Chinese); China Statistics Press, Beijing (2011)
17. NCGCC (National Coordination Group on Climate Change), ERI (Energy Research Institute, National Development and Reform Commission), 2007 NCGCC (National Coordination Group on Climate Change), ERI (Energy Research Institute, National Development and Reform Commission); The People’s Republic of China National Greenhouse Gas Inventory (in Chinese); China Environmental Science Press, Beijing (2007)
18. NDRC, 2011 NDRC (National Development and Reform Commission), 2011. Provincial guidelines for greenhouse gas inventories (trial) (in Chinese).
19. NDRC, 2012 NDRC (National Development and Reform Commission), 2012[2014-05-21]. Second national communication on climate change of the People’s Republic of China (in Chinese). Accessed http://www.ccchina.gov.cn/archiver/ccchinacn/UpFile/Files/Default/20130218142020138656.pdf .
20. Song et al., 2013 Song, R.-P., Zhu, J.-J., Hou, P., et al., 2013. Getting every ton of emissions right: An analysis of emission factors for purchased electricity in China (in Chinese). World Resources Institute, Working Paper. Accessed http://www.wri.org.cn/chuban/zhunquehesuanmei%E4%B8%80dunpaifang%EF%BC%9Aqiyewaigoudianliwenshiqitipaifangyinzijiexi .
21. Tang, 2013 Q.-H. Tang; CO₂ emissions characteristics of electricity and reduction pathway analysis in Shanghai  ; Environmental Science & Technology (in Chinese), 36 (1) (2013), pp. 192–196
22. UNEP, 2010 UNEP (United Nations Environment Programme), World UN-HABITAT, WB (World Bank), 2010[2014-05-21]. International standard for determining greenhouse gas emissions for cities (version 2.1). Accessed http://www.unep.org/urban_environment/PDFs/InternationalStd-GHG.pdf .
23. WRI et al., 2012 WRI (World Resources Institute), C40, ICLEI (Local Governments for Sustainability), 2012[2014-05-21]. Global protocol for community-scale greenhouse gas emissions (GPC) pilot version 1.0. Accessed http://www.ghgprotocol.org/files/ghgp/GPC_PilotVersion-1.0-May2012_20120514.pdf .
24. Zhao, 2004 Z.-L. Zhao; Development and prospect of power system in China; Electric Power (in Chinese), 37 (1) (2004), pp. 1–6
25. Zhou et al., 2012 S.-D. Zhou, M.-Z. Zhao, C.-X. Wang, et al.; Calculation of carbon dioxide emissions considering secondary energy deployment among provinces in China; China Population Resources and Environment (in Chinese), 22 (6) (2012), pp. 69–75
26. Zhou et al., 2011 Y. Zhou, B.-F. Cai, L.-C. Liu, et al.; Study on spatial distribution of CO₂ emissions from thermal power industry of China  ; Thermal Power Generation (in Chinese), 40 (10) (2011), pp. 1–7