Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams

Transcription

1 Energ. Ecol. Environ. (2017) 2(5): DOI /s y ORIGINAL ARTICLE Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams Feiyin Wang 1,2 Pengtao Wang 1,2 Xiaomeng Xu 1,2 Lihui Dong 1,2 Honglai Xue 1,2 Shuai Fu 1,2 Yingxu Ji 3 1 Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology, Beijing , China 2 Faculty of Resources and Safety Engineering, China University of Mining and Technology, Beijing , China 3 Langfang Power Supply Company, State Grid Jibei Electric Power Company Limited, Langfang , China Received: 6 April 2017 / Revised: 10 July 2017 / Accepted: 13 July 2017 / Published online: 22 July 2017 Ó Joint Center on Global Change and Earth System Science of the University of Maryland and Beijing Normal University and Springer-Verlag GmbH Germany 2017 Abstract China has promised to optimize its energy structure and reduce its CO 2 emission in the 13th Five- Year Plan. To track the energy structure, the conversions, efficiencies, end consumptions of total energy and coal and the whole CO 2 emission status, the energy flow, coal flow and CO 2 flow in 2015 were, respectively, drawn at the national level based on Sankey diagrams. Besides, each provincial fossil fuel structure, CO 2 structure and CO 2 intensity were calculated and plotted. It is mainly found that China s energy structure consisted of 69.2% of coal, 19.9% of oil, 6.3% of natural gas and 4.7% of non-fossil energy, where 45.5% of energy was consumed by industry and 23.9% by losses and statistical difference; coal was distributed to industry (55.6%), etc., with a utilization rate of 70.1%; and CO 2 were derived from coal (84.7%), oil (11.1%) and natural gas (4.2%), of which 39.0% was released through the process of thermal power generation and 19.4% by industry. The structures of fossil fuels and their CO 2 emissions together with the evolution of CO 2 intensity at the provincial level and the regional level were also given. Besides, two pieces of policy implications were proposed to provide the government with reference. Keywords Energy flow Coal flow Utilization efficiency Carbon dioxide flow Fossil fuel CO 2 intensity & Pengtao Wang pengtaowangscholar@163.com 1 Introduction China was the world s largest energy consumer, whose total energy consumption accounted for the world s 22.3% in The issues of energy consumption and environment, such as the released greenhouse gas and pollutants when costing energy, have become the focus of social concern. As early as the 12th Five-Year Plan period, the Chinese government clearly put forward to establish a green and low-carbon development concept, concentrate on energy saving and emission reduction, improve incentive and restraint mechanisms, speed up the construction of resource saving and environment-friendly production and consumption, and enhance the ability of sustainable development. China s 13th Five-Year Plan has defined explicitly the specific objectives that by the year 2020 the total primary energy consumption will have been controlled at approximate 4.8 billion tce (tonnes of coal equivalent) and the total coal consumption at about 4.2 billion tce, the domestic primary energy production will have reached 4.2 billion tce, the non-fossil energy consumption will have accounted for over 15% of the primary energy consumption, the proportion of natural gas consumption will have been more than 10%, and the proportion of coal consumption will have been limited to \62%. Both the national energy saving and emission reduction plan submitted to the United Nations Framework Convention on Climate Change in 2015 and the Paris Agreement China subscribed in 2016 call for China to put a longterm strategy of low-carbon development (low-emission growth) into practice. How is Chinese recent energy structure? How are various energies converted? Where does energy finally flow to? Why does China s energy

2 318 F. Wang et al. consumption lead to lots of carbon dioxide? How are the fossil fuel structure and CO 2 emission of GDP in each province? How can the government well meet targets China s 13th Five-Year Plan formulated? This study tried to find out the problems of energy consumption in China intuitively and effectively by using Sankey diagrams so as to provide a reference for China to perform energy policy well. Traditional energy balance sheet involves many energy varieties, complex intermediate conversion processes and many end consumers, which is cumbersome to professional staffs in this field and even unintelligible to laymen. After reading a sheet in a year, people often cannot directly get the ins and outs of the total energy or a variety of energy sources not to mention the proportions of terminal consumptions. The International Energy Agency (IEA) draws a global energy flow map each year to facilitate the direct observation of the flow of energy. China s energy consumption ranks the first in the world, and this developing country is suffering unparalleled pressure on energy saving and emission reduction. Therefore, it is very necessary to employ the Sankey diagram to draw Chinese energy flow map, coal flow map and carbon dioxide flow map. This paper looked forward to discovering the basic forms of energy and coal flows, the main emitters of CO 2, each provincial fossil fuel structure, and CO 2 intensity, and the direction of goal effort proposed by China s 13th Five-Year Plan from the drawing of these three maps in China. In order to accomplish objectives above, the Sankey diagram from 2001 to June 2017 searched in Web of Science, this work obtained 124 articles, of which over a half were energy and environment-related documents and mainly were published after 2010 (Cullen and Allwood 2010a, b; Paszota 2011; Caliskan et al. 2011; Nakamura et al. 2011; Bode et al. 2011; Giuffrida et al. 2011; Qiu and Davies 2011; Perez-Lombard et al. 2011; Skelton et al. 2011a, b; Kong and Liu 2012; Paszota 2012a, b; Ma et al. 2012a, b; Bode et al. 2012; Cullen et al. 2012; Paszota 2013; Hu et al. 2013; Nakajima et al. 2013; Curmi et al. 2013a, b; Yan et al. 2013; Kalt 2015; Huang et al. 2015; Guyonnet et al. 2015; Cullen and Allwood 2010a, b). Technical factors were taken into consideration when accelerating efficiency reflected by the 2005 Sankey map of energy flow in the world following the four processing steps, i.e., primary energy, conversion devices, passive systems and services (Ma et al. 2012a, b). Another similar work was done concerning China 2005 as well by the same team (Allwood et al. 2010). The flows of global industrial carbon dioxide emissions in 2006 were applied to assess the possibility of reaching goals in 2050 (Cullen and Allwood 2013). The Sankey diagram was also used in tracing aluminum to provide room for improvement and recycling (Soundararajan et al. 2014). How to design the Sankey diagram was presented and the energy flow and exergy flow diagrams were compared for saving energy in the UK The whole world, energy, efficiency, recycling were the hotspots most papers concentrated on. Besides, previous researchers have studied embodied energies using input output analysis, such as Chen et al. (2010), Chen and Chen (2011), and Chen and Wu (2017). There has been analysis on China s energy flow (Ma et al. 2012a, b; Zhang et al. 2017; Sun et al. 2014), coal flow (Chong et al. 2015; Yu et al. 2013) and carbon dioxide flow (Li et al. 2015) at the national level. But, no preceding studies focus on three flow diagrams for energy, coal and carbon dioxide together. Besides, data applied to previous research on Chinese energy, coal and carbon dioxide flow were relatively old, which cannot accurately reflect the current flows in China. In this paper, the blank of the 2015 energy flow, coal flow and carbon dioxide flow was filled with the up-to-date data when this study was done, and each provincial structure of fossil fuels and carbon dioxide emissions was compared together with the evolution of CO 2 emission of GDP (CO 2 intensity). 2 Data and methodology Intending to clearly show the framework of this work, Fig. 1 is plotted. This study was problem-oriented. At the beginning, there were three questions to answer. The energy flow diagram was employed to indicate Chinese energy structure, conversion and end consumption. After that, it is obvious that coal prevailed over other energies. That is why we made China s coal flowchart to witness its transformation and end use. Besides, the Sankey diagram was drawn as well for representing the CO 2 emitters. Finally, China s energy flow diagram also underlay the fossil fuel structure in each province and each region, and, furthermore, the distributions of 30 main provinces CO 2 emissions of GDP in 2005, 2008, 2011 and 2014 were drawn. Data of both the energy flow diagram, the coal flow diagram and the carbon dioxide flow diagram are derived from China Energy Statistical Yearbook (2016) (CESY 2016a, b). Known for the energy efficiency map of the steam engine by Matthew Henry Phineas Riall Sankey in 1898, this kind of diagram was named after his name Sankey. The Sankey diagram, the Santi energy shunt diagram, is also called the Sankey energy balance diagram. It is a specific type of flow chart, which is usually used in visualization analysis of energy, material composition, finance and other data. The width of the branch extension corresponds to the size of the data flow. This study used the electrothermal equivalent method to draw China s energy flow diagram in 2015, China s coal flow chart in 2015 and

3 Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams 319 How is the current energy structure? How are primary energies transformed? Where do energies finally flow? How is the coal that accounts the largest proportion of energies converted and consumed? China's coal flow diagram China's energy flow diagram How is each provincial fossil fuel structure? How does the CO 2 divided by GDP evolve in each province? Which fossil fuel dominates carbon emission? Which sectors are the main carbon dioxide emitters? China's carbon dioxide flow diagram The fossil fuel structure in each province and region The distribution and evolution of the CO 2 intensity Fig. 1 The sketch of research framework China s carbon dioxide flow diagram in 2015 based on the software of e!sankey 4.2. The variables are explained in Tables 1, 2 and 3. The method (Wu et al. 2016) shown as follows underlay how to computer CO 2 emissions from main fossil fuels CO 2 emission ¼ X6 i¼1 ðf i CCF i HE i COF i Þ ð1þ where F are fossil fuels consumption (10 4 m 3 or 10 4 tons) whose data are from China Energy Statistical Yearbook ( ) (CESY 2016a, b), CCF carbon content factors (tons carbon/trillion Joules) whose data are from (IPCC Guidelines for National Greenhouse Gas Inventories 2006) and HE heat equivalents (trillion Joules/10 4 m 3 or trillion Joules/10 4 tons) whose data are from (National Development and Reform Commission 2007) and COF carbon oxidation factors whose data are from (National Development and Reform Commission 2007). The corresponding parameters of various fossil fuels are presented in Table 4. As each calculated CO 2 emission of petrol, kerosene, diesel and fuel oil was too small to solely and visually exhibit compared with that of natural gas let alone coal when drawing the Sankey diagram of CO 2, this paper aggregated the CO 2 emissions of these four fossil fuels. As shown in Table 4, fossil fuels in each province were transformed from physical unit to coal equivalent using conversion factors (CESY 2016a, b) in which 12 tce/ 10,000 m 3 was taken as the conversion factor for natural gas. We also calculated the conversion factors presented in this table between CO 2 emission and coal equivalent, which indicates that natural gas emits less CO 2 than other fossil fuels and coal is the most CO 2 emitter when sending out the same heat. Thus, the consumption of natural gas is more environmentally friendly and affects climate change less than that of coal. When counting the CO 2 emission divided by GDP, the data of GDP are from China Statistical Yearbook ( ) (CESY 2016a, b). Table 1 Variables in energy flow diagram China s energy balance item Energy supply Import Including domestic airplanes and ships refueling in abroad Export Including oversea airplanes and ships refueling in China Stock Net additions or decreases to stocks Coal Aggregates raw coal, cleaned coal, other washed coal, briquettes Oil Aggregates crude oil and all the other petroleum products Natural gas Aggregates natural gas and liquefied natural gas Electricity Electricity generated from various aggregated fossil fuel and hydro, wind, solar, nuclear Heat Heat generated from raw coal, oil, natural gas, recovered energy Energy end use Industry Fishery and water conservancy, construction Transport Transport, storage and post, wholesale, retail trade and hotel, restaurants Residential Urban, rural and other Losses and statistical difference LSD Loss and statistical difference

4 320 F. Wang et al. Table 2 Variables in coal flow diagram China s coal balance item Coal supply Coal Export Stock Coal conversion Direct combustion Electricity generation Heat generation Coal washing Coal chemical Coking Coking products Other coking products Coal gas Energy end use Agriculture Industry Transport Construction Wholesale Residential Others Losses and statistical difference LSD Aggregates raw coal, cleaned coal, other washed coal, briquettes Net export quantity Net additions or decreases to stocks Including the combustion of raw coal, cleaned coal, petroleum refineries and gas works Electricity generated from various aggregated coal and coal products Heat generated from various aggregated coal and coal products Aggregates cleaned coal and other washed coal Aggregates petroleum refineries and gas works Generation of coke, coal gas and other coking products Aggregates petroleum refineries and gas works All coking products exclude coke Aggregates coke oven gas, blast furnace gas, converter gas and other gas Agriculture, forestry, animal husbandry, fishery and water conservancy Industry and non-energy use Transport, storage and post Construction Wholesale, retail trade and hotel, restaurants Residential consumption Others Loss and statistical difference 3 Results 3.1 Energy flow diagram Figure 2 clearly describes Chinese primary energy supply, transformation, final consumption, and loss and the statistical difference in the year of The indigenous energy production, import, export and stock change included, China s total primary energy supply was up to 3.97 billion tce in 2015 using calorific value calculation, of which coal (2.75 billion tce) accounted for about 69.2%, and oil (0.79 billion tce) 19.9%, natural gas (0.25 billion tce) 6.3% and non-fossil energy (0.18 billion tce) 4.7%. The total indigenous energy production (3.28 billion tce) consisted of coal (79.6%), oil (9.4%), natural gas (5.3%) and nonfossil energy (5.7%). The total energy import reached 773 million tce in 2015, while total energy export was 85 million tce. The energy dependence ratio was 17.3%, and the total energy net stock decreased by 661 million tce. In the given year, 0.53 billion tce of electricity was generated by 1.26 billion tce of other energies among which the percent of coal was 94.9%, natural gas 3.2%, heat 1.6% and oil 0.3%. 58.3% of these energies lost in the conversion process, and the power generation efficiency for the whole energy was 40.7%. The total of 0.71 billion tce of electricity whose 26.2% was primary energy and 73.8% was secondary energy flowed to the final use accompanied by the LSD of 5.2% of electricity. In terms of its end consumption, nearly two-thirds (66.5%) of the electricity was used by the industry, 1.9% by the transport, storage and post, 13.0% by residential consumption and 13.4% by other trades. Besides, the efficiency of combined heat and power reached 45.5%. In view of the total energy end use and LSD, the efficiency of energy utilization trended out to be 76.1%. End consumption was totaled up to 3.12 billion tce. The industry, the LSD, the transport, storage and post, the residential consumption, the non-energy use and others, respectively, accounted for 45.5, 23.9, 8.5, 8.3, 5.5 and 8.3%. The percentages of coal, oil, natural gas, electricity and heat that the industry costed were 58.3, 6.1, 5.0, 25.4 and 5.1%, respectively, those for transport, storage and post were 1.0, 86.3, 8.4, 4.0 and 0.3%, those for the residential consumptions were severally 21.2, 28.4, 13.7, 27.3 and 9.4%, those for non-energy uses were 40.3, 51.3, 8.4, 0 and 0%, and those for other trades were 26.2, 40.0, 3.8, 28.1 and 2.0%.

5 Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams 321 Table 3 Variables in carbon dioxide flow diagram China s carbon dioxide balance item CO 2 from fossil fuels Coal Oil Natural gas CO 2 emissions conversion Thermal power Heating supply Coal washing Coking Petroleum refineries Gas works End CO 2 emissions Agriculture Industry Construction Transport Wholesale Residential Others CO 2 from aggregates raw coal, cleaned coal, other washed coal, briquettes, gangue CO 2 from aggregates petrol, kerosene, diesel, fuel oil CO 2 from natural gas, excluding liquefied natural gas Agriculture, forestry, animal husbandry and fishery Industry Construction Transport, storage and post Wholesale, retail trade and hotel, restaurants Urban and rural Others Table 4 Conversion factors from physical unit to coal equivalent of the main fossil fuels Fuel CCF HE COF (%) CO 2 /F Coal equivalent/f CO 2 /coal equivalent Coal t/trillion Joules trillion Joules/10,000 t ,739.3 t/10,000 t 7143 tce/10,000 t 2.48 t/tce Natural gas t/trillion Joules trillion Joules/10,000 m t/10,000 m 3 12 tce/10,000 m t/tce Gasoline t/trillion Joules trillion Joules/10000 t ,425.5 t/10,000 t 14,714 tce/10,000 t 2.07 t/tce Kerosene t/trillion Joules trillion Joules/10,000 t ,710.1 t/10,000 t 14,714 tce/10,000 t 2.16 t/tce Diesel t/trillion Joules trillion Joules/10,000 t ,468.6 t/10,000 t 14,571 tce/10,000 t 2.16 t/tce Fuel oil t/trillion Joules trillion Joules/10,000 t ,612.7 t/10,000 t 14,286 tce/10,000 t 2.14 t/tce 3.2 Coal flow diagram China s coal flow map for 2015, Fig. 3, was also mapped based on Energy Balance of China (Standard Quantity) (CESY 2016a, b). The diagram was divided into four parts, namely coal supply, coal conversion, end use and LSD. Concerning 2.74 billion tce of its total primary coal supply in 2015, China domestically produced approximately 2.61 billion tce of raw coal, imported 125 million tce, exported net 5 million tce and reduced its stock by 13 million tce. At the beginning of coal conversion, 24.4% of the total raw coal supply was used to combust directly. 43.2% was costed when generating electricity and 5.5% when generating heat. 24.6% was for coal washing, 1.9% for coking, 0.4% for coal chemical and 0.1% for briquettes. Approximate 108 million tce of cleaned coal and 6 million tce of liquid and gaseous fuels from coal liquefaction and coal gasification was assembled to the direct combustion and flowed to the final consumption along with 669 million tce of raw coal. About 485 million tce of electricity and 121 million tce of heat were converted from 1.40 billion tce of coal and coal products with an efficiency of 43.3%. The power generation efficiency for coal was 39.4%. Six hundred and twenty-three million tce of washed coal was manufactured, whose 77.6% was put into coking. The process of coking produced 436 million tce of coke and 60 million tce of other coking product. There was a large number of recovery energy (112 million tce) helping to refine other coking products. The coal and coal products (1.95 billion tce) were provided to end use in the form of raw coal (40.1%, including part of washed coal, liquid fuels and coal gas),

6 322 F. Wang et al. Fig. 2 China s energy flow diagram in 2015 Fig. 3 China s coal flowchart in 2015 electricity (24.8%), heat (6.2%), coke (21.8%), other coking products (6.6%) and briquette (0.6%). According to various demands, coal and its terminal products were portioned to different sectors in different proportions. The industrial sector accounted for the largest proportion that was 79.4% of the whole coal and coal-based products. Among them, 70.3% of raw coal, 67.8% of electricity, 70.7% of heat, 95.7% of coke, 94.0% of other coking products and 69.3% of briquette flowed to the industry. It can be obtained from the graph that the rest were in descending order distributed to other costing sectors (7.9%), non-energy use (4.3%), residential sector (3.27%), wholesale (2.4%), agriculture (1.4%), construction (0.7%) and transport (0.7%). The whole utilization rate arrived at 70.1% from the primary supply of raw coal to the terminal. What is worthy of mention is that for coke the volume of net export was about 6 times that of the stock reduction, while for row coal the net import was nearly 9 times the volume of the stock reduction. 3.3 Carbon dioxide flow diagram The carbon dioxide emission in 2015 mostly came from coal (84.7%), followed by oil (11.1%) and natural gas (4.2%) and totaled up to 8320 million tons indicated in Fig. 4. Both the classified two parts, the conversion and the terminal consumption, emitted quite a lot of carbon dioxide in the certain year. Nearly 62.8% of CO 2 were released when the primary fossil fuels were carried on the conversion processes, of which almost two-thirds was sent by the process of thermal power generation. The surplus 37.2% of

7 Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams 323 Fig. 4 China s carbon dioxide flow diagram in 2015 the total CO 2 emission was let out by terminal consumption sectors. Industry accounted for more than a half of CO 2 emission of all the seven sectors. 3.4 Provincial fossil fuel structure and CO 2 intensity The Chineses government has assigned Beijing, Tianjin, Shanghai, Fujian, Guangdong, Hebei, Liaoning, Jiangsu, Shandong and Hainan to the East region, Jilin, Heilongjiang, Jiangxi, He nan, Hubei, Hu nan and Shanxi to the middle region, and Sichuan, Yunnan, Guizhou, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Tibet, Chongqing and Neimenggu to the West region since the year of This allocation has largely considered the geographical location and economic development level in each province. It is worth noticing that Chinese HongKong, Macao, Tibet and Taiwan are not included in this analysis due to their unavailable data. Figure 5 shows the counts and their ratios of fossil fuels and CO 2 emission in each province, as well as the mean counts and their ratios of provinces fossil fuels and CO 2 emissions in each region. Comparing graphs of ratios for the fossil fuel and CO 2 emission, the proportion of natural gas decreased from the graph for fossil fuel to that for CO 2 emission. That is because less CO 2 is released when burning natural gas than other fossil fuels. However, the coal shows an opposite trend to natural gas. Therefore, a better fossil fuel structure should be composed of more natural gas and less coal. Shandong consumed the most total fossil fuels (380 million tce), the most coal (292 million tce) and the most oil (78 million tce) of 30 provinces, and Sichuan cost the highest natural gas (21 million tce). Hainan spent the least total fossil fuels (18 million tce) and the least coal (8 million tce), Qinghai the lowest oil (2 million tce) and Yunnan the smallest natural gas (1 million tce). The means of total fossil fuels, coal, oil and natural gas were, respectively, 129, 101, 20 and 8 million tce. For the fossil fuel structure, the proportion of natural gas (40.5%) in Beijing was the largest of Chinese 30 provinces, while that of coal (19.1%) was the least. The rate of oil (42.9%) was biggest in Shanghai, while the smallest (3.9%) was in Shanxi. The percentage of coal (94.0%) in Neimenggu was the most, and the ratio of natural gas (1.1%) in Yunnan was the lowest. The percentages of the averages for coal, oil and natural gas were severally 73.7, 17.5 and 8.8%. Concerning the CO 2 emission, 307, 251, 42 and 14 million tons, in turn, were the averages of Chinese 30 main provinces total CO 2 emission, CO 2 emission from coal (coal CO 2 ), CO 2 emission from oil (oil CO 2 )and CO 2 emission from natural gas (natural gas CO 2 ). For a certain species of fossil fuel, the province who consumes more energy undoubtedly releases more CO 2. Hainan emitted the least total CO 2 emission (39 million tons) and the least coal CO 2 (19 million tons) of all the given provinces, Qinghai the smallest oil CO 2 (5 million tons) and Yunnan the lowest natural gas CO 2 (1.0 million tons), while the three maximums of the total CO 2 emission (910 million tons), coal CO 2 (726 million tons) and oil CO 2 (167 million tons) all came from Shandong and that of natural gas CO 2 (31 million tons) from Xinjiang. When it comes to the CO 2 structure, Beijing had the smallest percentage of coal CO 2 (23.2%) and the biggest rate of natural gas CO 2 (35.1%). That is why Beijing was considered to be the city with the optimal energy structure or CO 2 emission structure. Shanxi owned the most ratio of coal CO 2 (94.6%) and the least proportion of oil CO 2 (3.4%); thus, it suffered from its worst energy or CO 2 emission structure. The lowest rate of natural gas CO 2 (1.1%) belonged to Yunnan. 73.7, 17.5 and 8.8% were the

8 324 F. Wang et al. Fossil fuels (10 8 tce) Province Beijing Tianjin Shanghai Zhejiang Fujian Guangdong Hebei Liaoning Jiangsu Shandong Hainan Jilin Heilongjiang Jiangxi He'nan Hubei Hu'nan Shanxi Anhui Sichuan Yunnan Guizhou Shaanxi Gansu Qinghai Ningxia Xinjiang Chongqing Neimenggu Guangxi East Middle West Fossil fuels ratio Province Beijing Tianjin Shanghai Zhejiang Fujian Guangdong Hebei Liaoning Jiangsu Shandong Hainan Jilin Heilongjiang Jiangxi He'nan Hubei Hu'nan Shanxi Anhui Sichuan Yunnan Guizhou Shaanxi Gansu Qinghai Ningxia Xinjiang Chongqing Neimenggu Guangxi East Middle West Coal Natural gas Oil (a) Coal Natural gas Oil (b) CO 2 (10 8 ton) Province Beijing Tianjin Shanghai Zhejiang Fujian Guangdong Hebei Liaoning Jiangsu Shandong Hainan Jilin Heilongjiang Jiangxi He'nan Hubei Hu'nan Shanxi Anhui Sichuan Yunnan Guizhou Shaanxi Gansu Qinghai Ningxia Xinjiang Chongqing Neimenggu Guangxi East Middle West CO 2 ratio Province Beijing Tianjin Shanghai Zhejiang Fujian Guangdong Hebei Liaoning Jiangsu Shandong Hainan Jilin Heilongjiang Jiangxi He'nan Hubei Hu'nan Shanxi Anhui Sichuan Yunnan Guizhou Shaanxi Gansu Qinghai Ningxia Xinjiang Chongqing Neimenggu Guangxi East Middle West Coal Nature gas Oil Coal Nature gas Oil (c) (d) Fig. 5 Fossil fuels and CO 2 consumption in a Fossil fuels in 30 provinces of 3 regions, b fossil fuels ratios in 30 provinces of 3 regions, c CO 2 emissions in 30 provinces of 3 regions, d CO 2 emissions ratios in 30 provinces of 3 regions proportions of the mean values for coal CO 2, oil CO 2 and natural gas CO 2, respectively. It is illustrated that different provinces did not have similar amount or proportion of the total fossil fuel or total CO 2 emission within each of the three regions assigned by the government, both the mean (147 million tce) of provinces total fossil fuels and the average (346.7 million tons) of provinces total CO 2 emissions in the eastern region were slightly larger than those in the middle region (138 and 333 million tons), and the West was the one who obviously spent the least total fossil fuel (104 million tce) and emitted the least CO 2 emission (250 million tons). In view of the structure of fossil fuel and CO 2 emission in each region, provinces differences within the middle region were the least significant of the three regions, and the East region changed more obviously than the West. The eastern coal accounted less proportion (74.7%, accompanied by oil of 19.1% and natural gas of 6.2%) than other regions, the middle region had the worst fossil fuel structure (coal of 84.5%, oil of 12.0% and natural gas of 3.5%) of the 3 regions, and the western energy structure was made up of coal of 81.7%, oil of 11.5% and natural gas of 6.8%. In addition, the East region released coal CO 2 of 74.7%, oil CO 2 of 19.1% and natural gas CO 2 of 6.2%, while the proportions for the middle region were, respectively, 86.9, 10.5 and 2.6% and those for the West 84.8, 10.2 and 5.0%. The distribution maps of Chinese provinces CO 2 emissions divided by GDP at 2005 price were drawn every 3 years in the period of displayed in Fig. 6. The levels of Shanxi, Ningxia, Neimenggu and Xinjiang remained stable in the given period, while other 26 provinces all had decreased their degrees of CO 2 intensity. The relatively developed provinces with low levels of CO 2

9 Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams 325 Fig. 6 CO 2 intensities in China s 30 provinces intensity in 2005, such as Beijing, Shanghai and Guangdong, significantly began to cut down their levels in earlier years, while the northeastern and central provinces with relatively high levels of CO 2 intensity started to reduce their levels in later years. Qinghai was special as its level went up in 2008 and fell down in later years. More and more provinces had shrunk its CO 2 emissions of GDP to \2 tons/10,000 yuan at 2005 price. Overall, each province had a decline of CO 2 intensity. Compared with those in 2014, only Anhui and Guizhou, respectively, reduced a level of CO 2 intensity in the following year. In 2015, the intensities in southeastern coast were low and those in the northwestern high. The central region had medium CO 2 intensities. The gaps among provinces were still wide. It is reasonable if the measurements were given regionally. 4 Discussions For the drawing processes of the energy flow, coal flow and carbon dioxide flow, the overall data are based on China Energy Statistical Yearbook 2016 (CESY 2016a, b). The calorific value calculation was taken in plotting Sankey diagrams instead of coal equivalent calculation. In terms of energy conversion on the power and heat, energy loss was calculated by subtracting the total electricity and total heat from the total energy supply, leading to that the efficiency of combined heat and power was 45.5% which approximates 44.2% in the Efficiency of Energy Transformation of China Energy Statistical Yearbook 2016 (CESY 2016a, b). In addition, when plotting the energy flow and coal flow diagrams, the allocation of electricity and heat to the

10 326 F. Wang et al. terminal sectors involved the final consumption ratio of the electricity and heat balance table (CESY 2016a, b) to determine the consumption amount of each consumer. Concerning the carbon dioxide flow diagram, the six major fossil fuels commonly used by previous scholars were selected to computer CO 2 emission, and the total CO 2 emission from fossil fuels excluded that from fossil fuels as raw materials. The strictly calculated Chinese carbon dioxide emission (8.5 billion tons in 2014 and 8.3 billion tons in 2015) is close to and less than that IEA (International Energy Agency 2016) announced (9.1 billion tons in 2014). One of the two main reasons for this deviation is that this study focused on the dominant six fossil fuels (coal, natural gas, gasoline, kerosene, diesel and fuel oil), and other energy, such as liquefied natural gas and liquefied petroleum gas, was not included in this estimation. Another chief reason is that the data of Hong Kong, Macao, Taiwan, Tibet were not complete or not available in 2015, and this study only analyzed the fossil fuel structure, carbon dioxide emission and CO 2 intensity of the major 30 provinces in China. The two aspects directly resulted that the total CO 2 emission calculated by this work was under that by IEA. 5 Conclusions and policy implications 5.1 Conclusions With the total energy supply of 3.97 billion tce and the energy dependence ratio of 17.3% in 2015, China s energy structure consisted of coal (69.2%), oil (19.9%), natural gas (6.3%) and non-fossil energy (4.7%). Therefore, Chinese energy was heavily dominated by coal. Accompanied by a transformation rate of 41.7%, the 0.53 billion tce of thermal power rooted in 1.26 billion tce of other energies, i.e., coal (94.9%), natural gas (3.2%), heat (1.6%) and oil (0.3%). The efficiency of energy utilization was 76.1%. 45.5% of energy was cost on industry, 23.9% on energy loss and statistical difference, 8.5% on transport, storage and post, 8.3% on residential consumption, 5.5% on non-energy use and 8.3% on others. The total coal supply was 2.74 billion tce of which 43.2% flowed to generate electricity, 5.5% to generate heat, 24.4% to combust directly, 24.6% to be washed, 1.9% to be coked, 0.4% to petroleum refineries and gas works and 0.1% to briquettes. 485 million tce electricity and 121 million tce heat were converted basically from coal with the conversion efficiency of 43.3%. The energy (1.95 billion tce) was provided to final use in the form of direct combustion products (40.1%), electricity (24.8%), heat (6.2%), coke (21.8%), other coking products (6.6%) and briquette (0.6%). The efficiency of coal utilization was 70.1%. The proportions for the industry, other costing sectors, non-energy use, residential sector, wholesale, agriculture, construction, transport and LSD were 55.6, 5.6, 3.0, 2.3, 1.7, 1.0, 0.5,0.5 and 29.9% in turn. Coal emitted 84.7% of the total CO 2, oil 11.1% and natural gas only 4.2%. The total CO 2 emission was 8.3 billion tons, of which the converting process of thermal power released 39.0%, industry 19.4%, the converting process of coking 12.9%, transport 8.2%, the converting process of heating supply 5.4%, residential 4.3%, the converting process of coal washing 3.9%, etc. Most provinces, except for Beijing, Hainan and Shanghai, relied largely on coal, and the ratios of natural gas were still low. The middle region had the worst fossil fuel structure, while the structure in the East was the best. The East region consumed the most fossil fuels and released the most CO 2 than the other two regions. In both the two aspects, the West region showed better than the middle. The majority of provinces, apart from Shanxi, Ningxia, Neimenggu, Xinjiang, Jiangsu, Chongqing and Hainan, had cut down at least one level of CO 2 intensity. Seventeen provinces have realized their CO 2 intensity of no more than 2 tons/10,000 yuan by the end of The southeast region had a low level of CO 2 intensity and started to decline the intensity early, while the northwest region owned a high level of the intensity and began to cut down the intensity late. 5.2 Policy implications Through the results and conclusions above, three pieces of policy implications were given as follows. As the converting process of thermal power releases a mass of CO 2 (39.0% of the total CO 2 emission in 2015) and the emitting process is quite concentrated in thermal power plants, applying the technology of carbon capture, utilization and storage (CCUS which is also named as CCS) in thermal power plants could efficiently cut down China s CO 2 emission. The middle region is a region which has the worst energy structure, the least differences among provinces and a high fossil fuel consumption. The investment of the technology and its implementation for non-fossil energies, such as hydro, nuclear, wind and bioenergy, to provinces in the middle region, should be the most effective so as to reduce the use of coal and optimize the energy structure. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No ). Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest.

11 Tracing China s energy flow and carbon dioxide flow based on Sankey diagrams 327 References Allwood JM, Cullen JM, Milford RL (2010) Options for achieving a 50% cut in industrial carbon emissions by Environ Sci Technol 44(6): Bode A, Buerkle J, Hoffner B, Wisniewski T (2011) A view through the cost binocular: use of flow analysis to assess and improve chemical production processes. Chem Ing Tech 83(10, SI): Bode A, Buerkle J, Hoffner B, Wisniewski T (2012) Looking at the cost: using flow analysis to assess and improve chemical production processes. Chem Eng Technol 35(8, SI): Caliskan H, Hepbasli A, Dincer I (2011) Exergy analysis and sustainability assessment of a solar-ground based heat pump with thermal energy storage. J Sol Energy Eng 133(011005):1 8 CESY (2016a) China energy statistical yearbook ( ). China Statistical Press, Beijing CESY (2016b) China statistical yearbook ( ). China Statistical Press, Beijing Chen ZM, Chen GQ (2011) An overview of energy consumption of the globalized world economy. Energy Policy 39(10): Chen GQ, Wu XF (2017) Energy overview for globalized world economy: source, supply chain and sink. Renew Sustain Energy Rev 69: Chen ZM, Chen GQ, Zhou JB, Jiang MM, Chen B (2010) Ecological input output modeling for embodied resources and emissions in Chinese economy Commun Nonlinear Sci Numer Simul 15(7): Chong C, Ma L, Li Z, Ni W, Song S (2015) Logarithmic mean Divisia index (lmdi) decomposition of coal consumption in china based on the energy allocation diagram of coal flows. Energy 85: Cullen JM, Allwood JM (2010a) The efficient use of energy: tracing the global flow of energy from fuel to service. Energy Policy 38(1):75 81 Cullen JM, Allwood JM (2010b) Theoretical efficiency limits for energy conversion devices. Energy 35(5): Cullen JM, Allwood JM (2013) Mapping the global flow of aluminum: from liquid aluminum to end-use goods. Environ Sci Technol 47(7): Cullen JM, Allwood JM, Bambach MD (2012) Mapping the global flow of steel: from steelmaking to end-use goods. Environ Sci Technol 46(24): Curmi E, Richards K, Fenner R, Allwood JM, Kopec GM, Bajzelj B (2013a) An integrated representation of the services provided by global water resources. J Environ Manag 129: Curmi E, Fenner R, Richards K, Allwood JM, Bajzelj B, Kopec GM (2013b) Visualising a Stochastic model of Californian water resources using Sankey Diagrams. Water Resour Manag 27(8): Giuffrida A, Romano MC, Lozza G (2011) Thermodynamic analysis of air-blown gasification for IGCC applications. Appl Energy 88(11): Guyonnet D, Planchon M, Rollat A, Escalon V, Tuduri J, Charles N, Vaxelaire S, Dubois D, Fargier H (2015) Material flow analysis applied to rare earth elements in Europe. J Clean Prod 107: Hu G, Xunmin O, Zhang Q, Karplus VJ (2013) Analysis on energywater nexus by Sankey diagram: the case of Beijing. Desalin Water Treat 51(19 21): Huang C-W, Syed-Abdul S, Jian W-S, Iqbal U, Nguyen P-AA, Lee P, Lin S-H, Hsu W-D, Mai-Szu W, Wang C-F, Ma K-L, Li Y-CJ (2015) A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan. J Am Med Inform Assoc 22(2): International Energy Agency (2016) statisticssearch/report/?product=indicators&country=china IPCC (2006) IPCC guidelines for national greenhouse gas inventories. Kalt G (2015) Biomass streams in Austria: drawing a complete picture of biogenic material flows within the national economy. Resour Conserv Recycl 95: Kong L, Liu H (2012) A static energy model of conventional paper drying for multicylinder paper machines. Drying Technol 30(3): Li H, Wei Y, Mi Z (2015) China s carbon flow: Energy Policy 80:45 53 Ma L, Allwood JM, Cullen JM, Li Z (2012a) The use of energy in China: tracing the flow of energy from primary source to demand drivers. Energy 40(1): Ma L, Fu F, Li Z, Liu P (2012b) Oil development in China: current status and future trends. Energy Policy 45:43 53 Nakajima K, Ohno H, Kondo Y, Matsubae K, Takeda O, Miki T, Nakamura S, Nagasaka T (2013) Simultaneous material flow analysis of nickel, chromium, and molybdenum used in alloy steel by means of input output analysis. Environ Sci Technol 47(9): Nakamura S, Kondo Y, Matsubae K, Nakajima K, Nagasaka T (2011) UPIOM: a new tool of MFA and its application to the flow of iron and steel associated with car production. Environ Sci Technol 45(3): National Development and Reform Commission (NDRC) (2007) National greenhouse gas inventory of the People s Republic of China. Chinese Environmental Science Press, Beijing Paszota Z (2011) Hydrostatic drives as safe and energy saving machines the drive investigation method compatible with the diagram of power increase opposite to the direction of power flow. Pol Marit Res 18(1):3 9 Paszota Z (2012a) Effect of the working liquid compressibility on the picture of volumetric and mechanical losses in a high pressure displacement pump used in a hydrostatic drive part II mechanical losses in a pump. Polish Marit Res 19(3):36 44 Paszota Z (2012b) Effect of the working liquid compressibility on the picture of volumetric and mechanical losses in a high pressure displacement pump used in a hydrostatic drive part I energy losses in a drive system, volumetric losses in a pump. Pol Marit Res 19(2):3 10 Paszota Z (2013) Losses and energy efficiency of drive motors and systems Replacement of the Sankey diagram of power decrease in the direction of power flow by a diagram of power increase opposite to the direction of power flow opens a new perspective of research of drive motors and systems. Pol Marit Res 20(1):3 10 Perez-Lombard L, Ortiz J, Maestre IR (2011) The map of energy flow in HVAC systems. Appl Energy 88(12): Qiu TY, Davies PA (2011) The scope to improve the efficiency of solar-powered reverse osmosis. Desalin Water Treat 35(1 3):14 32 Skelton A, Guan D, Peters GP, Crawford-Brown D (2011a) Mapping flows of embodied emissions in the global production system. Environ Sci Technol 45(24): Skelton A, Guan D, Peters GP, Crawford-Brown D (2011b) Mapping flows of embodied emissions in the global production system. Environ Sci Technol 45(24): Soundararajan K, Ho HK, Su B (2014) Sankey diagram framework for energy and exergy flows. Appl Energy 136: Sun J, Ma L, Ni W, Zheng L (2014) Reducing China s energy consumption and CO 2 emissions by more efficient use of energy. In: International conference on materials for renewable energy and environment, vol 3. IEEE, pp

12 328 F. Wang et al. Wu J, Qingyuan Z, Liang L (2016) CO 2 emissions and energy intensity reduction allocation over provincial industrial sectors in China. Appl Energy 166: Yan L, Boshu H, Xiaohui P, Xusheng L, Chaojun W (2013) Energy and exergy analyses of a zero emission coal system. Energy 55: Yu F, Ma L, Jiang L, Li Z, Ni W (2013) Mapping the energy flows of coal utilization in China: the methodology and case studies for the year 2005 and Adv Mater Res : Zhang B, Qu X, Meng J, Sun X (2017) Identifying primary energy requirements in structural path analysis: a case study of china Appl Energy 191: