- Email: [email protected]
Coal flow of present and the future in China-A provincial perspective
Available online at www.sciencedirect.com
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy Procedia 142 Energy Procedia 00(2017) (2017)2448–2453 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Coal flow of present and the future in China-A provincial The 15th International Symposium on District Heating and Cooling perspective Assessing the feasibility using Chen the a,b* heat demand-outdoor Nan Lia,b,of Wenying temperature function for a long-term district heat demand forecast Research Center for Contemporary Management, Tsinghua University, Beijing 100084, China aa
b bInstitute
Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China
a,b,c
I. Andrić a
*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
Considering the vital role of coal in the energy structure and coal transportation being a bottleneck of development in China, this paper assessed the current and future flow from a provincial perspective in an integrated and systematical way. From coal flow in 2010-2015, we found that the Xinjiang and Southwest region can be more developed to supply coal and the Middle region can be Abstract developed as a transit area for the Coastal region. From the perspective of provincial China-TIMES model, there will be a substantial growth of Southwest output while Xinjiang increase much slower due to the transportation cost in the Base scenario. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the Moreover, policies should strengthen the capability of the Middle region to supply energy for the Coastal area. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat © 2017 The Authors. Published by Elsevier Ltd. sales. to the changed climate conditions and building renovation policies, heat demand in the future could decrease, © 2017 Due The Authors. Published by Ltd. committee Peer-review under responsibility of Elsevier the scientific of the 9th International Conference on Applied Energy. Peer-review responsibility the scientific committee of the 9th International Conference on Applied Energy. prolonging under the investment returnofperiod. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords: Coal flow; China; Provincial; China-TIMES forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1.renovation Introduction compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Energy and transportation arelower two than important and economic lifelines. On after one hand, passenger and (the error in annual demand was 20% for allinterdependent weather scenarios considered). However, introducing renovation freight transportation sectors require large amount of energy. On the other hand, energy delivery from supply side to scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). demand side needs to be unhindered. Coal has dominated the energy supply at 70% of the total, resulting in significant The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the air pollution carbon dioxidehours emissions thatduring reached 7.2 billion tons in 2010on[1]. China’s rapid economic decrease in theand number of heating of 22-139h the heating season (depending the combination of weather and renovation scenarios On the other hand, function intercept increased 7.8-12.7% permajor decade (dependingover on the development leads toconsidered). a similar increase in primary energy consumption, withfor coal being the component a coupled scenarios). values The suggested couldisbefurther used tocomplicated modify the function parameters the scenarios considerable periodThe of Time. challenge by the fact that thefor majority of coalconsidered, resources and is improveinthe heatnorthern demand estimations. located theaccuracy westernofand part of China while the most developed area is Coastal area in the southeast part, © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
* Corresponding author. [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.181
2
Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Nan Li/ Energy Procedia 00 (2017) 000–000
2449
as shown in Fig.1. Basic reserves of Shanxi, Inner Mongolia, Xinjiang, Shaanxi province take up 67% [2-3]. The energy consumption, GDP and population of the western and northern part are much lower than those of the other parts. Therefore, it’s worthwhile to analysis coal flow due to unevenly distributed resources and development.
Fig. 1. Provincial percentage of Basic reserves of coal, coal extraction, GDP in China in 2010.†
Nowadays, increasing attention has been drawn in the studies about coal transport in China. Gao applied Lorenz curve to describe the significant imbalance of coal supply and coal demand. He pointed out the coal output provinces was moving west and north by analyzing data from 1995-2009. Moreover, he optimized the flow path by digging into the flow path in the main output provinces [4]. Wang divided the provinces into two types like source regions, sink regions and conclude the center of sources is moving like “U” and the center of sinks like “I” with data from 19902010 [5]. These studies gave detailed interpretations about the change in the flow pattern in view of historical data and analyze the future arrangement qualitatively. Mou used the linear programming to figure out the change of coal transportation under different conditions including with Daqin Railway capacity constraint, shift of coal supply zones and the influence of inland waterway of Yangtze River. This study gave a good understanding of how the optimal coal flow will change under different conditions in the current coal demand. But its deficiency was coal supply and consumption remained unchanged with the 2009 energy statics. This study analyzes the current coal flow by the railway between provinces from 2010-2015. Secondly, it applies a bottom-up energy system model to figure out the future coal flow in the Baseline scenario 2. Current coal flow in 2010 and 2015 Figure 2 shows that currently Shanxi, Inner Mongolia and Shaanxi account for 34%, 30% and 12% of total coal moving out, respectively, while each of Hebei, Jiangsu and Shandong contribute to more than 10% of coal moving in of the total.
1. Taiwan, Hongkong and Marco are not shown due to statistical issue 2. The abbreviation can be found in the Appendix
†
Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Author name / Energy Procedia 00 (2017) 000–000
2450
3
Fig. 2. Inter provincial coal moving in and out in 2010
Coal transportation can be divided into three types: railway, railway-waterway joint operation and road. In 2010, coal delivered by railway occupied a large proportion of 67%, while that of the waterway and road was around 30% and 5% respectively [6]. As the railway-waterway joint operation still needs development of railway and after 2010, the share of railway has been always higher than 60% and keep increasing. So this paper will focus on the railway transportation of coal and the coal flow means coal flow by railway hereafter. Figure 3 shows the coal flow in 2010 and 2015. The percentage of Shanxi and Northwest in the region output increase from 77% in 2010 to 85% in 2015, with the amount reaching around 600 Mtce. The coal moving in of Coastal and JJJ remain around 63% in 2010 and 2015, which are the most energy intensive regions [6]. There are small shifts in the other region in the short run. The trend can be found more specifically from Fig.4. Rich in coal reserves, Xinjiang, Northwest, Southwest and Shanxi have few coal input and nowadays Northeast and Shanxi work as the two largest coal output base. But in the long run, there are great potential for Xinjiang and Southwest to grow into important output energy bases [7]. JJJ is the largest coal moving in region due to both the economic development and the surrounding industrial processes. The Northeast region still works as a heavy industry base which contributes to its large amount of energy consumption, The Middle region is the transition area which supplies the energy needed by the coastal area.
a Fig. 3. (a) Coal flow in 2010; (b) Coal flow in 2015.
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Fig. 4 (a) Coal moving in in 2010-2015; (b) Coal moving out in 2010-2015.
3. Model This study uses the provincial energy system model to analyze the future coal flow. This model is developed from China-TIMES model, which is an optimization model which figures out the lowest-cost combination of technologies and fuel mix over 2010-2050 to meet user specified energy service demand and it can provide a detailed insight for the future development based on rich technology database and flexible application [8,9,10]. The provincial model goes further to represent the provincial details, including energy extraction, convention and power plant and different end-use sectors. For the demand side, the model improves the methodology used in China-TIMES model to project the future energy service demand for residential (including commercial, urban and rural) , transportation sector and adopt the original methodology and downscaling methodology together to get the future industrial energy service demand [11,12]. The interactions between provinces are represented by the flow of coal, oil, gas and electricity. The current version of the provincial model the V1.0 and the V2.0 which with a modification for 2015 energy statistics will come out soon.
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Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Author name / Energy Procedia 00 (2017) 000–000
5
4. Coal flow in the future This study only explores the future coal flow under the Baseline scenario which means we don’t take into consideration of future policies like the planned capacity for renewable power plants or the coal consumption and CO 2 emission constraint. In this case, the percentage of Southwest will increase from 1.7% in 2015 to 24% in 2050, while the ratio of Shanxi Province will decrease to 36%, which is consistent with deduction from Fig.4. But since the Xinjiang is located far away from the energy intensive area as shown in Figure.1, without any policy impact, Xinjiang will not release its great potential in coal reserves due to the transportation cost. From the perspective of coal moving in, coastal area will increase to nearly 50% in 2050 and the percentage of JJJ will reduce to 23%, which means the sustainable future development requires more work on the energy flow and power plant capacity in the future.
Fig. 5. Coal flow in 2050
The further work can be extended to figure out the optimal coal flow under different policy scenarios and the functional orientation of different regions and improve railway capacity to release Xinjiang’s potential and shift the energy convention from the Coastal area to the Middle area, which can be a good transfer station. Acknowledgements This research is supported by National Natural Science Foundation of China (NSFC71690243) and Ministry of Science and Technology (2012BAC20B01).
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2453
Appendix A.1. Regional definition and representation Table 1. Region definition Regions Coastal Xinjiang Northwest Northeast Southwest JJJ SHANXI Middle
Provinces Jiangsu, Zhejiang, Fujian, Shandong, Shanghai, Guangdong, Hainan Xinjiang Shaanxi, Ningxia, Gansu, Inner Mongolia Heilongjiang, Jilin, Liaoning Tibet, Yunnan, Guizhou, Sichuan, Chongqing, Guangxi, Qinghai Beijing, Tianjin, Hebei Shanxi Hubei, Hunan, Henan, Anhui, Jiangxi
Province abbreviation JINU, ZHEJ, FUJI, SHAD, SHAN, GUAD, HAIN XING SHAA, NING, GANS, NEMO HEIL, JILI, LIAO TIBE, YUNN, GUIZ, SICH, CHON, GUAX, QING BEIJ, TIAN, HEBE SHNX HUBE, HUNA, HENA, ANHU, JINX
Fig. 6. Region definition
References [1] Wenying Chen, Xiang Yin, Hongjun Zhang, et al. The Role of Energy Service Demand in Carbon Mitigation: Combining Sector Analysis and China TIMES-ED Modelling. Informing Energy and Climate Policies Using Energy Systems Models. Springer, 2015: 293-312. [2] NBS (National Bureau of Statistics of China). China statistical yearbook. Beijing: China Statistics Press; 2011. [3]Cun-li Cui, China’s Coal Resources and Their Distribution Characteristics. SCI-TECH INFORMATION DEVELOPMENT ＆ ECONOMY.2011:21, 24:181-183. (Chinese) [4]Tianming Gao, Lei Shen, Litao,Liu, Yuexiang,Liu. The Inequality of China’s Coal Resources and Its Flow Paths. JOURNAL OF NATURAL RESOURCES.2013, 28 (1): 92-103. (Chinese) [5] Yi-qiang, Wang, Yuan Zhao. Spatial Pattern Evolution and Inner Differences of Source-sink Regions of China's Coal Resources Flow. SCIENTIA GEOGRAPHICA SINICA2014, 34 (10): 1153-1161. (Chinese) [6] Coal industry council. China’s coal industry yearbook 2011 [M]. Coal industry press. 2011. (Chinese) [7] The State Council. Energy Development Strategy Action Plan 2014-2020.2014. [8] Wenying Chen, Xiang Yin, Hongjun Zhang. Towards a low carbon development in China: a comparison of national and global models. Climatic Change, 2016,136: 95-108 [9] Ding Ma, Nan Li, Wenying Chen, 2015. Analysis of the impacts of water constraints on China's power sector. Energy Procedia, 75: 2665-2670. [10] Nan Li, Ding Ma, Wenying Chen. Quantifying the impacts of decarbonisation in China’s cement sector: A perspective from an integrated assessment approach [J]. Applied Energy, 2017, 185:1840-1848. [11] Wenying Chen, Xiang Yin, Ma Ding. A bottom up analysis of China’s iron and steel industrial energy consumption and CO2 emissions. Applied Energy, 2014, 136:1174-1183. [12] Nan Li, Ding Ma, Wenying Chen. Projection of cement demand and analysis of the impacts of carbon taxes on cement industry in China [J]. Energy Procedia, 2015, 75: 1766-1771.
ScienceDirect Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Energy Procedia 142 Energy Procedia 00(2017) (2017)2448–2453 000–000 www.elsevier.com/locate/procedia
9th International Conference on Applied Energy, ICAE2017, 21-24 August 2017, Cardiff, UK
Coal flow of present and the future in China-A provincial The 15th International Symposium on District Heating and Cooling perspective Assessing the feasibility using Chen the a,b* heat demand-outdoor Nan Lia,b,of Wenying temperature function for a long-term district heat demand forecast Research Center for Contemporary Management, Tsinghua University, Beijing 100084, China aa
b bInstitute
Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China
a,b,c
I. Andrić a
*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
Considering the vital role of coal in the energy structure and coal transportation being a bottleneck of development in China, this paper assessed the current and future flow from a provincial perspective in an integrated and systematical way. From coal flow in 2010-2015, we found that the Xinjiang and Southwest region can be more developed to supply coal and the Middle region can be Abstract developed as a transit area for the Coastal region. From the perspective of provincial China-TIMES model, there will be a substantial growth of Southwest output while Xinjiang increase much slower due to the transportation cost in the Base scenario. District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the Moreover, policies should strengthen the capability of the Middle region to supply energy for the Coastal area. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat © 2017 The Authors. Published by Elsevier Ltd. sales. to the changed climate conditions and building renovation policies, heat demand in the future could decrease, © 2017 Due The Authors. Published by Ltd. committee Peer-review under responsibility of Elsevier the scientific of the 9th International Conference on Applied Energy. Peer-review responsibility the scientific committee of the 9th International Conference on Applied Energy. prolonging under the investment returnofperiod. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Keywords: Coal flow; China; Provincial; China-TIMES forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1.renovation Introduction compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Energy and transportation arelower two than important and economic lifelines. On after one hand, passenger and (the error in annual demand was 20% for allinterdependent weather scenarios considered). However, introducing renovation freight transportation sectors require large amount of energy. On the other hand, energy delivery from supply side to scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). demand side needs to be unhindered. Coal has dominated the energy supply at 70% of the total, resulting in significant The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the air pollution carbon dioxidehours emissions thatduring reached 7.2 billion tons in 2010on[1]. China’s rapid economic decrease in theand number of heating of 22-139h the heating season (depending the combination of weather and renovation scenarios On the other hand, function intercept increased 7.8-12.7% permajor decade (dependingover on the development leads toconsidered). a similar increase in primary energy consumption, withfor coal being the component a coupled scenarios). values The suggested couldisbefurther used tocomplicated modify the function parameters the scenarios considerable periodThe of Time. challenge by the fact that thefor majority of coalconsidered, resources and is improveinthe heatnorthern demand estimations. located theaccuracy westernofand part of China while the most developed area is Coastal area in the southeast part, © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
* Corresponding author. [email protected]
Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 9th International Conference on Applied Energy. 10.1016/j.egypro.2017.12.181
2
Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Nan Li/ Energy Procedia 00 (2017) 000–000
2449
as shown in Fig.1. Basic reserves of Shanxi, Inner Mongolia, Xinjiang, Shaanxi province take up 67% [2-3]. The energy consumption, GDP and population of the western and northern part are much lower than those of the other parts. Therefore, it’s worthwhile to analysis coal flow due to unevenly distributed resources and development.
Fig. 1. Provincial percentage of Basic reserves of coal, coal extraction, GDP in China in 2010.†
Nowadays, increasing attention has been drawn in the studies about coal transport in China. Gao applied Lorenz curve to describe the significant imbalance of coal supply and coal demand. He pointed out the coal output provinces was moving west and north by analyzing data from 1995-2009. Moreover, he optimized the flow path by digging into the flow path in the main output provinces [4]. Wang divided the provinces into two types like source regions, sink regions and conclude the center of sources is moving like “U” and the center of sinks like “I” with data from 19902010 [5]. These studies gave detailed interpretations about the change in the flow pattern in view of historical data and analyze the future arrangement qualitatively. Mou used the linear programming to figure out the change of coal transportation under different conditions including with Daqin Railway capacity constraint, shift of coal supply zones and the influence of inland waterway of Yangtze River. This study gave a good understanding of how the optimal coal flow will change under different conditions in the current coal demand. But its deficiency was coal supply and consumption remained unchanged with the 2009 energy statics. This study analyzes the current coal flow by the railway between provinces from 2010-2015. Secondly, it applies a bottom-up energy system model to figure out the future coal flow in the Baseline scenario 2. Current coal flow in 2010 and 2015 Figure 2 shows that currently Shanxi, Inner Mongolia and Shaanxi account for 34%, 30% and 12% of total coal moving out, respectively, while each of Hebei, Jiangsu and Shandong contribute to more than 10% of coal moving in of the total.
1. Taiwan, Hongkong and Marco are not shown due to statistical issue 2. The abbreviation can be found in the Appendix
†
Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Author name / Energy Procedia 00 (2017) 000–000
2450
3
Fig. 2. Inter provincial coal moving in and out in 2010
Coal transportation can be divided into three types: railway, railway-waterway joint operation and road. In 2010, coal delivered by railway occupied a large proportion of 67%, while that of the waterway and road was around 30% and 5% respectively [6]. As the railway-waterway joint operation still needs development of railway and after 2010, the share of railway has been always higher than 60% and keep increasing. So this paper will focus on the railway transportation of coal and the coal flow means coal flow by railway hereafter. Figure 3 shows the coal flow in 2010 and 2015. The percentage of Shanxi and Northwest in the region output increase from 77% in 2010 to 85% in 2015, with the amount reaching around 600 Mtce. The coal moving in of Coastal and JJJ remain around 63% in 2010 and 2015, which are the most energy intensive regions [6]. There are small shifts in the other region in the short run. The trend can be found more specifically from Fig.4. Rich in coal reserves, Xinjiang, Northwest, Southwest and Shanxi have few coal input and nowadays Northeast and Shanxi work as the two largest coal output base. But in the long run, there are great potential for Xinjiang and Southwest to grow into important output energy bases [7]. JJJ is the largest coal moving in region due to both the economic development and the surrounding industrial processes. The Northeast region still works as a heavy industry base which contributes to its large amount of energy consumption, The Middle region is the transition area which supplies the energy needed by the coastal area.
a Fig. 3. (a) Coal flow in 2010; (b) Coal flow in 2015.
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Fig. 4 (a) Coal moving in in 2010-2015; (b) Coal moving out in 2010-2015.
3. Model This study uses the provincial energy system model to analyze the future coal flow. This model is developed from China-TIMES model, which is an optimization model which figures out the lowest-cost combination of technologies and fuel mix over 2010-2050 to meet user specified energy service demand and it can provide a detailed insight for the future development based on rich technology database and flexible application [8,9,10]. The provincial model goes further to represent the provincial details, including energy extraction, convention and power plant and different end-use sectors. For the demand side, the model improves the methodology used in China-TIMES model to project the future energy service demand for residential (including commercial, urban and rural) , transportation sector and adopt the original methodology and downscaling methodology together to get the future industrial energy service demand [11,12]. The interactions between provinces are represented by the flow of coal, oil, gas and electricity. The current version of the provincial model the V1.0 and the V2.0 which with a modification for 2015 energy statistics will come out soon.
2452
Nan Li et al. / Energy Procedia 142 (2017) 2448–2453 Author name / Energy Procedia 00 (2017) 000–000
5
4. Coal flow in the future This study only explores the future coal flow under the Baseline scenario which means we don’t take into consideration of future policies like the planned capacity for renewable power plants or the coal consumption and CO 2 emission constraint. In this case, the percentage of Southwest will increase from 1.7% in 2015 to 24% in 2050, while the ratio of Shanxi Province will decrease to 36%, which is consistent with deduction from Fig.4. But since the Xinjiang is located far away from the energy intensive area as shown in Figure.1, without any policy impact, Xinjiang will not release its great potential in coal reserves due to the transportation cost. From the perspective of coal moving in, coastal area will increase to nearly 50% in 2050 and the percentage of JJJ will reduce to 23%, which means the sustainable future development requires more work on the energy flow and power plant capacity in the future.
Fig. 5. Coal flow in 2050
The further work can be extended to figure out the optimal coal flow under different policy scenarios and the functional orientation of different regions and improve railway capacity to release Xinjiang’s potential and shift the energy convention from the Coastal area to the Middle area, which can be a good transfer station. Acknowledgements This research is supported by National Natural Science Foundation of China (NSFC71690243) and Ministry of Science and Technology (2012BAC20B01).
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Appendix A.1. Regional definition and representation Table 1. Region definition Regions Coastal Xinjiang Northwest Northeast Southwest JJJ SHANXI Middle
Provinces Jiangsu, Zhejiang, Fujian, Shandong, Shanghai, Guangdong, Hainan Xinjiang Shaanxi, Ningxia, Gansu, Inner Mongolia Heilongjiang, Jilin, Liaoning Tibet, Yunnan, Guizhou, Sichuan, Chongqing, Guangxi, Qinghai Beijing, Tianjin, Hebei Shanxi Hubei, Hunan, Henan, Anhui, Jiangxi
Province abbreviation JINU, ZHEJ, FUJI, SHAD, SHAN, GUAD, HAIN XING SHAA, NING, GANS, NEMO HEIL, JILI, LIAO TIBE, YUNN, GUIZ, SICH, CHON, GUAX, QING BEIJ, TIAN, HEBE SHNX HUBE, HUNA, HENA, ANHU, JINX
Fig. 6. Region definition
References [1] Wenying Chen, Xiang Yin, Hongjun Zhang, et al. The Role of Energy Service Demand in Carbon Mitigation: Combining Sector Analysis and China TIMES-ED Modelling. Informing Energy and Climate Policies Using Energy Systems Models. Springer, 2015: 293-312. [2] NBS (National Bureau of Statistics of China). China statistical yearbook. Beijing: China Statistics Press; 2011. [3]Cun-li Cui, China’s Coal Resources and Their Distribution Characteristics. SCI-TECH INFORMATION DEVELOPMENT ＆ ECONOMY.2011:21, 24:181-183. (Chinese) [4]Tianming Gao, Lei Shen, Litao,Liu, Yuexiang,Liu. The Inequality of China’s Coal Resources and Its Flow Paths. JOURNAL OF NATURAL RESOURCES.2013, 28 (1): 92-103. (Chinese) [5] Yi-qiang, Wang, Yuan Zhao. Spatial Pattern Evolution and Inner Differences of Source-sink Regions of China's Coal Resources Flow. SCIENTIA GEOGRAPHICA SINICA2014, 34 (10): 1153-1161. (Chinese) [6] Coal industry council. China’s coal industry yearbook 2011 [M]. Coal industry press. 2011. (Chinese) [7] The State Council. Energy Development Strategy Action Plan 2014-2020.2014. [8] Wenying Chen, Xiang Yin, Hongjun Zhang. Towards a low carbon development in China: a comparison of national and global models. Climatic Change, 2016,136: 95-108 [9] Ding Ma, Nan Li, Wenying Chen, 2015. Analysis of the impacts of water constraints on China's power sector. Energy Procedia, 75: 2665-2670. [10] Nan Li, Ding Ma, Wenying Chen. Quantifying the impacts of decarbonisation in China’s cement sector: A perspective from an integrated assessment approach [J]. Applied Energy, 2017, 185:1840-1848. [11] Wenying Chen, Xiang Yin, Ma Ding. A bottom up analysis of China’s iron and steel industrial energy consumption and CO2 emissions. Applied Energy, 2014, 136:1174-1183. [12] Nan Li, Ding Ma, Wenying Chen. Projection of cement demand and analysis of the impacts of carbon taxes on cement industry in China [J]. Energy Procedia, 2015, 75: 1766-1771.