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Natural Hazards

, Volume 99, Issue 3, pp 1345–1364 | Cite as

Carbon emissions accounting for China’s coal mining sector: invisible sources of climate change

  • Bing WangEmail author
  • Chao-Qun Cui
  • Yi-Xin ZhaoEmail author
  • Bo Yang
  • Qing-Zhou Yang
Original Paper

Abstract

Coal is the primary source of China’s carbon emissions due to the energy structure and its resource endowment. This reality creates enormous pressure and impetus for low-carbon pathways of coal production and consumption. Based on a literature review on carbon emissions accounting methods, this paper builds a source-driven CO2 emissions accounting model for the coal development sector using the emissions factor method. Scenario analysis is employed to predict future carbon emission equivalents and to indicate possible implications for climate change mitigation in this sector. Carbon emissions from coal development are mainly derived from coal mine gas emissions, which yield 62% of the sector’s total carbon emissions, followed by energy consumption. The recent decline in coal mining-driven CO2 emissions is mainly due to the strict deployment of coal mine gas and the changing structure of coal mines. The results from the scenarios suggest that the carbon emissions reduction potential will largely be determined by technology innovation in the coal mine gas industry. Policy implications for further addressing carbon emissions from the supply side of the coal industry include improvements in energy efficiency and coal mine gas extraction and utilization.

Keywords

Mining sector Carbon emissions accounting Coal development Emissions factor Coal mine gas 

1 Introduction

Carbon emissions produced by human activities are extremely likely to be the primary cause of global climate change. The vast majority of anthropogenic carbon emissions are derived from the combustion of fossil fuels. Coal, the fundamental energy sources in many countries, most notably China but also India and Indonesia, contributes a great deal to greenhouse gas emissions and air pollution in these nations. According to the statistics from the National Bureau of Statistics of China (NBSC), the preliminary calculation indicates that coal makes up 60.4% and 70% of China’s energy consumption and primary energy production, respectively. Most of the carbon emissions are sourced from raw coal in China, which is primarily consumed in the manufacturing and electricity generation sectors (Liu 2016).

However, what is noteworthy is that Beijing introduced a series of actions to cut down on total coal consumption and to transition to low-carbon energy systems. For example, the percentage of coal in China’s energy consumption changed from 72.4% in 2005 to 60.4% in 2017. This percentage is projected to be lower than 58% in 2020 by China’s 13th Five-Year Plan for Energy Development. The National Key Research and Development Program of China, which is supervised by the Ministry of Science and Technology of China, issued a program for clean and efficient coal utilization and new types of energy-saving technology to improve the cleanliness of the coal industry. Energy saving and carbon emissions reductions in the coal industry will become the first priority for coping with climate change in China.

China’s resource endowments, especially its energy reserves, have led to the long-standing dominating role of coal in the energy structure. Even though the percentage of coal in China’s energy mix has decreased, coal still ranked No. 1 in 2017 for its importance in energy consumption. Furthermore, due to the abundance of coal in China, it is also significant for the safeguarding of the national energy security. Under the framework of sustainable development, a clean coal system is inevitable for China’s low-carbon pathways, including clean production and utilization endeavors for the coal sector (Madzivire et al. 2017). The supply chain of the coal industry consists of coal mining, coal washing, coal dressing, coal transportation, coal utilization (including electricity, heating, direct consumption in steel, building materials and other industries) and coal chemical processing. Similar to acid mine drainage, carbon emissions reduction in the coal industry should be a cradle-to-cradle solution to low-carbon coal mining.

The carbon emissions from the coal industry are mainly derived from coal utilization, while in the upstream of the coal industry chain, the emissions of coal development (including coal mining and washing) are often ignored. However, as the first step for coal industry operation, energy saving and carbon emissions reduction in the coal development sector is key to national carbon emissions reduction by means of source control. The coal mining sector faces the challenges of the irrational layout of coal production capacity, lower ratios of green coal resources (only 10% of the total predicted coal resources in China) and the coal resource recovery rate (only approximately 50%, relatively lower than that of other coal production countries), and higher energy consumption in coal mining process. Carbon emissions accounting for China’s coal mining sector will be essential and imperative for us to find methods and pathways for sustainable coal resource utilization (Yuan 2018).

Carbon accounting is the premise of exploring the potentials of emission reduction and energy savings (Mi et al. 2018a). Numerous researches have been conducted on the carbon emissions accounting in macroeconomic sectors and different nations or regions (Mi et al. 2018b). The studies on calculating carbon emissions in different economic sectors could be the basis of a national carbon trading market. Fan et al. (2013) discussed the drivers of carbon emissions evolutions in urban–rural China and concluded that the end-use mode contributed more to the increase of aggregate carbon intensity. Other studies have compared the production-based and consumption-based accounting methods for carbon emissions and analyzed the application range of each model, but there is a controversial debate in this area (Wang et al. 2018b; Franzen and Mader 2018). However, the studies on the carbon emissions of the coal industry were relative estimations based on case studies, with fewer studies for scrutinizing the emissions database. Based on the indicators of carbon emissions in coal development (Wang et al. 2018a), the aim of this research is to target the accounting of carbon emissions in the production of raw coal sources. This calculation will be helpful for identifying challenges and opportunities for climate change mitigation in the coal mining sector and shed some light on coal production safety. This paper attempts to address three questions:
  1. 1.

    What are the recent trends in carbon emissions of coal development?

     
  2. 2.

    What is the predicted structural change and future forecast for carbon emissions in the coal development process?

     
  3. 3.

    What can we do to achieve ultralow emissions in the coal mining sector, considering potentials from policy, structure and technology?

     

The rest of this research will first review the major modes of carbon emissions accounting in Sect. 2 and build the source-driven CO2 emissions accounting model with a combination of emissions factors for different fuels in Sect. 3. Scenario analyses are conducted to identify changes in carbon emissions in the coal mining sector in Sect. 4. Finally, policy implications are advanced regarding the synergies between coal production safety and carbon emissions reduction in the coal mining sector.

2 Literature review

There are numerous studies on carbon emissions accounting and decomposition. The existing method for carbon emissions calculation is mainly based on the emissions factor method. Carbon emissions decomposition could be used to analyze the impact factors of carbon emissions.

2.1 Decomposition and the impact factors of carbon emissions

When conducting scenario analyses and policy simulations on carbon emissions at different levels, the common approach is to build a multi-parameter mathematical model in order to distinguish the impact factors of carbon emissions and to design carbon decomposition methods and life cycle assessment to assess components of carbon emissions. These models include the MARKAL model (Tsai and Chang 2015; Deng and Liang 2017), STIRPAT model (Ma et al. 2017), BP neural network model, SRIO model, logistic model, system dynamics model, input–output model (Huang et al. 2018; Mi et al. 2018b) and life cycle assessment model.

Regarding the multi-parameter mathematical model, Chen et al. (2007) built three editions of the MARKAL model to survey China’s carbon emissions control strategies and corresponding influences on the economy and social welfare. Yin et al. (2017) adopted the BP neural network model to predict the carbon content of burning coal and made comparisons with two other methods. Meng and Niu (2011) used the logistic model to estimate the parameters of CO2 emissions in each industry and checked the S-shape curves of total CO2 emissions from fossil fuel combustion. Sim (2018) proposed a system dynamics model to evaluate carbon emissions in a container terminal and found that vessel maneuvering led to large part of the emissions.

Regarding the carbon decomposition method, there are two kinds of decomposition: index decomposition analysis (Ma et al. 2017) and structural decomposition analysis (Mi et al. 2018b). Liu and Xiao (2018) established a STIRPAT model to study the factors of CO2 emissions and to identify the possible time of peak emissions. Sun et al. (2017) employed the SRIO model to calculate and analyze the carbon footprint of economic sectors in India. Wang et al. (2018b) constructed an input–output model to assess the characteristics of regional carbon flow and the changes in carbon emissions from different industries. Pan et al. (2018) established a multi-regional input–output model and a comprehensive framework to analyze the interregional carbon emissions in China. The life cycle assessment (LCA) model is also a widely accepted method for analyzing GHG emissions. For example, Kjendseth et al. (2018) used this model to evaluate and discuss the GHG emissions in buildings. Goglio et al. (2018) made a comparison between the LCA model and the IPCC approach for accounting for GHG emissions in soil.

The above quantitative models, which build complex frameworks, are mainly used to calculate national or regional carbon emissions. The qualitative methods, such as literature review and questionnaire survey, could be employed to investigate the research results or opinions among researchers and the public. Wang et al. (2018a) applied a literature review to demonstrate research trends and key indicators for carbon emissions in the coal development sector. This research will adopt these indicators for carbon emissions accounting.

2.2 Carbon emissions accounting methods

The emission factor method is the most widely used tool for directly calculating carbon emissions. According to this method, carbon emissions can be identified by setting the emissions factor (EF) of each fuel and multiplying by the fuel consumption. It has been formed into two types of accounting systems: the top-down system and down-up system. Among the examples of the top-down system, the 2006 Guidelines for National Greenhouse Gas Inventories published by the Intergovernmental Panel on Climate Change (IPCC) was the most typical case (Kalt et al. 2016; Schueler et al. 2018). The IPCC approach measured carbon emission sources through a layer-by-layer classification to obtain the final carbon emissions. It is one of the most commonly used methods for national carbon emissions calculation in the world (Shan et al. 2017).

The down-up accounting system is formed based on enterprise products or project carbon emissions. Among these application cases, the code 14064-1 greenhouse gas certification standards from the International Standardization Organization (Bastianoni et al. 2014) and World Resources Institute (WRI) are the most representative (Lee et al. 2018). However, this accounting system focuses on examining specific products or targets an enterprise’s carbon emissions. Therefore, it has limitations in accounting for regional and sectoral carbon emissions.

Carbon emissions calculation is relatively new in China, but the Chinese government attaches great importance to this issue, and a series of accounting studies have been carried out to determine China’s total carbon emissions. In May 2011, the National Development and Reform Commission (NDRC) published “Preparation Guide to Chinese Provincial Greenhouse Gas List (trial)” (GCPGGL) to strengthen the provincial greenhouse gas inventory capacity and released carbon emissions controls in 2016 (NDRC 2016). The GCPGGL applied the emission factor method to calculate direct and indirect carbon emissions from energy activities, industrial processes, agriculture, land use and waste disposal in provincial domain. In October 2013, this agency issued a notice of “Greenhouse Gas Emission Accounting Methods and Reporting Guidelines for 10 Industrial Enterprises (trial)” (GGEAME), which provided a universal method for carbon emissions accounting for China’s high-energy enterprises (NDRC 2013).

The comparisons of carbon accounting methods at the national, provincial and enterprise level are shown in Table 1. The IPCC accounting method, as one of the best-known emission factor method, is mostly applicable to national and regional greenhouse gas inventory compilation, and it provides default values of emission factors. The other two accounting methods are also based on similar measurements, which are established for the greenhouse gas inventory report. Furthermore, the accounting of the carbon sources in these methods is all classified into direct and indirect emissions. The only differences occur in the different emissions classifications in the production process and different selection of emissions factors.
Table 1

Comparison of different types of emission factor methods

Item

Time

Purpose

Scope

Method

EF

IPCC

2006

Compile the national greenhouse gas emission inventory

Nation

Emission factor method

Default

GCPGGL

2011

Compile provincial greenhouse gas inventory

Province

Emission factor method

Default/self-measured

GGEAME

2013

Establish a system for reporting and accounting greenhouse gas emissions for enterprises

Enterprise

Emission factor method

Default/self-measured

The literature review on carbon emissions accounting methods indicates that carbon emissions accounting for the coal development process is very difficult because of the absence of detailed information about coal mines in China. Thus, it is impracticable to calculate total carbon emissions by aggregating the carbon emissions of all coal mines. The solution is to calculate the annual carbon emissions of China’s coal mining sector by a source-driven CO2 emissions accounting model, which will be beneficial to both national emissions accounting and the industry’s emissions reduction.

3 Methodologies

3.1 Research framework for carbon emissions accounting in the coal mining sector

Based on the code 14064-1 from the International Standardization Organization, the research framework and technological roadmap for carbon emissions accounting in the coal mining sector is shown in Fig. 1. Carbon emissions accounting can be enacted based on the following three steps. Each step has its corresponding tasks to determine the core concepts in carbon emissions accounting.
Fig. 1

Roadmap for calculating carbon emissions of the coal development sector

3.1.1 Step 1 Determining the accounting boundary for carbon emissions from the coal development sector

Coal development is the main process in coal production. As shown in Fig. 2, this process could be divided into two parts: coal mining and coal washing. The carbon emissions accounting boundary of coal development ranges from raw coal mining to primary coal products (washing coal). The carbon emissions from energy consumption, such as electricity, oil, coal, and coal mine gas emissions, are required to be included in the calculation boundary. The auxiliary production system includes ventilation, drainage, power supply center and auxiliary transportation, which is also an energy consumer.
Fig. 2

Coal development process in this research

3.1.2 Step 2 Identifying carbon emissions sources from the coal development process

Energy consumption and coal mine gas emissions are two main sources of coal development carbon emissions (Wang et al. 2018a). In winter, the temperature of coal mines in Northern China is below 0 C, which seriously affects the normal operation of coal mines. The usual practice is to burn raw coal to provide heat, which emits large amounts of CO2 emissions. Coal transportation by ground vehicles and coal mine vehicles consumes some oil, which is another source of CO2 emissions. Electricity is the main power source for coal mine production operations, which is the major source of indirect CO2 emissions. It is necessary to identify the sources of electricity consumed in coal mines when calculating CO2 emissions from electricity consumption. In the process of raw coal mining, greenhouse gases such as coal mine gas and CO2, which are originally adsorbed in the coal seam, are released into the atmosphere. Coal mine gas, whose main component is CH4, is a very important but usually neglected source of carbon emissions. CH4 has a greater greenhouse effect, which has been verified by the IPCC. Additionally, with the increasing production of a coal mine and increasing mining depth, the discharge of coal mine gas and the amount of ventilated methane in the colliery also gradually increase.

The CO2 emissions sources of coal development can be divided into direct emissions sources and indirect emissions sources, among which coal mine gas emissions and fossil fuel consumptions belong to direct carbon emissions sources while electric energy falls into indirect CO2 emissions sources. It should be noted that spontaneous coal combustion emissions are difficult to measure due to the slow process. Thus, these emissions are not included in this research.

3.1.3 Step 3 Confirming carbon emissions factors for different sources

Carbon emissions factors (CO2 emission per unit product) have been proposed by many international institutions, such as the IPCC and the WRI; however, these emissions factors are sourced from a general benchmark and cannot be directly applied to China’s carbon emissions accounting since China’s fuel is much different in quality and combustion efficiency. The calculated results could be higher than China’s actual carbon emissions if we adopted the IPCC or WRI emission factor. Therefore, the emissions factors in this study are corrected according to “General Rules for Comprehensive Energy Consumption Calculation” and “Preparation Guide to Chinese Provincial Greenhouse Gas List,” and the results are provided in Table 6.

There are a large number of factors affecting coal mine gas emissions, such as coal production, the method of exploitation, the geological conditions and coal mine deployment. Thus, it is difficult to accurately estimate coal mine gas emissions. The common assessment methodologies include the emissions coefficient intensity factor, actual measurement and geological statistics method (Ji et al. 2017). However, the application conditions, estimation process and results of each method are quite different, which are shown in Table 7. The coefficient intensity factor method is mainly adopted in the major coal-producing countries (Jin et al. 2016; Booth et al. 2017). The IPCC recommended a large emissions factor range (10–25-fold) for the greenhouse effect of coal mine gas based on the global average, which may influence the accuracy of the accounting (IPCC 2006). The gas emissions coefficients determined by the appraising method for the grade of gas are from the perspective of mine safety, resulting in predictions that are higher than the actual value. Wang et al. (2015) invented a coefficient intensity factor methodology with IPCC methodology and obtained the national emissions intensity factor of 9.176 m3/t. The report by Chinese Academy of Engineering “Research on Unconventional Natural Gas Development Strategy in China” provided the average national coefficient intensity factor of 9 m3/t. Therefore, in order to simplify gas emissions forecasting, it is reasonable and feasible to update the national coal mine gas emissions intensity factor value over time: 7.4 m3/t (2020), 6.7 m3/t (2030), 6.6 m3/t (2035) and 6.3 m3/t (2050). The greenhouse effect of coal mine gas is set as 21-fold greater than that of CO2 and may be updated as 28-fold referenced from the similiar research (Zhu et al. 2018).

3.2 Construction of the accounting model

Based on the above analysis, the source-driven CO2 emissions accounting model for coal development is constructed. The calculation formulas in this model are given in Eqs. (1)–(4). The meanings, units and sources of the variables in Eqs. (1)–(4) are shown in Table 2.
Table 2

Description and measurement of the variables in CO2 emissions accounting

Variables

Meaning

Unit

Source

i

Types of fossil fuels

  

ADi

Consumption of fossil fuels

t; Nm3(gas)

NBSC (2017a)

EFi

Emissions factor of fuel i

kg CO2/kg

GCPGGL and general rules for comprehensive energy consumption calculation

\({\text{AD}}_{\text{electricity}}\)

Electricity consumption

KWh

NBSC (2017a)

\({\text{EF}}_{\text{electricity}}\)

Emissions factor of electricity

kg CO2/KWh

National Climate Center

\(Q_{\text{coal}}\)

Total coal production

t

NBSC (2017b)

e

Coal mine gas release per raw coal

m3/t

Report of the Chinese Academy of Engineering (Research on Unconventional Natural Gas Development Strategy in China) and Wang et al. (2015)

q

Coal mine gas utilization

t

Research of the China National Coal Association

\(\rho_{\text{gas}}\)

Gas density

t/10,000 Nm3

 

GWPgas

Greenhouse effect of coal mine gas

 

IPCC recommended value

Equation (1) is the calculation process of the carbon emissions of fossil fuel consumptions, including raw coal, coke, fuel oil, gasoline, kerosene, diesel and natural gas. In Eq. (1), \(E_{{{\text{co}}_{2} \;{\text{fuel}}}}\) is the CO2 emissions of fossil fuel consumption. Equation (2) is the calculation of CO2 emissions of electricity consumption. \(E_{{{\text{co}}_{2} \;{\text{electricity}}}}\) is CO2 emissions from electricity consumption. Equation (3) is the calculation of the carbon emissions equivalent of coal mine gas released into the atmosphere. \(E_{{{\text{co}}_{ 2} \;{\text{gas}}}}\) is CO2 emissions from coal mine gas releases. Equation (4) is the calculation of the total carbon emissions of the three parts. \(E_{{{\text{co}}_{ 2} \;{\text{sum}}}}\) is the total CO2 emissions. The meanings of the other variables could be found in Table 2.
$$E_{{{\text{co}}_{2} \;{\text{fuel}}}} = \sum {_{i} \left( {{\text{AD}}_{i} *{\text{EF}}_{i} } \right)}$$
(1)
$$E_{{{\text{co}}_{2} \;{\text{electricity}}}} = {\text{AD}}_{\text{electricity}} *{\text{EF}}_{\text{electricity}}$$
(2)
$$E_{{{\text{co}}_{2} \;{\text{gas}}}} = \left( {Q_{\text{coal}} *e - q} \right)*\rho_{\text{gas}} *{\text{GWP}}_{\text{gas}}$$
(3)
$$E_{{{\text{co}}_{2} \;{\text{sum}}}} = E_{{{\text{co}}_{2} \;{\text{fuel}}}} + E_{{{\text{co}}_{2} \;{\text{electricity}}}} + E_{{{\text{co}}_{2} \;{\text{gas}}}}$$
(4)

3.3 Scenario analysis

Scenario analysis is a common method of studying energy and environmental forecasts, which mainly synthesizes all kinds of uncertainties affecting future development and quantifies the influences and effects of various policies (Yang et al. 2017). As mentioned above, the main factors influencing carbon emissions in coal development are energy consumption and coal mine gas emissions. Therefore, this article performs the scenario analysis in three parts. With the forecast of CO2 emissions of energy consumption and coal mine gas emissions, the future CO2 emissions from China’s coal development could be predicted at the strategic time node in the future.

3.3.1 Part 1 Scenarios of comprehensive energy consumption in coal development

There are various types of coal mines in China, and their modernization and mechanization are quite different. These two facts result in differences in the indicator of comprehensive energy consumption per ton of raw coal production. With the current development situation in China’s coal industry and the long-term forecast of China’s coal development and energy conservation by the report “Strategic Studies of High-Efficient and Energy-Effective Coal Extractions in China” from the Chinese Academy of Engineering (CAE), the scenario of comprehensive energy consumption in coal development is designed in Table 3. Scenario 1 (S1) is based on the current progress in energy-saving technology development, while Scenario 2 (S2) depicts significant improvements achieved in energy-saving technology. The percentages of comprehensive energy consumption reduction are based on 2015 data.
Table 3

Scenarios for different energy consumption levels in the coal mining sector

 

Percentage of energy consumption reduction (%)

Energy efficiency (kgce/t)

Total energy consumption (10 thousand tce)

S1

S2

S1

S2

S1

S2

2020

7

11

25.11

24.03

8788.5

8410.5

2030

22

33

21.06

18.09

8424

7236

2035

30

40

18.90

16.20

7182

6156

2050

37

53

17.01

12.69

5783.4

4314.6

According to Eq. (3), the total amount of CH4 emissions in the air is determined by coal output (\(Q_{\text{coal}}\)) and the amount of gas utilization (e). The forecast of coal production is based on the integrated results of the National Energy Administration (NEA), the CAE, the National Development and Reform Commission and the International Energy Agency (IEA). Coal mine gas utilization is equal to the drainage amount times the utilization rate. Therefore, the draining rate and utilization rate should be projected separately.

3.3.2 Part 2 Scenarios of the coal mine gas draining rate in coal development

The average gas drainage rate for mines in China was 22% in 2010, which has increased significantly in the past 5 years. Provided that the input–output ratio is acceptable, it has at least 25–30% room for improvement. The drainage rate of 30–40% is slightly higher than the current level, which is the low scenario. Fifty percent can be realized under the condition of technological progress and national mandatory requirements, which is the medium scenario. The drainage rate of 60–70% is slightly higher than the level of most coal mines in the USA from 1990 to 2000. It may be reached by improving extraction technology, which may be achieved in the high scenario. Based on the above statements and the possible innovative development of extraction technology, the drainage rate scenarios are shown in Table 4. Scenario 3 (S3) is the normal technological progress scenario, and Scenario 4 (S4) is the significant technological progress scenario.
Table 4

Scenario design of coal mine gas drainage and utilization rate in China

 

Drainage rate (%)

Utilization rate (%)

S3

S4

S3

S4

2020

40

50

45

50

2030

50

60

60

70

2035

50

70

65

75

2050

60

90

80

90

3.3.3 Part 3 Scenario of coal mine gas utilization rates in coal development

The utilization rate of coal mine gas in China was relatively high in the 1990s, but since the twenty-first century, it has dropped sharply, and the utilization rate was only 31.6% in 2010. The national “13th Five-Year Plan for the development and utilization of coal mine gas” suggested that 14 billion cubic meters of coal mine gas will be pumped out and the utilization rate should be increased to over 50% in 2020, compared to 35.3% in 2015. The recovery and utilization of gas in low-concentration mines would be encouraged. If the facilities of China’s coal mine gas collection and transportation network are improved and the technology for ultralow concentration coal mine gas is developed, the utilization of coal mine gas will be further expanded. The utilization rate scenarios are provided in Table 4. Scenario 3 (S3) is the normal technological progress scenario, and Scenario 4 (S4) is the significant technological progress scenario.

3.4 Data resources

Two types of data are required for the calculations: activity data of energy consumption from the NBSC (2017a) and the emissions factors for different fuels. Emissions factors are corrected by the “General Rules for Comprehensive Energy Consumption Calculation” and “Preparation Guide to Chinese Provincial Greenhouse Gas List.” The emissions factor of electricity is replaced by the average value of the emissions factors of regional power grids, which were issued by the National Climate Center in 2012. The coal mine gas emissions intensity factor is referenced from Wang et al. (2015) and the report of Chinese Academy of Engineering “Research on Unconventional Natural Gas Development Strategy in China.”

4 Results and discussion

4.1 Decomposition and components of carbon emissions in the coal mining sector

Based on energy consumption and coal mine gas effusion in coal development, this research calculates the carbon emissions surrounding the coal mining sector in 2016. As shown in Table 5, the self-consumption of raw coal in this process is the major energy consumption source for this sector. Electricity consumed by coal developing mechanical devices is another essential factor in coal production. Most importantly, the results from Table 5 also indicate that coal mine gas emissions from coal mining, which are responsible for China’s total CH4 emissions, are the major source of carbon emissions from the coal mining sector in China.
Table 5

Carbon emissions from the coal mining sector in China (2016)

Category

Consumption

Emission factor

CO2 equivalent (104 tons)

Raw coal (104 tons)

9053

1.9003

17203.67

Coke (104 tons)

75

2.8604

215.12

Crude oil (104 tons)

0.01

3.0202

0.020

Gasoline (104 tons)

8.88

2.9251

25.99

Kerosene (104 tons)

1.59

3.0179

4.81

Diesel (104 tons)

153.04

3.0959

473.79

Fuel oil (104 tons)

0.5

3.1705

1.59

Natural gas (108 m3)

17.2

2.1622

371.88

Electricity (108 kWh)

847.04

0.6808

5766.67

Coal mine gas (108 m3)

258.9

38996.12

Total (104 tons)

63059.67

Based on the previous analysis on the key components of carbon emissions in the coal development process, this research classifies the emissions inventory into four categories: coal self-use, oil and gas consumption, electricity and coal mine gas, as shown in Fig. 3. Carbon emissions from coal development in China are mainly concentrated in coal mine gas dissipation, accounting for 61.8% of the total emissions, followed by carbon emissions from coal consumption (27.6%) and electricity (9.1%). Other energy uses account for smaller carbon emissions, approximately 2%. The emissions reduction in coal development should be mainly centered on the extraction and utilization of coal mine gas.
Fig. 3

Key components of carbon emissions from the coal mining process

4.2 Historical trends of carbon emissions in the coal mining sector

According to the results in Fig. 4 and Table 7, the total carbon emissions of China’s coal mining and washing industries have decreased substantially since 2013. It can be seen from the carbon emissions sources that the carbon emissions caused by electricity consumption in the coal mining process have decreased by a small amount in recent years, and coal mine gas emissions reductions have contributed a lot to this decrease, followed by the self-consumption of raw coal in coal production. There is still much room for carbon emissions reductions in coal development.
Fig. 4

Historical carbon emissions and intensity of the coal mining sector

When considering the energy intensity and carbon intensity, more information could be found for this decrease. At the first glance, the decline is caused by both the declining demand for raw coal and the falling escape of coal mine gas. Specifically, carbon emissions reductions in the coal development process are generally referenced for two important reasons: energy efficiency and coal mine gas utilization improvement for coal mining machines and changes in the total amount and the layout of coal production. First, the coal mine gas utilization rate has improved from 26.7% in 2005 to 35.3% in 2015, with 4.8 billion cubic meters of coal mine gas consumption in 2015. Energy efficiency, namely the energy consumption per coal production, demonstrates a similar trend with the indicator of carbon emissions per coal production.

Energy efficiency is gained in the coal development process when more coal production is delivered for the same energy input, or the same amount of services are delivered for less energy input. This can be achieved by reducing energy losses that occur during the conversion of primary source fuels, during energy transmission and distribution, and in final energy use, as well as by implementing other measures that reduce energy demand without diminishing the energy services delivered. Second, primary coal production has decreased from the peak of 3974 million tons in 2013–3411 million tons in 2016. This change is closely related to the decrease in total carbon emissions from the coal mining process in 2013. Third, the change in the layout of coal production is significant to energy savings and coal mine gas emissions due to the regional differences in the mechanization of mining and excavation and the existence of coal mine gas. Therefore, carbon emissions reductions in the coal mining sector could be achieved by technology innovation, industrial management and structural adjustment.

4.3 Long-term forecast for carbon emissions in the coal mining sector

Based on the scenarios in Sect. 3.3, this section will forecast the future carbon emissions of the coal mining sector in 2020, 2030, 2035 and 2050, which are shown in Table 8. It can be seen from Fig. 5 and Table 8 that even though different scenarios have depicted different coal development pathways, the downtrend was very obvious for each component of carbon emissions in the coal mining process.
Fig. 5

Forecast of carbon emissions of the coal development process

The reason for this downtrend could be revealed from two sides. One side is the undeniable fact that the coal demand will fall in the near future due to the economic drivers and the pursuit of energy transition. Another cause is the significant improvements in technology, such as energy savings in coal mining machines and coal mine gas utilization technologies for different concentrations. The results in Fig. 5 also indicate that the total emissions from the coal development process will have the potential to be limited to 200 million tons in 2050. However, the decoupling crevice of total carbon emissions in Fig. 5 demonstrates that there will be substantial uncertainties regarding future carbon emissions reductions. In addition, the uncertainty will mainly lie in coal mine gas extraction and utilization. The key technologies may be listed as coal and mine gas co-extraction, coal mine gas drainage, coal mine gas gathering and transportation, and coal mine gas utilization for different gas concentrations.

With normal technological advances, the total carbon dioxide emissions will be less than 400 million tons. From the perspective of carbon emission sources, it can be seen that, in the next few years, the carbon emissions caused by comprehensive energy consumption will decrease by a large margin, while the coal mine gas emissions will decrease by a relatively small percentage. Carbon emissions reduction in coal development still has great potential.

5 Conclusions and policy implications

Based on the fact that 90% of China’s fossil energy reserves are from coal, it is predicted that coal will still be the major energy source in China for a long period. This research tries to calculate the carbon emissions of the coal development process because of its significance in reducing carbon emissions from the original source. Using historical trends and future forecasts, carbon emissions reduction strategies have been put forward. Based on the analysis conducted in this research, the following conclusions are reached.

5.1 Main conclusions

  1. 1.

    Carbon emissions from the coal development process are not the major source of carbon emissions for the coal industry. However, this process influences subsequent emissions in coal conversion and utilization. The emissions from this process are 630 million tons, which accounts for approximately 6% of total national carbon emissions. The main component of carbon emissions for this sector is coal mine gas, with its 28-fold greenhouse effect. This fact indicates that, in addition to its role in the commitment to reducing our carbon emissions and achieving a carbon emissions peak, this sector is also significant for coal mine safety and source management for water and air pollution.

     
  2. 2.

    The carbon emissions curve has an arched shape, and the top point of the arch happened in 2013. Carbon emissions from the coal mining process experienced a peak in 2013, with 820 million tons and then presented a downward trend in recent years. The causes of the changes of carbon emissions from 2011 to 2016 could be explained by three factors: the decline in coal demand, energy efficiency improvements for coal mining machinery, and the promotion of coal mine gas utilization. Even though the total energy efficiency for coal development has improved, the indicator of electricity consumption per coal production unit shows an upward trend. This case reveals China’s efforts in enhancing the mechanization level of the coal industry, which may have technical substitution effects on labor from coal miners.

     
  3. 3.

    Carbon emission reductions could be achieved in this sector through technology innovation, industrial management and structural adjustments. The conclusion is derived from the recent changes and future scenario analyses of carbon emissions. Specifically, technology innovation could be effective in energy conversion and coal mine gas extraction and utilization. Industrial management should focus on two issues. The first one is the outdated capacity control, which is closely related to future coal production activity. The second issue is economic incentives for coal mine gas industry development. Structural adjustments in the layout of coal production are influential because of regional differences in coal development conditions and the coal mine gas-hearing properties. These incentives will go a long way toward reducing carbon emissions and promoting energy transition in China.

     

5.2 Policy implications

First, it is meaningful for carbon emissions reduction in the coal development process to close down the outdated production facilities in the coal industry. These outdated mines display poor performance regarding advanced and efficient equipment and strict rules for coal mine gas control. New coal mine projects should have the advantage of high adaptability, efficiency and mechanization. These requirements are significant for energy efficiency improvement and precise coal mining.

Second, there could be many meaningful actions around the uncontrollable carbon emissions from the coal development process. Small steps may provide good opportunities for big successes. These strategies could be depicted as business process improvements and resource recycling. Process reengineering may aim at shortening the exposure of raw coal during the production process and cutting coal loss during the coal washing process. The comprehensive utilization of coal gangue is influential in resource recycling and energy saving. In addition, managers can approach the steps of the coal development process as units in which to reduce energy consumption, such as measures to lower fuel consumption in the coalfield, to strengthen equipment management to minimize electric power consumption and to implement multi-stage recovery and reuse of waste heat.

Third, the supply chain management of coal mine gas should balance technology innovation and policy motivation in order to promote its role in coal mine safety and carbon emissions reduction. There are two directions for technology innovation in the extraction and utilization of coal mine gas. Extraction technology could adapt to multiple coal seams and different grades of coal. Coal mine gas utilization should focus on different technologies for different methane concentrations. Regarding policy incentives, because of the lower recovery efficiency and utilization rate of coal mine gas, the policy orientation could be centered on building a coal mine gas utilization market, especially the demand side and the transportation of coal mine gas. These two aspects will help the coal mine gas industry work its way up the value chain.

Finally, the recycling of abandoned or closed coal mines should be planned before coal mining activities. This measure would be beneficial for carbon emissions reductions in the following ways. Due to the relatively lower coal resource recovery rate, there are many valuable resources in these underground abandoned mines, including raw coal and associated minerals. The utilization of these discarded mines is a perfect opportunity for the conservation of energy and other resources. A second opportunity is presented by renewable energy deployment and land rehabilitation on the surface of abandoned mines, which could contribute to energy production and a soil carbon sink.

There are still many extended possibilities for this research. The source-driven CO2 emissions accounting for coal production by emission factor method in this paper is employed the fixed carbon emission factors and current gas emission coefficient to calculate CO2 emissions at the strategic time node in the future. This settlement may neglect the refinement in carbon emission factors of different energy types caused by technological progress and the changes of national coal mine gas emission intensity factor. IPCC has determined to refine the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Furthermore, the coal mining layouts would be adjusted, resulting in certain uncertainties in the future calculation of carbon emissions in the coal mining sector. The prediction in this research is just a trend rather than a projection. And there is no need to be intertwined with the exact number itself. Therefore, the measurement of carbon emissions needs to be further improved to obtain more accurate outcomes.

Notes

Acknowledgements

The authors gratefully acknowledge the financial support from National Natural Science Foundation of China (No. 71704178), Beijing Municipal Excellent Talents Foundation (No. 2017000020124G133), Yue Qi Distinguished Scholar Project of China University of Mining and Technology (Beijing), National Statistical Science Research Project by National Bureau of Statistics of China (No. 2017LY10), and Major Consulting Project of Chinese Academy of Engineering (Nos. 2016-ZD-07, 2017-ZD-03). We also greatly appreciate the comments from the seminar participants in workshop organized by Chinese Academy of Engineering.

Supplementary material

11069_2018_3526_MOESM1_ESM.xlsx (16 kb)
Supplementary material 1 (XLSX 15 kb)

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory for Precise Mining of Intergrown Energy and ResourcesChina University of Mining and Technology (Beijing)BeijingChina
  2. 2.College of Resources and Safety EngineeringChina University of Mining and Technology (Beijing)BeijingChina
  3. 3.School of ManagementChina University of Mining and Technology (Beijing)BeijingChina

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