The LCA methodology is a tool for assessing the environmental consequences of a product or an entire activity throughout its life. A complete LCA consists of four integral components, namely determination of purpose and scope, inventory analysis, impact assessment, and life cycle interpretation. LCA includes the following: identification and quantification of energy and resource use and environmental emissions to air, water, and land; characterization of technology quality and quantity; evaluation of the consequences of environmental impact analysis; and assessment of opportunities for environmental burden reduction and implementation. In this study, LCA was analyzed using SimaPro software, an internationally recognized database containing a large amount of data such as the Ecoinvent database and the input-output database, which is directly connected to the Eco-indicator 99 and ReCiPe Midpoint databases and contains well-known LCA calculation methods such as Eco-indicator 99 and ReCiPe Midpoint. The database provides a rich background of data, and the calculation methods are more efficient and accurate for quantitative calculations. Therefore, this software is widely used in the life cycle inventory assessment (LCIA) of coal mines.

In this study, quantitative calculations were performed using the Eco-indicator99 method, reflecting the direct and indirect environmental impacts of different mining methods. In this method, the inventory results were grouped into 11 impact categories, namely respiratory organics, respiratory inorganics, climate change, radiation, ozone layer, ecotoxicity, acidification/eutrophication, land use, minerals, and fossil fuels. When life cycle inventories of different mining methods are assigned to these impact categories, the results of the characterization analysis can reflect the relative contribution of each of these processes to the impact category. A normalized analysis reflects the specific value of each input unit in the overall environmental impact and allows for a comparison between different life cycles. Consequently, both aspects provide a clear picture of the environmental impacts of the different mining methods (Figure 1).

Before an LCA evaluation, selecting functional units and delineating system boundaries are essential steps. The selection of functional units is ultimately used to compare and analyze the LCA results and provide an accurate quantitative reference for the inputs and outputs of the relevant processes that have been investigated (ISO 14040, 2006). The main role of the functional units is to provide a uniform measure of input and output in a mathematical sense, providing a frame of reference for the relevant inputs and outputs to ensure comparability of the LCA results. With reference to the measurement size of the data in the Ecoinvent database, this study used the mining of 1 kg of coal as a functional unit; thus, all inputs and outputs were based on the mining of 1 kg of coal. The study was divided into two mining methods, namely non-pillar (reserved coal pillar width of 0 m) and reserved coal pillar mining, where the reserved coal pillar width was 5, 10, 20, and 25 m. All the input and output units were determined, and the environmental impacts caused by different mining methods in coal mining were evaluated.

There are complex subsystems interconnected with a production system and interconnected as subsystems with other production systems, making it is very difficult to identify all the interconnected inputs and outputs. Figure 2 shows the system boundary, which includes three subsystems, with the production of materials and energy within the system boundary and the production of equipment and maintenance of equipment not included within the boundary.

The life cycle inventory (LCI) is the basis for LCIA and for improving and optimizing engineering practices. The inventory analysis is an iterative process. Production data and waste emissions from coal with different mining methods are monitored. Where information is insufficient, data are supplemented by referencing government planning documents and literature, in addition to background information on production materials and energy sources provided by the Ecoinvent database. All the data were measured in functional units of 1 kg of coal mining. The LCI is given in Table 1, where the data on material production, energy consumption, and direct emissions are calculated on a functional unit basis.

Daily production parameters, operating procedures, and direct emissions are the main sources of field data, which are monitored by specialist departments studying coal mines. Data, including those on steel and wood, were derived from operating procedures and emissions from direct emissions, in addition to raw data such as electricity and sulfur dioxide, which were referred from existing research in China (Tao et al., 2021). The Ecoinvent database is one of the most advanced life-cycle inventory databases, covering basic data from China and other countries and providing background data on the production of energy and raw materials (Ecoinvent Center, 2015).

The LCI is the collection of the data required at each stage of the life cycle and calculation of the data collected. The collection process is the most labor-intensive part of the process. It involves not only the production of the materials and energy required for the different mining methods, materials, and energy consumed in various processes, but also the collection and calculation of data on various waste emissions.

The LCIA directly reflects the environmental relevance of all inputs and outputs. In this study, the environmental impact results were classified into 11 impact categories according to the Eco-indicator99 method and included carcinogens, respiratory organics, and respiratory inorganics. The parameters involved in the LCI, such as material production, energy consumption, and waste emissions, were assigned to these impact categories in the SimaPro software calculations to obtain the LCIA results.

In the Eco-indicator99 method, normalization and weighting were performed in levels within the damage categories. The three damage categories were as follows:

1. HH, human health (unit: DALY = disability-adjusted life years, which means different disabilities caused by diseases are weighted).

Table 2 provides the LCIA results, from which the potential impact of each impact category can be understood. For example, the impact values for carcinogens were 7.710E-08, 8.961E-08, 8.748E-08, 8.429E-08, and 8.270E-08 DALY for mining methods at 0, 5, 10, 15, 20, and 25 m coal pillar widths, respectively.

In the characterized results, all impact categories were counted at 100% to provide a more intuitive analysis of the proportion of each process in each impact category. However, the results of this study do not allow for an intuitive identification of which impact category or process has the greatest impact on the overall environment. Only which unit or process contributes the most to a particular impact category can be observed. Using a 20 m coal pillar as an example, Figure 3 shows the characterized results for each unit or process in the coal mining process. Figure 4 shows the characterized results for coal mining at pillar widths of 0–25 m.

All units or processes contribute in some way to each of the 11 impact categories, and the share of each unit in particular impact categories is clearly demonstrated in Figure 3. For example, electricity was found to make a substantial contribution to the impact of carcinogens, followed by steel. Pollution is not only related to direct emissions but also to the production of materials and energy at the previous level. In this way, the magnitude of the contribution of the impact units or processes in each of the 11 impact categories could be visualized. The contributions of the impact categories, impact units, and systems for the remaining coal pillar width mining processes were similar to those for the 20 m pillar width mining process; therefore, they were not repeated here.

The 11 impact types of coal mining at five coal pillar widths were compared centrally (Figure 4), with the most severe mining method for each impact type being counted at 100% in the comparison process. The impact of the remaining mining methods on each impact type was compared according to the most severe mining method. The environmental impact of each mining method could only be compared for one impact type, but it was seen that the environmental impact of pillarless mining is the lowest for each impact type, and therefore, the overall environmental contribution of pillarless mining is also the lowest. Based on this method, Figure 4 shows a comparison of the characterized results for coal mining at 0–25 m pillar widths, with each influence category increasing and then decreasing as the width of the coal column increases. The 5 m coal pillar mining method led the way for each impact type, and therefore, had the greatest overall environmental impact, followed by the 10, 20, and 25 m pillar width mining methods. The overall environmental impact of pillarless mining was the lowest. The proportion of respiratory organic impacts for each mining method was the highest compared to the other impact types in coal-free pillar mining, mainly because of organic particulate matter generated by blasting during the coal-free pillar mining process. Conversely, the 5 m coal pillar mining process had the highest impact of each category, mainly because the pillar was so small that it no longer had the capacity to carry the overlying rock seam. It required a large amount of steel, electricity, and wood to support the material in order to reach the carrying capacity of the mining and consumed a large amount of energy and material. As the width of the coal column increased, the ability to carry the overlying rock seam gradually increased, requiring less ancillary support material and energy, and the contribution to the environment from producing the corresponding energy and material gradually decreased, showing a gradual decrease in the contribution to the environment in each impact type.

In the characterized results, it was not possible to directly observe the extent to which each impact category contributes to the overall impact. Therefore, a further standardized analysis was required, in which the contribution of each impact category to the overall process can be quantified and the magnitude of the contribution of the impact categories can be visually analyzed and compared. As an example of coal mining with a 20 m coal pillar width, respiratory inorganics caused the greatest environmental burden in the coal mining process, followed by fossil fuels, carcinogens, and climate change (Figure 5).

An uncertainty analysis examines how uncertainty in the input data affects the results of the model and helps to understand the importance of the data source or model. The dataset was drawn from the field, literature, and databases and then averaged together to create a combined dataset; therefore, it was necessary to conduct an uncertainty analysis to ensure its accuracy. The uncertainty analysis was conducted using Simapro 9.0.0 software. The input data were analyzed using the uncertainty analysis method in the Eco-indicator99 method with a 95% confidence level and displaying the mean, median, and standard deviation. The uncertainty results using a 20 m coal column and calculated using the Eco-indicator99 method are displayed in Table 3 and Figure 8.

For the 20 m coal pillar mining process, the results based on Table 3 show no significant change in the significant impact categories of carcinogens, respiratory inorganics, fossil fuels, and climate change. Figure 8 shows the results of the Eco-indicator99 based method for the 20 m coal column mining process, with the marked categories being carcinogens, radiation, and respiratory inorganics. By comparing the uncertainty of carcinogens with the other impact types, followed by radiation and respiratory inorganics, the uncertainty results for the remaining impact types were found to be low and have high stability compared to the uncertainty results for these three impact categories. For low uncertainty data, there is a high degree of stability, whereas for high uncertainty data, double-checking is required to ensure the accuracy of the data and provide a reference for future related studies.

A sensitivity analysis can provide a basis for scientific decisions to reduce environmental burden. This is an indication of how changes in individual parameters and parameter sets affect the final results; that is, the importance or sensitivity of a parameter to the overall model can be determined by analyzing the impact of a change in the parameter when all other parameters are constant. The sensitivity analysis was conducted using Simapro 9.0.0 software, and the results were calculated using the Eco-indicator99 method. The results of the sensitivity analysis were calculated using the software and the methods described above. The sensitivity analysis was conducted for crucial impact categories and key processes based on functional units, reducing the input data for each key process by 5% and observing changes in the contribution of key impact categories. Taking 20 m coal pillar mining as an example, Figure 9 shows the sensitivity analysis results for the key processes.

The graph (Figure 9) demonstrated that that a 5% reduction in coal mining can provide significant environmental benefits for all impact categories, such as carcinogens, respiratory organics, respiratory inorganics, and climate change, with the rates of change being 5.03%, 4.70%, 5.04%, and 4.78%, respectively. The sensitivity of the coal mining process was high and particularly important because changes in coal mining have a greater impact on all impact types. When electricity consumption was reduced by 5%, the rates of change for respiratory inorganics, carcinogens, acidification/eutrophication, fossil fuels, and climate change were 4.75%, 4.25%, 4.01%, 3.04%, and 1.52%, respectively. Therefore, changes in electricity consumption have a greater impact on the important impact types and a higher sensitivity and are crucial for the important impact categories. When steel consumption was reduced by 5%, the rate of change was 4.94%, 2.77%, and 2.30% for the minerals, radiation, and ozone layer categories, respectively, with some sensitivity and importance for some of these impact categories.

Based on the results of the sensitivity analysis, to adopt scientific countermeasures for reducing the burden on the environment, the first step is to minimize the mining of coal resources. Coal mining has a greater burden on the environment, and scientific methods should be adopted to use clean energy as much as possible and improve the efficiency of the mining and utilization of coal. The consumption of electricity also has a significant impact on the environment; therefore, we should try to use less burdensome methods of power generation, such as wind power and water power generation, and improve the efficiency of electricity use. Finally, steel consumption damages the environment, and it is necessary to reduce the environmental impact of the steel production process and improve its efficiency.

With rapid economic development, environmental issues have become key global topics. As one of the primary energy sources, the production and consumption of coal has caused substantial environmental pollution. As different production methods are used, their impacts on the environment vary. This study investigated the environmental impact of underground coal resources under different mining methods and identified the production method with the least burden on the environment and, consequently, the optimal mining method.

The life cycle evaluation process is similar to different coal pillar mining methods, and this study used the mining method with a 20 m coal pillar left in place as an example of a life cycle evaluation of its production process. The characterization results (Figure 3) provide an understanding of the extent of each process for each of the 11 impact categories and the share of each unit in a particular impact type is also shown. For example, electricity makes the largest contribution to the impact of carcinogens, followed by steel. The results of the characterization of all coal pillar widths were compared (Figure 4), which initially concluded that the no-pillar mining method has the least environmental burden. A standardized analysis was then used to quantify the degree of impact of each impact category on the overall environment, with respiratory inorganics, carcinogens, and fossil fuels being the most dominant impact categories. With an understanding of the main impact categories, a contribution analysis was conducted to identify the face mining system as the subsystem with the greatest environmental burden through the contributions of subsystems and processes. The main processes affecting the main categories of impact were identified, with the main processes affecting respiratory inorganics being, for example, electricity. This analysis explains the main impact categories and processes and provides a basis for scientific decisions to reduce the environmental burden. However, to verify the accuracy of the data and provide a solid scientific strategy, uncertainty and sensitivity analyses were conducted. The sensitivity analysis results showed that a 5% reduction in the input data for the main impact processes would result in a greater environmental benefit. Therefore, with the mining method of leaving a 20 m coal pillar as an example, based on the above analysis, the mining method without a coal pillar was considered to have the least negative impact on the environment. Consequently, it was initially concluded that the mining method without a coal pillar is the optimal mining method. Respiratory inorganics, carcinogens, and fossil fuels were the most important impact types, and after determining the main impact, the main impact types were identified. The face mining system was the subsystem with the greatest environmental burden. Mining, electricity, and steel were considered to have high sensitivity or importance.

The main objective of this study was to determine the optimal extraction method. Using the 20 m coal pillar mining method as an example, the main categories of influence and processes affecting the mining method were recognized through characterized, normalized, and contribution analyses. The influence of respiratory inorganics had a greater weighting under the non-pillar mining method (Figure 4), mainly because of the blasting technique used in the non-pillar mining process, which generates more organic particles. The LCA process for the other mining methods is similar to the 20 m column mining method, and there is little difference in the primary impact categories and processes. The single score value for the mining method without coal pillars, that is, 0 m pillar width, was the smallest (Figure 10). Combined with the results of the characterization comparison, it is clear that mining without coal pillars has the lowest level of environmental impact, and therefore, mining without coal pillars is determined to be the optimal mining method. The final single score summary comparison shows that as the width of the coal pillar increases, the degree of negative environmental impact increases and then decreases. As the width of the pillar continues to increase to a certain width, the recovery methods used because of its increased carrying capacity change and the materials and energy used are reduced accordingly, making the mining process less environmentally burdensome. It is predicted that as the width of the pillar increases to a width equal to that of the working face, the mining method will be equivalent to pillarless mining, and the level of negative environmental impact will approach that of pillarless mining (Figure 10).