Life cycle assessment of synthetic graphite: Inventories and impact assessment

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Electrification requires batteries as the central technology in which graphite is widely used as an anode material. Investigating the environmental impacts of graphite is crucial, especially synthetic graphite, which dominates the market share of graphite. The environmental assessment was conducted using life cycle assessment (LCA) to quantify the impacts of manufacturing 1 kg of anode-grade synthetic graphite from cradle-to-gate within the Chinese context by implementing economic allocation. The assessment included scenario analysis that explored the outcomes when the geographical area of the manufacturing process was relocated to other countries. Moreover, contribution analysis was performed to examine the environmental hotspots. The study reported an impact of 10.44 kg CO 2 eq per 1 kg of synthetic graphite. Meanwhile, graphitization caused 81% of the overall climate change impact among seven production stages. When the contribution was classified based on the input-output parameters, more than 80% of the climate change impact originated from electricity consumption. The study presented comprehensive inventories for manufacturing synthetic graphite and demonstrated the importance of complete and reliable data and transparency for reproducibility. The study may assist stakeholders in making decisions to improve the impacts within the supply chain. Synthetic graphite anode material life cycle assessment life cycle inventory scenario analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Climate change poses a real threat, with its implications stretching into various aspects such as ecosystems, the economy, and society. Economists have reported that the cost of climate change could be six times higher than previously estimated (Bilal & Känzig, 2024 ; Kikstra et al., 2021 ). Many economic models have mainly considered short-term damage, assuming that long-term damage would not occur, while the latest study shows that the lasting effect of climate change could decrease global GDP by 30% (Kikstra et al., 2021 ). Different strategies are pursued to mitigate greenhouse gasses (GHG), including electrification, which makes graphite a pivotal player in the manufacturing of batteries (Abdelmotteleb, 2024 ; IEA, 2022 ). Because of its exceptional properties, graphite is used in various industries. It exhibits electrical and thermal conductivity as well as chemical resistance, which make it suitable for use as a lubricant, in sealing, a carbon brush in electric motors, in refractories, as electrode material for fuel cells, and more (Engels et al., 2022 ; Jara et al., 2019 ). Graphite is one of the most widely used anode materials for lithium-ion batteries (LIB) due to its low cost, safety, and good reversibility (Zhu et al., 2019 ). The global market size of graphite in 2022 was about 15.64 billion USD, estimated to grow at 6.4% from 2023 to 2030 (Grand View Research, 2023 ). In 2021, graphite production was around 3,403 kt, with synthetic graphite accounting for 66% of the supply and the remaining 34% coming from natural graphite (ECGA, 2022 ). The important role of synthetic graphite in the supply chain should be investigated from an economic and environmental perspective. Available studies on the environmental impacts of synthetic graphite manufacturing are limited. The notable ones include Surovtseva et al. ( 2022 ), Kulkarni et al. ( 2022 ), Dunn et al. ( 2015 ), Dai et al. ( 2019 ), and Whattoff ( 2022 ). The study by Kulkarni et al. ( 2022 ) applied the GREET model, an improved version of the model used by Dunn et al. ( 2015 ). Similarly, a study by Dai et al. ( 2019 ) was an updated version of Dunn et al. ( 2015 ). Surovtseva et al. ( 2022 ) laid a foundation by offering a framework for setting the boundaries in conducting an LCA of graphite. They also presented the inventories and impacts of anode-grade synthetic graphite from cradle-to-gate based on each production stage. However, their study only focused on the impacts of climate change and energy consumption, which meant the inventories presented were not complete and left room for improvement. Kulkarni et al. ( 2022 ), as well as Dunn et al. ( 2015 ) and Dai et al. ( 2019 ), presented LCA results of synthetic graphite produced using an Acheson furnace. The study investigated the product from cradle-to-gate, with direct inventory built only for the baking and graphitization stages. The inventories of the main inputs, petroleum coke and coal tar, were obtained from the Ecoinvent database. Meanwhile, Whattoff ( 2022 ) conducted a study for an Indian company, Epsilon, using the actual process. It presented comprehensive inputs and outputs, including the market price data for economic allocation. However, the broad inventories included the coal-based binder but not the graphite precursor, which constitutes about 80% of the main material inputs in the baking stage. The background of Whattoff ( 2022 ) study was India, allowing comparison with this study since its geographical location was in China. Considering the importance of synthetic graphite in the supply chain, further studies on the impacts of producing synthetic graphite are crucial. The overall aim of this study is to provide comprehensive inventories and assess the environmental impacts of the production of anode-grade synthetic graphite. The study intends to contribute to the environmental assessment of synthetic graphite, which is still scarce. Moreover, it aims to improve upon available studies by providing more complete inventories, covering impact categories beyond climate change and energy consumption, and increasing transparency while investigating the reproducibility of LCA studies on synthetic graphite compared to previous research. The goals will be achieved by focusing on the following objectives: (i) gathering all the inputs and outputs, including the market price of the products, in each production stage of synthetic graphite; (ii) assessing impact categories beyond climate change and energy consumption; (iii) applying contribution analysis to investigate the environmental hotspots; and (iv) applying sensitivity and scenario analysis to explore the model's behavior and possible alternatives. 2 Methods To quantify the environmental impacts and interpret the results, LCA was applied following ISO 14040 and ISO 14044 (ISO, 2006a , 2006b ). The following section adheres to LCA phases: goal and scope definition, life cycle inventory, impact assessment, and interpretation. 2.1 Goal and scope definition The study investigated the cradle-to-gate environmental impacts of the production of anode-grade synthetic graphite by applying attributional LCA. It also systematically inventoried each activity's inputs and outputs during synthetic graphite manufacturing. The inventory was as important as obtaining the results of environmental impacts since LCA studies about synthetic graphite production are still scarce, incomplete, and have relatively high uncertainties. The functional unit (FU) applied was per kg of anode-grade synthetic graphite. The graphite precursor used to produce synthetic graphite was derived from oil mixed with the processed coal as a binder. Graphite precursors can also be produced from coal; however, their intrinsic characteristics result in lower carbon content, which may not be graphitized completely and require further processing to make them suitable for battery applications (Surovtseva et al., 2022 ). The life cycle stages considered in this study included coal mining, coal coking, oil mining, oil refining, calcination, baking, and graphitization. Figure 1 shows the system boundaries, activities, general inputs and outputs to manufacture synthetic graphite as adopted from previous studies (Surovtseva et al., 2022 ; Whattoff, 2022 ). The manufacturing process was assumed to take place in China. Coal and oil mining occurred in China and the United States (US), respectively. The transportation of hard coal and crude oil to their subsequent facilities were included in the boundaries. The coal was transported via lorry for coking, while the oil was transported using both lorry and ship from the US to China. Other transportation activities were excluded, assuming the coking, oil refining, calcining, baking, and graphitizing facilities were nearby. Economic allocation was applied to handle co-products produced during coking, oil refining, baking, and calcination. The method indicated that the environmental burden would be assigned to all outputs with economic value based on market price (Ecoinvent, 2024 ). Hence, inventory data regarding market prices for the year 2023 were required. The means of dealing with co-products is left to the practitioner, and allocation should be avoided in the first place (ISO, 2006b ); however, four production stages generated 15 co-products. Using system expansion would be exhaustive and require too many assumptions. Moreover, many co-products from oil refining were considered products with high value that were not used as substitutions for which certain impacts from other production processes could be avoided. Economic allocation was used because the market price of these products was deemed to reflect their value within society. Electrification using batteries is related to policy subsidies, which made market price allocation appropriate. 2.2 Life cycle inventory The foreground data were gathered from previous academic studies, while the background data regarding emission factors, classifications, and characterization were obtained from the Ecoinvent 3.8 database. Surovtseva et al. ( 2022 ) underlined that the environmental impacts of synthetic graphite production were understated since the production of carbon black is usually used as a proxy for synthetic graphite, and previous studies only considered graphitization stage, resulting in lower impacts and incomplete data inventory. This study modelled the impacts of each production stage instead of using the readily available intermediate products found in the Ecoinvent database. For example, the impact of coal mining was modelled based on a study done by Tao et al. ( 2022 ) instead of using the hard coal process found in the Ecoinvent database. Table 1 summarizes the sources of inventory data used in the study. Table 1 References used for foreground data in life cycle inventory Items References Note General Surovtseva et al. ( 2022 ), Whattoff ( 2022 ) Used for determining the weight input of intermediate products needed. Coal mining Tao et al. ( 2022 ) Case study of underground mining in East China Coking Li et al. ( 2020 ), Banerjee et al. ( 2021 ), Ecoinvent ( 2021 ) Data from coking plants in North-west China Oil mining Meili et al. ( 2021 ) Onshore oil mining in the US Oil refining Askari et al. ( 2021 ), Liu et al. ( 2020 ); NCCP ( 2017 ) Case study of medium refinery in East China Calcination Edwards et al. ( 2020 ), (Tang et al., 2018 ) Mainly taken from Edwards et al. ( 2020 ), a case study of calciner in India, adjusted for Chinese background data. Baking Tang et al. ( 2018 ) Case study of prebaked anode for aluminum industry in East China Graphitization Lan et al. ( 2022 ), Shang et al. ( 2024 ) The Acheson method was used. The background data were country-specific for China and the US; a global process was applied when country-specific data was unavailable in the Ecoinvent database. The 'market group for electricity' in China and the US was used as electricity sources during production. Since China and the US are large countries and the inventory data of graphite production was from different parts of China and India, the 'market group' data set was applied, and later, the results would reveal the effect of choosing electricity sources. The market price of the products and co-products in 2023 was used to apply economic allocation. Table 2 indicates the activities that generated co-products and the references of the market price of the products and co-products used in this study. The complete operational and economic data of synthetic graphite production are shown in tables of supplementary material sections 1 and 2. Table 2 References used for determining market price (economic allocation) Activities Products and co-products References Coking Coal tar pitch, coal coke, crude benzene, ammonium sulphate, sulfur, coke oven gas. Abdalla ( 2024 ), Li et al. ( 2020 ), National Bureau of Statistics of China ( 2023 ), Procurement Resource ( 2024 ), SunSirs ( 2024b , 2023b , 2023a ) Oil refining Pet coke (as green coke), benzene, coke burning, diesel, gasoline, liquefied petroleum gas, naphtha, propylene, sulfur Abdalla ( 2024 ), Mysteel ( 2023 ), National Bureau of Statistics of China ( 2023 ), Procurement Resource ( 2024 ), SunSirs ( 2024a ) Calcination Calcined coke (as needle coke), electricity Mysteel ( 2023 ), Statista ( 2023 ) Baking Graphitization precursor, calcium sulphate Shanghai Metals Market ( 2023 ), Tang et al. ( 2018 ) 2.3 Life cycle impact assessment The impact assessment method for this research was the CML baseline, which resulted in midpoint impacts. The calculation used OpenLCA 2.3 ("OpenLCA," 2022). The assessed impact categories were abiotic depletion (AD), abiotic depletion fossil fuels (AD-FF), acidification (AC), eutrophication (EU), freshwater aquatic ecotoxicity (Ecotox-FW), climate change (CC), human toxicity (HT), marine aquatic ecotoxicity (Ecotox-MA), ozone layer depletion (ODP), photochemical oxidation (PO), and terrestrial ecotoxicity (Ecotox-tr). The CML baseline was deemed sufficient to cover a relatively wide range of impacts while not being too exhaustive. Moreover, cumulative energy demand (based on OpenLCA method 2) was also assessed because the production process was energy-intensive. Normalization and weighting were not applied since comparing unitless results across different impact categories and generating a single score was not the objective of this study. Further analysis and interpretation were focused on climate change because the impact affects the global population, and there is an urgency to tackle the problem. Besides overall results, contribution analysis was also implemented. It was applied based on the activities during synthetic graphite manufacturing to discover which part of the production stage caused the highest impact. Contribution analysis was also conducted based on the flows (input and output parameters) to understand the most important parameter. Contribution analysis can help stakeholders identify the environmental hotspot, which is part of the process that causes the highest impacts, and assist them in making improvements. 2.4 Interpretation, scenario and sensitivity analysis During interpretation, the results of the LCA were reflected in the study's objectives. Scenario and sensitivity analysis were applied as part of the interpretation to understand better the outcomes and how the model behaved. Uncertainties are an inherent part of conducting LCA. Changes in the foreground data, background data, methodological choices, and so on could result in different outcomes that steer the interpretation of the results in a different direction. Implementing scenario analysis can help manage uncertainties by exploring possible alternatives. Different assumptions regarding the geographical location of synthetic graphite production were applied during the scenario analysis while keeping the mining location the same as the baseline: China (coal mining) and the United States (oil mining). Five alternatives of the geographical locations in the production process were investigated: Germany, Japan, the United States, South Korea, and Finland. In the US scenario, the state of Pennsylvania was the specific location used for the calculation because it has one of the biggest graphite production facilities. The selection of a specific state was matched with the electricity provider in the Ecoinvent database. The countries in the scenario analysis were selected because they (except for Finland) and China were the top five exporters of synthetic graphite in 2022 (The Observatory of Economic Complexity, 2023 ). Finland was selected because it was where the study was conducted and as the Nordic representation. Furthermore, additional scenario analysis was applied by changing the economic allocation to physical allocation. Sensitivity analysis was applied to evaluate how the outcomes differ because of a change in the parameter's values. It was conducted by increasing a parameter's value by 10% one at a time while keeping the rest at the baseline levels. Sensitivity ratios (SRs) were calculated to measure the importance of each parameter concerning the LCA model. SR, the ratio of two relative changes, was calculated by dividing the percentage change in the results by the percentage change in the parameters, as shown by Eq. ( 1 ). $$\:\:SR=\:\frac{\left(\frac{\varDelta\:\:result}{initial\:result}\right)}{\left(\frac{\varDelta\:\:parameter}{initial\:parameter}\right)}\:$$ 1 When the calculation generates an SR of 3, it indicates that the rise of specific parameters by 10% will increase the results by 30%. Conducting sensitivity analysis could help evaluate the dynamic of the LCA outcomes and determine the most important parameters that can affect the outcomes significantly. 3 Results 3.1 Environmental impacts Eleven impact categories were analyzed based on CML baseline methods. The overall results of producing 1 kg of anode-grade synthetic graphite in China are shown in Table 3 . The climate change impact was 10.44 kg CO 2 eq per FU. A comparison across different impacts was not conducted due to differences in the unit, and normalization would be required. Hence, a complete contribution analysis based on activities/production stages of synthetic graphite across all impact categories was conducted and shown in Fig. 2 . Conducting a contribution analysis is crucial to understanding the different roles of different activities or process flows within the investigated system. Relative importance can be assigned, and improvement measures can be focused on what matters in the process. Table 3 Environmental impacts of synthetic graphite production per FU Name Results Unit Abiotic depletion (AD) 9.55E-06 kg Sb eq Abiotic depletion, fossil fuels (AD-FF) 1.32E + 02 MJ Acidification (AC) 4.37E-02 kg SO 2 eq Eutrophication (EU) 9.88E-03 kg PO 4 eq Freshwater aquatic ecotoxicity (Ecotox-FW) 2.67E + 00 kg 1,4-DB eq Climate change (CC) 1.04E + 01 kg CO 2 eq Human toxicity (HT) 3.62E + 00 kg 1,4-DB eq Marine aquatic ecotoxicity (Ecotox-MA) 1.43E + 04 kg 1,4-DB eq Ozone layer depletion (ODP) 1.34E-07 kg CFC-11 eq Photochemical oxidation (PO) 1.80E-03 kg C 2 H 4 eq Terrestrial ecotoxicity (Ecotox-Tr) 1.52E-02 kg 1,4-DB eq Figure 2 indicates a similar pattern in all impact categories, where the graphitization stage was the highest contributor by a large margin. An exception was found in ODP, where graphitization was not significantly higher than in other production stages. In ODP, the top three contributors were graphitization (30.1%), coking (26.6%), and calcination (18%). Meanwhile, the rest of the impact categories showed that graphitization contributed about 57.3% (AD-FF) to 93.4% (Ecotox-MA). In AD-FF, the second-highest contributor was about 32.3% (oil mining), whereas the other impact categories (other than ODP) showed that the second-highest contributors contributed less than 10%. Electricity was the only input during the graphitization stage; hence, the impacts in that stage were caused by the high electricity consumption in the Acheson furnace, which depended on the source of the electricity mix. Detailed numbers on the contribution analysis in each impact category can be found in the supplementary material Table 3.1. The cumulative energy demand (CED) was assessed to investigate the consumption of primary energy within the boundaries of the studied system. The primary energy consumption per kg of synthetic graphite was about 150.8 MJ, of which 95% was from non-renewable sources and renewable sources generated 5%. Non- renewable fossil and nuclear contributed for 139.9 MJ and 2.73 MJ, respectively. Meanwhile, the renewable comprised of water (5.91 MJ), biomass (1.12 MJ) and other renewable (1.11 MJ). More detailed analysis was done on the impact of climate change because it could have a catastrophic effect globally and the urgency to curb it. The contribution analysis classified the results based on activities and process flows, as shown in Fig. 3 . The former referred to different production stages in synthetic graphite manufacturing, starting from the raw material extraction until graphitization, while the latter denoted the input or output parameters during graphite manufacturing. The overall climate change impact per FU was 10.44 kg CO 2 eq. A contribution analysis based on the activities (Fig. 3 .a) showed that graphitization caused 81.3% of the impact of climate change, followed by 9.3% during calcination. Almost 91% of climate change impacts were caused by two activities out of seven in total. The other five activities contributed 9% of the overall impact, contributing about 1.3–3% of the climate change impact. The transportation of crude oil from the US, part of the oil refining stage, which required ocean transportation (about 26,000 km) and land transportation, did not affect climate change significantly, as refineries covered 1.3% of the overall climate change impact. Figure 3 .b displays contribution analysis classified by the flow of parameters, in which electricity consumption covered about 84% of total climate change impact, followed by 10.4% contribution from direct emissions. The impact caused by electricity relies on the sources of the electricity mix, which can be generated from renewable or non-renewable sources. The remaining 5.5% was from waste treatment, natural gas, transportation, coal coking gas, steam, and lime. Both classifications, activities and flows, presented similar patterns where two contributors caused more than 90% of the overall climate change impacts. It showed relative importance among activities and where the improvement was needed or wanted the most. Detail numbers of contribution analysis for climate change is in the supplementary material Table 3.2.1 and 3.2.2. 3.2 Scenario analysis The scenario analysis explored the outcomes of the manufacturing facilities in producing synthetic graphite located in different geographical areas without changing the location of raw material extraction. Figure 4 shows the comparison of climate change impacts of synthetic graphite production between baseline scenarios in China (CN) and in Germany (DE), Japan (JP), South Korea (KR), the United States (US), and Finland (FI). All the alternative scenarios showed lower impacts in comparison to the baseline. The decrease in impacts ranged from 27–39%, with the US being the country with the lowest impact. As shown by Fig. 3 .b, electricity was a hotspot parameter in the production of synthetic graphite. The lower impacts found in the alternative scenarios can be attributed to the production of auxiliaries that were greener, but mainly to the electricity sources, especially during the graphitization process that was energy intensive. The climate change impact of 'market group' electricity in China was 1.01 kg CO 2 eq per kWh. Meanwhile, the average impact in kg CO 2 eq per kWh electricity in DE, JP, KR, US, and FI were 0.55, 0.66, 0.69, 0.54, and 0.25 respectively. The lowest impact was from the FI where nuclear and renewable energy contributed significantly to their electricity mix. The scenario analysis demonstrated how the background system affects the impacts, where the same process could result in different outcomes. Numeric data corresponds to Fig. 4 can be found in supplementary material Table 3.2.3. Meanwhile, physical allocation was applied as well during scenario analysis. The environmental burden was assigned based on the weight of the products and co-products, which generated a result of 10.14 kg CO 2 eq per FU. This study did not find significant differences between two different allocation methods. The results obtained from economic allocation will change following the fluctuation of the market, while the outcomes derived from physical allocation could change if the operations in different production stages change. 3.3 Sensitivity analysis Sensitivity analysis was done to investigate the outcome change if the parameters were increased by 10% compared to the baseline value. Five parameter flows were used in sensitivity analysis: direct emission, electricity, natural gas, transportation, and waste. These five parameters were selected based on the contribution analysis results shown in Fig. 3 .b. There were multiple flows or parameters in the inputs and output of the LCA model, and their importance toward the impact of climate change was dissimilar. Hence, the sensitivity analysis was applied to the five most important parameters to investigate how they affected climate change impact, as shown in Fig. 5 . Each parameter was increased by 10% in each production activity when applicable (e.g., transportation distance was increased as part of coal coking and oil refining activities) and the overall impacts were calculated. Figure 5 shows how the parameters change affected the climate change impacts differently. Across all production stages, the SR ranged between 0 to 1. Electricity consumption was the most important input parameter throughout different production stages, with the highest effect during the graphitization stage, followed by baking. It indicated the importance of using electricity from more sustainable sources. The other parameter that was relatively important was direct emission. Direct emissions were input and process-specific; hence, improving or changing the production methods could alter the direct emissions. Data regarding Fig. 5 can be found in supplementary material Table 3.2.4. 4 Discussions 4.1 Comparison with previous studies Not many studies discussed a comprehensive inventory of synthetic graphite and its environmental impacts. Available studies regarding the LCA of anode-grade synthetic graphite reported different results for different explanations. Kulkarni et al. ( 2022 ) studied the impacts of different types of graphite produced using different methods, such as synthetic graphite being manufactured using the Acheson method, causing 5.45 kg CO 2 eq per kg synthetic graphite. The study excluded any transportation stage which would not change the results significantly, as shown by this study, where main materials were transported across the continent and contributed to less than 1% of the overall climate change impact. Their study built direct modelling of the baking and graphitization stages, while the rest of the production stage was taken from the Ecoinvent database by using petroleum coke and coal tar as input in the baking process. Their result was 48% lower than this study's, although, in theory, their study had the same boundaries and geographical location (even the same electricity provider) as this study. Their lower results could be attributed to the lower electricity consumption during the graphitization process, which was 4.1 kWh, whereas this study assumed 8.38 kWh. Literature reported that electricity consumption during the Acheson process to produce 1 kg graphite ranged between 5.5–12 kWh, with the lowest value of 7.35 kWh after the process was optimized (Lan et al., 2022 ; Shang et al., 2024 ). A higher value was found in a study by a company in India that consumed 14 kWh during the graphitization process (Whattoff, 2022 ). Results generated by Kulkarni et al. ( 2022 ) were similar to the impact of synthetic graphite found in Ecoinvent 3.8 (5.37 kg CO 2 eq/kg graphite) since they used the same basis data from the GREET model (Dunn et al., 2015 ) Epsilon Ltd, an Indian-based company, conducted LCA of synthetic graphite, resulting in a climate change impact of 5.2 kg CO 2 eq/kg graphite (Whattoff, 2022 ). The boundaries were cradle-to-gate, and it applied economic allocation. It had a comprehensive and transparent input-output throughout all production stages, although the detailed and comprehensive inputs were mainly for producing binders out of coal. The weight contribution of the binder was about 20%, while the remaining 80% was the precursor derived from petroleum, which in the LCA calculation was taken from readily available data from Ecoinvent as petroleum coke. Although the overall energy consumption was high, including electricity consumption during graphitization, which was 14 kWh/kg, the overall climate change impact was low, even lower than Kulkarni et al. ( 2022 ). This result could be linked to the energy used by Epsilon, the majority of which was from waste gas streams (during coal tar production) or renewable sources (during graphitization). Up to 80% of electricity sources during graphitization were renewable, and the remaining was from the grid. Surovtseva et al. ( 2022 ) proposed the generalized boundaries for graphite production, life cycle inventories for synthetic graphite, and environmental impact calculation. Their study covered cradle-to-gate with the inventory to specifically calculate climate change impact and energy demand. The impact of climate change on manufacturing a kg of synthetic graphite was 13.8 kg CO2 eq, which was 24% higher than this study. Surovtseva et al. ( 2022 ) provided a framework for any future study, and my study followed their framework boundaries; hence, the comparison could be done fairly. Their study argued that graphitization caused little climate change impact of 0.243 kg CO 2 eq. An opposite result was obtained in this study, where graphitization caused 8.49 kg CO 2 Eq. (81% of total climate change impact) due to electricity consumption, which was supported by a previous study about electricity consumption by Acheson furnace (Lan et al., 2022 ; Shang et al., 2024 ; Whattoff, 2022 ). It was unclear if Surovtseva et al. ( 2022 ) used a Castner furnace that consumed less electricity. Nevertheless, electricity consumption by the Castner furnace was around 2–3 kWh/kg graphite (Iyer & Kelly, 2022 ), which could be three times higher than the value used in Surovtseva et al. ( 2022 ). Other major differences were found during the oil mining and calcination stages, where these were the environmental hotspots of Surovtseva et al. ( 2022 ). Their study showed impacts of 5.2 kg CO 2 eq and 6 kg CO 2 eq for oil mining and calcination, respectively. Meanwhile, this study generated 0.23 kg CO 2 eq and 0.97 kg CO 2 eq during oil mining and calculation. Producing 1 kg of green needle coke requires 5.9 kWh of energy input, of which 10% is from electricity (Surovtseva et al., 2022 ). Meanwhile, this study referred to Edwards et al. ( 2020 ), who did a case study on the calcination of green coke where the energy consumption to produce 1 kg calcined product was 0.11 kWh in which 90% of the energy was from electricity. Tang et al. ( 2018 ) showed even lower energy consumption in producing 1 kg prebaked anode through analysis of a real case study in which the energy consumption was about 0.04 kWh and 100% was derived from electricity. As for the oil mining stage, the energy consumption was quite similar between Surovtseva et al. ( 2022 ) and this study, 0.94 kWh and 0.804 kWh per kg crude oil, although the energy source was different. Hence, the difference might be ascribed to the allocation during the oil mining and refining. 4.2 Data, impact assessment method, transparency, and clarity Using the database in LCA applies to the exact inputs or proxy input. The former one is about using a database that matches the requirement. For example, the model needs 'hydrated lime', and the database has 'hydrated lime,' although the geographical location may not be precise, the global average is still possible. The latter is when the exact input is unavailable in the database, and another input is used because it is considered similar and representative enough. This study applied both where the database acted as exact and proxy input; for example, manufacturing lubricating oil was used to represent dedusting oil during the calcination phase. This practice is common in LCA; however, more careful consideration is necessary when the data is used as a proxy. As Surovtseva et al. ( 2022 ) pointed out, using a proxy could lead to an underestimation of the impacts, such as using carbon black as a proxy for anode-grade graphite. Assumptions and decisions should be made throughout the LCA calculation process and left to the LCA practitioner. These decisions can steer the results in different directions. One of them is that the choice of life cycle impact assessment (LCIA) methods can result in different outcomes, and there was no specific requirement in selecting the LCIA methods. In some ways, it can be straightforward; for example, if a single score and endpoint results are needed, a specific LCIA method will be used (e.g., ReCiPe) since not all methods can accommodate it. Different methods can also employ different characterization factors (CF) that lead to different outcomes, as shown by CML, ILCD, ReCiPe 2016, and TRACI, where the CF of 'methane fossil' ranges between 25–36 (Ecoinvent, 2021 ). This factor could affect the differences between the results of this study and previous studies. The difference can also be found in the inclusion of CO as greenhouse gas in the IPCC method describing the CF between 2–3 (IPCC, 2019 ), while CML, ILCD, ReCiPe, or TRACI did not include CO. This study employed CML, while Kulkarni et al. ( 2022 ) applied TRACI and Surovtseva et al. ( 2022 ) and Whattoff ( 2022 ) used IPCC to calculate climate change impacts. However, it is unclear if Whattoff ( 2022 ) included CO in the calculation as Surovtseva et al. ( 2022 ) did. Since assumptions are inevitable in conducting LCA, clarity and transparency are important. It not only concerns the completeness of inventory data (when confidentiality is not an issue) but also discloses assumptions, geographical location, the LCIA, allocation, etc. When other practitioners replicate the available studies, they will be able to pinpoint where the source of differences and deduce whether their study is aligned with the previous ones or not. 4.3 Implications and future directions This research has implications for multiple stakeholders. For LCA practitioners and databases, it is important to continuously check and update the current inventories and impacts of any products or services. For the practitioners, providing clarity and transparency of the LCA process is also important to ensure the study's reproducibility and correct interpretation. Meanwhile, companies manufacturing synthetic graphite could select their suppliers by considering their environmental impacts. The scenario analysis demonstrated how location can affect the impacts even when the production process is the same. Asking for an environmental declaration, either self-declaration or third-party verification like EPD (ISO, 2006c, 1999), could help with the decision-making. They may as well keep looking for other alternatives for graphite that could serve the same function in batteries with better technical, economic, and environmental aspects. Policymakers can also consider what kind of support could be provided to transition into electrification by considering the role of synthetic or natural graphite. Among all the previous studies on synthetic graphite, the closest results were obtained by Surovtseva et al. ( 2022 ). Nonetheless, when the contribution analysis was compared, there was a significant difference concerning the environmental hotspots. One of the directions of the future study is to focus on inventories and impacts in each production stage, especially during oil mining, calcination, and graphitization. Building a good inventory in which the data is reliable and accurate is important. It is also important to study the impacts of bio-based graphite as an alternative. 5 Conclusions There have not been many studies regarding synthetic graphite's environmental impacts despite the importance of synthetic graphite in the supply chain. This research helps to improve the estimation of the greenhouse gas emissions produced while making synthetic graphite. This study gathered complete inventories of synthetic graphite and its environmental impacts from cradle-to-gate. The inventories in this study allow the calculation of a wide range of impact categories and the disclosure of all assumptions and allocation, providing a way for others to replicate or revise this study. Previous scientific studies showed a range of climate change impacts in manufacturing 1 kg synthetic graphite (5.4–13.8 kg CO 2 eq), while this research presented a result of 10.44 kg CO 2 eq. The most striking difference was the energy consumption between this study and others during graphitization, which was reported to be low in other studies. This research also presented a contribution analysis based on production stages and parameters, which can assist relevant stakeholders in pinpointing the environmental hotspots within the process. The scenario analysis and sensitivity analysis could also provide insights to stakeholders in applying strategic decisions in the production process, selecting suppliers, and even choosing electricity contracts to reduce the impacts. Although the study focused on climate change's impacts, other categories were also analyzed for more comprehensive decision-making. Researchers and companies could use pattern and contribution analysis across impact categories to develop approaches to reduce overall impacts to manufacture greener batteries. Statements and declarations Acknowledgement Financial support for this study was provided by a grant from the EU – Interreg Aurora. Funding This work is part of the Green Battery project funded by EU - Interreg Aurora. Author’s contribution The author has conducted conceptualization, methodology, material collection, formal analysis and visualization, writing the draft, review, and editing. Ethical approval Not applicable. Consent to participate Not applicable. Consent to publish Not applicable. Competing interests The authors have no relevant financial or non-financial interests to disclose. Data availability statement The data that supports the finding of the study derived from published literature. All the data used are openly summarized in the supporting material, including their references. Clinical trial number: not applicable References Abdalla, A. (2024). Sulfur price in China . https://sulfur-price.com/today/china Abdelmotteleb, I. (2024). The battery's role in decarbonizing the electricity grid . https://www.rabobank.com/knowledge/d011408711-the-batterys-role-in-decarbonizing-the-electricity-grid Askari, M., Aliofkhazraei, M., Jafari, R., Hamghalam, P., & Hajizadeh, A. (2021). Downhole corrosion inhibitors for oil and gas production – a review. Applied Surface Science Advances , 6 , 100128. https://doi.org/10.1016/j.apsadv.2021.100128 Banerjee, C., Agarwal, S., Dash, P. S., & Roy, A. (2021). Effect of wash oil inlet temperature in naphthalene scrubber on the absorptivity of naphthalene in coke oven by product plant. 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Y., Thapaliya, B. P., Luo, H., Dai, S., & Zhao, F. (2022). Prospective Life Cycle Assessment of Synthetic Graphite Manufactured via Electrochemical Graphitization. In ACS Sustainable Chemistry and Engineering (Vol. 10, Issue 41). https://doi.org/10.1021/acssuschemeng.2c02937 Lan, Y., Zhao, X., Zhang, W., Mu, L., & Wang, S. (2022). Investigation of the waste heat recovery and pollutant emission reduction potential in graphitization furnace. Energy , 245 . https://doi.org/10.1016/j.energy.2022.123292 Li, J., Zhang, S., Nie, Y., Ma, X., Xu, L., & Wu, L. (2020). A holistic life cycle evaluation of coking production covering coke oven gas purification process based on the subdivision method. Journal of Cleaner Production , 248 . https://doi.org/10.1016/j.jclepro.2019.119183 Liu, Y., Lu, S., Yan, X., Gao, S., Cui, X., & Cui, Z. (2020). Life cycle assessment of petroleum refining process: A case study in China. Journal of Cleaner Production , 256 , 120422. https://doi.org/10.1016/J.JCLEPRO.2020.120422 Meili, C., Jungbluth, N., & Bussa, M. (2021). Life cycle inventories of crude oil and natural gas extraction Life cycle inventories of crude oil and natural gas extraction R . https://doi.org/10.13140/RG.2.2.29142.78409 Mysteel. (2023). Needle coke price wanes on weaker downstream demand . https://www.mysteel.net/news/5045743-needle-coke-price-wanes-on-weaker-downstream-demand National Bureau of Statistics of China. (2023). Market Prices of Important Means of Production in Circulation, November 11-20, 2023 . https://www.stats.gov.cn/english/PressRelease/202311/t20231129_1944997.html NCCP. (2017). Zeolite catalysts | NCCP . http://www.nccp.ru/en/products/zeolite_catalysts/?PAGEN_1=3 OpenLCA . (2022). https://www.openlca.org/openlca/new/ Procurement Resource. (2024). Procurement Resource . https://www.procurementresource.com/ Shang, T., Zhan, H., Gong, Q., Zeng, T., Li, P., & Zeng, Z. (2024). Insights into the thermal and electric field distribution and the structural optimization in the graphitization furnace. Energy , 297 , 131269. https://doi.org/10.1016/j.energy.2024.131269 Shanghai Metals Market. (2023). Prices of Prebaked Anode Unlikely To Stop Falling In Short Term amid Weak Raw Material Market . https://news.metal.com/newscontent/102216471/Prices-of-Prebaked-Anode-Unlikely-To-Stop-Falling-In-Short-Term-amid-Weak-Raw-Material-Market Statista. (2023). Average electricity prices for enterprises in China from September 2019 to September 2023 . https://www.statista.com/statistics/1373596/business-electricity-price-china/ SunSirs. (2023a). SunSirs: It is Expected that There is Currently Room for a Decline in Coal Tar Prices . https://www.sunsirs.com/uk/detail_news-14267.html SunSirs. (2023b). SunSirs: The Ammonium Sulfate Market Stopped Falling and Rose . https://www.sunsirs.com/uk/detail_news-15968.html SunSirs. (2024a). SunSirs: Analysis of Naphtha Trends in 2023 and Future Prospects . https://www.sunsirs.com/uk/detail_news-16958.html SunSirs. (2024b). SunSirs: China Coking Coal Price Fell and Rose in 2023, or May Fluctuate in 2024 . https://www.sunsirs.com/uk/detail_news-16705.html Surovtseva, D., Crossin, E., Pell, R., & Stamford, L. (2022). Toward a life cycle inventory for graphite production. Journal of Industrial Ecology , 26 (3), 964–979. https://doi.org/10.1111/jiec.13234 Tang, Y., Li, Y., Shi, Y., Wang, Q., Yuan, X., & Zuo, J. (2018). Environmental and economic impacts assessment of prebaked anode production process: A case study in Shandong Province, China. Journal of Cleaner Production , 196 , 1657–1668. https://doi.org/10.1016/J.JCLEPRO.2018.06.121 Tao, M., Cheng, W., Nie, K., Zhang, X., & Cao, W. (2022). Life cycle assessment of underground coal mining in China. Science of the Total Environment , 805 . https://doi.org/10.1016/j.scitotenv.2021.150231 The Observatory of Economic Complexity. (2023). Artificial Graphite . https://oec.world/en/profile/hs/tall-oil Whattoff, P. (2022). Prospective life cycle assessment study of Epsilon Carbon Ltd synthetic graphite anode manufacturing . https://www.epsilonam.com/pdf/life-cycle-report.pdf Zhu, G. L., Zhao, C. Z., Huang, J. Q., He, C., Zhang, J., Chen, S., Xu, L., Yuan, H., & Zhang, Q. (2019). Fast Charging Lithium Batteries: Recent Progress and Future Prospects. Small , 15 (15). https://doi.org/10.1002/smll.201805389 Supplementary Files SupMat.docx Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 26 Aug, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 31 Mar, 2025 Editor invited by journal 28 Mar, 2025 Editor assigned by journal 19 Mar, 2025 First submitted to journal 16 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6204323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436243737,"identity":"790d8496-cad6-4b93-89e9-6e37cf4a07c2","order_by":0,"name":"Bening Mayanti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAyA+gMS1gTJsJIjUcsAgDcpKw68FAQ4wHIZpwe0wc/beg4cLfjFE889Ifvb4Q8H5xA3Hjz/8wJBggVOLZc+5hMMz+xhyZ9xIMzc4YHA7ccOZHGMJhgQ8DruRY3CYt4cht+FGgpkESMu2AzkMEow/8Gi5/waiZf6N9G9ALecSt51//vgHflt4DA7z/GDI3XAjB2TLgcRtIOvwabHsATmsQSJ345k3ZRJnDJKN9994Y2aRgEeLOfsZ4888f2xy5x1P3yZR8cdOdmZ/+uMbHxLqcGoBA8Y2oJkCCUgiCThUIsAfIOY/QFDZKBgFo2AUjFAAAOvhXxWUhmVPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5073-7375","institution":"University of Vaasa: Vaasan Yliopisto","correspondingAuthor":true,"prefix":"","firstName":"Bening","middleName":"","lastName":"Mayanti","suffix":""}],"badges":[],"createdAt":"2025-03-11 15:07:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6204323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6204323/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-37288-1","type":"published","date":"2025-12-13T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81022689,"identity":"2564b6ed-581e-49a4-9a99-b07f8624b0f4","added_by":"auto","created_at":"2025-04-21 10:04:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323442,"visible":true,"origin":"","legend":"\u003cp\u003eSystem boundaries of synthetic graphite production\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/ea244fe7da017d9dab48209d.png"},{"id":81022341,"identity":"6dafeedb-647b-4bb2-9621-a918a39e03b4","added_by":"auto","created_at":"2025-04-21 09:56:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156006,"visible":true,"origin":"","legend":"\u003cp\u003eContribution analysis across impact categories\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/cce39863dc019d0e817544ee.png"},{"id":81022345,"identity":"16637e96-3c0c-4f21-9215-dd797e3f6170","added_by":"auto","created_at":"2025-04-21 09:56:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":236851,"visible":true,"origin":"","legend":"\u003cp\u003eContribution analysis for climate change impact based on activities and flows\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/9ab47f6b5c83112417f17f6a.png"},{"id":81022687,"identity":"57cd7283-9ac0-43ae-8ab5-f0cfa983a899","added_by":"auto","created_at":"2025-04-21 10:04:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41175,"visible":true,"origin":"","legend":"\u003cp\u003eClimate change impact results for scenario analysis (DE, JP, KR, US, and FI used economic allocation same as the baseline).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/54f100e222145f24ccd899f2.png"},{"id":81022347,"identity":"7755d688-f72d-45b6-8e4e-cd6f9f479d8e","added_by":"auto","created_at":"2025-04-21 09:56:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139966,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity ratio for climate change impact in each production activity: 1. Coal mining, 2. Coal coking, 3. Oil mining, 4. Oil refining, 5. Calcination, 6. Baking, 7. Graphitization.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/fd68b2e036bf88ad8b2797c5.png"},{"id":98243625,"identity":"5f656286-9d12-4ee8-adba-2e3e0a9ce12c","added_by":"auto","created_at":"2025-12-15 16:09:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1533063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/3ff4c5d6-ebbc-47c7-a9ca-81c32f14c974.pdf"},{"id":81022690,"identity":"765b60d6-6d5c-453e-a4eb-5a1a3614182e","added_by":"auto","created_at":"2025-04-21 10:04:12","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":84367,"visible":true,"origin":"","legend":"","description":"","filename":"SupMat.docx","url":"https://assets-eu.researchsquare.com/files/rs-6204323/v1/d94e9ab1f9875d9c33ccbabb.docx"}],"financialInterests":"","formattedTitle":"Life cycle assessment of synthetic graphite: Inventories and impact assessment","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change poses a real threat, with its implications stretching into various aspects such as ecosystems, the economy, and society. Economists have reported that the cost of climate change could be six times higher than previously estimated (Bilal \u0026amp; K\u0026auml;nzig, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kikstra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many economic models have mainly considered short-term damage, assuming that long-term damage would not occur, while the latest study shows that the lasting effect of climate change could decrease global GDP by 30% (Kikstra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Different strategies are pursued to mitigate greenhouse gasses (GHG), including electrification, which makes graphite a pivotal player in the manufacturing of batteries (Abdelmotteleb, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; IEA, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause of its exceptional properties, graphite is used in various industries. It exhibits electrical and thermal conductivity as well as chemical resistance, which make it suitable for use as a lubricant, in sealing, a carbon brush in electric motors, in refractories, as electrode material for fuel cells, and more (Engels et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jara et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Graphite is one of the most widely used anode materials for lithium-ion batteries (LIB) due to its low cost, safety, and good reversibility (Zhu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The global market size of graphite in 2022 was about 15.64\u0026nbsp;billion USD, estimated to grow at 6.4% from 2023 to 2030 (Grand View Research, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In 2021, graphite production was around 3,403 kt, with synthetic graphite accounting for 66% of the supply and the remaining 34% coming from natural graphite (ECGA, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The important role of synthetic graphite in the supply chain should be investigated from an economic and environmental perspective.\u003c/p\u003e \u003cp\u003eAvailable studies on the environmental impacts of synthetic graphite manufacturing are limited. The notable ones include Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Dunn et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Dai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study by Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) applied the GREET model, an improved version of the model used by Dunn et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, a study by Dai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was an updated version of Dunn et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) laid a foundation by offering a framework for setting the boundaries in conducting an LCA of graphite. They also presented the inventories and impacts of anode-grade synthetic graphite from cradle-to-gate based on each production stage. However, their study only focused on the impacts of climate change and energy consumption, which meant the inventories presented were not complete and left room for improvement. Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as Dunn et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Dai et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), presented LCA results of synthetic graphite produced using an Acheson furnace. The study investigated the product from cradle-to-gate, with direct inventory built only for the baking and graphitization stages. The inventories of the main inputs, petroleum coke and coal tar, were obtained from the Ecoinvent database. Meanwhile, Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a study for an Indian company, Epsilon, using the actual process. It presented comprehensive inputs and outputs, including the market price data for economic allocation. However, the broad inventories included the coal-based binder but not the graphite precursor, which constitutes about 80% of the main material inputs in the baking stage. The background of Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) study was India, allowing comparison with this study since its geographical location was in China. Considering the importance of synthetic graphite in the supply chain, further studies on the impacts of producing synthetic graphite are crucial.\u003c/p\u003e \u003cp\u003eThe overall aim of this study is to provide comprehensive inventories and assess the environmental impacts of the production of anode-grade synthetic graphite. The study intends to contribute to the environmental assessment of synthetic graphite, which is still scarce. Moreover, it aims to improve upon available studies by providing more complete inventories, covering impact categories beyond climate change and energy consumption, and increasing transparency while investigating the reproducibility of LCA studies on synthetic graphite compared to previous research. The goals will be achieved by focusing on the following objectives: (i) gathering all the inputs and outputs, including the market price of the products, in each production stage of synthetic graphite; (ii) assessing impact categories beyond climate change and energy consumption; (iii) applying contribution analysis to investigate the environmental hotspots; and (iv) applying sensitivity and scenario analysis to explore the model's behavior and possible alternatives.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eTo quantify the environmental impacts and interpret the results, LCA was applied following ISO 14040 and ISO 14044 (ISO, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e). The following section adheres to LCA phases: goal and scope definition, life cycle inventory, impact assessment, and interpretation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Goal and scope definition\u003c/h2\u003e \u003cp\u003eThe study investigated the cradle-to-gate environmental impacts of the production of anode-grade synthetic graphite by applying attributional LCA. It also systematically inventoried each activity's inputs and outputs during synthetic graphite manufacturing. The inventory was as important as obtaining the results of environmental impacts since LCA studies about synthetic graphite production are still scarce, incomplete, and have relatively high uncertainties. The functional unit (FU) applied was per kg of anode-grade synthetic graphite. The graphite precursor used to produce synthetic graphite was derived from oil mixed with the processed coal as a binder. Graphite precursors can also be produced from coal; however, their intrinsic characteristics result in lower carbon content, which may not be graphitized completely and require further processing to make them suitable for battery applications (Surovtseva et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The life cycle stages considered in this study included coal mining, coal coking, oil mining, oil refining, calcination, baking, and graphitization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the system boundaries, activities, general inputs and outputs to manufacture synthetic graphite as adopted from previous studies (Surovtseva et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Whattoff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The manufacturing process was assumed to take place in China. Coal and oil mining occurred in China and the United States (US), respectively. The transportation of hard coal and crude oil to their subsequent facilities were included in the boundaries. The coal was transported via lorry for coking, while the oil was transported using both lorry and ship from the US to China. Other transportation activities were excluded, assuming the coking, oil refining, calcining, baking, and graphitizing facilities were nearby.\u003c/p\u003e \u003cp\u003eEconomic allocation was applied to handle co-products produced during coking, oil refining, baking, and calcination. The method indicated that the environmental burden would be assigned to all outputs with economic value based on market price (Ecoinvent, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, inventory data regarding market prices for the year 2023 were required. The means of dealing with co-products is left to the practitioner, and allocation should be avoided in the first place (ISO, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e); however, four production stages generated 15 co-products. Using system expansion would be exhaustive and require too many assumptions. Moreover, many co-products from oil refining were considered products with high value that were not used as substitutions for which certain impacts from other production processes could be avoided. Economic allocation was used because the market price of these products was deemed to reflect their value within society. Electrification using batteries is related to policy subsidies, which made market price allocation appropriate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Life cycle inventory\u003c/h2\u003e \u003cp\u003eThe foreground data were gathered from previous academic studies, while the background data regarding emission factors, classifications, and characterization were obtained from the Ecoinvent 3.8 database. Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) underlined that the environmental impacts of synthetic graphite production were understated since the production of carbon black is usually used as a proxy for synthetic graphite, and previous studies only considered graphitization stage, resulting in lower impacts and incomplete data inventory. This study modelled the impacts of each production stage instead of using the readily available intermediate products found in the Ecoinvent database. For example, the impact of coal mining was modelled based on a study done by Tao et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) instead of using the hard coal process found in the Ecoinvent database. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the sources of inventory data used in the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReferences used for foreground data in life cycle inventory\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsed for determining the weight input of intermediate products needed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoal mining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTao et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase study of underground mining in East China\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLi et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Banerjee et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Ecoinvent (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData from coking plants in North-west China\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil mining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeili et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOnshore oil mining in the US\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil refining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAskari et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Liu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); NCCP (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase study of medium refinery in East China\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdwards et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Tang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMainly taken from Edwards et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a case study of calciner in India, adjusted for Chinese background data.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase study of prebaked anode for aluminum industry in East China\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraphitization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Shang et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Acheson method was used.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe background data were country-specific for China and the US; a global process was applied when country-specific data was unavailable in the Ecoinvent database. The 'market group for electricity' in China and the US was used as electricity sources during production. Since China and the US are large countries and the inventory data of graphite production was from different parts of China and India, the 'market group' data set was applied, and later, the results would reveal the effect of choosing electricity sources.\u003c/p\u003e \u003cp\u003eThe market price of the products and co-products in 2023 was used to apply economic allocation. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates the activities that generated co-products and the references of the market price of the products and co-products used in this study. The complete operational and economic data of synthetic graphite production are shown in tables of supplementary material sections 1 and 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReferences used for determining market price (economic allocation)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducts and co-products\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoal tar pitch, coal coke, crude benzene, ammonium sulphate, sulfur, coke oven gas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbdalla (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Li et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), National Bureau of Statistics of China (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Procurement Resource (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), SunSirs (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil refining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePet coke (as green coke), benzene, coke burning, diesel, gasoline, liquefied petroleum gas, naphtha, propylene, sulfur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbdalla (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Mysteel (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), National Bureau of Statistics of China (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Procurement Resource (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), SunSirs (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcined coke (as needle coke), electricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMysteel (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Statista (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraphitization precursor, calcium sulphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShanghai Metals Market (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Tang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Life cycle impact assessment\u003c/h2\u003e \u003cp\u003eThe impact assessment method for this research was the CML baseline, which resulted in midpoint impacts. The calculation used OpenLCA 2.3 (\"OpenLCA,\" 2022). The assessed impact categories were abiotic depletion (AD), abiotic depletion fossil fuels (AD-FF), acidification (AC), eutrophication (EU), freshwater aquatic ecotoxicity (Ecotox-FW), climate change (CC), human toxicity (HT), marine aquatic ecotoxicity (Ecotox-MA), ozone layer depletion (ODP), photochemical oxidation (PO), and terrestrial ecotoxicity (Ecotox-tr). The CML baseline was deemed sufficient to cover a relatively wide range of impacts while not being too exhaustive. Moreover, cumulative energy demand (based on OpenLCA method 2) was also assessed because the production process was energy-intensive. Normalization and weighting were not applied since comparing unitless results across different impact categories and generating a single score was not the objective of this study. Further analysis and interpretation were focused on climate change because the impact affects the global population, and there is an urgency to tackle the problem. Besides overall results, contribution analysis was also implemented. It was applied based on the activities during synthetic graphite manufacturing to discover which part of the production stage caused the highest impact. Contribution analysis was also conducted based on the flows (input and output parameters) to understand the most important parameter. Contribution analysis can help stakeholders identify the environmental hotspot, which is part of the process that causes the highest impacts, and assist them in making improvements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Interpretation, scenario and sensitivity analysis\u003c/h2\u003e \u003cp\u003eDuring interpretation, the results of the LCA were reflected in the study's objectives. Scenario and sensitivity analysis were applied as part of the interpretation to understand better the outcomes and how the model behaved. Uncertainties are an inherent part of conducting LCA. Changes in the foreground data, background data, methodological choices, and so on could result in different outcomes that steer the interpretation of the results in a different direction. Implementing scenario analysis can help manage uncertainties by exploring possible alternatives. Different assumptions regarding the geographical location of synthetic graphite production were applied during the scenario analysis while keeping the mining location the same as the baseline: China (coal mining) and the United States (oil mining). Five alternatives of the geographical locations in the production process were investigated: Germany, Japan, the United States, South Korea, and Finland. In the US scenario, the state of Pennsylvania was the specific location used for the calculation because it has one of the biggest graphite production facilities. The selection of a specific state was matched with the electricity provider in the Ecoinvent database. The countries in the scenario analysis were selected because they (except for Finland) and China were the top five exporters of synthetic graphite in 2022 (The Observatory of Economic Complexity, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finland was selected because it was where the study was conducted and as the Nordic representation. Furthermore, additional scenario analysis was applied by changing the economic allocation to physical allocation.\u003c/p\u003e \u003cp\u003eSensitivity analysis was applied to evaluate how the outcomes differ because of a change in the parameter's values. It was conducted by increasing a parameter's value by 10% one at a time while keeping the rest at the baseline levels. Sensitivity ratios (SRs) were calculated to measure the importance of each parameter concerning the LCA model. SR, the ratio of two relative changes, was calculated by dividing the percentage change in the results by the percentage change in the parameters, as shown by Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\:SR=\\:\\frac{\\left(\\frac{\\varDelta\\:\\:result}{initial\\:result}\\right)}{\\left(\\frac{\\varDelta\\:\\:parameter}{initial\\:parameter}\\right)}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhen the calculation generates an SR of 3, it indicates that the rise of specific parameters by 10% will increase the results by 30%. Conducting sensitivity analysis could help evaluate the dynamic of the LCA outcomes and determine the most important parameters that can affect the outcomes significantly.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Environmental impacts\u003c/h2\u003e \u003cp\u003eEleven impact categories were analyzed based on CML baseline methods. The overall results of producing 1 kg of anode-grade synthetic graphite in China are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The climate change impact was 10.44 kg CO\u003csub\u003e2\u003c/sub\u003e eq per FU. A comparison across different impacts was not conducted due to differences in the unit, and normalization would be required. Hence, a complete contribution analysis based on activities/production stages of synthetic graphite across all impact categories was conducted and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Conducting a contribution analysis is crucial to understanding the different roles of different activities or process flows within the investigated system. Relative importance can be assigned, and improvement measures can be focused on what matters in the process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental impacts of synthetic graphite production per FU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbiotic depletion (AD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.55E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg Sb eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbiotic depletion, fossil fuels (AD-FF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMJ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification (AC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg SO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophication (EU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.88E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg PO\u003csub\u003e4\u003c/sub\u003e eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreshwater aquatic ecotoxicity (Ecotox-FW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg 1,4-DB eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate change (CC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman toxicity (HT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.62E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg 1,4-DB eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarine aquatic ecotoxicity (Ecotox-MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg 1,4-DB eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOzone layer depletion (ODP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg CFC-11 eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhotochemical oxidation (PO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003e eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrestrial ecotoxicity (Ecotox-Tr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekg 1,4-DB eq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates a similar pattern in all impact categories, where the graphitization stage was the highest contributor by a large margin. An exception was found in ODP, where graphitization was not significantly higher than in other production stages. In ODP, the top three contributors were graphitization (30.1%), coking (26.6%), and calcination (18%). Meanwhile, the rest of the impact categories showed that graphitization contributed about 57.3% (AD-FF) to 93.4% (Ecotox-MA). In AD-FF, the second-highest contributor was about 32.3% (oil mining), whereas the other impact categories (other than ODP) showed that the second-highest contributors contributed less than 10%. Electricity was the only input during the graphitization stage; hence, the impacts in that stage were caused by the high electricity consumption in the Acheson furnace, which depended on the source of the electricity mix. Detailed numbers on the contribution analysis in each impact category can be found in the supplementary material Table\u0026nbsp;3.1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cumulative energy demand (CED) was assessed to investigate the consumption of primary energy within the boundaries of the studied system. The primary energy consumption per kg of synthetic graphite was about 150.8 MJ, of which 95% was from non-renewable sources and renewable sources generated 5%. Non- renewable fossil and nuclear contributed for 139.9 MJ and 2.73 MJ, respectively. Meanwhile, the renewable comprised of water (5.91 MJ), biomass (1.12 MJ) and other renewable (1.11 MJ).\u003c/p\u003e \u003cp\u003eMore detailed analysis was done on the impact of climate change because it could have a catastrophic effect globally and the urgency to curb it. The contribution analysis classified the results based on activities and process flows, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The former referred to different production stages in synthetic graphite manufacturing, starting from the raw material extraction until graphitization, while the latter denoted the input or output parameters during graphite manufacturing. The overall climate change impact per FU was 10.44 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;A contribution analysis based on the activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.a) showed that graphitization caused 81.3% of the impact of climate change, followed by 9.3% during calcination. Almost 91% of climate change impacts were caused by two activities out of seven in total. The other five activities contributed 9% of the overall impact, contributing about 1.3\u0026ndash;3% of the climate change impact. The transportation of crude oil from the US, part of the oil refining stage, which required ocean transportation (about 26,000 km) and land transportation, did not affect climate change significantly, as refineries covered 1.3% of the overall climate change impact. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.b displays contribution analysis classified by the flow of parameters, in which electricity consumption covered about 84% of total climate change impact, followed by 10.4% contribution from direct emissions. The impact caused by electricity relies on the sources of the electricity mix, which can be generated from renewable or non-renewable sources. The remaining 5.5% was from waste treatment, natural gas, transportation, coal coking gas, steam, and lime. Both classifications, activities and flows, presented similar patterns where two contributors caused more than 90% of the overall climate change impacts. It showed relative importance among activities and where the improvement was needed or wanted the most. Detail numbers of contribution analysis for climate change is in the supplementary material Table\u0026nbsp;3.2.1 and 3.2.2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Scenario analysis\u003c/h2\u003e \u003cp\u003eThe scenario analysis explored the outcomes of the manufacturing facilities in producing synthetic graphite located in different geographical areas without changing the location of raw material extraction. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the comparison of climate change impacts of synthetic graphite production between baseline scenarios in China (CN) and in Germany (DE), Japan (JP), South Korea (KR), the United States (US), and Finland (FI). All the alternative scenarios showed lower impacts in comparison to the baseline. The decrease in impacts ranged from 27\u0026ndash;39%, with the US being the country with the lowest impact. As shown by Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.b, electricity was a hotspot parameter in the production of synthetic graphite. The lower impacts found in the alternative scenarios can be attributed to the production of auxiliaries that were greener, but mainly to the electricity sources, especially during the graphitization process that was energy intensive. The climate change impact of 'market group' electricity in China was 1.01 kg CO\u003csub\u003e2\u003c/sub\u003e eq per kWh. Meanwhile, the average impact in kg CO\u003csub\u003e2\u003c/sub\u003e eq per kWh electricity in DE, JP, KR, US, and FI were 0.55, 0.66, 0.69, 0.54, and 0.25 respectively. The lowest impact was from the FI where nuclear and renewable energy contributed significantly to their electricity mix. The scenario analysis demonstrated how the background system affects the impacts, where the same process could result in different outcomes. Numeric data corresponds to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e can be found in supplementary material Table\u0026nbsp;3.2.3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, physical allocation was applied as well during scenario analysis. The environmental burden was assigned based on the weight of the products and co-products, which generated a result of 10.14 kg CO\u003csub\u003e2\u003c/sub\u003e eq per FU. This study did not find significant differences between two different allocation methods. The results obtained from economic allocation will change following the fluctuation of the market, while the outcomes derived from physical allocation could change if the operations in different production stages change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis was done to investigate the outcome change if the parameters were increased by 10% compared to the baseline value. Five parameter flows were used in sensitivity analysis: direct emission, electricity, natural gas, transportation, and waste. These five parameters were selected based on the contribution analysis results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.b. There were multiple flows or parameters in the inputs and output of the LCA model, and their importance toward the impact of climate change was dissimilar. Hence, the sensitivity analysis was applied to the five most important parameters to investigate how they affected climate change impact, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Each parameter was increased by 10% in each production activity when applicable (e.g., transportation distance was increased as part of coal coking and oil refining activities) and the overall impacts were calculated.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows how the parameters change affected the climate change impacts differently. Across all production stages, the SR ranged between 0 to 1. Electricity consumption was the most important input parameter throughout different production stages, with the highest effect during the graphitization stage, followed by baking. It indicated the importance of using electricity from more sustainable sources. The other parameter that was relatively important was direct emission. Direct emissions were input and process-specific; hence, improving or changing the production methods could alter the direct emissions. Data regarding Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e can be found in supplementary material Table\u0026nbsp;3.2.4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Comparison with previous studies\u003c/h2\u003e \u003cp\u003eNot many studies discussed a comprehensive inventory of synthetic graphite and its environmental impacts. Available studies regarding the LCA of anode-grade synthetic graphite reported different results for different explanations. Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied the impacts of different types of graphite produced using different methods, such as synthetic graphite being manufactured using the Acheson method, causing 5.45 kg CO\u003csub\u003e2\u003c/sub\u003e eq per kg synthetic graphite. The study excluded any transportation stage which would not change the results significantly, as shown by this study, where main materials were transported across the continent and contributed to less than 1% of the overall climate change impact. Their study built direct modelling of the baking and graphitization stages, while the rest of the production stage was taken from the Ecoinvent database by using petroleum coke and coal tar as input in the baking process. Their result was 48% lower than this study's, although, in theory, their study had the same boundaries and geographical location (even the same electricity provider) as this study. Their lower results could be attributed to the lower electricity consumption during the graphitization process, which was 4.1 kWh, whereas this study assumed 8.38 kWh. Literature reported that electricity consumption during the Acheson process to produce 1 kg graphite ranged between 5.5\u0026ndash;12 kWh, with the lowest value of 7.35 kWh after the process was optimized (Lan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A higher value was found in a study by a company in India that consumed 14 kWh during the graphitization process (Whattoff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Results generated by Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were similar to the impact of synthetic graphite found in Ecoinvent 3.8 (5.37 kg CO\u003csub\u003e2\u003c/sub\u003e eq/kg graphite) since they used the same basis data from the GREET model (Dunn et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eEpsilon Ltd, an Indian-based company, conducted LCA of synthetic graphite, resulting in a climate change impact of 5.2 kg CO\u003csub\u003e2\u003c/sub\u003e eq/kg graphite (Whattoff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The boundaries were cradle-to-gate, and it applied economic allocation. It had a comprehensive and transparent input-output throughout all production stages, although the detailed and comprehensive inputs were mainly for producing binders out of coal. The weight contribution of the binder was about 20%, while the remaining 80% was the precursor derived from petroleum, which in the LCA calculation was taken from readily available data from Ecoinvent as petroleum coke. Although the overall energy consumption was high, including electricity consumption during graphitization, which was 14 kWh/kg, the overall climate change impact was low, even lower than Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This result could be linked to the energy used by Epsilon, the majority of which was from waste gas streams (during coal tar production) or renewable sources (during graphitization). Up to 80% of electricity sources during graphitization were renewable, and the remaining was from the grid.\u003c/p\u003e \u003cp\u003eSurovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed the generalized boundaries for graphite production, life cycle inventories for synthetic graphite, and environmental impact calculation. Their study covered cradle-to-gate with the inventory to specifically calculate climate change impact and energy demand. The impact of climate change on manufacturing a kg of synthetic graphite was 13.8 kg CO2 eq, which was 24% higher than this study. Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provided a framework for any future study, and my study followed their framework boundaries; hence, the comparison could be done fairly. Their study argued that graphitization caused little climate change impact of 0.243 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;An opposite result was obtained in this study, where graphitization caused 8.49 kg CO\u003csub\u003e2\u003c/sub\u003e Eq.\u0026nbsp;(81% of total climate change impact) due to electricity consumption, which was supported by a previous study about electricity consumption by Acheson furnace (Lan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Whattoff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It was unclear if Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used a Castner furnace that consumed less electricity. Nevertheless, electricity consumption by the Castner furnace was around 2\u0026ndash;3 kWh/kg graphite (Iyer \u0026amp; Kelly, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which could be three times higher than the value used in Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther major differences were found during the oil mining and calcination stages, where these were the environmental hotspots of Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Their study showed impacts of 5.2 kg CO\u003csub\u003e2\u003c/sub\u003e eq and 6 kg CO\u003csub\u003e2\u003c/sub\u003e eq for oil mining and calcination, respectively. Meanwhile, this study generated 0.23 kg CO\u003csub\u003e2\u003c/sub\u003e eq and 0.97 kg CO\u003csub\u003e2\u003c/sub\u003e eq during oil mining and calculation. Producing 1 kg of green needle coke requires 5.9 kWh of energy input, of which 10% is from electricity (Surovtseva et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, this study referred to Edwards et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who did a case study on the calcination of green coke where the energy consumption to produce 1 kg calcined product was 0.11 kWh in which 90% of the energy was from electricity. Tang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) showed even lower energy consumption in producing 1 kg prebaked anode through analysis of a real case study in which the energy consumption was about 0.04 kWh and 100% was derived from electricity. As for the oil mining stage, the energy consumption was quite similar between Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and this study, 0.94 kWh and 0.804 kWh per kg crude oil, although the energy source was different. Hence, the difference might be ascribed to the allocation during the oil mining and refining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Data, impact assessment method, transparency, and clarity\u003c/h2\u003e \u003cp\u003eUsing the database in LCA applies to the exact inputs or proxy input. The former one is about using a database that matches the requirement. For example, the model needs 'hydrated lime', and the database has 'hydrated lime,' although the geographical location may not be precise, the global average is still possible. The latter is when the exact input is unavailable in the database, and another input is used because it is considered similar and representative enough. This study applied both where the database acted as exact and proxy input; for example, manufacturing lubricating oil was used to represent dedusting oil during the calcination phase. This practice is common in LCA; however, more careful consideration is necessary when the data is used as a proxy. As Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out, using a proxy could lead to an underestimation of the impacts, such as using carbon black as a proxy for anode-grade graphite.\u003c/p\u003e \u003cp\u003eAssumptions and decisions should be made throughout the LCA calculation process and left to the LCA practitioner. These decisions can steer the results in different directions. One of them is that the choice of life cycle impact assessment (LCIA) methods can result in different outcomes, and there was no specific requirement in selecting the LCIA methods. In some ways, it can be straightforward; for example, if a single score and endpoint results are needed, a specific LCIA method will be used (e.g., ReCiPe) since not all methods can accommodate it. Different methods can also employ different characterization factors (CF) that lead to different outcomes, as shown by CML, ILCD, ReCiPe 2016, and TRACI, where the CF of 'methane fossil' ranges between 25\u0026ndash;36 (Ecoinvent, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This factor could affect the differences between the results of this study and previous studies. The difference can also be found in the inclusion of CO as greenhouse gas in the IPCC method describing the CF between 2\u0026ndash;3 (IPCC, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while CML, ILCD, ReCiPe, or TRACI did not include CO. This study employed CML, while Kulkarni et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) applied TRACI and Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used IPCC to calculate climate change impacts. However, it is unclear if Whattoff (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) included CO in the calculation as Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) did.\u003c/p\u003e \u003cp\u003eSince assumptions are inevitable in conducting LCA, clarity and transparency are important. It not only concerns the completeness of inventory data (when confidentiality is not an issue) but also discloses assumptions, geographical location, the LCIA, allocation, etc. When other practitioners replicate the available studies, they will be able to pinpoint where the source of differences and deduce whether their study is aligned with the previous ones or not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Implications and future directions\u003c/h2\u003e \u003cp\u003eThis research has implications for multiple stakeholders. For LCA practitioners and databases, it is important to continuously check and update the current inventories and impacts of any products or services. For the practitioners, providing clarity and transparency of the LCA process is also important to ensure the study's reproducibility and correct interpretation. Meanwhile, companies manufacturing synthetic graphite could select their suppliers by considering their environmental impacts. The scenario analysis demonstrated how location can affect the impacts even when the production process is the same. Asking for an environmental declaration, either self-declaration or third-party verification like EPD (ISO, 2006c, 1999), could help with the decision-making. They may as well keep looking for other alternatives for graphite that could serve the same function in batteries with better technical, economic, and environmental aspects. Policymakers can also consider what kind of support could be provided to transition into electrification by considering the role of synthetic or natural graphite.\u003c/p\u003e \u003cp\u003eAmong all the previous studies on synthetic graphite, the closest results were obtained by Surovtseva et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nonetheless, when the contribution analysis was compared, there was a significant difference concerning the environmental hotspots. One of the directions of the future study is to focus on inventories and impacts in each production stage, especially during oil mining, calcination, and graphitization. Building a good inventory in which the data is reliable and accurate is important. It is also important to study the impacts of bio-based graphite as an alternative.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThere have not been many studies regarding synthetic graphite's environmental impacts despite the importance of synthetic graphite in the supply chain. This research helps to improve the estimation of the greenhouse gas emissions produced while making synthetic graphite. This study gathered complete inventories of synthetic graphite and its environmental impacts from cradle-to-gate. The inventories in this study allow the calculation of a wide range of impact categories and the disclosure of all assumptions and allocation, providing a way for others to replicate or revise this study. Previous scientific studies showed a range of climate change impacts in manufacturing 1 kg synthetic graphite (5.4\u0026ndash;13.8 kg CO\u003csub\u003e2\u003c/sub\u003e eq), while this research presented a result of 10.44 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;The most striking difference was the energy consumption between this study and others during graphitization, which was reported to be low in other studies. This research also presented a contribution analysis based on production stages and parameters, which can assist relevant stakeholders in pinpointing the environmental hotspots within the process. The scenario analysis and sensitivity analysis could also provide insights to stakeholders in applying strategic decisions in the production process, selecting suppliers, and even choosing electricity contracts to reduce the impacts. Although the study focused on climate change's impacts, other categories were also analyzed for more comprehensive decision-making. Researchers and companies could use pattern and contribution analysis across impact categories to develop approaches to reduce overall impacts to manufacture greener batteries.\u003c/p\u003e"},{"header":"Statements and declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support for this study was provided by a grant from the EU – Interreg Aurora.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of the Green Battery project funded by EU - Interreg Aurora.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has conducted conceptualization, methodology, material collection, formal analysis and visualization, writing the draft, review, and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the finding of the study derived from published literature. All the data used are openly summarized in the supporting material, including their references.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdalla, A. (2024). \u003cem\u003eSulfur price in China\u003c/em\u003e. https://sulfur-price.com/today/china\u003c/li\u003e\n \u003cli\u003eAbdelmotteleb, I. (2024). \u003cem\u003eThe battery\u0026apos;s role in decarbonizing the electricity grid\u003c/em\u003e. https://www.rabobank.com/knowledge/d011408711-the-batterys-role-in-decarbonizing-the-electricity-grid\u003c/li\u003e\n \u003cli\u003eAskari, M., Aliofkhazraei, M., Jafari, R., Hamghalam, P., \u0026amp; Hajizadeh, A. (2021). 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Fast Charging Lithium Batteries: Recent Progress and Future Prospects. \u003cem\u003eSmall\u003c/em\u003e,\u0026nbsp;\u003cem\u003e15\u003c/em\u003e(15). https://doi.org/10.1002/smll.201805389\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Synthetic graphite, anode material, life cycle assessment, life cycle inventory, scenario analysis","lastPublishedDoi":"10.21203/rs.3.rs-6204323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6204323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe role of graphite as a raw material has become more important due to the urgency of curbing the climate change problem through electrification. Electrification requires batteries as the central technology in which graphite is widely used as an anode material. Investigating the environmental impacts of graphite is crucial, especially synthetic graphite, which dominates the market share of graphite. The environmental assessment was conducted using life cycle assessment (LCA) to quantify the impacts of manufacturing 1 kg of anode-grade synthetic graphite from cradle-to-gate within the Chinese context by implementing economic allocation. The assessment included scenario analysis that explored the outcomes when the geographical area of the manufacturing process was relocated to other countries. Moreover, contribution analysis was performed to examine the environmental hotspots. The study reported an impact of 10.44 kg CO\u003csub\u003e2\u003c/sub\u003e eq per 1 kg of synthetic graphite. Meanwhile, graphitization caused 81% of the overall climate change impact among seven production stages. When the contribution was classified based on the input-output parameters, more than 80% of the climate change impact originated from electricity consumption. The study presented comprehensive inventories for manufacturing synthetic graphite and demonstrated the importance of complete and reliable data and transparency for reproducibility. The study may assist stakeholders in making decisions to improve the impacts within the supply chain.\u003c/p\u003e","manuscriptTitle":"Life cycle assessment of synthetic graphite: Inventories and impact assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 09:56:07","doi":"10.21203/rs.3.rs-6204323/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-08-26T04:52:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-03-31T19:42:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T09:37:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-03-28T18:51:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T04:19:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-03-17T03:10:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1303743-2ec6-4a83-a922-173f62ef0ab7","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:01:47+00:00","versionOfRecord":{"articleIdentity":"rs-6204323","link":"https://doi.org/10.1007/s11356-025-37288-1","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-12-13 15:57:46","publishedOnDateReadable":"December 13th, 2025"},"versionCreatedAt":"2025-04-21 09:56:07","video":"","vorDoi":"10.1007/s11356-025-37288-1","vorDoiUrl":"https://doi.org/10.1007/s11356-025-37288-1","workflowStages":[]},"version":"v1","identity":"rs-6204323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6204323","identity":"rs-6204323","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}