Debunking the 'Local is Greener' Myth: Life Cycle Assessment of Local vs. Imported Ingredients in Ice Cream Production

Debunking the 'Local is Greener' Myth: Life Cycle Assessment of Local vs. Imported Ingredients in Ice Cream Production | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Debunking the 'Local is Greener' Myth: Life Cycle Assessment of Local vs. Imported Ingredients in Ice Cream Production Yu-Ching Huang, Wei-Lun Zhu, Chao-Kai Chang, Mohsen Gavahian, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6526193/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Although locally sourced ingredients are perceived to have a lower carbon footprint due to shorter transportation distances, their comprehensive environmental impact has been less studied. The study compares Taiwanese locally sourced, Sri Lanka, and New Zealand-imported raw materials using the life cycle assessment (LCA) model, employing both Midpoint and Endpoint methods from ReCiPe to assess the carbon footprint and environmental impacts of industrially produced black tea latte and sesame ice cream. Results show that locally sourced black tea latte ice cream ingredients contribute to more obvious global warming (0.563 kg CO 2 eq) and terrestrial ecotoxicity (0.433 kg 1,4-DCB) than imported. The land use impact of domestic sesame is 2.9 times higher than that of imported. Endpoint analysis reveals that locally sourced black tea ingredients have a 22% to 24% higher impact on human health, ecosystem damage, and resource scarcity, while locally sourced sesame has a 55% to 60% higher impact than imported. The carbon footprint analysis shows products made with imported ingredients have lower emissions during production stages, including 0.04 kg CO 2 e during raw material acquisition and reductions of 0.03 and 0.05 kg CO 2 e for production and transportation. Scenario simulations suggest that replacing whole milk powder with domestically sourced fresh milk can reduce emissions by 37% to 67%, and using imported fresh milk instead of powder could reduce emissions by 41% to 70%. The findings offer strategic recommendations for balancing cost, flavor, and environmental impact to achieve sustainable production models and effective carbon reduction strategies. Life cycle assessment (LCA) Carbon Footprint Environmental Impact Assessment Food Origin of raw materials Ice cream Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction According to the "AR6 Synthesis Report: Climate Change 2023" published by the Intergovernmental Panel on Climate Change (IPCC) in 2023, greenhouse gas emissions must start to decline immediately to achieve the Paris Agreement’s goal of limiting temperature increase to 1.5°C. 2030 emissions must be reduced by nearly half (Lee et al., 2023 ). The global food system is associated with climate change through its impact on carbon footprint, land-use change, and biodiversity loss (Boakes et al., 2024 ). Food production systems contribute approximately 35% to global greenhouse gas emissions, with significant emissions arising from processing, packaging, transportation, household consumption, and waste management within the food production process. (Crippa et al., 2021 ). Life Cycle Assessment (LCA) is a method recommended by international organizations such as the European Commission and the United Nations Environment Programme (UNEP) to assess the environmental impacts of a product throughout its entire life cycle—from raw material input to waste disposal (Cucurachi et al., 2019 ). LCA is considered the preferred method to evaluate greenhouse gas emissions and environmental impacts in food production processes (Wang et al., 2021 ). Scientists have emphasized the need for the application of LCA to elaborate on the environmental impacts of the food production chain, from waste management to the entire industry chain (Gavahian et al., 2025 ; Sasaki et al., 2024 ). In the food industry, there is considerable interest in the concept of "food miles," which refers to the distance food travels from production to consumption. During the COVID-19 pandemic, European Union countries actively shortened the "farm-to-table" distance to reduce food transportation distances and related carbon emissions while enhancing domestic economic resilience and reducing reliance on global supply chains (EC, 2020). However, the contribution of transportation to most impact categories is limited (San Miguel & Ruiz, 2021 ). Previous studies have shown that domestic food systems might require more intensive agriculture to meet local demand, potentially increasing environmental impacts and not necessarily being more sustainable than large-scale operations (Boakes et al., 2024 ). Moreover, land use, production processes, or storage methods can impact a product’s carbon footprint more than transportation (Ritchie & Roser, 2022 ). In the context of earlier studies, the carbon emissions of food loss and waste account for about half of the global food industry chain (Zhu et al., 2023 ), especially for perishable foods such as fruits (Liu et al., 2024 ). Therefore, research suggests reducing food miles to avoid waste and loss caused by transportation (Li et al., 2023 ). However, production and processing methods are crucial for reducing environmental impacts, and local or short-distance supply does not necessarily equate to being more environmentally friendly (Evola et al., 2022 ). For example, in monosodium glutamate (MSG) processing, the primary sources of greenhouse gas emissions are the production process and raw material acquisition. Additionally, irrigation water and ammonia volatilization from corn cultivation, the raw material for MSG, are considered significant environmental impact factors (Ding et al., 2022 ). The carbon footprint of food is more influenced by land use, production efficiency, transportation economies of scale, and whether the food is plant-based rather than merely the transportation distance (Stein & Santini, 2022 ), large-scale production can also help reduce the environmental impact of a unit of product (Wang et al., 2023 ). Because soil microorganisms may metabolize CH 4 when agricultural products are grown, applying nitrogen fertilizers will increase N 2 O emissions (Crippa et al., 2021 ). In addition to the escape of CH 4 generated during the metabolism of animal products, the environmental impact caused by the production of edible feed must also be calculated (Mrówczyńska-Kamińska et al., 2021 ). Ice cream presents an ideal case for examining these intertwined factors as a product with energy-intensive processing, diverse raw material requirements, and extensive food miles associated with imported ingredients. Previous studies have conducted LCA of ice cream from the perspective of processing procedures (Konstantas et al., 2019 ; Wróbel-Jędrzejewska & Polak, 2023 ). However, there is a lack of research and published studies on whether raw materials or transportation contribute more significantly to the LCA of ice cream products in countries without a domestic dairy industry. In island nations, where land availability is limited, and production costs are high, the local food industry heavily relies on imported raw materials, often cheaper than locally sourced alternatives (Godenau et al., 2022 ). While this practice supports cost efficiency, it conflicts with the sustainability expectations of modern consumers. In Taiwan, sesame and black tea are essential raw food ingredients that enhance flavor. The annual production value of sesame is USD 8.45 million, while tea reaches USD 222.75 million (Agriculture, 2023 ). However, domestic production remains insufficient to meet market demand, leading to heavy reliance on imports. Taiwan also lacks a sufficient dairy industry to provide an adequate raw milk supply, making the food processing sector highly reliant on imported dairy products (Andoko et al., 2020 ). By investigating ice cream's LCA in this context, our study seeks to address these critical gaps and contribute to sustainable food production strategies. We use a food factory that processes ice cream as a case study, evaluate the main contributions stage of carbon footprint and life cycle environmental impacts from raw material acquisition to production, transportation, use, and final disposal, investigating whether domestic ingredients have advantages over imported ones in the processing procedure to achieve a more sustainable production model and effective carbon emission reduction strategies. 2. Materials and methods 2.1. Functional unit, goal, and scope This study employs the LCA methodology based on the international standards ISO 14040 and ISO 14044. These standards establish the four main phases of LCA: goal and scope definition, inventory analysis, impact assessment, and interpretation. The evaluation covers the entire life cycle of the studied product, from raw material acquisition to final disposal, compiling and evaluating inputs, outputs, and potential environmental impacts. The study also follows the ISO 14067 standard to calculate the carbon footprint. This study focuses on processed food products—specifically, ice cream produced in a factory setting. It assesses the life cycle from the raw material acquisition stage, including the transportation and packaging waste from sourcing ingredients and packaging materials, the energy inputs and waste disposal during the manufacturing stage, transportation logistics and distances for distribution, energy consumption at the point of sale during the use phase, and final disposal. The life cycle environmental impact assessment and carbon footprint calculations are conducted using the SimaPro 9.4.0.1 software. The functional unit is defined as 85 mL per cup, which aligns with the study's objectives and scope. It serves as the reference basis for correlating raw material inputs with product outputs. This definition is clear, precise, and measurable (Suer et al., 2021 ). 2.2. System Boundaries This study's selected ice cream products are black tea latte and sesame flavors. The product composition includes raw materials, accessories (such as spoons), and packaging materials (such as cup lids and sealing films). The manufacturing process encompasses blending, cooking, sterilization, homogenization, aging, filling, rapid freezing, and packaging. The distribution, sales, product usage, and final disposal stages are also considered. Because the geographical distribution of consumers is different, and the storage equipment and time after purchase are also significantly different, To reduce uncertainty to an acceptable level, under the premise of complying with the provisions of ISO 14064-1, the use process of the product after purchase by consumers is excluded from the boundary. Figure 1 shows the system boundaries of this study. 2.3. Inventory Analysis The inventory data for this study is provided by the company involved in the case study, covering the period from January 1, 2022, to December 31, 2022. The inventory encompasses raw material acquisition, manufacturing, distribution, transportation, and final disposal stages. The primary raw materials listed in Table 1 include whole milk powder from New Zealand, sesame powder (paste) from Taiwan or Sri Lanka, sugar from Brazil, black tea powder from Taiwan or Sri Lanka, cream from New Zealand, and all water used is from domestic sources in Taiwan. L powder, a composite colloid powder, is excluded from the inventory due to data unavailability and its proportion in the formula being less than 0.1%, hence not affecting the overall data. Table 2 provides inventory data on packaging materials and final disposal methods, including ice cream cups, lids, spoons, sealing films, polystyrene boxes, and tape. Table 3 details the freight transportation distances for each domestic raw or packaging material type. The transportation distance of raw materials and packaging materials to the factory varies depending on their origin. For domestic raw materials and packaging, land transportation using trucks covers a distance of 687.6 kilometers. In contrast, imported raw materials and packaging involve both land transportation via trucks for 687.6 kilometers and sea transportation via cargo ships for an additional 11,936 kilometers. Table 4 details the energy inputs for the manufacturing stage. The distribution and sales stage includes transporting products to sales points and energy usage at these points, such as tap water, electricity, and refrigerant leakage. The use stage assumes immediate consumption at the sales point, resulting in no life cycle greenhouse gas emissions. Table 1 Raw material inventory data per functional unit Raw Materials Black Tea Ice Cream (kg/85mL) Sesame Ice Cream (kg/85mL) Whole Milk Powder 6.54×10 − 3 4.51×10 − 3 Water 5.48×10 − 2 5.36×10 − 2 Sesame Powder - 7.82×10 − 3 Sesame Paste - 3.51×10 − 3 Sugar 8.36×10 − 3 8.15×10 − 3 Black Tea Powder 5.32×10 − 3 - Cream 4.71×10 − 3 2.35×10 − 3 L Powder - - Table 2 Inventory data on packaging materials and waste disposal methods per functional unit Item Weight (kg) Waste disposal methods Cup Lid 4.12×10 − 3 Polypropylene (PP) Recycle Ice Cream Cup 4.21×10 − 3 Recycle Spoon 1.70×10 − 3 Polystyrene (PS) Recycle Cup Sealing Film 6.20×10 − 4 incineration Styrofoam Box 1.47×10 − 6 Recycling in the factory PVC Tape 1.12×10 − 4 incineration Textile Tape 8.81×10 − 5 incineration Table 3 The freight transportation distances for each type of domestic raw or packaging material. Raw materials/packaging materials Freight transportation distances (km) Whole Milk Powder 155.0 Water - Sesame Powder 65.2 Sesame Paste 65.2 Sugar 27.5 Black Tea Powder 73.4 Cream 8.5 Cup Lid 16.0 Ice Cream Cup 22.7 Spoon 16.0 Cup Sealing Film 16.9 Styrofoam Box 159.0 PVC Tape 13.3 Textile Tape 13.3 Table 4 Inventory data for manufacturing, distribution and sales per functional unit Phase Item Consumption Unit Manufacturing Tap Water 1.84×10 − 3 m 3 Process Electricity Consumption 3.22×10 − 1 kWh LPG 1.01×10 − 5 kL Gasoline 9.12×10 − 4 L Diesel Fuel 2.00×10 − 3 L Refrigerant Escape 19.7 kg Distribution Sales Tap Water 4.13×10 − 4 m 3 Refrigerant Escape 2.62×10 − 1 kg Electricity Usage 3.24×10 − 2 kWh Transport to the point of Sale 6.41×10 − 4 tkm 2.4. Allocation Principles The allocation principles are based on weight calculations, where the actual input weight of raw materials is calculated and directly allocated to each product. Overall factory data, such as electricity and water consumption during the manufacturing stage, are assigned to the specific product category and each product. 2.5. Carbon Footprint Calculation Principles The carbon footprint calculation for the products follows the environmental carbon footprint calculation standards, consolidating primary and secondary data across various stages of the product life cycle according to raw material names, categories, inputs, distances, etc. This study utilizes the IPCC 2021 GWP100 V1.01 methodology with public carbon emission factors provided by (EPA, 2024 ) and corresponding coefficients from Ecoinvent 3.8 and Agri-footprint version 6. The collected activity data is multiplied by the corresponding emission factors and summed to calculate the total carbon emissions per functional unit of the product. The calculation formula is as follows: Carbon Footprint (CO 2 eq)=∑(Activity Data × Emission Factor × GWP) (1) 2.6. Environmental Impact Assessment Methodology The ReCiPe methodology is employed to calculate Midpoint results and Endpoint results. The ReCiPe methodology, developed based on CML 2001 and Eco-indicator 99, is a comprehensive and novel approach to environmental impact assessment (Zahedi et al., 2025 ). The data were then input into SimaPro 9.4.0.1 and converted to generate 18 midpoint environmental impact categories and 3 endpoint damage categories. The usage quantities were based on the actual inventory results and were not modified in terms of coefficients or inventory data. 3. Results and discussion 3.1. Midpoint Environmental Impact Results of Different Ice Cream Flavors Assessed Using the ReCiPe Method 3.1.1 Black Tea Latte Ice Cream As shown in Fig. 2 , the ReCiPe midpoint method calculates the environmental impact for 18 categories for each functional unit of black tea latte ice cream produced by the company, using local and imported ingredients. The y-axis shows the environmental impact of each category. Specific units for each category are provided in the figure note. The main contributors to environmental impact include global warming, ionizing radiation, terrestrial ecotoxicity, human non-carcinogenic toxicity, land use, and fossil resource scarcity. For GWP, the impact of ice cream made with local ingredients is 5.63×10 − 1 kg CO 2 eq, compared to 5.39×10 − 1 kg CO 2 eq for those made with imported ingredients. Both variants show significant greenhouse gas emissions due to high energy consumption during the manufacturing stage. In the IR, the value for ice cream made with local ingredients is 4.46×10 − 2 kBq Co-60 eq, while for imported ingredients, it is 4.42×10 − 2 kBq Co-60 eq, showing a minor difference. For TETX, the data for local ingredients is 4.33×10 − 1 kg 1,4-DCB, compared to 3.95×10 − 1 kg 1,4-DCB for imported ingredients. The higher ecotoxicity is associated with using chemicals in raw material acquisition and pollution during production. The results for HNCTX indicate a value of 3.64×10 − 1 kg 1,4-DCB for local ingredients and 3.52×10 − 1 kg 1,4-DCB for imported ingredients linked to pesticide and chemical use, posing more risks to human health. In terms of LU and FRS, the values are 1.23×10 − 1 m 2 crop eq versus 1.02×10 − 1 m 2 crop eq and 1.14×10 − 1 kg oil eq versus 1.11×10 − 1 kg oil eq, respectively, reflecting the demand for land and fossil fuels during the manufacturing process. The possible reasons for the higher environmental impact of using domestic raw materials is that Sri Lanka's agriculture is highly labor-intensive and the planting density is low (Munasinghe et al., 2017 ), while Taiwan has a seriously aging population, uses mechanized harvesting and has a high amount of fertilizer (Hu et al., 2019 ). 3.1.2 Sesame Ice Cream Figure 3 illustrates the results of the ReCiPe midpoint method for sesame ice cream, showing the environmental impact data for 18 categories for each functional unit produced using both local and imported ingredients. The y-axis shows the environmental impact of each category. Specific units for each category are provided in the figure note. The main contributors to environmental impact are global warming, ionizing radiation, terrestrial ecotoxicity, human non-carcinogenic toxicity, land use, and fossil resource scarcity. For GWP, the impact of ice cream made with local ingredients is 5.79×10 − 1 kg CO 2 eq, compared to 5.74×10 − 1 kg CO 2 eq for imported ingredients. Both types show significant greenhouse gas emissions due to high energy consumption during the manufacturing stage. In the IR, the value for local ingredients is 4.42×10 − 2 kBq Co-60 eq, while for imported ingredients, it is 4.45×10 − 2 kBq Co-60 eq, indicating a slight difference. For TETX, the value for local ingredients is 4.80×10 − 1 kg 1,4-DCB, compared to 4.77×10 − 1 kg 1,4-DCB for imported ingredients. The higher ecotoxicity is related to using chemicals in raw material acquisition and pollution during production. The results for HNCTX show a value of 4.85×10 − 1 kg 1,4-DCB for local ingredients and 3.97×10 − 1 kg 1,4-DCB for imported ingredients associated with pesticide and chemical use, increasing risks to human health. Only two types of pesticides can be used to grow sesame in Taiwan, and environmentally friendly farming methods will likely cause higher environmental impacts (van der Werf et al., 2020 ). In the FRS category, the values are 1.17×10 − 1 kg oil eq for local ingredients and 1.19×10 − 1 kg oil eq for imported ingredients, indicating the demand for fossil fuels during manufacturing. This may be because sesame in Sri Lanka is typically cultivated on marginal land with minimal labor and attention, resulting in a relatively lower environmental impact (Dissanayake & Sithara, 2021 ). 3.2. Midpoint Environmental Impact Results of Different Ice Cream Flavors in the Raw Material Acquisition Stage Assessed Using the ReCiPe Method 3.2.1 Black Tea Latte Ice Cream Figure 4 and Fig. 5 present the percentage of environmental impacts for different black tea latte ice cream ingredients, depending on whether local or imported ingredients were used. Since Taiwan rarely produces milk powder and cream, local and imported milk powder and cream in this case study use whole cream milk powder and cream imported from New Zealand, with coefficients based on the global average from the Ecoinvent 3.8 database. The environmental impact results for whole cream milk powder and cream in black tea lattes and sesame ice creams are as follows: For GWP, whole cream milk powder contributes 57% of ice cream with local ingredients, 59.9% for imported black tea latte ice cream, and 61.4% and 53% for sesame ice cream, respectively. Finnegan et al. ( 2017 ) state that converting raw milk into milk powder is the most significant contributor to GWP. In the stratospheric ozone depletion (SOD) category, the impact of whole cream milk powder is 66.2% for ice cream with local ingredients and 67.8% for imported ingredients in black tea latte ice cream, and 52.9% and 62.3% for sesame ice cream, respectively, related to the use of refrigerants containing chlorofluorocarbons (CFCs) in dairy production. This result is lower than the research result of Konstantas et al. ( 2019 ) because the number of days of frozen storage is highly uncertain, and we exclude the frozen storage process outside the system boundary. Particulate matter formation (PMF) is measured in PM 2.5 , particles with a diameter of less than 2.5 micrometers composed of complex organic and inorganic substances that can penetrate deep into the lungs and enter the bloodstream, affecting the cardiovascular, cerebrovascular, and respiratory systems. PMF is commonly used to measure the concentration of these particles (Huijbregts et al., 2017 ). The contribution of whole cream milk powder in the PMF category is 72.5% for ice cream with local ingredients, 71.4% for imported ingredients in black tea latte ice cream, and 67.8% and 61.6% for sesame ice cream, respectively. Kumar et al. ( 2021 ) pointed out that dairy products such as butter and ice cream, due to undergoing more extensive thermal processing, may generate particulate matter formation (PMF) during energy usage. A study on the environmental LCA of Brazilian milk production by Silva et al. ( 2024 ) demonstrates that raw milk yield exerts the most pronounced influence on AC, FEUT, MUET, and GWP categories, mainly due to methane emissions from enteric fermentation, manure, and cattle urine. In AC, the contribution of whole cream milk powder is 43.2% for ice cream with local ingredients and 45.1% for imported ingredients in black tea latte ice cream, and 31.3% and 35.9% for sesame ice cream, respectively, with cream contributing 17.3% and 26.5% for black tea latte ice cream, and 5.36% and 6.14% for sesame ice cream. In MEUT, the impact of whole cream milk powder is higher, with 60.8% and 64.9% for black tea latte ice cream using local and imported ingredients, respectively, and 41.6% and 65.3% for sesame ice cream. In LU, the contribution of whole cream milk powder is 24% and 21.5% for black tea latte ice cream using local and imported ingredients, respectively, and 21.3% and 36.5% for sesame ice cream. The contribution of cream is 4.53% and 45.6% for black tea latte ice cream and 18.9% and 32.5% for sesame ice cream, respectively. Konstantas et al. ( 2019 ) mention that land use is related to the use of raw milk, and more cream requires more agricultural land for milk production. In the water consumption (WC) category, the contribution of whole cream milk powder is 4.71% and 7.71% for black tea latte ice cream using local and imported ingredients, respectively, and 12.2% and 12.4% for sesame ice cream. The water footprint of skim milk powder indicates that dairy production has a minimal impact on freshwater scarcity compared with other agricultural production (Sharma et al., 2024 ). For black tea in WU, the contribution of local tea is 83.6%. In comparison, tea from Sri Lanka is 76.1%, showing a positive relationship between the use of local ingredients and reduced water consumption. Zhang et al. ( 2023 ) evaluated the life cycle of black tea under different production scenarios during the cultivation and processing stages. They found that water use during the cultivation stage varies from 4.55×10 − 3 to 17.4 kg/kg tea, indicating a high demand for water during cultivation, which may also be influenced by different factors such as chemical fertilizer and pesticide use and processing methods. Reducing chemical fertilizers during the cultivation stage can reduce GWP, non-renewable energy use, and WU. For sugar, in most environmental impact categories, the use of local ingredients shows higher values than imported ingredients, with LU accounting for 54.8% for local ingredients and only 1.49% for imported ingredients. This may be because the cultivation methods in Brazil, where sugarcane is sourced, are more intensive, resulting in lower land use (Bordonal et al., 2018 ). 3.2.2 Sesame Ice Cream Figure 6 and Fig. 7 show the percentage of environmental impacts for different ingredients in sesame ice cream, depending on whether local or imported ingredients were used. Since full-cream milk powder and cream are not produced in Taiwan, this case study uses imported ingredients, with coefficients based on the global average from the Ecoinvent 3.8 database. According to the ReCiPe methodology defined by Huijbregts et al. ( 2017 ), ozone is formed through photochemical reactions involving nitrogen oxides (NOx) and non-methane volatile organic compounds (NMVOCs), which can cause respiratory distress and lung damage, such as asthma and chronic obstructive pulmonary disease (COPD). The human health ozone formation potential (HOFP) is typically used to quantify these environmental impacts. In this study, the values for sesame powder and sesame paste using local ingredients in ozone formation and its effects on human health (OHH) are 22.7% and 7.26%, respectively, while the values for sesame powder and sesame paste using ingredients from Sri Lanka are 48.5% and 15.5%, respectively. The values for sesame powder and paste using local ingredients in ozone impact on terrestrial ecosystems (OTE) are 19% and 6.06%, respectively. In comparison, the values for ingredients from Sri Lanka are 43.9% and 14%. In the FEUT category, the values for sesame powder and sesame paste using local ingredients are 29.5% and 9.43%, respectively. In comparison, the values for ingredients from Sri Lanka are 18.2% and 5.82%, respectively. In the MUET category, the values for sesame powder and paste using local ingredients are 35.5% and 11.3%, respectively. In comparison, the values for ingredients from Sri Lanka are 13.2% and 4.21%, respectively. This is due to the negative impact of wastewater generated during sesame powder and paste processing on water bodies. For example, sesame shells, residues, and chemicals used in washing may contain organic matter and nutrients, which, when discharged into water bodies, lead to eutrophication, causing excessive plant growth, algae blooms, water quality degradation, and aquatic ecosystem imbalance (Sadeghy et al., 2024 ). In the TACID category, the values for sesame powder and paste using local ingredients are 34.3% and 11%, respectively. In comparison, the values for ingredients imported from Sri Lanka are 28.7% and 9.18%, respectively. Sesame paste production methods showed significant impacts in the categories of acidification potential, eutrophication potential, and ozone layer depletion. Similar results were found in a recent study by Fereidani and Üçtuğ ( 2024 ). 3.3 Endpoint Environmental Impact Results for Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins for Each Functional UnitProduced 3.3.1 Black Tea Latte Ice Cream Using the ReCiPe endpoint method, Table 5 shows the results for three environmental damage categories for each 85 mL cup of black tea latte ice cream produced using ingredients from different origins during the raw material acquisition stage. In all three categories, the values for ice cream made with imported ingredients are lower than those made with local ingredients. In the Damage to Human Health category, the value for ice cream made with local ingredients is 3.57×10 − 7 DALY, compared to 2.70×10 − 7 DALY for imported ingredients, indicating a 24% higher impact for local ingredients. Hu et al. ( 2019 ) noted in their study on the environmental effects of agricultural winter tea in Taiwan that the most significant environmental impact in the human health category is due to fertilizer use during the raw material stage. In the Damage to Ecosystems category, the value for ice cream made with local ingredients is 1.58×10 − 9 species. yr, compared to 1.23×10 − 9 species.yr for imported ingredients, showing a 22% higher impact for local ingredients. In the Damage to Resources category, the value for ice cream made with local ingredients is 4.06×10 − 3 $ , compared to 3.12×10 − 3 $ for imported ingredients, indicating a 23% higher impact for local ingredients. Table 5 Environmental damage results of the Endpoint method for black tea latte ice cream using ingredients from different origins during the raw material acquisition stage for each 85mL/cup produced. Environmental damage category Unit Black tea latte ice cream raw material acquisition stage Domestic Foreign origin Damage to human health DALY 3.57×10 − 7 2.70×10 − 7 Damage to ecosystems species.yr 1.58×10 − 9 1.23×10 − 9 Damage to Resources $ (USD2013) 4.06×10 − 3 3.12×10 − 3 3.3.2 Sesame Ice Cream Using the ReCiPe endpoint method, Table 6 shows the results for three environmental damage categories for each functional unit cup of sesame ice cream produced using local and imported ingredients during the raw material acquisition stage. In all three categories, the values for imported ingredients are lower than those for local ingredients. In the Damage to Human Health category, the value for ice cream made with local ingredients is 4.50×10 − 7 DALY, compared to 2.01×10 − 7 DALY for imported ingredients, indicating a 55% higher impact for local ingredients. In the Damage to Ecosystems category, the value for ice cream made with local ingredients is 2.06×10 − 9 species. yr, compared to 8.18×10 –10 species.yr for imported ingredients, showing a 60% higher impact for local ingredients. In the Damage to Resources category, the value for ice cream made with local ingredients is 5.29×10 − 3 $ , compared to 4.76×10 − 3 $ for imported ingredients, indicating a 10% higher impact for local ingredients. Table 6 Environmental damage results of the Endpoint method for sesame ice cream using raw materials from different origins during the raw material acquisition stage for each functional unit produced. Environmental damage category Unit Sesame ice cream raw material acquisition stage Domestic Foreign origin Damage to human health DALY 4.50×10 − 7 2.01×10 − 7 Damage to ecosystems species.yr 2.06×10 − 9 8.18×10 –10 Damage to Resources $ (USD2013) 5.29×10 − 3 4.76×10 − 3 3.4 Carbon Footprint of Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins During the Raw Material Acquisition Stage and Per Functional Unit This study references the carbon footprint database from the Taiwan Ministry of Environment's Carbon Footprint Calculation Service Platform (EPA, 2024 ) and collects greenhouse gas emission factors, allowing for a comparative analysis of the ice cream products' carbon footprint per functional unit. The IPCC 2021 GWP100 V1.01 methodology is used to calculate the total carbon emissions at each stage because the IPCC is considered to be more relevant to assessing environmental impacts on a global scale when making decisions related to the sustainability of different processes, and the ReCiPe method is more focused on specific environmental issues in different countries (Salazar-Sogamoso et al., 2024 ). The carbon emissions for producing each functional unit of ice cream using ingredients from different origins are calculated for each stage, including raw material acquisition, processing, transportation, and final disposal, and then summed. As shown in Table 7 , the carbon footprint for each functional unit of black tea latte ice cream using local ingredients is 0.57 kg CO 2 e, while for imported ingredients, it is 0.53 kg CO 2 e. For each functional unit of sesame ice cream, the carbon footprint for local ingredients is 0.60 kg CO 2 e, while for imported ingredients, it is 0.56 kg CO 2 e. During the raw material acquisition stage, the carbon footprints for both ice cream flavors are as follows: for each functional unit of black tea latte ice cream, the carbon footprint using local ingredients is 0.12 kg CO 2 e, and for imported ingredients, it is 0.09 kg CO 2 e; for each functional unit of sesame ice cream, the carbon footprint using local ingredients is 0.16 kg CO 2 e, and for imported ingredients, it is 0.11 kg CO 2 e. Table 7 Carbon footprints of each functional unit and raw material acquisition stage using ingredients from different origins for black tea latte and sesame ice cream. Project Total (kgCO 2 e/unit) Raw material acquisition stage (kgCO 2 e/unit) Black tea latte: domestic 0.57 0.12 Black tea latte: foreign origin 0.53 0.09 Sesame: domestic 0.60 0.16 Sesame: foreign origin 0.56 0.11 However, Wróbel-Jędrzejewska and Polak ( 2023 ) studied the carbon footprint of ice cream produced at a laboratory scale with different fats and sugars and found that the carbon footprint per functional unit of ice cream ranges from 0.2021 to 0.3579 kg CO 2 e. This lower carbon footprint is primarily because the study was conducted at a laboratory scale, excluding the sales, transportation, and waste disposal stages. In contrast, the background of this study is set during the COVID-19 pandemic in 2022, when the company’s production volume was only 60% of that in 2021 due to the pandemic. Despite reduced production, the factory still needed to maintain essential equipment operation, including electricity and refrigeration equipment. Therefore, reduced production may have led to an increase in the carbon footprint per unit product. 3.5 Greenhouse Gas Emissions of Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins During the Raw Material Acquisition Stage 3.5.1 Black Tea Latte Ice Cream According to Fig. 8 , the greenhouse gas emissions for whole cream milk powder in black tea latte ice cream using local and imported ingredients from New Zealand are 0.0501 kg CO 2 e, for cream are 0.0153 kg CO 2 e, and for sugar are also 0.0579 kg CO 2 e. The greenhouse gas emissions for the ice cream cup are 0.0947 kg CO 2 e. The emissions for black tea using local ingredients from Taiwan are 0.0329 kg CO 2 e, while for tea from Sri Lanka, it is 0.0044 kg CO 2 e. These data show that the hotspots for greenhouse gas emissions in the raw material acquisition stage of black tea latte ice cream are whole cream milk powder, cream, tea, and the ice cream cup. Based on the life cycle stages, the following improvement suggestions are proposed: Substitution of Full Cream Milk Powder : Local fresh milk and UHT milk can replace whole cream milk powder, which can be evaluated according to the scenario simulation in section 3.6. Finding Low-Emission Products : It is recommended that the company look for low-emission cream products and ice cream cups. Comparison of Tea Emission Coefficients : The greenhouse gas emission coefficient for Taiwanese black tea is 7.04 kg CO 2 e, while that for Sri Lankan black tea is 1.99 kg CO 2 e. The processing of Taiwanese black tea includes steps such as withering, fixing, rolling, drying, mass rolling, unrolling, sorting, and vacuum packaging. According to the Taiwan Carbon Footprint Information Network, the emission ratios for each life cycle stage are as follows (EPA, 2024 ): raw material stage accounts for 35.15% (2.473 kg CO 2 e), manufacturing stage 18.67% (1.313 kg CO 2 e), transportation stage 0.29% (0.020 kg CO 2 e), usage stage 45.58% (3.207 kg CO 2 e), and disposal stage 0.31% (0.022 kg CO 2 e), totaling 7.035 kg CO 2 e. To reduce the carbon footprint, the following improvement suggestions are proposed: c-1. Raw Material Stage : Use organic and chemical fertilizers in agricultural cultivation to reduce greenhouse gas emissions. Soil management: Improve soil health and carbon sequestration capacity through composting and cover-cropping techniques (Ouikhalfan et al., 2022 ). c-2. Manufacturing Stage : Enhance energy efficiency: Use more efficient and energy-saving equipment and technologies, such as more efficient drying equipment and fixing machines (Yudhistira et al., 2024 ). Use renewable energy: Incorporate renewable energy sources such as solar and wind power in manufacturing to reduce fossil fuel use. Reduce waste: Optimize production processes to minimize raw material and energy waste (Zhu et al., 2023 ). c-3. Transportation Stage : Opt for low-carbon transportation methods: Use electric vehicles, natural gas vehicles, or other alternatives. Localize production and consumption: Reduce transportation distances and prioritize local market sales to lower carbon emissions from long-distance transport (Li et al., 2022 ). c-4. Usage Stage : Promote energy-saving methods: Encourage consumers to use energy-efficient equipment for brewing and storing tea. Raise consumer awareness: Educate and promote the importance of energy conservation and carbon footprint reduction. c-5. Disposal Stage : Promote recycling: Establish a recycling system for tea packaging and waste and promote biodegradable or recyclable packaging materials (Qiang et al., 2024 ). Reuse waste: Utilize discarded tea leaves for composting or other agricultural purposes to reduce waste-related carbon emissions (Seth et al., 2025 ). By adopting these methods, the carbon footprint at each stage of the tea life cycle can be effectively reduced, achieving a more environmentally friendly production and consumption model. 3.5.2 Sesame Ice Cream According to Fig. 9 , the greenhouse gas emissions for sesame ice cream using both local and imported ingredients show the following: New Zealand milk powder has an emission of 0.0583 kg CO 2 e, cream 0.0075 kg CO 2 e, sugar 0.0577 kg CO 2 e, and ice cream cups 0.0095 kg CO 2 e. The emissions for sesame powder made from local ingredients are 0.0656 kg CO 2 e, whereas for sesame powder sourced from India, the emissions are 0.0209 kg CO 2 e. These data show that the hotspots for greenhouse gas emissions in the raw material acquisition stage of sesame ice cream are whole cream milk powder, cream, sesame powder, and ice cream cups. The following are recommendations for improvement: Substitution of Full Cream Milk Powder : Use local fresh and UHT milk to replace whole cream milk powder. This can be evaluated according to the scenario simulation in section 3.12. Finding Low-Emission Products : It is recommended that the company find low-emission cream products and ice cream cups, such as using PLA to place PE (Peantham & Varabuntoonvit, 2024 ). The greenhouse gas emission coefficient for Taiwanese sesame powder is 8.32 kg CO 2 e, while for Indian sesame powder, it is 2.65 kg CO 2 e. According to the Taiwan Carbon Footprint Information Network, the production process for Taiwanese sesame powder includes roasting, grinding, re-roasting, and final packaging (EPA, 2024 ). The following are suggestions for improving the sesame powder production process: c-1. Roasting ( Yudhistira et al., 2024 ): Use high-efficiency electric heating equipment instead of traditional gas heating equipment. Utilize renewable energy for secondary roasting, such as installing solar water heaters to provide a stable heat source. Increase the thermal efficiency of roasting equipment to reduce energy loss. c-2. Grinding : Implement variable-frequency controlled high-efficiency equipment to adjust grinding speed as needed, reducing unnecessary energy consumption (Noorani et al., 2023 ). Use a water cooling system during grinding, recycling the cooling water to reduce water waste. c-3. Packaging ( Peantham & Varabuntoonvit, 2024 ) : Use biodegradable or recyclable packaging materials to reduce the carbon footprint of packaging materials. Optimize packaging design to reduce the amount of packaging materials used and lower carbon emissions during transportation. 3.6 Scenario Simulation: Greenhouse Gas Emissions from Substituting Local Fresh Whole Milk and Imported Whole Milk for Full Cream Milk Powder The main ingredient in ice cream is whole-cream milk powder. As noted in section 3.2, whole cream milk powder is not produced in Taiwan, and this study case uses milk powder imported from New Zealand. This study uses the milk coefficients from Ecoinvent 3.8 and the carbon footprint coefficients of whole fresh milk from the Taiwan Ministry of Environment’s Carbon Footprint Information Network to explore the greenhouse gas emissions during the raw material acquisition stage when substituting local fresh milk and imported whole milk for full cream milk powder. The proportions of entire fresh and whole milk used are based on the average moisture content of 87.9% for whole fresh milk, as indicated by the Ministry of Health and Welfare Nutrition Database. (TFDA, 2023 ). This data calculates the amount of whole cream milk powder replaced in the formulation. As shown in Table 8 , the greenhouse gas emissions for using local ingredients in black tea latte ice cream at the raw material acquisition stage are 0.1239 kg CO 2 e /functional unit, and for sesame ice cream, they are 0.1590 kg CO 2 e /functional unit. The greenhouse gas emissions for substituting local fresh whole milk for full cream milk powder are 0.0780 kg CO 2 e/functional unit for black tea latte ice cream and 0.0524 kg CO 2 e/functional unit for sesame ice cream. The greenhouse gas emissions for substituting whole milk for full cream milk powder are 0.0736 kg CO 2 e/functional unit for black tea latte ice cream and 0.0484 kg CO 2 e/functional unit for sesame ice cream. Substituting full cream milk powder with whole fresh milk reduced greenhouse gas emissions at the raw material acquisition stage by 0.0459 kg CO 2 e/functional unit for black tea latte ice cream and 0.1066 kg CO 2 e/functional unit for sesame ice cream. Substituting full cream milk powder with whole milk reduced greenhouse gas emissions at the raw material acquisition stage by 0.0503 kg CO 2 e/functional unit for black tea latte ice cream and 0.1166 kg CO 2 e/functional unit for sesame ice cream. The results indicate that substituting full cream milk powder with local whole fresh milk or imported whole milk can significantly reduce greenhouse gas emissions during the raw material acquisition stage of ice cream production. Since ice cream formulations inherently require a large amount of water, drying raw milk into milk powder for transportation generates substantial carbon emissions. After importation, additional water must be reintroduced to produce ice cream. The carbon emissions from transportation account for only a small proportion; even if raw milk were imported directly, the increased weight would result in lower transportation-related carbon emissions than those generated during spray drying. Patil et al. ( 2021 ) also indicated that spray drying accounts for the largest share of total energy consumption in the process. Using concentration technology can reduce a lot of carbon emissions and environmental impact compared with spray drying. Robertson et al. ( 2024 ) study exhibited a similar trend, with results indicating that importing frozen concentrated milk significantly reduces carbon emissions compared to importing milk powder. However, raw milk still has the lowest carbon emission. The carbon emission of fresh dairy products is 1.1 kg CO2e/kg, while the carbon emission of butter products is 6.5 kg CO2e/kg, and that of milk powder products is 7.4 kg CO2e/kg (Sahu & Agarwal, 2021 ). Table 8 Greenhouse gas emissions of black tea latte and sesame ice cream using local ingredients and replacing milk powder with domestic fresh and imported milk in the raw material procurement stage. Category Whole milk powder (kgCO 2 e/unit) Domestic fresh milk (kgCO 2 e/unit) Imported milk (kgCO 2 e/unit) Black tea latte ice cream raw material acquisition stage: Domestic 0.1239 0.0780 0.0736 Sesame ice cream raw material acquisition stage: Domestic 0.1590 0.0524 0.0484 4. Research Limitations and Considerations for Future Research Assumptions constrain the LCA model, and not all potential impacts have models available. Data availability or the quality of existing data may limit the accuracy of the LCA. —Since domestic factory-developed coefficients, mass balance-derived coefficients, similar process/equipment experience coefficients, manufacturer-provided coefficients, and regional emission factors are not yet mature, this study uses some national coefficient databases and foreign databases built into the software. The inventory data used may lack spatial and temporal specificity, leading to uncertainties in impact results. —Ice cream products' formulas and product sources may change depending on industry and taste. In the future, we can explore more differences in the environmental impact caused by raw materials from different origins and products with other flavors. According to the above limitations, future studies must improve data specificity by developing coefficient databases that are specific regionally, enhancing inventory data resolution, and incorporating domestic manufacturers’ primary data. Enlarging impact categories and considering additional life cycle stages might improve the assessment comprehensiveness. Also, industry-specific LCA modeling refinement by comparative studies of various processing techniques and ingredient sourcing strategies can enhance sustainability aspects. Besides, sensitivity and uncertainty assessments should be considered carefully to analyze data variability while assessing the environmental impact of various formulations, raw material origins, and flavors to develop a more sustainable product. 5. Conclusions Our findings highlight significant differences in using local versus imported ingredients in carbon emissions and resource utilization, suggesting that locally sourced ingredients, despite the common belief of lower carbon footprints, often result in higher environmental impacts in categories such as GWP and resource scarcity. Notably, domestic sesame's land use impact was 2.9 times greater than that of imported sesame, and black tea ingredients sourced locally had a 22—24% higher impact on human health and ecosystem damage. These results emphasize the complexity of environmental sustainability in food production, where transportation is not the sole determinant of carbon footprint. Instead, factors such as agricultural intensity and production efficiency play a critical role. Future strategies for more sustainable production should consider cost and flavor and the broader environmental implications of ingredient sourcing. This research is a foundation for further exploration into balancing local economic support with global environmental sustainability in the food industry. Declarations Credit authorship contribution statement Yu-Ching Huang : Conceptualization, Methodology, Software, Data Curation, Writing—Original Draft Preparation, Visualization, Investigation, Validation. Chao-Kai Chang : Conceptualization, Methodology, Software, Validation, Writing—Reviewing and Editing. Wei-Lun Zhu : Visualization, Investigation, Validation, Writing—Reviewing and Editing Mohsen Gavahian : Validation, Writing—Reviewing and Editing. Yeh Chen: Writing—Reviewing and Editing. Chang-Wei Hsieh : Conceptualization, Methodology, Software, Supervision, Validation, Writing—Reviewing and Editing. Funding declaration This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability declaration The data that supported the finding of this study are available from the corresponding author upon reasonable request. Competing Interest declaration The authors declare no competing interests. References Agriculture, M. (2023). Agricultural statistics . https://agrstat.moa.gov.tw/sdweb/public/inquiry/InquireAdvance.aspx Andoko, E., Liu, W. Y., Zeng, H. J., & Sjoblom, A. (2020). Review of Taiwan’s food security strategy. Food and Fertilizer Technology Center for the Asian and Pacific Region (FFTC-AP)(website) . Boakes, E. H., Dalin, C., Etard, A., & Newbold, T. (2024). Impacts of the global food system on terrestrial biodiversity from land use and climate change. Nature Communications , 15 (1), 5750. https://doi.org/10.1038/s41467-024-49999-z Bordonal, R. O., Carvalho, J. L. N., Lal, R., de Figueiredo, E. B., de Oliveira, B. G., & La Scala, N. (2018). Sustainability of sugarcane production in Brazil. A review. Agronomy for Sustainable Development , 38 (2), 13. https://doi.org/10.1007/s13593-018-0490-x Crippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., & Leip, A. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. Nature Food , 2 (3), 198–209. https://doi.org/10.1038/s43016-021-00225-9 Cucurachi, S., Scherer, L., Guinée, J., & Tukker, A. (2019). Life cycle assessment of food systems. One Earth , 1 (3), 292–297. https://doi.org/10.1016/j.oneear.2019.10.014 Ding, J., Hu, X., Feng, Z., & Dong, L. (2022). Environmental life cycle assessment of monosodium glutamate production in China: Based on the progress of cleaner production in recent ten years. Science of the Total Environment , 818 , 151706. https://doi.org/10.1016/j.scitotenv.2021.151706 Dissanayake, I. A. J. K., & Sithara, P. (2021). Is Sesame a Potential Crop that Contributes to the Bioeconomy of Sri Lanka? In (pp. 72–90). EPA (2024). Carbon footprint coefficient database . Environmental Protection Administration Taiwan . https://cfp-calculate.tw/eng/WebPage/WebSites/CoefficientDB.aspx Evola, R. S., Peira, G., Varese, E., Bonadonna, A., & Vesce, E. (2022). Short food supply chains in Europe: scientific research directions. Sustainability , 14 (6), 3602. https://doi.org/10.3390/su14063602 Fereidani, B. M., & Üçtuğ, F. G. (2024). Towards sustainable production of sesame products: Comparison of traditional and modern production systems via a life cycle assessment approach. Cleaner and Responsible Consumption , 12 , 100166. https://doi.org/10.1016/j.clrc.2023.100166 Finnegan, W., Goggins, J., Clifford, E., & Zhan, X. (2017). Environmental impacts of milk powder and butter manufactured in the Republic of Ireland. Science of the Total Environment , 579 , 159–168. https://doi.org/10.1016/j.scitotenv.2016.10.237 Gavahian, M., Chaosuan, N., Yusraini, E., & Sastry, S. (2025). Roles of ohmic heating to achieve sustainable development goals in the food industry: From reduced energy consumption and resource optimization to innovative production pathways with reduced carbon footprint. Trends in Food Science & Technology , 159 , 104947. https://doi.org/https://doi.org/10.1016/j.tifs.2025.104947 Godenau, D., Martin-Rodriguez, G., Gonzalez-Gomez, J. I., & Caceres-Hernandez, J. J. (2022). Food consumption in the Canary Islands: nutritional implications of food imports and local production. Bmc Public Health , 22 (1), 404. https://doi.org/10.1186/s12889-022-12805-w Hu, A. H., Chen, C. H., Huang, L. H., Chung, M. H., Lan, Y. C., & Chen, Z. (2019). Environmental Impact and Carbon Footprint Assessment of Taiwanese Agricultural Products: A Case Study on Taiwanese Dongshan Tea. Energies , 12 (1), 138. https://www.mdpi.com/1996-1073/12/1/138 Huijbregts, M. A., Steinmann, Z. J., Elshout, P. M., Stam, G., Verones, F., Vieira, M., Zijp, M., Hollander, A., & Van Zelm, R. (2017). ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. The International Journal of Life Cycle Assessment , 22 , 138–147. https://doi.org/10.1007/s11367-016-1246-y Konstantas, A., Stamford, L., & Azapagic, A. (2019). Environmental impacts of ice cream. Journal of cleaner production , 209 , 259–272. https://doi.org/10.1016/j.jclepro.2018.10.237 Kumar, M., Kumar Choubey, V., Deepak, A., Gedam, V. V., & Raut, R. D. (2021). Life cycle assessment (LCA) of dairy processing industry: A case study of North India. Journal of cleaner production , 326 , 129331. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.129331 Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., Trisos, C., Romero, J., Aldunce, P., & Barrett, K. (2023). Climate change 2023: synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change. The Australian National University . https://doi.org/10.59327/IPCC/AR6-9789291691647.001 Li, M., Jia, N., Lenzen, M., Malik, A., Wei, L., Jin, Y., & Raubenheimer, D. (2022). Global food-miles account for nearly 20% of total food-systems emissions. Nature Food , 3 (6), 445–453. https://doi.org/10.1038/s43016-022-00531-w Li, Y., Zhong, H., Shan, Y., Hang, Y., Wang, D., Zhou, Y., & Hubacek, K. (2023). Changes in global food consumption increase GHG emissions despite efficiency gains along global supply chains. Nature Food , 4 (6), 483–495. https://doi.org/10.1038/s43016-023-00768-z Liu, G., Kuang, Z., Tang, J., Kuang, S., Tian, Q., Zou, Y., & Li, Q. (2024). Calculation and utility analysis of lychee life-cycle carbon emissions considering food loss and waste. Journal of cleaner production , 434 , 140013. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.140013 Mrówczyńska-Kamińska, A., Bajan, B., Pawłowski, K. P., Genstwa, N., & Zmyślona, J. (2021). Greenhouse gas emissions intensity of food production systems and its determinants. PloS one , 16 (4), e0250995. https://doi.org/10.1371/journal.pone.0250995 Munasinghe, M., Deraniyagala, Y., Dassanayake, N., & Karunarathna, H. (2017). Economic, social and environmental impacts and overall sustainability of the tea sector in Sri Lanka. Sustainable Production and Consumption , 12 , 155–169. https://doi.org/https://doi.org/10.1016/j.spc.2017.07.003 Noorani, M. H., Asakereh, A., & Siahpoosh, M. R. (2023). Investigating cumulative energy and exergy consumption and environmental impact of sesame production systems, a case study. International Journal of Exergy , 42 (1), 96–114. https://doi.org/10.1504/ijex.2023.134289 Ouikhalfan, M., Lakbita, O., Delhali, A., Assen, A. H., & Belmabkhout, Y. (2022). Toward Net-Zero Emission Fertilizers Industry: Greenhouse Gas Emission Analyses and Decarbonization Solutions. Energy & Fuels , 36 (8), 4198–4223. https://doi.org/10.1021/acs.energyfuels.2c00238 Patil, M. H., Gaëlle, T., Le, F. F. C., Romain, J.,and, & Murphy, E. G. (2021). Energy usage in the manufacture of dairy powders: Advances in conventional processing and disruptive technologies. Drying Technology , 39 (11), 1595–1613. https://doi.org/10.1080/07373937.2021.1903489 Peantham, K., & Varabuntoonvit, V. (2024). Sustainable solutions in single-use food containers: A comprehensive life cycle assessment comparing plastic (PP) and its green alternative (PLA coated kraft paper, PLA). Environmental Engineering Research , 29 (5), 230729–230720. https://doi.org/10.4491/eer.2023.729 Qiang, Z., Nan, Q., Chi, W., Qin, Y., Yang, S., Zhu, W., & Wu, W. (2024). Recycling packaging waste from residual waste reduces greenhouse gas emissions. Journal of Environmental Management , 371 , 123028. https://doi.org/https://doi.org/10.1016/j.jenvman.2024.123028 Ritchie, H., & Roser, M. (2022). Environmental impacts of food production. Our World in Data. https://ourworldindata.org/environmental-impacts-of-food Robertson, L. J., Morel, J. P., Archer, R. H., & McLaren, S. J. (2024). GHG comparison of frozen milk concentrate and milk powder. Journal of Food Process Engineering , 47 (8), e14710. https://doi.org/https://doi.org/10.1111/jfpe.14710 Sadeghy, S., Pormazar, S. M., Ghaneian, M. T., Ehrampoush, M. H., & Dalvand, A. (2024). Modeling and optimization of direct dyes removal from aqueous solutions using activated carbon produced from sesame shell waste. Scientific Reports , 14 (1), 24867. https://doi.org/10.1038/s41598-024-76081-x Sahu, R., & Agarwal, T. (2021). Carbon Footprint of Raw Milk and Other Dairy Products. In R. S. Mor, A. Panghal, & V. Kumar (Eds.), Challenges and Opportunities of Circular Economy in Agri-Food Sector: Rethinking Waste (pp. 177–189). Springer Singapore. https://doi.org/10.1007/978-981-16-3791-9_10 Salazar-Sogamoso, L. M., Gómez-García, M. Á., & Dobrosz-Gómez, I. (2024). Comparative life cycle assessment of sequential chemical and electrochemical processes for the treatment of industrial textile wastewater. Journal of Solid State Electrochemistry . https://doi.org/10.1007/s10008-024-05976-6 San Miguel, G., & Ruiz, D. (2021). Environmental sustainability of a pork and bean stew. Science of the Total Environment , 798 , 149203. https://doi.org/10.1016/j.scitotenv.2021.149203 Sasaki, Y., Orikasa, T., Takaoka, R., Nakamura, N., Hayashi, K., Yasaka, Y., Makino, N., Shobatake, K., Koide, S., & Shiina, T. (2024). A novel simplified model to evaluate environmental impact based on life cycle assessment methodology–Case study of spinach considering food waste reduction through modified atmosphere packaging–. Future Foods , 100456. https://doi.org/10.1016/j.fufo.2024.100456 Seth, D., Athparia, M., Singh, A., Rathore, D., Venkatramanan, V., Channashettar, V., Prasad, S., Maddirala, S., Sevda, S., & Kataki, R. (2025). Sustainable environmental practices of tea waste—a comprehensive review. Environmental Science and Pollution Research , 32 (12), 7449–7467. https://doi.org/10.1007/s11356-023-30848-3 Sharma, H., Singh, P. K., Kaur, I., & Singh, R. (2024). Water Footprints of Dairy Milk Processing Industry: A Case Study of Punjab (India). Water , 16 (3), 435. https://doi.org/10.3390/w16030435 Silva, D. V., Pavan, A. L. R., Faria, L. C., d., Piekarski, C. M., Saavedra, Y. M. B., & Lopes Silva, D. A. (2024). Opportunities to integrate Ecosystem Services into Life Cycle Assessment (LCA): a case study of milk production in Brazil. Ecosystem Services , 69 , 101646. https://doi.org/https://doi.org/10.1016/j.ecoser.2024.101646 Stein, A. J., & Santini, F. (2022). The sustainability of local food: A review for policy-makers. Review of Agricultural Food and Environmental Studies , 103 (1), 77–89. https://doi.org/10.1007/s41130-021-00148-w Suer, J., Traverso, M., & Ahrenhold, F. (2021). Carbon footprint of scenarios towards climate-neutral steel according to ISO 14067. Journal of cleaner production , 318 , 128588. https://doi.org/10.1016/j.jclepro.2021.128588 TFDA (2023). Ministry of Health and Welfare Nutrition Database . Ministry of Health and Welfare Food and Drug Administration. https://consumer.fda.gov.tw/Food/TFND.aspx?nodeID=178 van der Werf, H. M. G., Knudsen, M. T., & Cederberg, C. (2020). Towards better representation of organic agriculture in life cycle assessment. Nature Sustainability , 3 (6), 419–425. https://doi.org/10.1038/s41893-020-0489-6 Wang, R., Chen, J., Li, Z., Bai, W., & Deng, X. (2023). Factors analysis for the decoupling of grain production and carbon emissions from crop planting in China: A discussion on the regulating effects of planting scale and technological progress. Environmental Impact Assessment Review , 103 , 107249. https://doi.org/10.1016/j.eiar.2023.107249 Wang, X., Ledgard, S., Luo, J., Chen, Y., Tian, Y., Wei, Z., Liang, D., & Ma, L. (2021). Life cycle assessment of alfalfa production and potential environmental improvement measures in Northwest China. Journal of cleaner production , 304 , 127025. https://doi.org/10.1016/j.jclepro.2021.127025 Wróbel-Jędrzejewska, M., & Polak, E. (2023). Carbon Footprint Analysis of Ice Cream Production. Sustainability , 15 (8), 6887. https://doi.org/10.3390/su15086887 Yudhistira, B., Adi, P., Mulyani, R., Chang, C. K., Gavahian, M., & Hsieh, C. W. (2024). Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint. Comprehensive Reviews in Food Science and Food Safety , 23 (5), e13413. https://doi.org/10.1111/1541-4337.13413 Zahedi, R., Shoaei, M., & Aslani, A. (2025). Life cycle assessment and comparison of the conventional and third generation of photovoltaic panels. Journal of the Brazilian Society of Mechanical Sciences and Engineering , 47 (2), 1–11. https://doi.org/10.1007/s40430-024-05371-2 Zhang, C., Ye, X., Zhang, H., Xie, J., & Yang, X. (2023). Environmental footprint performance improvement of black tea using the life cycle assessment approach. Journal of cleaner production , 430 , 139574. https://doi.org/10.1016/j.jclepro.2023.139574 Zhu, J., Luo, Z., Sun, T., Li, W., Zhou, W., Wang, X., Fei, X., Tong, H., & Yin, K. (2023). Cradle-to-grave emissions from food loss and waste represent half of total greenhouse gas emissions from food systems. Nature Food , 4 (3), 247–256. https://doi.org/10.1038/s43016-023-00710-3 Additional Declarations No competing interests reported. Supplementary Files floatimage1.jpeg Graphical Abstract Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 18 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 28 Apr, 2025 First submitted to journal 25 Apr, 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. 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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-6526193","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451492966,"identity":"63ffcd63-0d64-4da5-a61e-f9e81cbcf445","order_by":0,"name":"Yu-Ching Huang","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Ching","middleName":"","lastName":"Huang","suffix":""},{"id":451492967,"identity":"107a6434-650f-4537-90bb-1f330764252b","order_by":1,"name":"Wei-Lun Zhu","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Lun","middleName":"","lastName":"Zhu","suffix":""},{"id":451492968,"identity":"1c64e55c-0264-4dbd-881c-00628b337577","order_by":2,"name":"Chao-Kai Chang","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Chao-Kai","middleName":"","lastName":"Chang","suffix":""},{"id":451492969,"identity":"9294dd6d-810a-41dd-bc59-9af43a7eb26b","order_by":3,"name":"Mohsen Gavahian","email":"","orcid":"","institution":"National Pingtung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Gavahian","suffix":""},{"id":451492977,"identity":"677c0aec-37c6-480e-a94f-ef0641b775ca","order_by":4,"name":"Yeh Chen","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Yeh","middleName":"","lastName":"Chen","suffix":""},{"id":451492978,"identity":"e7d7ded5-4043-4b39-b6f4-474865e280aa","order_by":5,"name":"Chang-Wei Hsieh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFAC5gYgYcPAIAHisBGlhRGkJQ2hhYdILYdJ0GJwvLH5w8cd5/P4Z/cYMHwoO8xgL5FAQMuZg22SM8/cLpa4c8aAcca5www8hLSY3UhsY+Ztu524QSLHAMgAapEmpOX+w+bPvG3nIFr+EqXlBmODNG/bAYgWRmK02J9JBPqlLTlxxo20goM959J5eO4/wK9Fsv3w4Q8f2+wS+2ckb3zwo8xajr3nAH4tKACkloiYHAWjYBSMglFAEAAACllE3u2eoQsAAAAASUVORK5CYII=","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":true,"prefix":"","firstName":"Chang-Wei","middleName":"","lastName":"Hsieh","suffix":""}],"badges":[],"createdAt":"2025-04-25 07:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6526193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6526193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81998977,"identity":"47a77843-791e-48a7-abaf-6f1c17e03325","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196263,"visible":true,"origin":"","legend":"\u003cp\u003eSystem Boundary diagram for carbon emissions of ice cream production.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/6dfa9e513cf2f4354a14a641.png"},{"id":81998978,"identity":"237f65df-dfe1-4e01-aa94-42a22802d569","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129615,"visible":true,"origin":"","legend":"\u003cp\u003eThe environmental impact results of the Midpoint method per 85mL/cup of black tea latte ice cream using ingredients from different origins. GWP: Global warming (kg CO₂ eq), SOD: Stratospheric ozone depletion (kg CFC11 eq), IR: Ionizing radiation (kBq Co-60 eq), OHH: Ozone formation, Human health (kg NOx eq), FPMF: Fine particulate matter formation (kg PM₂.₅ eq), OTE: Ozone formation, Terrestrial ecosystems (kg NOx eq), TACID: Terrestrial acidification (kg SO₂ eq), FEUT: Freshwater eutrophication (kg P eq), MEUT: Marine eutrophication (kg N eq), TETX: Terrestrial ecotoxicity (kg 1,4-DCB eq), FETX: Freshwater ecotoxicity (kg 1,4-DCB eq), MATX: Marine ecotoxicity (kg 1,4-DCB eq), HCTX: Human carcinogenic toxicity (kg 1,4-DCB eq), HNCTX: Human non-carcinogenic toxicity (kg 1,4-DCB eq), LU: Land use (m²·a crop eq), MRS: Mineral resource scarcity (kg Cu eq), FRS: Fossil resource scarcity (kg oil eq), WC: Water consumption (m³).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/9faaf84307a956fa472d4f16.jpeg"},{"id":81999730,"identity":"4f2b8b8f-0a80-4731-8566-e9526f09ede6","added_by":"auto","created_at":"2025-05-05 19:15:34","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129822,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental impact results of the Midpoint method per unit of sesame ice cream using raw materials from different origins. GWP: Global warming (kg CO₂ eq), SOD: Stratospheric ozone depletion (kg CFC11 eq), IR: Ionizing radiation (kBq Co-60 eq), OHH: Ozone formation, Human health (kg NOx eq), FPMF: Fine particulate matter formation (kg PM₂.₅ eq), OTE: Ozone formation, Terrestrial ecosystems (kg NOx eq), TACID: Terrestrial acidification (kg SO₂ eq), FEUT: Freshwater eutrophication (kg P eq), MEUT: Marine eutrophication (kg N eq), TETX: Terrestrial ecotoxicity (kg 1,4-DCB eq), FETX: Freshwater ecotoxicity (kg 1,4-DCB eq), MATX: Marine ecotoxicity (kg 1,4-DCB eq), HCTX: Human carcinogenic toxicity (kg 1,4-DCB eq), HNCTX: Human non-carcinogenic toxicity (kg 1,4-DCB eq), LU: Land use (m²·a crop eq), MRS: Mineral resource scarcity (kg Cu eq), FRS: Fossil resource scarcity (kg oil eq), WC: Water consumption (m³).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/320eca85f24cfe738aa87fe0.jpeg"},{"id":81999731,"identity":"15f297f8-3843-4097-be99-e7400b30afcb","added_by":"auto","created_at":"2025-05-05 19:15:34","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416668,"visible":true,"origin":"","legend":"\u003cp\u003eThe environmental impact percentage of the Midpoint method for Black tea latte ice cream using local ingredients for each raw material produced per unit.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/2e765db2e6779b15635b3739.jpeg"},{"id":81998981,"identity":"a2a10d4b-8b0c-43ee-891f-4fadc7af1b3d","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":477795,"visible":true,"origin":"","legend":"\u003cp\u003eThe environmental impact percentage of each raw material produced by the Midpoint method for each unit of black tea latte ice cream using ingredients from foreign origins.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/122c31df6481d6d826201008.jpeg"},{"id":81998987,"identity":"94537e32-f41f-45c0-a5fc-5ce884452473","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":352106,"visible":true,"origin":"","legend":"\u003cp\u003eThe environmental impact percentage of the Midpoint method for Sesame ice cream using local ingredients for each raw material produced per unit.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/b6cf655b641642ad76ac85d9.jpeg"},{"id":81998989,"identity":"e4667577-2ae2-4701-9d42-c78c7b4547e8","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":386702,"visible":true,"origin":"","legend":"\u003cp\u003eThe environmental impact percentage of each raw material produced by the Midpoint method for sesame ice cream using ingredients from foreign origins per unit.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/b0fbe411d62d29b7db61e4ea.jpeg"},{"id":81999210,"identity":"2d9ea5e3-27a2-4dca-9747-0a0a714b8774","added_by":"auto","created_at":"2025-05-05 19:07:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":146976,"visible":true,"origin":"","legend":"\u003cp\u003eGreenhouse gas emissions of various raw materials used in black tea latte ice cream from different origins.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/c56556c500fc6ccc5af1cd8a.jpeg"},{"id":81999215,"identity":"ab96e76e-fbbd-40dc-b13f-a3540a6b965c","added_by":"auto","created_at":"2025-05-05 19:07:34","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":160553,"visible":true,"origin":"","legend":"\u003cp\u003eGreenhouse gas emissions of various raw materials used in sesame ice cream from different origins.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/03d751ac2ebc3edf911d2b4a.jpeg"},{"id":82000201,"identity":"22aedaa9-9ff7-45e0-8e50-b5b254e2247a","added_by":"auto","created_at":"2025-05-05 19:23:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4146670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/5175b620-585d-445b-9e71-9d8aa17dc6db.pdf"},{"id":81998984,"identity":"94d0c879-0ddb-4d02-a454-fe9cc082331e","added_by":"auto","created_at":"2025-05-05 18:59:34","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":491968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6526193/v1/b1da8bfb6f998cd0aa2015e7.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Debunking the 'Local is Greener' Myth: Life Cycle Assessment of Local vs. Imported Ingredients in Ice Cream Production","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the \"AR6 Synthesis Report: Climate Change 2023\" published by the Intergovernmental Panel on Climate Change (IPCC) in 2023, greenhouse gas emissions must start to decline immediately to achieve the Paris Agreement\u0026rsquo;s goal of limiting temperature increase to 1.5\u0026deg;C. 2030 emissions must be reduced by nearly half (Lee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The global food system is associated with climate change through its impact on carbon footprint, land-use change, and biodiversity loss (Boakes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Food production systems contribute approximately 35% to global greenhouse gas emissions, with significant emissions arising from processing, packaging, transportation, household consumption, and waste management within the food production process. (Crippa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Life Cycle Assessment (LCA) is a method recommended by international organizations such as the European Commission and the United Nations Environment Programme (UNEP) to assess the environmental impacts of a product throughout its entire life cycle\u0026mdash;from raw material input to waste disposal (Cucurachi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). LCA is considered the preferred method to evaluate greenhouse gas emissions and environmental impacts in food production processes (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Scientists have emphasized the need for the application of LCA to elaborate on the environmental impacts of the food production chain, from waste management to the entire industry chain (Gavahian et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sasaki et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the food industry, there is considerable interest in the concept of \"food miles,\" which refers to the distance food travels from production to consumption. During the COVID-19 pandemic, European Union countries actively shortened the \"farm-to-table\" distance to reduce food transportation distances and related carbon emissions while enhancing domestic economic resilience and reducing reliance on global supply chains (EC, 2020). However, the contribution of transportation to most impact categories is limited (San Miguel \u0026amp; Ruiz, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have shown that domestic food systems might require more intensive agriculture to meet local demand, potentially increasing environmental impacts and not necessarily being more sustainable than large-scale operations (Boakes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, land use, production processes, or storage methods can impact a product\u0026rsquo;s carbon footprint more than transportation (Ritchie \u0026amp; Roser, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of earlier studies, the carbon emissions of food loss and waste account for about half of the global food industry chain (Zhu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially for perishable foods such as fruits (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, research suggests reducing food miles to avoid waste and loss caused by transportation (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, production and processing methods are crucial for reducing environmental impacts, and local or short-distance supply does not necessarily equate to being more environmentally friendly (Evola et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, in monosodium glutamate (MSG) processing, the primary sources of greenhouse gas emissions are the production process and raw material acquisition. Additionally, irrigation water and ammonia volatilization from corn cultivation, the raw material for MSG, are considered significant environmental impact factors (Ding et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The carbon footprint of food is more influenced by land use, production efficiency, transportation economies of scale, and whether the food is plant-based rather than merely the transportation distance (Stein \u0026amp; Santini, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), large-scale production can also help reduce the environmental impact of a unit of product (Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Because soil microorganisms may metabolize CH\u003csub\u003e4\u003c/sub\u003e when agricultural products are grown, applying nitrogen fertilizers will increase N\u003csub\u003e2\u003c/sub\u003eO emissions (Crippa et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to the escape of CH\u003csub\u003e4\u003c/sub\u003e generated during the metabolism of animal products, the environmental impact caused by the production of edible feed must also be calculated (Mr\u0026oacute;wczyńska-Kamińska et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIce cream presents an ideal case for examining these intertwined factors as a product with energy-intensive processing, diverse raw material requirements, and extensive food miles associated with imported ingredients. Previous studies have conducted LCA of ice cream from the perspective of processing procedures (Konstantas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wr\u0026oacute;bel-Jędrzejewska \u0026amp; Polak, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, there is a lack of research and published studies on whether raw materials or transportation contribute more significantly to the LCA of ice cream products in countries without a domestic dairy industry. In island nations, where land availability is limited, and production costs are high, the local food industry heavily relies on imported raw materials, often cheaper than locally sourced alternatives (Godenau et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While this practice supports cost efficiency, it conflicts with the sustainability expectations of modern consumers. In Taiwan, sesame and black tea are essential raw food ingredients that enhance flavor. The annual production value of sesame is USD 8.45\u0026nbsp;million, while tea reaches USD 222.75\u0026nbsp;million (Agriculture, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, domestic production remains insufficient to meet market demand, leading to heavy reliance on imports. Taiwan also lacks a sufficient dairy industry to provide an adequate raw milk supply, making the food processing sector highly reliant on imported dairy products (Andoko et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy investigating ice cream's LCA in this context, our study seeks to address these critical gaps and contribute to sustainable food production strategies. We use a food factory that processes ice cream as a case study, evaluate the main contributions stage of carbon footprint and life cycle environmental impacts from raw material acquisition to production, transportation, use, and final disposal, investigating whether domestic ingredients have advantages over imported ones in the processing procedure to achieve a more sustainable production model and effective carbon emission reduction strategies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Functional unit, goal, and scope\u003c/h2\u003e \u003cp\u003eThis study employs the LCA methodology based on the international standards ISO 14040 and ISO 14044. These standards establish the four main phases of LCA: goal and scope definition, inventory analysis, impact assessment, and interpretation. The evaluation covers the entire life cycle of the studied product, from raw material acquisition to final disposal, compiling and evaluating inputs, outputs, and potential environmental impacts. The study also follows the ISO 14067 standard to calculate the carbon footprint.\u003c/p\u003e \u003cp\u003eThis study focuses on processed food products\u0026mdash;specifically, ice cream produced in a factory setting. It assesses the life cycle from the raw material acquisition stage, including the transportation and packaging waste from sourcing ingredients and packaging materials, the energy inputs and waste disposal during the manufacturing stage, transportation logistics and distances for distribution, energy consumption at the point of sale during the use phase, and final disposal. The life cycle environmental impact assessment and carbon footprint calculations are conducted using the SimaPro 9.4.0.1 software.\u003c/p\u003e \u003cp\u003eThe functional unit is defined as 85 mL per cup, which aligns with the study's objectives and scope. It serves as the reference basis for correlating raw material inputs with product outputs. This definition is clear, precise, and measurable (Suer et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. System Boundaries\u003c/h2\u003e \u003cp\u003eThis study's selected ice cream products are black tea latte and sesame flavors. The product composition includes raw materials, accessories (such as spoons), and packaging materials (such as cup lids and sealing films). The manufacturing process encompasses blending, cooking, sterilization, homogenization, aging, filling, rapid freezing, and packaging. The distribution, sales, product usage, and final disposal stages are also considered. Because the geographical distribution of consumers is different, and the storage equipment and time after purchase are also significantly different, To reduce uncertainty to an acceptable level, under the premise of complying with the provisions of ISO 14064-1, the use process of the product after purchase by consumers is excluded from the boundary. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the system boundaries of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Inventory Analysis\u003c/h2\u003e \u003cp\u003eThe inventory data for this study is provided by the company involved in the case study, covering the period from January 1, 2022, to December 31, 2022. The inventory encompasses raw material acquisition, manufacturing, distribution, transportation, and final disposal stages. The primary raw materials listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e include whole milk powder from New Zealand, sesame powder (paste) from Taiwan or Sri Lanka, sugar from Brazil, black tea powder from Taiwan or Sri Lanka, cream from New Zealand, and all water used is from domestic sources in Taiwan. L powder, a composite colloid powder, is excluded from the inventory due to data unavailability and its proportion in the formula being less than 0.1%, hence not affecting the overall data. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides inventory data on packaging materials and final disposal methods, including ice cream cups, lids, spoons, sealing films, polystyrene boxes, and tape. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the freight transportation distances for each domestic raw or packaging material type. The transportation distance of raw materials and packaging materials to the factory varies depending on their origin. For domestic raw materials and packaging, land transportation using trucks covers a distance of 687.6 kilometers. In contrast, imported raw materials and packaging involve both land transportation via trucks for 687.6 kilometers and sea transportation via cargo ships for an additional 11,936 kilometers. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e details the energy inputs for the manufacturing stage. The distribution and sales stage includes transporting products to sales points and energy usage at these points, such as tap water, electricity, and refrigerant leakage. The use stage assumes immediate consumption at the sales point, resulting in no life cycle greenhouse gas emissions.\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\u003eRaw material inventory data per functional unit\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\u003eRaw Materials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack Tea Ice Cream (kg/85mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSesame Ice Cream (kg/85mL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole Milk Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.54\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.48\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame Paste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.51\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.15\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack Tea Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.71\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\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 \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\u003eInventory data on packaging materials and waste disposal methods per functional unit\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=\"char\" char=\"\u0026times;\" 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\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste disposal methods\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCup Lid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolypropylene (PP) Recycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIce Cream Cup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.70\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolystyrene (PS) Recycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCup Sealing Film\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e6.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eincineration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStyrofoam Box\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.47\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycling in the factory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVC Tape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eincineration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTextile Tape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e8.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eincineration\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\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\u003eThe freight transportation distances for each type of domestic raw or packaging material.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw materials/packaging materials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreight transportation distances (km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole Milk Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame Paste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack Tea Powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCup Lid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIce Cream Cup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpoon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCup Sealing Film\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStyrofoam Box\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVC Tape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTextile Tape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInventory data for manufacturing, distribution and sales per functional unit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcess Electricity Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekWh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGasoline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiesel Fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefrigerant Escape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDistribution Sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTap Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.13\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefrigerant Escape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectricity Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekWh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransport to the point of Sale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etkm\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Allocation Principles\u003c/h2\u003e \u003cp\u003eThe allocation principles are based on weight calculations, where the actual input weight of raw materials is calculated and directly allocated to each product. Overall factory data, such as electricity and water consumption during the manufacturing stage, are assigned to the specific product category and each product.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Carbon Footprint Calculation Principles\u003c/h2\u003e \u003cp\u003eThe carbon footprint calculation for the products follows the environmental carbon footprint calculation standards, consolidating primary and secondary data across various stages of the product life cycle according to raw material names, categories, inputs, distances, etc. This study utilizes the IPCC 2021 GWP100 V1.01 methodology with public carbon emission factors provided by (EPA, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and corresponding coefficients from Ecoinvent 3.8 and Agri-footprint version 6. The collected activity data is multiplied by the corresponding emission factors and summed to calculate the total carbon emissions per functional unit of the product. The calculation formula is as follows:\u003c/p\u003e \u003cp\u003eCarbon Footprint (CO\u003csub\u003e2\u003c/sub\u003e eq)=\u0026sum;(Activity Data \u0026times; Emission Factor \u0026times; GWP) (1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Environmental Impact Assessment Methodology\u003c/h2\u003e \u003cp\u003eThe ReCiPe methodology is employed to calculate Midpoint results and Endpoint results. The ReCiPe methodology, developed based on CML 2001 and Eco-indicator 99, is a comprehensive and novel approach to environmental impact assessment (Zahedi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The data were then input into SimaPro 9.4.0.1 and converted to generate 18 midpoint environmental impact categories and 3 endpoint damage categories. The usage quantities were based on the actual inventory results and were not modified in terms of coefficients or inventory data.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Midpoint Environmental Impact Results of Different Ice Cream Flavors Assessed Using the ReCiPe Method\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Black Tea Latte Ice Cream\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the ReCiPe midpoint method calculates the environmental impact for 18 categories for each functional unit of black tea latte ice cream produced by the company, using local and imported ingredients. The y-axis shows the environmental impact of each category. Specific units for each category are provided in the figure note. The main contributors to environmental impact include global warming, ionizing radiation, terrestrial ecotoxicity, human non-carcinogenic toxicity, land use, and fossil resource scarcity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor GWP, the impact of ice cream made with local ingredients is 5.63\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg CO\u003csub\u003e2\u003c/sub\u003e eq, compared to 5.39\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg CO\u003csub\u003e2\u003c/sub\u003e eq for those made with imported ingredients. Both variants show significant greenhouse gas emissions due to high energy consumption during the manufacturing stage. In the IR, the value for ice cream made with local ingredients is 4.46\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e kBq Co-60 eq, while for imported ingredients, it is 4.42\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e kBq Co-60 eq, showing a minor difference. For TETX, the data for local ingredients is 4.33\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB, compared to 3.95\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for imported ingredients. The higher ecotoxicity is associated with using chemicals in raw material acquisition and pollution during production. The results for HNCTX indicate a value of 3.64\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for local ingredients and 3.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for imported ingredients linked to pesticide and chemical use, posing more risks to human health. In terms of LU and FRS, the values are 1.23\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e crop eq versus 1.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e crop eq and 1.14\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg oil eq versus 1.11\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg oil eq, respectively, reflecting the demand for land and fossil fuels during the manufacturing process. The possible reasons for the higher environmental impact of using domestic raw materials is that Sri Lanka's agriculture is highly labor-intensive and the planting density is low (Munasinghe et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while Taiwan has a seriously aging population, uses mechanized harvesting and has a high amount of fertilizer (Hu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Sesame Ice Cream\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the results of the ReCiPe midpoint method for sesame ice cream, showing the environmental impact data for 18 categories for each functional unit produced using both local and imported ingredients. The y-axis shows the environmental impact of each category. Specific units for each category are provided in the figure note. The main contributors to environmental impact are global warming, ionizing radiation, terrestrial ecotoxicity, human non-carcinogenic toxicity, land use, and fossil resource scarcity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor GWP, the impact of ice cream made with local ingredients is 5.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg CO\u003csub\u003e2\u003c/sub\u003e eq, compared to 5.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg CO\u003csub\u003e2\u003c/sub\u003e eq for imported ingredients. Both types show significant greenhouse gas emissions due to high energy consumption during the manufacturing stage. In the IR, the value for local ingredients is 4.42\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e kBq Co-60 eq, while for imported ingredients, it is 4.45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e kBq Co-60 eq, indicating a slight difference. For TETX, the value for local ingredients is 4.80\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB, compared to 4.77\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for imported ingredients. The higher ecotoxicity is related to using chemicals in raw material acquisition and pollution during production. The results for HNCTX show a value of 4.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for local ingredients and 3.97\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg 1,4-DCB for imported ingredients associated with pesticide and chemical use, increasing risks to human health. Only two types of pesticides can be used to grow sesame in Taiwan, and environmentally friendly farming methods will likely cause higher environmental impacts (van der Werf et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the FRS category, the values are 1.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg oil eq for local ingredients and 1.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e kg oil eq for imported ingredients, indicating the demand for fossil fuels during manufacturing. This may be because sesame in Sri Lanka is typically cultivated on marginal land with minimal labor and attention, resulting in a relatively lower environmental impact (Dissanayake \u0026amp; Sithara, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. Midpoint Environmental Impact Results of Different Ice Cream Flavors in the Raw Material Acquisition Stage Assessed Using the ReCiPe Method\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Black Tea Latte Ice Cream\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the percentage of environmental impacts for different black tea latte ice cream ingredients, depending on whether local or imported ingredients were used. Since Taiwan rarely produces milk powder and cream, local and imported milk powder and cream in this case study use whole cream milk powder and cream imported from New Zealand, with coefficients based on the global average from the Ecoinvent 3.8 database. The environmental impact results for whole cream milk powder and cream in black tea lattes and sesame ice creams are as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor GWP, whole cream milk powder contributes 57% of ice cream with local ingredients, 59.9% for imported black tea latte ice cream, and 61.4% and 53% for sesame ice cream, respectively. Finnegan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) state that converting raw milk into milk powder is the most significant contributor to GWP. In the stratospheric ozone depletion (SOD) category, the impact of whole cream milk powder is 66.2% for ice cream with local ingredients and 67.8% for imported ingredients in black tea latte ice cream, and 52.9% and 62.3% for sesame ice cream, respectively, related to the use of refrigerants containing chlorofluorocarbons (CFCs) in dairy production. This result is lower than the research result of Konstantas et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) because the number of days of frozen storage is highly uncertain, and we exclude the frozen storage process outside the system boundary.\u003c/p\u003e \u003cp\u003eParticulate matter formation (PMF) is measured in PM\u003csub\u003e2.5\u003c/sub\u003e, particles with a diameter of less than 2.5 micrometers composed of complex organic and inorganic substances that can penetrate deep into the lungs and enter the bloodstream, affecting the cardiovascular, cerebrovascular, and respiratory systems. PMF is commonly used to measure the concentration of these particles (Huijbregts et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The contribution of whole cream milk powder in the PMF category is 72.5% for ice cream with local ingredients, 71.4% for imported ingredients in black tea latte ice cream, and 67.8% and 61.6% for sesame ice cream, respectively. Kumar et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) pointed out that dairy products such as butter and ice cream, due to undergoing more extensive thermal processing, may generate particulate matter formation (PMF) during energy usage. A study on the environmental LCA of Brazilian milk production by Silva et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrates that raw milk yield exerts the most pronounced influence on AC, FEUT, MUET, and GWP categories, mainly due to methane emissions from enteric fermentation, manure, and cattle urine. In AC, the contribution of whole cream milk powder is 43.2% for ice cream with local ingredients and 45.1% for imported ingredients in black tea latte ice cream, and 31.3% and 35.9% for sesame ice cream, respectively, with cream contributing 17.3% and 26.5% for black tea latte ice cream, and 5.36% and 6.14% for sesame ice cream. In MEUT, the impact of whole cream milk powder is higher, with 60.8% and 64.9% for black tea latte ice cream using local and imported ingredients, respectively, and 41.6% and 65.3% for sesame ice cream.\u003c/p\u003e \u003cp\u003eIn LU, the contribution of whole cream milk powder is 24% and 21.5% for black tea latte ice cream using local and imported ingredients, respectively, and 21.3% and 36.5% for sesame ice cream. The contribution of cream is 4.53% and 45.6% for black tea latte ice cream and 18.9% and 32.5% for sesame ice cream, respectively. Konstantas et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) mention that land use is related to the use of raw milk, and more cream requires more agricultural land for milk production. In the water consumption (WC) category, the contribution of whole cream milk powder is 4.71% and 7.71% for black tea latte ice cream using local and imported ingredients, respectively, and 12.2% and 12.4% for sesame ice cream. The water footprint of skim milk powder indicates that dairy production has a minimal impact on freshwater scarcity compared with other agricultural production (Sharma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor black tea in WU, the contribution of local tea is 83.6%. In comparison, tea from Sri Lanka is 76.1%, showing a positive relationship between the use of local ingredients and reduced water consumption. Zhang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) evaluated the life cycle of black tea under different production scenarios during the cultivation and processing stages. They found that water use during the cultivation stage varies from 4.55\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e to 17.4 kg/kg tea, indicating a high demand for water during cultivation, which may also be influenced by different factors such as chemical fertilizer and pesticide use and processing methods. Reducing chemical fertilizers during the cultivation stage can reduce GWP, non-renewable energy use, and WU. For sugar, in most environmental impact categories, the use of local ingredients shows higher values than imported ingredients, with LU accounting for 54.8% for local ingredients and only 1.49% for imported ingredients. This may be because the cultivation methods in Brazil, where sugarcane is sourced, are more intensive, resulting in lower land use (Bordonal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Sesame Ice Cream\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show the percentage of environmental impacts for different ingredients in sesame ice cream, depending on whether local or imported ingredients were used. Since full-cream milk powder and cream are not produced in Taiwan, this case study uses imported ingredients, with coefficients based on the global average from the Ecoinvent 3.8 database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the ReCiPe methodology defined by Huijbregts et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), ozone is formed through photochemical reactions involving nitrogen oxides (NOx) and non-methane volatile organic compounds (NMVOCs), which can cause respiratory distress and lung damage, such as asthma and chronic obstructive pulmonary disease (COPD). The human health ozone formation potential (HOFP) is typically used to quantify these environmental impacts. In this study, the values for sesame powder and sesame paste using local ingredients in ozone formation and its effects on human health (OHH) are 22.7% and 7.26%, respectively, while the values for sesame powder and sesame paste using ingredients from Sri Lanka are 48.5% and 15.5%, respectively. The values for sesame powder and paste using local ingredients in ozone impact on terrestrial ecosystems (OTE) are 19% and 6.06%, respectively. In comparison, the values for ingredients from Sri Lanka are 43.9% and 14%.\u003c/p\u003e \u003cp\u003eIn the FEUT category, the values for sesame powder and sesame paste using local ingredients are 29.5% and 9.43%, respectively. In comparison, the values for ingredients from Sri Lanka are 18.2% and 5.82%, respectively. In the MUET category, the values for sesame powder and paste using local ingredients are 35.5% and 11.3%, respectively. In comparison, the values for ingredients from Sri Lanka are 13.2% and 4.21%, respectively. This is due to the negative impact of wastewater generated during sesame powder and paste processing on water bodies. For example, sesame shells, residues, and chemicals used in washing may contain organic matter and nutrients, which, when discharged into water bodies, lead to eutrophication, causing excessive plant growth, algae blooms, water quality degradation, and aquatic ecosystem imbalance (Sadeghy et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the TACID category, the values for sesame powder and paste using local ingredients are 34.3% and 11%, respectively. In comparison, the values for ingredients imported from Sri Lanka are 28.7% and 9.18%, respectively. Sesame paste production methods showed significant impacts in the categories of acidification potential, eutrophication potential, and ozone layer depletion. Similar results were found in a recent study by Fereidani and \u0026Uuml;\u0026ccedil;tuğ (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Endpoint Environmental Impact Results for Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins for Each Functional UnitProduced\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Black Tea Latte Ice Cream\u003c/h2\u003e \u003cp\u003e Using the ReCiPe endpoint method, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results for three environmental damage categories for each 85 mL cup of black tea latte ice cream produced using ingredients from different origins during the raw material acquisition stage. In all three categories, the values for ice cream made with imported ingredients are lower than those made with local ingredients. In the Damage to Human Health category, the value for ice cream made with local ingredients is 3.57\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e DALY, compared to 2.70\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e DALY for imported ingredients, indicating a 24% higher impact for local ingredients. Hu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted in their study on the environmental effects of agricultural winter tea in Taiwan that the most significant environmental impact in the human health category is due to fertilizer use during the raw material stage. In the Damage to Ecosystems category, the value for ice cream made with local ingredients is 1.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e species. yr, compared to 1.23\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e species.yr for imported ingredients, showing a 22% higher impact for local ingredients. In the Damage to Resources category, the value for ice cream made with local ingredients is 4.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u003cspan\u003e$\u003c/span\u003e, compared to 3.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u003cspan\u003e$\u003c/span\u003e for imported ingredients, indicating a 23% higher impact for local ingredients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental damage results of the Endpoint method for black tea latte ice cream using ingredients from different origins during the raw material acquisition stage for each 85mL/cup produced.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironmental damage category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBlack tea latte ice cream raw material acquisition stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomestic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForeign origin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to human health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDALY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.70\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to ecosystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003especies.yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e (USD2013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e4.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e3.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Sesame Ice Cream\u003c/h2\u003e \u003cp\u003eUsing the ReCiPe endpoint method, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results for three environmental damage categories for each functional unit cup of sesame ice cream produced using local and imported ingredients during the raw material acquisition stage. In all three categories, the values for imported ingredients are lower than those for local ingredients. In the Damage to Human Health category, the value for ice cream made with local ingredients is 4.50\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e DALY, compared to 2.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e DALY for imported ingredients, indicating a 55% higher impact for local ingredients. In the Damage to Ecosystems category, the value for ice cream made with local ingredients is 2.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e species. yr, compared to 8.18\u0026times;10\u003csup\u003e\u0026ndash;10\u003c/sup\u003e species.yr for imported ingredients, showing a 60% higher impact for local ingredients. In the Damage to Resources category, the value for ice cream made with local ingredients is 5.29\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u003cspan\u003e$\u003c/span\u003e, compared to 4.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e \u003cspan\u003e$\u003c/span\u003e for imported ingredients, indicating a 10% higher impact for local ingredients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental damage results of the Endpoint method for sesame ice cream using raw materials from different origins during the raw material acquisition stage for each functional unit produced.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironmental damage category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSesame ice cream raw material acquisition stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomestic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForeign origin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to human health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDALY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e4.50\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e2.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to ecosystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003especies.yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e2.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e8.18\u0026times;10\u003csup\u003e\u0026ndash;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamage to Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e (USD2013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e5.29\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e4.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\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 \u003cp\u003e \u003cb\u003e3.4 Carbon Footprint of Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins During the Raw Material Acquisition Stage and Per Functional Unit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study references the carbon footprint database from the Taiwan Ministry of Environment's Carbon Footprint Calculation Service Platform (EPA, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and collects greenhouse gas emission factors, allowing for a comparative analysis of the ice cream products' carbon footprint per functional unit. The IPCC 2021 GWP100 V1.01 methodology is used to calculate the total carbon emissions at each stage because the IPCC is considered to be more relevant to assessing environmental impacts on a global scale when making decisions related to the sustainability of different processes, and the ReCiPe method is more focused on specific environmental issues in different countries (Salazar-Sogamoso et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The carbon emissions for producing each functional unit of ice cream using ingredients from different origins are calculated for each stage, including raw material acquisition, processing, transportation, and final disposal, and then summed.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the carbon footprint for each functional unit of black tea latte ice cream using local ingredients is 0.57 kg CO\u003csub\u003e2\u003c/sub\u003ee, while for imported ingredients, it is 0.53 kg CO\u003csub\u003e2\u003c/sub\u003ee. For each functional unit of sesame ice cream, the carbon footprint for local ingredients is 0.60 kg CO\u003csub\u003e2\u003c/sub\u003ee, while for imported ingredients, it is 0.56 kg CO\u003csub\u003e2\u003c/sub\u003ee. During the raw material acquisition stage, the carbon footprints for both ice cream flavors are as follows: for each functional unit of black tea latte ice cream, the carbon footprint using local ingredients is 0.12 kg CO\u003csub\u003e2\u003c/sub\u003ee, and for imported ingredients, it is 0.09 kg CO\u003csub\u003e2\u003c/sub\u003ee; for each functional unit of sesame ice cream, the carbon footprint using local ingredients is 0.16 kg CO\u003csub\u003e2\u003c/sub\u003ee, and for imported ingredients, it is 0.11 kg CO\u003csub\u003e2\u003c/sub\u003ee.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon footprints of each functional unit and raw material acquisition stage using ingredients from different origins for black tea latte and sesame ice cream.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (kgCO\u003csub\u003e2\u003c/sub\u003ee/unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw material acquisition stage (kgCO\u003csub\u003e2\u003c/sub\u003ee/unit)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack tea latte: domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack tea latte: foreign origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame: domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame: foreign origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\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\u003eHowever, Wr\u0026oacute;bel-Jędrzejewska and Polak (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied the carbon footprint of ice cream produced at a laboratory scale with different fats and sugars and found that the carbon footprint per functional unit of ice cream ranges from 0.2021 to 0.3579 kg CO\u003csub\u003e2\u003c/sub\u003ee. This lower carbon footprint is primarily because the study was conducted at a laboratory scale, excluding the sales, transportation, and waste disposal stages. In contrast, the background of this study is set during the COVID-19 pandemic in 2022, when the company\u0026rsquo;s production volume was only 60% of that in 2021 due to the pandemic. Despite reduced production, the factory still needed to maintain essential equipment operation, including electricity and refrigeration equipment. Therefore, reduced production may have led to an increase in the carbon footprint per unit product.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Greenhouse Gas Emissions of Black Tea Latte and Sesame Ice Creams Using Different Ingredient Origins During the Raw Material Acquisition Stage\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Black Tea Latte Ice Cream\u003c/h2\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the greenhouse gas emissions for whole cream milk powder in black tea latte ice cream using local and imported ingredients from New Zealand are 0.0501 kg CO\u003csub\u003e2\u003c/sub\u003ee, for cream are 0.0153 kg CO\u003csub\u003e2\u003c/sub\u003ee, and for sugar are also 0.0579 kg CO\u003csub\u003e2\u003c/sub\u003ee. The greenhouse gas emissions for the ice cream cup are 0.0947 kg CO\u003csub\u003e2\u003c/sub\u003ee. The emissions for black tea using local ingredients from Taiwan are 0.0329 kg CO\u003csub\u003e2\u003c/sub\u003ee, while for tea from Sri Lanka, it is 0.0044 kg CO\u003csub\u003e2\u003c/sub\u003ee. These data show that the hotspots for greenhouse gas emissions in the raw material acquisition stage of black tea latte ice cream are whole cream milk powder, cream, tea, and the ice cream cup. Based on the life cycle stages, the following improvement suggestions are proposed:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSubstitution of Full Cream Milk Powder\u003c/b\u003e: Local fresh milk and UHT milk can replace whole cream milk powder, which can be evaluated according to the scenario simulation in section 3.6.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFinding Low-Emission Products\u003c/b\u003e: It is recommended that the company look for low-emission cream products and ice cream cups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComparison of Tea Emission Coefficients\u003c/b\u003e: The greenhouse gas emission coefficient for Taiwanese black tea is 7.04 kg CO\u003csub\u003e2\u003c/sub\u003ee, while that for Sri Lankan black tea is 1.99 kg CO\u003csub\u003e2\u003c/sub\u003ee. The processing of Taiwanese black tea includes steps such as withering, fixing, rolling, drying, mass rolling, unrolling, sorting, and vacuum packaging. According to the Taiwan Carbon Footprint Information Network, the emission ratios for each life cycle stage are as follows (EPA, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): raw material stage accounts for 35.15% (2.473 kg CO\u003csub\u003e2\u003c/sub\u003ee), manufacturing stage 18.67% (1.313 kg CO\u003csub\u003e2\u003c/sub\u003ee), transportation stage 0.29% (0.020 kg CO\u003csub\u003e2\u003c/sub\u003ee), usage stage 45.58% (3.207 kg CO\u003csub\u003e2\u003c/sub\u003ee), and disposal stage 0.31% (0.022 kg CO\u003csub\u003e2\u003c/sub\u003ee), totaling 7.035 kg CO\u003csub\u003e2\u003c/sub\u003ee. To reduce the carbon footprint, the following improvement suggestions are proposed:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003ec-1. \u003cb\u003eRaw Material Stage\u003c/b\u003e: Use organic and chemical fertilizers in agricultural cultivation to reduce greenhouse gas emissions. Soil management: Improve soil health and carbon sequestration capacity through composting and cover-cropping techniques (Ouikhalfan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ec-2. \u003cb\u003eManufacturing Stage\u003c/b\u003e: Enhance energy efficiency: Use more efficient and energy-saving equipment and technologies, such as more efficient drying equipment and fixing machines (Yudhistira et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Use renewable energy: Incorporate renewable energy sources such as solar and wind power in manufacturing to reduce fossil fuel use. Reduce waste: Optimize production processes to minimize raw material and energy waste (Zhu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ec-3. \u003cb\u003eTransportation Stage\u003c/b\u003e: Opt for low-carbon transportation methods: Use electric vehicles, natural gas vehicles, or other alternatives. Localize production and consumption: Reduce transportation distances and prioritize local market sales to lower carbon emissions from long-distance transport (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ec-4. \u003cb\u003eUsage Stage\u003c/b\u003e: Promote energy-saving methods: Encourage consumers to use energy-efficient equipment for brewing and storing tea. Raise consumer awareness: Educate and promote the importance of energy conservation and carbon footprint reduction.\u003c/p\u003e \u003cp\u003ec-5. \u003cb\u003eDisposal Stage\u003c/b\u003e: Promote recycling: Establish a recycling system for tea packaging and waste and promote biodegradable or recyclable packaging materials (Qiang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Reuse waste: Utilize discarded tea leaves for composting or other agricultural purposes to reduce waste-related carbon emissions (Seth et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy adopting these methods, the carbon footprint at each stage of the tea life cycle can be effectively reduced, achieving a more environmentally friendly production and consumption model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Sesame Ice Cream\u003c/h2\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the greenhouse gas emissions for sesame ice cream using both local and imported ingredients show the following: New Zealand milk powder has an emission of 0.0583 kg CO\u003csub\u003e2\u003c/sub\u003ee, cream 0.0075 kg CO\u003csub\u003e2\u003c/sub\u003ee, sugar 0.0577 kg CO\u003csub\u003e2\u003c/sub\u003ee, and ice cream cups 0.0095 kg CO\u003csub\u003e2\u003c/sub\u003ee. The emissions for sesame powder made from local ingredients are 0.0656 kg CO\u003csub\u003e2\u003c/sub\u003ee, whereas for sesame powder sourced from India, the emissions are 0.0209 kg CO\u003csub\u003e2\u003c/sub\u003ee. These data show that the hotspots for greenhouse gas emissions in the raw material acquisition stage of sesame ice cream are whole cream milk powder, cream, sesame powder, and ice cream cups. The following are recommendations for improvement:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSubstitution of Full Cream Milk Powder\u003c/b\u003e: Use local fresh and UHT milk to replace whole cream milk powder. This can be evaluated according to the scenario simulation in section 3.12.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFinding Low-Emission Products\u003c/b\u003e: It is recommended that the company find low-emission cream products and ice cream cups, such as using PLA to place PE (Peantham \u0026amp; Varabuntoonvit, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe greenhouse gas emission coefficient for Taiwanese sesame powder is 8.32 kg\u003c/b\u003e CO\u003csub\u003e2\u003c/sub\u003ee, \u003cb\u003ewhile for Indian sesame powder, it is 2.65 kg\u003c/b\u003e CO\u003csub\u003e2\u003c/sub\u003ee. According to the Taiwan Carbon Footprint Information Network, the production process for Taiwanese sesame powder includes roasting, grinding, re-roasting, and final packaging (EPA, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The following are suggestions for improving the sesame powder production process:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003ec-1. Roasting (\u003c/b\u003eYudhistira et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e):\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUse high-efficiency electric heating equipment instead of traditional gas heating equipment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUtilize renewable energy for secondary roasting, such as installing solar water heaters to provide a stable heat source.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIncrease the thermal efficiency of roasting equipment to reduce energy loss.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003ec-2. Grinding\u003c/b\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImplement variable-frequency controlled high-efficiency equipment to adjust grinding speed as needed, reducing unnecessary energy consumption (Noorani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse a water cooling system during grinding, recycling the cooling water to reduce water waste.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003ec-3. Packaging (\u003c/b\u003ePeantham \u0026amp; Varabuntoonvit, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUse biodegradable or recyclable packaging materials to reduce the carbon footprint of packaging materials.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOptimize packaging design to reduce the amount of packaging materials used and lower carbon emissions during transportation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6 Scenario Simulation: Greenhouse Gas Emissions from Substituting Local Fresh Whole Milk and Imported Whole Milk for Full Cream Milk Powder\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe main ingredient in ice cream is whole-cream milk powder. As noted in section 3.2, whole cream milk powder is not produced in Taiwan, and this study case uses milk powder imported from New Zealand. This study uses the milk coefficients from Ecoinvent 3.8 and the carbon footprint coefficients of whole fresh milk from the Taiwan Ministry of Environment\u0026rsquo;s Carbon Footprint Information Network to explore the greenhouse gas emissions during the raw material acquisition stage when substituting local fresh milk and imported whole milk for full cream milk powder. The proportions of entire fresh and whole milk used are based on the average moisture content of 87.9% for whole fresh milk, as indicated by the Ministry of Health and Welfare Nutrition Database. (TFDA, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This data calculates the amount of whole cream milk powder replaced in the formulation.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the greenhouse gas emissions for using local ingredients in black tea latte ice cream at the raw material acquisition stage are 0.1239 kg CO\u003csub\u003e2\u003c/sub\u003ee /functional unit, and for sesame ice cream, they are 0.1590 kg CO\u003csub\u003e2\u003c/sub\u003ee /functional unit. The greenhouse gas emissions for substituting local fresh whole milk for full cream milk powder are 0.0780 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for black tea latte ice cream and 0.0524 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for sesame ice cream. The greenhouse gas emissions for substituting whole milk for full cream milk powder are 0.0736 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for black tea latte ice cream and 0.0484 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for sesame ice cream. Substituting full cream milk powder with whole fresh milk reduced greenhouse gas emissions at the raw material acquisition stage by 0.0459 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for black tea latte ice cream and 0.1066 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for sesame ice cream. Substituting full cream milk powder with whole milk reduced greenhouse gas emissions at the raw material acquisition stage by 0.0503 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for black tea latte ice cream and 0.1166 kg CO\u003csub\u003e2\u003c/sub\u003ee/functional unit for sesame ice cream. The results indicate that substituting full cream milk powder with local whole fresh milk or imported whole milk can significantly reduce greenhouse gas emissions during the raw material acquisition stage of ice cream production. Since ice cream formulations inherently require a large amount of water, drying raw milk into milk powder for transportation generates substantial carbon emissions. After importation, additional water must be reintroduced to produce ice cream. The carbon emissions from transportation account for only a small proportion; even if raw milk were imported directly, the increased weight would result in lower transportation-related carbon emissions than those generated during spray drying. Patil et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also indicated that spray drying accounts for the largest share of total energy consumption in the process. Using concentration technology can reduce a lot of carbon emissions and environmental impact compared with spray drying. Robertson et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) study exhibited a similar trend, with results indicating that importing frozen concentrated milk significantly reduces carbon emissions compared to importing milk powder. However, raw milk still has the lowest carbon emission. The carbon emission of fresh dairy products is 1.1 kg CO2e/kg, while the carbon emission of butter products is 6.5 kg CO2e/kg, and that of milk powder products is 7.4 kg CO2e/kg (Sahu \u0026amp; Agarwal, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGreenhouse gas emissions of black tea latte and sesame ice cream using local ingredients and replacing milk powder with domestic fresh and imported milk in the raw material procurement stage.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole milk powder (kgCO\u003csub\u003e2\u003c/sub\u003ee/unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomestic fresh milk (kgCO\u003csub\u003e2\u003c/sub\u003ee/unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImported milk (kgCO\u003csub\u003e2\u003c/sub\u003ee/unit)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack tea latte ice cream raw material acquisition stage: Domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSesame ice cream raw material acquisition stage: Domestic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0484\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 \u003c/div\u003e"},{"header":"4. Research Limitations and Considerations for Future Research","content":"\u003cp\u003eAssumptions constrain the LCA model, and not all potential impacts have models available. Data availability or the quality of existing data may limit the accuracy of the LCA.\u003c/p\u003e \u003cp\u003e\u0026mdash;Since domestic factory-developed coefficients, mass balance-derived coefficients, similar process/equipment experience coefficients, manufacturer-provided coefficients, and regional emission factors are not yet mature, this study uses some national coefficient databases and foreign databases built into the software. The inventory data used may lack spatial and temporal specificity, leading to uncertainties in impact results.\u003c/p\u003e \u003cp\u003e\u0026mdash;Ice cream products' formulas and product sources may change depending on industry and taste. In the future, we can explore more differences in the environmental impact caused by raw materials from different origins and products with other flavors.\u003c/p\u003e \u003cp\u003eAccording to the above limitations, future studies must improve data specificity by developing coefficient databases that are specific regionally, enhancing inventory data resolution, and incorporating domestic manufacturers\u0026rsquo; primary data. Enlarging impact categories and considering additional life cycle stages might improve the assessment comprehensiveness. Also, industry-specific LCA modeling refinement by comparative studies of various processing techniques and ingredient sourcing strategies can enhance sustainability aspects. Besides, sensitivity and uncertainty assessments should be considered carefully to analyze data variability while assessing the environmental impact of various formulations, raw material origins, and flavors to develop a more sustainable product.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur findings highlight significant differences in using local versus imported ingredients in carbon emissions and resource utilization, suggesting that locally sourced ingredients, despite the common belief of lower carbon footprints, often result in higher environmental impacts in categories such as GWP and resource scarcity. Notably, domestic sesame's land use impact was 2.9 times greater than that of imported sesame, and black tea ingredients sourced locally had a 22\u0026mdash;24% higher impact on human health and ecosystem damage. These results emphasize the complexity of environmental sustainability in food production, where transportation is not the sole determinant of carbon footprint. Instead, factors such as agricultural intensity and production efficiency play a critical role. Future strategies for more sustainable production should consider cost and flavor and the broader environmental implications of ingredient sourcing. This research is a foundation for further exploration into balancing local economic support with global environmental sustainability in the food industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYu-Ching Huang\u003c/strong\u003e: Conceptualization, Methodology, Software, Data Curation, Writing—Original Draft Preparation, Visualization, Investigation, Validation. \u003cstrong\u003eChao-Kai Chang\u003c/strong\u003e: Conceptualization, Methodology, Software, Validation, Writing—Reviewing and Editing. \u0026nbsp;\u003cstrong\u003eWei-Lun Zhu\u003c/strong\u003e: Visualization, Investigation, Validation, Writing—Reviewing and Editing\u003cbr\u003e\u003cstrong\u003eMohsen Gavahian\u003c/strong\u003e: Validation, Writing—Reviewing and Editing. \u003cstrong\u003eYeh Chen:\u003c/strong\u003e Writing—Reviewing and Editing. \u003cstrong\u003eChang-Wei Hsieh\u003c/strong\u003e: Conceptualization, Methodology, Software, Supervision, Validation, Writing—Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edeclaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edeclaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supported the finding of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgriculture, M. (2023). \u003cem\u003eAgricultural statistics\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://agrstat.moa.gov.tw/sdweb/public/inquiry/InquireAdvance.aspx\u003c/span\u003e\u003cspan address=\"https://agrstat.moa.gov.tw/sdweb/public/inquiry/InquireAdvance.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndoko, E., Liu, W. Y., Zeng, H. J., \u0026amp; Sjoblom, A. (2020). Review of Taiwan\u0026rsquo;s food security strategy. \u003cem\u003eFood and Fertilizer Technology Center for the Asian and Pacific Region (FFTC-AP)(website)\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoakes, E. H., Dalin, C., Etard, A., \u0026amp; Newbold, T. (2024). Impacts of the global food system on terrestrial biodiversity from land use and climate change. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 5750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-024-49999-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-49999-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordonal, R. O., Carvalho, J. L. N., Lal, R., de Figueiredo, E. B., de Oliveira, B. G., \u0026amp; La Scala, N. (2018). Sustainability of sugarcane production in Brazil. A review. \u003cem\u003eAgronomy for Sustainable Development\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13593-018-0490-x\u003c/span\u003e\u003cspan address=\"10.1007/s13593-018-0490-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrippa, M., Solazzo, E., Guizzardi, D., Monforti-Ferrario, F., Tubiello, F. N., \u0026amp; Leip, A. (2021). Food systems are responsible for a third of global anthropogenic GHG emissions. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), 198\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-021-00225-9\u003c/span\u003e\u003cspan address=\"10.1038/s43016-021-00225-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCucurachi, S., Scherer, L., Guin\u0026eacute;e, J., \u0026amp; Tukker, A. (2019). Life cycle assessment of food systems. \u003cem\u003eOne Earth\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), 292\u0026ndash;297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.oneear.2019.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.oneear.2019.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, J., Hu, X., Feng, Z., \u0026amp; Dong, L. (2022). Environmental life cycle assessment of monosodium glutamate production in China: Based on the progress of cleaner production in recent ten years. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e818\u003c/em\u003e, 151706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.151706\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.151706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDissanayake, I. A. J. K., \u0026amp; Sithara, P. (2021). Is Sesame a Potential Crop that Contributes to the Bioeconomy of Sri Lanka? In (pp. 72\u0026ndash;90).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPA (2024). \u003cem\u003eCarbon footprint coefficient database\u003c/em\u003e. \u003cem\u003eEnvironmental Protection Administration Taiwan\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cfp-calculate.tw/eng/WebPage/WebSites/CoefficientDB.aspx\u003c/span\u003e\u003cspan address=\"https://cfp-calculate.tw/eng/WebPage/WebSites/CoefficientDB.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvola, R. S., Peira, G., Varese, E., Bonadonna, A., \u0026amp; Vesce, E. (2022). Short food supply chains in Europe: scientific research directions. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(6), 3602. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su14063602\u003c/span\u003e\u003cspan address=\"10.3390/su14063602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFereidani, B. M., \u0026amp; \u0026Uuml;\u0026ccedil;tuğ, F. G. (2024). Towards sustainable production of sesame products: Comparison of traditional and modern production systems via a life cycle assessment approach. \u003cem\u003eCleaner and Responsible Consumption\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 100166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clrc.2023.100166\u003c/span\u003e\u003cspan address=\"10.1016/j.clrc.2023.100166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinnegan, W., Goggins, J., Clifford, E., \u0026amp; Zhan, X. (2017). Environmental impacts of milk powder and butter manufactured in the Republic of Ireland. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e579\u003c/em\u003e, 159\u0026ndash;168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2016.10.237\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2016.10.237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavahian, M., Chaosuan, N., Yusraini, E., \u0026amp; Sastry, S. (2025). Roles of ohmic heating to achieve sustainable development goals in the food industry: From reduced energy consumption and resource optimization to innovative production pathways with reduced carbon footprint. \u003cem\u003eTrends in Food Science \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e159\u003c/em\u003e, 104947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.tifs.2025.104947\u003c/span\u003e\u003cspan address=\"10.1016/j.tifs.2025.104947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodenau, D., Martin-Rodriguez, G., Gonzalez-Gomez, J. I., \u0026amp; Caceres-Hernandez, J. J. (2022). Food consumption in the Canary Islands: nutritional implications of food imports and local production. \u003cem\u003eBmc Public Health\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-022-12805-w\u003c/span\u003e\u003cspan address=\"10.1186/s12889-022-12805-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, A. H., Chen, C. H., Huang, L. H., Chung, M. H., Lan, Y. C., \u0026amp; Chen, Z. (2019). Environmental Impact and Carbon Footprint Assessment of Taiwanese Agricultural Products: A Case Study on Taiwanese Dongshan Tea. \u003cem\u003eEnergies\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/1996-1073/12/1/138\u003c/span\u003e\u003cspan address=\"https://www.mdpi.com/1996-1073/12/1/138\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuijbregts, M. A., Steinmann, Z. J., Elshout, P. M., Stam, G., Verones, F., Vieira, M., Zijp, M., Hollander, A., \u0026amp; Van Zelm, R. (2017). ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. \u003cem\u003eThe International Journal of Life Cycle Assessment\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 138\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11367-016-1246-y\u003c/span\u003e\u003cspan address=\"10.1007/s11367-016-1246-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonstantas, A., Stamford, L., \u0026amp; Azapagic, A. (2019). Environmental impacts of ice cream. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e209\u003c/em\u003e, 259\u0026ndash;272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2018.10.237\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2018.10.237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, M., Kumar Choubey, V., Deepak, A., Gedam, V. V., \u0026amp; Raut, R. D. (2021). Life cycle assessment (LCA) of dairy processing industry: A case study of North India. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e326\u003c/em\u003e, 129331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jclepro.2021.129331\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2021.129331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., Trisos, C., Romero, J., Aldunce, P., \u0026amp; Barrett, K. (2023). Climate change 2023: synthesis report. Contribution of working groups I, II and III to the sixth assessment report of the intergovernmental panel on climate change. \u003cem\u003eThe Australian National University\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59327/IPCC/AR6-9789291691647.001\u003c/span\u003e\u003cspan address=\"10.59327/IPCC/AR6-9789291691647.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M., Jia, N., Lenzen, M., Malik, A., Wei, L., Jin, Y., \u0026amp; Raubenheimer, D. (2022). Global food-miles account for nearly 20% of total food-systems emissions. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(6), 445\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-022-00531-w\u003c/span\u003e\u003cspan address=\"10.1038/s43016-022-00531-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., Zhong, H., Shan, Y., Hang, Y., Wang, D., Zhou, Y., \u0026amp; Hubacek, K. (2023). Changes in global food consumption increase GHG emissions despite efficiency gains along global supply chains. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(6), 483\u0026ndash;495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-023-00768-z\u003c/span\u003e\u003cspan address=\"10.1038/s43016-023-00768-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, G., Kuang, Z., Tang, J., Kuang, S., Tian, Q., Zou, Y., \u0026amp; Li, Q. (2024). Calculation and utility analysis of lychee life-cycle carbon emissions considering food loss and waste. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e434\u003c/em\u003e, 140013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jclepro.2023.140013\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2023.140013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMr\u0026oacute;wczyńska-Kamińska, A., Bajan, B., Pawłowski, K. P., Genstwa, N., \u0026amp; Zmyślona, J. (2021). Greenhouse gas emissions intensity of food production systems and its determinants. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), e0250995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0250995\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0250995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunasinghe, M., Deraniyagala, Y., Dassanayake, N., \u0026amp; Karunarathna, H. (2017). Economic, social and environmental impacts and overall sustainability of the tea sector in Sri Lanka. \u003cem\u003eSustainable Production and Consumption\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 155\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.spc.2017.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.spc.2017.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoorani, M. H., Asakereh, A., \u0026amp; Siahpoosh, M. R. (2023). Investigating cumulative energy and exergy consumption and environmental impact of sesame production systems, a case study. \u003cem\u003eInternational Journal of Exergy\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(1), 96\u0026ndash;114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1504/ijex.2023.134289\u003c/span\u003e\u003cspan address=\"10.1504/ijex.2023.134289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuikhalfan, M., Lakbita, O., Delhali, A., Assen, A. H., \u0026amp; Belmabkhout, Y. (2022). Toward Net-Zero Emission Fertilizers Industry: Greenhouse Gas Emission Analyses and Decarbonization Solutions. \u003cem\u003eEnergy \u0026amp; Fuels\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(8), 4198\u0026ndash;4223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.energyfuels.2c00238\u003c/span\u003e\u003cspan address=\"10.1021/acs.energyfuels.2c00238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatil, M. H., Ga\u0026euml;lle, T., Le, F. F. C., Romain, J.,and, \u0026amp; Murphy, E. G. (2021). Energy usage in the manufacture of dairy powders: Advances in conventional processing and disruptive technologies. \u003cem\u003eDrying Technology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(11), 1595\u0026ndash;1613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07373937.2021.1903489\u003c/span\u003e\u003cspan address=\"10.1080/07373937.2021.1903489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeantham, K., \u0026amp; Varabuntoonvit, V. (2024). Sustainable solutions in single-use food containers: A comprehensive life cycle assessment comparing plastic (PP) and its green alternative (PLA coated kraft paper, PLA). \u003cem\u003eEnvironmental Engineering Research\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(5), 230729\u0026ndash;230720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4491/eer.2023.729\u003c/span\u003e\u003cspan address=\"10.4491/eer.2023.729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiang, Z., Nan, Q., Chi, W., Qin, Y., Yang, S., Zhu, W., \u0026amp; Wu, W. (2024). Recycling packaging waste from residual waste reduces greenhouse gas emissions. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e371\u003c/em\u003e, 123028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.jenvman.2024.123028\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2024.123028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, H., \u0026amp; Roser, M. (2022). \u003cem\u003eEnvironmental impacts of food production. Our World in Data.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ourworldindata.org/environmental-impacts-of-food\u003c/span\u003e\u003cspan address=\"https://ourworldindata.org/environmental-impacts-of-food\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobertson, L. J., Morel, J. P., Archer, R. H., \u0026amp; McLaren, S. J. (2024). GHG comparison of frozen milk concentrate and milk powder. \u003cem\u003eJournal of Food Process Engineering\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(8), e14710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1111/jfpe.14710\u003c/span\u003e\u003cspan address=\"10.1111/jfpe.14710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadeghy, S., Pormazar, S. M., Ghaneian, M. T., Ehrampoush, M. H., \u0026amp; Dalvand, A. (2024). Modeling and optimization of direct dyes removal from aqueous solutions using activated carbon produced from sesame shell waste. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 24867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-76081-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-76081-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahu, R., \u0026amp; Agarwal, T. (2021). Carbon Footprint of Raw Milk and Other Dairy Products. In R. S. Mor, A. Panghal, \u0026amp; V. Kumar (Eds.), \u003cem\u003eChallenges and Opportunities of Circular Economy in Agri-Food Sector: Rethinking Waste\u003c/em\u003e (pp. 177\u0026ndash;189). Springer Singapore. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-16-3791-9_10\u003c/span\u003e\u003cspan address=\"10.1007/978-981-16-3791-9_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalazar-Sogamoso, L. M., G\u0026oacute;mez-Garc\u0026iacute;a, M. \u0026Aacute;., \u0026amp; Dobrosz-G\u0026oacute;mez, I. (2024). Comparative life cycle assessment of sequential chemical and electrochemical processes for the treatment of industrial textile wastewater. \u003cem\u003eJournal of Solid State Electrochemistry\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10008-024-05976-6\u003c/span\u003e\u003cspan address=\"10.1007/s10008-024-05976-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSan Miguel, G., \u0026amp; Ruiz, D. (2021). Environmental sustainability of a pork and bean stew. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e798\u003c/em\u003e, 149203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.149203\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.149203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki, Y., Orikasa, T., Takaoka, R., Nakamura, N., Hayashi, K., Yasaka, Y., Makino, N., Shobatake, K., Koide, S., \u0026amp; Shiina, T. (2024). A novel simplified model to evaluate environmental impact based on life cycle assessment methodology\u0026ndash;Case study of spinach considering food waste reduction through modified atmosphere packaging\u0026ndash;. \u003cem\u003eFuture Foods\u003c/em\u003e, 100456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fufo.2024.100456\u003c/span\u003e\u003cspan address=\"10.1016/j.fufo.2024.100456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeth, D., Athparia, M., Singh, A., Rathore, D., Venkatramanan, V., Channashettar, V., Prasad, S., Maddirala, S., Sevda, S., \u0026amp; Kataki, R. (2025). Sustainable environmental practices of tea waste\u0026mdash;a comprehensive review. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(12), 7449\u0026ndash;7467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-023-30848-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-023-30848-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma, H., Singh, P. K., Kaur, I., \u0026amp; Singh, R. (2024). Water Footprints of Dairy Milk Processing Industry: A Case Study of Punjab (India). \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w16030435\u003c/span\u003e\u003cspan address=\"10.3390/w16030435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva, D. V., Pavan, A. L. R., Faria, L. C., d., Piekarski, C. M., Saavedra, Y. M. B., \u0026amp; Lopes Silva, D. A. (2024). Opportunities to integrate Ecosystem Services into Life Cycle Assessment (LCA): a case study of milk production in Brazil. \u003cem\u003eEcosystem Services\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e, 101646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.ecoser.2024.101646\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoser.2024.101646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein, A. J., \u0026amp; Santini, F. (2022). The sustainability of local food: A review for policy-makers. \u003cem\u003eReview of Agricultural Food and Environmental Studies\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e(1), 77\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s41130-021-00148-w\u003c/span\u003e\u003cspan address=\"10.1007/s41130-021-00148-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuer, J., Traverso, M., \u0026amp; Ahrenhold, F. (2021). Carbon footprint of scenarios towards climate-neutral steel according to ISO 14067. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e318\u003c/em\u003e, 128588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2021.128588\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2021.128588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTFDA (2023). \u003cem\u003eMinistry of Health and Welfare Nutrition Database\u003c/em\u003e. Ministry of Health and Welfare Food and Drug Administration. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://consumer.fda.gov.tw/Food/TFND.aspx?nodeID=178\u003c/span\u003e\u003cspan address=\"https://consumer.fda.gov.tw/Food/TFND.aspx?nodeID=178\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Werf, H. M. G., Knudsen, M. T., \u0026amp; Cederberg, C. (2020). Towards better representation of organic agriculture in life cycle assessment. \u003cem\u003eNature Sustainability\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(6), 419\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41893-020-0489-6\u003c/span\u003e\u003cspan address=\"10.1038/s41893-020-0489-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R., Chen, J., Li, Z., Bai, W., \u0026amp; Deng, X. (2023). Factors analysis for the decoupling of grain production and carbon emissions from crop planting in China: A discussion on the regulating effects of planting scale and technological progress. \u003cem\u003eEnvironmental Impact Assessment Review\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e, 107249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eiar.2023.107249\u003c/span\u003e\u003cspan address=\"10.1016/j.eiar.2023.107249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Ledgard, S., Luo, J., Chen, Y., Tian, Y., Wei, Z., Liang, D., \u0026amp; Ma, L. (2021). Life cycle assessment of alfalfa production and potential environmental improvement measures in Northwest China. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e304\u003c/em\u003e, 127025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2021.127025\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2021.127025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWr\u0026oacute;bel-Jędrzejewska, M., \u0026amp; Polak, E. (2023). Carbon Footprint Analysis of Ice Cream Production. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(8), 6887. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15086887\u003c/span\u003e\u003cspan address=\"10.3390/su15086887\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYudhistira, B., Adi, P., Mulyani, R., Chang, C. K., Gavahian, M., \u0026amp; Hsieh, C. W. (2024). Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint. \u003cem\u003eComprehensive Reviews in Food Science and Food Safety\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), e13413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1541-4337.13413\u003c/span\u003e\u003cspan address=\"10.1111/1541-4337.13413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahedi, R., Shoaei, M., \u0026amp; Aslani, A. (2025). Life cycle assessment and comparison of the conventional and third generation of photovoltaic panels. \u003cem\u003eJournal of the Brazilian Society of Mechanical Sciences and Engineering\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40430-024-05371-2\u003c/span\u003e\u003cspan address=\"10.1007/s40430-024-05371-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, C., Ye, X., Zhang, H., Xie, J., \u0026amp; Yang, X. (2023). Environmental footprint performance improvement of black tea using the life cycle assessment approach. \u003cem\u003eJournal of cleaner production\u003c/em\u003e, \u003cem\u003e430\u003c/em\u003e, 139574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2023.139574\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2023.139574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, J., Luo, Z., Sun, T., Li, W., Zhou, W., Wang, X., Fei, X., Tong, H., \u0026amp; Yin, K. (2023). Cradle-to-grave emissions from food loss and waste represent half of total greenhouse gas emissions from food systems. \u003cem\u003eNature Food\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 247\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43016-023-00710-3\u003c/span\u003e\u003cspan address=\"10.1038/s43016-023-00710-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-and-bioprocess-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food and Bioprocess Technology](https://www.springer.com/journal/11947)","snPcode":"11947","submissionUrl":"https://submission.nature.com/new-submission/11947/3","title":"Food and Bioprocess Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Life cycle assessment (LCA), Carbon Footprint, Environmental Impact Assessment, Food, Origin of raw materials, Ice cream","lastPublishedDoi":"10.21203/rs.3.rs-6526193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6526193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough locally sourced ingredients are perceived to have a lower carbon footprint due to shorter transportation distances, their comprehensive environmental impact has been less studied. The study compares Taiwanese locally sourced, Sri Lanka, and New Zealand-imported raw materials using the life cycle assessment (LCA) model, employing both Midpoint and Endpoint methods from ReCiPe to assess the carbon footprint and environmental impacts of industrially produced black tea latte and sesame ice cream. Results show that locally sourced black tea latte ice cream ingredients\u0026nbsp;contribute to more obvious global warming (0.563 kg CO\u003csub\u003e2\u003c/sub\u003e eq) and terrestrial ecotoxicity (0.433 kg 1,4-DCB) than imported. The land use impact of domestic sesame is 2.9 times higher than that of\u0026nbsp;imported. Endpoint analysis reveals that locally sourced black tea ingredients have a 22% to 24% higher impact on human health, ecosystem damage, and resource scarcity, while locally sourced sesame has a 55% to 60% higher impact than imported. The carbon footprint analysis shows products made with imported ingredients have lower emissions during production stages, including 0.04 kg CO\u003csub\u003e2\u003c/sub\u003ee during raw material acquisition and reductions of 0.03 and 0.05 kg CO\u003csub\u003e2\u003c/sub\u003ee for production and transportation. Scenario simulations suggest that replacing whole milk powder with domestically sourced fresh milk can reduce emissions by 37% to 67%, and using imported fresh milk instead of powder could reduce emissions by 41% to 70%. The findings offer strategic recommendations for balancing cost, flavor, and environmental impact to achieve sustainable production models and effective carbon reduction strategies.\u003c/p\u003e","manuscriptTitle":"Debunking the 'Local is Greener' Myth: Life Cycle Assessment of Local vs. Imported Ingredients in Ice Cream Production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 18:59:29","doi":"10.21203/rs.3.rs-6526193/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T11:38:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T07:53:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269632593497131090842075849465060317091","date":"2025-07-13T07:59:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186809457931130690810974456062073248811","date":"2025-05-18T08:30:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T09:19:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184913939098267573661134881676632233518","date":"2025-04-28T11:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T11:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T07:07:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T05:18:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food and Bioprocess Technology","date":"2025-04-25T07:03:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-and-bioprocess-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food and Bioprocess Technology](https://www.springer.com/journal/11947)","snPcode":"11947","submissionUrl":"https://submission.nature.com/new-submission/11947/3","title":"Food and Bioprocess Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bc72b05d-497e-4e84-92a0-5c6b42548935","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T09:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 18:59:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6526193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6526193","identity":"rs-6526193","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}