Current carbon policies and emerging emission control technologies in the dairy supply chain

Current carbon policies and emerging emission control technologies in the dairy supply chain | 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 Article Current carbon policies and emerging emission control technologies in the dairy supply chain Bei Wang, Fanyu Meng, Weijun Wang, Guofeng Wang, Chuanwang Sun, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5672225/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Addressing the dynamic changes in the carbon footprint (CF) of the dairy industry to meet the diverse demands of different countries and achieve the global net-zero emission target presents a significant challenge. This study integrates a systematic scoping of CF and reduction strategies, with a policy analysis utilizing Term Frequency–Inverse Document Frequency (TF-IDF) statistics to provide a comprehensive assessment of current efforts to mitigate CF in the dairy industry. Across 29 countries, the CF of dairy production ranges from 0.57 kg CO₂-eq/kg FPCM (Norway) to 5.85 kg CO₂-eq/kg FPCM (Tanzania). Mitigation strategies are implemented across the entire dairy supply chain, with a primary focus on the milk production stage. Among the six countries analyzed over a 10-year period, New Zealand demonstrated the highest policy effectiveness, largely due to measures targeting feeding practices and dairy cattle breeding. This article offers an in-depth evaluation of CF reduction in the dairy sector, integrating environmental, technological, and policy dimensions. Earth and environmental sciences/Environmental social sciences/Environmental economics Social science/Science, technology and society Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global dairy production is expected to grow at an annual rate of 1.4%, driven by economic and population growth, and is projected to reach 9.1 billion by 2050 1 . However, dairy products are significant contributors to greenhouse gas (GHG) emissions, with the thirteen largest dairy corporations emitting over 338 million tons of GHG annually 2 . Reducing the GHG emission intensity of dairy production has become a critical challenge for ensuring the sustainability of the industry. Life cycle assessment (LCA) is the primary methodology used to measure GHG emissions, with the carbon footprint (CF) being the most relevant metric for assessing emissions in the dairy sector 3 . CF, as defined within the LCA framework, quantifies the direct and indirect GHG emissions (e.g., CO₂, CH₄, N₂O) throughout the lifecycle of a product, service, or activity 4 . In the dairy industry, CH₄ accounts for 63.3% of the CF, followed by N₂O (24.5%) and CO₂ (12.2%) 5 . Evaluating CF serves as a critical reference point for technological innovation and policy formulation. GHG emissions are generated across the entire dairy supply chain, encompassing five key stages: milk production, dairy processing, transportation, retail, and disposal. Notably, the milk production stage contributes approximately 80% of total emissions 6, 7 . Consequently, most studies have focused on analyzing the CF of dairy farms, either directly or through representative models 8 . However, emissions from subsequent stages of the supply chain, such as processing and transportation, also play a significant role and must not be overlooked 9 . Additionally, variations in CF are influenced by factors such as geographical regions and the types of dairy products, yet comprehensive and consistent research on these factors remains limited. Reducing CF across the dairy supply chain requires a systematic review and comparison of innovative technologies. While some reviews have focused on new technologies, they primarily emphasize the milk production stage 8, 10 . Beyond milk production, energy-intensive processes during dairy processing (e.g., heat treatment, separation, pasteurization, and cooling) and activities in transportation, retail, and consumption stages also contribute significantly to CF. For example, transporting raw milk through cold chains, refrigeration during retail and consumption, and energy use in transportation collectively add to GHG emissions. Research on novel technologies aimed at reducing CF in these stages is still sparse and warrants further exploration. Globally, CF reduction has become a policy priority 11 , with initiatives targeting raw material supply, manufacturing operations, logistics, and consumer behavior. Key areas of intervention include green ranching, green factories, green logistics, and green consumption. Major dairy-producing countries such as the United States, New Zealand, Australia, China, Denmark, and Ireland have implemented various policies to address the industry's CF. However, the effectiveness of these policies remains underexplored, and further analysis is necessary to support the development of new technologies, foster cross-country learning, and advance efforts toward the global net-zero emission target. This study aims to systematically review CF evaluations across different countries, supply chain stages, and dairy product types. It further seeks to identify and summarize innovative technologies for reducing CF across the dairy supply chain and to evaluate and compare the effectiveness of dairy carbon policies implemented by various countries. This work provides a comprehensive overview of the progress made in CF reduction within the dairy sector, offering valuable insights into future advancements. Results Analyzing carbon emissions from dairy This study examines the CF of dairy production across 29 countries, collectively accounting for over 60% of global dairy production (Fig. 1). The existing literature predominantly focuses on Europe, America, and East Asia. Among these regions, European countries generally report lower CF values, whereas the United States and China, as major milk producers, exhibit relatively higher CFs. Across all countries analyzed, the CF of dairy production ranges from 0.57 kg CO₂-eq/kg fat- and protein-corrected milk (FPCM) in Norway to 5.85 kg CO₂-eq/kg FPCM in Tanzania, with an average of 1.70 kg CO₂-eq/kg FPCM. A substantial portion of current research emphasizes the "cradle-to-gate" stage, specifically the milk production phase. As depicted in Fig. 2, CH₄ emissions from enteric fermentation represent the largest contributor at this stage, ranging from 0.41 to 1.42 kg CO₂-eq/kg FPCM. These emissions are primarily influenced by the cow's dry matter intake. Emissions from manure management, which depend on handling and storage practices, contribute an average of 0.24 kg CO₂-eq/kg FPCM and constitute a critical area for GHG reduction. Although farm energy consumption contributes less to the CF (0.19 kg CO₂-eq/kg FPCM on average) compared to other components of milk production, it surpasses emissions from any stage in the processing-to-disposal phases and tends to increase with industrial agglomeration. The processing stage represents another critical phase in the dairy life cycle, with average emissions estimated at 0.15 kg CO₂-eq/kg FPCM, primarily due to energy consumption. Similarly, the transportation, retail, and disposal stages contribute 0.11, 0.10, and 0.09 kg CO₂-eq/kg FPCM, respectively. Although these stages account for a smaller share of overall CF compared to milk production, their contribution to GHG emissions remains significant. The CF of different dairy products varies considerably (Fig. 3). Using fresh milk as a baseline (1.42 kg CO₂-eq/kg), the CF of yogurt, cream, and cheese is approximately 1.53, 2.41, and 6.58 times higher, respectively. Among dairy products, milk powder has the highest CF (11.8 kg CO₂-eq/kg), followed by butter (9.9 kg CO₂-eq/kg) and cheese (9.34 kg CO₂-eq/kg). These findings underscore the significant variability in emissions among conventional dairy products. In contrast, plant-based milk alternatives have an average CF of 0.42 kg CO₂-eq/kg product, ranging from 0.28 to 0.75 kg CO₂-eq/kg product—roughly 30% of the CF of cow's milk. Unlike dairy milk, where over 80% of emissions occur during milk production, plant-based milk alternatives primarily generate emissions during the processing stage, which accounts for 79.2% of their total CF. This distinction highlights differences in emissions profiles between conventional and plant-based dairy products. Novel strategies to decrease CF in dairy supply chain This study screened 4,233 titles published between 2014 and 2024, ultimately including 73 relevant studies for strategies to decrease CF in the dairy supply chain. As shown in Figure 4, technological advancements have predominantly targeted the production phase of the dairy supply chain (56.8%), followed by the processing phase (28.4%). Among the 11 identified strategic categories, feeding enhancement, manure treatment, and sludge management were the most frequently studied, accounting for 27.0%, 23.0%, and 10.8% of the research, respectively. Within the production phase, feeding strategies are divided into roughage and concentrate improvements. Soybean straw-based feeds demonstrated the highest reduction in enteric methane emissions (20.8%), while corn-based feeds, including genetically modified varieties expressing α-amylase, show significant potential for GHG mitigation. Concentrate enhancements, particularly the use of methane inhibitors like 3-nitrooxypropanol (3-NOP), achieved an 11.7% reduction in GHG emissions (kg CO₂-eq/kg energy-corrected milk). Red macroalgae is another notable approach, reducing enteric methane emissions by 20.3% to 42.7%. Manure management strategies, including co-composting, advanced energy production technologies, and optimized storage management, have proven effective in reducing emissions. Membrane cover technology for manure storage is the most efficient method for mitigating methane emissions. Innovations in anaerobic digestion systems significantly reduce fugitive methane emissions and increase biogas production. Thermo-chemical treatments, such as converting manure into biochar, and process improvements like solid-liquid separation further decrease GHG emissions while creating value-added products. Additionally, selective breeding for low-CF cattle, based on traits such as survival rates, feed efficiency, and residual methane production, represents a promising mitigation strategy. In the processing phase, innovations in sludge treatment have garnered attention, focusing on wastewater reduction, recycling, value-added product creation, and facility upgrades. Recycling sludge or repurposing it for new products minimizes GHG emissions. Simple modifications, such as using recycled shredded plastics in fixed-bed bioreactors, have reduced emissions by 49%. Ultra-high-pressure homogenization in heat treatment demonstrated an 88% reduction in CF at the pilot scale. Similarly, improvements to steam ejectors during the concentration process reduced GHG emissions by 14.5% to 47.3%. Integrated heat pump systems were the most effective, achieving a 91.7% reduction in total energy use and GHG emissions. Research on novel technologies for reducing CF during transportation, retail, storage, and waste disposal remains limited. Producing ambient-stable products to minimize energy consumption offers potential benefits in transportation. Hyperbaric storage trials on raw milk have eliminated the need for cold chains, while lightweight packaging in cold chain logistics reduced GHG emissions by 30%. Innovations in refrigeration and vehicle propulsion systems have also contributed to emissions reduction. On the consumer side, diets with low CF—including small poultry, eggs, yogurt, and plant-based dairy alternatives—have demonstrated a 50% reduction in CF, warranting further exploration. Although carbon trust labeling has been studied, its impact on CF requires more investigation. In the waste disposal phase, advancements in recycling techniques and materials have been reported. For example, multilayer flexible packaging treated with switchable hydrophilicity solvents reduced CF by 2,266 kg CO₂-eq/t. Bioplastics like polylactic acid (PLA) outperform conventional polyethylene terephthalate (PET) in landfills in terms of CF. Policy effectiveness Our study evaluates the effectiveness of carbon reduction policies in six countries: New Zealand, Australia, China, Ireland, the United States, and Denmark. Among these, New Zealand demonstrated the highest policy effectiveness over a 10-year period, with an average score of 0.287 and a peak of 0.878 in 2023. The success of New Zealand's policies can be attributed to comprehensive measures targeting feeding practices, dairy cattle breeding, and agricultural intensification, including feedlots and stockholding areas. Australia ranked second in policy effectiveness, with an average score of 0.184. Initiatives such as the Australian Dairy Sustainability Framework and the Climate Change Strategy 2020–25 contributed significantly to its performance. China’s emission reduction index steadily improved over the decade, increasing from 0.008 to 0.523, driven by policies like the Green Factory Evaluation Criteria targeting the dairy processing stage. However, China’s policy effectiveness remained lower than that of New Zealand and Australia. Ireland ranked fourth, with an average score of 0.118, comparable to China. The United States ranked fifth, with an average score of 0.057, supported by national programs such as Dairy Farmers of America and state-level initiatives like Dairy Cares in California. Although Denmark implemented the Dairy Products Administrative Order in 2022, which improved carbon reduction outcomes, its overall policy effectiveness was the lowest among the six countries, with an average score of 0.020. In summary, the policies analyzed—including Agricultural Intensification, Feedlots and Stockholding Areas, the Australian Dairy Sustainability Framework, the Climate Change Strategy, the Green Factory Evaluation Criteria, Dairy Farmers of America, Dairy Cares, and the Dairy Products Administrative Order—played varying roles in each country. New Zealand's comprehensive and targeted approach resulted in the highest policy effectiveness, followed by Australia, China, Ireland, the United States, and Denmark. Discussion The dairy industry operates across the primary, secondary, and tertiary economic sectors. Given the significant contribution of production emissions to the CF of dairy, most studies and reports have concentrated on the primary sector, particularly on CF analysis, innovations, and effective policies. Notably, most of the literature focuses exclusively on the milk production stage. Exceptions include studies conducted in the USA, Iran, and Italy, which examine additional stages of the dairy supply chain 12-14 . Our study includes 29 countries and conducts comparative analyses using standardized functional units. However, the data is limited, excluding major dairy-producing countries such as Brazil, Russia, and Turkey due to a lack of relevant literature. The average CF in our study, 1.70 kg CO2-eq/kg FPCM, is lower than the FAO global estimate of 2.40 kg CO2-eq/kg FPCM 15 . This discrepancy might stem from the inclusion of countries with relatively low CF values, particularly in Europe. Additionally, CF comparisons across countries remain challenging because many studies base their calculations on single or representative farms, which do not adequately capture regional emission variations. Differences in allocation methods and functional units further contribute to inconsistencies in findings. Standardized guidelines, such as those outlined by the IDF (2015, updated in 2022), should be followed to ensure comparability 16 . Decisions regarding calculation boundaries, allocation methods, and functional units should align with study objectives, with additional details reported in the main text or supplementary materials. The most effective strategies for CF reduction also focus primarily on milk production stages. However, these strategies often have limitations. For example, using palm oil to enhance fat content in animal feed can lead to deforestation and biodiversity loss if not sustainably sourced 17 . Similarly, condensed tannins may adversely affect rumen function and nutrient digestibility, while algae-based feed additives require government approval for production and application 18, 19 . Techniques like co-composting with bacterial inoculants and wheat straw demand additional resources, and certain manure treatment methods are suitable only for specific farm scales 20 . Breeding index approaches may also conflict with other breeding goals, limiting their effectiveness in reducing GHG emissions 21 . Thus, CF reduction efforts often involve trade-offs between environmental, economic, and practical considerations. Policy measures play a critical role in CF reduction. For milk production, the CF rankings of six countries from lowest to highest are: Australia (0.75), New Zealand (0.81), Ireland (1.11), Denmark (1.23), China (1.35), and the United States (1.47) kg CO2-eq/kg FPCM. Policy effectiveness rankings align with these figures, with New Zealand leading due to its comprehensive policies targeting agricultural intensification, feedlots, and stockholding areas. Policies such as Dairy Farmers of America and Dairy Cares (California) in the United States, the Australian Dairy Sustainability Framework, and Australia’s Climate Change Strategy have also proven instrumental in CF reduction during milk production. According to our study, the CF of dairy products varies widely, underscoring the need to address emissions across the entire supply chain. While milk production contributes the most to CF, emissions from energy management, concentration, heating, sludge treatment, cold chain logistics, consumer use, and packaging recycling are significant. Strategies for these stages, such as renewable energy use, closed-loop spray drying, and packaging recycling, have limitations. For instance, renewable energy adoption is constrained by resource availability, and closed-loop spray drying requires substantial investments. Balancing economic feasibility with technical advancement remains a challenge, and knowledge gaps persist in processing, consumer behavior, and recycling. Interestingly, dietary shifts toward plant-based dairy alternatives could reduce CF by up to 50%. However, these substitutes often lack the nutritional profile of bovine milk, which offers superior protein, calcium, potassium, and vitamin D content. Additionally, the bioavailability of nutrients differs between plant-based and bovine milk. Research indicates that substituting plant-based milk (e.g., soy milk) for bovine milk cannot significantly reduce GHG emissions unless by-product consumption from milk production is minimized 22 . Policy initiatives in the secondary and tertiary sectors also play a crucial role. The Australian Dairy Sustainability Framework, Green Factory Evaluation Guidelines (China), and the Dairy Industry Executive Order (United States) effectively address emissions beyond milk production. For example, Australia aims to ensure all packaging is recyclable, compostable, or reusable by 2025, with the additional goal of halving food waste. Emerging trends in smart management, low-carbon technologies, and digitization are redefining dairy industry practices, warranting further exploration. Finally, it should be noted that the data for this study was sourced using Google News, a platform with extensive global news coverage and algorithmic aggregation. While this ensures representativeness and technical neutrality, the accuracy of results may be affected by variations in carbon reduction reporting by different countries. Despite these limitations, the findings of this study provide innovative insights into CF reduction in the dairy industry. Conclusion This study provides a comprehensive evaluation of the emission mitigation potential and practical effectiveness of emerging technologies implemented across five critical stages of the dairy supply chain over the past decade. Achieving a zero-carbon dairy supply chain will be a central policy objective for nations worldwide, with upstream interventions—particularly in feed optimization and mitigation of GHG emissions from manure—emerging as key priorities. Our findings underscore the interconnected nature of the dairy industry's carbon footprint, spanning breeding practices, feed strategies, manure management, sludge treatment, and packaging systems. To advance global carbon neutrality goals, it is imperative to establish a standardized and credible database that facilitates cross-country assessments of carbon emissions within the dairy sector. Such a framework would provide the foundation for robust benchmarking, policy formulation, and the development of globally aligned strategies to decarbonize the dairy supply chain. Methods This study employed two primary methodological approaches: Scoping Reviews: Reviews adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (2020) to analyze carbon emissions and identify novel technologies. Policy Analysis via News Scoping: Policy effectiveness was evaluated through keyword-based searches using Google News. Scoping Reviews A comprehensive search was conducted in October 2024 using the Web of Science Core Collection to identify peer-reviewed literature. Studies published between 2010 and 2024 were included for carbon emissions analysis across different countries or regions. For novel strategies, literature from 2014 to 2024 was considered, reflecting a sharp rise in publications on emerging technologies aimed at reducing carbon emissions. Search keywords were determined through expert panel discussions involving industry professionals and academic researchers, as well as existing reviews on carbon reduction strategies and technologies. Detailed search strategies are provided in Supplementary Figure 1. For CF analysis, keywords included “milk” combined with terms like “LCA,” “carbon footprint,” and “carbon emission.” To identify novel strategies in the dairy industry for reducing carbon emissions, topics were framed around “carbon footprint,” “greenhouse gas,” “energy consumption,” or “techno-assessment.” Abstracts had to reference dairy products, such as “milk,” while keywords described the dairy supply chain, including terms like “feed*,” “process,” and “cold chain”. After screening, 138 studies were included in the analysis: 65 focused on CF assessment and 73 on the identification of strategies for emission reduction. Policy News Scoping Identification of Keywords Keyword extraction was applied to identify relevant reports. The research was divided into two sets of keywords: "dairy industry chain" and "carbon policies". The keywords related to the "dairy industry chain" encompassed various critical links within the industry chain, based on the upstream, midstream, and downstream segments of the dairy industry chain, along with the primary activities, resources, and technologies involved in each segment. After conducting tests, irrelevant keywords and those that did not appear alongside policies in the news texts were filtered out. The final list of keywords obtained is presented in Table 2. It should be noted that during the reports’ selection process, the word "diary" (though potentially a typo for "dairy") was included when necessary to narrow down the search scope. This study introduced the "Global Carbon Market Progress" report published by the International Carbon Action Partnership (ICAP) to reflect global actions on carbon policies. Term Frequency-Inverse Document Frequency (TF-IDF) word frequency statistics from Python (v. 3.10.10) were applied for text processing, analyzing word meanings, and screening for characteristic words that met the requirements. Identification of News Reports Referring to Galloway et al. 23 , the time range was controlled within a decade. The total volume of news reports was counted. Through searches, it was found that the total number of news samples from the United States, New Zealand, Australia, China, Denmark, and Ireland over the ten-year period from 2014 to 2023 was 1,022,800,000. Then, using Google News, news reports containing two sets of keywords ("dairy supply chain" and "carbon policy") were obtained. After manual verification, the news reports were organized into a dataset, which includes the title, date, source, and content summary of each article, facilitating subsequent analysis and research. In this study, a total of 184,880 news samples were obtained through web scraping techniques using Python software version 3.10.10. Calculation of Policy Index Drawing on the measurement methods of climate policies from Huang et al. 24 and Ma et al. 25 , and combining the research focus of the study, a word frequency method is adopted to identify and measure the carbon policy index within the dairy supply chain. A higher index indicates better policy effectiveness, while a lower index suggests weaker policy effectiveness. The final policy index was calculated using the conditional publication volume and the total publication volume. The conditional publication volume refers to the number of reports in which both the keywords "dairy industry chain" and "carbon policies" appear simultaneously. The total publication volume refers to the total number of articles related to a specific country (obtained by searching using the name of country). The first step is to obtain the annual conditional publication frequency for each country by dividing the conditional publication volume of each country in each year. is the total number of articles for that country ： The second step is to normalize the index to a range between 0 and 1. Declarations Competing interests The authors declare no competing interests. Data availability The authors confirm that the data that we collected from the literature are available within the article and/or its supplementary materials. References Opio, C. I., Gerber, P. J., Mottet, A., Falcucci, A., Tempio, G., MacLeod, M., Vellinga, T. V., Henderson, B. B., & Steinfeld, H. 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Frontiers in Sustainable Food Systems 8, 1333981 (2024). Huang, Y. & Luk, P. Measuring economic policy uncertainty in China. China Economic Review 59, 101367 (2020). Ma, Y.-R., Liu, Z., Ma, D., Zhai, P., Guo, K., Zhang, D., & Ji, Q. A news-based climate policy uncertainty index for China. Scientific Data 10 (1), 881 (2023). Tables Table 1: Strategies to decrease carbon emissions in dairy supply chain. Stage Strategies Effects/advantages Limitations References Production Stage Feeding Roughage improvement Corn-based feed: Corn-based feed ↓13% GHG emission compared to barley silage Corn containing a bacterial transgene expressing α-amylase ↓7.2% enteric CH 4 emission intensity Cassava-based feed (30%): ↓6.6% CH 4 emission (kg CO 2 eq/kg FPCM) Crushed wheat grain-based feed (33%): ↓5.91% CH 4 emission (g/d) compared to pasture Soyabean straw-based feed (20%): ↓20.79% CH 4 emission (g/d) compared to wheat straw Greater fertilizer inputs associated with corn silage; lower milk fat concentration Cassava based feed only slightly reduced the carbon footprint, depending on breed; limited studies Milk fat levels↓with 30% crushed wheat grain (Guyader et al., 2017) (Cueva et al., 2021) (Molina-Botero et al., 2024) (Moate et al., 2020) (Katwal et al., 2021) Concentrate improvement - Fat Enteric CH 4 emissions (g/d): Supplemental fat (Meta-analysis):↓3.77% Palm oil:↓4% for every 10 g/kg palm oil Oilseeds:↓14% Hydroxy-methionine-analog-isobutyrate + palm oil:↓15.7% Oilseed: effects did not persist after 19 weeks of supplementation (de Ondarza et al., 2024) (Flores-Santiago et al., 2022) (Munoz et al., 2021) (Alstrup et al., 2015) Concentrate improvement - Tannins Meta-analysis (condensed tannins≥2.32 %):↓1% CH 4 emissions in vivo (L/kg dry matter) Biologically active condensed tannins ≥6%: lower fiber digestibility and poorer animal performance (Berça et al., 2023) Concentrate improvement - 3-NOP Meta-analysis (70.5 mg/kg dry matter): ↓32.7% CH 4 emission (g/d) LCA analysis:↓11.7% GHG emission (kg CO 2 eq /kg energy corrected milk) Increased H 2 ; a 1% (10 g/kg DM) decrease in dietary crude fat content (Dijkstra et al., 2018) (Feng & Kebreab, 2020) Concentrate improvement – Macroalgae Enteric CH 4 emissions (g/d): Red macroalgae ( Asparagopsis armata ):↓20.3%-42.7% Brown macroalgae ( Sargassum johnstonii ):↓16.53% Addition needs to be approved by regulatory bodies (Roque et al., 2019) (Katwal et al., 2021) Other Concentrate improvement Enteric CH 4 emissions (g/d): Cashew nut shell liquid:↓10.64% Crushed rapeseed:↓11.95% Garlic and citrus extract:↓10.3% Grapeseed:↓11.8% - 16.4% (60 - 120 mg) Lack of evidence on economically successful (Sarmikasoglou et al., 2024) (Storlien et al., 2017) (Khurana et al., 2023) (F. Zhang et al., 2023) Feeding management Enteric CH 4 emission (g/d): Separate offering of feed ingredients:↓9% High concentrate proportion:↓27.2% (Holstein) and 13.8% (Jersey cows) Further research is required (Martins et al., 2024) (Olijhoek et al., 2018) Manure management Co-composting Co-composting with biochar: ↓58% (+/- 22%) CH 4 emissions；produce 0.81 g CH 4 and 280 g CO 2 (kg –1 dry feedstock) Co-composting with biochar and bacterial inoculum: ↓20.7% CO2-C Co-composting with Lignite: ↓12-23% CO 2 and 52-59% CH 4 emissions Require high dry matter levels and additional technologies (e.g., drying system) for biochar co-composting (Harrison et al., 2024) (Harrison et al., 2022) (Awasthi et al., 2020) (Impraim et al., 2020) Energy production -Anaerobic digestion (AD) Co-substrate with wheat straw: ↑156% CH 4 yield Anaerobic sequencing batch reactors (ASBR) using a cationic polymer:↓18–56% fugitive CH 4 emissions Cellulase pretreatment (CP) and microvoltage application: ↑18.7% and 10.0% CH 4 production rate Bioeconomy model is required to be developed; a capital-intensive technology, suitable at large farms; ensuring consistent yields and product quality remains elusive (Imeni et al., 2020) (Zeb et al., 2019) (Cai et al., 2022) Energy production - thermo-chemical processes Using CO 2 as a co-feed reactant for catalytic pyrolysis: ↑25% gas formation Hydrothermal liquefaction: ↓50% energy consumption and 3.5 years for energy payback The liquid products contain high amounts of oxygenated and nitrogenated chemicals (Lee et al., 2020) (Chen et al., 2018) Storage management -Solid-liquid separation Manure fractionation:↓87% CH 4 emissions from outdoor liquid digestate storage Solid-liquid separation alone:↓38% GHG emission Solid-liquid separation + anaerobic digestion:↓41% GHG emission Higher N 2 O and NH 3 emissions from storing the solid fraction Annual total GHG emissions ↑with the increasing separator screen size and reached approximately 0.7 t CO 2 eq / cow (Feng, Smith, & VanderZaag, 2023) (Aguirre-Villegas, Larson, & Sharara, 2019) (Y. X. Zhang et al., 2024) Storage management - Membrane cover Application of locally manufactured membrane: ↓85.9% CO 2 emission; ↓55.6% CH4 emission Semi-permeable membrane + intermittent aeration: ↓99.89% CH 4 emission Semi-permeable membrane + forced aeration composting: ↓27.48% CH 4 emission Further studies is required for the membrane properties affecting fermentation process (Varga et al., 2024) (Fang et al., 2021) (Fang et al., 2022) Storage management -others Slurry treatment with sulphuric acid (46%), gypsum (39%) and biochar (15%) ↓CH 4 emissions Applying acid to residual manure following storage emptying has long-term effect on CH 4 removal Gypsum increases H 2 S production in the anaerobic environment, which is released in dangerous concentrations when the manure is agitated (Owusu-Twum et al., 2024) (Sokolov et al., 2021) Energy management Energy utilization Energy self-sufficiency from biogas produced from manure:↓32.8 Mt CO 2 eq annually Biogas yield factor can be different (Villarroel-Schneider et al., 2022) Breeding Breeding index Reducing 6–10 kg CO 2 -eq per ton milk with optimal indices Survival (−8.55 kg of CO 2 -eq) had the most favorable genetic improvement with feed saved (−0.53 CO2-eq) being the only other trait that lowered emissions Selecting for feed efficiency and residual methane traits ↓methane emissions Bio-economic model is required to be improved; limited traits are considered; low heritability for predicted CH 4 trait (Shi et al., 2024) (Richardson et al., 2021) (Manzanilla-Pech et al., 2021; Manzanilla-Pech et al., 2022) Processing Stage Energy management Renewable energy Predict the energy demand with the solar resource using multiple regression in a cheese factory: ↓14% - 42% consumption rate Technical issue due to intermittent nature of solar energy production; high investment cost (Miserocchi, Franco, & Testi, 2024) Energy recovery The integrated heat pump system: ↓91.7% GHG emission Economical cost in factory design (Ahrens et al., 2021) Concentration Evaporation Replacing steam ejectors with mechanical vapor recompression fans: energy ↓13.7 % - 41.6 %, GHG emissions ↓ 14.5 % - 47.3 %. Increased cost and new design is required (Lincoln et al., 2024) Membrane filtration Hybrid forward osmosis (FO) - membrane distillation (MD) system: ↓50 % energy consumption Preconcentrating using reverse osmosis: ↓36% natural gas consumption + ↓10% electricity consumption Spiral-wound membrane for microfiltration (MF): low energy consumption (0.015-0.024 kWh/kg of permeate collected) Hot MF:↓4.4 WhL -1 permeate energy consumption compared to cold MF FO-MD is still conceptually applied for the non-thermal concentration of skim milk (Zhou et al., 2024) (Chamberland et al., 2020) (Mercier-Bouchard et al., 2017) (Subhir et al., 2023) Heating process Ohmic heating ↓73% energy compared to conventional heating in liquid dairy products Quality parameters need to be improved (Rocha et al., 2022) Pulsed electric fields Moderate electric fields:↓98.6% energy consumption compared to conventional heating ↓16% energy in sweet whey demineralization 5.3 ± 0.4 Wh/g of lactic acid in acid whey processing Electrode corrosion of milk may occur and therefore produce toxic compounds. (Alsaedi et al., 2023) (Lemay, Mikhaylin, & Bazinet, 2019) (Dufton et al., 2020) Ultra-high-pressure homogenization ↓88% carbon footprint Milk is prone to bubbles and accelerates rancidity (Valsasina et al., 2017) Spray drying Optimization of closed-loop spray drying energy: ↓3.5 MJ heat per kg milk powder Plant layout, possibilities for energy transport between units, and interference with other operations may limit the application (Moejes et al., 2018) Sludge treatment Low wastewater production Fouling-mitigating surface modification: ↓84% climate change (ecosystem) scores Require new technologies; higher cost (Zouaghi et al., 2019) Recycling for use Milk whey is recycled for cattle slurry acidification: ↓37.2-68.1% CH 4 emissions - (Gioelli et al., 2022) Add-value products Hydrochar:↓8.36% GHG emission Biochar:↓25.63% GHG emission Require new technologies (Hu et al., 2021) Improved treatment facilities/ Process modification Application of an intermittently ASBR: ↓36–68% energy Optimization of the aeration process: ↓45% GHG emission Recycled shredded plastics for a fixed bed bioreactor: ↓49% GHG emission Application of microwave radiation heating in a multi-section hybrid anaerobic bioreactor: ↑5% COD removal efficiency; ↑12-20% biogas yields Electrocoagulation-flotation: Ultrasonic pretreatment (40 W 5min) of cheese whey before AD:↑16.4% potential CH 4 yield Require new technologies (Leonard et al., 2021) (Yapicioglu, Yalçin, & Yesilnacar, 2023) (Abyar et al., 2024) (Zielinski, Debowski, & Kazimierowicz, 2021) (Mainardis et al., 2019) Transportation Stage Cold chain Cold chain removal UHT dairy products: No cold chain during distribution and retail led to energy saving Hyperbaric storage: prolong storage time of raw milk at room temperature to 60 days; nearly no energy consumption compared to refrigeration The manufacturing of packaging materials contributed 7% of the total CF of 1 L of packaged UHT milk (Roibás et al., 2016) (Duarte et al., 2022) New energy application Battery-powered vapor compression refrigeration: ↓8320 kg CO 2 per year per trailer Require new technologies; higher cost (Bagheri, Fayazbakhsh, & Bahrami, 2017) Package design Low weight pillow pouches:↓30% GHG emissions of 4 L HDPE jugs and 2 L LPB cartons Consumers might be hesitant to purchase milk in pillow pouches (Sun et al., 2021) Retail stage Consumer use Carbon trust label Consumers preferred the dairy products branded with the Carbon trust label Not effective to price-sensitive consumers (Asioli et al., 2023) (Canavari & Coderoni, 2020) Low carbon-footprint diet Diet with only small poultry, eggs and yogurt or plant-based dairy alternatives:↓50% carbon footprint Reduction in solid dairy foods and meat:↓39% GHG emissions Limited by income in lower-middle-income countries (Gaillac & Marbach, 2021) (Colombo et al., 2021) Waste Disposal Stage Packaging recycling Recycle process Recycling of multilayer flexible packaging using switchable hydrophilicity solvents:↓2266 kg CO 2 -eq/t Higher mineral resource use and higher environmental damage in the marine eutrophication (Blazejewski et al., 2021) Package material A mix of reusable stainless-steel churns and reusable glass bottles compared to single-use HDPE bottles: ↓6.5 t CO 2 -eq per year PLA vs. PET: PLA bottle is more sustainable in terms of waste taken to landfills (14.8 vs. 27.3 kg CO 2 emission) to the incinerator and by increasing the recycling and composting rate The production process of PLA requires an optimization (Mumladze et al., 2018) (Desole et al., 2022) Table 2: Keywords Used for Identifying "Dairy Supply Chain - Emission Reduction Policies" Types Terms Diary Supply Chain Feeding Feeding; Leguminous based feed; Algal based feed; Cassava-based feed; Insect-based feed; Low emissions of greenhouse gases and ammonia; Dairy feed; Sustainable feeding Manure management Manure management; Biochar; Bacterial inoculums; Gypsum-based products; Pecan; Manure fractionation; Co-substrate; Manure handling; Manure recycling Energy Input Energy Input; Polygeneration system; solar energies; geothermal energies; biomass energies; The integrated heat pump system Heating process Heating process; Alternatives-ohmic heating; Microwave heating; Pulsed electric fields; High pressure processing; Ultra-high-pressure homogenization Sludge treatment Sludge treatment; Add-value products; Membrane improvement; Power to gas - biomethanation Cold chain Cold chain; Products stored at room temperature; Mathematical programming model; Intermodal road-rail operations; Doored display; Consumer use Consumer use; Carbon footprint label; Carbon trust brand; Low carbon diet Packaging recycling Bioplastics; Sustainable packaging; Nanocellulose materials; Bioplastics; Dairy packaging waste; Eco-friendly packaging Carbon Policy Carbon policy Carbon Policy; Policy; Regulation; Energy policy; Energy tax; Carbon tax; Pollution controls; Environmental restrictions; Clean Air Act; Clean Water Act; Environmental Protection Agency; Carbon emissions; Cap-and-trade; Carbon offsetting; Carbon neutrality; Carbon credits; Carbon pricing; Decarbonization; Carbon sequestration; Emission reduction targets; Carbon Labels Additional Declarations There is NO Competing Interest. Supplementary Files SupplementarymaterialScopingpapers.xlsx Current carbon policies and emerging carbon footprint control technologies in the dairy supply chain SupplementaryFigure1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5672225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400558923,"identity":"a8cdae0b-6fce-49a5-abb1-8dbe2a995afc","order_by":0,"name":"Bei 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Sun","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Baoguo","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-12-18 21:50:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5672225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5672225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73614661,"identity":"392655d6-f799-4b64-b565-c992e4577a7b","added_by":"auto","created_at":"2025-01-13 02:35:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon footprint of dairy by countries.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eMap of the countries selected with carbon footprint. \u003cstrong\u003eb,\u003c/strong\u003eBreakdown of the carbon footprint in different countries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/b2fb09bb2095d88cf75118f1.png"},{"id":73614662,"identity":"b2f66844-7dac-43be-8af7-d431f59f4f0b","added_by":"auto","created_at":"2025-01-13 02:35:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon footprint from dairy supply chain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e carbon emissions from different sections of dairy supply chain. \u003cstrong\u003eb,\u003c/strong\u003ecarbon emissions from typical dairy products and plant-based milk. Error bars represent standard deviation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/107ea82bbaa8d0f8961c6343.png"},{"id":73614665,"identity":"7c0665e2-6ae3-4537-83e0-7382bdb5715e","added_by":"auto","created_at":"2025-01-13 02:35:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA sunburst diagram of studies included on novel strategies to reduce carbon emissions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSunburst diagram of all the included studies (n = 73) showing the frequencies of study designs employed to investigate the effects of technologies used at different stages of dairy supply chain.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/9000d8c1762ec2cb4421f604.png"},{"id":73615629,"identity":"2735b21b-f6ad-4ab1-8dde-5aec92b5557c","added_by":"auto","created_at":"2025-01-13 02:43:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e“Dairy supply chain - emission reduction” policy index trends of six countries from 2014 to 2023.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolicy index was calculated from the amount of policy news using a word frequency method. Index range from 0 to 1. The higher the index, the higher the policy effectiveness, and \u003cem\u003evice versa\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/08a5146c68143907ef760fc2.png"},{"id":81116086,"identity":"f92b5c7b-51be-46f2-96ae-a5fc81953947","added_by":"auto","created_at":"2025-04-22 11:40:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1021434,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/c53fe693-e7a1-4f23-8b39-6a9c3f6a4638.pdf"},{"id":73615631,"identity":"ea62bcf7-7062-44c1-b1b4-2abd3fab24f2","added_by":"auto","created_at":"2025-01-13 02:43:56","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49568,"visible":true,"origin":"","legend":"Current carbon policies and emerging carbon footprint control technologies in the dairy supply chain","description":"","filename":"SupplementarymaterialScopingpapers.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/4d3ca2dcd1c12c53d79b365d.xlsx"},{"id":73615718,"identity":"ebe17f8e-4f77-42d2-9fb5-a000531af04a","added_by":"auto","created_at":"2025-01-13 02:51:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59864,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5672225/v1/682cbbc7dfb46597a12d8dd9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Current carbon policies and emerging emission control technologies in the dairy supply chain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal dairy production is expected to grow at an annual rate of 1.4%, driven by economic and population growth, and is projected to reach 9.1 billion by 2050\u003csup\u003e1\u003c/sup\u003e. However, dairy products are significant contributors to greenhouse gas (GHG) emissions, with the thirteen largest dairy corporations emitting over 338 million tons of GHG annually\u003csup\u003e2\u003c/sup\u003e. Reducing the GHG emission intensity of dairy production has become a critical challenge for ensuring the sustainability of the industry.\u003c/p\u003e\n\u003cp\u003eLife cycle assessment (LCA) is the primary methodology used to measure GHG emissions, with the carbon footprint (CF) being the most relevant metric for assessing emissions in the dairy sector\u003csup\u003e3\u003c/sup\u003e. CF, as defined within the LCA framework, quantifies the direct and indirect GHG emissions (e.g., CO₂, CH₄, N₂O) throughout the lifecycle of a product, service, or activity\u003csup\u003e4\u003c/sup\u003e. In the dairy industry, CH₄ accounts for 63.3% of the CF, followed by N₂O (24.5%) and CO₂ (12.2%)\u003csup\u003e5\u003c/sup\u003e. Evaluating CF serves as a critical reference point for technological innovation and policy formulation.\u003c/p\u003e\n\u003cp\u003eGHG emissions are generated across the entire dairy supply chain, encompassing five key stages: milk production, dairy processing, transportation, retail, and disposal. Notably, the milk production stage contributes approximately 80% of total emissions\u003csup\u003e6, 7\u003c/sup\u003e. Consequently, most studies have focused on analyzing the CF of dairy farms, either directly or through representative models\u003csup\u003e8\u003c/sup\u003e. However, emissions from subsequent stages of the supply chain, such as processing and transportation, also play a significant role and must not be overlooked\u003csup\u003e9\u003c/sup\u003e. Additionally, variations in CF are influenced by factors such as geographical regions and the types of dairy products, yet comprehensive and consistent research on these factors remains limited.\u003c/p\u003e\n\u003cp\u003eReducing CF across the dairy supply chain requires a systematic review and comparison of innovative technologies. While some reviews have focused on new technologies, they primarily emphasize the milk production stage\u003csup\u003e8, 10\u003c/sup\u003e. Beyond milk production, energy-intensive processes during dairy processing (e.g., heat treatment, separation, pasteurization, and cooling) and activities in transportation, retail, and consumption stages also contribute significantly to CF. For example, transporting raw milk through cold chains, refrigeration during retail and consumption, and energy use in transportation collectively add to GHG emissions. Research on novel technologies aimed at reducing CF in these stages is still sparse and warrants further exploration.\u003c/p\u003e\n\u003cp\u003eGlobally, CF reduction has become a policy priority\u003csup\u003e11\u003c/sup\u003e, with initiatives targeting raw material supply, manufacturing operations, logistics, and consumer behavior. Key areas of intervention include green ranching, green factories, green logistics, and green consumption. Major dairy-producing countries such as the United States, New Zealand, Australia, China, Denmark, and Ireland have implemented various policies to address the industry\u0026apos;s CF. However, the effectiveness of these policies remains underexplored, and further analysis is necessary to support the development of new technologies, foster cross-country learning, and advance efforts toward the global net-zero emission target.\u003c/p\u003e\n\u003cp\u003eThis study aims to systematically review CF evaluations across different countries, supply chain stages, and dairy product types. It further seeks to identify and summarize innovative technologies for reducing CF across the dairy supply chain and to evaluate and compare the effectiveness of dairy carbon policies implemented by various countries. This work provides a comprehensive overview of the progress made in CF reduction within the dairy sector, offering valuable insights into future advancements.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalyzing carbon emissions from dairy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines the CF of dairy production across 29 countries, collectively accounting for over 60% of global dairy production (Fig. 1). The existing literature predominantly focuses on Europe, America, and East Asia. Among these regions, European countries generally report lower CF values, whereas the United States and China, as major milk producers, exhibit relatively higher CFs. Across all countries analyzed, the CF of dairy production ranges from 0.57 kg CO₂-eq/kg fat- and protein-corrected milk (FPCM) in Norway to 5.85 kg CO₂-eq/kg FPCM in Tanzania, with an average of 1.70 kg CO₂-eq/kg FPCM.\u003c/p\u003e\n\u003cp\u003eA substantial portion of current research emphasizes the \u0026quot;cradle-to-gate\u0026quot; stage, specifically the milk production phase. As depicted in Fig. 2, CH₄ emissions from enteric fermentation represent the largest contributor at this stage, ranging from 0.41 to 1.42 kg CO₂-eq/kg FPCM. These emissions are primarily influenced by the cow\u0026apos;s dry matter intake. Emissions from manure management, which depend on handling and storage practices, contribute an average of 0.24 kg CO₂-eq/kg FPCM and constitute a critical area for GHG reduction. Although farm energy consumption contributes less to the CF (0.19 kg CO₂-eq/kg FPCM on average) compared to other components of milk production, it surpasses emissions from any stage in the processing-to-disposal phases and tends to increase with industrial agglomeration.\u003c/p\u003e\n\u003cp\u003eThe processing stage represents another critical phase in the dairy life cycle, with average emissions estimated at 0.15 kg CO₂-eq/kg FPCM, primarily due to energy consumption. Similarly, the transportation, retail, and disposal stages contribute 0.11, 0.10, and 0.09 kg CO₂-eq/kg FPCM, respectively. Although these stages account for a smaller share of overall CF compared to milk production, their contribution to GHG emissions remains significant.\u003c/p\u003e\n\u003cp\u003eThe CF of different dairy products varies considerably (Fig. 3). Using fresh milk as a baseline (1.42 kg CO₂-eq/kg), the CF of yogurt, cream, and cheese is approximately 1.53, 2.41, and 6.58 times higher, respectively. Among dairy products, milk powder has the highest CF (11.8 kg CO₂-eq/kg), followed by butter (9.9 kg CO₂-eq/kg) and cheese (9.34 kg CO₂-eq/kg). These findings underscore the significant variability in emissions among conventional dairy products.\u003c/p\u003e\n\u003cp\u003eIn contrast, plant-based milk alternatives have an average CF of 0.42 kg CO₂-eq/kg product, ranging from 0.28 to 0.75 kg CO₂-eq/kg product\u0026mdash;roughly 30% of the CF of cow\u0026apos;s milk. Unlike dairy milk, where over 80% of emissions occur during milk production, plant-based milk alternatives primarily generate emissions during the processing stage, which accounts for 79.2% of their total CF. This distinction highlights differences in emissions profiles between conventional and plant-based dairy products.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNovel strategies\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eto decrease CF in dairy supply chain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study screened 4,233 titles published between 2014 and 2024, ultimately including 73 relevant studies for strategies to decrease CF in the dairy supply chain. As shown in Figure 4, technological advancements have predominantly targeted the production phase of the dairy supply chain (56.8%), followed by the processing phase (28.4%). Among the 11 identified strategic categories, feeding enhancement, manure treatment, and sludge management were the most frequently studied, accounting for 27.0%, 23.0%, and 10.8% of the research, respectively.\u003c/p\u003e\n\u003cp\u003eWithin the production phase, feeding strategies are divided into roughage and concentrate improvements. Soybean straw-based feeds demonstrated the highest reduction in enteric methane emissions (20.8%), while corn-based feeds, including genetically modified varieties expressing \u0026alpha;-amylase, show significant potential for GHG mitigation. Concentrate enhancements, particularly the use of methane inhibitors like 3-nitrooxypropanol (3-NOP), achieved an 11.7% reduction in GHG emissions (kg CO₂-eq/kg energy-corrected milk). Red macroalgae is another notable approach, reducing enteric methane emissions by 20.3% to 42.7%.\u003c/p\u003e\n\u003cp\u003eManure management strategies, including co-composting, advanced energy production technologies, and optimized storage management, have proven effective in reducing emissions. Membrane cover technology for manure storage is the most efficient method for mitigating methane emissions. Innovations in anaerobic digestion systems significantly reduce fugitive methane emissions and increase biogas production. Thermo-chemical treatments, such as converting manure into biochar, and process improvements like solid-liquid separation further decrease GHG emissions while creating value-added products. Additionally, selective breeding for low-CF cattle, based on traits such as survival rates, feed efficiency, and residual methane production, represents a promising mitigation strategy.\u003c/p\u003e\n\u003cp\u003eIn the processing phase, innovations in sludge treatment have garnered attention, focusing on wastewater reduction, recycling, value-added product creation, and facility upgrades. Recycling sludge or repurposing it for new products minimizes GHG emissions. Simple modifications, such as using recycled shredded plastics in fixed-bed bioreactors, have reduced emissions by 49%. Ultra-high-pressure homogenization in heat treatment demonstrated an 88% reduction in CF at the pilot scale. Similarly, improvements to steam ejectors during the concentration process reduced GHG emissions by 14.5% to 47.3%. Integrated heat pump systems were the most effective, achieving a 91.7% reduction in total energy use and GHG emissions.\u003c/p\u003e\n\u003cp\u003eResearch on novel technologies for reducing CF during transportation, retail, storage, and waste disposal remains limited. Producing ambient-stable products to minimize energy consumption offers potential benefits in transportation. Hyperbaric storage trials on raw milk have eliminated the need for cold chains, while lightweight packaging in cold chain logistics reduced GHG emissions by 30%. Innovations in refrigeration and vehicle propulsion systems have also contributed to emissions reduction.\u003c/p\u003e\n\u003cp\u003eOn the consumer side, diets with low CF\u0026mdash;including small poultry, eggs, yogurt, and plant-based dairy alternatives\u0026mdash;have demonstrated a 50% reduction in CF, warranting further exploration. Although carbon trust labeling has been studied, its impact on CF requires more investigation. In the waste disposal phase, advancements in recycling techniques and materials have been reported. For example, multilayer flexible packaging treated with switchable hydrophilicity solvents reduced CF by 2,266 kg CO₂-eq/t. Bioplastics like polylactic acid (PLA) outperform conventional polyethylene terephthalate (PET) in landfills in terms of CF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study evaluates the effectiveness of carbon reduction policies in six countries: New Zealand, Australia, China, Ireland, the United States, and Denmark. Among these, New Zealand demonstrated the highest policy effectiveness over a 10-year period, with an average score of 0.287 and a peak of 0.878 in 2023. The success of New Zealand\u0026apos;s policies can be attributed to comprehensive measures targeting feeding practices, dairy cattle breeding, and agricultural intensification, including feedlots and stockholding areas.\u003c/p\u003e\n\u003cp\u003eAustralia ranked second in policy effectiveness, with an average score of 0.184. Initiatives such as the Australian Dairy Sustainability Framework and the Climate Change Strategy 2020\u0026ndash;25 contributed significantly to its performance. China\u0026rsquo;s emission reduction index steadily improved over the decade, increasing from 0.008 to 0.523, driven by policies like the Green Factory Evaluation Criteria targeting the dairy processing stage. However, China\u0026rsquo;s policy effectiveness remained lower than that of New Zealand and Australia.\u003c/p\u003e\n\u003cp\u003eIreland ranked fourth, with an average score of 0.118, comparable to China. The United States ranked fifth, with an average score of 0.057, supported by national programs such as Dairy Farmers of America and state-level initiatives like Dairy Cares in California. Although Denmark implemented the Dairy Products Administrative Order in 2022, which improved carbon reduction outcomes, its overall policy effectiveness was the lowest among the six countries, with an average score of 0.020.\u003c/p\u003e\n\u003cp\u003eIn summary, the policies analyzed\u0026mdash;including Agricultural Intensification, Feedlots and Stockholding Areas, the Australian Dairy Sustainability Framework, the Climate Change Strategy, the Green Factory Evaluation Criteria, Dairy Farmers of America, Dairy Cares, and the Dairy Products Administrative Order\u0026mdash;played varying roles in each country. New Zealand\u0026apos;s comprehensive and targeted approach resulted in the highest policy effectiveness, followed by Australia, China, Ireland, the United States, and Denmark.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe dairy industry operates across the primary, secondary, and tertiary economic sectors. Given the significant contribution of production emissions to the CF of dairy, most studies and reports have concentrated on the primary sector, particularly on CF analysis, innovations, and effective policies. Notably, most of the literature focuses exclusively on the milk production stage. Exceptions include studies conducted in the USA, Iran, and Italy, which examine additional stages of the dairy supply chain\u003csup\u003e12-14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur study includes 29 countries and conducts comparative analyses using standardized functional units. However, the data is limited, excluding major dairy-producing countries such as Brazil, Russia, and Turkey due to a lack of relevant literature. The average CF in our study, 1.70 kg CO2-eq/kg FPCM, is lower than the FAO global estimate of 2.40 kg CO2-eq/kg FPCM\u003csup\u003e15\u003c/sup\u003e. This discrepancy might stem from the inclusion of countries with relatively low CF values, particularly in Europe. Additionally, CF comparisons across countries remain challenging because many studies base their calculations on single or representative farms, which do not adequately capture regional emission variations. Differences in allocation methods and functional units further contribute to inconsistencies in findings. Standardized guidelines, such as those outlined by the IDF (2015, updated in 2022), should be followed to ensure comparability\u003csup\u003e16\u003c/sup\u003e. Decisions regarding calculation boundaries, allocation methods, and functional units should align with study objectives, with additional details reported in the main text or supplementary materials.\u003c/p\u003e\n\u003cp\u003eThe most effective strategies for CF reduction also focus primarily on milk production stages. However, these strategies often have limitations. For example, using palm oil to enhance fat content in animal feed can lead to deforestation and biodiversity loss if not sustainably sourced\u003csup\u003e17\u003c/sup\u003e. Similarly, condensed tannins may adversely affect rumen function and nutrient digestibility, while algae-based feed additives require government approval for production and application\u003csup\u003e18, 19\u003c/sup\u003e. Techniques like co-composting with bacterial inoculants and wheat straw demand additional resources, and certain manure treatment methods are suitable only for specific farm scales\u003csup\u003e20\u003c/sup\u003e. Breeding index approaches may also conflict with other breeding goals, limiting their effectiveness in reducing GHG emissions\u003csup\u003e21\u003c/sup\u003e. Thus, CF reduction efforts often involve trade-offs between environmental, economic, and practical considerations.\u003c/p\u003e\n\u003cp\u003ePolicy measures play a critical role in CF reduction. For milk production, the CF rankings of six countries from lowest to highest are: Australia (0.75), New Zealand (0.81), Ireland (1.11), Denmark (1.23), China (1.35), and the United States (1.47) kg CO2-eq/kg FPCM. Policy effectiveness rankings align with these figures, with New Zealand leading due to its comprehensive policies targeting agricultural intensification, feedlots, and stockholding areas. Policies such as Dairy Farmers of America and Dairy Cares (California) in the United States, the Australian Dairy Sustainability Framework, and Australia\u0026rsquo;s Climate Change Strategy have also proven instrumental in CF reduction during milk production.\u003c/p\u003e\n\u003cp\u003eAccording to our study, the CF of dairy products varies widely, underscoring the need to address emissions across the entire supply chain. While milk production contributes the most to CF, emissions from energy management, concentration, heating, sludge treatment, cold chain logistics, consumer use, and packaging recycling are significant. Strategies for these stages, such as renewable energy use, closed-loop spray drying, and packaging recycling, have limitations. For instance, renewable energy adoption is constrained by resource availability, and closed-loop spray drying requires substantial investments. Balancing economic feasibility with technical advancement remains a challenge, and knowledge gaps persist in processing, consumer behavior, and recycling.\u003c/p\u003e\n\u003cp\u003eInterestingly, dietary shifts toward plant-based dairy alternatives could reduce CF by up to 50%. However, these substitutes often lack the nutritional profile of bovine milk, which offers superior protein, calcium, potassium, and vitamin D content. Additionally, the bioavailability of nutrients differs between plant-based and bovine milk. Research indicates that substituting plant-based milk (e.g., soy milk) for bovine milk cannot significantly reduce GHG emissions unless by-product consumption from milk production is minimized\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePolicy initiatives in the secondary and tertiary sectors also play a crucial role. The Australian Dairy Sustainability Framework, Green Factory Evaluation Guidelines (China), and the Dairy Industry Executive Order (United States) effectively address emissions beyond milk production. For example, Australia aims to ensure all packaging is recyclable, compostable, or reusable by 2025, with the additional goal of halving food waste. Emerging trends in smart management, low-carbon technologies, and digitization are redefining dairy industry practices, warranting further exploration.\u003c/p\u003e\n\u003cp\u003eFinally, it should be noted that the data for this study was sourced using Google News, a platform with extensive global news coverage and algorithmic aggregation. While this ensures representativeness and technical neutrality, the accuracy of results may be affected by variations in carbon reduction reporting by different countries. Despite these limitations, the findings of this study provide innovative insights into CF reduction in the dairy industry.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive evaluation of the emission mitigation potential and practical effectiveness of emerging technologies implemented across five critical stages of the dairy supply chain over the past decade. Achieving a zero-carbon dairy supply chain will be a central policy objective for nations worldwide, with upstream interventions\u0026mdash;particularly in feed optimization and mitigation of GHG emissions from manure\u0026mdash;emerging as key priorities. Our findings underscore the interconnected nature of the dairy industry\u0026apos;s carbon footprint, spanning breeding practices, feed strategies, manure management, sludge treatment, and packaging systems. To advance global carbon neutrality goals, it is imperative to establish a standardized and credible database that facilitates cross-country assessments of carbon emissions within the dairy sector. Such a framework would provide the foundation for robust benchmarking, policy formulation, and the development of globally aligned strategies to decarbonize the dairy supply chain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study employed two primary methodological approaches:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eScoping Reviews:\u003c/strong\u003e Reviews adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (2020) to analyze carbon emissions and identify novel technologies.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Analysis via News Scoping:\u003c/strong\u003e Policy effectiveness was evaluated through keyword-based searches using Google News.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eScoping Reviews\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive search was conducted in October 2024 using the Web of Science Core Collection to identify peer-reviewed literature. Studies published between 2010 and 2024 were included for carbon emissions analysis across different countries or regions. For novel strategies, literature from 2014 to 2024 was considered, reflecting a sharp rise in publications on emerging technologies aimed at reducing carbon emissions.\u003c/p\u003e\n\u003cp\u003eSearch keywords were determined through expert panel discussions involving industry professionals and academic researchers, as well as existing reviews on carbon reduction strategies and technologies. Detailed search strategies are provided in Supplementary Figure 1. For CF analysis, keywords included \u0026ldquo;milk\u0026rdquo; combined with terms like \u0026ldquo;LCA,\u0026rdquo; \u0026ldquo;carbon footprint,\u0026rdquo; and \u0026ldquo;carbon emission.\u0026rdquo; To identify novel strategies in the dairy industry for reducing carbon emissions, topics were framed around \u0026ldquo;carbon footprint,\u0026rdquo; \u0026ldquo;greenhouse gas,\u0026rdquo; \u0026ldquo;energy consumption,\u0026rdquo; or \u0026ldquo;techno-assessment.\u0026rdquo; Abstracts had to reference dairy products, such as \u0026ldquo;milk,\u0026rdquo; while keywords described the dairy supply chain, including terms like \u0026ldquo;feed*,\u0026rdquo; \u0026ldquo;process,\u0026rdquo; and \u0026ldquo;cold chain\u0026rdquo;. After screening, 138 studies were included in the analysis: 65 focused on CF assessment and 73 on the identification of strategies for emission reduction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy News Scoping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Keywords\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKeyword extraction was applied to identify relevant reports. The research was divided into two sets of keywords: \u0026quot;dairy industry chain\u0026quot; and \u0026quot;carbon policies\u0026quot;. The keywords related to the \u0026quot;dairy industry chain\u0026quot; encompassed various critical links within the industry chain, based on the upstream, midstream, and downstream segments of the dairy industry chain, along with the primary activities, resources, and technologies involved in each segment. After conducting tests, irrelevant keywords and those that did not appear alongside policies in the news texts were filtered out. The final list of keywords obtained is presented in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt should be noted that during the reports\u0026rsquo; selection process, the word \u0026quot;diary\u0026quot; (though potentially a typo for \u0026quot;dairy\u0026quot;) was included when necessary to narrow down the search scope. This study introduced the \u0026quot;Global Carbon Market Progress\u0026quot; report published by the International Carbon Action Partnership (ICAP) to reflect global actions on carbon policies. Term Frequency-Inverse Document Frequency (TF-IDF) word frequency statistics from Python (v. 3.10.10) were applied for text processing, analyzing word meanings, and screening for characteristic words that met the requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of News Reports\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReferring to Galloway et al.\u003csup\u003e23\u003c/sup\u003e, the time range was controlled within a decade. The total volume of news reports was counted. Through searches, it was found that the total number of news samples from the United States, New Zealand, Australia, China, Denmark, and Ireland over the ten-year period from 2014 to 2023 was 1,022,800,000. Then, using Google News, news reports containing two sets of keywords (\u0026quot;dairy supply chain\u0026quot; and \u0026quot;carbon policy\u0026quot;) were obtained. After manual verification, the news reports were organized into a dataset, which includes the title, date, source, and content summary of each article, facilitating subsequent analysis and research. In this study, a total of 184,880 news samples were obtained through web scraping techniques using Python software version 3.10.10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of Policy Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrawing on the measurement methods of climate policies from Huang et al.\u003csup\u003e24\u003c/sup\u003e and Ma et al.\u003csup\u003e25\u003c/sup\u003e, and combining the research focus of the study, a word frequency method is adopted to identify and measure the carbon policy index within the dairy supply chain. A higher index indicates better policy effectiveness, while a lower index suggests weaker policy effectiveness.\u003c/p\u003e\n\u003cp\u003eThe final policy index was calculated using the conditional publication volume and the total publication volume. The conditional publication volume refers to the number of reports in which both the keywords \u0026quot;dairy industry chain\u0026quot; and \u0026quot;carbon policies\u0026quot; appear simultaneously. The total publication volume refers to the total number of articles related to a specific country (obtained by searching using the name of country).\u003c/p\u003e\n\u003cp\u003eThe first step is to obtain the annual conditional publication frequency for each country by dividing the conditional publication volume of each country in each year. \u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAWCAYAAADAQbwGAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEBSURBVDhP7ZTREUQwEIY31wFK4N0LowQlqEELOqACJWhABRpQgQrwoAF3/0rucHGY83b3zWT+ZGV+ye4ixgd0ITepl/Hrho7jkBCCTNOkoihklMj3/Wd8F1RZ0bbt6HkejzllWXIMz/dYGIKqqtBGrAqYzdefeDMEcRyPtm3zPM/zMUkSnh9Ba4irGYbBZkevqtAagjRN+erI3xk2DVUu14RhyC/b4rThHKRivWezsYdhYG2ahhVkWcb9CAVBELAihsFI4wV1XXNR8BiKtQJFml95baE9oeu61Pc9drJirbAsS870XPItd133Ss100GOoQkVRJCNT1RFTzf//Y38L0R1o4qNz7aXYGgAAAABJRU5ErkJggg==\" width=\"20\" height=\"22\"\u003e\u0026nbsp;is the total number of articles for that country ：\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"33\" width=\"320\"\u003e\u003c/p\u003e\n\u003cp\u003eThe second step is to normalize the index to a range between 0 and 1.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"21\" width=\"335\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data that we collected from the literature are available within the article and/or its supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOpio, C. I., Gerber, P. J., Mottet, A., Falcucci, A., Tempio, G., MacLeod, M., Vellinga, T. V., Henderson, B. B., \u0026amp; Steinfeld, H. \u003cem\u003eGreenhouse gas emissions from ruminant supply chains \u0026ndash; a global life cycle assessment\u003c/em\u003e (Food and Agriculture Organization of the United Nations (FAO), 2013).\u003c/li\u003e\n\u003cli\u003eSharma, S. \u003cem\u003eMilking the planet: How Big Dairy is heating up the planet and hollowing rural communities\u003c/em\u003e (Institute for Agriculture \u0026amp; Trade Policy, 2020).\u003c/li\u003e\n\u003cli\u003eWiedmann, T. \u0026amp; Minx, J. A definition of \u0026lsquo;carbon footprint\u0026rsquo;. \u003cem\u003eEcological Economics Research Trends \u003c/em\u003e1, 1-11 (2008).\u003c/li\u003e\n\u003cli\u003eda Silva, L. P. \u0026amp; da Silva, J. C. E. Evaluation of the carbon footprint of the life cycle of wine production: A review. \u003cem\u003eCleaner and Circular Bioeconomy \u003c/em\u003e2, 100021 (2022).\u003c/li\u003e\n\u003cli\u003eFAO \u0026amp; GDP. \u003cem\u003eClimate change and the global dairy cattle sector\u0026ndash;The role of the dairy sector in a low-carbon future\u003c/em\u003e. (FAO \u0026amp; Global Dairy Platform Inc.,\u003cem\u003e \u003c/em\u003e2018).\u003c/li\u003e\n\u003cli\u003eGerber, P., Vellinga, T., Opio, C., Steinfeld, H., Productivity gains and greenhouse gas emissions intensity in dairy systems. \u003cem\u003eLivestock Science \u003c/em\u003e139 (1-2), 100-108 (2011).\u003c/li\u003e\n\u003cli\u003eHerrero, M., Henderson, B., Havl\u0026iacute;k, P., Thornton, P. K., Conant, R. T., Smith, P., Wirsenius, S., Hristov, A. N., Gerber, P., \u0026amp; Gill, M. Greenhouse gas mitigation potentials in the livestock sector. \u003cem\u003eNature Climate Change\u003c/em\u003e 6 (5), 452-461 (2016).\u003c/li\u003e\n\u003cli\u003eMazzetto, A. M., Falconer, S., \u0026amp; Ledgard, S. Mapping the carbon footprint of milk production from cattle: A systematic review. \u003cem\u003eJournal of Dairy Science\u003c/em\u003e 105 (12), 9713-9725 (2022).\u003c/li\u003e\n\u003cli\u003eGuerci, M., Proserpio, C., Famiglietti, J., Zanchi, M., \u0026amp; Bilato, G. Carbon footprint of Grana Padano PDO cheese in a full life cycle perspective,\u003cem\u003e in: 10\u003csup\u003eth\u003c/sup\u003e International Conference on LCA Food.\u003c/em\u003e (2016).\u003c/li\u003e\n\u003cli\u003eHristov, A. N. Invited review: Advances in nutrition and feed additives to mitigate enteric methane emissions. \u003cem\u003eJournal of Dairy Science 107\u003c/em\u003e (7), 4129-4146 (2024).\u003c/li\u003e\n\u003cli\u003eHenderson, B., Golub, A., Pambudi, D., Hertel, T., Godde, C., Herrero, M., Cacho, O., \u0026amp; Gerber, P. The power and pain of market-based carbon policies: a global application to greenhouse gases from ruminant livestock production. \u003cem\u003eMitigation and Adaptation Strategies for Global Change\u003c/em\u003e 23 (3), 349-369 (2018).\u003c/li\u003e\n\u003cli\u003eThoma, G., Popp, J., Nutter, D., Shonnard, D., Ulrich, R., Matlock, M., Kim, D. S., Neiderman, Z., Kemper, N., \u0026amp; East, C. Greenhouse gas emissions from milk production and consumption in the United States: A cradle-to-grave life cycle assessment circa 2008. \u003cem\u003eInternational Dairy Journal \u003c/em\u003e31, S3-S14 (2013).\u003c/li\u003e\n\u003cli\u003eFamiglietti, J., Guerci, M., Proserpio, C., Ravaglia, P., \u0026amp; Motta, M. Development and testing of the product environmental footprint milk tool: a comprehensive LCA tool for dairy products. \u003cem\u003eScience of the Total Environment \u003c/em\u003e648, 1614-1626 (2019).\u003c/li\u003e\n\u003cli\u003eDaneshi, A., Esmaili-Sari, A., Daneshi, M., \u0026amp; Baumann, H. Greenhouse gas emissions of packaged fluid milk production in Tehran. \u003cem\u003eJournal of cleaner Production \u003c/em\u003e80, 150-158 (2014).\u003c/li\u003e\n\u003cli\u003eFAO. \u003cem\u003eGreenhouse gas emissions from the dairy sector: A life cycle assessment. \u003c/em\u003e(FAO, 2010).\u003c/li\u003e\n\u003cli\u003eInternational Dairy Federation (IDF). \u003cem\u003eCommon carbon footprint approach for the dairy sector. The IDF guide to standard life cycle assessment methodology \u003c/em\u003e(International Dairy Federation, 2015).\u003c/li\u003e\n\u003cli\u003ePacheco-Pappenheim, S., Yener, S., Nichols, K., Dijkstra, J., Hettinga, K., \u0026amp; van Valenberg, H. J. F. Feeding hydrogenated palm fatty acids and rumen-protected protein to lactating Holstein-Friesian dairy cows modifies milk fat triacylglycerol composition and structure, and solid fat content. \u003cem\u003eJournal of Dairy Science \u003c/em\u003e105 (4), 2828-2839 (2022).\u003c/li\u003e\n\u003cli\u003eBer\u0026ccedil;a, A. S., Tedeschi, L. O., da Silva Cardoso, A., \u0026amp; Reis, R. A. Meta-analysis of the relationship between dietary condensed tannins and methane emissions by cattle. \u003cem\u003eAnimal Feed Science and Technology\u003c/em\u003e 298, 115564 (2023).\u003c/li\u003e\n\u003cli\u003eKatwal, S., Pandya, P. R., Trivedi, M. M., Sorathiya, K. K., \u0026amp; Shah, S. V. Antimethanogenic effects of soybean straw and seaweed (\u003cem\u003eSargassum johnstonii\u003c/em\u003e) based total mixed ration in crossbred cows. \u003cem\u003eIndian Journal of Dairy Science\u003c/em\u003e 74 (6), 498-503 (2021).\u003c/li\u003e\n\u003cli\u003eImeni, S. M., Puy, N., Ovejero, J., Busquets, A. M., Bartroli, J., Pelaz, L., Ponsa, S., \u0026amp; Col\u0026oacute;n, J. Techno-Economic Assessment of Anaerobic Co-digestion of Cattle Manure and Wheat Straw (Raw and Pre-treated) at Small to Medium Dairy Cattle Farms. \u003cem\u003eWaste and Biomass Valorization\u003c/em\u003e 11 (8), 4035-4051 (2020).\u003c/li\u003e\n\u003cli\u003eShi, R., Wang, Y., van Middelaar, C. E., Ducro, B., Oosting, S. J., Hou, Y., Wang, Y., \u0026amp; van der Linden, A. Balancing farm profit and greenhouse gas emissions along the dairy production chain through breeding indices. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 451, 142099 (2024).\u003c/li\u003e\n\u003cli\u003ePorto Costa, M., Saget, S., Zimmermann, B., Petig, E., Angenendt, E., Rees, R. M., Chadwick, D., Gibbons, J., Shrestha, S., Williams, M., \u0026amp; Styles, D. Environmental and land use consequences of replacing milk and beef with plant-based alternatives. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e 424, 138826 (2023).\u003c/li\u003e\n\u003cli\u003eGalloway, C., Swanepoel, P. A., \u0026amp; Haarhoff, S. J. A carbon footprint assessment for pasture-based dairy farming systems in South Africa. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e 8, 1333981 (2024).\u003c/li\u003e\n\u003cli\u003eHuang, Y. \u0026amp; Luk, P. Measuring economic policy uncertainty in China. \u003cem\u003eChina Economic Review\u003c/em\u003e 59, 101367 (2020).\u003c/li\u003e\n\u003cli\u003eMa, Y.-R., Liu, Z., Ma, D., Zhai, P., Guo, K., Zhang, D., \u0026amp; Ji, Q. A news-based climate policy uncertainty index for China. \u003cem\u003eScientific Data\u003c/em\u003e 10 (1), 881 (2023).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Strategies to decrease carbon emissions in dairy supply chain.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"702\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eStrategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEffects/advantages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLimitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 702px;\"\u003e\n \u003cp\u003e\u003cem\u003eProduction Stage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 75px;\"\u003e\n \u003cp\u003eFeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRoughage improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorn-based feed:\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eCorn-based feed \u0026darr;13% GHG emission compared to barley silage\u003c/p\u003e\n \u003cp\u003eCorn containing a bacterial transgene expressing \u0026alpha;-amylase \u0026darr;7.2% enteric CH\u003csub\u003e4\u003c/sub\u003e emission intensity\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCassava-based feed (30%):\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026darr;6.6% CH\u003csub\u003e4\u003c/sub\u003e emission (kg CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eeq/kg FPCM)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCrushed wheat grain-based feed (33%):\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026darr;5.91% CH\u003csub\u003e4\u003c/sub\u003e emission (g/d) compared to pasture\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSoyabean straw-based feed (20%):\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026darr;20.79% CH\u003csub\u003e4\u003c/sub\u003e emission (g/d) compared to wheat straw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eGreater fertilizer inputs associated with corn silage; lower milk fat concentration\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCassava based feed only slightly reduced the carbon footprint, depending on breed; limited studies\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMilk fat levels\u0026darr;with 30% crushed wheat grain\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Guyader et al., 2017)\u003c/p\u003e\n \u003cp\u003e(Cueva et al., 2021)\u003c/p\u003e\n \u003cp\u003e(Molina-Botero et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Moate et al., 2020)\u003c/p\u003e\n \u003cp\u003e(Katwal et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eConcentrate improvement - Fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEnteric CH\u003csub\u003e4\u003c/sub\u003e emissions (g/d):\u003c/p\u003e\n \u003cp\u003eSupplemental fat (Meta-analysis):\u0026darr;3.77%\u003c/p\u003e\n \u003cp\u003ePalm oil:\u0026darr;4% for every 10 g/kg palm oil\u003c/p\u003e\n \u003cp\u003eOilseeds:\u0026darr;14%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHydroxy-methionine-analog-isobutyrate + palm oil:\u0026darr;15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eOilseed: effects did not persist after 19 weeks of supplementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(de Ondarza et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Flores-Santiago et al., 2022)\u003c/p\u003e\n \u003cp\u003e(Munoz et al., 2021)\u003c/p\u003e\n \u003cp\u003e(Alstrup et al., 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eConcentrate improvement - Tannins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eMeta-analysis (condensed tannins\u0026ge;2.32 %):\u0026darr;1% CH\u003csub\u003e4\u003c/sub\u003e emissions \u003cem\u003ein vivo\u003c/em\u003e (L/kg dry matter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBiologically active condensed tannins \u0026ge;6%: lower fiber digestibility and poorer animal performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Ber\u0026ccedil;a et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eConcentrate improvement - 3-NOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eMeta-analysis (70.5 mg/kg dry matter): \u0026darr;32.7% CH\u003csub\u003e4\u003c/sub\u003e emission (g/d)\u003c/p\u003e\n \u003cp\u003eLCA analysis:\u0026darr;11.7% GHG emission (kg CO\u003csub\u003e2\u003c/sub\u003e eq /kg energy corrected milk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eIncreased H\u003csub\u003e2\u003c/sub\u003e; a 1% (10 g/kg DM) decrease in dietary crude fat content\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Dijkstra et al., 2018)\u003c/p\u003e\n \u003cp\u003e(Feng \u0026amp; Kebreab, 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eConcentrate improvement \u0026ndash; Macroalgae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEnteric CH\u003csub\u003e4\u003c/sub\u003e emissions (g/d):\u003c/p\u003e\n \u003cp\u003eRed macroalgae (\u003cem\u003eAsparagopsis armata\u003c/em\u003e):\u0026darr;20.3%-42.7%\u003c/p\u003e\n \u003cp\u003eBrown macroalgae (\u003cem\u003eSargassum johnstonii\u003c/em\u003e):\u0026darr;16.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eAddition needs to be approved by regulatory bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Roque et al., 2019)\u003c/p\u003e\n \u003cp\u003e(Katwal et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOther Concentrate improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEnteric CH\u003csub\u003e4\u003c/sub\u003e emissions (g/d):\u003c/p\u003e\n \u003cp\u003eCashew nut shell liquid:\u0026darr;10.64%\u003c/p\u003e\n \u003cp\u003eCrushed rapeseed:\u0026darr;11.95%\u003c/p\u003e\n \u003cp\u003eGarlic and citrus extract:\u0026darr;10.3%\u003c/p\u003e\n \u003cp\u003eGrapeseed:\u0026darr;11.8% - 16.4% (60 - 120 mg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLack of evidence on economically successful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Sarmikasoglou et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Storlien et al., 2017)\u003c/p\u003e\n \u003cp\u003e(Khurana et al., 2023)\u003c/p\u003e\n \u003cp\u003e(F. Zhang et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFeeding management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEnteric CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eemission (g/d):\u003c/p\u003e\n \u003cp\u003eSeparate offering of feed ingredients:\u0026darr;9%\u003c/p\u003e\n \u003cp\u003eHigh concentrate proportion:\u0026darr;27.2% (Holstein) and 13.8% (Jersey cows)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFurther research is required\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Martins et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Olijhoek et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 75px;\"\u003e\n \u003cp\u003eManure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eCo-composting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eCo-composting with biochar:\u003c/p\u003e\n \u003cp\u003e\u0026darr;58% (+/- 22%) CH\u003csub\u003e4\u003c/sub\u003e emissions；produce 0.81 g CH\u003csub\u003e4\u003c/sub\u003e and 280 g CO\u003csub\u003e2\u003c/sub\u003e (kg\u003csup\u003e\u0026ndash;1\u003c/sup\u003e dry feedstock)\u003c/p\u003e\n \u003cp\u003eCo-composting with biochar and bacterial inoculum:\u003c/p\u003e\n \u003cp\u003e\u0026darr;20.7% CO2-C\u003c/p\u003e\n \u003cp\u003eCo-composting with Lignite:\u003c/p\u003e\n \u003cp\u003e\u0026darr;12-23% CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eand 52-59% CH\u003csub\u003e4\u003c/sub\u003e emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRequire high dry matter levels and additional technologies (e.g., drying system) for biochar co-composting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Harrison et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Harrison et al., 2022)\u003c/p\u003e\n \u003cp\u003e(Awasthi et al., 2020)\u003c/p\u003e\n \u003cp\u003e(Impraim et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEnergy production -Anaerobic digestion\u003c/p\u003e\n \u003cp\u003e(AD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eCo-substrate with wheat straw: \u0026uarr;156% CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eyield\u003c/p\u003e\n \u003cp\u003eAnaerobic sequencing batch reactors (ASBR) using a cationic polymer:\u0026darr;18\u0026ndash;56% fugitive CH\u003csub\u003e4\u003c/sub\u003e emissions\u003c/p\u003e\n \u003cp\u003eCellulase pretreatment (CP) and microvoltage application: \u0026nbsp;\u0026uarr;18.7% and 10.0% CH\u003csub\u003e4\u003c/sub\u003e production rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBioeconomy model is required to be developed; a capital-intensive technology, suitable at large farms; ensuring consistent yields and product quality remains elusive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Imeni et al., 2020)\u003c/p\u003e\n \u003cp\u003e(Zeb et al., 2019)\u003c/p\u003e\n \u003cp\u003e(Cai et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEnergy production - thermo-chemical processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eUsing CO\u003csub\u003e2\u003c/sub\u003e as a co-feed reactant for catalytic pyrolysis: \u0026uarr;25% gas formation\u003c/p\u003e\n \u003cp\u003eHydrothermal liquefaction: \u0026darr;50% energy consumption and 3.5 years for energy payback\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eThe liquid products contain high amounts of oxygenated and nitrogenated chemicals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Lee et al., 2020)\u003c/p\u003e\n \u003cp\u003e(Chen et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eStorage management -Solid-liquid separation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eManure fractionation:\u0026darr;87% CH\u003csub\u003e4\u003c/sub\u003e emissions from outdoor liquid digestate storage\u003c/p\u003e\n \u003cp\u003eSolid-liquid separation alone:\u0026darr;38% GHG emission\u003c/p\u003e\n \u003cp\u003eSolid-liquid separation + anaerobic digestion:\u0026darr;41% GHG emission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHigher N\u003csub\u003e2\u003c/sub\u003eO and NH\u003csub\u003e3\u003c/sub\u003e emissions from storing the solid fraction\u003c/p\u003e\n \u003cp\u003eAnnual total GHG emissions \u0026uarr;with the increasing separator screen size and reached approximately 0.7 t CO\u003csub\u003e2\u003c/sub\u003e eq / cow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Feng, Smith, \u0026amp; VanderZaag, 2023)\u003c/p\u003e\n \u003cp\u003e(Aguirre-Villegas, Larson, \u0026amp; Sharara, 2019)\u003c/p\u003e\n \u003cp\u003e(Y. X. Zhang et al., 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eStorage management - Membrane cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eApplication of locally manufactured membrane:\u003c/p\u003e\n \u003cp\u003e\u0026darr;85.9% CO\u003csub\u003e2\u003c/sub\u003e emission; \u0026darr;55.6% CH4 emission\u003c/p\u003e\n \u003cp\u003eSemi-permeable membrane + intermittent aeration:\u003c/p\u003e\n \u003cp\u003e\u0026darr;99.89% CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eemission\u003c/p\u003e\n \u003cp\u003eSemi-permeable membrane + forced aeration composting:\u003c/p\u003e\n \u003cp\u003e\u0026darr;27.48% CH\u003csub\u003e4\u0026nbsp;\u003c/sub\u003eemission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFurther studies is required for the membrane properties affecting fermentation process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Varga et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Fang et al., 2021)\u003c/p\u003e\n \u003cp\u003e(Fang et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eStorage management -others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eSlurry treatment with sulphuric acid (46%), gypsum (39%) and biochar (15%) \u0026darr;CH\u003csub\u003e4\u003c/sub\u003e emissions\u003c/p\u003e\n \u003cp\u003eApplying acid to residual manure following storage emptying has long-term effect on CH\u003csub\u003e4\u003c/sub\u003e removal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eGypsum increases H\u003csub\u003e2\u003c/sub\u003eS production in the anaerobic environment, which is released in dangerous concentrations when the manure is agitated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Owusu-Twum et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Sokolov et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eEnergy management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEnergy utilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eEnergy self-sufficiency from biogas produced from manure:\u0026darr;32.8 Mt CO\u003csub\u003e2\u003c/sub\u003e eq annually\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBiogas yield factor can be different\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Villarroel-Schneider et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eBreeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eBreeding index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eReducing 6\u0026ndash;10 kg CO\u003csub\u003e2\u003c/sub\u003e-eq per ton milk with optimal\u0026nbsp;indices\u003c/p\u003e\n \u003cp\u003eSurvival (\u0026minus;8.55 kg of CO\u003csub\u003e2\u003c/sub\u003e-eq) had the most favorable genetic improvement with feed saved (\u0026minus;0.53 CO2-eq) being the only other trait that lowered emissions\u003c/p\u003e\n \u003cp\u003eSelecting for feed efficiency and residual methane traits \u0026darr;methane emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBio-economic model is required to be improved; limited\u0026nbsp;traits are considered; low heritability for predicted CH\u003csub\u003e4\u003c/sub\u003e trait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Shi et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Richardson et al., 2021)\u003c/p\u003e\n \u003cp\u003e(Manzanilla-Pech et al., 2021; Manzanilla-Pech et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 702px;\"\u003e\n \u003cp\u003e\u003cem\u003eProcessing Stage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003eEnergy management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRenewable energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003ePredict the energy demand with the solar resource using multiple regression in a cheese factory:\u0026nbsp;\u0026darr;14% - 42% consumption rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eTechnical issue due to intermittent nature of solar energy production; high investment cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Miserocchi, Franco, \u0026amp; Testi, 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEnergy recovery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eThe integrated heat pump system:\u0026nbsp;\u0026darr;91.7% GHG emission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eEconomical cost in factory design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Ahrens et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003eConcentration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEvaporation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eReplacing steam ejectors with mechanical vapor recompression fans: energy\u0026nbsp;\u0026darr;13.7 % - 41.6 %, GHG emissions\u0026nbsp;\u0026darr;\u0026nbsp;14.5 % - 47.3 %.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eIncreased cost and new design is required\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Lincoln et al., 2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMembrane filtration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eHybrid forward osmosis (FO) - membrane distillation (MD) system: \u0026darr;50 % energy consumption\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePreconcentrating using reverse osmosis: \u0026darr;36% natural gas consumption + \u0026darr;10% electricity consumption\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSpiral-wound membrane for microfiltration (MF): low energy consumption (0.015-0.024 kWh/kg of permeate collected)\u003c/p\u003e\n \u003cp\u003eHot MF:\u0026darr;4.4 WhL\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003epermeate\u0026nbsp;energy consumption compared to cold MF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFO-MD is still conceptually applied for the non-thermal concentration of skim milk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Zhou et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Chamberland et al., 2020)\u003c/p\u003e\n \u003cp\u003e(Mercier-Bouchard et al., 2017)\u003c/p\u003e\n \u003cp\u003e(Subhir et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eHeating process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOhmic heating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026darr;73% energy compared to conventional heating in liquid dairy products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eQuality parameters need to be improved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Rocha et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003ePulsed electric fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eModerate electric fields:\u0026darr;98.6% energy consumption compared to conventional heating\u003c/p\u003e\n \u003cp\u003e\u0026darr;16% energy in sweet whey demineralization\u003c/p\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.4 Wh/g of lactic acid in acid whey processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eElectrode corrosion of milk may occur and therefore produce toxic compounds.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Alsaedi et al., 2023)\u003c/p\u003e\n \u003cp\u003e(Lemay, Mikhaylin, \u0026amp; Bazinet, 2019)\u003c/p\u003e\n \u003cp\u003e(Dufton et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eUltra-high-pressure homogenization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026darr;88% carbon footprint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMilk is prone to bubbles and accelerates rancidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Valsasina et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSpray drying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eOptimization of closed-loop spray drying energy:\u003c/p\u003e\n \u003cp\u003e\u0026darr;3.5 MJ heat per kg milk powder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePlant layout, possibilities for energy transport between units, and interference with other operations may limit the application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Moejes et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 75px;\"\u003e\n \u003cp\u003eSludge treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eLow wastewater production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eFouling-mitigating surface modification:\u003c/p\u003e\n \u003cp\u003e\u0026darr;84% climate change (ecosystem) scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRequire new technologies; higher cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Zouaghi et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRecycling for use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eMilk whey is recycled for cattle slurry acidification:\u003c/p\u003e\n \u003cp\u003e\u0026darr;37.2-68.1% CH\u003csub\u003e4\u003c/sub\u003e emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Gioelli et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eAdd-value products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eHydrochar:\u0026darr;8.36% GHG emission\u003c/p\u003e\n \u003cp\u003eBiochar:\u0026darr;25.63% GHG emission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRequire new technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Hu et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eImproved treatment facilities/ Process modification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eApplication of an intermittently ASBR: \u0026darr;36\u0026ndash;68% energy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOptimization of the aeration process: \u0026darr;45% GHG emission\u003c/p\u003e\n \u003cp\u003eRecycled shredded plastics for a fixed bed bioreactor: \u0026darr;49% GHG emission\u003c/p\u003e\n \u003cp\u003eApplication of microwave radiation heating in a multi-section hybrid anaerobic bioreactor:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026uarr;5% COD removal efficiency; \u0026uarr;12-20% biogas yields\u003c/p\u003e\n \u003cp\u003eElectrocoagulation-flotation:\u003c/p\u003e\n \u003cp\u003eUltrasonic pretreatment (40 W 5min) of cheese whey before AD:\u0026uarr;16.4% potential CH\u003csub\u003e4\u003c/sub\u003e yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRequire new technologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Leonard et al., 2021)\u003c/p\u003e\n \u003cp\u003e(Yapicioglu, Yal\u0026ccedil;in, \u0026amp; Yesilnacar, 2023)\u003c/p\u003e\n \u003cp\u003e(Abyar et al., 2024)\u003c/p\u003e\n \u003cp\u003e(Zielinski, Debowski, \u0026amp; Kazimierowicz, 2021)\u003c/p\u003e\n \u003cp\u003e(Mainardis et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 576px;\"\u003e\n \u003cp\u003e\u003cem\u003eTransportation Stage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 75px;\"\u003e\n \u003cp\u003eCold chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eCold chain removal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eUHT dairy products: No cold chain during distribution and retail led to energy saving\u003c/p\u003e\n \u003cp\u003eHyperbaric storage: prolong storage time of raw milk at room temperature to 60 days; nearly no energy consumption compared to refrigeration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eThe manufacturing of packaging materials contributed 7% of the total CF of 1 L of packaged UHT milk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Roib\u0026aacute;s et al., 2016)\u003c/p\u003e\n \u003cp\u003e(Duarte et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eNew energy application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eBattery-powered vapor compression refrigeration:\u003c/p\u003e\n \u003cp\u003e\u0026darr;8320 kg CO\u003csub\u003e2\u003c/sub\u003e per year per trailer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eRequire new technologies; higher cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Bagheri, Fayazbakhsh, \u0026amp; Bahrami, 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003ePackage design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eLow weight pillow pouches:\u0026darr;30% GHG emissions of 4 L HDPE jugs and 2 L LPB cartons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eConsumers might be hesitant to purchase milk in pillow pouches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Sun et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 702px;\"\u003e\n \u003cp\u003eRetail stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003eConsumer use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eCarbon trust label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eConsumers preferred the dairy products branded with the Carbon trust label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eNot effective to price-sensitive consumers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Asioli et al., 2023)\u003c/p\u003e\n \u003cp\u003e(Canavari \u0026amp; Coderoni, 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eLow carbon-footprint diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eDiet with only small poultry, eggs and yogurt or plant-based dairy alternatives:\u0026darr;50% carbon footprint\u003c/p\u003e\n \u003cp\u003eReduction in solid dairy foods and meat:\u0026darr;39% GHG emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLimited by income in lower-middle-income countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Gaillac \u0026amp; Marbach, 2021)\u003c/p\u003e\n \u003cp\u003e(Colombo et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 702px;\"\u003e\n \u003cp\u003e\u003cem\u003eWaste Disposal Stage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePackaging recycling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRecycle process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eRecycling of multilayer flexible packaging using switchable hydrophilicity solvents:\u0026darr;2266 kg CO\u003csub\u003e2\u003c/sub\u003e-eq/t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHigher mineral resource use and higher environmental damage in the marine eutrophication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Blazejewski et al., 2021)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003ePackage material\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eA mix of reusable stainless-steel churns and reusable glass bottles compared to single-use HDPE bottles: \u0026darr;6.5 t CO\u003csub\u003e2\u003c/sub\u003e-eq per year\u003c/p\u003e\n \u003cp\u003ePLA vs. PET: PLA bottle is more sustainable in terms of waste taken to landfills (14.8 vs. 27.3 kg CO\u003csub\u003e2\u003c/sub\u003e emission) to the incinerator and by increasing the recycling and composting rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eThe production process of PLA requires an optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e(Mumladze et al., 2018)\u003c/p\u003e\n \u003cp\u003e(Desole et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Keywords Used for Identifying \u0026quot;Dairy Supply Chain - Emission Reduction Policies\u0026quot;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiary Supply Chain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eFeeding; Leguminous based feed; Algal based feed; Cassava-based feed; Insect-based feed; Low emissions of greenhouse gases and ammonia; Dairy feed; Sustainable feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eManure management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eManure management; Biochar; Bacterial inoculums; Gypsum-based products; Pecan; Manure fractionation; Co-substrate; Manure handling; Manure recycling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEnergy Input\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eEnergy Input; Polygeneration system; solar energies; geothermal energies; biomass energies; The integrated heat pump system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eHeating process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eHeating process; Alternatives-ohmic heating; Microwave heating; Pulsed electric fields; High pressure processing; Ultra-high-pressure homogenization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSludge treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eSludge treatment; Add-value products; Membrane improvement; Power to gas - biomethanation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCold chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eCold chain; Products stored at room temperature; Mathematical programming model; Intermodal road-rail operations; Doored display;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eConsumer use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eConsumer use; Carbon footprint label; Carbon trust brand; Low carbon diet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePackaging recycling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eBioplastics; Sustainable packaging; Nanocellulose materials; Bioplastics; Dairy packaging waste; Eco-friendly packaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbon Policy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCarbon policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 459px;\"\u003e\n \u003cp\u003eCarbon Policy; Policy; Regulation; Energy policy; Energy tax; Carbon tax; Pollution controls; Environmental restrictions; Clean Air Act; Clean Water Act; Environmental Protection Agency; Carbon emissions; Cap-and-trade; Carbon offsetting; Carbon neutrality; Carbon credits; Carbon pricing; Decarbonization; Carbon sequestration; Emission reduction targets; Carbon Labels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5672225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5672225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Addressing the dynamic changes in the carbon footprint (CF) of the dairy industry to meet the diverse demands of different countries and achieve the global net-zero emission target presents a significant challenge. This study integrates a systematic scoping of CF and reduction strategies, with a policy analysis utilizing Term Frequency–Inverse Document Frequency (TF-IDF) statistics to provide a comprehensive assessment of current efforts to mitigate CF in the dairy industry. Across 29 countries, the CF of dairy production ranges from 0.57 kg CO₂-eq/kg FPCM (Norway) to 5.85 kg CO₂-eq/kg FPCM (Tanzania). Mitigation strategies are implemented across the entire dairy supply chain, with a primary focus on the milk production stage. Among the six countries analyzed over a 10-year period, New Zealand demonstrated the highest policy effectiveness, largely due to measures targeting feeding practices and dairy cattle breeding. This article offers an in-depth evaluation of CF reduction in the dairy sector, integrating environmental, technological, and policy dimensions.","manuscriptTitle":"Current carbon policies and emerging emission control technologies in the dairy supply chain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 02:35:51","doi":"10.21203/rs.3.rs-5672225/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e849dd34-9d7b-4335-b541-b35c46958ebf","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42693900,"name":"Earth and environmental sciences/Environmental social sciences/Environmental economics"},{"id":42693901,"name":"Social science/Science, technology and society"}],"tags":[],"updatedAt":"2025-04-22T11:32:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 02:35:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5672225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5672225","identity":"rs-5672225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}