IJFS#1523_bozza Ital. J. Food Sci., vol. 31, 2019 - 808 SURVEY PRODUCT CARBON FOOTPRINT: STILL A PROPER METHOD TO START IMPROVING THE SUSTAINABILITY OF FOOD AND BEVERAGE ENTERPRISES A. CIMINI and M. MORESI* Department for Innovation in the Biological, Agrofood and Forestry Systems, University of Tuscia, Via S. C. de Lellis, 01100 Viterbo, Italy *Corresponding author: Tel.: +39 0761357497; Fax: +39 0761357498 E-mail: mmoresi@unitus.it ABSTRACT Given the complexity of food production, supply chains and distribution, this paper sustains that the mere assessment of the product carbon footprint might still be regarded as a first trial in the field of improving the sustainability of the food and drink industry. After having reviewed the greenhouse gas (GHG) emissions associated with the agro-food system in industrialized countries, and summarized the main direct environmental impacts of the food industry, the pros and cons of the Life Cycle assessment (LCA) methodology were briefly examined together with the current standard methods used to assess the environmental impact of food and drink products. Once a cradle-to-grave product carbon footprint modelling had been developed, some mitigating actions might be tested with the final goal of reducing the GHG emissions associated with the most impacting product life cycle stages. As an example, such a procedure was applied to approximately halve the cradle-to-grave carbon footprint of two cereal-based products (i.e., dry pasta and malt beer). A cost/benefit analysis is required to relate the marginal increase in the product processing costs to each reduction in the product environmental load. Keywords: beer, carbon footprint, dry pasta, environmental impact, GHG emissions Ital. J. Food Sci., vol. 31, 2019 - 809 1. INTRODUCTION The current food system is regarded as ecologically unsustainable (CHURCH, 2005; FOODDRINKEUROPE, 2012; WRI, 2013), since fossil fuels are essential requirements for running crop production, animal husbandry, food production and distribution, as well for the construction and maintenance of machinery and processing equipment, transportation vehicles, and infrastructures. The greenhouse gas (GHG) emissions associated with food production and consumption were evaluated to constitute 19-29% of the global GHG emissions (VERMEULEN et al., 2012). By referring to the major environmental impact categories, such as climate change (CC), ozone depletion (OD), photochemical ozone creation (POC), acidification (A), eutrophication (NP), resource depletion, human toxicity, and eco-toxicity, the food, drink, tobacco and narcotics area of consumption in the EU-25 was estimated to generate up to 20-30% of the main impact categories (including 22-31% for CC), with the exception of 59% for NP (TUKKER et al., 2006). An increasing number of studies has dealt with the long-term sustainability of the current trends in the production and consumption of food. In particular, the EU Standing Committee on Agriculture Research (SCAR) observed that food production is near to exceed environmental limits; land use change and land degradation, as well as the dependence on non-renewable fossil energy sources, contribute about one-fourth of the GHG emissions; agriculture, including fisheries, is the single largest driver of biodiversity loss (EUROPEAN COMMISSION, 2011). The average USA and EU diet, being rich in meat, fat and sugar, is a risk for individual health, social systems and the environment. Since the world population is expected to grow from about 7 billion to 9.6 billion people in 2050, as well as the global meat and milk consumption, especially in China and India, the promotion of healthy diets can reduce the environmental footprint of food consumption (EUROPEAN COMMISSION, 2011; FAO, 2018; MORESI and VALENTINI, 2010; WRI, 2013). In addition, food processing and retail industries are asked to stimulate the necessary changes in production and consumption patterns (WRI, 2013). The food and beverage industry is a major contributor to the EU economy (FOODDRINKEUROPE, 2018), followed by the automotive, machinery and equipment, and chemical industries. As of 2015, it was the major driver of the economy, with turnover of € 1.109 trillion, employment of 4.57 million employees with 294,000 total number of companies. Actually, 99.1% (i.e., 280,000) of the companies are small and medium-sized enterprises (SMEs), these generating 48.1% (i.e., €538 billion) of the overall turnover, 48.4% (i.e., €107 billion) of the value added and 61.3% (i.e., 2.8 million employees) of employments. Owing to its environmental and economic importance, an intergovernmental set of 17 Sustainable Development Goals has already been identified in the food sector (FOODDRINKEUROPE, 2019), this being a core part of the 2030 Agenda for Sustainable Development (UN, 2015). Beyond the general statement of decreasing environmental burdens, such as GHG emissions, waste generation, as well as water and energy consumption, Goal 9 aims at building resilient infrastructures, promoting inclusive and sustainable industrialization and fostering innovations. The complex relation between innovation and agro-food sustainability was deeply analyzed by EL BILALI (2018) in order to identify what type of innovation should be promoted to foster transition towards a more sustainable food system. Given the complexity of food production, supply chains and distribution, this paper aimed to present how the mere assessment of the product carbon footprint might effectively help food and drink industries to improve their sustainability. Section 2 Ital. J. Food Sci., vol. 31, 2019 - 810 focused on the GHG emissions associated with the agro-food system in industrialized countries, and especially in Italy. Section 3 summarized the main direct environmental impacts of the food industry. Section 4 briefly reviewed the basic of the Life Cycle Analysis (LCA) methodology with the pros and cons of the main standard methods used to assess the food and drink environmental impact. Section 5 further discussed if the key elements for sustainable food processing are a priori identifiable or should be considered on a case-by-case basis. Finally, the importance of prioritizing the life cycle stages with the highest environmental impact as derived from business-to-consumer LCA studies was addressed in Section 6. More specifically, by resorting to the cradle-to-grave carbon footprint (CFCG) modelling for two typical cereal-based food and drink products (i.e., dried pasta and malt lager beer), several mitigation options were selected in order to reduce their climate change impact. In spite of assessing the effect of such options on other environmental impact categories, the only estimation of the CFCG was regarded as intrinsically sufficient to promote a first improvement in the sustainability of the great majority of the food and drink enterprises. 2. GHG EMISSIONS FOR THE AGRO-FOOD SYSTEM IN INDUSTRIALIZED COUNTRIES Although from the millenary climate observations the warming since the middle of the 20th century might be primarily attributed to natural causes, such as solar activity and random variations (DE LARMINAT, 2016), the human contribution cannot be considered negligible (IPCC, 2013). The human population has grown from about 3.0 to 7.7 billion people since 1960 (ANONIMOUS, n.d.), and in all probability has exerted a primary impact on the environment. It is, indeed, responsible for the huge release of the so-called greenhouse gases (GHG), namely CO2, CH4, N2O, hydrochlorofluorocarbons (HFCs), perfluorinated chemicals (PFCs) and SF6, in the atmosphere. Since 1980 the volumetric concentrations of CO2, CH4 and N2O in the atmosphere over marine surface sites have definitely increased from about 380 to 405 ppm (NOAA, n.d.), 1566 to 1835 ppb and 301 to 328 ppb (EEA, 2017), respectively. To allow any person now living on the Earth and those expected to live until 2100 the same rights to emit GHGs, the GHG emission space per capita and in a year should be limited to 2400 kg of CO2, 59 kg of CH4, and 0.67 kg of N2O, provided the atmospheric concentration of CO2 is less than 450 ppm with CH4 and N2O emissions kept at the same levels measured in 1995 (CARLSSON-KANYAMA, 1998; IPCC, 1996). Thus, the per capita GHG emissions permitted each year within a 20-yr time perspective, as estimated by summing the mass of each GHG times its corresponding global warming potential (IPCC, 2013), would amount to (1x2400+84x59+264x0.67=) 7533 kg CO2e yr-1. By referring to the national inventory reports (NIR) published by UNCC (2018), it is possible to assess whether such permitted GHG emissions are congruent with the ones currently in several countries. In 2007, the direct per capita emissions ranged from 24.0 to 1.6 Mg CO2e yr-1 for the USA and India, respectively (BERNERS-LEE, 2010). As shown in Table 1, in 2016 the Italian GHG emissions (including those adsorbed by land use, land use change and forestry, LULUCF) amounted to circa 398 Tg CO2e (ISPRA, 2018), equivalent to the Italian per capita CF of about 6.7 Mg CO2e yr-1. Altogether, these emissions were mainly composed of CO2, followed by CH4 and N2O, while the contribution of the halogenated compounds (i.e., HFCs, PFCs, NF3, and SF6) was negligible. The main GHG emissions were from the energy sector (347.1 Tg CO2e), this was followed by the industrial Ital. J. Food Sci., vol. 31, 2019 - 811 (32.1 Tg CO2e), agricultural (30.4 Tg CO2e), and waste (18.3 Tg CO2e) sectors, while the category LULUCF was the main GHG sink (-29.9 Tg CO2e). More specifically, the agriculture sector mainly emitted CH4 from animal husbandry [i.e., enteric fermentation (14.0 Tg CO2e) and manure management (3.1 Tg CO2e)] and rice cultivation (1.7 Tg CO2e), and N2O from agricultural soils (8.9 Tg CO2e) and manure management (2.1 Tg CO2e). The industrial processing ones were mainly due to the iron and steel industry, followed by the chemical, and pulp, paper and print ones. The food processing, beverages and tobacco sector emitted ~3.7 Tg CO2e (ISPRA (2018). The contribution of the agro-food sector to the overall direct GHG emissions cannot be directly extracted from any NIR. In fact, most of its subsectors (namely, agro-food product transportation; production and transportation of packaging materials; food transport from retailer to consumer’s house; electric energy consumed to preserve foods in the home freezer, fridge, etc.; gas and/or electric energy consumed to cook foods; disposal of food losses or wastes) are aggregated in other sectors. The Italian contribution without the consumer and post-consumer phases was found to be about 19% of the overall GHG emissions (MORESI, 2014), this falls within the range estimated by TUKKER et al. (2006). The main direct impacts of food processing are derived from waste generation, water use, and energy use (DIEU, 2009). Food waste is intense in farms due to spoilage (~21% of supply), but limited to ~7% throughout food processing. Food waste may be the loss of inedible materials or rejected products from sorting, grading, peeling, trimming, and squeezing. It may amount to the 50-70% of fresh citrus fruits or crab and shrimp processed (DIEU, 2009). Packaging materials (i.e., paper- and card-board, plastics, glass, metals, and wood) are largely used to protect processed foods not only from deterioration and/or contamination (primary packaging), but also from mechanical damage through the distribution and retailing operations (secondary and tertiary packaging). In food processing large volumes of water are used as the main ingredient, particularly in drink production, as the initial and intermediate cleaning source, transportation conveyor of raw materials, and principal agent used in sanitizing plant areas and machinery (DIEU, 2009). The water consumption in fruit and vegetable processing ranges from 4 to 32 m3 per Mg of product treated, of which approximately 50% is used just for washing and rinsing. The water used to make beer or milk products may vary from 9 to 18 m3 Mg-1. The resulting wastewaters are generally rich in organic matter, and sometimes are contaminated with pesticide residues from raw material treatments. Up to 50-60% of the water might be reclaimed and reused after screening, filtering or dilution with fresh water. Air emissions during food processing may contain fine particles, combustion products (CO, CO2, NOX), volatile organic compounds, and in the case of fish by-products may contain unpleasant odorous contaminants, such as H2S, and (CH3)3N (DIEU, 2009). The energy needs of food industry are of low or medium intensity. Some sectors (e.g., wet corn milling, beet sugar, soybean oil mills, malt beverages, meat packaging, canned and frozen fruits and vegetables, bread, and baked products) are however high-energy users (DIEU, 2009). The 38% of all the energy consumed by the Italian agro-food industry is electric, while the remainder is thermal (MISE, n.d.). The total impact of energy use might be lessened by minimizing the energy needs of production, producing energy from waste, and using renewable energy sources. Ital. J. Food Sci., vol. 31, 2019 - 812 Table 1. Summary report for the overall Italian direct CO2 equivalent emissions, including or excluding the net GHG emissions adsorbed from Land Use, Land-Use Change and Forestry (LULUCF), as referred to the main GHG sources (i.e., CO2, CH4, N2O, and halogenated compounds, HC, as HFCs, PFCs, NF3, and SF6) and sink categories in the year 2016, as extracted from ISPRA (2018). GHG Source CO2 CH4 N2O HC Subtotal 1 Subtotal 2 Total Sink Categories Tg CO2e 1. Energy 334.93 7.66 4.49 0 347.08 A. Fuel combustion 332.44 2.93 4.48 0 339.86 1. Energy industries 103.79 0.13 0.44 0 104.36 2. Manufacturing industries and construction 46.96 0.28 0.71 0 47.94 3. Transport 103.38 0.22 0.91 0 104.51 4. Other sectors 77.81 2.30 2.41 0 82.52 5. Other 0.52 0.00 0.02 0 0.53 B. Fugitive emissions from fuels 2.48 4.73 0.01 0 7.22 1. Solid fuels 0.00 0.04 0.00 0 0.04 2. Oil and natural gas 2.48 4.69 0.01 0 7.18 2. Industrial processes and product use 14.76 0.05 0.57 16.72 32.10 32.10 A. Mineral industry 10.61 0.00 0.00 0.00 10.61 B. Chemical industry 1.46 0.00 0.12 1.49 3.08 C. Metal industry 1.71 0.04 0.00 0.01 1.76 D. Non-energy products from fuels and solvent use 0.98 0.00 0.00 0.00 0.98 E. Electronic Industry 0.00 0.00 0.00 0.22 0.22 F. Products used as substitutes for Ozone-depleting substances 0.00 0.00 0.00 14.66 14.66 G. Other product manufacture and use 0.00 0.00 0.46 0.33 0.79 3. Agriculture 0.54 18.87 10.98 0.00 30.39 30.39 A. Enteric fermentation - 14.04 - 0.00 14.04 B. Manure management - 3.11 2.12 0.00 5.23 C. Rice cultivation - 1.71 - 0.00 1.71 D. Agricultural soils - - 8.86 0.00 8.86 E. Prescribed burning of savannas - - - 0.00 0.00 F. Field burning of agricultural residues - 0.017 0.004 0.00 0.02 G. Liming 0.01 - - 0.00 0.01 H. Urea application 0.53 - - 0.00 0.53 4. LULUCF -31.08 0.40 0.76 0.00 -29.93 -29.93 A. Forest land -36.08 0.28 0.001 0.00 -35.80 B. Cropland 2.46 0.002 0.03 0.00 2.49 C. Grassland -6.64 0.12 0.04 0.00 -6.48 D. Wetlands - - - - - E. Settlements 9.01 - 0.68 0.00 9.69 F. Harvested wood products 0.17 - - 0.00 0.17 5. Waste 0.09 16.29 1.90 0.00 18.29 18.29 A. Solid waste disposal - 13.62 - 0.00 13.62 B. Biological treatment of solid waste - 0.12 0.53 0.00 0.65 C. Incineration and open burning of waste 0.09 0.06 0.02 0.00 0.18 D. Waste water treatment and discharge 2.49 1.35 0.00 3.84 Total CO2 equivalent emissions without LULUCF 427.86 Total CO2 equivalent emissions with LULUCF 397.94 Ital. J. Food Sci., vol. 31, 2019 - 813 3. THE ENVIRONMENTAL IMPACT OF FOOD PROCESSING The complete supply chain of the food industry from the production of raw materials via food processing to the consumption and disposal by the consumer is quite complex and is schematically sketched in Fig. 1. Figure 1. Simplified flow sheet of the supply chain of the food industry, as adapted from MORESI (2014). 4. LIFE-CYCLE ASSESSMENT: PROS AND CONS Life-cycle assessment (LCA) is a technique capable of assessing the environmental impact associated with a product, process or activity during its life cycle from raw material extraction via material processing, packaging, distribution, use, repair and maintenance to the final disposal, that is from cradle to grave (MINKOV et al., 2016). Its procedure is standardized by the International Organization for Standardization (ISO, 2006ab) and is performed in four different phases: i) Goal and scope of the study to set the functional unit (i.e., the reference unit), system boundaries, allocation methods and impact categories of choice, as well as the assumptions and limitations used. ii) Inventory analysis by constructing a flow chart including all the activities involved in the system boundaries and a flow model to relate all input and output data to and from the environment in order to account for 99% of the mass and energy used in the system under study. iii) Impact assessment to convert the inventory analysis results into specific environmental impact categories. These may be also categorized under the development, manufacture, use, and disposal phases of the product examined. iv) Interpretation to discuss the outcomes of the above stages, identify the data elements contributing most significantly to each impact category and measure their sensitivity, assess the completeness and consistency of the study, and provide a basis for conclusions and recommendations. Several impact categories are used to measure the potential impacts to the natural environment, human health or depletion of natural resources. Table 2 lists the main ones together with their characterization models, as derived from MANFREDI et al. (2012) and MORAWICKI (2012). Thus, by summing up any release to air, water or soil Yi (expressed in mass, energy, mass-km basis) associated to the system boundaries times its corresponding science-based conversion factor, called characterization factor (Fi,j), it is possible to estimate the score of the generic impact category (ICj) as: )F (ΨIC ji, i ij ∑= (1) Ital. J. Food Sci., vol. 31, 2019 - 814 In particular, the environmental impact of climate change can be directly calculated by using the 100-year time horizon Global Warming Potentials (GWP) relative to the CO2 of the GHGs, which were recently reassessed by IPCC (2013). Table 2. Main impact categories used in several LCA standard methods, as extracted from MANFREDI et al. (2012) and MORAWICKI (2012). Impact category Category definition Indicator Unit Ref.s Climate Change (CC) The potential change on the Earth climate is due to human activity and GHG release. kg CO2e IPCC(2007) Ozone Depletion (OD) The industrial gas concentrations accelerating O3 decomposition in the Earth’s stratosphere affect living organisms kg CFC-11e WMO (1999) Acidification (A) The release of NOX and SO2 which combine with water in the atmosphere forms HNO3 and H2SO3. mol H+e SEPPÄLÄ et al. (2006) Eutrophication- aquatic (NPA) The release of N- and P-rich nutrients in surface waters results in excessive plant growth. Fresh water: kg Pe; Marine water: kg Ne STRUIJS et al. (2009) Eutrophication- terrestrial (NPT) The deposition of N from the emissions released by N-rich nutrients affects terrestrial ecosystems too. mol Ne SEPPÄLÄ et al. (2006) Photochemical Ozone Creation (POC) The formation of ground-level O3, as due to the reaction of NOX and volatile organic compounds, causes irritation for humans and damage for plants. kg NMVOCe VAN ZELM et al. (2008) Ecotoxicity-aquatic, freshwater (ET) Interaction among chemical compounds and organisms in the environment. CTUe ROSENBAUM et al. (2008) Human Toxicity- cancer effects (HTC) Chemical compounds may cause several types of cancer in humans or CTUh ROSENBAUM et al. (2008) non-cancer effects (HTNC) chronic non-cancer effects including mutagenicity, toxicity, etc. CTUh ROSENBAUM et al. (2008) Particulate Matter (PM) Particulate matter causes respiratory problems. kg PM2.5e HUMBERT et al.(2011) Ionizing Radiation- human health effects (IR) Ionizing radiation affects the risk for human cancer incidence and mortality increase. kg U235e DREICER et al. (1995) Resource Depletion- water (RDW) Use and depletion of fresh water, minerals and fossil resources impact ecosystems and many species survival. m3 of water related to local water scarcity FRISCHKNECHT et al. (2008) mineral/fossil (RDMF) kg Sbe VAN OERS et al. (2002) Land Transformation (LT) The extent of changes in land properties and effects on the area affected. kg Soil Organic Matter MILÀ I CANALS et al. (2007) The environmental performance of food and drink production may be currently assessed by various standard methods, such as those listed in Table 3. Some of them (i.e., Product Carbon Footprint; PAS2050; Bilan Carbone®, BC; GHG Protocol) make use of only the impact category of climate change and give no hint about the overall environmental impact of the products, even if the emissions from direct land-use changes over the previous 20 years are generally included (Table 3). Other standard methods evaluate from seven (i.e., LCA, and Environmental Product Declaration, EPD®) to 14 (Product Environmental Footprint, PEF) impact categories. Their scores are estimated using a series Ital. J. Food Sci., vol. 31, 2019 - 815 of LCA data sources and characterization factors, which obviously are strongly dependent on the LCA databases used. There is thus a strong need for reliable databases to achieve a trustworthy assessment of a product life cycle environmental performance, as observed by the food and drink companies involved in several PEF pilot tests (FOODDRINKEUROPE, 2017). Table 3. Brief description of some international standard methods for product and service environmental assessment together with the impact categories (IC) accounted for (same labels as in Table 2). Standard method Description Impact categories chosen Ref.s Life Cycle Assessment (LCA) Specifies requirements and provides guidelines for LCA studies. CC; OD; A; NP; POC; RD; LU. ISO (2006ab) Carbon Footprint of Product Allows the calculation of PCF, based on LCA specified in ISO (2006ab). CC; LUC. ISO/TS (2013) PAS 2050 Provides a standardized guidance for calculating the PCF of goods and services. CC; LUC. BSI (2008) Bilan Carbone® Tool developed by the French Environment & Energy Management Agency GHG to assess GHG emissions. CC; LUC. ADEME (2010) Environmental Product Declaration (EPD®) Tool supported by the Swedish government. CC; OD; A; NP; POC; RD; LU. ISO (2006c) GHG Protocol Defines how measuring, and reporting GHG emissions in the USA. CC BHATIA et al. (2011) Product Environmental Footprint (PEF) Novel European Community methodology under development. CC; OD; A; NPA; NPT; POC; ET; HTC; HTNC; PM; IR; RDW; RDMF; LT. MANFREDI et al. (2012) The greater the number of impact categories accounted for, the more precise the environmental profile of the product under study will be. Nevertheless, the estimation with as many as 14 impact categories (Table 3) was harshly criticized by numerous stakeholders, such as academia (CIMINI and MORESI, 2018a; FINKBEINER, 2014; LEHMANN et al., 2016), industry (ACEA, 2013; BDI, 2015), policy-makers (BMUB/UBA/TUB, 2014), and consumer associations (ANEC, 2012), for being uselessly complex and very expensive. In fact, the Federation of German Industry (BDI, 2015) estimated an average cost of about 100 k€ for assessing the PEF profile of a single product. Furthermore, some critical issues were identified to ensure that the LCA delivered robust results (NOTARNICOLA et al., 2017). In particular, the intrinsic variability of the agricultural system affected the inventory analysis, impact assessment, and interpretation phases. The higher the output per hectare the higher will be the eco-efficiency of the final product. However, long-term sustainability of food production in a given production area is not considered in the current LCA method. Many LCA studies give no details about the soil, climate and weather conditions, timescale adopted, transport distances and modes used to deliver raw materials and final products, as well as the use phase and related wastes. A more meaningful functional unit for food products was also proposed by SONESSON et al. (2017) in order to relate the nutritional function of foods to their LCA results and account for the sustainable food consumption and food security. How to represent such variability in LCA studies without having to collect an enormous number of extra data that would make such studies disproportionately expensive is a primary Ital. J. Food Sci., vol. 31, 2019 - 816 challenge for LCA researchers and practitioners. Thus, to allow small- and medium-sized food and drink enterprises to improve their sustainability in the most direct and economical method, the assessment of the Product Carbon Footprint (PCF) appeared to be more useful. In fact, not only was the climate change impact category with the lowest levels of uncertainty (CIMINI and MORESI, 2018a), but also was the major contributor to acidification (r2=0.82), eutrophication (r2=0.66), and photochemical ozone formation (r2=0.86) categories (HUIJBREGTS et al., 2006). 5. IDENTIFICATION OF THE KEY ELEMENTS FOR SUSTAINABLE FOOD PROCESSING The food and beverage industry is seeking to improve its environmental performance and identify which actions are suitable for a more sustainable production (MORESI, 2014). No food processing nowadays is 100% sustainable owing to the lack of energy, ingredients and packaging materials derived from renewable resources; excessive water use; the inherent CH4 and N2O emissions associated with crop production and animal husbandry; and lack of biodegradable packaging materials (MORAWICKI, 2012). Nevertheless, by accounting for only the impact category of climate change, MORAWICKI (2012) suggested a simple and progressive approach to relieve the environmental impact of a food company. First, food processing plant efficiencies for energy, water, and raw and packaging material consumption should be improved and fossil energy usage replaced with renewable one by purchase or self-generation. Second, the GHG emissions associated with the transportation of raw materials and final products should be reduced. Third, the GHG emissions resulting from the field phase should be minimized. Fourth, the impact of the post-consumer disposal of packaging materials, as well as food loss, is to be reduced. Despite being firm-oriented, such an approach might result in mitigation actions exerting a minimum reduction in the product carbon footprint. Thus, the mitigation opportunities should be prioritized starting from the life cycle stages with the highest contribution to PCF, as previously assessed (CIMINI and MORESI, 2018b). This procedure was specifically applied to improve the sustainability of two typical cereal-based food and drink products, as detailed in the following cases studies. 6. CASE STUDY NO. 1: LAGER BEER PRODUCTION The cradle-to-grave carbon footprint (CFCG) of a malt lager beer was previously estimated (CIMINI and MORESI, 2016, 2018c) by applying the PAS 2050 standard method (BSI, 2008). All the aforementioned four LCA canonical stages were referred to a functional unit consisting of 1 hL of malt beer, as produced in a large-sized brewery with an annual beer capacity of 3x106 hL and packed in 66-cL glass bottles. The system boundaries for this case study are shown in Fig. 2. According to PAS 2050 (Section 7.2), the geographical and time scopes of this LCA study are the Western Europe and from the years 2006-2016. Main process data were of the primary type (CIMINI and MORESI, 2016). By using all the essential data previously given (CIMINI and MORESI, 2018b), the LCA model was able to estimate the CFCG as 127 kg CO2e per hL of beer. The contribution of the different life cycle stages are shown in Fig. 3. Ital. J. Food Sci., vol. 31, 2019 - 817 Figure 2. Beer system boundaries, as adapted from CIMINI and MORESI (2018c). The main identification items are listed in the Abbreviations and Nomenclature section. Figure 3. Contribution of the different life cycle phases to the cradle-to-grave carbon footprint (CFCG) of 1 hL of beer packed in 66-cL glass bottles in a large-sized brewery, as estimated from the LCA model previously developed (CIMINI and MORESI, 2018b), and its cumulative score (see broken line). For the identification items refer to the Abbreviations and Nomenclature section. Ital. J. Food Sci., vol. 31, 2019 - 818 The life cycle phases contributing mostly to the CFCG, in descending order, were associated with packaging material manufacture (~56 kg CO2e hL-1), overall transportation (~29 kg CO2e hL-1), production of malted barley and processing aids (~23 kg CO2e hL-1), consumer use (~19 kg CO2e hL-1), beer production and packaging (~12 kg CO2e hL-1), and waste disposal (1.2 kg CO2e hL-1). CO2e credits derived from the use of spent grains and surplus yeast as animal feed (2.1 kg CO2e hL-1) and from recycling of glass bottles, paper and cardboard wastes (11 kg CO2e hL-1). Instead of adopting the aforementioned MORAWICKI’s approach to sustainability, a series of improvement opportunities was scheduled to sequentially reduce the contribution of the most impacting life cycle phases of the above reference case. Firstly, the replacement of 10% recycled glass bottles with 100% recycled ones reduced the CFCG by about 21 % with respect to the reference case. By shifting the transportation mode from 100% of road freight to 100% of rail freight to manage logistics flows, an additional 10% decrease in CFCG was achieved. The use of organic instead of conventional barley grown locally had the effect of decreasing the CFCG by another 9%. A quasi zero-carbon alternative for electricity generation is solar-photovoltaic electricity. Such a shift further lessened the CFCG by 13%. On the contrary, by reducing the delivery distance of malted barley from 500 to 250 km, no significant change was observed in the CFCG; hence, reducing distance had a negligible effect. Table 4 shows all the emission factors (EFi) that were varied and how the above sequential series of mitigation options practically halved the beer carbon footprint from about 127 to 60 kg CO2e hL-1. Since the per capita consumption of beer in Italy is about 31.8 L yr-1 (ASSOBIRRA, 2018) and the current Italian population is 59,228,336 (WORLDOMETERS, 2019), the GHG emissions associated with the Italian consumption of beer would be reduced from 2.39 to 1.13 Tg CO2 yr-1. The application of the aforementioned mitigating actions had the effect of limiting the contribution from beer to 0.28% of the overall Italian GHG emissions (Table 1). Table 4. Effect of the sequential mitigation strategies used to minimize the cradle-to-grave beer carbon footprint (CFCG) and its cumulative percentage variation with respect to that pertaining to the reference case ( !"#!"# !"!" ∗ ). The sequential stepwise procedure started from the most impacting life cycle phase as resulting from Fig. 3. Mitigation strategy Parameter varied Unit CFCG [kg CO2e hL -1] ∆𝑪𝑭𝑪𝑮𝒋 𝑪𝑭𝑪𝑮 ∗ [%] Beer reference case (*) 127.2 0 100% recycled glass bottles EFRB 1.08à0.48 kg CO2e kg -1 100.3 -21 Malt & beer rail transport EFRT 0.168à0.039 kg CO2e (Mg km) -1 88.2 -31 Organic malt EFOC 1.143à0.546 kg CO2e kg -1 76.6 -40 Local malt dRM 500à250 km 76.5 -40 Photovoltaic electric energy EFPEE 0.324à0.055 kg CO2e kWh -1 60.2 -53 7. CASE STUDY NO. 2: DRY PASTA PRODUCTION The cradle-to-grave CFCG of an organic durum wheat semolina pasta was previously estimated (CIBELLI et al., 2017; CIMINI et al., 2019c) using the PAS 2050 standard method (BSI, 2008). All the LCA canonical stages were referred to a functional unit consisting of 1 Ital. J. Food Sci., vol. 31, 2019 - 819 kg of dry pasta, produced in a medium-sized pasta factory with a capacity of approximately 125 Gg yr-1 and packed in 0.5-kg polypropylene (PP) bags. The system boundaries for this case study are shown in Fig. 4. According to the PAS 2050 (Section 7.2), the geographical and time scopes of this LCA study were the Western Europe and from the years 2006-2016. Finally, the process data were of the primary type, as reported by CIMINI et al. (2019c). The estimated dry pasta CFCG was about 1.8 kg CO2e kg-1, the contribution of all the life cycle phases being plotted in Fig. 5. Their impacts were therefore ranked as follows: field phase (~0.67 kg CO2e kg-1), home pasta cooking (0.65 kg CO2e kg-1), pasta production and packaging (~0.20 kg CO2e kg-1), transportation (~0.15 kg CO2e kg-1), packaging material manufacture (~0.11 kg CO2e kg-1), durum wheat milling (~0.05 kg CO2e kg-1), end of life of packaging materials (~0.03 kg CO2e kg-1) and pasta losses (~0.02 kg CO2e kg-1). CO2e credits resulted from using wheat milling by-products, and pasta making and packaging wastes for animal feed (~0.07 kg CO2e kg-1) as an alternative to soybean meal fodder (CIMINI et al., 2019c). Figure 4. Dry pasta system boundaries, as adapted from CIMINI et al. (2019c). The main identification items are listed in the Abbreviations and Nomenclature section. Figure 5. Contribution of the different life cycle stages to the cradle-to-grave carbon footprint (CFCG) of 1 kg of dried organic pasta packed in 0.5-kg PP bags in a medium-sized pasta factory, as estimated from the LCA model previously developed (CIMINI et al., 2019c), and its cumulative score (see broken line). The main identification items are listed in the Abbreviations and Nomenclature section. 666 52 201 107 12 136 19 -69 651 19 14 -200 100 400 700 1,000 1,300 1,600 1,900 FP MI PPP PaM TRpaw TRfp STPEoL BPC CP PS PPEoL C F i [g C O 2e k g- 1 ] CFCG = 1.81 kg CO2e kg-1 Ital. J. Food Sci., vol. 31, 2019 - 820 To improve the sustainability of such product, a series of mitigating actions were programmed to reduce the contribution of the most impacting life cycle phases of the above reference case. In particular, to limit the impact of the primary hotspot (i.e., the consumer and post-consumer ones), the eco-sustainable pasta cooking procedure suggested by CIMINI et al. (2019ab) was applied by setting the cooking water-to-dry pasta ratio at 2 L kg-1 and the nominal cooking power at 0.4 kW. In this way, the CFCG was cut by 29% with respect to the reference case. Use of organic crop rotation enabled the CFCG to be decreased by another 13%. By replacing the methane needed for the steam generating boilers with biogas, the CFCG was further reduced by 7%. Use of solar-photovoltaic electricity also lessened the CFCG by an extra 9%. Similarly, by shifting from road to rail freight transport, a supplementary 2% reduction in the CFCG was obtained. Finally, when the final product or grain delivery distance was shortened from 900 or 150 km to as low as 250 or 50 km, respectively, the CFCG still reduced by 2 or 1%. In total, such a sequential series of mitigating options allowed the dry pasta carbon footprint to be reduced from 1.81 to 0.68 kg CO2e kg-1 (Table 5). Table 5. Effect of the sequential mitigation strategies used to minimize the cradle-to-grave dry pasta carbon footprint (CFCG) and its cumulative percentage variation with respect to that pertaining to the reference case ( !!"!"# !"!" ∗ ). The sequential stepwise procedure started from the most impacting life cycle phase as shown in Fig. 5. Mitigation strategy Parameter varied Unit CFCG [kg CO2e kg -1] 𝜟𝑪𝑭𝑪𝑮𝒋 𝑪𝑭𝑪𝑮 ∗ [%] Dry pasta reference case (*) 1.81 0 Eco-sustainable cooking PC 2.3à0.4 kWh kg -1 1.28 -29 Organic rotation cropping EFOC 0.534à0.36 kg CO2e kg -1 1.06 -42 Thermal energy from biogas EFBG 0.231à0.029 kg CO2e kWh -1 0.92 -49 Photovoltaic electric energy EFPEE 0.513à0.055 kg CO2e kWh -1 0.77 -58 Pasta rail transport EFRT 0.168à0.047 kg CO2e (Mg km) -1 0.72 -60 Pasta regional distribution dP 900à250 km 0.70 -62 Durum wheat local supply dRM 150à50 km 0.68 -63 Since the per capita consumption of pasta in Italy is about 23.5 kg yr-1 (UNAFPA, 2015), the GHG emissions associated with the Italian consumption of dry pasta would reduce from 2.52 to 0.95 Tg CO2 yr-1. The aforementioned mitigating actions had the effect of reducing the impact of the dry pasta sector to the 0.24% of the overall Italian GHG emissions (Table 1). 8. CONCLUSIONS In this work, the main direct environmental impacts of the food industry and GHG emissions for the agro-food system in industrialized countries were analyzed together with the main advantages and disadvantages of the standard methods currently used to assess the food and drink environmental impact. Ital. J. Food Sci., vol. 31, 2019 - 821 Owing to the great deal of money needed to characterize the whole environmental profile of a single product, and the fact that the climate change impact category was by far more reliable than all the other ones used in the EPD® and PEF standard methods made the assessment of the product carbon footprint a cheaper tool to identify the major hotspots of the food supply chain. Thus, it is probably the best method to start improving the sustainability of the 99% of the food and beverage SMEs. It was used here to select a sequential series of mitigating actions in order to reduce the cradle-to-grave product carbon footprint (CFCG) of 1 hL of beer packed in 66-cL glass bottles from about 127 to 60 kg CO2e hL-1, and that of 1 kg of dry organic pasta packed in 0.5-kg PP bags from 1.81 to 0.68 kg CO2e kg-1. A cost/benefit analysis might help SMEs to relate the marginal increase in the overall final product costs to each reduction in the product environmental load. Since only the assessment of GHG emissions might result in burden shifting, a further step should investigate the effect of the selected mitigating actions on other environmental impact categories. ABBREVIATIONS AND NOMENCLATURE A Acidification BC Bilan Carbone® BPC CO2e credits from by-product use as cattle feed; BPP Brewing and packaging processing BSG Brewer’s spent grain CC Climate Change CFCG Cradle-to-grave product carbon footprint [kg CO2e hL-1 or kg-1] CFC Trichlorofluoromethane or Freon-11 CO2e Carbon dioxide equivalent CP Consumer phase CTUe Comparative Toxic Unit for ecosystems CTUh Comparative Toxic Unit for humans DE Diatomaceous earth DC distribution centers dP Distribution distance of packed dry pasta [km] dRM Supply distance of raw materials [km] EE Electric energy EFBG Emission factor for biogas [kg CO2e kWh-1] EFOC Emission factor for organic crop [kg CO2e kg-1] EFPEE Emission factor for photovoltaic electric energy [kg CO2e kWh-1] EFRB Emission factor for 100% recycled glass bottles [kg CO2e kg-1] EFRT Emission factor for rail freight transport [kg CO2e (Mg km)-1] EoL End of life EPD Environmental Product Declaration ET Ecotoxicity – aquatic, freshwater EU European Union Fij Generic i-th characterization factor of the j-th impact category FP Field phase GHG Greenhouse gas HC Halogenated compound HFC Hydrochlorofluorocarbon HTC Human toxicity - cancer effects HTNC Human toxicity - non-cancer effects IC Impact category IR Ionizing radiation – human health effects LCA Life cycle assessment LT Land transformation LULUCF Land use, land use change and forestry MI Milling NIR National Inventory Report NMVOC Non-methane volatile organic compound Ital. J. Food Sci., vol. 31, 2019 - 822 NP Eutrophication NPA Eutrophication- aquatic NPT Eutrophication- terrestrial OD Ozone depletion PaM Packaging materials PAS Publicly available specification PC Specific food cooking power [kWh kg-1] PCF Product carbon footprint PCWD Post-consumer waste disposal PEF Product environmental footprint PFC Perfluorinated chemical PM Particulate matter/respiratory inorganics POC Photochemical ozone creation PP Polypropylene PPEoL Primary packaging end of life. PPP Pasta production and packaging PS Pasta scraps PVPP Polyvinylpolypyrrolidone Q Thermal energy RD Resource depletion RDMF Resource depletion – mineral/fossil RDW Resource depletion – water RPM Raw and processing materials RR Retailer refrigeration r2 Coefficient of determination SME Small- and medium-sized enterprise STPEoL Secondary and tertiary packaging end of life TR Transportation TRBPW Transportation of byproducts and wastes TRBR Transportation of beer in cartons from DCs to retailers TRBDC Transportation of palletized beer to distribution centers TRfp Transport of final product TRIR Transportation of input resources to the brewery gate TRpaw Transport of packaging and auxiliary materials, and wastes TW Process water WED Waste and effluent disposal ΔCFCG/CFCG* Relative percentage variation of CFCG with respect to that pertaining to the reference case [%] Ψi Generic i-th activity, expressed in mass, energy or mass-km basis. 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