Parametric assessment of climate change impacts of automotive material substitution

Quantifying the net climate change impact of automotive material substitution is not a trivial task. It requires the assessment of the mass reduction potential of automotive materials, the greenhouse gas (GHG) emissions from their production and recycling, and their impact on GHG emissions from vehicle use. The model presented in this paper is based on life cycle assessment (LCA) and completely parameterized, i.e., its computational structure is separated from the required input data, which is not traditionally done in LCAs. The parameterization increases scientific rigor and transparency of the assessment methodology, facilitates sensitivity and uncertainty analysis of the results, and also makes it possible to compare different studies and explain their disparities. The state of the art of the modeling methodology is reviewed and advanced. Assessment of the GHG emission impacts of material recycling through consequential system expansion shows that our understanding of this issue is still incomplete. This is a critical knowledge gap since a case study shows thatfor materials such as aluminum, the GHG emission impacts of material production and recycling are both of the same size as the use phase savings from vehicle mass reduction.     

Greenhouse Gas Emission Evaluation of the GTL Pathway

Gas to liquids (GTL) products have the potential to replace petroleum-derived products, but the efficacy with which any sustainability goals can be achieved is dependent on the lifecycle impacts of the GTL pathway. Life cycle assessment (LCA) is an internationally established tool (with GHG emissions as a subset) to estimate these impacts. Although the International Standard Organization's ISO 14040 standard advocates the system boundary expansion method (also known as the "displacement method" or the "substitution method") for life-cycle analyses, application of this method for the GTL pathway has been limited until now because of the difficulty in quantifying potential products to be displaced by GTL coproducts. In this paper, we use LCA methodology to establish the most comprehensive GHG emissions evaluation to date of the GTL pathway. The influence of coproduct credit methods on the GTL GHG emissions results using substitution methodology is estimated to afford the Well-to-Wheels (WTW) greenhouse gas (GHG) intensity of GTL Diesel. These results are compared to results using energy-based allocation methods of reference GTL diesel and petroleum-diesel pathways. When substitution methodology is used, the resulting WTW GHG emissions of the GTL pathway are lower than petroleum diesel references. In terms of net GHGs, an interesting way to further reduce GHG emissions is to blend GTL diesel in refineries with heavy crudes that require severe hydrotreating, such as Venezuelan heavy crude oil or bitumen derived from Canadian oil sands and in jurisdictions with tight aromatic specifications for diesel, such as California. These results highlight the limitation of using the energy allocation approach for situations where coproduct GHG emissions reductions are downstream from the production phase.     

Uncertainty in life cycle greenhouse gas emissions from United States natural gas end-uses and its effects on policy

Increasing concerns about greenhouse gas (GHG) emissions in the United States have spurred interest in alternate low carbon fuel sources, such as natural gas. Life cycle assessment (LCA) methods can be used to estimate potential emissions reductions through the use of such fuels. Some recent policies have used the results of LCAs to encourage the use of low carbon fuels to meet future energy demands in the U.S., without, however, acknowledging and addressing the uncertainty and variability prevalent in LCA. Natural gas is a particularly interesting fuel since it can be used to meet various energy demands, for example, as a transportation fuel or in power generation. Estimating the magnitudes and likelihoods of achieving emissions reductions from competing end-uses of natural gas using LCA offers one way to examine optimal strategies of natural gas resource allocation, given that its availability is likely to be limited in the future. In this study, the uncertainty in life cycle GHG emissions of natural gas (domestic and imported) consumed in the U.S. was estimated using probabilistic modeling methods. Monte Carlo simulations are performed to obtain sample distributions representing life cycle GHG emissions from the use of 1 MJ of domestic natural gas and imported LNG. Life cycle GHG emissions per energy unit of average natural gas consumed in the U.S were found to range between -8 and 9% of the mean value of 66 g CO(2)e/MJ. The probabilities of achieving emissions reductions by using natural gas for transportation and power generation, as a substitute for incumbent fuels such as gasoline, diesel, and coal were estimated. The use of natural gas for power generation instead of coal was found to have the highest and most likely emissions reductions (almost a 100% probability of achieving reductions of 60 g CO(2)e/MJ of natural gas used), while there is a 10-35% probability of the emissions from natural gas being higher than the incumbent if it were used as a transportation fuel. This likelihood of an increase in GHG emissions is indicative of the potential failure of a climate policy targeting reductions in GHG emissions.     

Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: A case study

The objective of this study was to conduct a life-cycle assessment (LCA) of greenhouse gas (GHG) emissions from a typical nongrazing dairy production system in Eastern Canada. Additionally, as dairying generates both milk and meat, this study assessed several methods of allocating emissions between these coproducts. An LCA was carried out for a simulated farm based on a typical nongrazing dairy production system in Quebec. The LCA was conducted over 6 yr, the typical lifespan of dairy cows in this province. The assessment considered 65 female Holstein calves, of which 60heifers survived to first calving at 27mo of age. These animals were subsequently retained for an average of 2.75 lactations. Progeny were also included in the analysis, with bulls and heifers in excess of replacement requirements finished as grain-fed veal (270kg) at 6.5mo of age. All cattle were housed indoors and fed forages and grains produced on the same farm. Pre-farm gate GHG emissions and removals were quantified using Holos, a whole-farm software model developed by Agriculture and Agri-Food Canada and based on the Intergovernmental Panel for Climate Change Tier 2 and 3methodologies with modifications for Canadian conditions. The LCA yielded a GHG intensity of 0.92kg of CO(2) Eq/kg of fat- and protein-corrected milk yield. Methane (CH(4)) accounted for 56% of total emissions, with 86% originating from enteric fermentation. Nitrous oxide accounted for 40% of total GHG emissions. Lactating cows contributed 64% of total GHG emissions, whereas calves under 12mo contributed 10% and veal calves only 3%. Allocation of GHG emissions between meat and milk were assessed as (1) 100% allocation to milk, (2) economics, (3) dairy versus veal animals, and (4) International Dairy Federation equation using feed energy demand for meat and milk production. Comparing emissions from dairy versus veal calves resulted in 97% of the emissions allocated to milk. The lowest allocation of emissions to milk (78%) was associated with the International Dairy Federation equation. This LCA showed that greatest reductions in GHG emissions would be achieved by applying mitigation strategies to reduce enteric CH(4) from the lactating cow, with minimal reductions being achievable in young stock. Choice of coproduct allocation method can also significantly affect the relative allocation of GHG emissions to milk and meat.     

Beef production in balance: considerations for life cycle analyses

Life Cycle Assessments (LCA) are useful tools to analyze a product's "carbon footprint" (e.g., the net greenhouse gas (GHG) emissions expressed as standardized carbon dioxide equivalents per unit of product) considering all phases of the production chain. For beef, an LCA would include the GHG emissions from feed production, from the enteric fermentation of the cattle, from the cattle's waste, and from processing and transportation. Identifying the scope and scale of the LCA is critical and key to preventing inappropriate applications of the analysis (e.g., applying a global LCA for beef to the regional or national scale). Ideally, a LCA can integrate the complex biogeochemical processes responsible for GHG emissions and the disparate animal and agricultural management techniques used be different phases of the beef production chain (e.g., feedlot vs. cow-calf) and different production systems (e.g., conventional vs. organic).     

Relevance of emissions timing in biofuel greenhouse gases and climate impacts

Employing life cycle greenhouse gas (GHG) emissions as a key performance metric in energy and environmental policy may underestimate actual climate change impacts. Emissions released early in the life cycle cause greater cumulative radiative forcing (CRF) over the next decades than later emissions. Some indicate that ignoring emissions timing in traditional biofuel GHG accounting overestimates the effectiveness of policies supporting corn ethanol by 10-90% due to early land use change (LUC) induced GHGs. We use an IPCC climate model to (1) estimate absolute CRF from U.S. corn ethanol and (2) quantify an emissions timing factor (ETF), which is masked in the traditional GHG accounting. In contrast to earlier analyses, ETF is only 2% (5%) over 100 (50) years of impacts. Emissions uncertainty itself (LUC, fuel production period) is 1-2 orders of magnitude higher, which dwarfs the timing effect. From a GHG accounting perspective, emissions timing adds little to our understanding of the climate impacts of biofuels. However, policy makers should recognize that ETF could significantly decrease corn ethanol's probability of meeting the 20% GHG reduction target in the 2007 Energy Independence and Security Act. The added uncertainty of potentially employing more complex emissions metrics is yet to be quantified.     

Assessment of fuel-cycle energy use and greenhouse gas emissions for Fischer-Tropsch diesel from coal and cellulosic biomass

This study expands and uses the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model to assess the effects of carbon capture and storage (CCS) technology and cellulosic biomass and coal cofeeding in Fischer-Tropsch (FT) plants on energy use and greenhouse gas (GHG) emissions of FT diesel (FTD). To demonstrate the influence of the coproduct credit methods on FTD life-cycle analysis (LCA) results, two allocation methods based on the energy value and the market revenue of different products and a hybrid method are employed. With the energy-based allocation method, fossil energy use of FTD is less than that of petroleum diesel, and GHG emissions of FTD could be close to zero or even less than zero with CCS when forest residue accounts for 55% or more of the total dry mass input to FTD plants. Without CCS, GHG emissions are reduced to a level equivalent to that from petroleum diesel plants when forest residue accounts for 61% of the total dry mass input. Moreover, we show that coproduct method selection is crucial for LCA results of FTD when a large amount of coproducts is produced.     

Life cycle energy and greenhouse gas analysis of a large-scale vertically integrated organic dairy in the United States

In order to manage strategies to curb climate change, systemic benchmarking at a variety of production scales and methods is needed. This study is the first life cycle assessment (LCA) of a large-scale, vertically integrated organic dairy in the United States. Data collected at Aurora Organic Dairy farms and processing facilities were used to build a LCA model for benchmarking the greenhouse gas (GHG) emissions and energy consumption across the entire milk production system, from organic feed production to post-consumer waste disposal. Energy consumption and greenhouse gas emissions for the entire system (averaged over two years of analysis) were 18.3 MJ per liter of packaged fluid milk and 2.3 kg CO(2 )equiv per liter of packaged fluid milk, respectively. Methane emissions from enteric fermentation and manure management account for 27% of total system GHG emissions. Transportation represents 29% of the total system energy use and 15% of the total GHG emissions. Utilization of renewable energy at the farms, processing plant, and major transport legs could lead to a 16% reduction in system energy use and 6.4% less GHG emissions. Sensitivity and uncertainty analysis reveal that alternative meat coproduct allocation methods can lead to a 2.2% and 7.5% increase in overall system energy and GHG, respectively. Feed inventory data source can influence system energy use by -1% to +10% and GHG emission by -4.6% to +9.2%, and uncertainties in diffuse emission factors contribute -13% to +25% to GHG emission.     

A demand-centered, hybrid life-cycle methodology for city-scale greenhouse gas inventories

Greenhouse gas (GHG) accounting for individual cities is confounded by spatial scale and boundary effects that impact the allocation of regional material and energy flows. This paper develops a demand-centered, hybrid life-cycle-based methodology for conducting city-scale GHG inventories that incorporates (1) spatial allocation of surface and airline travel across colocated cities in larger metropolitan regions, and, (2) life-cycle assessment (LCA) to quantify the embodied energy of key urban materials--food, water, fuel, and concrete. The hybrid methodology enables cities to separately report the GHG impact associated with direct end-use of energy by cities (consistent with EPA and IPCC methods), as well as the impact of extra-boundary activities such as air travel and production of key urban materials (consistent with Scope 3 protocols recommended by the World Resources Institute). Application of this hybrid methodology to Denver, Colorado, yielded a more holistic GHG inventory that approaches a GHG footprint computation, with consistency of inclusions across spatial scale as well as convergence of city-scale per capita GHG emissions (approximately 25 mt CO2e/person/year) with state and national data. The method is shown to have significant policy impacts, and also demonstrates the utility of benchmarks in understanding energy use in various city sectors.     

Characterizing model uncertainties in the life cycle of lignocellulose-based ethanol fuels

Renewable and low carbon fuel standards being developed at federal and state levels require an estimation of the life cycle carbon intensity (LCCI) of candidate fuels that can substitute for gasoline, such as second generation bioethanol. Estimating the LCCI of such fuels with a high degree of confidence requires the use of probabilistic methods to account for known sources of uncertainty. We construct life cycle models for the bioconversion of agricultural residue (corn stover) and energy crops (switchgrass) and explicitly examine uncertainty using Monte Carlo simulation. Using statistical methods to identify significant model variables from public data sets and Aspen Plus chemical process models,we estimate stochastic life cycle greenhouse gas (GHG) emissions for the two feedstocks combined with two promising fuel conversion technologies. The approach can be generalized to other biofuel systems. Our results show potentially high and uncertain GHG emissions for switchgrass-ethanol due to uncertain CO? flux from land use change and N?O flux from N fertilizer. However, corn stover-ethanol,with its low-in-magnitude, tight-in-spread LCCI distribution, shows considerable promise for reducing life cycle GHG emissions relative to gasoline and corn-ethanol. Coproducts are important for reducing the LCCI of all ethanol fuels we examine.     

Variability and uncertainty in life cycle assessment models for greenhouse gas emissions from Canadian oil sands production

Because of interest in greenhouse gas (GHG) emissions from transportation fuels production, a number of recent life cycle assessment (LCA) studies have calculated GHG emissions from oil sands extraction, upgrading, and refining pathways. The results from these studies vary considerably. This paper reviews factors affecting energy consumption and GHG emissions from oil sands extraction. It then uses publicly available data to analyze the assumptions made in the LCA models to better understand the causes of variability in emissions estimates. It is found that the variation in oil sands GHG estimates is due to a variety of causes. In approximate order of importance, these are scope of modeling and choice of projects analyzed (e.g., specific projects vs industry averages); differences in assumed energy intensities of extraction and upgrading; differences in the fuel mix assumptions; treatment of secondary noncombustion emissions sources, such as venting, flaring, and fugitive emissions; and treatment of ecological emissions sources, such as land-use change-associated emissions. The GHGenius model is recommended as the LCA model that is most congruent with reported industry average data. GHGenius also has the most comprehensive system boundaries. Last, remaining uncertainties and future research needs are discussed.     

Predicting manure volatile solid output of lactating dairy cows

Organic matter (OM) in livestock manure consisting of biodegradable and nonbiodegradable fractions is known as volatile solids (VS). According to the Intergovernmental Panel on Climate Change (IPCC) Tier 2 guidelines, methane produced by stored manure is determined based on VS. However, only biodegradable OM generates methane production. Therefore, estimates of biodegradable VS (dVS; dVS = VS ? lignin) would yield better estimates of methane emissions from manure. The objective of the study was to develop mathematical models for estimating VS and dVS outputs of lactating dairy cows. Dry matter intake, dietary nutrient contents, milk yield and composition, body weight, and days in milk were used as potential predictor variables. Multicollinearity, model simplicity, and random study effects were taken into account during model development that used 857 VS and dVS measurements made on individual cows (kg/cow per day) from 43 metabolic trials conducted at the USDA Energy and Metabolism laboratory in Beltsville, Maryland. The new models and the IPCC Tier 2 model were evaluated with an independent data set including 209 VS and dVS measurements (kg/cow per day) from 2 metabolic trials conducted at the University of California, Davis. Organic matter intake (kg/d) and dietary crude protein and neutral detergent fiber contents (% of dry matter) were significantly associated with VS. A new model including these variables fitted best to data. When evaluated with independent data, the new model had a root mean squared prediction error as a percentage of average observed value (RMSPE) of 12.5%. Mean and slope biases were negligible at <1% of total prediction bias. When energy digestibility of the diet was assumed to be 67%, the IPCC Tier 2 model had a RMSPE of 13.7% and a notable mean bias for VS to be overpredicted by 0.4 kg/cow per day. A separate model including OM intake as well as dietary crude protein and neutral detergent fiber contents as predictor variables fitted best to dVS data and performed well on independent data (RMSPE = 12.7%). The Cornell Net Carbohydrate and Protein System model relying on fat-corrected milk yield and body weight more successfully predicted dry matter intake (DMI; RMSPE = 14.1%) than the simplified (RMSPE = 16.9%) and comprehensive (RMSPE = 23.4%) models to predict DMI in IPCC Tier 2 methodology. New models and the IPCC Tier 2 model using DMI from the Cornell Net Carbohydrate and Protein System model predicted VS (RMSPE = 17.7–19.4%) and dVS (RMSPE = 20%) well with small systematic bias (<10% of total bias). The present study offers empirical models that can accurately predict VS and dVS of dairy cows using routinely available data in dairy farms and thereby assist in efficiently determining methane emissions from stored manure.     

Improving the environmental profile of wood panels via co-production of ethanol and acetic acid

The oriented strand board (OSB) biorefinery is an emerging technology that could improve the building, transportation, and chemical sectors' environmental profiles. By adding a hot water extraction stage to conventional OSB panel manufacturing, hemicellulose polysaccharides can be extracted from wood strands and converted to renewably sourced ethanol and acetic acid. Replacing fossil-based gasoline and acetic acid has the potential to reduce greenhouse gas (GHG) emissions, among other possible impacts. At the same time, hemicellulose extraction could improve the environmental profile of OSB panels by reducing the level of volatile organic compounds (VOCs) emitted during manufacturing. In this study, the life cycle significance of such GHG, VOC, and other emission reductions was investigated. A process model was developed based on a mix of laboratory and industrial-level mass and energy flow data. Using these data a life cycle assessment (LCA) model was built. Sensitive process parameters were identified and used to develop a target production scenario for the OSB biorefinery. The findings suggest that the OSB biorefinery's deployment could substantially improve human and ecosystem health via reduction of select VOCs compared to conventionally produced OSB, gasoline, and acetic acid. Technological advancements are needed, however, to achieve desirable GHG reductions.     

Forest bioenergy or forest carbon? Assessing trade-offs in greenhouse gas mitigation with wood-based fuels

The potential of forest-based bioenergy to reduce greenhouse gas (GHG) emissions when displacing fossil-based energy must be balanced with forest carbon implications related to biomass harvest. We integrate life cycle assessment (LCA) and forest carbon analysis to assess total GHG emissions of forest bioenergy over time. Application of the method to case studies of wood pellet and ethanol production from forest biomass reveals a substantial reduction in forest carbon due to bioenergy production. For all cases, harvest-related forest carbon reductions and associated GHG emissions initially exceed avoided fossil fuel-related emissions, temporarily increasing overall emissions. In the long term, electricity generation from pellets reduces overall emissions relative to coal, although forest carbon losses delay net GHG mitigation by 16-38 years, depending on biomass source (harvest residues/standing trees). Ethanol produced from standing trees increases overall emissions throughout 100 years of continuous production: ethanol from residues achieves reductions after a 74 year delay. Forest carbon more significantly affects bioenergy emissions when biomass is sourced from standing trees compared to residues and when less GHG-intensive fuels are displaced. In all cases, forest carbon dynamics are significant. Although study results are not generalizable to all forests, we suggest the integrated LCA/forest carbon approach be undertaken for bioenergy studies.     

Environmental impacts of cultured meat production

Cultured meat (i.e., meat produced in vitro using tissue engineering techniques) is being developed as a potentially healthier and more efficient alternative to conventional meat. Life cycle assessment (LCA) research method was used for assessing environmental impacts of large-scale cultured meat production. Cyanobacteria hydrolysate was assumed to be used as the nutrient and energy source for muscle cell growth. The results showed that production of 1000 kg cultured meat requires 26-33 GJ energy, 367-521 m(3) water, 190-230 m(2) land, and emits 1900-2240 kg CO(2)-eq GHG emissions. In comparison to conventionally produced European meat, cultured meat involves approximately 7-45% lower energy use (only poultry has lower energy use), 78-96% lower GHG emissions, 99% lower land use, and 82-96% lower water use depending on the product compared. Despite high uncertainty, it is concluded that the overall environmental impacts of cultured meat production are substantially lower than those of conventionally produced meat.     

Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling

The evaluation of uncertainty is relatively new in environmental life-cycle assessment (LCA). It provides useful information to assess the reliability of LCA-based decisions and to guide future research toward reducing uncertainty. Most uncertainty studies in LCA quantify only one type of uncertainty, i.e., uncertainty due to input data (parameter uncertainty). However, LCA outcomes can also be uncertain due to normative choices (scenario uncertainty) and the mathematical models involved (model uncertainty). The present paper outlines a new methodology that quantifies parameter, scenario, and model uncertainty simultaneously in environmental life-cycle assessment. The procedure is illustrated in a case study that compares two insulation options for a Dutch one-family dwelling. Parameter uncertainty was quantified by means of Monte Carlo simulation. Scenario and model uncertainty were quantified by resampling different decision scenarios and model formulations, respectively. Although scenario and model uncertainty were not quantified comprehensively, the results indicate that both types of uncertainty influence the case study outcomes. This stresses the importance of quantifying parameter, scenario, and model uncertainty simultaneously. The two insulation options studied were found to have significantly different impact scores for global warming, stratospheric ozone depletion, and eutrophication. The thickest insulation option has the lowest impact on global warming and eutrophication, and the highest impact on stratospheric ozone depletion.     

Improvements to Emergy evaluations by using Life Cycle Assessment

Life Cycle Assessment (LCA) is a widely recognized, multicriteria and standardized tool for environmental assessment of products and processes. As an independent evaluation method, emergy assessment has shown to be a promising and relatively novel tool. The technique has gained wide recognition in the past decade but still faces methodological difficulties which prevent it from being accepted by a broader stakeholder community. This review aims to elucidate the fundamental requirements to possibly improve the Emergy evaluation by using LCA. Despite its capability to compare the amount of resources embodied in production systems, Emergy suffers from its vague accounting procedures and lacks accuracy, reproducibility, and completeness. An improvement of Emergy evaluations can be achieved via (1) technical implementation of Emergy algebra in the Life Cycle Inventory (LCI); (2) selection of consistent Unit Emergy Values (UEVs) as characterization factors for Life Cycle Impact Assessment (LCIA); and (3) expansion of the LCI system boundaries to include supporting systems usually considered by Emergy but excluded in LCA (e.g., ecosystem services and human labor). Whereas Emergy rules must be adapted to life-cycle structures, LCA should enlarge its inventory to give Emergy a broader computational framework. The matrix inversion principle used for LCAs is also proposed as an alternative to consistently account for a large number of resource UEVs.     

Life cycle assessment of switchgrass- and corn stover-derived ethanol-fueled automobiles

Utilizing domestically produced cellulose-derived ethanol for the light-duty vehicle fleet can potentially improve the environmental performance and sustainability of the transport and energy sectors of the economy. A life cycle assessment model was developed to examine environmental implications of the production and use of ethanol in automobiles in Ontario, Canada. The results were compared to those of low-sulfur reformulated gasoline (RFG) in a functionally equivalent automobile. Two time frames were evaluated, one near-term (2010), which examines converting a dedicated energy crop (switchgrass) and an agricultural residue (corn stover) to ethanol; and one midterm (2020), which assumes technological improvements in the switchgrass-derived ethanol life cycle. Near-term results show that, compared to a RFG automobile, life cycle greenhouse gas (GHG) emissions are 57% lower for an E85-fueled automobile derived from switchgrass and 65% lower for ethanol from corn stover, on a grams of CO2 equivalent per kilometer basis. Corn stover ethanol exhibits slightly lower life cycle GHG emissions, primarily due to sharing emissions with grain production. Through projected improvements in crop and ethanol yields, results for the mid-term scenario show that GHG emissions could be 25-35% lower than those in 2010 and that, even with anticipated improvements in RFG automobiles, E85 automobiles could still achieve up to 70% lower GHG emissions across the life cycle.     

Top-down技术控制图法评估电感耦合等离子体质谱法测定大米中镉含量的不确定度

采用Top-down技术控制图法评估电感耦合等离子体质谱法（ICP-MS）测定大米中镉含量的不确定度。按照GB 5009.268-2016《食品中多元素的测定》规定的分析方法[1]，在期间精密度条件下测定大米质控样中镉（Cd）的含量，采用Top-down技术原理对半年所得30组数据进行分析和评估。通过测量系统的性能检验可得出正态性的检验（As2*）和独立性检验（A2*MR）均＜1，表明99%概率下测量系统正态独立，计算得到扩展不确定度的结果为U=0.52μg/kg。Top-down技术可用于食品检验中对食用农产品检测结果不确定度的评估，该技术在建立数学模型上更简单，对测量不确定度结果的评价更灵活、方便。     

Carbon footprint as an instrument for enhancing food quality: overview of the wine,olive oil and cereals sectors

The quantification of greenhouse gases (GHG) emissions represents a critical issue for the future development of agro‐food produces. Consumers' behaviour could play an important role in requiring environmental performance as an essential element for food quality. Nowadays, the carbon footprint (CFP) is a tool used worldwide by agro‐food industries to communicate environmental information. This paper aims to investigate the role that CFP could have in consumers' choices in three significant agro‐food sectors in the Mediterranean area: wine, olive oil and cereals. A critical review about the use of CFP was carried out along the supply chain of these three sectors, in order to identify opportunities for enhancing food quality and environmental sustainability and highlighting how environmental information could influence consumers' preferences. The analysis of the state of the art shows a great variability of the results about GHG emissions referred to agricultural and industrial processes. In many cases, the main environmental criticisms are linked to the agricultural phase, but the other phases of the supply chain could also contribute to the increased CFP. However, despite the wide use of CFP by companies as a communication tool to help consumers' choices in agro‐food products, some improvements are needed in order to provide clearer and more understandable information. © 2016 Society of Chemical Industry