A comprehensive review of carbon footprint analysis as an extended environmental indicator in the wine sector

Journal of Cleaner Production 54 (2013) 61e77

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Review

A comprehensive review of carbon footprint analysis as an extended environmental indicator in the wine sector Benedetto Rugani a, *, Ian Vázquez-Rowe a, Graziella Benedetto b, Enrico Benetto a a

Public Research Centre Henri Tudor (CRPHT)/Resource Centre for Environmental Technologies (CRTE), 6A, Avenue des Hauts-Fourneaux, L-4362 Esch-surAlzette, Luxembourg b Department of Science for Nature and Environmental Resources, University of Sassari, Via Piandanna 4, I-07100 Sassari, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 December 2012 Received in revised form 28 April 2013 Accepted 28 April 2013 Available online 9 May 2013

Currently, carbon footprint (CF) analysis is gaining a role of primary interest within the extensive literature regarding wine sustainability issues. It envisages the quantification of greenhouse gas emissions that underpin the life-cycle of wine, from viticulture and vinification to wine bottling, distribution, consumption and waste end-of-life. This critical review pursues several methodological and conceptual issues behind wine carbon footprinting, such as calculation approaches, labeling and standardization purposes, combinations with other methods and theories, and CF trends in the wine sector. Most studies have only addressed specific methodological issues from an attributional life-cycle perspective, or have directly reported the CF profile of a given wine product. Future studies, however, will have to deal with increasingly complex market interactions linked to the entire life cycle of wine-making. A comprehensive discussion is presented concerning the benefits the CF indicator may provide both to producers and consumers and on the needs for reducing uncertainties and misinterpretations within a growing globalized wine market. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Carbon footprint (CF) Greenhouse gases (GHGs) Life Cycle Assessment (LCA) Viticulture Wine

1. Introduction Wine production constitutes one of the most ancient industries in the agri-food sector, providing important economic revenues in many regions worldwide. Wine consumption has been linked in many cases to religious and pagan celebrations. Moreover, an entire culture around wine tasting has developed on a global scale. These issues have slowly created a demand for higher quality standards for commercial wines and an increasing competition between wine brands, appellations or wineries to attain consumer recognition and quality awards. Consequently, improvements in wine quality and a growing number of appellations have resulted in a steady decrease in wine production in Europe since the early 1980s (OIV, 2010). Standardized quality criteria in the wine sector initiated in the 18th century in Hungary, Italy and Portugal (UNESCO, 2012), where the first appellations were created. These first criteria were linked mainly to soil characteristics, sun hours or the risk of developing fungus such as Botrytis cinerea, and were possible thanks to improvements in cultivation methods and technology in the wine

* Corresponding author. Tel.: þ352 425 991 682. E-mail addresses: [email protected], (B. Rugani).

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0959-6526/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jclepro.2013.04.036

production phase (Estreicher, 2004). However, changing social demands and expectations have progressively widened these criteria over the decades. For instance, recent efforts to categorize aspects linked to wine quality include harvest year, age, aroma, region, reputation or color (Botonaki and Tsakiridou, 2004; Jover et al., 2004). However, the increase in consumer interest concerning the environmental profile of consumption products, especially those linked to the food and beverages sector, as well as pressures from local communities and governments have started a race to disseminate environmentally relevant results in order to improve market quota or consumer satisfaction (Garnett, 2007; McLaughlin, 2007). The mounting concern regarding increases in greenhouse gases (GHGs) with the potential to modify regional climate patterns has prompted many firms to move toward sustainable grape growing and wine production practices (Soosay et al., 2012). Therefore, improvement actions in terms of energy and water consumption, pesticide use and the polluting effects that these inputs may have on the biosphere have centered recent research activities (Marshall et al., 2005). With the aim of performing an integrated assessment of the different environmental problems linked to wine production, certain environmental management tools, such as Life Cycle Assessment (LCA), have been implemented (ISO, 2006a; EU, 2010).

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B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

Wine LCA studies have proved to be useful methods to account for the environmental burdens associated with the different life cycle stages of wine (Petti et al., 2010; Neto et al., 2013; VázquezRowe et al., 2013), thanks to the multiple impact categories that can be considered. However, the holism and comprehensiveness of LCA also presents disadvantages when it comes to reporting and communicating results to stakeholders and the general public (Weidema et al., 2008). Consequently, the development of single issue indicators, such as water footprint or carbon footprint (CF),   has proliferated in life cycle thinking (Cu cek et al., 2012; Laurent et al., 2012; Scipioni et al., 2012). The appropriateness of using CF as a mechanism for communicating life cycle environmental results through eco-labeling has been heavily discussed in literature because the LCA community is wary of the risks linked to relying on one single environmental impact for dissemination (Udo de Haes, 2006; Weidema et al., 2008). However, they do acknowledge an increase in visibility of life cycle thinking thanks to the popularity that CF has gained in the market. In fact, two main factors can be highlighted as being responsible for the acceptance of CF as a valid dissemination indicator by the LCA community. On the one hand, public interest in global warming is overwhelming in comparison to other environmental concerns. On the other hand, despite the misleading depiction that a sole indicator may have on reporting results, CF is strongly linked to energy use and may therefore represent other underlying environmental impacts (Weidema et al., 2008). To better understand the role of this single indicator approach in the dissemination of life cycle-oriented results, the main objective of this critical review is to identify the key advantages and constraints of using CF as an extended indicator in the wine sector based on knowledge from existing publications and stakeholder actions. 2. Materials and methods 2.1. Carbon footprint in the wine sector CF is a worldwide standardized indicator of GHG emissions throughout the life cycle phases of any goods, service or activity according to the Kyoto Protocol and Life Cycle Thinking principles (BSI, 2011a). CF has become extremely relevant both in the public and private sectors because it is a useful and manageable tool for identifying areas of emissions reduction and for driving necessary changes to improve the eco-profile of products and the socioenvironmental awareness and responsibility of people and organizations. Further readings concerning the CF framework and the strengths of product carbon footprinting can be found in BSI (2011b), Finkbeiner (2009), Jensen (2012), Scipioni et al. (2012) or EU (2007). In Section 3, a comprehensive review regarding CF applications in the wine supply chain is provided by analyzing studies from existing literature based on assessment parameters, results and conclusions. 2.2. Requirements for the literature review 2.2.1. Selection of studies A total of 35 LCA and CF-related studies have been selected and discussed according to the following criteria: scientific recognition, international audience, method, system boundaries and data requirement, and deliverable characteristics (see further in the Supplementary information e SI e file, section S1). In general, the variations of CO2 emissions from the wine making process are attributable to technical differences linked to the methodological frameworks and assumptions selected in each study, but may also reflect the fact that there are substantial geographical differences between appellations and wineries (Vázquez-Rowe et al., 2013). In

fact, the geographic diversity increases variability in the wine production process based on differing soil and climate characteristics, production scales and efficiencies, and intended wine markets (low or high-end consumer cost) (Garnett, 2007). To account for this variability, nine key issues intrinsically related to CF analysis in the wine industry were identified from the selected literature (Section 2.2.2). 2.2.2. Key investigation issues Several aspects were explored to evaluate the use of CF as a single indicator throughout the 35 studies reviewed. In order to examine what improvements can be made to CF in terms of data sources and uncertainty reduction, methods (e.g. boundaries, analysis type e attributional or consequential), calculations, tools, standards, or specialization by viticulture and product type, we identified and analyzed the following 9 key issues: 1. LCA Methodology and Inventory e CF is an environmental indicator that originated from LCA. Therefore, the limitations, assumptions and strengths underlying the LCA method directly influence the use of CF. For instance, the reliability and representativeness of results depend on the Functional Unit (FU) and the background data sources to conduct the Life Cycle Inventory (LCI) and the subsequent Life Cycle Impact Assessment (LCIA). The choice of life cycle perspective, usually oriented toward an attributional LCA (A-LCA) (EU, 2010), also determines the scope and the variability of CF results. 2. (Life Cycle) System Boundaries e The delimitation of the production system constitutes a key issue when analyzing the completeness and comparability of results in LCA studies. For example, the use of a gate to gate perspective (e.g. from wine making to wine bottling), may provide more accurate and comparable CF scores, because data are usually collected within the same appellation and for a limited number of production processes. In contrast, phases including upstream agricultural practices (e.g., organic farming, conventional viticulture, etc.), transport, consumption and end-of-life processes usually need to be modeled on the basis of several case studies and scenarios, which may depend on local scale conditions and on data availability (Vázquez-Rowe et al., 2013). Therefore, the variability of wine CF results across the literature may be strongly influenced by the delineation of the system boundaries. 3. CF results (absolute and relative) e The performance (in terms of GHG emissions) of each single process of the wine life-cycle is useful to comprehensively interpret the discrepancies in the absolute values of CF. The reviewed literature offers abundant material for depicting an average estimation of CF per wine bottle and per phase of production as a first global proxy. 4. Allocation criteria e Besides the bottled or bulk product, the life cycle of a wine production system generates several biological wastes, such as pomace or marc (i.e., pulpy material remaining after pressing), grape lees (i.e., residues from the fermentation process), or grape stalks. All these components can be considered co-products rather than just wastes because they may be recovered and recycled/reused for other purposes (Barry, 2011; Bosco et al., 2011; Da Porto, 1998; Hwang et al., 2009; Miralles et al., 2008; Valderrama et al., 2010; Vázquez-Rowe et al., 2012a). Therefore, allocation criteria become a key issue in this review, where studies should suggest if splitting the CF among co-products (and not just wine) is a reasonable procedure. 5. Product type e Wine types, brands and appellations show a high degree of variability worldwide (e.g. red, white or rosé wine; sweet or dry; etc.). This large variability is likely to have an important influence on the CF results and on its adaptability to the different production systems.

B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

6. Viticulture Practices e This aspect is related to the cutting-edge debate regarding organic vs. conventional agricultural practices. In the wine sector, a certificate for organic viticulture can be obtained if the farmer avoids using synthetic fertilizers and pesticides according to specific standards and regulations (NOP, 2012; BFA, 2010; CGSB, 2006). In addition, organic production may also differ from conventional activities in the tillage practice and the level of organic carbon added to the soil (Venkat, 2012). The most recent European standards focus mainly on the problem of SO2 levels and the application of certain additives, substances and wine-making processes (EU, 2012). In contrast, criteria for reducing GHG emissions are not monitored through these regulations, and thus distinctions between organic and conventional varieties are not based on CF monitoring. Consequently, organic practices may not necessarily lead to lower CF values than conventional, more agricultural intensive viticulture (Schlich, 2010; Van Hauwermeiren et al., 2007; Venkat, 2012). 7. CF calculations, methods, standards and tools e Several GHG accounting standards, protocols and requirements have been publicly disclosed to support companies in the monitoring and reporting of their products’ CF. A commonly used and representative standard is ISO 14040, which provides robust and practice-proven requirements for performing transparent and accepted CF calculations (EU, 2007). ISO 14040 is also the basis to elaborate other specifications established for the assessment of GHG emissions of products and systems, such as the PAS 2050 (BSI, 2011a), the GHG Protocol Product Life Cycle Accounting and Reporting Standard (WRI and WBCSD, 2011), and the ISO 14067 (still under development; ISO, 2014). Deeper analysis related to the similarities and discrepancies between these guidelines, as well as their advantages and limitations, is outside the scope of this critical review, but there is ample literature that may be consulted (Jensen, 2012; Pattara et al., 2012; Scipioni et al., 2012; Barry, 2011; Bosco et al., 2011; Brandão and Levasseur, 2011; Pandey et al., 2011; Iribarren et al., 2010; Del Borghi et al., 2009; Finkbeiner, 2009; Sinden, 2009). In the current review, analysis linked to the underlying CF computational procedures, calculators and reporting guidelines represents a meaningful issue to define the needs and challenges for CF standardization in the wine sector. 8. Biogenic carbon e The handling of biogenic carbon balances in LCA is noteworthy and constitutes an issue of great interest in the sustainability and climate science community. Accounting for CO2 at each stage of the life cycle offers the advantage of allowing the dynamic modeling of emission and removal, making the analysis consistent with the ‘polluter pays’ principle and the Kyoto rules, implying that each GHG contribution (positive or negative) should be allocated to the causing agent (Rabl et al., 2007). In fact, viticulture and vinification are intrinsically related to a biogenic carbon balance because carbon is sequestered during vine growth (Martin, 1997; Poni et al., 2006; Soja et al., 2010) and released during the alcoholic fermentation of wine. The anaerobic reaction of yeasts with the sugar contained in pressed grapes (wine fermentation) is of particular importance because it generates the alcoholic content of wine and CO2 emissions. Hence, the relationship between biogenic carbon and its influence on the CF balance and use remains unclear and deserves further examination. 9. Uncertainty e Uncertainty is a relevant issue in LCA and CF analysis (Pandey et al., 2011; Laurent et al., 2012; Lenzen, 2006). For example, while data for CO2 and CH4 are usually readily available or easily gathered, difficulties arise in data acquisition for the remaining GHGs and uncertainties are

63

increased with the use of GWPs (Wright et al., 2011; Guinée et al., 2009; IPCC, 2006). Moreover, a debate is currently ongoing in terms of the meaning and representativeness of the word ‘footprint’, where uncertainties and errors would arise if converting the total emissions from carbon-equivalent mass to   a land-based metric (Cu cek et al., 2012; Wright et al., 2011; Wiedmann and Minx, 2008). In LCA, the data quality requirements should address information uncertainty (e.g., data, models and assumptions) for studies used in comparative assertions intended for public divulgation (ISO, 2006b). Similarly, ISO 14067 and the GHG Protocol Product Standard recommend providing at least a qualitative statement related to the source of inventory uncertainty and methodological choices, giving opportunities for harmonization with PAS 2050, which currently claims that uncertainty should be minimized through the application of data-quality requirements and hence the identification of poor-quality data (BSI, 2011b). All these issues are relevant to assess how uncertainty, if any, influences the CF of wine throughout the literature reviewed. 3. Results Wine CF has been calculated on the basis of different data sources and methodological assumptions, such as LCA and related inventory datasets, GHGs analysis guidelines and CF calculators. Most studies have been based on LCA applications, according to the impact characterization phase, where Global Warming Potential (GWP) scores are assessed. Nevertheless, great variability in the definition of the FU and in the choice of the environmental impact categories that accompany GWP in the assessment (i.e. GWP is the only impact category present in all the analyzed studies) can be observed, together with differences in the assessment of the environmental impacts of co-products, the management of allocation, or the choice of analyzing processes such as packaging and end-of-life (Petti et al., 2010). 3.1. LCA literature results 3.1.1. LCA methodology and inventory Table 1 compares the LCA characteristics (i.e., FU, system boundary, LCI, LCIA methods, database and tools, and allocation criteria) for 35 inventoried studies, 24 of which are merely based on an LCA application. A total of 16/24 studies are explicitly based on a conventional ALCA perspective. In parallel, 63% of the reviewed studies used common LCA tools and databases. These resources include dedicated softwares like GaBi4 (PE International, 2012) and SimaPro (PRé Consultants, 2012), LCIA methodologies like CML 2001 (Guinée et al., 2002) and Eco-Indicator ‘99 (Goedkoop and Spriensma, 2000), and LCI datasets such as ecoinventÒ (The Ecoinvent Centre, 2012). Other types of methodological criteria have also been tested recently. These include specific selections of LCI data to enhance the process operational efficiency analysis (Vázquez-Rowe et al., 2012b) or the use of lab-experiments to add specific agronomic information to the LCI models (Ruggieri et al., 2009). Moreover, 22 studies explicitly refer to one single FU: a 0.75 L bottle of wine, which is a highly consistent way of meeting the purpose of LCA to name and quantify efficiently the qualitative and quantitative aspects of the function(s) (see in EU, 2010) in the wine production process (Table 1). All these life-cycle modeling features increase the comparability of the CF results between the various LCA-based analyses. However, disparate assumptions for data collection and elaboration have been adopted that largely characterize the different studies. For instance, the highest total CF scores (>3 kg CO2-eq./FU) are usually recorded when various sources for

Study

Functional unit

LCA-related approachb

Databaseb

Bottle 0.75 L A-LCA

Ardente et al., 2006

Bottle 0.75 L POEMS; (Simplified) LCA

Barry, 2011

Bottle 0.75 L A-LCA

Benedetto, 2010

Bottle 0.75 L A-LCA

Bosco et al., 2011

Bottle 0.75 L A-LCA GaBi professional; oriented to a Ecoinvent database 2009 CF analysis

Carballo Penela et al., 2009

Bottle 0.75 L n.a.

Carta, 2009

Bottle 0.75 L A-LCA

Cholette and Venkat, 2009 a

6-Bottles box n.a. transported

CIV, 2008

1 L wine

LCA for EPD

Colman and Bottle 0.75 L n.a. Päster, 2009

Comandaru et al., 2012

Bottle 0.75 L A-LCA a

Methodb

Various dataset sources implemented in SimaPro, e.g. IDEMAT, BUWAL, ETH-ESU, etc. Various dataset sources, e.g. Boustead, GEMIS, DMU, etc.

SimaPro 5

Eco-Indicator’99 H/A

n.s.

n.a.

GaBi professional, PlasticsEurope, etc. EDIP database

GaBi4

CO2 emissions estimated from energyintensity and land-use factors Ecoinvent database; EMEP-Corinair Emission factors n.a.

Various LCI datasets, i.e. Ecoinvent, BUWAL, ETH-ESU Various emission factors to calculate GHGs, e.g. taken from GHG protocol, CE Delft et al., 2006; etc. Ecoinvent database

GaBi4

GaBi4

n.a.

SimaPro 7.3

Allocation criteria

System boundaries

n.e.c.

U

n.s.: mass allocation apparently adopted (bottled vs. loose wine) CML 2001 Allocation avoided CML 2001 Only mass allocation between bulk and bottled wine GWP from CML 2001; Stalks, direct and indirect IPCC- skins, pips: GHGs from fertilizers mass allocation MC3 (method composed n.e.c. of financial accounts)

IPCC 2007

n.e.c.

Vineyard Viticulture Wine Packaging Transport Storage End-ofplanting & grape making processes & distribution and life growing consumption processes U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

CargoScopeÔ Life-cycle GHGs and n.e.c. energy use analysis according to ISO 14040, PAS 2050, and GHG Protocol SimaPro 7 n.s. n.e.c.

U

U

U

U

U

n.a.

Development of a n.e.c. Carbon Calculator model

U

U

U

U

U

SimaPro 7.3

Eco-Indicator’99; addition of a new water impact category in the “AoP-resources”

U

U

U

U

U

n.e.c.

U

U

U

U

B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

Aranda et al., 2005

Softwareb

64

Table 1 Comparison of the life-cycle modeling and carbon footprint characteristics across #35 studies in the wine production sector. Further details are available in the supporting information material, Tables S1eS3.

Pomace, lees and press syrup; economic allocation (98.5% to F.U.) n.e.c.

U

U

U

U

U

U

U

U

U

U

No allocation issues identified

U

U

U

U

n.e.c.

U U

U

U

U

n.e.c.

U

U

U

Mass allocation between bottled wine and pressed wine Allocation criteria and system expansion tools not available in IWCC Mass allocation between bulk and bottled wine n.e.c.

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

U

CML 2 baseline 2000

n.e.c.

U

U

U

U

GHG impact analysis of shipping and distribution systems CML 2001

n.e.c.

Bottle 0.75 L A-LCA

GaBi professional

GaBi4

CML 2001

Gonzalez et al., 2006

1 L wine

SimaPro 6.0

Eco-Indicator ’99 v2.03 E/E

Greenhaigh et al., 2011

Bottle 0.75 L n.a.

Various LCI datasets stored in SimaPro, e.g. Ecoinvent, Idemat, ETHESU, þ EPD sources, etc. Use of several GHG emission factors collected from the literature

n.a.

Kavargiris et al., 2009 Montedonico, 2005 a

1 ha

GWPs

n.a.

Bottle 0.75 L A-LCA

Ecoinvent database

SimaPro 5

Bottle 0.75 L A-LCA

Ecoinvent database

SimaPro 7.3

GHG product accounting guidelines for the wine industry, NZ IPCC-GHG inventory Eco-Indicator ’99; EPS 2000; EDIP96 CML 2002

Bottle 0.75 L A-LCA

Different LCI sources of literature; LCA databases n.s.

n.s.

CML 1992 and CML 2000

Pattara et al., 2012

(¼in Petti et al., 2006)

n.a.

n.a.

GWPs and GHG emission factors: different sources

Petti et al., 2006

Bottle 0.75 L VerdEE: streamlined LCA

Various dataset sources, e.g. I-LCA, IDEMAT, LCA literature, etc.

GaBi4

CML 2001

Pizzigallo et al., 2008

1 t wine

n.s.

SimaPro 6

LCIA not performed

Various dataset sources for LCI inputs and emissions Different sources of GHGs factors for transportation: DEFRA, 2008; Ecoinvent v2.1; CE Delft et al., 2006; etc. Various dataset sources, e.g. EDIP database, LCA literature, etc.

SimaPro 7.1.6 n.a.

Neto et al., 2013 Notarnicola et al., 2003

Point et al., 2012

(Stand alone) LCA

n.a.

a

LCA vs IWCC

LCI vs. Emergy analysis Bottle 0.75 L A-LCA

Reich-Weiser et al., 2010

Bottle 0.75 L n.a.

Rugani et al., 2009

100 kg bulk wine

A-LCA vs. Emergy analysis

GaBi4

n.e.c.

n.e.c.

U

U

U

B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

Gazulla et al., 2010

U

U

U

U

U

U

U

U

U

(continued on next page) 65

66

Table 1 (continued ) Functional unit

LCA-related approachb

Databaseb

Ruggieri et al., 2009

1 kg N

Various dataset sources, e.g. Ecoinvent, LCA literature, etc.

SimaPro 7.0

CML 2 baseline 2000 v2.02

n.e.c.

SAWIA, 2004

1 t grape

LCA þ lab exp. and economic estimations n.a.

n.a.; GHGs are calculated from energy use, i.e. electricity, transport fuels and stationary fuels i.n.a. n.a.; GHGs calculated from the application of different models (e.g. RothCmodel application and IPCC-based estimations) n.a.

n.a.

AGO factors and Methods Workbook v.3

n.s.

U

U

U

i.n.a. n.a.

i.n.a. GHGs inventory (factors from different literature and dataset sources) Sustainable Value Chain Analysis e SVCA

i.n.a. n.s.

U U

U U

U U

U U

Impacts of U byproducts reprocessing: allocation details not disclosed Mass allocation assumed in the winery stage n.e.c.

U

U

U

U

U

U

U

U

U

U

U

Study

Schlich, 2010 Soja et al., 2010

Bottle 0.75 L A-LCA 1 L wine n.a.

i.n.a.

n.a.

n.a.

a

Vázquez-Rowe Bottle 0.75 L A-LCA et al., 2012a

Ecoinvent database; various other models and sources to estimate direct emissions, etc. (¼in Vázquez-Rowe et al., 2012a)

SimaPro 7.3

A-LCA þ SimaPro 7.3 DEA (Data Envelopment Analysis) Vázquez-Rowe Bottle 0.75 L A-LCA Ecoinvent database; EMEP- SimaPro 7.3 et al., 2013 Corinair Emission factors Venkat, 2012 1 kg of grape n.a. GHGs from agricultural FoodCarbon soils and carbon sequest. ScopeÔ modeled based on the IPCC tier 1 guidelines (CarbonScopeDataÔ) Vázquez-Rowe 1.1 kg grape et al., 2012b

WRAP, 2007

Bottle 0.75 L n.a.

Zabalza et al., 2003

100 L wine

(Simplified) LCA

Source of emission factors i) for transport processes: Smith et al. (2005) and ii) for bottle production: n.s. BUWAL 250

n.a.

n.s.

Methodb

CML 2 baseline 2000; USEtox (¼in VázquezRowe et al., 2012a)

Allocation criteria

System boundaries Vineyard Viticulture Wine Packaging Transport Storage End-ofplanting & grape making processes & distribution and life growing consumption processes U

U

U

IPCC 2007

n.e.c.

Life cycle GHG inventory according to PAS 2050: 2008 and ISO 14040:2006 standards CO2 emissions inventory analysis

n.e.c.

U

n.s.

U

U

U

U

n.s.

n.e.c.

U

U

U

U

U

U

n.a. ¼ not applicable; n.s. ¼ not specified; n.e.c. ¼ not explicitly considered; i.n.a. ¼ information not available. a Studies that do not (or only partially) disclose absolute values of CF (i.e. they are excluded from the calculations provided in Fig. 1; see also Tables S1 and S2). b Full references for LCI databases, LCIA methods and other acronyms of data sources can be found in the information material.

U

B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

Soosay et al., 2012

a

Softwareb

B. Rugani et al. / Journal of Cleaner Production 54 (2013) 61e77

67

evaluation). On the other hand, a gate to gate perspective is usually developed when there is a precise aim of decisional support, obtained by comparing different scenarios, or the LCA is not expressly within the scope priorities. Accordingly, 6 analyses have only dealt with a single process or stage within the life cycle to increase the completeness of the CF study by addressing specific questions on the viticulture phase (Kavargiris et al., 2009; Venkat, 2012), transportation (Cholette and Venkat, 2009; Reich-Weiser et al., 2010), and end-of-life (Ruggieri et al., 2009). For boundary consistency, companies should reference the currently available guidelines (see below) to calculate and report product CFs. Ultimately, boundary selection will depend on the objectives of the CF study. For example, the New Zealand guidelines for GHG reduction at the sector level (Greenhaigh et al., 2011) assume that all processes from vineyard planting to consumption should be included. However, a contract grape grower will only consider the annual growing and harvesting of grapes if the vinification activities are performed by another company. In addition, GHG emissions related to vineyard construction, capital equipment, winery infrastructures, and retail and consumption processes are considered optional because they are either a one-time occurrence or are beyond the direct control of the wine company (Greenhaigh et al., 2011).

3.1.2. System boundaries To analyze the completeness of the life-cycle evaluation, the system boundaries of 35 studies were compared by dividing the wine supply chain into six main processes according to the most commonly applied framework (see Table 1). Independently of the FU, the cut-off processes and the method used, 14 studies quantify a CF value for wine production from cradle to grave; 71% of these studies assume that the use phase impacts are negligible (Gonzales et al., 2006). In contrast, 6 studies use a cradle to gate perspective, with boundaries set at the gate of the winery, while another 8 studies also model the transportation and distribution phase. The choice of embedding a cradle to grave rather than a cradle to gate approach mostly depends on data availability. While data are typically available for phases like viticulture and wine-making, which are the most representative of the wine LCA boundaries, sufficient information may not always be accessible to consistently model transportation or end-of-life. As a consequence, for these latter phases, the variability of the CF values can be larger and more influenced by value choices (see Section 3.2.2 for more quantitative

3.2. CF literature results 3.2.1. Worldwide estimated average The average CF values collected for 29 literature studies are shown in Fig. 1A. To assess the variability of the CF data, standard deviation values were also quantified given the considerable amount of data retrieved (see Table S2 in the SI). On average, the CF for a generic bottle of wine is 2.2  1.3 kg CO2-eq. Given the variability in technological, geographical and viticulture conditions and the uncertainty they imply, Fig. 1 is essentially used here to give a global proxy of the total CF distribution among the life cycle phases of wine production. According to this assumption, the contribution of the wine sector to the global annual CF of worldwide human activities can be roughly estimated as 0.3%, a value that should definitely not be overlooked (see Table S4 in the SI). 3.2.2. Contribution analysis of life cycle processes Viticulture activities (17%), packaging processes (22%) and endof-life (22%) are the most significant processes with regard to the

2.4

3.6

2.17 ± 1.34

3.2

2.8 2.4

2.0 0.48 ± 1.02

1.6

0.47 ± 0.24

1.2

0.38 ± 0.31

0.26 ± 0.33

0.8 0.4 0.0

0.07 ± 0.12

0.25 ± 0.29

0.26 ± 0.29

kg CO2- eq./wine bottled, 0.75 L

kg CO2- eq./wine bottled, 0.75 L

GHG emissions and/or emission factors are mixed with LCI databases (Carballo Penela et al., 2009; Colman and Päster, 2009; Gonzales et al., 2006; Point et al., 2012; Ruggieri et al., 2009; Vázquez-Rowe et al., 2012a). In fact, the adoption of datasets other than conventional LCI sources such as ecoinventÒ is usually made to increase the accuracy and specificity of certain emission factors (e.g. for transportation or end-of-life) or the representativeness and completeness of emission models which are possibly missing (e.g. for in situ modeling of fertilizer emissions). Most of the studies are intended to provide information on the critical steps of LCA, to improve or update the eco-profile of different types of wine, or to combine and compare impact scores (see also Table S1 in the SI for additional information). It is worth highlighting that most studies calculate a broad range of LCIA impact categories (e.g., potentials of acidification, eutrophication or resource depletion), including GWP, or are oriented to designing new or complementary indicators for the wine sector (Comandaru et al., 2012; Pizzigallo et al., 2008; Rugani et al., 2009). In contrast, some authors specifically address the problem of CF assessment (Bosco et al., 2011; Pattara et al., 2012; Vázquez-Rowe et al., 2012a). The use of different LCIA methods and software tools, however, does not ultimately influence the variability of CF values observed in the literature.

1.09 ± 0.83

1.36 ± 0.66 1.06 ± 0.73

1.6

0.79 ± 0.30

0.8

0.0 Red

A)

White

Organic

Conventional

B)

Fig. 1. A) average values and standard deviation range of the carbon footprint (CF) of wine per life cycle phase from a cradle to grave approach; 29 studies of wine life cycle are considered starting from a selection of 35 studies (see Table 1 and Table S2 in the Supporting Information - SI - for further details); B) average values and standard deviation range of the CF of different wine typologies (red vs. white) and agricultural practices of wine production (organic vs. conventional); the average CF scores are quantified from cradle to gate (i.e. wine bottled at winery gate) starting from a selection of 22 studies (see Table S3 in the SI).

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total CF score (Fig. 1A). However, the CF values reported by Ruggieri et al. (2009) for two landfill scenarios greatly influence the variability and increase the average end-of-life score (see Fig. 1A and Table S2 in the SI). In fact, if these values are excluded from the analysis, results change considerably in terms of absolute value and variability, i.e., the contribution from end-of-life management decreases to 12% (see Fig. S2 in the SI). The large uncertainty in CF scores for the life cycle processes is dependent on the same issues identified for LCA studies. For example, large variability was observed in results for wine-making, transportation and end-oflife. These processes are influenced by assumptions made when modeling the technology, industry, and market rather than by the local ecosystems and physico-chemical parameters that control agricultural processes. In the latter case, local climate conditions, land texture and different agricultural practices and grape types will determine yields, which can substantially differ between harvest years (Vázquez-Rowe et al., 2012a), products (Bosco et al., 2011; Pizzigallo et al., 2008) and locations (Montedonico, 2005; Colman and Päster, 2009; Vázquez-Rowe et al., 2013). 3.3. Allocation strategy The problem of allocating the CF-impact among wine products (high vs. low quality wines, the latter derived from second grapejuice pressing) and co-products is limited by the scope of the analysis and the definition of the FU (Notarnicola et al., 2003). Only some authors explicitly address the allocation problem by applying specific cut-off rules and criteria to avoid assigning the full impact burden exclusively to the bottled wine. Gazulla et al. (2010) allocated the calculated environmental indicators to the co-products of wine on the basis of the associated economic revenues. As a result of an allocation larger than 98%, the total CF was essentially due to only wine production rather than pomace, lees and press syrup. This allocation criterion is justified by the authors stating that economic allocation can reflect the actual thrust behind the whole wine industry much better than either mass- or energy-based allocation, because the main product by far is obviously wine itself and not any of the other by-products (Gazulla et al., 2010). In contrast, Bosco et al. (2011) applied a mass allocation approach to assign a CF impact to stalk, skin and pip products. In this case, we can observe an increase of CF from 24% to 34% if the allocation is applied 100% to bottled wine only (see Footnotes to Table S2 in the SI), which may approximately demonstrate the extent to which we can estimate the variability of CF results on the basis of different allocation criteria. Finally, a third group of studies explicitly excluded treatment of rasp, lees and marc as solid waste by considering them feedstock for other production processes (e.g., compost from rasps and tartaric acid from marc) (Notarnicola et al., 2003). This is a general case in which the mass allocation choice can determine a decrease in the CF of the bottled wine, because the model assumes co-products are recovered and not disposed. Literature findings suggest co-product allocation has not been analyzed any further due to the lack of useful data and information to expand the evaluation of the system boundaries. Although ISO standard 14044 (ISO, 2006b) recommends avoiding allocation whenever possible, through either system subdivision or expansion, Gazulla et al. (2010) found that neither of these strategies was viable in their case study. The grape residues and fermentation sediments cannot be produced separately, so it makes no sense to divide the wine-making process into two or more independent sub-processes. Moreover, detailed information was unavailable regarding the alternative products that could be replaced by pomace, lees, and press syrup, making an appropriate system expansion unfeasible (Gazulla et al., 2010). Similarly, VázquezRowe et al. (2012a) failed to expand the system to include the

avoided emissions from the landfill, because this would imply going back to waste disposal measures that are not recommended by current waste policy legislation in Spain. As a result, the compost process they included in the LCI model substantially increased the CF value of the wine under analysis (Vázquez-Rowe et al., 2012a). If compost production were excluded assuming these impacts are attributable to waste treatment phase of prior processes, then the CF could be reduced by 38%. 3.4. Product type The literature analyzed includes the evaluation of several types and brands of wine, which derive from different viticulture practices and from a variety of grapevines (Jackson, 2009). A comparison of average CF values for both wine type (red vs. white) and viticulture practice (organic vs. conventional) is shown in Fig. 1B based on data from 19 studies (see Table S3 in the SI). For the purpose of comparison, CF values were calculated from cradle to gate. It can be observed that white wines typically have higher CF values than red ones, despite the higher variability of the latter (SD ¼ 76%, Fig. 1B). Several factors may help explain this variance, though finding a general rule is not easy due to the multiple abovementioned factors. For example, harvest yield could play an important role because yields for white varieties are usually higher than those for red ones, but may require more inputs per unit of crop area (Venkat, 2012). However, while some authors have analyzed both white and red wines (Bosco et al., 2011; Point et al., 2012; Carta, 2009; Colman and Päster, 2009), or vine systems of white and red grapes (Venkat, 2012), none specifically focused on identifying the sources of variability between the two types, a potential topic for future research in this area. 3.5. Viticulture practices Numerous studies have explicitly compared organic and conventional wine productions (Gonzales et al., 2006; Kavargiris et al., 2009; Pizzigallo et al., 2008; Rugani et al., 2009; Aranda et al., 2012; Venkat, 2012; Vázquez-Rowe et al., 2013). The average CF value for organic wine from cradle to gate was found to be around 25% lower than conventional wine (Fig. 1B). However, this result should not be considered unconditionally. It is worth noting that LCIs for specialized organic inputs to agriculture and associated emission factors after application are generally not available (see also in Vázquez-Rowe et al., 2013). Therefore, the absence of these datasets should not be interpreted as providing “no impact”, but as a lack of ability to quantify those impacts or benefits. Current literature suggests that increased CF values for conventional wines are linked to a higher use of synthetic substances and other inputs (e.g., diesel, wood) during the agricultural phase (Niccolucci et al., 2008; Point et al., 2012; Pizzigallo et al., 2008), as well as other factors including wine age (Bosco et al., 2011; Gazulla et al., 2010; Vázquez-Rowe et al., 2013), bunch selection (Niccolucci et al., 2008), and harvest year (Vázquez-Rowe et al., 2012a). In contrast, other authors find that GHG emissions show relatively minimal differences between the two due to the higher yield of conventional viticulture (Colman and Päster, 2009). This is even more noticeable on a large scale, where conventional grape growing and wine-making practices may result in marginally less GHG emissions per hectare than in the case of organic production (Venkat, 2012; Pizzigallo et al., 2008; Waye, 2008; Aranda et al., 2012). Consequently, the interpretation of CF values remains too uncertain and inexplicit when comparing viticulture practices, while at the same time it is associated with many other environmental factors involved in the organic vs. conventional debate (e.g. impact on local riparian habitats, biodiversity loss, soil type,

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etc.) (Colman and Päster, 2009). These could directly or indirectly influence the dynamics of carbon emissions in the life cycle and thus be worthy of inclusion in future CF assertions of organic and conventional wines. 3.6. CF calculations, methods, standards, and tools As depicted in Table 2, wine CFs reporting is centered around the ISO 14040 and 14044 standards, although only some authors have explicitly followed those standards (Barry, 2011; Bosco et al., 2011; Cholette and Venkat, 2009; Soja et al., 2010; Venkat, 2012).

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Table 2 also lists other A-LCA oriented guidelines and standards that can be potentially applied in the carbon footprint of wine, such as the PAS 2050, the GHG Protocol Product Life Cycle Accounting and Reporting Standard, or the forthcoming ISO 14067. However, methods that do not necessarily follow an A-LCA perspective also exist (Carballo Penela et al., 2009; Pattara et al., 2012; Reich-Weiser et al., 2010; WRAP, 2007), but may range from carbon calculators to classical IPCC GHG-based analyses (see Table 1). In the former case, software is developed to allow stakeholders to calculate and monitor their CF trends in an automatic and simplified way, while in the latter case different emission factors enable the profiling of

Table 2 Summary of potential international standardization tools and guidelines to report carbon footprint in the wine sector. Standard/Reporting guideline

Scope and objectives

Alignment with carbon footprint concept

Adoption in the wine sector

British standard PAS 2050:2011 (BSI, 2011a)

Publicly available specification for the assessment of the life cycle GHG emissions of goods and services based on key life cycle techniques and principles (explicitly built on ISO 14040) Step-by-step guide for companies to use in quantifying and reporting their GHG emissions

Full

Yes/No

Venkat, 2012 (via FoodCarbonScopeÔ)

Full

Yes/No

Indirect (organizations can monitor GHGs performance and adopt solutions for emission reduction)

Yes/No

Cholette and Venkat, 2009 (via Cargo ScopeÔ);Colman and Päster, 2009 (data source of GHG emission factors) Ardente et al., 2006 (via POEMS);Hughey et al., 2005

Partial/Full (CF as GWP indicator can be included within the set of analyzed LCIA indicators or accounted for separately)

Yes

Barry, 2011;Bosco et al., 2011; Cholette and Venkat, 2009; Soja et al., 2010; Venkat, 2012

Yes

CIV, 2008

Partial/Full (the scope is to address carbon emissions only but with criteria different from other full CF alignments)

Yes

NZWC, 2010

Full (developed to increase transparency in quantifying and reporting carbon emissions over the entire lifecycle of products and services) Full

No

e

Yes

Pattara et al., 2012; Soja et al., 2010

Full

Yes

Carballo Penela et al., 2009

GHG Protocol Product Life Cycle Accounting and Reporting Standard (WRI and WBCSD, 2011)

ISO 14001:2004 e Environmental Management System (EMS)

ISO 14040:2006 e Environmental Management e Life Cycle Assessment e Principles and Framework (ISO, 2006a) ISO 14044:2006 e Environmental Management e Life Cycle Assessment e Requirements and Guidelines (ISO, 2006b) ISO 14025:2006 e Environmental labels and declarations e Type III environmental declarations e Principles and procedures (ISO, 2006c) Greenhouse gases-Parts 1e3: Family of ISO 14064 (see ISO, 2006d)

Carbon footprint of products e Requirements and guidelines for quantification and communication (ISO/DIS 14067.2 e Under development; ISO, 2013) International Wine Carbon Calculator Protocol (FIVS, 2010)

Method composed of financial statements (MC3) (Carballo-Penela and Doménech, 2010)

Certification process for environmental management, established as a voluntary standard to support environmental protection and prevent pollution through monitoring according to organization’s environmental policies Guidelines on the principles and conduct of LCA studies that provide an organization with information on how to reduce the overall environmental impact of its products and services

Specifically established to use ISO 14040 in the development of Environmental Product Declarations (EPD), eco-labels intended for use in business-tobusiness communication International GHG accounting and verification standards providing a set of clear and verifiable requirements to support organizations and proponents of GHG emission reduction projects Will provide specific requirements for the quantification and communication of GHGs associated with products

Designed primarily as an enterprise and/or facility level calculating tool for the International Wine Industry in compliance with current international standards and practices for GHG accounting Method to assess and communicate CF of products with an organization-based LCA perspective

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the CF of wine according to various guidelines and protocols listed in Table 2 (IPCC, 2006; Russell, 2011). Among the several existing carbon calculators for products (e.g. FoodCarbonScopeÔ; in: Cholette and Venkat, 2009; CargoScopeÔ; Venkat, 2012; Tables 1 and 2), some are specifically implemented to assess the CF of wine. For example, the International Wine Carbon Calculator-IWCC is a wine industry specific GHG protocol and calculator in compliance with international GHG accounting guidelines such as PAS 2050 (Pattara et al., 2012; Soja et al., 2010). Because of the highly enterprise-oriented scope of IWCC, results from the application of this calculator seem to be consistent only for communication purposes and improvement at the specific wine industry level. As comprehensively observed by Pattara et al. (2012), CF values calculated with IWCC are scarcely comparable to classical LCAbased results because of several departures underlying IWCC and LCA with regard to modeling assumptions (e.g. use of different GHG emission factors, system boundary, etc.) and the aggregation of items per life cycle phase. In fact, results can differ substantially in absolute terms, i.e. CF/FU calculated with IWCC >40% of CF/FU calculated with LCA (see Table 1 in Pattara et al., 2012). Moreover, the process contributions are extremely variable, whereby they are compliant across IWCC and LCA only in that the greatest contribution in terms of emissions (more than 70%) arrives from packaging, followed by product distribution and agricultural operations (Pattara et al., 2012). Besides these calculator and reporting systems, other authors (Colman and Päster, 2009; Greenhaigh et al., 2011; Kavargiris et al., 2009; Reich-Weiser et al., 2010; SAWIA, 2004; Soja et al., 2010; WRAP, 2007) computed the CF of wine with GHG emission factors for fuel production and combustion, fertilizer use and energy generation (WRI and WBCSD, 2011; DEFRA, 2008; CE Delft et al., 2006; IPCC, 2006; Smith et al., 2005; AGO, 2004; Table 1). Most of the latter sources are usually applied in programs of GHG accounting and policy support on different scales of product and economic system. Therefore, they include a set of GHG emission factors that can be used to assess wine CF in combination and/or as alternative databases to traditional LCI frameworks, although this could imply reduction in the accuracy and representativeness of the inventoried inputs, as well as substantial discrepancies in the final CF results. Whilst it is obviously difficult to evaluate the extent to which CF is varying depending on the use of different GHG inventory tools, some observations can be made. For example, it is worth noting that emissions from specific life cycle processes such as lime and nitrogen application in viticulture soils, when emission factors from the IPCC method are used, can greatly influence the record of total wine CF (around 25% in Point et al., 2012, or around 50% in Neto et al., 2013). However, when only ecoinventÒ is used, GHG emissions from these phases can show lower relative contributions in comparison with the GHG emissions generated by fossil fuel-based processes, although these are clearly dependent on the inventoried input (Vázquez-Rowe et al., 2013). The use of generic emission factors seems a reasonable way of estimating process CF at least to cover possible gaps in the LCI. However, trusted emission factors, in particular estimating transportation emissions relative to total CF, can vary depending on sources and assumptions of the study, regional variability in fuels, and regional variability in vehicles. Therefore, the use of these estimates for conclusive results is risky (Reich-Weiser et al., 2010). Similar conclusions can be drawn for the estimates of emissions for fertilizers, where considerations are usually omitted about fertilizer application timing, soil characteristics, and climatic conditions at the time of application, all of which could be important factors for a more detailed analysis of a vineyard’s nutrient flow dynamics (Point et al., 2012; Vázquez-Rowe et al., 2013).

3.7. Biogenic carbon issues Despite the potential relevance of biogenic carbon emissions for the wine CF calculation (see Section 2.2.2), only 5 out of the 35 studies reviewed quantitatively accounted for biogenic emissions. For example, CO2 emissions from fermentation contributed from 15% to 24% of the CF of the vinification process and, thus, circa 2e3% to the total CF of white wine. This observation is compliant with the findings of Colman and Päster (2009) and Neto et al. (2013), while it is only comparable to the calculations performed by Zabalza et al. (2003). Indeed, they found that fermentation produces 0.13 kg of CO2 emissions per liter of wine, which represents 9% of the total life cycle CO2 emissions. However, they did not account for GHGs other than CO2. On the other hand, Soosay et al. (2012) observed that viticulture in the vineyards may provide up to 28% of the emissions due to decomposing biomass, timber decay and sequestration linked to vine growth and sugar production in the grapes. However, the authors did not disclose any additional information to support a comprehensive understanding of the impact due to these biogenic emissions. As a result, the contribution from biogenic carbon emissions to the overall CF of wine is only marginal. Moreover, neither the balance with carbon removals during for example photosynthesis nor the notion of time is usually taken into account. Indeed, the reason for the widespread lack of biogenic carbon assessment can be attributed to the common LCA practice of assigning an impartial weighing between emission and removal activities. The general belief is that fermentation is considered negative due to the CO2 sequestered by the vines and grape growth (Ardente et al., 2006; Barry, 2011; Benedetto, 2010; Greenhaigh et al., 2011; Neto et al., 2013; Notarnicola et al., 2003; Point et al., 2012; SAWIA, 2004; Vázquez-Rowe et al., 2012a) and that CO2 derived from the processes of photosynthesis and must fermentation may be calculated but excluded from the balance. This is either because it is part of the short-term carbon cycle (e.g., CO2 from wine fermentation, emissions from combustion or breakdown of vine pruning, etc.) (Pattara et al., 2012) or because it is assumed to be equal to the amount of CO2 released back into the atmosphere on account of the oxidation of carbon contained in the pruning wastes and in the grapes (Neto et al., 2013). Moreover, Bosco et al. (2011) explicitly excluded carbon emissions due to stalk degradation in soil on account of the difficulties in obtaining a specific spatial estimate without a sampling campaign or validated models. As a consequence, wine CF usually includes only fossil-based GHG sources, although the biogenic carbon issues (in particular with regard to carbon removals) have been recently taken up by CF guidelines such as the GHG Protocol Product Standard and PAS 2050 (see further in Section S3.6 of the SI). 3.8. CF uncertainty Only a small group of studies analyzed here dealt with uncertainty issues related to the accuracy of model parameters, by performing sensitivity analyses and evaluating the significance of key inventory inputs, processes and emission factors used in wine LCA (Bosco et al., 2011; Barry, 2011; Kavargiris et al., 2009; Montedonico, 2005; Neto et al., 2013; Point et al., 2012; Venkat, 2012; Soja et al., 2010; Vázquez-Rowe et al., 2012b). For instance, Bosco et al. (2011) performed a sensitivity analysis in order to validate the robustness of the LCA model, by applying a 20% SD factor to the main parameters affecting the CF (e.g. electricity for vinification, bottle weight, fuel consumption for field operation, fertilizer in production, etc.), and a 70% SD factor to the N2O emissions according to the uncertainty values reported in the IPCC methodology (IPCC, 2006). They found that the most sensitive

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parameters affecting CF were the harvest yield (potential change in CF between 3 and 8%) and the glass bottle weight (potential change in CF between 6 and 10%) for the four wine brands analyzed, which ultimately allow to claim that the impact of the inputs per hectare is affected by the yield obtained (Bosco et al., 2011). Similarly, Montedonico (2005) and Point et al. (2012) applied different scenarios of container substitution and observed that the use of lighter bottles could yield important environmental benefits, even greater than those associated with the adoption of organic grape growing practices. Moreover, Barry (2011) extended the sensitivity analysis to a wider number of life cycle issues, varying parameters, such as frost events, field-based spray emissions, harvest yield or energy efficiency in the winery. However, authors could also provide variability and error estimations with greater margin of accuracy when the sample of available farms for data collection was numerous (Vázquez-Rowe et al., 2012b; Soja et al., 2010; Kavargiris et al., 2009). Even though uncertainty analysis may add valuable information regarding the uncertainties in food product CFs (Röös et al., 2011), to date uncertainty in wine CF has not been treated in depth. Therefore, the need to strengthen research toward a standardization action for an acceptable level of uncertainty analysis and reporting in the carbon footprinting of wine should be tackled in future studies. 4. Discussion 4.1. Open research issues in the environmental assessment of wine The study of wine CF is inherently linked to many other environmental issues and management procedures as LCA has been shown to provide contrasting information concerning a wide set of impact categories. In Section 4.1, we identify a number of useful insights with a view to improving the evaluation of wine CF. 4.1.1. The use of consequential LCA in the wine sector A-LCA applied to the wine sector is primarily aimed at attributing part of the total environmental burden of the economy to the wine production processes, to allow companies to identify the ‘hot-spots’ or critical phases of the life cycle of wine and for communication purposes (Aranda et al., 2005; Benedetto, 2010; Vázquez-Rowe et al., 2013). Therefore, A-LCA is of particular relevance when the aim of the wine firm is to reduce the CF of their product using specific environmental strategies such as eco-labeling (Ardente et al., 2006; CIV, 2008; Barry, 2011; Gonzales et al., 2006), mitigation of GHG emissions (Bosco et al., 2011), and, to a limited extent, eco-design for the delivery of more environmentally sound products (Petti et al., 2006). However, a CF indicator based on an A-LCA approach disregards possible consequences on the market and economic system engendered by large modifications in the wine production system, e.g. following an eco-design approach. The C-LCA approach was therefore proposed to evaluate the (direct and indirect) environmental consequences generated by (large scale) changes implemented on the production system, most often by modeling the physical and market cause and effect chains originating from the decision to change the production system (Mason Earles and Anthony Halog, 2011; Weidema, 2003; Marvuglia et al., 2013). None of the studies analyzed explicitly identifies the materials or energy inputs in wine LCI which may indirectly affect the provision of commodities for other production systems, nor do they take into account the possible marginal effects on the market of increasing or decreasing wine production. Wineries can have a limited share of the total national wine production, so changes in their operations would have limited to negligible consequences on the market

71

(Barry, 2011). Nevertheless, future research addressing CF evaluation should definitely consider a larger scope including substitution scenarios, thus enhancing the value of results interpretation in the larger context of the agri-food and energy sectors (Vázquez-Rowe et al., 2013). Broadening the scope of CF analysis by considering the causal relationships of the wine life cycle processes to other commodity supply-chains would provide winemakers with more robust information to take decisions about their materials supply and savings. In addition, the reuse or recycling of wine co-products should be thoroughly assessed to enhance the characterization of CF and consider positive and negative feedbacks to the wine product itself. By including those effects that may occur outside the system boundaries and affect other production systems, the C-LCA perspective could provide a source of results enrichment through the analysis of the consequences of changes in the levels of production (Ekvall and Weidema, 2004; Thomassen et al., 2008). A consequential approach applied to the wine sector would certainly allows enhancing the utility of LCA (including CF assessments) by providing policy making activities with concrete profiles regarding the environmental consequences linked to future policy amendments. 4.1.2. Labor inclusion in wine LCA The contribution of human activities and in particular of Human Labor (HL) to climate change is not usually included in LCA and CF analyses (Rugani et al., 2012; Zhang and Dornfeld, 2007), despite the fact that this phenomenon is directly and indirectly linked to anthropogenic activities. In contrast, other environmental assessment and embodied energy or exergy analysis methods (Fluck, 1992; Giampietro and Pimentel, 1990; Pimentel, 1993; Krausmann, 2004; Sciubba et al., 2008) do explicitly evaluate the role of HL as a resource input and its intrinsic influence on the final production of specific goods or services. This is particularly true for agricultural systems (Loake, 2001; Guzmán and Alonso, 2008) and viticulture in particular (Gabzdylova et al., 2009; Trioli and Hofmann, 2009; Delmas and Grant, 2008). In fact, the emergy evaluation method (Odum, 1988) considers accounting for HL as an integral part of the human-dominated systems analysis. Within the emergy-oriented wine production analyses explored in this study, the HL contribution to the total emergy of both organic and conventional systems is highly relevant (Pizzigallo et al., 2008; Rugani et al., 2009). HL can fully support the wine production during the entire life cycle, and in turn consume resources and release emissions. Based on this assumption, Rugani et al. (2012) determined that 1 h of work can generate up to 0.4e0.5 kg CO2-eq through hybridization between inputeoutput tables and LCI. The HL-based CF component can provide information linked to the ‘indirect’ GHG emissions associated with the goods and services used by the person to live (Rugani et al., 2012). As a first approximation, this contribution may line up to an additional 6% in the production of 1 bottle of wine (see Table S5 in the SI), a score which is eventually noteworthy to bear in mind for future CF assertions (see Section S3.3 in the SI for further analysis on HL). 4.1.3. Delving into the single-issue perspective of CF Narrowing the focus on CF, rather than performing an integrated environmental assessment of a cluster of different indicators, may allow wine stakeholders to obtain straightforward solutions to solve broad decisional questions in compliance with environmental value improvement purposes. This is mainly due to the fact that GHG emissions arise diffusely and massively throughout the most relevant inputs of the wine life cycle system (e.g. energy and fuel consumptions, fertilizers production and application, land use and land use change, packaging products’ use and end-of-life, etc.). As a matter of fact, CF may be perceived as the most significant indicator

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to assess the environmental sustainability of wine, at least when considering impacts that have a global scale perspective. However, wine producers have to face economic, institutional and social risks, which can occur simultaneously and are not commonly considered in climate change and viticulture assessments (Hadarits et al., 2010). Therefore, the possibility of relying on integrated sustainability analysis systems, such as the recently proposed Life Cycle Sustainability Analysis (LCSA) framework (Guinée et al., 2011), can become a necessity (see Section S3.4 in the SI). The natural step subsequent to the use of specific CF tools and calculators is voluntary adaptation to ecolabeling standards. In this respect, Mobius wine, from The New Zealand Wine Company (NZWC) e Mobius Marlborough Sauvignon Blanc e is highly representative because it constitutes the first certified wine for the Australian and New Zealand markets with a Carbon Reduction Label, which is an easily recognizable on-pack tag for consumers to check whether the purchased wine is committed to reducing carbon emissions according to PAS 2050 and the Carbon Trust Code of Good Practice (NZWC, 2010). Other environmental standards also exist to enable wine companies to undertake voluntary processes of certification, such as the adoption of certified Environmental Management System (EMS) or Environmental Product Declaration (EPD) (see Table 2), which include analysis of CO2 emissions while broadening the scope of the impact assessment. For instance, the Italian company CIV&CIV pioneered on a worldwide scale when it obtained the first EPD and related Climate Declaration for its wine Lambrusco Grasparossa doc Righi (CIV, 2008). In fact, wine companies aligned with such or similar GHG reporting standards can benefit notably from continuous CF monitoring and emission reduction programs, improvements in the wine eco-profile and industrial efficiency and increases in the attractiveness for consumers (Delmas and Grant, 2008; Hughey et al., 2005; Röös and Tjärnemo, 2011). Nevertheless, an in-depth survey reveals that CF may benefit significantly from being placed alongside other indicators to uncover issues that would be missed by focusing on GHGs alone. In fact, some studies (Vázquez-Rowe et al., 2012b) have not only demonstrated the benefits of expanding on the environmental dimensions that are included from a life-cycle perspective, but have also shown that the interpretation of results and discussion of environmental assessment studies in the wine sector may benefit from applying integrated methodologies, in which CF or LCA are combined with external tools (e.g. Geographic Information Systems e GIS) with the objective of enhancing a specific research question. A common practice to allow mutual assertions and scope benefits for CF reporting is to combine LCA with other methodological approaches (Table 1), such as Emergy (to enhance ecosystem services’ biophysical evaluation; Pizzigallo et al., 2008; Rugani et al., 2009; Neri et al., 2012), Data Envelopment Analysis-DEA (to identify wine production operational efficiencies, see Section S3.5 in the SI; Vázquez-Rowe et al., 2012b), lab experiments and benefit-cost estimations (Ruggieri et al., 2009), the use of key performance indicators such as management systems, environmental investment costs, amount of wastes or water consumption, or biodiversity areas (SAWIA, 2004), and the analysis of trade-offs between environmental benefits in the wine supply chain and the consumer perceptions of wine product value (Soosay et al., 2012). Other strategies, such as Product-Oriented Environmental Management Systems (POEMS), aim at individualizing the ‘hot spots’ of the production chain across the entire wine life cycle, to foster dialog among stakeholders, while implementing efficient improvement strategies (Ardente et al., 2006). In these cases, the strength of using not only one single indicator, but a larger range of metrics is unchallenged and adds several advantages to the consistency of

information behind an environmental certificate. Comparing and reporting CF with indicators or techniques other than LCA can ultimately i) support the identification of hidden critical aspects related to wine supply-chain, and ii) provide an added value to broaden the coverage of environmental as well as economic and social issues related to the wine’s sustainability. However, in the context of an environmental certification, the use of a single-issue indicator (i.e. CF) may aid in terms of interpretation. Accordingly, recent surveys have revealed either the policy-decisional or methodological strategy needs to assess the potential threat to regional and local water reservoirs (in terms of freshwater depletion and pollution) due to the increase of wine production practices in certain areas of the globe, such as New Zealand (Herath et al., 2013) or Romania (Ene et al., 2013). Moreover, another issue that remains partially out of the wine CF scope, but of great environmental concern (Neto et al., 2013; Vázquez-Rowe et al., 2012a,b; Ruggieri et al., 2009), is represented by the use of chemicals for wine growing and making processes. However, not much attention has been paid to this issue within the reviewed LCA studies, while the (eco)toxicological impacts related to the consumption of synthetic chemicals in conventional wine productions may be significant. For example, the spreading of fertilizers and the application of pesticides and fungicides containing different metal compounds can lead to an increase in the amounts of these elements in soil, grapes and wine (Diaz et al., 2003; Fiket et al., 2011). The combined use of CF with other single-issue indicators that have largely different scope and claims seems to offer a better platform for impacts’ depiction than the use of single-score methods (common practice in wine-LCAs, see Table 1), which aggregate and weight multi-objective and multi-scale indicators in a way that typically does not rely on the system boundary requirements of the wine product investigated, and, thus, may hamper a meaningful and transparent interpretation of results. The following section further explores the potentials to address multicriteria approaches related to CF in the sustainability analysis of wine. 4.2. Consequences of using CF as an extended indicator The information gathered in the previous sections broadened the discussion on the market and consumption effects of applying the CF indicator in the wine sector. The choice of a wine-making organization of introducing a CF reporting system does not lack certain consequences. While the ‘internal’ benefits and limitations of this introduction have been researched before, analyzing the ‘external’ impacts associated with the adoption of CF reduction actions at the level of single wineries or extended consortiums remains an open issue. This brings up the question of the effects emerging upstream and downstream the supply chain due to innovative actions achieved to improve the wine sustainability profile, with the aim of reflecting the criticality and necessary adjustments the winery system will have to face for reorganizing the process under a life cycle thinking perspective (on both a global and local scale). The focus here is on the following areas of observation: market supply, demand and instruments. 4.2.1. The market supply This market supply component specifically refers to the modification of the intra- and extra-supply chain interrelation mechanisms that appear with the winery and which underpinned a CF improvement oriented strategy. To present the idea, it is useful to start from the classical Supply Chain-LCA scheme, which is directly superimposable to the representation of winery chain à la Malassis defined in agri-food economics (Malassis and Ghersi, 1995). The chain is a series of technological productions, inter-dependent but

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vertically distinct and separate, exchanging raw materials and finished products to satisfy the food needs of the population in a given place and given time (Christopher, 1998). In wine economics, the different steps in the supply chain are not always managed by different actors. Accordingly, two situations may be described: i) the wine integrated enterprise, which manages all the production phases from the grape-growing to the distribution of wine and disposal of industrial waste, and ii) the single wine-producing company, which is the integral part of a larger system of companies that, together, manage one or more production phases (e.g. viticulture, winemaking, wine sales, etc.). In the former case, the implementation of LCA may lead to an extensive involvement of all the company’s functions and, in the best scenario, to a change in the company’s philosophy toward sustainability (e.g., reduction of synthetic fertilizer use, efficiency enhancement in irrigation and pesticide spreading, etc.). This virtuous process may have positive external feedback in terms of better selection of suppliers and customers (e.g., air companies that ship only ‘eco-friendly’ wines). Therefore, the enterprise can provide backward and forward modifications in market relationships, soliciting a shakedown of the system and a demand for alternative solutions. This case matches the on-going situation with the production of lighter glass bottles, or the retail of bulk wine that is bottled only at the destination market, allowing a reduction in transport costs and environmental burdens (see e.g. WRAP, 2007). In the latter case, the production of inputs such as manure, pesticides or glass, and the associated emissions remain out of the enterprise’s control. The introduction of a system of emissions’ allocation (i.e. system of GHGs responsibility assignment) may contribute to a better distribution of responsibilities among the supply chain’s actors which may also improve transparency (Bastianoni et al., 2004; Röös and Tjärnemo, 2011). Regardless of the position along the supply chain covered by the wine production system, the assignation of individual responsibilities for GHG emissions may also contribute to the distrust of many wine firms, and even category associations, of adhering to GHG emission reduction protocols or carbon footprinting (Gadema and Oglethorpe, 2011) (refer to Section 4.2.3 for further discussion on this concept). 4.2.2. Market demand Regarding consumption demand, there are numerous economic implications deriving from the introduction of a CF label, including the consumer interpretation capacity and the related purchase decision model when a variation in the qualitative characteristics of the product occurs. Indeed, reporting GHG emission levels may entail an element of quality differentiation among the wines sold, transforming quality attributes from a credence (i.e., food qualities which are invisible to the consumer both before and after the purchase; Grunert, 2002) to a search (i.e., dimensions whose quality can be ascertained by the buyer at the time of purchase; Grunert, 2002) (Darby and Karni, 1973). This would offer a beneficial judgment factor to reduce the time of wine selection by consumers careful to maximize their own utility function but also the community’s welfare. Hence, it would be of interest to evaluate the willingness to pay for wines that, through ‘eco-sustainable’ labeling, communicate the commitment of the appellation or winery to safeguarding their environmental impact and show sensibility to environmental sustainability issues. One of the conditions for effective eco-labels is that customers should be willing to pay a price premium that may help defray the higher cost of improved environmental management practices (Delmas and Grant, 2008). Thereafter, criticality may arise from communication aspects between the enterprise and the consumer when dealing with responsibility as issued before. In other words, it

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remains an open question whether the system boundaries behind the CF number on the label must include the total GHG emissions of the wine life cycle, only the direct gate-to-gate emissions, or a combination of the two. Even informed consumers will find it hard to interpret the meaning of that number. The reason for this misunderstanding should probably be examined given that there are several different tools which are used for defining the CF of wine. In fact, the problem of information overload may also add dysfunctional consequences in consumer behavior, due to the difficulty a person may have in interpreting issues and thus making choices when faced with ‘too much’ information (Jacoby, 1984). A desirable scenario would be to standardize a unique wine CF calculation method and labeling form, which should be internationally-scaled to meet the purposes of a globalized winemarket demand, allowing consumers worldwide to perform conscious purchases. In connection with this discussion, the recent resolution of OIV (Organisation Internationale de la Vigne et du Vin) concerning GHG accounting for the vine and wine sector (OIV, 2011) is clearly addressed to unifying the CF calculation procedures defined by several international standardization methods (e.g. ISO 14040, ISO 14064, IWCC; see Table 2) in one common and consensual protocol. This international effort is claimed to encompass the most critical aspects behind the CF accounting and reporting, distinguishing between enterprise- and product-oriented protocols and embracing issues which have been only rarely included in traditional CF analyses (e.g. short and long-term biogenic carbon assessment, emissions arising from land use change, use of biomass and biofuels, oak barrels etc.; see Section 3). 4.2.3. Market instruments for wine carbon footprinting As pointed out in Section 4.2.1, the most challenging issue is to find solutions to assign responsibilities throughout the phases coming after the company’s gate, e.g. a company that produces grapes but does not manage their distribution and further winemaking process. In that case, the meaning of a possible CF value reported on the final wine bottle’s label, albeit highly accurate, can be misleading and not transparent because it misses identification of upstream responsibilities and includes confusing information. Such a problem could be overcome by introducing a voluntary procedure of supply chain and product traceability (Banterle and Stranieri, 2008; Dani and Deep, 2010; Sanfiel-Fumero et al., 2012) for the wine product carbon footprinting, which may foresee the management of GHG emissions’ information throughout the life cycle steps of wine production and consumption. A traceability procedure of product quality is already internationally envisaged, for example by the standard ISO 22005:2007 (ISO, 2007). Moreover, several chemometric methods already exist and are internationally adopted for the authentication of wine quality and samples control (Arvanitoyannis et al., 1999; Reid et al., 2006). Therefore, the introduction of a CF traceability system would definitely enable transferring consistent and comprehensive information to the final consumer, and also facilitate the development of systems that wine-management organizations and public institutions can use to monitor carbon trades in the wine market. An effective instrument to allow such a traceability system could be a Product Category Rule (PCR) for wine, created with support from industry representatives, LCA experts, and third parties.1 PCRs are defined in ISO 14025 as the necessary framework to outline

1 The reference refers particularly to the recent PCR guidance development initiative (http://www.pcrguidance.org/) and to the available documentation provided by the PCR committee of the American Center for Life Cycle Assessment (ACLCA) (http://www.lcacenter.org/product-category-rule.aspx).

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specific rules for products serving the same function, providing a harmonized methodology and greater transparency (ISO, 2006c). However, PCRs may also be used for multiple purposes beyond the strict form of EPD proposed in the standard, e.g., consumer labeling, general guidance to business to aid uptake, etc. (Subramanian et al., 2012). They refer to product category-specific rules that govern quantitative assessment of products for all other environmental claims that are life-cycle based and primarily quantitative, including product CF claims or other quantitative product sustainability claims or indices (Ingwersen and Stevenson, 2012). PCRs are linked to labeling programs; therefore, auditing and verification could be performed to provide traceability. In fact, Ingwersen and Stevenson (2012) provide useful insights and recommendations on how to define, propose, manage/align, apply and communicate PCRs and related labeling instruments. There is currently great interest among program operators, academics, consultants, researchers, and industry personnel to establish consensus on the use of the PCR framework and to provide effective actions for global alignment of PCRs (Ingwersen et al., 2012). Therefore, it seems as if wine carbon footprint reporting attempts could benefit in future from a definition and standardization under a specific PCR framework, to allow tuning an accurate process of inventory harmonization, GHGs traceability, comparability and monitoring at different scales of the market. However, side-effects underlying the implementation of traceability, standardization, labeling or other global-based reporting initiatives in the wine market may exist, which are due to rebound effects and transport-related issues (see Sections S3.1 and S3.2, respectively, for a more comprehensive overview). A high demand for environmental certifications and labels in the wine sector is expected in years to come. This is particularly true for wineries located in the New World, which seem to be more sensitive to environmental care and to organic production transitions than Old World wineries (Cholette et al., 2005; White et al., 2009). This is probably due to the relatively younger tradition in producing wine, and thus to the higher flexibility in shifting and conforming to eco-friendly practices according to market changes, which is increasingly oriented to ecosystems preservation and climate change mitigation. 5. Conclusions Despite the strong proliferation of wine CF studies in recent years, this extensive review demonstrates the wide range of applications that remain unexplored in this field. To date, most studies have focused on analyzing specific methodological issues from an A-LCA perspective, or to directly reporting the CF profile of a given wine product. Future studies, however, will have to deal with increasingly complex interactions linked to the entire life cycle of wine-making. Current methodological hot topics in life cycle thinking suggest that an integrated sustainability assessment (i.e. LCSA), which includes the evaluation of economic and social issues, will also develop in the wine sector, whereby it may become easier beginning with a CF analysis and then broadening the scope to the other two pillars of sustainability. However, from an exclusively environmental sustainability approach, it remains to be seen if current climate change focalization, through CF reporting, will be maintained, or if it will be expanded to cover other environmental issues such as impacts related to land use change (BSI, 2011b; IPCC, 2006). Parallelly, a widespread use of the CF in combination with other single-issue indicators would be recommended to increase transparency and impacts coverage, avoid value judgments and arbitrary weighing and promote integrated perspectives. While CF is an outstanding indicator for supply chain improvements and

enterprise communications (internal and external), it seems less useful to evaluate strategies and take decisions at macro scale. Finally, from a market supply and demand perspective, future standardization developments in wine CF will start to provide feedback on the success or failure of eco-labeling dissemination strategies and how their implementation will influence consumer behavior and patterns. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2013.04.036. References AGO-Australian Greenhouse Office, 2004. AGO Factors and Methods Workbook e August 2004 e For Use in Australian Greenhouse Office Programmes. Available at: http://www.soe-townsville.org/data/factors_methods_workbook.pdf (last access December 2012). Aranda, A., Scarpellini, S., Zabalza, I., 2005. Economic and environmental analysis of the wine bottle production in Spain by means of life cycle assessment. Int. J. Agric. Res. Gov. 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