Drivers of Environmental Sustainability in the Wine Industry: A Life Cycle Assessment Approach

Abstract

The primary aim of this study is to conduct a comprehensive review of the existing literature to identify the most relevant environmental variables and other factors influencing the life cycle assessment of the wine industry. This research seeks to determine whether the type of wine significantly impacts the carbon footprint and to highlight the importance of production strategies over wine typology or grape variety in reducing greenhouse gas emissions. This review encompasses an extensive analysis of previous studies on the environmental impact of wine production. This method involves synthesizing findings from life cycle inventory assessments to identify key variables contributing to greenhouse gas emissions. This analysis also considers regional variations and the effectiveness of different production strategies in mitigating environmental impacts. This review indicates that wine typology (red or white) and grape variety are less significant in determining the carbon footprint than the production strategies employed. It identifies specific variables that contribute substantially to greenhouse gas emissions in wine production. The analysis highlights the need for standardized assessment methods to ensure accurate determination of influential factors in reducing emissions. This study concludes that achieving environmental sustainability in the wine industry requires a balanced approach that integrates environmental, economic, and social aspects. It emphasizes the necessity of developing standardized and universal strategies for assessing wine sustainability. The application of artificial intelligence is proposed as a crucial tool for improving data gathering, trend analysis, and formulating customized sustainability strategies for different wine regions. Addressing the challenges of sustainability in the wine industry is imperative for environmental preservation and the wellbeing of future generations.

1. Introduction

Full of symbolic meanings, imbued with religiosity and mysticism, wine has appeared in our literature since early times, becoming a source of legendary stories and a catalyst for myths, while also being an ancient and revered beverage throughout the centuries [1]. Furthermore, it is important to emphasize that the grape, being one of the most cultivated fruits worldwide, plays a fundamental role in the production of this iconic drink [2].

In 2022, global wine production, excluding juices and musts, is estimated at 258 million hectoliters (mhl), indicating a decrease of nearly 3 mhl (−1%) from 2021. This decline can be attributed to higher-than-expected harvest volumes in Europe, despite spring and summer droughts and heat waves, along with average production levels in the Southern Hemisphere. Overall, the 2022 global wine production volume remained slightly below its 20-year average for the fourth consecutive year. As of 2022, there are 85 countries worldwide that produce wine. Italy (19%), France (18%), and Spain (14%) collectively account for more than half of the world’s production. Portugal ranks tenth globally in terms of wine production volume [3].

The wine production process is increasingly aligned with the prioritization of sustainability in agriculture, driven by rising concerns about environmental issues and consumer demand for transparency regarding the environmental impact of the products they purchase [4]. Following this trend, farmers are adapting and enhancing their production methods to meet updated standards. Wine producers and vineyard owners are also actively involved in sustainable initiatives driven by environmental considerations [5,6].

Due to the increasing urgency of present environmental challenges, it is essential to evaluate the sustainability of products and industrial processes. This assessment aims to promote alternative solutions that help alleviate environmental pressures on the planet [7].

1.1. Sustainability in the Wine Sector

The concept of “sustainability” was initially introduced in 1987 in the Brundtland Report, overseen by Gro Harlem Brundtland, the Prime Minister of Norway, and the World Commission on Environment and Development. This influential document advocates for social and economic progress that supports a healthy and productive life for all, while ensuring the preservation of opportunities for future generations [8,9].

The wine sector, like much of the agri-food industry, faces the challenge of achieving sustainability [10,11,12,13]. Sustainability is a significant goal of the European Union (EU) and the development policy of other international organizations, including the International Organization of Vine and Wine (OIV). To assist vintners in enhancing sustainability, guidelines and standards are required. This has prompted various organizations representing vintners and wine producers, including our own [14]. In 2004, the International Organization of Vine and Wine defined sustainability in the wine sector as a “global strategy on the scale of the grape production and processing systems, incorporating at the same time the economic sustainability of structures and territories, producing quality products, considering requirements of precision in sustainable viticulture, risks to the environment, products safety and consumer health and valuing of heritage, historical, cultural, ecological and landscape aspects” [15]. This approach encompasses three dimensions: environmental, economic, and social [16,17].

The environmental dimension involves the protection of natural resources such as soil, water, air, and biodiversity, and the reduction in negative impacts from viticulture activities, including erosion, pollution, greenhouse gas emissions, and habitat loss.

The economic dimension aims to ensure the viability and profitability of wine companies while promoting sustainability. This involves adopting efficient practices such as the rational use of resources, cost reduction in production, proper inventory management, and seeking new markets and opportunities.

The social dimension refers to the impact of wine-related activities on local communities, the wellbeing of workers, and the preservation of regional culture and traditions. It is essential to promote social inclusion by ensuring adequate working conditions, respecting human rights, and ensuring occupational safety and health [18,19].

The assessment of sustainability should be primarily based on quantitative measures or indicators, whenever possible. In 1994, the Organization for Economic Co-operation and Development (OECD) defined indicator as “a parameter, or a value derived from parameters, which points to/provides information about/describes the state of a phenomenon/environment/area with a significance extending beyond that directly associated with a parameter value” [20].

Before determining the most suitable indicators to assess the performance of the wine industry, it is essential to comprehend and identify the environmental impacts arising from wine production.

To establish consistent methods for assessing and communicating the potential environmental impact throughout the life cycle of products and organizations (goods or services), the European Commission developed two frameworks, known as the Product Environmental Footprint (PEF) and the Organization Environmental Footprint (OEF). These frameworks utilize sixteen indicators to evaluate production impacts (e.g., acidification, ozone depletion, eutrophication, use of mineral and metal resources, and climate change) [21,22,23]. The two methodologies are closely related and share many components [24]. Figure 1 illustrates the impact categories considered in PEF/OEF and the indicators used to assess them.

Using Product Environmental Footprint Category Rules (PEFCRs) enables comparisons of a product’s environmental performance against similar products in the European market. These rules offer detailed guidance on key aspects and parameters crucial for calculating the PEF within a specific product category. Adoption of PEFCRs enhances result consistency and reduces the costs associated with conducting a PEF study [25].

Choosing the appropriate impact category primarily depends on the question the study aims to address. The central issue concerning impact categories involves the selection of various assessment methods, each addressing different impact categories. To achieve this goal, various methodological approaches can be employed, with life cycle assessment (LCA) stands out as a primary tool.

1.2. Life Cycle Assessment

The methodology of life cycle assessment (LCA), as described in the ISO 14040:2006 standards [26] “Environmental management–Life cycle assessment–Principles and framework” and ISO 14044:2006 [27] “Environmental management–Life cycle assessment–Requirements and guidelines”, is an internationally recognized environmental accounting tool that provides a standardized framework for quantifying the environmental impacts of a product or production system throughout its life cycle [28].

LCA considers all the phases involved in the journey of a product, from raw material acquisition (cradle) to end-of-life processes (to grave) [29,30,31,32]. The assessment process comprises four distinct stages: goal definition, inventory analysis, impact assessment, and interpretation of results (Figure 2) [33].

1.3. Life Cycle Inventory

The inventory analysis involves gathering information and using calculation methods to determine the quantity of items entering and leaving a production system. With this information, the evaluation of the life cycle impact begins. The more detailed the inventory data collection, the greater the precision of the life cycle assessment study, thus reflecting the real situation under analysis and resulting in reliable information for effective decision making [34]. The overall quality of the study can be influenced by the data collection phase, which justifies the need for a significant amount of information for conducting the inventory [35]. To carry out this procedure, interviews and questionnaires are conducted with various winery managers or even farmers. Primary inventory data are obtained internally by companies through direct measurements and specific documents, while supplementary information is primarily sourced from LCA databases [1,36]. In the absence of primary data, secondary data from the relevant literature are used [37]. The temporal reference during which the data were collected is also taken into consideration because it is important that this type of information does not pertain to just one productive year, as the product under analysis is an agricultural product subject to climatic conditions, diseases, and pests [38,39,40,41,42]. Indeed, climate change has significant consequences on both the quantity and quality of wine production. Climate changes, such as higher temperatures, extreme weather events, and variations in rainfall patterns, can impact the growth and development of grapes, the maturation cycle, and vine health, resulting in smaller harvests, earlier or later vintages, and potentially lower wine quality [43,44]. Therefore, it is recommended to use data from multiple years for grape cultivation. Typically, data from at least three reference years are collected, and this is the most commonly adopted approach [35,45,46,47,48].

The product’s life cycle includes the stages of grape production (viticulture), wine production, bottling, and the disposal subsystem [49].

1.4. Interpretation of Inventory Results

The interpretation of the results of an LCA study marks the closure of the process, in which they are carefully explored through sensitivity analyses [50,51]. Sensitivity analysis of the data was conducted in several studies [49,52,53,54,55,56,57,58]. Moreover, it is necessary to discuss the robustness and completeness of the information, also taking into account the study’s limitations. At this point, the reliability of the results is confirmed, which is crucial for making decisions. This can be illustrated by the need to redo previous steps, such as in cases of changes in the company’s strategies or to ensure compliance with current laws and regulations [46].

Through interpretation, one can discern the primary factors influencing and hindering environmental impact assessment, providing valuable insights for professionals engaged in LCA and stakeholders in the wine production industry [34].

1.5. Material and Mass Flow

Another crucial point in LCA studies is the definition of system boundaries, which shows significant variation due to the inclusion of multiple functional units and vineyards located in various global regions. This variability directly influences the results of the environmental impact [59]. It is of utmost importance to include a detailed flowchart that encompasses all phases of the production cycle. This flowchart will enable the analysis of various inputs of energy and materials, such as chemicals, water, fuel, and raw materials, among other components, as well as outputs, including emissions and waste. Therefore, it will be possible to estimate indirect environmental burdens related to material production and energy source generation, as well as to analyze the impacts resulting from the transportation of raw materials and finished products. This way, a comprehensive analysis of the wine’s life cycle is ensured, rigorously addressing all relevant aspects [35,50].

1.6. Carbon Footprint

According to research by Pattara et al. (2012) [60], it was found that the carbon footprint (CF) is the most suitable indicator among the available environmental impact categories in LCA-based methodologies to assess greenhouse gas emissions, both direct and indirect, from the wine industry. These emissions are expressed in terms of carbon dioxide equivalent (CO₂eq) [61,62]. The CF is calculated in a detailed manner by multiplying the emissions of various greenhouse gases (GHGs) such as carbon dioxide (CO₂), methane (CH₄), ozone (O₃), nitrous oxide (N₂O), chlorofluorocarbons (CFCs and HCFCs), and hydrofluorocarbons (HFCs), among others, by their respective characterization factors, known as global warming potentials (GWP) [63].

The factor that converts an elementary exchange into an equivalent amount of CO₂, measured in kilograms, is called the “characterization factor” (CF) [64]. This way, the total impact of GHG emissions from a specific activity is quantified, taking into account the relative contribution of each gas to global warming.

The Intergovernmental Panel on Climate Change (IPCC) is a scientific and political organization established in 1988 within the United Nations (UN) framework, initiated by the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO). Its primary objective is to synthesize and disseminate the most advanced knowledge on climate change that currently affects the world, especially global warming. The group aims to explain its causes, effects, and risks to humanity and the environment, as well as to propose ways to address these issues [65].

The global warming potential is a metric defined by the IPCC, which integrates radiative forcing (RF) over a specific time horizon, T, the selection of which is subjective and context-dependent, for instance, global warming potential 20 years (GWP20) or global warming potential 100 years (GWP100). GWP20 assigns greater weight to effects over a shorter time horizon, whereas GWP100 considers a longer period. The Kyoto Protocol is based on the GWPs of emissions from a pulse over a 100-year period. This metric takes into account the radiative efficiency of various substances and their lifetimes in the atmosphere, providing values relative to the reference gas CO₂. GWP measures how much energy the emissions of 1 kg of a gas will absorb over a given period, relative to the emissions of 1 kg of CO₂ [58,65]. That means:

$$\mathbf{GWP}\mathbf{=}\frac{{\int }_0^{\mathit{T}}{\mathit{a}}_{\mathit{i}}{\mathit{c}}_{\mathit{i}}\mathit{d}\mathit{t}}{{\int }_0^{\mathit{T}}{\mathit{a}}_{{\mathit{C}\mathit{O}}_2}{\mathit{c}}_{{\mathit{C}\mathit{O}}_2}\mathit{d}\mathit{t}}$$

In the equation, a_i is the instantaneous radiative forcing due to a unit increase in the concentration of trace gas, i. Conversely, c_i indicates the concentration of trace gas i remaining in the atmosphere t time units after its release. Finally, T represents the period over which the calculation is conducted. The respective carbon dioxide values are denoted in the denominator [66,67].

Analyzing the carbon footprint across activities in the wine industry can assist companies in pinpointing “hotspots” where emissions are most significant, aiming to enhance the current status. According to [68], the carbon footprint has some advantages over LCA when it comes to communicating with stakeholders and the general public, as the interpretation of the results is simpler and easier to understand, making the information more accessible to everyone.

2. Materials and Methods

The methodology of this study can be categorized into two components: the systematic process of searching the literature and the methodological approach used to develop and assess the research findings. This study has as its main objective to conduct an innovative systematic review, focusing on collecting information for the formation of the inventory. In other words, we analyze how the authors develop the questionnaire, identify its target audience, and explore all aspects related to the inventory in the context of sustainability and carbon footprint, including its limitations. It is also our aim to compare among the analyzed articles the presence of the three dimensions of sustainability, as well as the possible absence of any of them. Furthermore, we are dedicated to understanding whether, in a general sense, the circular economy is a concern addressed in the analyzed studies, seeking to identify its presence within the context of sustainability research.

The study involved gathering articles from two databases: ScienceDirect and Scopus. Searches in these databases used combinations of keywords: (“Life Cycle Assessment” OR LCA) AND “Wine”; (“Life Cycle Assessment” OR LCA) AND “Inventory” AND “Wine”; (“Global Warming Potential” OR GWP) AND “Wine” AND “Carbon footprint” and “Sustainability” AND (“Life Cycle Assessment” OR LCA) AND “Wine”, with the “AND” Boolean operator in the “Article title, Abstract, Key Terms”. Data collection for the review took place from February to July 2023. As shown in

Table 1. Sources from the scientific literature and the keywords employed.

Article Title, Abstract, Key Terms	Number of Scopus Documents Located	Number of Science Direct Documents Located
(“Life Cycle Assessment” OR LCA) AND “Wine”	150	180
(“Life Cycle Assessment” OR LCA) AND “Inventory” AND “Wine”	28	75
(“Global Warming Potential” OR GWP) AND “Wine” AND “Carbon footprint”	10	56
“Sustainability” AND (“Life Cycle Assessment” OR LCA) AND “Wine”	55	137

, during the identification phase, a total of 691 initial items were retrieved, comprising 243 from Scopus and 448 from Science Direct.

After the exclusion of duplicate copies, there were a total of 343 items remaining. In the figure below (Figure 3), a bibliometric mapping is depicted, which aims to identify the authors who have significantly contributed to the areas of research interest, highlighting those considered the most influential or prominent within this set of publications. The use of the VOSviewer program allows for an effective visualization of connections and patterns in academic works, providing valuable insights into which researchers have played a pivotal role in advancing knowledge in these fields of study. This bibliometric analysis is essential for recognizing the primary contributors and tracking the evolution of these research domains.

After the screening phase, which involved only the analysis of abstracts and titles and the publication year (starting from 2000), the number of articles was reduced to 76. Next, all these articles underwent a full reading (in the eligibility phase), focusing exclusively on studies that addressed life cycle assessment, life cycle inventory, sustainability and carbon footprint. Review articles, book chapters, books, reports, errata, and conference abstracts were excluded, resulting in a total of 53 articles. There were no restrictions based on language, time period, or publication status.

3. Results and Discussion

The qualitative analysis was structured as follows: an initial assessment was conducted, taking into account all the necessary information to establish the purpose of each study. In this context, data related to the author(s), year of publication, geographic location of the studies, the three pillars of sustainability, circular economy (end of life), the methodology used, inventory data source, inclusion or exclusion of data or tables addressing the inventory in the articles, availability of the questionnaire, and, finally, the time frame during which inventory-related data were collected were collected and summarized. These pieces of information were compiled and are presented in Table 2 and Table 3.

3.1. Temporal Classification

All the studies included in the present review cover a wide range of years: the temporal references reported in the review indicate that the studies are ranging from 2005 to 2023. Dividing this period into three distinct groups, it can be observed that from 2005 to 2011, a total of 6 articles were published; this number rose to 24 articles from 2012 to 2017. Finally, in the most recent years, from 2018 to 2023, 21 articles were published (Figure 4). The increase in publications on sustainable viticulture can be attributed to several reasons. Since 2004, when the OIV began promoting viticultural sustainability as a priority, consumers and producers have become more concerned with environmental, economic, and social issues. Additionally, governments have implemented measures to promote more sustainable products, such as carbon environmental taxes and market restrictions on nonsustainable products. Wine cooperatives also face pressures to adopt sustainable practices due to market and consumer demand. This pressure has resulted in the creation of various sustainable certification programs, encouraging winegrowers to improve resource efficiency and implement more ecological and socially responsible practices [69].

Table 2. An analytical summary of the key features of the papers reviewed.

References	Country	Functional Unit (UF)	Sustainability			System Boundary
			Environmental	Social	Economic
Amienyo et al. (2014) [70]	Australia	0.75 L	✓	X	✓	Cradle to grave
Aranda et al. (2005) [71]	Spain	0.75 L	✓	X	✓	Cradle to grave
Ardente et al. (2006) [50]	Italy	0.75 L	✓	X	X	Cradle to market
Bellon-Maurel et al. (2015) [72]	France	1 kg of grape	✓	X	X	Cradle to gate
Benedetto et al. (2013) [73]	Italy	0.75 L	✓	X	X	Cradle to gate
Bonamente et al. (2016) [53]	Italy	0.75 L	✓	X	X	Cradle to grave
Bosco et al. (2011) [74]	Italy	0.75 L	✓	X	X	Cradle to grave
Casson et al. (2022) [75]	Italy	1 ha	✓	X	✓	Cradle to gate
Chiusano et al. (2015) [11]	Italy	1 L	✓	X	X	Cradle to gate
Cichelli et al. (2016) [76]	Italy	1 ton of grape	✓	X	X	Cradle to gate
Csiba-Herczeg et al. (2023) [77]	Hungary	Not identifiable	✓	X	X	Cradle to gate
De Marco et al. (2015) [78]	Italy	0.75 L	✓	X	X	Gate to gate
Falcone et al. (2016) [79]	Italy	1 kg of grape	✓	X	✓	Cradle to grave
Ferrara et al. (2023) [80]	Italy	3 L	✓	X	✓	Cradle to grave
Ferrari et al. (2018) [81]	Italy	566 t of grape	✓	X	X	Cradle to gate
Fusi et al. (2014) [82]	Italy	0.75 L	✓	X	✓	Cradle to gate
García et al. (2023) [83]	Spain	0.75 dm3	✓	✓	✓	Cradle to grave
Gazulla et al. (2010) [84]	Italy, Spain, Luxembourg	0.75 L	✓	X	X	Cradle to grave or gate
Hefler and Kissinger (2023) [85]	Israel	1 ton of grape	✓	X	X	Cradle to gate
Iannone et al. (2014) [86]	Italy	0.75 L	✓	X	X	Cradle to gate
Iannone et al. (2016) [87]	Italy	0.75 L	✓	X	X	Gate to gate or grave
Jiménez et al. (2013) [88]	Spain	1 ha	✓	X	X	Cradle to grave
Jradi et al. (2018) [47]	France	Not identifiable	✓	X	✓	Not identifiable
Laca et al. (2020) [56]	Spain	0.75 L	✓	X	X	Cradle to gate
Letamendi et al. (2022) [89]	Chile	0.75 L	✓	X	✓	Cradle to market
Litskas et al. (2017) [90]	Cyprus	1 kg of grape	✓	X	X	Vineyard to market
Litskas et al. (2020) [91]	Cyprus	0.75 L	✓	X	X	Vineyard to market
Liu et al. (2023) [92]	China	0.75 L	✓	X	X	Cradle to gate
Martins et al. (2018) [46]	Portugal	0.75 L	✓	X	✓	Gate to gate
Masotti et al. (2022) [93]	Italy	0.75 L	✓	X	X	Gate to gate
Meneses et al. (2016) [49]	Spain	0.75 L	✓	X	X	Cradle to grave
Mura et al. (2023) [37]	Italy	0.75 L	✓	X	✓	Cradle to gate
Navarro et al. (2017) [94]	Italy, Spain	0.75 L	✓	X	X	Cradle to gate
Neto et al. (2013) [95]	Portugal	0.75 L	✓	X	X	Cradle to market
Notarnicola et al. (2015) [96]	Italy	1000 L	✓	X	✓	Cradle to grave
Pattara et al. (2012) [60]	Italy	0.75 L	✓	X	X	Cradle to market
Pizzigallo et al. (2008) [97]	Italy	1 ton of wine	✓	X	X	Cradle to gate
Point et al. (2012) [98]	Canada	0.75 L	✓	X	X	Cradle to grave
Recchia et al. (2018) [99]	Italy	0.75 L	✓	X	X	Viticulture
Rinaldi et al. (2016) [62]	Italy	0.75 L	✓	X	X	Cradle to grave
Ruggieri et al. (2009) [100]	Spain	1 kg of N	✓	X	✓	End-of-life processes
Russo et al. (2021) [101]	Africa	1 kg of grape	✓	X	X	Viticulture
Scrucca et al. (2019) [102]	Italy	0.75 L	✓	X	X	Cradle to grave
Sinisterra-Solís et al. (2023) [103]	Spain	1 kg of grape	✓	X	✓	Viticulture
Steenwerth et al. (2015) [104]	California	1 ton of grape	✓	X	X	Vineyard to market
Trombly and Fortier (2019) [105]	New York	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2012a) [45]	Spain	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2012b) [106]	Spain	1.1 kg of grape	✓	X	✓	Not identifiable
Vázquez-Rowe et al. (2013) [61]	Italy	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2017) [107]	Peru	0.50 L	✓	X	X	Cradle to gate
Vinci et al. (2022) [108]	Italy	0.75 L	✓	X	X	Cradle to gate
Wang et al. (2023) [109]	China	1 ton of grape	✓	X	X	Cradle to gate
Zhang and Rosentrater (2019) [110]	USA	0.75 L	✓	X	✓	Cradle to grave

✓–yes; X–no.

Table 2. An analytical summary of the key features of the papers reviewed.

References	Country	Functional Unit (UF)	Sustainability			System Boundary
			Environmental	Social	Economic
Amienyo et al. (2014) [70]	Australia	0.75 L	✓	X	✓	Cradle to grave
Aranda et al. (2005) [71]	Spain	0.75 L	✓	X	✓	Cradle to grave
Ardente et al. (2006) [50]	Italy	0.75 L	✓	X	X	Cradle to market
Bellon-Maurel et al. (2015) [72]	France	1 kg of grape	✓	X	X	Cradle to gate
Benedetto et al. (2013) [73]	Italy	0.75 L	✓	X	X	Cradle to gate
Bonamente et al. (2016) [53]	Italy	0.75 L	✓	X	X	Cradle to grave
Bosco et al. (2011) [74]	Italy	0.75 L	✓	X	X	Cradle to grave
Casson et al. (2022) [75]	Italy	1 ha	✓	X	✓	Cradle to gate
Chiusano et al. (2015) [11]	Italy	1 L	✓	X	X	Cradle to gate
Cichelli et al. (2016) [76]	Italy	1 ton of grape	✓	X	X	Cradle to gate
Csiba-Herczeg et al. (2023) [77]	Hungary	Not identifiable	✓	X	X	Cradle to gate
De Marco et al. (2015) [78]	Italy	0.75 L	✓	X	X	Gate to gate
Falcone et al. (2016) [79]	Italy	1 kg of grape	✓	X	✓	Cradle to grave
Ferrara et al. (2023) [80]	Italy	3 L	✓	X	✓	Cradle to grave
Ferrari et al. (2018) [81]	Italy	566 t of grape	✓	X	X	Cradle to gate
Fusi et al. (2014) [82]	Italy	0.75 L	✓	X	✓	Cradle to gate
García et al. (2023) [83]	Spain	0.75 dm3	✓	✓	✓	Cradle to grave
Gazulla et al. (2010) [84]	Italy, Spain, Luxembourg	0.75 L	✓	X	X	Cradle to grave or gate
Hefler and Kissinger (2023) [85]	Israel	1 ton of grape	✓	X	X	Cradle to gate
Iannone et al. (2014) [86]	Italy	0.75 L	✓	X	X	Cradle to gate
Iannone et al. (2016) [87]	Italy	0.75 L	✓	X	X	Gate to gate or grave
Jiménez et al. (2013) [88]	Spain	1 ha	✓	X	X	Cradle to grave
Jradi et al. (2018) [47]	France	Not identifiable	✓	X	✓	Not identifiable
Laca et al. (2020) [56]	Spain	0.75 L	✓	X	X	Cradle to gate
Letamendi et al. (2022) [89]	Chile	0.75 L	✓	X	✓	Cradle to market
Litskas et al. (2017) [90]	Cyprus	1 kg of grape	✓	X	X	Vineyard to market
Litskas et al. (2020) [91]	Cyprus	0.75 L	✓	X	X	Vineyard to market
Liu et al. (2023) [92]	China	0.75 L	✓	X	X	Cradle to gate
Martins et al. (2018) [46]	Portugal	0.75 L	✓	X	✓	Gate to gate
Masotti et al. (2022) [93]	Italy	0.75 L	✓	X	X	Gate to gate
Meneses et al. (2016) [49]	Spain	0.75 L	✓	X	X	Cradle to grave
Mura et al. (2023) [37]	Italy	0.75 L	✓	X	✓	Cradle to gate
Navarro et al. (2017) [94]	Italy, Spain	0.75 L	✓	X	X	Cradle to gate
Neto et al. (2013) [95]	Portugal	0.75 L	✓	X	X	Cradle to market
Notarnicola et al. (2015) [96]	Italy	1000 L	✓	X	✓	Cradle to grave
Pattara et al. (2012) [60]	Italy	0.75 L	✓	X	X	Cradle to market
Pizzigallo et al. (2008) [97]	Italy	1 ton of wine	✓	X	X	Cradle to gate
Point et al. (2012) [98]	Canada	0.75 L	✓	X	X	Cradle to grave
Recchia et al. (2018) [99]	Italy	0.75 L	✓	X	X	Viticulture
Rinaldi et al. (2016) [62]	Italy	0.75 L	✓	X	X	Cradle to grave
Ruggieri et al. (2009) [100]	Spain	1 kg of N	✓	X	✓	End-of-life processes
Russo et al. (2021) [101]	Africa	1 kg of grape	✓	X	X	Viticulture
Scrucca et al. (2019) [102]	Italy	0.75 L	✓	X	X	Cradle to grave
Sinisterra-Solís et al. (2023) [103]	Spain	1 kg of grape	✓	X	✓	Viticulture
Steenwerth et al. (2015) [104]	California	1 ton of grape	✓	X	X	Vineyard to market
Trombly and Fortier (2019) [105]	New York	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2012a) [45]	Spain	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2012b) [106]	Spain	1.1 kg of grape	✓	X	✓	Not identifiable
Vázquez-Rowe et al. (2013) [61]	Italy	0.75 L	✓	X	X	Cradle to gate
Vázquez-Rowe et al. (2017) [107]	Peru	0.50 L	✓	X	X	Cradle to gate
Vinci et al. (2022) [108]	Italy	0.75 L	✓	X	X	Cradle to gate
Wang et al. (2023) [109]	China	1 ton of grape	✓	X	X	Cradle to gate
Zhang and Rosentrater (2019) [110]	USA	0.75 L	✓	X	✓	Cradle to grave

✓–yes; X–no.

3.2. Geographical Classification

Figure 5 helps classify the geographical regions where the different studies were conducted. It shows that most research was conducted in Europe, especially in Italy and Spain, with additional case studies in North America (such as the United States and Canada), South America (including Chile and Peru), Australia, Asia (like China and Israel), and South Africa. Europe stands out as the leading continent in terms of productivity. Italy tops the list with the most articles on wine life cycle assessment (26), followed by Spain (11). Other European nations, including Portugal, France, and Cyprus, each contribute around 4% of the total studies.

3.3. Functional Unit (FU)

The functional unit (FU) serves as a standardized measure against which inputs and outputs can be assessed, facilitating the comparison of various scenarios. The choice of functional unit varies depending on the specific goals of the studies reviewed. According to the research findings, three main types of functional units—volume, mass, and surface—were utilized in the studies. The predominant choice for the functional unit is a volume measure of wine production, with 54.71% of articles using 0.75 L of wine. In terms of mass-based functional units, 9.43% of the studies (5) employed 1 kg of harvested grapes, and only 1.89% of the studies (1) utilized 1.1 kg of harvested grapes, while 5.66% of the studies (3) used 1 ton of harvested grapes. Lastly, 3.77% of the studies (2) employed 1 hectare (“ha”) of vineyard as the functional unit based on surface area. The functional unit was unspecified in two studies (3.77%). Different functional units are established if the purpose of the study is distinct. A case in point is the research by [100], which established the functional unit as 1 kg of nitrogen added to the vineyard soil.

3.4. The Three Pillars of Sustainability

With regard to the three pillars of sustainability, namely, environmental, social, and economic, it is important to note that environmental sustainability is addressed by all of them, representing a point of convergence among these studies. Only 30.19% of these studies go further, also incorporating the dimension of economic sustainability, thus demonstrating an additional commitment to financial issues associated with sustainability. However, it is crucial to highlight that only a single study mentions social sustainability, standing out as a notable exception within this set of research. Consequently, we can conclude that only one study stands out by comprehensively addressing the three pillars of sustainability, incorporating environmental, economic, and social dimensions in its analysis. This study provides a more comprehensive and integrated view of sustainability issues, allowing for a deeper and broader analysis of the topic.

3.5. Circular Economy

The circular economy is an increasingly relevant and stimulating field of study, reflecting a growing awareness and recognition of the importance of adopting sustainable practices, especially in the wine sector. Our goal was to investigate the extent to which this concept is being integrated into the variables considered to calculate the carbon footprint, thus highlighting its influence in the broader context of sustainability in the wine industry.

The notion of circular economy is emphasized in 35.85% of the analyzed articles. This approach, which promotes resource efficiency, waste reduction, and material recycling, emerges as a significant point of interest in the studies examined [111,112]. The implementation of the circular economy can positively impact company performance through corporate sustainability, aiming to ensure long-term business growth and development [113].

3.6. System Boundary

Most of the studies reviewed take two main approaches: cradle to gate (39.63% of the articles) and cradle to grave (26.42% of the articles). The cradle to gate concept in the life cycle assessment of a wine bottle encompasses the extraction of natural resources (or “cradle”) to the winery’s exit point (or “gate”). It considers stages such as viticulture, vinification, bottling, and distribution. In other words, it covers all stages from obtaining raw materials to manufacturing the product. In contrast, the cradle to grave concept also includes consumption and its disposal or recycling until the end of its complete life cycle (or “grave”), providing a more comprehensive view of the life cycle. The “gate to gate” approach focuses solely on activities within the facilities of the wine-producing company, from the entry of raw materials (grapes) to the exit of the final product (wine bottle ready for distribution). Therefore, other phases such as viticulture (grape cultivation) and its disposal are not covered in the “gate to gate” analysis as they occur outside the winery’s facilities [114].

Table 3. Details and information related to the inventory of the reviewed articles.

References	Methodology	Inventory Database	Inventory Tables or Data	Questionnaire Available	Data Collection Interval
Amienyo et al. (2014) [70]	LCA	Ecoinvent	✓	X	n.s.
Aranda et al. (2005) [71]	LCA	Various dataset	X	X	n.s.
Ardente et al. (2006) [50]	LCA	Various dataset	✓	X	n.s.
Bellon-Maurel et al. (2015) [72]	LCA	Ecoinvent v.2.2	✓	X	per year
Benedetto et al. (2013) [73]	LCA	EDIP	✓	X	n.s.
Bonamente et al. (2016) [53]	LCA	Ecoinvent v.3.1	✓	X	n.s.
Bosco et al. (2011) [74]	LCA	Ecoinvent v.2	✓	X	n.s.
Casson et al. (2022) [75]	LCA	Ecoinvent v.3.6	✓	X	2 years
Chiusano et al. (2015) [11]	LCA	n.a.	✓	X	2 years
Cichelli et al. (2016) [76]	LCA	n.a.	✓	X	per year
Csiba-Herczeg et al. (2023) [77]	LCA	Ecoinvent v.3; USLCI	X	X	n.s.
De Marco et al. (2015) [78]	LCA	Ecoinvent	✓	X	n.s.
Falcone et al. (2016) [79]	LCA and LCC	Ecoinvent v.2.2	✓	X	3 years
Ferrara et al. (2023) [80]	LCA and LCC	Ecoinvent v.3.8	X	X	n.s.
Ferrari et al. (2018) [81]	LCA	Ecoinvent	✓	X	n.s.
Fusi et al. (2014) [82]	LCA	Ecoinvent v.2.2	✓	X	1–5 years
García et al. (2023) [83]	LCA and LCC	Ecoinvent v.3.8	✓	X	n.s.
Gazulla et al. (2010) [84]	LCA	n.a.	✓	X	n.s.
Hefler and Kissinger (2023) [85]	LCA	Ecoinvent v.3.9	✓	X	1 year
Iannone et al. (2014) [86]	LCA	Ecoinvent	✓	X	n.s.
Iannone et al. (2016) [87]	LCA	Ecoinvent	✓	X	n.s.
Jiménez et al. (2013) [88]	LCA	n.a.	✓	X	n.s.
Jradi et al. (2018) [47]	DEA	n.a.	✓	X	n.s.
Laca et al. (2020) [56]	LCA	Ecoinvent v.3; Agri-footprint	✓	X	n.s.
Letamendi et al. (2022) [89]	LCA	Ecoinvent v.3	✓	X	3 years
Litskas et al. (2017) [90]	LCA	n.a.	✓	X	n.s.
Litskas et al. (2020) [91]	LCA	n.a.	✓	X	5 years
Liu et al. (2023) [92]	LCA	CLCD v.0.8	✓	X	n.s.
Martins et al. (2018) [46]	LCA and EBITDA	Ecoinvent v.2	✓	X	3 years
Masotti et al. (2022) [93]	LCA	Ecoinvent v.3	✓	X	1 year
Meneses et al. (2016) [49]	LCA	Ecoinvent v.3.1	✓	X	n.s.
Mura et al. (2023) [37]	LCA and LCC	Literature; Ecoinvent	✓	X	5 years
Navarro et al. (2017) [94]	LCA	Ecoinvent; Thinkstep; ELCD	✓	✓	n.s.
Neto et al. (2013) [95]	LCA	Ecoinvent v.2.2	✓	X	1 year
Notarnicola et al. (2015) [96]	LCA	Ecoinvent	✓	X	2 years
Pattara et al. (2012) [60]	LCA and IWCC v.1.3	ELCD	✓	X	n.s.
Pizzigallo et al. (2008) [97]	LCA and Emergy	n.a.	✓	X	n.s.
Point et al. (2012) [98]	LCA	Various dataset	✓	X	n.s.
Recchia et al. (2018) [99]	LCA	Various dataset	✓	X	2 years
Rinaldi et al. (2016) [62]	LCA	Ecoinvent v.4	✓	X	n.s.
Ruggieri et al. (2009) [100]	LCA	Literature; Ecoinvent	✓	X	n.s.
Russo et al. (2021) [101]	LCA	Ecoinvent v.3.6	✓	X	1 year
Scrucca et al. (2019) [102]	LCA	Ecoinvent v.3	✓	✓	n.s.
Sinisterra-Solís et al. (2023) [103]	LCA	Ecoinvent v.3.8	X	X	8 years
Steenwerth et al. (2015) [104]	LCA	Ecoinvent v.2.2; PEPD	✓	X	n.s.
Trombly and Fortier (2019) [105]	LCA	Ecoinvent v.3; USLCI	X	X	n.s.
Vázquez-Rowe et al. (2012a) [45]	LCA	Ecoinvent; ELCD	✓	X	4 years
Vázquez-Rowe et al. (2012b) [106]	LCA and DEA	Ecoinvent	✓	X	1 year
Vázquez-Rowe et al. (2013) [61]	LCA	EDIP	✓	X	n.s.
Vázquez-Rowe et al. (2017) [107]	LCA	Ecoinvent v.3.1	✓	✓	n.s.
Vinci et al. (2022) [108]	LCA	Ecoinvent v.3.8	✓	X	1 year
Wang et al. (2023) [109]	LCA	CLCD	✓	X	n.s.
Zhang and Rosentrater (2019) [110]	LCA and TEA	n.a.	✓	X	n.s.

Chinese Life Cycle Database (CLCD); data envelopment analysis (DEA); earnings before interest, taxes, depreciation, and amortization (EBITDA); European reference Life Cycle Database (ELCD); International Wine Carbon Calculator (IWCC); life cycle cost (LCC); technical efficiency analysis (TEA); United States Life Cycle Inventory (USLCI); ✓–yes; X–no; n.s. = not specified; n.a. = not applicable.

Table 3. Details and information related to the inventory of the reviewed articles.

References	Methodology	Inventory Database	Inventory Tables or Data	Questionnaire Available	Data Collection Interval
Amienyo et al. (2014) [70]	LCA	Ecoinvent	✓	X	n.s.
Aranda et al. (2005) [71]	LCA	Various dataset	X	X	n.s.
Ardente et al. (2006) [50]	LCA	Various dataset	✓	X	n.s.
Bellon-Maurel et al. (2015) [72]	LCA	Ecoinvent v.2.2	✓	X	per year
Benedetto et al. (2013) [73]	LCA	EDIP	✓	X	n.s.
Bonamente et al. (2016) [53]	LCA	Ecoinvent v.3.1	✓	X	n.s.
Bosco et al. (2011) [74]	LCA	Ecoinvent v.2	✓	X	n.s.
Casson et al. (2022) [75]	LCA	Ecoinvent v.3.6	✓	X	2 years
Chiusano et al. (2015) [11]	LCA	n.a.	✓	X	2 years
Cichelli et al. (2016) [76]	LCA	n.a.	✓	X	per year
Csiba-Herczeg et al. (2023) [77]	LCA	Ecoinvent v.3; USLCI	X	X	n.s.
De Marco et al. (2015) [78]	LCA	Ecoinvent	✓	X	n.s.
Falcone et al. (2016) [79]	LCA and LCC	Ecoinvent v.2.2	✓	X	3 years
Ferrara et al. (2023) [80]	LCA and LCC	Ecoinvent v.3.8	X	X	n.s.
Ferrari et al. (2018) [81]	LCA	Ecoinvent	✓	X	n.s.
Fusi et al. (2014) [82]	LCA	Ecoinvent v.2.2	✓	X	1–5 years
García et al. (2023) [83]	LCA and LCC	Ecoinvent v.3.8	✓	X	n.s.
Gazulla et al. (2010) [84]	LCA	n.a.	✓	X	n.s.
Hefler and Kissinger (2023) [85]	LCA	Ecoinvent v.3.9	✓	X	1 year
Iannone et al. (2014) [86]	LCA	Ecoinvent	✓	X	n.s.
Iannone et al. (2016) [87]	LCA	Ecoinvent	✓	X	n.s.
Jiménez et al. (2013) [88]	LCA	n.a.	✓	X	n.s.
Jradi et al. (2018) [47]	DEA	n.a.	✓	X	n.s.
Laca et al. (2020) [56]	LCA	Ecoinvent v.3; Agri-footprint	✓	X	n.s.
Letamendi et al. (2022) [89]	LCA	Ecoinvent v.3	✓	X	3 years
Litskas et al. (2017) [90]	LCA	n.a.	✓	X	n.s.
Litskas et al. (2020) [91]	LCA	n.a.	✓	X	5 years
Liu et al. (2023) [92]	LCA	CLCD v.0.8	✓	X	n.s.
Martins et al. (2018) [46]	LCA and EBITDA	Ecoinvent v.2	✓	X	3 years
Masotti et al. (2022) [93]	LCA	Ecoinvent v.3	✓	X	1 year
Meneses et al. (2016) [49]	LCA	Ecoinvent v.3.1	✓	X	n.s.
Mura et al. (2023) [37]	LCA and LCC	Literature; Ecoinvent	✓	X	5 years
Navarro et al. (2017) [94]	LCA	Ecoinvent; Thinkstep; ELCD	✓	✓	n.s.
Neto et al. (2013) [95]	LCA	Ecoinvent v.2.2	✓	X	1 year
Notarnicola et al. (2015) [96]	LCA	Ecoinvent	✓	X	2 years
Pattara et al. (2012) [60]	LCA and IWCC v.1.3	ELCD	✓	X	n.s.
Pizzigallo et al. (2008) [97]	LCA and Emergy	n.a.	✓	X	n.s.
Point et al. (2012) [98]	LCA	Various dataset	✓	X	n.s.
Recchia et al. (2018) [99]	LCA	Various dataset	✓	X	2 years
Rinaldi et al. (2016) [62]	LCA	Ecoinvent v.4	✓	X	n.s.
Ruggieri et al. (2009) [100]	LCA	Literature; Ecoinvent	✓	X	n.s.
Russo et al. (2021) [101]	LCA	Ecoinvent v.3.6	✓	X	1 year
Scrucca et al. (2019) [102]	LCA	Ecoinvent v.3	✓	✓	n.s.
Sinisterra-Solís et al. (2023) [103]	LCA	Ecoinvent v.3.8	X	X	8 years
Steenwerth et al. (2015) [104]	LCA	Ecoinvent v.2.2; PEPD	✓	X	n.s.
Trombly and Fortier (2019) [105]	LCA	Ecoinvent v.3; USLCI	X	X	n.s.
Vázquez-Rowe et al. (2012a) [45]	LCA	Ecoinvent; ELCD	✓	X	4 years
Vázquez-Rowe et al. (2012b) [106]	LCA and DEA	Ecoinvent	✓	X	1 year
Vázquez-Rowe et al. (2013) [61]	LCA	EDIP	✓	X	n.s.
Vázquez-Rowe et al. (2017) [107]	LCA	Ecoinvent v.3.1	✓	✓	n.s.
Vinci et al. (2022) [108]	LCA	Ecoinvent v.3.8	✓	X	1 year
Wang et al. (2023) [109]	LCA	CLCD	✓	X	n.s.
Zhang and Rosentrater (2019) [110]	LCA and TEA	n.a.	✓	X	n.s.

Chinese Life Cycle Database (CLCD); data envelopment analysis (DEA); earnings before interest, taxes, depreciation, and amortization (EBITDA); European reference Life Cycle Database (ELCD); International Wine Carbon Calculator (IWCC); life cycle cost (LCC); technical efficiency analysis (TEA); United States Life Cycle Inventory (USLCI); ✓–yes; X–no; n.s. = not specified; n.a. = not applicable.

3.7. Type of Methodology

All studies (100%) utilized life cycle assessment as their environmental approach. Furthermore, in articles addressing economic aspects, specific methodologies such as life cycle cost (LCC), data envelopment analysis (DEA), earnings before interest, taxes, depreciation, and amortization (EBITA), and technical efficiency analysis (TEA) were applied.

3.8. Inventory Database

A significant portion of the studies we analyzed (~64.16%) used the Ecoinvent database, regardless of the version, as the main source for life cycle information on products and processes, enabling global-scale environmental assessments. This database supports a wide range of sustainability assessments, spanning areas such as agriculture, construction, chemicals, energy, forestry, metals, textiles, transportation, hospitality, waste treatment and recycling, as well as water supply, among other industrial sectors. Approximately 16.98% of the studies did not make use of any database in their analyses.

3.9. Inventory Tables or Data

Nearly 90.57% of the investigations prominently included tables, data, or graphs that provide insights into the inventory construction process or a part of it, based on databases and the scientific literature. This approach offers a more in-depth and enlightening understanding of the inventory’s evolution and its inherent characteristics.

3.10. Questionnaire Available

Only a very few studies, specifically three, showed the initiative to design a unique inventory, carefully developing a set of questions that were created independently by these researchers. This portion, accounting for a remarkably small proportion, amounts to only 5.66% of the existing literature corpus in the field of study.

3.11. Data Collection Interval

It was observed that the majority of studies did not reference the data collection timeframe, accounting for approximately 62.26% of the total. In contrast, 11.32% of the publications indicated their period to be 1 year, and approximately 7.55% claimed it to be 2 years. These results differ from the conclusions presented in previous studies [35,45,46,47,48], which suggest a reference period of at least 3 years, ideally.

3.12. Analysis Variables

Of the analyzed wines, 30% are red (16 cases), while white wines contribute 17% (9 cases). The combination of red and white wines represents 25% (13 cases). It is worth mentioning that 15 cases (28% of the total) were categorized as “Not identifiable” (Figure 6).

Considering a 0.75 L bottle of wine as the functional unit, the carbon footprint varies across different studies. It is important to understand this variability in carbon footprint values, and to do so, it is necessary to identify the variables highlighted in the analysis of this impact.

In the case of red wine, the lowest carbon footprint found was 0.78 kg CO₂-eq, while the highest was 2.94 kg CO₂-eq, with an average value of 1.30 kg CO₂-eq. For white wine, the carbon footprint ranged from 0.10 kg CO₂-eq at the minimum to 4.00 kg CO₂-eq at the maximum, with an average value of 1.39 kg CO₂-eq. For both types of wine, the smallest carbon footprint found was 0.07 kg CO₂-eq, while the largest was 3.77 kg CO₂-eq (

Table 4. Carbon footprint values for red, white, and both wines (functional unit: 0.75 L).

Carbon Footprint/Functional Unit (0.75 L)
References	Red Wines	White Wines	Red and White Wines
Amienyo et al. (2014) [70]	1.25 kg CO2-eq
Ardente et al. (2006) [50]	1.60 kg CO2-eq
Benedetto et al. (2013) [73]		1.64 kg CO2-eq
Bonamente et al. (2016) [53]	1.07 ± 0.09 kg CO2-eq
Bosco et al. (2011) [74]			[0.60–1.30] kg CO2-eq
De Marco et al. (2015) [78]		0.10 kg CO2-eq
Fusi et al. (2014) [82]		1.01 kg CO2-eq
Gazulla et al. (2010) [84]	0.93 kg CO2-eq
Iannone et al. (2014) [86]			[0.18–1.28] kg CO2-eq
Iannone et al. (2016) [87]			[0.07–0.99] kg CO2-eq
Laca et al. (2020) [56]			2.35 kg CO2-eq
Letamendi et al. (2022) [89]	2.94 kg CO2-eq
Litskas et al. (2020) [91]		1.31 kg CO2-eq
Masotti et al. (2022) [93]		[0.51–0.91] kg CO2-eq
Meneses et al. (2016) [49]	0.95 kg CO2-eq
Mura et al. (2023) [37]			0.86 kg CO2-eq
Navarro et al. (2017) [94]			0.85 kg CO2-eq
Neto et al. (2013) [95]		2.0 kg CO2-eq
Pattara et al. (2012) [60]	0.78 kg CO2-eq
Point et al. (2012) [98]			3.22 kg CO2-eq
Rinaldi et al. (2016) [62]			[1.38; 1.43] kg CO2-eq
Trombly and Fortier (2019) [105]		[0.62; 1.03] kg CO2-eq
Vázquez-Rowe et al. (2012b) [106]		[1.65–3.21] kg CO2-eq
Vázquez-Rowe et al. (2013) [61]			[1.13–3.77] kg de CO2-eq
Vázquez-Rowe et al. (2017) [107]		[1.70–4.00] kg CO2-eq
Vinci et al. (2022) [109]	1.10 kg CO2-eq
Zhang and Rosentrater (2019) [110]	1.09 kg CO2-eq

).

Various factors have influenced this range of values, but are these factors consistent across all studies? What criteria differentiate them? Upon closely examining this information, we observe that the authors do not provide a clear answer to these questions. However, it is important to discuss whether these factors were actually considered in the reviewed articles and if they are relevant to the study.

Can grape variety play a decisive role in carbon footprint calculation? Among the studies analyzed, not all authors commented on the grape variety used. Among those that did (

Table 5. Papers references and their associated grape varieties.

References	Grape Variety
Bellon-Maurel et al. (2015) [72]	Syrah
Bosco et al. (2011) [74]	Sangiovese
Cichelli et al. (2016) [76]	Montepulciano, Trebbiano, Pecorino, Incrocio Manzoni, Syrah, Primitivo di Manduria
Fusi et al. (2014) [82]	Cannonau, Monica, Carignano, Vermentino
Litskas et al. (2017) [90]	Xynisteri, Cabernet Sauvignon, Thompson Seedless
Masotti et al. (2022) [93]	Ribolla Gialla, Friulano, Verduzzo, Malvasia Istriana
Meneses et al. (2016) [49]	Cabernet Sauvignon and Tempranillo
Pattara et al. (2012) [60]	Montepulciano
Point et al. (2012) [98]	Chardonnay, Pinot Noir
Recchia et al. (2018) [99]	Sangiovese
Trombly and Fortier (2019) [105]	Riesling
Vázquez-Rowe et al. (2013) [61]	Cannonau and Vermentino

), there was a tendency to focus on varieties such as Cabernet Sauvignon, Cannonau, Montepulciano, Sangiovese, Syrah (red grape varieties), and Vermentino (white grape variety). However, it was not clear if the choice of variety had a significant impact on carbon footprint results, as few studies directly compared different grape varieties. Therefore, more studies would be necessary to determine if grape variety is a relevant variable and how it influences the outcomes.

Could vineyard irrigation be an indispensable variable? Only Bellon-Maurel et al. (2015) [72] and Steenwerth et al. (2015) [104] emphasized that the vineyards in their studies were not irrigated. In contrast, Casson et al. (2022) [75] investigated six different irrigation scenarios, indicating that irrigation practices can vary significantly among vineyards. No other study provided detailed information on whether vineyards were irrigated and how frequently. This inconsistency complicates the assessment of the impact of irrigation on carbon footprint. For a more robust analysis, it would be necessary for more studies to include this information and systematically evaluate different irrigation regimes.

Is the use of fertilizers a common practice? If so, which fertilizers are used and in what quantities? What is the vineyard management method? What type of bottle or packaging is employed? There is ambiguity surrounding these questions. There seems to be no clear consensus among experts regarding the variables to be considered in calculating the carbon footprint, and there is a lack of information about them. Future research should aim to standardize the collection and presentation of these data to enable a better understanding of how each of these factors influences the carbon footprint in grape cultivation and wine production. Does the geographical location of the vineyard play a significant role? Vines from geographically distinct regions have divergent needs throughout their life cycle, thereby contributing to a wide range of dynamics and unique characteristics. The research conducted by [85] highlighted and assigned considerable importance to this aspect, incorporating a thorough analysis of the influence of region and climate in their findings.

The complexity and diversity associated with the carbon footprint in wine production are evident. By addressing these gaps, there is an urgent need for standardization and transparency in this field. This can help promote more sustainable practices in the wine industry, working towards a greener and more conscious future.

4. Conclusions

Sustainable viticulture plays a crucial role in environmental preservation and the wellbeing of future generations, but upon examining the existing literature, it becomes evident that it faces substantial challenges in terms of approach. Most existing research prioritizes environmental sustainability, while social and economic aspects are often neglected. This lack of balance compromises our ability to adopt a holistic approach to vineyard sustainability, which requires not only environmental preservation measures but also policies that promote social inclusion and economic viability in winemaking practices.

The concept of “greenwashing” involves practices that camouflage, lie, or omit information about the true environmental impacts of a company’s activities on the environment. Companies that engage in greenwashing make statements without concrete evidence regarding the veracity of a service or product. In the wine industry, it is important to distinguish between genuine sustainable actions and purely superficial marketing strategies. Integrity and transparency play crucial roles in this context, emphasizing the urgent need for a rigorous and impartial assessment of sustainability initiatives adopted by wineries. Life cycle assessment emerges as a means of combating greenwashing and preventing misleading environmental marketing practices. Interestingly, none of the reviewed articles address this issue.

One of the major challenges of the future is using artificial intelligence (AI) to reduce the amount of data required for different inventories. The potential of AI lies in reducing the number of variables that need to be collected from wine producers. Currently, data collection is time-consuming, and the responses from vintners or other professionals in the field are limited due to the large volume of data requested. With a robust database in place, the number of variables could be halved, simplifying inventory complexity. Furthermore, AI promises far-reaching benefits across the wine industry: in production and agriculture, optimizing vineyard management and predicting weather conditions; in quality and vinification, enhancing consistency and reducing waste; in marketing and distribution, analyzing market data for more effective strategies; and in sustainability, integrating practices to reduce carbon footprint and manage waste.

It has been observed that the development of sustainable inventories and the consideration of regional variations are often neglected in most studies. The differences between wine regions are substantial and require a more detailed approach to understand and address the specific needs of each locality. Artificial intelligence provides an opportunity to organize information by different geographic zones and, consequently, to compare the environmental impact across different wine regions.

In summary, wine sustainability faces significant challenges, ranging from the lack of balance between pillars to the absence of standardized strategies. However, we recognize the critical importance of this area and the need to address it more comprehensively and effectively. The application of artificial intelligence may be the key to overcoming these challenges and promoting sustainable practices in the wine industry worldwide.