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Logistics practitioners face a significant challenge in meeting local and international regulations and the United Nations' Sustainable Development Goals (SDGs) due to the complexities of measuring and assessing the CO2e emissions of logistics processes. This challenge is pronounced in distribution processes, where the literature currently lacks a structured approach based on existing guidelines and regulations or real-case implementation examples.
Design/methodology/approach
To analyse the environmental performance of distribution processes, a model with Visual Basic for Applications (VBA) algorithms compliant with the Global Logistics Emissions Council (GLEC) framework was developed. An integrative review identified key elements for evaluating the environmental impact of distribution processes, leading to model development. The model was validated through a business case in the agri-food supply chain, demonstrating its applicability and enabling the analysis of optimisation scenarios.
Findings
The findings suggest potential savings in CO2e emissions of up to 50% by improving vehicle efficiency and maximising vehicle capacity utilisation. Further savings of up to 30% are highlighted for the business case company by increasing intermodal transport modes use.
Originality/value
This study offers several academic and managerial contributions. On the one hand, it offers a structured approach to assess the environmental performance of the logistics distribution processes based on a comprehensive European standard and enriches the literature by providing an industrial application of GLEC framework guidelines. On the other hand, it empowers logistics practitioners with a model to assess the environmental impact of distribution processes, and it enables an enhanced decision-making process in selecting transport modes to achieve the company’s sustainability goals.
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Supply chains and related logistics processes have recently experienced major challenges due to several changes and paradigm shifts. Outsourcing and global sourcing have made supply chains longer, more complex, and more fragmented, with logistics and transport activities negatively impacting the environment (Layaoen et al., 2023; Mena et al., 2013). Recent disruptions have further exacerbated these issues (Graves et al., 2023). Consequently, sustainability has become crucial in logistics and supply chain management (Feng et al., 2018; Vinayavekhin et al., 2023), driven by companies' growing social and environmental responsibilities (Mashud et al., 2024; Sayed et al., 2017). Stakeholders now pressure organisations to incorporate environmental considerations in decision-making to reduce the environmental impact, often linked to greenhouse gas (GHG) emissions (Abbasi and Nilsson, 2012; De Stefano and Montes-Sancho, 2024). Logistics processes, particularly transport, have a significant impact on the environment. Emissions from the transport sector have increased by an annual average of 1.7%, faster than any other end-use sector, accounting for around 24% of global CO2e emissions, and freight transport contributes up to 40% of these emissions (International Energy Agency, 2023). Moreover, freight transport demand is expected to more than double over the next three decades since it will be driven by the growth in demand for e-commerce (International Transport Forum, 2023). Several governments and policymakers have implemented strict environmental regulations, urging sustainability-oriented strategies in logistics (Centobelli et al., 2017).
Academically, green logistics has emerged as a key area of focus, aiming to mitigate the environmental impact of logistics operations (Dekker et al., 2012) and has appeared in previous research as one of the main concerns of practitioners and policymakers (Colicchia et al., 2016; Helo and Ala-Harja, 2018; Maji et al., 2023; Prataviera et al., 2023). Research has explored measures to reduce the environmental footprint and resource consumption, identifying key factors influencing the environmental performance of logistics processes (Osman et al., 2022). Implementing green logistics requires mid- and long-term strategies, beginning with assessing and measuring emissions to identify bottlenecks and track the effectiveness of green logistics initiatives (Marchet et al., 2014; Centobelli et al., 2017; Maji et al., 2023). Furthermore, with the recent directive issued by the EU parliament, more companies are increasingly forced to communicate their commitment to sustainability (CSRD, 2023), prompting them to keep up to date with environmental policies by ensuring compliance with legislation and regulations (Dobers et al., 2019). This highlights the importance for companies of measuring the environmental performance of overall logistics processes to align their sustainability and corporate strategy with the United Nations' Sustainable Development Goals (SDGs) by 2025 (i.e. setting targets to decrease GHG emissions) and 2050 (i.e. achieving net-zero carbon emissions) (Colicchia et al., 2011, 2016; Carmagnac et al., 2023; Prataviera et al., 2023).
However, practitioners struggle to integrate sustainability into logistics due to challenges in measuring and assessing the environmental impact of logistics processes (Laari et al., 2018). Existing standards and guidelines vary, causing ambiguity in assessing and reporting (Kumar and Anbanandam, 2022). Specifically, three main issues have been observed: scope and approach – each guideline focuses on one or more parts of the logistics process using different approaches, which is why it is essential to choose the guideline that is best suited to environmental assessment objectives –, data asymmetries – most of the guidelines provide databases that differ from each other in terms of industry sector, country-specificity, and level of detail of KPIs and emissions factors –, and emissions reporting – a global measurement and reporting system is still lacking, forcing companies to refer to national directives. Despite the several options currently available, the literature lacks a structured approach based on current standards and guidelines to rigorously assess the environmental impact of logistics (Perotti et al., 2022). Furthermore, the previously mentioned issues are further amplified in the distribution process, which is the most environmentally inefficient segment of the supply chain (Raghunatha et al., 2023). These inefficiencies are generated by suboptimal vehicle utilisation, high-emission fuel vehicles, and the escalating demand for faster shipping from customers (Muñoz-Villamizar et al., 2021), thus offering the opportunity to obtain the most significant savings in terms of GHG emissions by employing the correct sustainability practices.
For all these reasons, practitioners are currently looking for practical methods to assess, report and benchmark the environmental impact of distribution processes to ensure transparency and compliance with regulations and stakeholders' demands. Given these challenges, this study aims to:
(1)Contribute to the current body of knowledge on environmental sustainability assessment of logistics distribution processes through a regulation-compliant tool.
(2)Provide guidance for managers defining their company’s sustainability assessment method in logistics based on international standards and guidelines.
(3)Understand key drivers for improving logistics distribution processes' sustainability performance.
An integrative literature review on methods and guidelines for the environmental assessment of logistics processes was conducted to achieve the abovementioned objectives (Snyder, 2019). Based on the findings, a practical model to assess the environmental performance of distribution processes was developed, offering insights into key drivers for logistics sustainability. The model’s application is demonstrated through a real-world business case using empirical data from semi-structured interviews. As the proposed method can be applied to any country and sector, to increase the practical relevance of the study, the application is offered for one of the world’s most critical logistics networks, the agri-food logistics network (Helo and Ala-Harja, 2018; Yakovleva et al., 2012). The logistics network of a global company that acknowledged logistics environmental sustainability as a key prospect for the immediate future was examined by focusing on the Italian context. Italy presents a unique case study for investigating the improvement of logistics environmental sustainability (Prataviera et al., 2021) due to a highly fragmented market characterised by extensive subcontracting and some of the highest traffic volumes within Europe (Ministero delle Infrastrutture e dei Trasporti, 2023; Perotti et al., 2015). Finally, a sensitivity analysis was performed to assess variations in key model parameters.
This study provides both academic and managerial implications. On the one hand, it provides several contributions to current literature: (1) it offers a structured approach to assess the environmental performance of the logistics distribution processes based on one of the most comprehensive European standards (i.e. GLEC framework), not used before in the literature; (2) it enriches the literature, by providing an industrial application of GLEC framework guidelines to a logistics distribution process; (3) it offers detailed efficiency metrics that improve theoretical models with practical and quantifiable measures; (4) it supports with empirical data previous research highlighting intermodal transport as more environmentally sustainable than full road transport; (5) it extends sustainability management literature by connecting the fields of logistics, environmental science, and policy studies to offer critical insights into the sustainability solutions and challenges of the logistics sector. On the other hand, it provides interesting inferences for several supply chain actors: (1) it empowers logistics practitioners with a valuable model to thoroughly assess the environmental impact of distribution processes in line with European standards; (2) it enables an enhanced decision-making process in selecting transport modes to achieve the company’s sustainability goals; (3) it offers a deeper understanding of how key logistics parameters affect overall emissions to enable data-driven decisions for company’s environmental strategies; (4) it facilitates shippers and LSPs to include environmental metrics in their data collection and to share data to achieve the United Nation’s SDGs; (5) it paves the way for policymakers to take on their responsibilities in guiding practitioners through the development of standards and regulations to efficiently support vehicle utilisation and intermodal transport shifts as crucial elements in shaping the logistics sector’s environmental sustainability strategies.
The remainder of this paper is structured as follows. The next section contains a review of the literature (Section 2). Section 3 presents the methodological approach, followed by the description of the proposed model (Section 4) and its application to the agri-food industry in the Italian context (Section 5). The results are then discussed, and a sensitivity analysis is carried out (Section 6). Finally, in Section 7 conclusions are drawn, the study’s limitations are highlighted, and recommendations for future research are provided.
2. Research background
2.1 Environmental impact of logistics processes
The literature offers abundant contributions to sustainability and logistics management and practices using different research methods. For instance, Abareshi and Molla (2013) conducted a survey of 279 Australian Logistics Service Providers (LSPs) and analysed it using structural equation modelling. The paper illustrates a comprehensive understanding of the mechanisms involved in implementing green practices in the logistics and transport sector by showing that the implementation procedure requires a long-term process of acquiring, absorbing, transforming, and utilising environmental information through various channels and practices. Using the same methodology, Maji et al. (2023) have shown that the categories of logistics operations and years of industry experience directly impact most indicators of green logistics initiatives. The survey results showed that most logistics managers are aware of the negative impact of logistics activities on the environment. Still, only a small portion of them have taken steps towards green logistics initiatives. This emphasises that the government and policymakers need to work with the most experienced logistics companies to promote green logistics initiatives.
On the other hand, some studies have also tried to assess and quantify the environmental performance of a logistics network using empirical data. Tuni and Rentizelas (2022) conducted a quantitative multicriteria environmental sustainability performance assessment method in a multi-tier food supply chain comprising small-medium enterprises to improve the development of an environmentally efficient supply chain. Findings indicate that prioritising areas of intervention for each environmental impact can improve supply chain sustainability. This assessment method adopted a holistic approach. However, it lacks a specific focus on distribution processes and does not provide a comprehensive environmental assessment of this crucial supply chain segment. Additionally, Bonilla et al. (2024) conducted a comprehensive mapping of sustainable practices applicable to last-mile logistics processes. However, their focus was only on last-mile logistics processes, neglecting a broader view of distribution processes from an environmental perspective.
2.2 Standards and guidelines for environmental performance assessment and evaluation
Standards and guidelines play a crucial role in the environmental performance assessment of logistics, providing support and guidance to practitioners in evaluating and benchmarking the environmental performance of logistics processes (Perotti et al., 2022; Baglio et al., 2020; Ferreira et al., 2016). While standards are often based on regulations and provide technical specifications and criteria for achieving legally prescribed levels of quality and performance, guidelines offer recommendations on practices and measures to be applied voluntarily (European Commission, 2024). Nevertheless, standards and guidelines vary significantly in scope, boundaries, and impact categories, leading to their application and interpretation ambiguities.
The lack of consistency and harmonisation among them can be attributed to several factors. Firstly, the diversity of scope and approaches used in the various environmental assessment standards contributes to the lack of uniformity. For instance, the IS0 14083 standard provides comprehensive documentation on logistics and transport processes. However, a global approach is used here, so the data provided is less accurate than in the EN 16258 standard, which has a level of detail specific to each European country. Regarding guidelines, only a few are specific to the logistics sector and include a comprehensive map of all logistics-related processes (e.g. GLEC and CLECAT frameworks).
Secondly, most guidelines and standards provide databases of emission factors that vary widely, leading to data asymmetry. In particular, each database is unique and idiosyncratic as it strongly depends on the type of approach used for the calculation, the scope of application, the industry sector of reference and the geographical reference location. For this reason, the choice of database to be used is critical to ensure high reliability and accuracy of the data, and it varies strictly according to the context.
Thirdly, emissions reporting initiatives are highly dependent on the standards and guidelines they refer to, as the level of detail required and the recommended KPIs to be reported on can vary significantly from case to case. The Global Reporting Initiative (GRI) standard is considered the most comprehensive reporting standard that practitioners can use to report on their impact on economic, environmental, and social sustainability (GSSB, 2024). Nevertheless, these initiatives hardly align with other standards and guidelines, such as the GLEC framework (GLEC, 2023), which leads to ambiguities and inconsistencies.
2.3 Models assessing the environmental performance of distribution processes
Despite the studies and the computational tools currently available on the market that have emerged in recent years, the environmental impact of distribution processes has rarely been systematically analysed, as it requires an in-depth breakdown and a high level of detail of logistics activities (Kihel et al., 2022). For instance, Mangiaracina et al. (2016) developed a model to assess the logistics environmental impact of the purchasing process in the apparel industry. Findings indicate that the online purchasing process is more sustainable than the offline process, as the environmental impact is lower in the pre-sale, sale, and delivery phases. However, the model is not transferable to other contexts as it focuses on a specific industry sector and is affected by data asymmetries due to the lack of coherence between the scope, guidelines and KPIs adopted. Another example comes from Helo and Ala-Harja (2018), who assessed the CO2e emissions of a temperature-controlled food distribution chain to illustrate the potential savings of green logistics practices. Results showed that analysing the environmental impact of distribution processes helps understand the impact of different activities and identify barriers to environmental improvements and potential savings in terms of CO2e emissions. Cooling has been identified as the most significant source of fuel consumption and can be minimised by real-time systems that optimise the cooling temperature required during transport activities. With the same objective but a different methodology, Colicchia et al. (2016) developed a multi-objective mathematical programming model to identify the most environmentally friendly configuration for the distribution network of chocolate products in a real case study. Even though the improvement of environmental performance is traditionally perceived as requiring considerable additional costs, the research findings showed that such improvements can be achieved even with only a slight increase in distribution costs. As far as the computational tools currently available on the market are concerned, they are usually quite complex, require a more significant effort for collecting data and may need to be adapted to fit specific company needs.
However, environmental assessment models developed so far often lack a structured approach based on standards and guidelines, as well as the use of country-specific KPIs that increase the accuracy and rigour of the results (GLEC, 2023; ISO 14083, 2023; ISPRA, 2024). Under these conditions, a rigorous environmental assessment of distribution processes and logistics-related activities based on globally recognised regulations, standards, and guidelines has been neglected so far. Table 1 reports a comparative analysis of different guidelines and standards for environmental assessment and reporting.
3. Methodology and research design
This paper aims to address the identified gap and pave the way for further empirical research. To achieve these objectives, the research methodology progressed through two main steps, as depicted in Figure 1: developing a model for environmental performance quantification grounded and supported by an integrative review and applying it to a business case to test and refine it, thereby demonstrating its practical application. Previous research has consolidated this approach (Mangiaracina et al., 2016; Marchet et al., 2012).
In the first phase, a deductive logic was employed to develop the environmental assessment model. The integrative review was the most adequate methodology to map and describe the distribution processes (i.e. from the company’s warehouse to the final customer) and identify the most relevant logistics environmental performance factors. The integrative review is a well-established methodology in logistics research (Pereira et al., 2022), which facilitates the rigorous gathering and synthesis of data to enable the critical analysis and enhancement of the theoretical foundation of a particular subject (Snyder, 2019). To obtain a rigorous assessment, two typologies of sources were employed: on the one hand, a literature review of scientific papers detailing the environmental impact of logistics processes was performed; on the other hand, several secondary sources, such as regulations, standards, and guidelines at the European level, that promote clear and straightforward environmental assessment techniques for logistics processes (e.g. GHG protocol report, GLEC framework) were consulted. As an output, this phase led to the development of a model for quantifying the environmental impact of logistics distribution processes and identifying the parameters and variables necessary to run the model. A detailed description of the model is presented in Section 4. It was decided to adopt the standard developed by the Global Logistics Emission Council (GLEC) of the most recent GLEC framework (2023 edition) based on the latest ISO 14000 standard (i.e. ISO14083) to compute, report, and reduce logistics emissions based on a European-specific perspective. The GLEC framework is compliant with several other standards (e.g. GHG protocol report, IPCC guidelines and CDP reporting), thus increasing the result’s validity.
In the second phase, an abductive logic was employed to test and refine the developed model through a real-world business case. It is essential to perform a business case as it allows for a detailed investigation of a company to obtain a holistic description of its network (Rashid et al., 2019), which is necessary to apply the model and validate its results. This fundamental requirement bridges the gap between model-based methods and empirical research (Seuring, 2013). A research protocol was developed to ensure the reliability of the business case. The company’s distribution logistics processes represent the unit of analysis. For the case selection step, to increase the relevance of our contribution, it was decided to test our model on a company operating in the supply chain of one of the most essential and critical logistics networks in the world, which is the agri-food sector (Helo and Ala-Harja, 2018; Yakovleva et al., 2012). In addition, the company was selected as entirely motivated and interested in moving towards greener distribution processes. Triangulation was ensured by employing multiple sources of evidence to build the business case database (i.e. documents and interviews). An interview protocol was developed to include the following aspects: (1) different company profiles with various roles were identified as interviewees (i.e. CEOs, heads of supply chain, logistics managers, and sustainability managers); (2) data were collected through semi-structured interviews, as this approach is particularly suited to business cases due to its inherent flexibility (Yin, 2018) – unlike structured interviews with predetermined questions, semi-structured interviews allow for a more fluid conversation, enabling interviewees to elaborate on their experiences and perspectives (Rubin and Rubin, 2011); (3) the semi-structured interviews were conducted via phone and online meetings to ensure the coherency and reliability of the answers (Yin, 2009); (4) the interviews were recorded and then transcribed to support the coding and analysis of the data collected. The interview questionnaire was divided into four sections (Appendix). The first section included general information on the company and related logistics distribution processes (e.g. transport volumes, logistics network description, product seasonality, transport modes). The other three sections covered specific transport modes, i.e. road transport (e.g. information on the vehicle fleet, propulsion type, average vehicle saturation, percentage of empty trips), rail-road and sea-road intermodal transport (e.g. intermodal operators involved, intermodal hubs, average distance travelled, information on the vehicle fleet involved for first- and last-mile segments) respectively. Overall, several sources refined and validated the model: collected documents/information (i.e. distribution processes database and environmental sustainability reports) and semi-structured interviews with the company operating in the agri-food supply chain and its 8 LSPs. The output of this phase was the refinement and validation of the model developed in the previous phase to represent the distribution processes of the case company and assess its environmental performance. Furthermore, a sensitivity analysis of the impact of different sustainability approaches on the overall CO2e emissions generated by the company’s logistics distribution processes was performed. Specifically, variations of vehicle saturation and an increase in the employment of intermodal transport were considered during the sensitivity analysis. These additional analyses provided the company with further insights into the changes in CO2e emissions depending on the variation of some of the most relevant parameters for logistics and environmental sustainability. The detailed application of the model is presented in Section 5.
4. Model for the environmental assessment of distribution processes
The model was developed using Microsoft Office Excel and Excel Visual Basic for Applications (VBA). Figure 2 reports the model structure. It includes three main parts: an input section, i.e. where the user interacts directly by filling in all required data to run the model; a computational engine, i.e. the computation spreadsheets with the formulae required to derive missing input data and to perform the environmental assessment; an output section, i.e. a dashboard with charts and tables summarising the environmental assessment of the company’s distribution processes.
4.1 Inputs
This category includes the input data users can fill in and modify (Table 2). The main data required by the model include the case-specific network information, namely the secondary distribution network nodes and related features – i.e. information about all the company’s logistics nodes, such as type (e.g. warehouse, transit point), address, and geographical coordinates (i.e. longitude and latitude) – and the secondary distribution network transport data – i.e. information about all trips performed within distribution processes, including pick-up and delivery dates, routing of round-trips, product weight [kg], origin and destination points (geographical location), and distance travelled [km]. All of the above represent the mandatory data necessary to run the model, except the routing of the round-trips and the distance travelled, which may be obtained from other information, as described in the next section. Users may fill in two additional input categories: “fixed parameters related to transport modes and vehicles” and “vehicle utilisation-related variables”. The first category includes information about the characteristics of transport modes (i.e. road, rail-road, and sea-road) employed in distribution processes, such as vehicle type, maximum payload [kg], and emission intensity [kgCO2e/ton∙km]. The second category includes all data related to road vehicle usage, such as vehicle saturation [%], vehicle capacity occupied by the company’s products [%] (in case distribution logistics processes are outsourced to an LSP), empty runnings [%], and average vehicle consumption [l/km]. If no information is available to fill the additional categories, these inputs are computed or pre-set according to default values taken from the GLEC framework (GLEC, 2023), as specified in the following section.
4.2 Computational engine
Different types of formulae compose the computational engine of the model. First, some formulae are related to input estimation when no primary data are available. It is widely acknowledged that data quality significantly influences the results' reliability. Therefore, primary source data (i.e. information collected by the company) should be preferred over secondary source data (i.e. assumed or indirectly derived). However, data collection can be challenging due to the scarce availability of data and process detail, especially regarding sustainability assessment (Perotti et al., 2022; Lu et al., 2019), leading to the potential unavailability of primary source data. For this reason, the computational section of the model was integrated with an algorithm developed with Excel VBA that could derive unavailable data from the user’s initial inputs (defined as “case-specific network information”). To provide an estimation of the missing data, the steps shown in Figure 3 are proposed:
(1)When distances among logistics nodes are not provided as inputs by the user, these data are derived by computing the Euclidean distance and then adjusting it according to a country-specific correction factor known as the “circuity factor” (Ballou et al., 2002) as per formula (1) in Table 3.
(2)When information on the typologies and characteristics of the vehicles employed for the distribution logistics processes is unavailable, then the fleet considered for the distribution processes assessment is assumed to be composed of the vehicles described in the EcoTransIT database (EcoTransIT, 2023), which is part of GLEC framework. The vehicle type is then assigned to each delivery according to the payload weight delivered (i.e. the heavier the products to be delivered, the larger the vehicle required to be used).
(3)When information about the specific vehicle emission intensity [kgCO2e/ton∙km] is unavailable, then three possible methods are proposed: (1) if information about the average fuel consumption [l/km] is available, then the specific vehicle emission intensity can be tailored according to the formula (3) in Table 3; (2) if information about the vehicle saturation [%] and empty runnings [%] is available, then the specific vehicle emission intensity can be tailored according to the formula (4) in Table 3. Otherwise, (3) standard GLEC framework values for the intensity emission factor are used, based on an average vehicle saturation and empty runnings of 60% and 17%, respectively (GLEC, 2023).
(4)When information about round-trip delivery routing (i.e. identifying the sequence in which the delivery drops are performed) is unavailable, it is defined with a multi-trip vehicle routing optimisation problem, as shown in Zhen et al. (2020).
Finally, the environmental assessment is computed using different formulae for each transport mode, as they have different characteristics. Two main cases were considered:
(1)Road transport – i.e. distribution processes performed using only road vehicles, as per formula (6) in Table 3.
(2)Intermodal transport – i.e. distribution processes performed using multiple transport modes (e.g. rail-road, sea-road), as per formula (7) in Table 3.
4.3 Outputs
The model output consists of metrics and KPIs to assess the environmental performance of the distribution processes under consideration, summarised into a dashboard. Such metrics and KPIs are expressed in terms of CO2e emissions emitted by the company’s distribution processes and are displayed in various charts and tables according to transport mode – i.e. GHG emissions [tonCO2e] – (divided into road, road-rail intermodal, and road-sea intermodal transport modes); month and year – i.e. both GHG emissions [tonCO2e] and average intensity factor [kgCO2e/ton∙km] are temporally represented per month of activity –; and transport activity – i.e. GHG emissions [tonCO2e] can be computed for transport activity.
5. Model application
The business case revolves around a company which operates in the Italian agri-food supply chain: Syngenta Italia S.p.A. The company is a distributor/producer of solutions to enhance crop productivity and sustainability through a range of products such as high-quality seeds, crop production chemicals and biological solutions. As a business strategy, Syngenta Italia S.p.A. outsources its distribution processes to 8 LSPs (the main features of which are reported in Table 4), each with its own geographical coverage, thus reaching customers across the whole national (i.e. Italian) territory. Data collected through company documents (i.e. distribution processes database and environmental sustainability reports) and semi-structured interviews with the company’s and its LSPs' managers allow understanding the structure of the logistics network at play in the distribution processes, which is composed of four main warehouses owned by the LSPs that are dedicated to product storage purposes, and 12 logistics platforms owned by the LSPs that are used as transit points (TPs) to perform cross-docking activities before the product finally reaches the end customer. Furthermore, the outbound processes are broken down into individual phases to enhance level of granularity and accuracy of the model (Havenga and Simpson, 2014) – i.e. hub-to-hub transfers” (i.e. physical flows required to perform orders consolidation in the main warehouses), “pick-up flows” (i.e. physical flows going from the main warehouses to the TP for the cross-docking activities), and “product distribution” (i.e. physical flows leaving the TP to deliver the products to the customers). For confidentiality reasons, numerical data reported in this research have been disguised.
5.1 Application of the model to the business case
Data collected from Syngenta Italia S.p.A. for the distribution processes in 2022 and 2023 allowed us to have enough information for each trip to run the model (i.e. the vehicle’s payload [kg], the date of delivery, the addresses of the starting and destination logistics points), in addition to the average vehicle saturation [%] and average empty runnings [%]. Therefore, using the algorithm in Figure 3, it was possible to obtain all missing data. Specifically:
(1)As the distance travelled per trip was unknown, it was computed with the equation at step (1) in Table 3. Latitude and longitude of the logistics nodes were obtained by running their addresses through ArcGIS (i.e. a cloud-based mapping and analysis solution software).
(2)Since the vehicle type per trip was unavailable for some LSPs, the fleet was assumed to comprise the vehicles in the EcoTransIT database. Characteristics of trains and vessels employed for intermodal transport were obtained from the GLEC framework, such as the emission intensity factors – both Tank-to-Wheel (TTW) and Well-to-Tank (WTT) – for each vehicle (Table 5).
(3)Since the average vehicle consumption [l/km] was unknown, case-specific emission intensity factors for each vehicle were computed with the equation at step (4) in Table 3.
(4)The average capacity occupied by the company’s products [%] was obtained through the semi-structured interviews and varied depending on the LSP (Table 5).
(5)According to the transport mode employed in each trip, the CO2e emissions were computed with the equations at step (6) or (7) in Table 3.
The main parameters set to run the model applied to the business case are reported in Table 5.
6. Application results and discussion
The results highlight road transport as the primary mode, accounting for 92.1% of the product weight moved and contributing to 92.5% of the total CO2e emissions over the considered timeframe. Rail-road intermodal transport follows with 4.7% of the product weight moved, producing only 1.5% of the total CO2e emissions. Lastly, sea-road intermodal transport represents the least adopted transport mode, with only 3.2% of the product weight moved, producing 6% of the total CO2e emissions. The current adoption of the various transport modes suggests the opportunity for Syngenta Italia S.p.A. to increase intermodal transport usage to benefit from an overall reduction in GHG emissions, as it is considered more environmentally friendly than full road transport (Bask and Rajahonka, 2017).
Looking at the environmental performance split by LSP (Figure 4), LSP8 and LSP3 are the most environmentally impacting, representing 40.2% and 21.8% of total CO2e emissions, respectively, due to the high volumes of products managed (32% and 25%). However, their logistics activities appear to be among the most efficient from an environmental perspective, showing an average emission intensity factor of 0.18 and 0.16 kgCO2e /ton∙km, respectively. They both make use of intermodal transport modes, with LSP8 adopting sea-road intermodal transport for 1.5% of its transported volume of products (which produces less than 5% of its CO2e emissions) and LSP3 using both rail- and sea-road intermodal transport accounting for 38.3% of its transported volume of products (20% of its CO2e emissions). LSP5 and LSP1 appear to have the least environmental impact, both representing roughly 1.3% of total CO2e emissions due to the low volume of products managed (1% and 2%, respectively). However, their logistics activities appear to be among the least efficient from an environmental perspective, showing an average emission intensity factor of 0.83 and 0.32 kgCO2e /ton∙km, respectively. They both make use of intermodal transport modes, with LSP5 adopting sea-road intermodal transport for 27.5% of its transported volumes of products (80% of its CO2e emissions) and LSP1 using sea-road intermodal transport for 0.5% of its transported volumes of products (3% of its CO2e emissions). The values related to LSP5 suggest that sea-road intermodal transport may not be more environmentally friendly than the other transport modes considered. This can be associated with the low product volumes moved with this transport mode, which curbed the environmental advantage provided by the sea transport segment.
Considering the environmental performance split by phase of the distribution process (Figure 5), it is possible to notice that product distribution represents the most impacting, representing 81.8% of the total GHG emissions (which manages 69.6% of the transported volumes of products) – with an average emission intensity factor of 0.19 kgCO2e/ton∙km – of which only 3.5% is emitted by intermodal transport modes. However, due to the physical inability to increase the intermodal transport modes in this phase to improve the environmental performance, other sustainable solutions would be required, such as improving the average saturation of the vehicles and optimising the round-trip deliveries. The pick-up comes second regarding GHG emissions, with 12.1% (19.6% of the volumes) and an average emission intensity factor of 0.34 kgCO2e/ton∙km. Lastly, hub-to-hub transfer represents 6.1% (10.7% of the volumes) of the total GHG emissions, and it results as the only transport phase where each transport mode has roughly the same GHG emissions, with an average emissions intensity factor of 0.07 kgCO2e/ton∙km.
Considering the environmental performance split by month (2022 and 2023) and transport mode (Figure 6), results show a consistent seasonality trend in both years, with the spring months, from March to May, showing higher GHG emissions and corresponding periods of increased transport activities. Conversely, the months from July to October show a significant reduction in GHG emissions, with a slight increase in the late autumn months (November and December). The average emissions intensity factors reflect these patterns, with relatively stable values ranging between 0.19 and 0.28 kgCO2e/ton∙km throughout the year, with a noticeable peak in September 2023, mainly due to a reduction in monthly product volumes transported and the high percentage of road transport mode (96%). Road transport is the most significant contributor to GHG emissions across the considered timeframe, followed by sea-road transport and rail-road, respectively. These results confirm the opportunity to further optimise the distribution processes by shifting towards intermodal transport modes to mitigate overall emissions.
6.1 Sensitivity analyses
A sensitivity analysis was finally performed to understand how the model results might vary while changing the main parameters used to run the model. Three parameters were identified as critical to improving the distribution processes' environmental sustainability, following previous studies on factors affecting freight transport GHG emissions (Piecyk and McKinnon, 2010): vehicle saturation, empty runnings, and intermodal transport usage. Two scenarios were analysed based on the identified parameters. First, vehicle saturation showed a significant potential for improvement – as LSPs had a range of values for this parameter between 60% and 90% – and empty runnings were set to 17% for all LSPs, in accordance with GLEC framework – as they lacked any detailed data about this aspect, therefore allowing to perform a sensitivity analysis on the impact that the variation of these two parameters would have on GHG emissions [tonCO2e]. Second, as previously mentioned, the results highlighted a broad preference for full road transport, therefore offering the opportunity to perform a sensitivity analysis on the impact of the increase of intermodal transport modes on GHG emissions [tonCO2e]. As such, two different scenarios were investigated. First, a sensitivity analysis was performed, varying the average vehicle saturation – values ranging from 50% to 100%, with 5% increments and empty runnings – values ranging from 0% to 50%, with increments of 10%. The results are displayed in Figure 7, and highlighted savings of up to almost 50% of total CO2e emissions in the best cases as the vehicles would be used more efficiently (i.e. a lower percentage of empty trips during distribution processes) and maximise capacity utilisation (i.e. better use of the vehicle’s volume or payload). This has also been confirmed in previous studies (Miklautsch and Woschank, 2022).
Second, a sensitivity analysis was performed considering the possibility of using intermodal transport modes where full road transport is currently employed. A preliminary analysis of the three transport phases showed that only hub-to-hub transfer and product distribution could be considered. Two approaches were adopted to assess the feasibility of a rail-road intermodal shift: cost-based and environmental-based. The first approach employed an empirical formula to determine economic viability in line with Banomyong and Beresford (2001). This computation considered intermodal transport expenditure [€] and distance travelled by transport mode [km]. In particular, each transport mode can be represented as a curve with a specific steepness (i.e. the unit cost [€/km]). In contrast, the material passage from one transport mode to the other at intermodal terminals can be represented as a vertical “step” of the cost curve (i.e. the fixed cost incurred for material handling charges).
Therefore, the empirical formula considers rail-road intermodal transport more economically convenient than full road transport when the sum of the distances travelled by road vehicles – i.e. the distances between the logistics nodes and the intermodal terminals “Road Distance1 (RoD1) + Road Distance2 (RoD2)” – is five times lower than the distance covered by train – i.e. the distance between the two intermodal terminals “Rail Distance (RaD)” – as shown in Figure 8.
This approach identified roughly 4% of the trips in the product distribution phase that currently travel the longest distance on the road with a weekly average of 3 tons of product weight that, if shifted to rail-road intermodal transport, would decrease GHG emissions by 81.2% (Figure 9, left histogram) – a potential 1.5% reduction over the product distribution phase of the distribution process. The second approach selected only full road transport trips that would environmentally benefit from the intermodal shift – which includes trips that could also economically benefit from the shift; it identified roughly 20% of the trips in the product distribution phase that currently travel the longest distance on road with a weekly average of 4.2 tons of product weight that, if shifted to rail-road intermodal transport, would decrease GHG emissions by 50.5% (see Figure 9, central histogram) – a potential 3% reduction over the product distribution phase of the distribution process.
Lastly, to assess the feasibility of a sea-road intermodal shift, only an environmental-based approach was adopted: trips that would environmentally benefit from the intermodal shift were considered. Overall, roughly 20% of the trips in the hub-to-hub transfer phase were identified with a weekly average of 10.3 tons of product weight that, if shifted to sea-road intermodal transport, would decrease GHG emissions by 65% (Figure 9, right histogram) – a potential 22% reduction over the hub-to-hub transfer phase of the distribution process.
7. Conclusions
This paper proposes an environmental assessment model, compliant with the GLEC framework, for CO2e emissions computation that can be applied to logistics distribution processes. The model’s application is shown in the business case of Syngenta Italia S.p.A., a company operating in the agri-food supply chain.
The first objective – i.e. to contribute to the current body of knowledge on environmental sustainability assessment of logistics processes through a regulation-compliant methodology – was achieved by developing a CO2e emission assessment model for distribution processes. This model uses MS Excel VBA algorithms to process case-specific network information, such as the company’s logistics nodes and secondary distribution network transport data. When data is missing, the model computes an estimate of case-specific values or sets standard values according to the GLEC framework. Based on the transport mode(s) adopted, the model computes the overall GHG emissions in tonCO2e of the distribution process by reporting results for each month, year, and distribution phase.
The second objective – i.e. to guide managers in defining their company’s sustainability assessment method in logistics based on international standards and guidelines – was met by applying the model to Syngenta Italia S.p.A., a company operating in the agri-food supply chain. The company outsources its distribution process to 8 LSPs in the business case analysed. It adopts three transport modes (i.e. full road transport, rail- and sea-road intermodal transport) to deliver the company’s products across Italy. The outputs of the model highlight a significant reliance on full road transport, thus suggesting that shifting to intermodal transport modes could improve the environmental sustainability of the distribution process, which is currently represented by rail- and sea-road intermodal transport in a low percentage.
The third objective – i.e. to understand key drivers for improving distribution processes' sustainability performance – was achieved with sensitivity analyses based on the investigated business case. The analyses show that the improvement in vehicles' saturation and empty runnings (first sensitivity analysis) and increased employment of intermodal transport modes (second sensitivity analysis) are viable methods for improving the overall environmental sustainability of the distribution processes, in accordance with previous research (Miklautsch and Woschank, 2022; Colicchia et al., 2017). On the one hand, the results of the first sensitivity analysis highlight savings of up to almost 50% of total CO2e emissions in the best cases. On the other hand, the second sensitivity analysis shows that increasing rail-road intermodal transport reduces GHG emissions by up to 3% during product distribution, while sea-road intermodal transport cuts emissions by up to 22% during hub-to-hub transfers.
7.1 Academic implications
From a theoretical perspective, this research addresses several significant gaps in the literature by offering a structured approach to assess the environmental impact of distribution processes and enriches the literature with empirical data.
Logistics processes are becoming increasingly complex, necessitating in-depth and meticulous analysis (Kihel et al., 2022). In response to these challenges, the literature needs more structured and rigorous approaches to the environmental assessment of logistics distribution processes based on standards and guidelines. This assessment model for CO2e emissions offers a finer granularity to evaluate logistics distribution processes and complies with the GLEC framework, one of the most comprehensive European standards for environmental performance assessment. To the best of the authors’ knowledge, this is the first time that an industrial application of GLEC framework guidelines to a logistics distribution process has been performed. This model provides a rigorous assessment technique and a valid methodology that could be extended to other business cases, showcasing its broad applicability. Furthermore, this study introduces detailed efficiency metrics that not only enrich theoretical models but also provide practical and quantifiable measures, thereby providing a robust and reassuring framework for evaluating and comparing the environmental performance of logistics operations.
Previous research has emphasised the pressing need to bridge the gap between empirical research and the valuable data assessments provided by quantitative models (Seuring, 2013). With its strong empirical basis, this study contributes to the literature by applying an environmental assessment model to a business case. Its results also provide further empirical support for specific hypotheses proposed in previous research, such as intermodal transport modes being more environmentally sustainable than full road transport (Colicchia et al., 2017).
As research in the context of sustainability management grows in size and complexity, obtaining a comprehensive understanding of the issues under analysis is becoming increasingly challenging. Therefore, it is essential to conduct interdisciplinary research to offer researchers a clear picture of the field and novel perspectives (Vinayavekhin et al., 2023). This research contributes to this novel discourse by connecting the fields of logistics, environmental science, and policy studies to provide interdisciplinary insights that enhance the theoretical landscape and foster a more holistic understanding of sustainability challenges and solutions in the logistics sector.
7.2 Managerial implications
As the United Nation’s SDGs objective dates of 2025 and 2050 are looming, the industrial sector is forced to implement sustainability practices. Among the various areas that require greater attention, logistics remains one of the most environmentally impacting but also among the most difficult to mitigate. The complexity of integrating sustainability into logistics is highlighted by the significant challenges in measuring and assessing the environmental impact of logistics processes (Laari et al., 2018). The findings reveal that most companies struggle to both identify and collect relevant data for environmental assessment, primarily due to ambiguities in existing standards and guidelines (Kumar and Anbanandam, 2022). This study provides interesting implications for several actors in the supply chain: practitioners (i.e. logistics companies), stakeholders (e.g. shippers, LSPs, investors), and policymakers.
For logistics practitioners, this model offers a valuable, replicable, and user-friendly tool to assess the environmental impact of their distribution processes and enables a better decision-making process in selecting transport modes to achieve the company’s sustainability goals. Furthermore, the sensitivity analyses conducted in this research offer a deeper understanding of how key logistics parameters affect overall emissions, enabling practitioners to make data-driven decisions for their environmental strategies (Piecyk and McKinnon, 2010). Moreover, breaking down outbound logistics processes into distinct phases improves the assessment model’s level of granularity and enables targeted interventions (Havenga and Simpson, 2014).
Stakeholders' collaboration has become paramount in today’s global market, where competitiveness is transitioning from individual companies to supply chains (Feng et al., 2018). Sustainability is growing critical for the success of the whole supply chain, and data-sharing plays a crucial role in reducing the logistics environmental impact (Marconi et al., 2017). Information sharing is critical to collaboration, which enables stakeholders to optimise routes, reduce fuel consumption, and minimise GHG emissions (Argyropoulou et al., 2023), paving the way for innovative and sustainable growth in the industry. The tool facilitates shippers and LSPs in incorporating environmental sustainability metrics into their data collection process rather than focusing solely on cost-efficiency performance. This further highlights the importance of stakeholders' collaboration and data-sharing in achieving a sustainable and competitive supply chain (Feng et al., 2018).
From a policymakers’ perspective, in response to the increasing demand for consistent and globally applicable methods of measuring sustainability performance, they need to take on the responsibility of providing support to practitioners through tools and guidance. This will help companies effectively measure and communicate their impact on sustainability, as policies are critical in defining sustainability performance indicators (Sangwan et al., 2018). As a result, policymakers should play a key role in promoting the creation and distribution of training programs that equip workers with the necessary knowledge and skills to carry out environmental sustainability assessments of logistics processes following existing standards and regulations. The results of this research can pave the way for policymakers to develop robust environmental standards and regulations to efficiently support vehicle utilisation and intermodal transport shifts as crucial elements in shaping the logistics sector’s environmental sustainability strategies. Thus, these findings promote further research on how company strategies can be aligned with policies and regulations to achieve environmental sustainability goals, as in line with previous research (Prataviera et al., 2023).
7.3 Limitations and future research directions
While this study fills a significant gap in the current literature, it is essential to acknowledge certain limitations that could provide interesting future research directions. First, the current model did not consider the environmental assessment of warehouse operations, and we focused on transport processes only as a first step. This could provide an opportunity for future research by integrating the environmental impacts of logistics nodes, as it would provide logistics managers with a comprehensive understanding of the entire environmental footprint of the company’s logistics processes. Second, it should be highlighted that the model requires maintenance over time, as some parameters and assumptions change and need to be manually fixed to account for the evolution of distribution processes, standards, and regulations. This could provide an opportunity for integrating the model with real-time data analytics with potential digital technologies (e.g. machine learning, Big Data Analytics, cloud computing) to enhance model accuracy and applicability across different sectors and industries, as highlighted by previous research (Wang et al., 2016). Third, the economic impact of the analysed sustainable practices was not examined. This paves the way for future research directions to study the economic impact of sustainable logistics practices that could enhance the understanding of the related financial implications. Furthermore, this study enables several additional future research directions: (1) expanding the knowledge in the industrial and logistics research area with benchmark studies among different logistics and supply chain contexts to provide a clear and structured overview of how the various environmental assessment methods are influenced by the case-specific network information (i.e. distribution network, industrial context, country and technological level), parameters related to transport modes, and vehicle utilisation-related variables; (2) investigating the model’s applicability in different geographical regions with varying regulatory environments to provide insights that would help understand how local regulations and standards influence the environmental impact of logistics operations; (3) engaging with a broader range of stakeholders, including LSPs, industry associations, and environmental organisations, to enhance the model’s relevance and applicability; (4) conducting longitudinal studies to assess the long-term impact of various logistics strategies on environmental sustainability with the aim to offer valuable knowledge about their effectiveness over time.
By addressing these limitations and exploring future research directions, the field can gain a more comprehensive understanding of logistics operations' environmental impacts, ultimately contributing to more sustainable logistics practices. This comprehensive approach will enrich the scientific literature with practical insights and provide operative strategies for industry practitioners and policymakers. Embracing these advancements will lead to more informed decision-making, promote innovation in sustainable logistics solutions, and stimulate global efforts towards achieving environmental sustainability. Ultimately, this will support realising the United Nations' SDGs, paving the way for a greener and more efficient logistics sector.
LSP’s average vehicle capacity occupied by the company’s products
30%–80%
Average empty runnings
17%
GLEC (2023)
Source(s): Authors’ own work
We would like to thank the business case company, Syngenta Italia S.p.A., for providing data and technical support during the analysis. This study is framed into a broader project (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033–17/06/2022, Spoke 10 “Sustainable Logistics”).
Disclosure statement: No potential conflict of interest was reported by the author(s).
Data availability statement: The main data underlying this research (individual detailed shipment data) cannot be made publicly available, as Syngenta Italia S.p.A. considers them proprietary and business sensitive.
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Appendix Interview questionnaire
Section 1 – general information
(1)Indicate the volumes handled and the regions/geographical areas served for the client company.
(2)Indicate the percentage breakdown compared to the total flows handled (or compared to the total number of deliveries made) of the flows/deliveries operated for the client company. Specify any seasonality during the year.
(3)Describe the current logistical-distribution network (structure, nodes, activities carried out in-house vs outsourced, etc.).
(4)Describe the main phases/activities of the logistical-distribution process (by product-service type, deliveries, etc.).
(5)Indicate if there are any seasonalities during the week/month/year concerning the handled goods.
(6)What are the managed transport methods? (e.g. all road, intermodal rail-road, intermodal road-sea.) In what percentage of volumes/flows?
(7)Indicate if the goods transported for the client company require special conditions (e.g. controlled temperature, dangerous goods, etc.).
(8)Indicate what information is collected for each trip (e.g. kilometres travelled, quantity and weight of material, fuel consumption, etc.).
Section 2 – focus on road transport
(9)Characteristics of the vehicle fleet (owned, leased, managed by third parties, etc.) and percentage breakdown.
(10)Additional information about the owned vehicle fleet (number of vehicles for each type, vehicle registration year, autonomy, characteristics, payload, etc.).
(11)Types of propulsion (internal combustion, electric, hybrid) and fuels used (fossil fuels, first or second-generation biodiesel, hydrogen, etc.) with percentage breakdown.
(12)Concerning the client company, indicate the vehicles predominantly used.
(13)Concerning the client company, describe the main distribution strategies implemented (direct, cross-docking, milk run, etc.).
(14)Indicate the average vehicle saturation used.
(15)Indicate the number or percentage of empty trips.
(16)(Optional) – Concerning the client company, indicate the average consumption (litres/km) for each type of vehicle/autonomy for electric vehicles.
(17)(Optional) – Concerning the client company, indicate the average distance (km) travelled for each trip and for each type of vehicle.
Section 3 – focus on intermodal rail-road transport
(18)Is the rail route management subcontracted to a railway transport operator? If yes, which one(s)?
(19)Concerning the client company, specify the main railway routes used, the average distance travelled by rail (km), and the intermodal terminals of departure/destination.
(20)Concerning the client company, indicate the characteristics of the vehicles predominantly used for the first and last miles (whether owned vehicles, leased, vehicles managed by third parties, etc.) and the percentage breakdown.
(21)Concerning the client company, provide further information about the vehicles used for the first and last miles (number of vehicles for each type, vehicle registration year, type of propulsion and fuel used, autonomy, characteristics, payload, etc.).
(22)Indicate the average vehicle saturation for the first and last miles.
(23)(Optional) – Indicate the average consumption (litres/km) or autonomy for electric vehicles for each type of vehicle mentioned above.
(24)(Optional) – Concerning the client company, indicate the average distance travelled by road for the first and last miles for each trip and for each type of vehicle.
Section 4 – focus on intermodal road-sea transport (specific for island operators)
(25)Is the sea route management subcontracted to a maritime carrier? If yes, which one(s)?
(26)In the case of own fleet or use of the internal maritime carrier, indicate the number and structure of the fleet.
(27)Concerning the client company, specify the main routes used, the average distance travelled (km), and the ports of departure/destination.
(28)Concerning the client company, indicate the characteristics of the vehicles predominantly used for the first and last miles (whether owned vehicles, leased, vehicles managed by third parties, etc.) and the percentage breakdown.
(29)Concerning the client company, provide further information about the vehicles used for the first and last miles (number of vehicles for each type, vehicle registration year, type of propulsion and fuel used, autonomy, characteristics, payload, etc.).
(30)Indicate the average vehicle saturation for the first and last miles.
(31)(Optional) – Concerning the vehicles used for the client company, indicate the average consumption (litres/km) for each type of ship.
(32)(Optional) – Indicate the average consumption (litres/km) or autonomy for electric vehicles for each type of vehicle mentioned above.
(33)(Optional) – Concerning the client company, indicate the average distance travelled by road for the first and last miles for each trip and for each type of vehicle.
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