02cb_sust_scm_2025_annotated.pdf

# Introduction to sustainable supply chains and carbon footprinting This section introduces the concept and motivations behind sustainable supply chains, defining sustainability in this context and exploring the significant challenges associated with carbon footprinting within these complex networks. ### 1.1 Recap of sustainability in supply chains #### 1.1.1 Motivation for sustainable supply chains The study of "sustainable" supply chains is driven by an understanding of the broader context of sustainability, which traditionally encompasses three pillars: economic, environmental, and social [2](#page=2) [3](#page=3). #### 1.1.2 Meaning of a sustainable supply chain A sustainable supply chain considers the environmental impact of its operations, among other factors. Environmental sustainability is a key component, focusing on aspects like emission reduction [2](#page=2) [3](#page=3) [4](#page=4). ### 1.2 The carbon footprinting problem in supply chains #### 1.2.1 Urgency and scale of emission reduction There is a significant and urgent need for emission reduction within supply chains. Estimates suggest that technological advancements alone can only achieve a 30% emission reduction by 2030. However, to limit global temperature increase to 2 degrees Celsius by 2050, a required reduction of 95% is necessary. Without a change in current trajectories, the transportation sector alone could account for 100-300% of the greenhouse gas (GHG) target, highlighting that technology alone is insufficient and smarter logistics are crucial [2](#page=2) [7](#page=7) [9](#page=9). #### 1.2.2 Sources of emissions Emissions stem from various sources within the supply chain. Understanding these sources is critical for effective management and reduction strategies [5](#page=5) [6](#page=6). #### 1.2.3 The challenge of measurement A fundamental challenge in managing emissions is the ability to measure them accurately. As famously stated, "If you can't measure it, you can't manage it". This underscores the importance of developing robust carbon footprinting methodologies within supply chain management [40](#page=40) [41](#page=41). > **Tip:** The concept of sustainability in supply chains is not new, but its urgency and the scale of the problem, particularly concerning carbon emissions, have become increasingly prominent. > **Example:** A company that sources raw materials internationally, manufactures products domestically, and distributes them globally faces emissions from multiple stages: extraction of raw materials, transportation of raw materials, manufacturing processes, packaging, and final delivery to consumers. ### 1.3 Opportunities in sustainable supply chains Despite the challenges, sustainability presents significant opportunities within supply chain management. These include the impact of regulatory policies, energy source selection, life cycle assessment, managing carbon emissions in logistics, implementing closed-loop supply chains, carbon footprinting and allocation, sustainable sourcing practices, addressing water risk, handling hazardous materials, fostering SC collaboration, and designing sustainable supply chains [17](#page=17) [18](#page=18) [19](#page=19). ### 1.4 Carbon reduction in transport Carbon reduction is a particularly critical area within the broader scope of sustainable supply chains. Given the significant contribution of transportation to overall emissions focused efforts in this domain are essential [16](#page=16) [9](#page=9). --- # Smart logistics and supply chain redesign for carbon reduction This section delves into strategies for reducing carbon emissions by optimizing logistics and redesigning supply chains for greater efficiency and sustainability [10](#page=10). ### 2.1 Modal shift and intermodal transport A key strategy for carbon reduction in logistics is the modal shift, which involves moving freight from more carbon-intensive transport modes (like road or air) to less carbon-intensive ones (like rail or barge). Intermodal transport, the use of multiple modes of transportation for a single shipment, is closely related and facilitates modal shifts by enabling seamless transfers between different transport types [10](#page=10). ### 2.2 Supply chain redesign for carbon reduction Redesigning the supply chain itself can significantly impact carbon emissions. This includes strategic decisions regarding facility location, which influences delivery radii and the overall transportation network. The number and placement of distribution centers (DCs) have direct consequences on inventory costs, transportation costs, and ultimately, the total carbon footprint. A more consolidated or strategically located network can reduce overall mileage and associated emissions [11](#page=11) [12](#page=12). ### 2.3 Synchromodality Synchromodality represents an advanced form of intermodal transport where the mode of transport is chosen dynamically, closer to the actual transport time, based on real-time conditions and availability. This allows for greater flexibility and optimization, potentially selecting the most carbon-efficient mode at any given moment [13](#page=13). ### 2.4 Increasing load factors A critical factor in the carbon efficiency of any transport mode is its load factor, which is the ratio of actual load to the maximum capacity. Increasing load factors means carrying more goods per trip, thereby reducing the carbon emissions per unit of goods transported. For example, a fully loaded truck will have a lower per-kilogram carbon footprint than a half-empty one [14](#page=14). > **Tip:** Always consider the load factor when comparing transport modes. A mode that appears greener per kilometer might not be if it consistently operates at low load factors. ### 2.5 Carbon-efficient vehicle routing problems (VRP) Vehicle Routing Problems (VRPs) are mathematical optimization problems that aim to find the optimal set of routes for a fleet of vehicles to serve a given set of customers. Carbon-efficient VRPs extend this by incorporating carbon emissions as a primary objective or constraint. This involves developing routing strategies that minimize not only travel distance and time but also fuel consumption and associated greenhouse gas emissions [15](#page=15). #### 2.5.1 Quantifying carbon footprints Quantifying carbon footprints is essential for understanding and reducing environmental impact. A comprehensive carbon footprint analysis considers the entire "cradle-to-grave" lifecycle of a product. This includes [21](#page=21) [29](#page=29): * **Well to Tank (WTT):** Emissions from the extraction, refining, and transportation of fuels [29](#page=29). * **Tank to Wheels (TTW):** Emissions generated during the actual usage of the vehicle, such as burning fuel [29](#page=29). * **Manufacturing and disposal:** The environmental impact associated with producing and eventually disposing of the vehicle and its components [29](#page=29). The Greenhouse Gas (GHG) Protocol provides a framework for reporting and managing greenhouse gas emissions, categorizing them into different scopes. Scope 1 emissions are direct emissions from owned or controlled sources, Scope 2 emissions are indirect emissions from the generation of purchased energy, and Scope 3 emissions are all other indirect emissions that occur in the value chain of the reporting company [30](#page=30). #### 2.5.2 Transport mode selection and emissions calculation Choosing the right transport mode is crucial for minimizing carbon emissions. The calculation of emissions per unit of freight often involves [31](#page=31): * Fuel efficiency (e.g., kilometers per liter) [31](#page=31). * CO2 emissions per unit of fuel (e.g., kg CO2 per liter) [31](#page=31). * Load size (e.g., tons or kilograms) [31](#page=31). For instance, comparing a truck and a smaller vehicle: * **Truck:** 2.55 km/l of diesel, 2.75 kg CO2 per liter, load size: 20 tons [31](#page=31). * Emissions per km per kg: $\frac{2.75 \text{ kg CO}_2/\text{liter}}{2.55 \text{ km/liter} \times 20000 \text{ kg}} \approx 5.4 \times 10^{-5}$ kg CO2/km/kg [32](#page=32). * **Smaller vehicle:** 7.65 km/l of benzine, 2.34 kg CO2 per liter, load size: 700kg [31](#page=31). * Emissions per km per kg: $\frac{2.34 \text{ kg CO}_2/\text{liter}}{7.65 \text{ km/liter} \times 700 \text{ kg}} \approx 4.3 \times 10^{-4}$ kg CO2/km/kg [32](#page=32). In this example, the truck is approximately 10 times greener per unit of transported mass, assuming full loads [32](#page=32). #### 2.5.3 The impact of assumptions It is critical to recognize that assumptions heavily influence the results of any carbon footprint analysis or transport comparison. These assumptions can include [33](#page=33) [36](#page=36): * **Load factors:** The percentage of vehicle capacity utilized. A study might assume full loads, which may not always be realistic [33](#page=33). * **Fuel efficiency:** Actual fuel consumption can vary significantly based on driving conditions, maintenance, and vehicle age [37](#page=37). * **Emission factors:** The rate of CO2 emissions per unit of fuel can differ [37](#page=37). * **Route characteristics:** Terrain, traffic, and weather can impact emissions [63](#page=63). * **Distance:** For air travel, the assumed flight path might differ from road distances [59](#page=59). > **Tip:** Always scrutinize the assumptions behind any study or report. Consider who funded the study and whether there might be an agenda. Performing sensitivity analysis and clearly stating all assumptions are crucial for objective reporting [36](#page=36) [39](#page=39). #### 2.5.4 Net Transport Emissions (NTM) example calculation The Net Transport Emissions (NTM) methodology provides a way to compare the emissions of different transport modes for a specific shipment. The process requires detailed data, including emission factors per mode, load factors, and shipment volume and density [55](#page=55). **Example Scenario:** Shipping 1 kg of sugar from Eindhoven to Lausanne (817 km) [54](#page=54). **Data required:** * Emission factors for different modes (kg CO2 per unit of fuel or per km) [55](#page=55). * Load factors for each mode [55](#page=55). * Volume and density of the shipment [55](#page=55). **Considerations for different modes:** * **Air Travel (e.g., B757-200SF):** * Maximum freight load: ~29,000 kg [57](#page=57). * Assuming a load factor of 0.8, the usable capacity is approximately 23,200 kg [58](#page=58). * Flight distance is often assumed to be 20% shorter than road distance, e.g., 640 km for Lausanne [59](#page=59). * Unit emissions for 800 km (approximated): 0.61 kg CO2 per kg [60](#page=60). * **Road (Truck+Trailer):** * Assumed load factor: 0.7 [60](#page=60). * Unit emissions for 800 km: 0.039 kg CO2 per kg [60](#page=60). * For hilly terrain to Switzerland, emissions might increase by 5% due to constant and variable factors [63](#page=63). * **Rail (Diesel):** * Assumed load factor: 0.5 [60](#page=60). * Unit emissions for 800 km: 0.025 kg CO2 per kg [60](#page=60). * For hilly terrain (e.g., crossing to Switzerland), specific factors like T(hilly) = 153.08 g/Km, FE = 3.175, and W = 1000 ton are used [64](#page=64). * **Barge (Inland Vessel):** * Assumed load factor: 0.5 [60](#page=60). * Unit emissions for 800 km: 0.011 kg CO2 per kg [60](#page=60). * Maximum capacity (W) = 3840 tons [65](#page=65). * Fuel consumption (FC) = 0.007 t diesel/Km [65](#page=65). * Emissions factor (FE) = 3178 Kg CO2/t diesel [65](#page=65). * Due to geography, the barge distance might be 20% longer than the truck distance [65](#page=65). **Findings:** Generally, barges are consistently the greenest option, with emissions often linear to distance. However, practical constraints mean barges are not always feasible [60](#page=60) [61](#page=61). #### 2.5.5 Decision-making challenges and Pareto frontiers Choosing the "greenest" mode is not always straightforward. Often, multiple criteria beyond just carbon emissions need to be considered, such as cost, speed, reliability, and accessibility. In situations with competing objectives, a Pareto frontier can emerge, illustrating trade-offs where improving one aspect (e.g., reducing emissions) necessitates worsening another (e.g., increasing cost or transit time). Making an informed choice requires balancing these factors [61](#page=61) [62](#page=62). --- # Estimating and reporting CO2 emissions in supply chains Estimating and reporting CO2 emissions in supply chains is a complex process, involving various methodologies, data challenges, and distinct emission scopes [46](#page=46). ### 3.1 Methodologies for CO2 emission estimation Estimating CO2 emissions is heavily dependent on the information available with different perspectives and objectives for customers and Logistics Service Providers (LSPs) [44](#page=44) [46](#page=46). #### 3.1.1 Energy-based vs. Activity-based calculations * **Energy-based calculation**: Relies on the actual fuel combusted or estimates based on detailed vehicle and route characteristics. A potential drawback is that carriers, who are the primary holders of fuel consumption data, may have little incentive to share this information truthfully and for free, as fuel is a main cost driver [42](#page=42) [43](#page=43). * **Activity-based calculation**: Based on shipment characteristics. This is considered the current best practice. Carriers may provide an overall estimate without specifics on vehicles or routes [42](#page=42) [45](#page=45). #### 3.1.2 The Network for Transport Measures (NTM) framework The NTM is a Swedish non-profit organization that provides a standard methodology for estimating emissions and maintains a database at transportmeasures.org. It covers various transport modes [49](#page=49): * **Air transport**: The calculation considers factors like effective weight, maximum cargo allocation, and the total mission of the plane. It aims to estimate the average utilization [50](#page=50). * **Road transport**: This involves considering factors such as fuel consumption per distance, contract time, and vehicle characteristics [51](#page=51). * **Diesel train**: Emission estimations are country-dependent and consider factors like fuel efficiency and weight percentage [52](#page=52). * **Diesel water transport (barge)**: This method also considers fuel consumption and emissions per distance [53](#page=53). > **Tip:** NTM's approach requires a significant amount of data, including emission factors per transport mode or fuel, load factors, and the volume and density of shipments [55](#page=55). **NTM assumptions and examples:** * **Maximum load per plane type**: For instance, a B757-200SF has a maximum freight load of approximately 29,000 kg (#page=56, 57). With a load factor of 0.8, this allows for interpolation to estimate emissions [56](#page=56) [57](#page=57) [58](#page=58). * **Other "airplane" assumptions**: Planes are assumed to fly in a straight line, making flight distances 20% shorter than road distances. Volumetric considerations are often disregarded [59](#page=59). * **Unit emissions comparison**: For an 800 km distance, air transport (B757-200, 0.8 load factor) emits 0.61 kg CO2 per unit, while road transport (truck+trailer, 0.7 load factor) emits 0.039 kg CO2, and rail (diesel, 0.5 load factor) emits 0.025 kg CO2. Barges (inland vessel, 0.5 load factor) are the lowest at 0.011 kg CO2. Barges are consistently greener, with emissions being linear to distance, while other modes can have non-linear relationships [60](#page=60). > **Example:** The document notes that the solution for detailed calculations on unit emissions for different modes and distances can be found in Hoen et al. on pages 189-193 [60](#page=60). #### 3.1.3 Data requirements for estimation The accuracy of emission estimates is fundamentally tied to the quality and availability of data. Vehicle characteristics significantly matter; "a truck is not just a truck" [46](#page=46) [48](#page=48). **Data for specific transport modes include:** * **Road**: Additional 5% is added to constant and variable emissions to account for changes in terrain elevation [63](#page=63). * **Rail**: Assumes hilly terrain for crossing to Switzerland, with specific factors for terrain, fuel efficiency (FE), and weight (W) provided. For example, `T(hilly) = 153.08 g/Km`, `FE = 3.175`, `W = 1000 ton` [64](#page=64). * **Barge**: Includes maximum capacity (W), fuel consumption per kilometer (FC), and CO2 emissions per ton of diesel (FE). It's assumed the distance required on a barge is 20% longer than by truck due to geography. For example, `W = 3840 tons`, `FC = 0.007 t diesel/Km`, `FE = 3178 Kg CO2/t diesel` [65](#page=65). ### 3.2 Understanding Scopes 1, 2, and 3 emissions The concept of emission scopes is crucial for comprehensive reporting. * **Scope 1**: Direct emissions from owned or controlled sources [67](#page=67). * **Scope 2**: Indirect emissions from the generation of purchased energy (electricity, steam, heating, cooling) [67](#page=67). * **Scope 3**: All other indirect emissions that occur in a company's value chain, both upstream and downstream [67](#page=67). #### 3.2.1 The importance and challenges of Scope 3 Scope 3 emissions matter because they often represent the largest portion of a company's total carbon footprint (#page=68, 69). However, Scope 3 estimation faces theoretical and practical challenges, leading to a lack of strict formal definitions or frameworks. The general advice is to "do what's possible" and start small [68](#page=68) [69](#page=69) [70](#page=70). > **Tip:** The goal of reporting emissions data is to enable better decision-making and policy-setting, understand how supply chain sustainability evolves over time, and allow for comparisons across firms and time periods (#page=72, 73) [72](#page=72) [73](#page=73). #### 3.2.2 A potential way forward for Scope 3 estimation A promising approach combines supply chain (SC) data with Scope 1 reported data to estimate Scope 3 emissions as the sum of a supply chain's Scope 1 emissions (#page=78, 79, 80). This method suggests that with reliable supply chain and Scope 1 data, transactional data may not be required [78](#page=78) [79](#page=79) [80](#page=80) [94](#page=94). ##### 3.2.2.1 Supply chain visibility as a key enabler The primary problem for Scope 3 estimation is not the estimation itself, but rather supply chain visibility. Mapping even a simple supply chain is non-trivial [83](#page=83) [84](#page=84). * **Supply chain data sources**: Tools like FactSet Revere data offer worldwide coverage by identifying buyer-supplier relationships from conference call transcripts, press releases, and investor presentations, including start and end dates of these links [85](#page=85). * **Dynamic nature of supply networks**: Supply networks are highly dynamic, with approximately a quarter of the network being refreshed annually. New links constitute about 26.8% of all links, and deletions represent 23.1%. Most new links are added between existing nodes, and deletions typically do not lead to node deletions [87](#page=87). ##### 3.2.2.2 Application and results of the model The proposed model combines SC data and Scope 1 reported data to estimate "scope 3". Modelling assumptions are critical in this process [89](#page=89) [90](#page=90). > **Example:** Research applying this method shows promising results where the model tends to over-estimate compared to reported Scope 3 data. The discrepancy appears to stem from downstream emission estimations (#page=91, 92, 93, 94) [91](#page=91) [92](#page=92) [93](#page=93) [94](#page=94). **Conclusions and insights from this approach:** * Scope 3 reporting presents theoretical and practical challenges [94](#page=94). * Supply chain and Scope 1 data show promise for rigorous Scope 3 estimation [94](#page=94). * Reliable SC and Scope 1 data can negate the need for detailed transactional data [94](#page=94). * The model's tendency to over-estimate suggests an area for refinement in downstream emission calculations [94](#page=94). --- ## Common mistakes to avoid - Review all topics thoroughly before exams - Pay attention to formulas and key definitions - Practice with examples provided in each section - Don't memorize without understanding the underlying concepts

| Term | Definition | |------|------------| | Sustainable Supply Chain | A supply chain that integrates environmental, social, and economic considerations into its design, operation, and management to minimize negative impacts and maximize positive contributions throughout its lifecycle. | | Carbon Footprint | The total amount of greenhouse gases (including carbon dioxide and methane) generated by our actions, often expressed in equivalent tons of carbon dioxide ($CO_2e$). | | Greenhouse Gas (GHG) | A gas that absorbs and emits radiant energy within the solar spectral. The primary GHGs in Earth's atmosphere are water vapor, carbon dioxide, methane, nitrous oxide, and ozone. | | GHG Protocol Scopes | A framework for accounting and reporting greenhouse gas emissions. Scope 1 covers direct emissions from owned or controlled sources. Scope 2 covers indirect emissions from the generation of purchased electricity, steam, heating, and cooling. Scope 3 covers all other indirect emissions that occur in a company's value chain, both upstream and downstream. | | Modal Shift | The transition of freight or passenger traffic from one mode of transport to another, typically from road to rail or water, to achieve environmental or economic benefits. | | Intermodal Transport | A shipment that uses multiple modes of transportation (e.g., ship, rail, truck) but is handled as a single shipment, often using standardized containers. | | Supply Chain Redesign | The process of restructuring a supply chain to improve its performance, often involving changes to network structure, facility locations, inventory policies, and transportation strategies. | | Synchromodality | A logistics concept that aims to optimize transport by dynamically selecting the most efficient mode of transport (road, rail, or inland waterway) for each leg of a journey, often in real-time, based on availability and cost. | | Load Factor | The ratio of actual cargo carried to the maximum capacity of a transport unit (e.g., truck, train, aircraft, ship). A higher load factor generally leads to lower emissions per unit of transported goods. | | Carbon-Efficient VRP | A variant of the Vehicle Routing Problem that aims to find the optimal routes for a fleet of vehicles to deliver goods, specifically minimizing the total carbon emissions generated during transportation. | | Life Cycle Assessment (LCA) | A systematic analysis of the potential environmental impacts of products, processes, or services throughout their entire life cycle, from raw material extraction to end-of-life disposal. | | Closed-Loop Supply Chain | A supply chain that includes the return flow of products from the consumer back to the producer for the purpose of recapturing value or proper disposal, often involving repair, refurbishment, remanufacturing, or recycling. | | Carbon Footprinting and Allocation | The process of calculating the carbon emissions associated with a product, service, or activity, and then attributing these emissions to specific entities or stages within a supply chain. | | Scope 1 Emissions | Direct greenhouse gas emissions from sources owned or controlled by an organization, such as emissions from company-owned vehicles or on-site fuel combustion. | | Scope 3 Emissions | Indirect greenhouse gas emissions that occur as a result of an organization's activities but are from sources not owned or controlled by the organization. This includes emissions from the supply chain, use of sold products, and employee commuting. | | Supply Chain Visibility | The ability to track and trace goods, information, and finances as they move through the supply chain, providing real-time insights into operations and potential disruptions. | | Network for Transport Measures (NTM) | A Swedish non-profit organization that develops and maintains a standard methodology and database for estimating emissions from various transport modes, used to compare the environmental impact of different logistics choices. | | Emission Factor | A coefficient that relates an amount of a greenhouse gas or air pollutant to an activity that generates them. For example, the amount of $CO_2$ emitted per liter of diesel fuel combusted. | | Pareto Frontier | In multi-objective optimization, the Pareto frontier represents a set of solutions where it is impossible to improve one objective without degrading at least one other objective. In logistics, this might represent trade-offs between cost and emissions. | | Upstream Emissions | Greenhouse gas emissions that occur before a company's direct operations, such as emissions from the extraction of raw materials or the production of purchased goods and services. | | Downstream Emissions | Greenhouse gas emissions that occur after a company's direct operations, such as emissions from the transportation of sold products to customers or the use and disposal of those products. |