The Carbon Footprint of Pharmaceutical Logistics: Calculating Distribution Emissions

Abstract

Calculating greenhouse gas (GHG) emissions across the supply chain presents a significant challenge for the pharmaceutical industry in achieving environmental sustainability. This article develops a comprehensive methodology for the data collection and calculation of GHG emissions in pharmaceutical distribution, with a focus on road transport and warehousing. The methodology specifies key data requirements and sources, enhancing transparency and alignment with industry standards, such as the GLEC Framework. Real-world pharmaceutical data were collected from a global logistics company operating in Southern Africa. The methodology was applied, which yielded significantly variable results. The calculated emission intensity factors differ significantly from those in the literature. Emissions from road transport ranged from 239.57 to 6156.80 gCO₂e/t-km, depending on the vehicle size, load factor, and empty running. Warehousing emissions results show a smaller variance, ranging from 6.07 to 8.85 kgCO₂e/m³ or 81.70 to 104.42 kgCO₂e/t. The insights from this article support the logistics company and other stakeholders in understanding their emissions and data requirements for enhanced assessments to advance sustainable practices in pharmaceutical logistics.

1. Introduction

The need for a continuous, reliable, and safe supply of medicines has intensified in recent years, as access to affordable pharmaceutical products is fundamental to achieving good health for billions of people worldwide [1]. Driven by the 2030 Agenda for Sustainable Development, the pharmaceutical sector faces increasing expectations to contribute to several key Sustainable Development Goals (SDGs) [2], particularly SDG 3, which aims to promote well-being and ensure healthy lives for all people [3]. In addition, SDG 13 is creating more pressure for all industries to be more environmentally conscious to reduce climate change. However, over the past three years, there has been a significant disruption or deviation in the planned overall progress towards achieving the SDGs by 2030 [3], predominantly due to the COVID-19 pandemic, several global supply chain disruptions, and global conflicts.

As a result of globalisation, freight logistics has become an increasingly important part of the supply chain of all products and is fundamental to our way of life [4,5]. In the pharmaceutical industry, the role of logistics is critical due to the perishable nature of products, which are sensitive to humidity, lighting intensity, and temperature [6]. Pharmaceutical products, therefore, require a temperature-controlled supply chain and a special type of distribution to manage environmental changes. However, the decarbonisation of freight logistics in this context requires more attention, highlighting the need for targeted research and guidance of the pharmaceutical industry [7].

1.1. Pharmaceutical Supply Chains and Logistics

Pharmaceutical supply chains are responsible for supplying healthcare facilities (such as pharmacies, hospitals, clinics, and surgeries) with essential medical products [8]. A defining characteristic of pharmaceutical supply chains, which differentiates them from other types of supply chains, is the prioritisation of human lives, as the primary goal is to ensure the availability of life-saving medications [9]. Pharmaceutical supply chains are complex and include many role players, such as raw materials manufacturing, pharmaceutical producers, distribution centres, pharmacies, hospitals, clinics, doctors, and patients [8,10].

The pharmaceutical supply chain is crucial for ensuring consistent and sufficient access to medicine [11,12]. However, the pharmaceutical supply chain faces numerous challenges [13], such as drug shortages, complex legislation, global pandemics leading to rapid changes in demand, high operational costs, market competition, and stringent quality requirements [14]. Effective management of pharmaceutical supply chains, covering essential processes such as transportation, storage, procurement, manufacturing, quality control, packaging, and waste disposal, is, therefore, vital for maintaining an uninterrupted supply of medicines [11,13,15].

Pharmaceutical logistics is unique from other commodities’ logistics due to several distinct characteristics of the products it handles. According to Ashworth et al. [7], some factors to consider include the type of pharmaceutical product (lightweight and bulky in some cases), characteristics of the product (shorter shelf life, possibly requiring extensive temperature control), and mode of transport required (different regions of the world have different types of vehicles and operating conditions). This highlights the uniqueness of road transportation and warehousing activities in pharmaceutical product distribution.

Due to these unique product characteristics, using the volume of a product as the allocation method (kgCO₂e/m³) instead of the weight allocation method (kg CO₂e/t) might be required since volume is the limiting factor in pharma supply chains—not the weight [7]. This mandates special attention to this commodity group.

1.2. Emissions of Pharmaceutical Supply Chains and Logistics Processes

With the heightened global pressure to reduce GHG emissions, many nations have pledged to achieve carbon neutrality by 2050 [16]. Enhancing emissions transparency across supply chains is recognised as an essential step towards broader decarbonisation efforts [17]. In addition to the increased pressure for transparency of supply chain emissions, there is also a demand for products with a lower carbon footprint [18]. The demand for environmentally sustainable pharmaceutical products has grown, pressuring the industry to account for emissions across all supply chain activities and not only during the production phase of the life cycle [19].

Reporting emissions on a supply chain activity level is increasingly important to estimate a product’s carbon footprint [17]. This is required for increased transparency of the life cycle emissions of different products. Calculating only corporate emissions is problematic since this does not give details of product or supply chain level emissions. Emission reporting frameworks, such as the Greenhouse Gas Protocol, which reports emissions based on equity or ownership or business boundaries, categorise emissions into three scopes for the efficient reporting and management of emissions on a corporate level [20]. However, accurately calculating upstream or downstream Scope 3 emissions associated with logistical activities, among other supply chain activities, remains challenging due to the unavailability of data and methodological inconsistencies [21]. Therefore, this article aims to assist the pharmaceutical and logistics industry by:

(a)

Developing a data collection and emission calculation methodology for the distribution of pharmaceutical products;

(b)

Collecting real-world data and applying the developed methodology to these data to determine the carbon intensity of distribution-related activities such as road transport and warehousing of pharmaceutical products.

2. Literature Review

A review of the current literature is discussed in two separate sections. Section 2.1 discusses the available frameworks for pharmaceutical distribution while existing emission intensity factors are illustrated in Section 2.2.

2.1. Frameworks for Pharmaceutical Distribution

Ashworth et al. [7] conducted a systematic literature review and determined that no standardised methodological approach is available to assess pharmaceutical distribution emissions.

To address emissions in logistics more broadly, the Smart Freight Centre [22] developed the Global Logistics Emissions Council (GLEC) Framework for transparently calculating and reporting logistics greenhouse gas emissions. The GLEC Framework serves as an ideal starting point for estimating fuel consumption emissions in road transportation. In addition to the GLEC Framework, ISO 14063 [23] provides guidelines for communicating environmental performance transparently and consistently. However, the GLEC framework and ISO 14063 provide generic data requirements and some calculation methods, enabling logistics providers to quantify emissions and share and report their progress to stakeholders, but on a high level.

2.2. Emission Intensity Factors

In addition to the lack of a framework or methodology specifically for pharmaceutical distribution, there are also no emission intensity factors specifically developed for the logistics of pharmaceutical products. Current emission intensity factors for logistical activities, such as road transportation and warehousing, are generic or commodity blind. Examples of these emission intensity factors found in the literature are shown in

Table 1. Suitable emission intensity factors for road transportation.

Activity	Basis	Vehicle Size	Emission Intensity (gCO2e/t-km)
			WTW
Road transport (region dependent; Europe and South America are given as examples)	Combined load factor and empty running: 24% Fuel: Petrol	Van < 3.5 t	1007
	Load factor: 60% Empty running: 17% Fuel: Diesel	Rigid truck 3.5–7.5 t GVW	335
		Rigid truck 7.5–12 t GVW	223
		Rigid truck 12–20 t GVW	191

Source: adapted from the Smart Freight Centre [22].

(road transport) and

Table 2. Suitable emission intensity factors for warehousing.

Hub Type Unit	Emission Intensity (kgCO2e/t)
	Ambient	Mixed
Transshipment	1.3	2.5
Storage and transshipment	5.6	18.4
Warehouse	45.5	≥50.0

Source: adapted from the Smart Freight Centre [22].

(warehousing). Factors derived from the GLEC Framework focus on weight and distance metrics and do not consider the volume aspect of freight and certain vehicle operation characteristics, which is important in pharmaceutical distribution as pharmaceutical products are lightweight and less dense.

Due to the limitations of current frameworks, emission intensity factors, and challenges discussed in Section 1, applying these generic factors to pharmaceutical distribution activities may result in the inaccurate estimation of emissions. This emphasises the need for a methodology and emission intensity factors specifically for pharmaceutical distribution bound to a geographical region.

3. Methodology

The development of the emission calculation methodology in this study was informed by insights and data provided by a division of an international logistics company operating in Southern Africa (Company X). A combination of qualitative and quantitative data collection methods was employed to develop, validate and ensure that the methodology and the developed emission intensity factors could be practically applied by logistics companies. The data collection methods are outlined in Figure 1 and include facility visits, a systematic literature review (SLR), informal interviews, focus groups and iterative data requirement collection.

Facility visits were conducted at multiple pharmaceutical distribution centres of Company X to gain insights into the movement of goods within the facility as well as different operations such as receiving, storage, picking, packing, and shipping processes. The facility visits enabled the identification of emission-generating activities (warehousing and road transport) and emission sources (such as cold stores, handling equipment, and refrigerated vehicles). This ensured that the emission intensity factors in the emission calculation methodology accurately and comprehensively reflected real-world operations and scenarios. In addition, focus group discussions that were held with industry professionals at Company X served as a platform to critique and improve the preliminary emission calculation methodology. Further, focus group discussions were useful in identifying data availability, measurement limitations, and practical constraints.

The use of an SLR as a data collection method was useful, as it identified gaps in current knowledge in this field and emphasised the need for emission intensity factors tailored for the distribution of pharmaceutical products. An unpublished SLR conducted by Ashworth et al. [7] assessed existing frameworks, energy consumption values, and emission intensity factors related to logistical activities in pharmaceutical supply chains. The results indicated that currently, there is no standardised methodology used to assess emissions of pharmaceutical distribution, a lack of literature on energy consumption values is evident, and no specific emission factors are used to convert energy usage to GHG emissions.

Multiple informal interviews were conducted between the primary researcher and staff at Company X. These interviews provided valuable insights into data availability, operation constraints, protocols, processes, and scenarios related to pharmaceutical distribution. Using informal interviews as a data collection method provided participants with the flexibility to share practical knowledge and details that other qualitative methods alone might have missed.

The researchers employed an iterative approach to data collection. This is represented in Figure 1 by the feedback loop to data collection. Initial data requirements were identified based on stakeholder input from previous data collection methods as well as the available literature. These data requirements were refined through additional site visits, focus groups and informal interviews with stakeholders at Company X to ensure that the emission intensity factors and the methodology were based on the most relevant data and accurate and that the results of the study were implementable in the pharmaceutical supply chain.

The data collected from the various methods in Figure 1 were integrated into an emission calculation methodology accounting for the road transport and warehousing components of pharmaceutical distribution, where specific emission intensity factors were derived for each activity.

4. Emission Calculation Methodology for Pharmaceutical Distribution

This section presents a comprehensive overview of how to calculate the emissions of distributing pharmaceutical products from the first principles. The methodology guides data collection and how to calculate the emissions of both road transportation (Section 4.1) and warehousing (Section 4.2) activities within the pharmaceutical distribution process. Each variable is defined, its reason for inclusion is explained and justified, and potential sources to identify and retrieve the relevant data are suggested.

4.1. Road Transportation

Equation (1) in Figure 2 and its expanded annotations should be used to determine the carbon emissions associated with the distribution of pharmaceutical products via road transportation or to calculate emission intensity factors. The remainder of this section describes the methodology used to calculate road freight transport emissions.

(1) Variable influencing the g CO₂e for the trip:

(1.1) ℓ for the trip

The ‘ℓ for the trip’ variable refers to the total number of litres of fuel consumed by the vehicle during a specific shipment. It is necessary to include this variable because the amount of carbon dioxide equivalent (CO₂e) for the trip varies based on the litres of fuel burned by the vehicle. This is determined by multiplying the litres consumed by a fuel emission factor. The fuel emission factor for petrol and the factor for diesel can be found in the GLEC framework [22]. As fuel consumption increases, so does the carbon footprint associated with the trip. This fuel consumption for a specific trip or shipment can be found on fuel bills or the fuel fill refill sheets of vehicles, the Original Equipment Manufacturer (OEM) vehicle data system, as well as the Transport Management System (TMS) of the company.

(2) Variables influencing the litres of fuel consumed for the specific road transportation activity:

(2.1) Vehicle description

The ‘vehicle description’ variable refers to all the characteristics of the vehicle used to transport the pharmaceutical goods during the specific shipment. Some key dimensions of the variable include vehicle size, type, and payload capacity in terms of weight and volume. Each vehicle category is unique since different-sized vehicles have different fuel consumption. Therefore, it is important to distinguish between the different vehicle categories when calculating the overall emissions for the trip. The vehicle description variable will often be found in the Transport Management System (TMS), the Fleet Management System (FMS), or the asset register of a company.

(2.2) Route

The ‘route’ refers to the path taken by the vehicle during the transportation of pharmaceutical products from origin to destination. Incorporating the route into the emission calculation is necessary since each route is unique. Different routes each have a unique elevation profile (gains or losses), stops or delays, traffic, and road conditions—all factors that significantly impact the fuel efficiency of a vehicle. Route-related information can be found in various company systems, such as the global positioning system (GPS) tracking system where vehicles are traced, the TMS or FMS, and external GPS devices and dispatch and delivery logs.

(2.3) Refrigerated or ambient

The ‘Refrigerated or ambient’ refers to whether the vehicle has a refrigerator unit, commonly called a reefer unit, fitted on the truck or not. Refrigerated vehicles have a reefer unit fixed to the truck to regulate the conditions (temperature, humidity, and gaseous concentration) in which the pharmaceuticals are transported, whereas vehicles that do not have a reefer unit transport pharmaceuticals in ambient conditions. It is necessary to include this variable because the reefer unit consumes fuel during refrigeration (frozen and refrigerated products) and contributes to the overall fuel consumption of the trip, meaning ambient trucks consume less fuel and have lower emissions than refrigerated trucks. This variable can be found in the FMS, TMS, or company asset register.

(2.4) Driver

The ‘Driver’ refers to the person responsible for the vehicle’s operation during the transportation of the pharmaceutical goods from origin to destination. Each driver is unique in the sense that their driving behaviour and style directly influence the fuel consumption and efficiency of the transportation process. For example, some drivers accelerate faster or let the vehicle idle for excessive periods, resulting in higher fuel consumption. Drivers also have different levels of experience and training, leading to different methods of vehicle operation. The driver variable can be found in driver rosters as well as the company’s FMS and can then be linked to specific vehicles or routes.

(3) Variables influencing the cargo (payload) for the specific road transportation leg:

(3.1) Weight

The variable ‘weight’ refers to how heavy the shipment of pharmaceutical products is. The gross weight (t) and nett (t) shipment weight of the pharmaceutical payload should be incorporated in the calculations. Emissions are calculated based on the gross weight transported (medicine and packaging weight), while the emissions are allocated to the net weight of pharmaceutical products. In addition, weight data are necessary as vehicles have maximum payload capacities that need to be adhered to. Pharmaceutical products are unique from other product categories. Pharmaceutical products differ in terms of weight and volume, for example, a vehicle can weigh out due to heavy pharmaceutical products being loaded but not reach volumetric constraints, or a vehicle can meet volumetric constraints due to lightweight goods being loaded but not necessarily weighing out. Therefore, both weight and volume should be considered in the calculation, and the limiting constraint of the two should be chosen. This variable can be found in the Bill of Lading (BOL), waybills, and the company’s TMS.

(3.2) Volume

The variable ‘Volume’ refers to the cubic space occupied by the cargo, i.e., the physical dimensions (length, height, and width) of the pharmaceutical product (including packaging) shipped. This variable is necessary to include because there are volume restrictions on vehicles, and seeing that some pharmaceutical products require a significant amount of packing to protect them during transportation, this affects the volume of goods that can be transported. This variable can be found in the Bill of Lading (BOL), waybills, and the company’s TMS.

(3.3) Repositioning circumstances

The ‘repositioning circumstances’ variable denotes whether the vehicle is loaded with cargo or returns empty to the point of origin from a delivery destination. It is necessary to assess the repositioning circumstances of a vehicle in a shipment because the empty movement or partial loading of transport vehicles during deadhead trips generates higher emissions per unit. This variable can typically be found in FMS, which records where loaded and empty distances and trips are commonly recorded.

(3.4) Type of cargo

The variable ‘Type of cargo’ refers to the temperature class of the pharmaceutical product shipped, which can be classified either as ambient or dry, chilled, or frozen. It is necessary to include this variable as it indicates the transportation requirements of the product, either ambient or refrigerated transportation, which ultimately influences the energy intensity and emissions of the transported pharmaceutical goods. This variable can be found in the Warehouse Management System (WMS), the Inventory Management System (IMS) of the warehouse, and the TMS of a company.

4.2. Warehousing

Equation (2) in Figure 3 should be used to determine the carbon emissions associated with the storage and handling of pharmaceutical products. The remainder of this section describes the methodology used to calculate the emissions of storing pharmaceutical products in a warehouse.

(4) Variables influencing the kgCO₂e for the facility:

(4.1) Electricity consumption

The ‘Electricity consumption’ variable refers to the total amount of electricity consumed by the facility over a specific period of time, and it is measured in kilo-Watt-hours (kWh). It is necessary to include electricity consumption data for various sections of the warehouse to determine the proportional impact of the facility on emissions. It is essential to differentiate between electricity sourced from the national grid and electricity generated on-site, such as through rooftop photovoltaic (PV) solar panels, as this distinction directly impacts emission calculations. For facilities relying on grid electricity, the emission factor specific to the country must retrieved. If solar electricity is used, then the emission factor is zero. Emissions due to electricity consumption are a significant contributor to the majority of these facilities and are, therefore, the most important variable to include in the calculation of the total emissions of the facility. This variable can oftentimes be found in the utility bills of the facility or other forms of the energy management system of a company.

(4.2) Units destroyed

The variable ‘Units destroyed’ refers to the number of pharmaceutical products that are destroyed due to being damaged, contaminated, or reached their expiry date and are no longer safe for use. It is necessary to include this variable because destroyed units take up space in the facility, which influences the energy consumption of the facility as well as emissions. Destroying products that have been stored increases the emission of the remaining products that are successfully moved through a facility since the emissions have to be transferred to the remaining products. This variable can be found in the Inventory Management System, specifically in disposal as well as expiry reports or in the WMS of a company.

(4.3) Liquid or gaseous fuels

The ‘Liquid or gaseous fuels’ variable refers to the total amount of liquid fuels (e.g., diesel) or gaseous fuels (e.g., natural gas, liquified petroleum gas (LPG)) consumed by the facility over a specific time period. This is usually measured in litres or kilograms, depending on the type of fuel consumed. Accurately identifying the fuel type is important as it will determine the emission factor used in the calculation. The emissions produced through diesel consumption, predominantly by backup diesel generators and other handling equipment, are significantly higher than those associated with gaseous fuel consumption and are necessary to include when calculating the facility’s total emissions. This variable can be found in generator and equipment logbooks, fuel purchase records, or a company’s WMS.

(5) Variables influencing the consumption of electricity or liquid or gaseous fuels in the facility:

(5.1) Temperature classes

The variable ‘Temperature classes’ refers to the different temperature regulating systems in the facility that consume energy. This includes:

• Cold stores: Separated allocated areas with controlled temperatures are designed to store temperature-sensitive pharmaceutical products. Cold stores include refrigerators, freezers, and larger refrigerated rooms, for example, and should be included due to the significant contribution to the electricity consumption in the facility, which can be found in the company’s utility bill.
• Ambient cooling systems: The heating, ventilation, and air cooling (HVAC) systems used to control the temperature and humidity levels of ambient storage areas. These systems consume electricity in the process of cooling and circulating air in the warehouse, and this can be found in the energy management system and HVAC logs of the company.

(5.2) Offices

The variable ‘offices’ refers to dedicated spaces in the facility where administrative activities are conducted. Offices consume electricity through HVAC systems, lighting, and appliances and are viewed as a contributor to the overall energy consumption of the facility. The level of electricity consumption by offices varies based on working hours, the number of staff and the type of appliances used in the office. Electricity consumption values for this variable can often be found in the company utility bill or sub-metre records.

(5.3) Miscellaneous

The variable ’Miscellaneous’ includes various sources such as lighting and specific machinery that contribute to overall electricity consumption. It is often difficult to measure the direct electricity consumption of these sources, and not many companies do so. Therefore, during an emission assessment, these are disclaimers that should be made to provide context for the developed emission intensity factor.

• Lighting refers to types of lighting fixtures used to ensure visibility in a facility. If different parts of the facility have different types of lighting, then this variable is applicable. The contribution that lighting makes to the electricity consumption in the facility varies based on the number of fixtures, the duration of time in which the lights are operational, and their wattage. This variable can be found in utility bills as well as energy audits in the company.
• Specific machinery refers to different types of mechanical equipment that are used in the facility, such as conveyor belts, automated systems, and sorting and packing machines. This variable contributes to the electricity consumption of the facility, and its impact depends on the power rating and duration of use of the different types of machinery. This variable can be found in machine logs, OEM data sheets, energy metres on individual machines, and utility bills in the company.

(5.4) Generators

The ‘Generators’ variable refers to a backup power source used in the case of power outages or emergencies. It is important to include this variable in the facility’s liquid or gaseous fuel consumption calculation, as its impact varies based on hours of operation and the load during that time. Data on diesel consumption for generators are typically recorded in the company’s diesel fuel logs, while data on gaseous fuel consumption can be obtained from generator usage logs and fuel purchase records.

(5.5) Handling Equipment

Handling equipment refers to machinery used to move, lift, and stack pharmaceutical goods in the facility. This variable only includes handling equipment that consumes liquid or gaseous fuels, for example, forklifts and cranes. The amount of fuel consumed by each type of handling equipment depends on the duration of use. Data on the fuel consumption of handling equipment can be found in fuel purchase records and diesel fuel logs in the company.

(6) Variables influencing the inventory in the facility:

(6.1) Storage duration

The variable ‘Storage duration’ refers to the length of time that a pharmaceutical product spends in the facility before being picked for shipping or moved out to a customer. The average storage duration of a pharmaceutical product varies depending on the average stock on hand, the amount of safety stock, the type of product, and the demand for the specific product. Some pharmaceutical goods are fast movers, resulting in a shorter average duration of storage; therefore, they have a smaller impact on energy consumption and emissions from storage. On the contrary, slow-moving pharmaceutical products spend more time in storage and, therefore, have a higher relative emission contribution. The storage durations of stock in the warehouse can be found in an IMS or WMS.

(6.2) Units on hand

The variable ‘Units on hand’ refers to the total physical quantity or number of units of a given pharmaceutical product in the facility at a given time. The number of units on hand should be included in the assessment since this directly determines the energy consumption and subsequent emissions per unit. This number of units on a Stock Keeping Unit (SKU) level can be found in a company’s IMS or WMS.

(6.3) Temperature classes

The variable ‘Temperature classes’ refers to the temperature ranges in which a specific pharmaceutical product or SKU needs to be stored to maintain the quality and safety of the product. Pharmaceutical products can be classified into either dry, chilled or frozen temperature classes. Dry temperature class refers to pharmaceutical products being stored in ambient conditions, between 8 °C and 25 °C, that do not require refrigeration. The chilled temperature class ranges from 2 to 8 °C, while the frozen temperature class ranges from −2 to −30 °C. It is necessary to differentiate SKUs on this level because the different temperature classes have different impacts on energy consumption and subsequent emission intensity values. This variable can be found in the IMS or the WMS of the company, in which the storage type description and the temperature range for each SKU can be identified.

(6.4) Volume

The ‘Volume’ variable refers to the space occupied by the product and is a function of the packaging dimensions (length, height, and width) of the pharmaceutical product. Determining the volume of a pharmaceutical good is important because it will indicate how much space it takes up in the facility. It is necessary to include this variable because the volume of an SKU influences the energy consumption and eventual emissions of a product. For example, the more volumetric space a packaged pharmaceutical product consumes, the higher the proportional energy consumption of the product, resulting in higher emissions. This variable can be found in the IMS or the WMS of the company, in which dimension data of the SKUs are documented to calculate volume.

(6.5) Weight

The ‘Weight’ variable refers to the mass of a packaged pharmaceutical product, typically measured in grams. Determining the weight of pharmaceutical goods is important as heavier goods often require more energy for handling, cooling, and storage, resulting in higher proportional energy consumption and related emissions. This variable can be found in the IMS or the WMS of the company.

5. Emission Intensity Factors for Distributing Pharmaceutical Products

In this section, the emission methodology developed in Section 4 is applied to a real-world case study scenario. The available data for this research, which was provided by an international logistics company operating in Southern Africa (Company X), are summarised in Section 5.1. The results of the application for road transportation are discussed in Section 5.2, followed by the results for warehousing in Section 5.3.

5.1. Data Availability

Obtaining all the desired data, as defined in the methodology in Section 4, presented some challenges. The dataset that Company X provided to the researchers is summarised in

Table 3. Data provided by Company X.

Logistical Activity	Data Type	Data Field or Description
Road Transport	Fuel data	Vehicle description: Vehicle size; Fuel type; Vehicle make; Vehicle model; Reefer unit fitted.
		Distance travelled between refills (odometer readings)
		Refill values (ℓ of fuel)
Warehousing	Facility data	Total amount of grid electricity (kWh) used
		Total amount of diesel (ℓ) used at the facility by generators
	Inventory data	Average stock on hand (units)
		Storage type description per SKU
		Unit dimensions per SKU
		Units destroyed per SKU

. Multiple desired data elements or fields were not collected by Company X and could, therefore, not be provided in the shared dataset. However, given the available data, the researchers could apply the methodology to calculate emission intensity factors for road transport and warehousing distributional activities.

A primary limitation in the available road transportation data was the absence of (i) fuel records on a shipment or trip level and (ii) cargo or payload information as to what was transported. This means a direct link between fuel consumption and the cargo carried on each vehicle during a shipment could not be established. To address issue (i), the researchers calculated a fuel consumption ratio (using total refill values (ℓ of fuel) and total distance travelled in kilometres) and worked further with the modelled value. To address issue (ii), the maximum permissible payload capacities (weight and volume) were obtained from online user manuals, and the online tool Truck Science was also employed to overcome this data limitation of different vehicles. By uploading details such as vehicle make and model, maximum load capacity, and fitted refrigeration unit, the researchers could accurately configure each vehicle model with a representative body, enabling the precise calculation of each truck’s volumetric and payload capacity.

Due to limitations with the availability of shipment data, the load factor and empty running percentages had to be assumed during the application of the emission calculation methodology. This was necessary for the purposes of consistency of calculations across multiple vehicle sizes. The two main assumptions are viewed as limitations that create a logically feasible space and are as follows:

(a)

The maximum empty running (%) is set at 50%. This implies that vehicles return empty for half of their trips. The reasoning behind this is that if it is greater than 50%, then logistics service providers will incur a loss and go bankrupt.

(b)

The minimum load factor (%) is set at 10% in terms of weight and volume. The load factor percentage represents the degree to which the vehicle’s capacity is utilised during transportation. The logic behind this assumption is that a 3PL will not load below 10% because it will cause a loss and skew the results disproportionately.

Primary limitations in the available warehousing data were the absence of Issue (1) storage duration, Issue (2) electricity consumption (kWh) for different parts of the warehouse and Issue (3) a consistent period of comparable data. To address Issue (1), the total volume (measured in m³) and weight (measured in tonne) of goods stored in the facility each month were estimated using the average stock on hand data. For Issue (2), only a single aggregate figure representing the facility’s total monthly electricity consumption was available, which was used by the researchers in the analysis. The lack of granular electricity consumption data constrained the researchers’ ability to link specific parts of the warehouse to their respective energy usage.

Electricity and diesel consumption records were only accessible until October 2023, whereas inventory data were extended through July 2024. To address Issue (3) and calculate warehousing emission intensity over this extended period, certain assumptions and estimates for electricity and diesel consumption were necessary:

(a)

For the months beyond October 2023 (November 2023–July 2024), electricity consumption was estimated by averaging the first four months of recorded data (July 2023–October 2023), resulting in an assumed consistent monthly consumption of 504,839 kWh. This average was applied uniformly to maintain consistency in the absence of specific monthly data.

(b)

To estimate diesel usage for the period November 2023–July 2024, the researchers utilised historical data on load-shedding stages. Load-shedding is a South African term used to describe deliberate and scheduled power outages implemented by Eskom to prevent the entire power grid from collapsing. Each month from July 2023 to October 2023 had a recorded load-shedding stage alongside a corresponding diesel consumption value. Where historical load-shedding stages matched those in months lacking data, the researchers assumed similar diesel consumption levels.

5.2. Results—Road Transport

The results for road transport are discussed in two sections. The first Section 5.2.1 discusses the impact of load factor and empty running based on the weight and volume of the payload. The remaining Section 5.2.2 suggests novel formulae for the emissions intensity of the 1-tonne, 2.5-tonne, 4-tonne, and 8-tonne road transport vehicles.

An important consideration is whether the emission intensity factor is more sensitive to a proportional change in weight or volume. The 1-tonne vehicles assessed in this study have a maximum load capacity of 1.36 tonnes and a maximum volumetric capacity of 5.92 m³. The 2.5-tonne vehicles have a maximum load capacity of 2 tonnes and a volumetric load capacity of 13.25 m³. The 4-tonne vehicles have a maximum load capacity of 2.95 tonnes and a volumetric load capacity of 23.59 m³. Finally, the 8-tonne vehicles have a maximum load capacity of 7.56 tonnes and a volumetric load capacity of 39.54 m³. Since there are larger volumes, a relative change in weight is much more sensitive than a similar change in volume. For instance, loading one tonne of payload into a 1-tonne capacity vehicle nearly utilises its full payload capacity. In contrast, loading one cubic metre of payload into the same vehicle represents only approximately one-sixth of its maximum payload capacity. This example illustrates that proportional unit changes have a substantially greater impact on weight than on volume.

5.2.1. The Impact of Load Factor and Empty Running

For simplicity of the discussion, only the results for 1-tonne vehicles were used in this section as an example to illustrate the relative impacts of load factor and empty running.

Figure 4 shows the impacts of different load factors and empty running percentages based on the total kilometres travelled and total litres of fuel consumed by all the 1-tonne vehicles in a month. An important observation from Figure 4 is that as empty running increases and the load factor decreases, the emission intensity factor increases significantly.

Ranges of emission intensity factors (measured in gCO₂e/t-km) were calculated across three different levels of analysis, as shown in Figure 5. The first level of analysis was for a whole month of data (September), including the combined total kilometres driven and the total litres of fuel consumed by all the 1-tonne vehicles in the fleet. The second level of analysis (on a vehicle level) was based on the total kilometres driven and litres of fuel consumed by each vehicle during the month of September. Lastly, the third level of analysis (specific trip) was for randomly chosen trips that included the distance travelled and the amount of fuel consumed during the trip. The emissions data captures two contrasting scenarios:

• Worst case scenario (blue data points)—this scenario represents the highest emissions, with conditions of 50% empty running and a 10% load factor.
• Best case scenario (orange points)—this scenario represents the lowest emissions, with conditions of a 0% empty running and a 100% load factor.

The wide ranges between the best and worst-case scenarios at each level in Figure 5 highlight the impact of operational efficiency on emissions. This indicates how optimising the load factor and reducing empty running can significantly reduce emissions.

Figure 6 illustrates the impacts of using the volumetric load factor and empty running percentages based on all the vehicles in one month. A trend similar to that in Figure 4 is noticeable in Figure 6, where an increase in the empty running and a decrease in the load factor results in a sharp increase in the emission intensity factor. The most noticeable difference between the weight and volume analysis, however, is that the amount of gCO₂e emitted per t-km is significantly higher compared to m³-km.

Ranges of emission intensity factors (measured in gCO₂e/m³-km) for volume are illustrated in Figure 7 and are based on the same three levels of analysis (month of September, vehicle level for a month, and individual trip level) used in Figure 5. The results for volume in Figure 7 are similar to those of weight in Figure 5, as there are wide ranges between the best and worst-case scenarios across all three levels of analysis.

The average emission intensity over 12 months was calculated and is shown in Figure 8 and Figure 9. The two graphs demonstrate that as the load factor percentage increases, the emission intensity factor decreases. Vehicles operating at a higher capacity have a lower emission intensity per unit of transported weight or volume over distance (t-km or m³-km). At lower load factors (e.g., 10–30%), the emission intensity is significantly higher, suggesting a higher environmental impact. The weight analysis, Figure 8, shows higher absolute amounts of emission intensity compared to the volume graph, Figure 9, indicating that weight constraints may be more impactful than volume.

5.2.2. Emission Intensity Factors of Road Transport

This section suggests novel formulae for determining the emission intensity of different vehicle sizes, assuming a constant 50% empty running. The weight- and volume-based equations are provided in

Table 4. Emission intensity formulae and values for different vehicle sizes on a 50% empty running basis, where the variable X is the load factor (0 to 100).

	Weight-Based Emission Intensity Factor (EIF) (gCO2e/t-km)		Volume-Based Emission Intensity Factor (EIF) (gCO2e/m3-km)
Truck Size	Equation	Value Range	Equation	Value Range
1-tonner	EIF1-tonner = 61,568/X	615.68 to 6156.80	EIF1-tonner = 14,138/X	141.38 to 1413.84
2.5-tonner	EIF2.5-tonner = 57,037/X	570.37 to 5703.70	EIF2.5-tonner = 8610.7/X	86.11 to 861.07
4-tonner	EIF4-tonner = 53,243/X	532.43 to 5324.26	EIF4-tonner = 6653.1/X	66.53 to 665.31
8-tonner	EIF8-tonner = 23,957/X	239.57 to 2395.66	EIF8-tonner = 4574.8/X	45.75 to 457.48

, with the variable ‘X’ representing the percentage load factor. In addition, Table 4 illustrates a range of emission intensity values derived from the application of these equations. The minimum value in each range corresponds to a 100% load factor under the assumption of 50% empty running, while the maximum value reflects a 10% load factor with the same empty-running assumption. For the calculations, a conversion factor of 3.12 kg CO₂e/ℓ was applied for 1-tonne petrol vehicles, whereas a conversion factor of 3.43 kg CO₂e/ℓ was utilised for 2.5-tonne, 4-tonne, and 8-tonne diesel vehicles in deriving their respective emission intensity equations. Details on the derived petrol and diesel factors can be found in Appendix A. Also, note that the values in Table 4 are applicable to all product categories in Appendix B and Appendix C.

Across all vehicle sizes in Table 4, a consistent trend emerges where an increase in the load factor percentage results in a decrease in emission intensity. Of the different vehicle sizes analysed, 8-tonne vehicles emit the lowest gCO₂e emissions in terms of weight and volume. These results are expected for larger vehicle sizes because emissions are distributed over a greater amount of cargo as the load factor increases, improving the fuel efficiency per unit weight and volume transported. Note that different brands of the same sized vehicles can influence the emissions of a specific transport service. However, this level of detail in assessing different brands of trucks is not within the scope of the current paper.

The equations in Table 4 are novel since they allow for the calculation of a specific shipment’s emission intensity factor and overall emissions—both on a weight and volume level. For example, the 1-tonne vehicle (maximum load capacity of 1.36 tonnes and a maximum volumetric capacity of 5.92 m³) is used to transport 800 kg of goods with a volume of 2.2 m³. The loaded distance that the vehicle travels is 230 km, and the vehicle travels back empty to the warehouse, resulting in an empty running of 50%. The load factor of this scenario in terms of weight is 58.82%, while the volumetric load factor is 37.16%. Using these load factors and the equations in Table 4 results in a weight-based emission intensity factor (EIF) of 1046.71 gCO₂e/t-km and a volume-based EIF of 380.46 gCO₂e/m³-km. In both scenarios, irrespective of using the weight or volume-based load factor, the resulting emissions for the shipment are approximately 192.5 kgCO₂e.

In comparison to existing emission intensity factors in the literature, the developed factors differ significantly. Although some calculated factors are lower than those reported in the literature, the majority of the calculated factors are up to 100% higher than reported in, for example, the GLEC framework [22]. However, note that the calculated emission intensity factors’ empty running and load factor are specific to Company X and should be compared with caution. Compare the 1-tonne vehicle’s emission intensity factor in Table 4 (615.68 to 6156.80 gCO₂e/t-km) to that of the literature in Table 1 (1007 gCO₂e/t-km). Ignoring the load factor and empty running, the calculated emissions are either 39% lower or up to 511% higher than that specified in the literature. Other vehicle sizes, such as the 8-tonner, have similar differences. The emission intensity factor of an 8-tonne truck in Table 4 (239.57 to 2395.66 gCO₂e/t-km) is significantly higher than that of a 7.5–12-tonne truck in Table 1 (223 gCO₂e/t-km). The calculated emission intensity factors are between 9% and 974% higher than those reported in the literature [22]. However, caution should be used when attempting to compare any emission intensity factors since they are scenario-specific, as is the case for Company X.

5.3. Results—Warehousing

The results for warehousing are discussed in two sections. The first section (Section 5.3.1) provides an overview of the facility and discusses the types of inventory stored as well as the energy consumption of the facility. The second section (Section 5.3.2) assesses the corresponding impact and emission intensity of warehousing. Emission intensity factors are standardised in terms of kg to align with established industry frameworks, such as the GLEC Framework [22], ensuring consistency.

5.3.1. Overview of the Facility

Company X operates a state-of-the-art logistics facility in South Africa, covering an area of approximately 130 000 m² and providing storage capacity for around 43,000 pallet positions. On average, Company X handles 15654 unique SKUs a month with a total combined weight of 6198 tonnes per month and a total combined volume of 76,666 m³ per month. The total GHG emissions of Company X’s facility, which was assessed, is 7668 tonnes of CO₂e in 2022. This proves the scale of operations at Company X.

Inventory Storage Types

Figure 10 illustrates the average weight of the type of product stored in the facility over a 13-month period. The results show that 91.88% of the unique SKUs that make up the total weight stored in the warehouse are ‘normal products’, followed by ‘fridge products’ that make up 3.11%, and ‘hazardous drugs’ with 2.46% of the total. Figure 11 shows that 80.77% of the unique SKUs that make up the total volume stored in the warehouse are ‘normal products’, followed by ‘animal health’, which contributes 16.31% of the total, followed by fridge products that make up 2.03% of the total. Additional details on the storage types grouped under the ‘other’ category in Figure 10 and Figure 11 are available in Appendix B,

Table A1. List of ’other’ product storage descriptions for weight and volume analysis.

‘Other’ for Weight Analysis	‘Other’ for Volume Analysis
HMC—Humidity CTRL 15 to 25’C; max 60%RH	HFD—Habitat Forming drugs
IOD—Products containing Iodine	HFG—Hazardous and Fridge Product
FLM—Flammable Products	HMC—Humidity Control 15 to 25C; max 60%RH
FZN—Freezer Product −20 Degrees	IOD—Products containing Iodine
GLI—GLIADEL FRZ −20 to −25 Degrees	FRZ—Refrigerated freezer normal −15
FRZ—Refrigerated freezer normal −15	FLM—Flammable Products
AHH—Animal Health Hazardous	FZN—Freezer Product −20 Degrees
FRR—Freezer Product −10 degrees	GLI—Gliadel Freeze −20 to −25 Degrees
RNM—Section 21 Normal	FRR—Freezer Product −10 degrees
RFF—Section 21 Fridge products	FLH—Flammable and Hazardous Products
HFZ—Refrigerated freezer hazardous −15	RNM—Section 21 Normal
RHF—Section 21 Habit-Forming Drugs	RFF—Section 21 Fridge products
FLH—Flammable and Hazardous Products	HFZ—Refrigerated freezer hazardous −15
	AHH—Animal Health Hazardous
	RHF—Section 21 Habit-Forming Drugs

.

Energy Consumption of the Facility

The facility’s kgCO₂e emissions were influenced by three variables: electricity consumption, generator diesel consumption, and units destroyed. Figure 12 illustrates the facility’s electricity consumption (measured in kWh) and generator diesel consumption (measured in litres) over 13 months. For the months beyond October 2023 (November 2023–July 2024), electricity consumption was estimated by averaging the first four months of recorded data (July 2023–October 2023), resulting in an assumed consistent monthly consumption of 504,839 kWh.

Generator diesel consumption, however, showed large fluctuations due to varying stages of load-shedding, as depicted in Figure 12. Diesel consumption increased in response to higher load-shedding stages, reflecting the facility’s reliance on generators during these periods. From April 2024 to July 2024, load-shedding did not occur, resulting in zero generator usage and, consequently, zero diesel consumption, as indicated in Figure 12.

The researchers analysed inventory destruction data provided by Company X over a five-month period. From these data, average percentages were calculated to determine the proportion of goods sold versus destroyed. The pie chart in Figure 13 illustrates the distribution of goods sold and destroyed, segmented by storage conditions (chilled and ambient). Of the total weight of goods in the facility, 99.58% is sold, while 0.42% is destroyed.

On the other hand, the pie chart in Figure 14 illustrates the distribution of goods sold and destroyed, segmented by storage conditions (chilled and ambient). Of the total volume of goods in the facility, 99.32% are sold, while 0.68% are destroyed.

5.3.2. Emission Intensity Factor of Warehousing

The carbon footprint of warehoused goods was determined by considering both their weight and volume, with calculations based on specific emission intensity factors for energy consumption. To quantify the facility’s carbon emissions in terms of kgCO₂e, a calculated conversion factor of 1.03 kgCO₂e per kWh [24,25] was applied for electricity use, while diesel consumption was converted using a factor of 3.43 kgCO₂e per litre, see Appendix A.

Figure 15 displays the monthly emission intensity per tonne of goods (measured in kgCO₂e/tonne) associated with various pharmaceutical product categories from July 2023 to the end of July 2024. Different coloured segments within each bar represent the product categories. Emission intensity per tonne varied from July 2023 through March 2024, correlating with the fluctuations in diesel consumption (as shown in Figure 12).

From April 2024 onward, diesel consumption for generators ceased entirely due to the end of load-shedding, resulting in zero litres of diesel use. Consequently, emission intensity per tonne stabilised, with values consistently ranging between 80 and 90 kgCO₂e per tonne of goods. There is a similar trend for the product categories across all months, as normal products (that are stored in a temperature range of 15 °C to 25 °C) contribute the largest amount of emissions. In contrast, hazardous drugs, fridge products, and animal health contribute smaller but consistent proportions to the total emissions. Products classified under the ‘Other’ category show minimal fluctuations in emissions contributions. A complete list of product storage type descriptions that make up the ‘other’ category in Figure 15 can be found in

Table A2. List of ’other’ product storage descriptions for the carbon footprint of goods in terms of weight and volume analysis.

‘Other’ for Weight Analysis	‘Other’ for Volume Analysis
HMC—Humidity CTRL 15 to 25’C; max 60%RH	HFD—Habitat Forming drugs
IOD—Products containing Iodine	HFG—Hazardous and Fridge Product
FLM—Flammable Products	HMC—Humidity CTRL 15 to 25’C; max 60%RH
FZN—Freezer Product −20 Degrees	IOD—Products containing Iodine
GLI—Gliadel Freeze −20 to −25 Degrees	FRZ—Refrigerated freezer normal −15
FRZ—Refrigerated freezer normal −15	FLM—Flammable Products
AHH—Animal Health Hazardous	FZN—Freezer Product −20 Degrees
FRR—Freezer Product −10 degrees	GLI—Gliadel Freeze −20 to −25 Degrees
RNM—Section 21 Normal	FRR—Freezer Product −10 degrees
RFF—Section 21 Fridge products	FLH—Flammable and Hazardous Products
HFZ—Refrigerated freezer hazardous −15	RNM—Section 21 Normal
RHF—Section 21 Habit-Forming Drugs	RFF—Section 21 Fridge products
FLH—Flammable and Hazardous Products	HFZ—Refrigerated freezer hazardous −15 Degrees
HFD—Habitat Forming drugs	AHH—Animal Health Hazardous
HFG—Hazardous and Fridge Product	RHF—Section 21 Habit-Forming Drugs
	HAZ—Hazardous Drugs

of Appendix C.

Overall, the weight-based emission intensity factor for warehousing is 92.41 kg CO₂e/tonne of goods handled and stored.

The volume analysis, on the other hand, produced significantly different results than the weight analysis. The graph in Figure 16 illustrates the monthly emission intensity per cubic metre of goods (measured in kgCO₂e/m³) for various pharmaceutical product categories from July 2023 to the end of July 2024. Different colours in each bar represent the product categories. The emissions per cubic metre show fluctuations across the months, with notable peaks in August 2023 and March 2024.

In contrast to the weight-based analysis shown in Figure 15, no consistent trend is observed in volume-based emissions per cubic metre corresponding to fluctuations in facility electricity or diesel generator usage. A key distinction between the two analytical approaches is that the weight-based analysis in Figure 15 reveals higher absolute emission intensity values compared to those in the volume-based analysis shown in Figure 16. Therefore, there is a big difference between the volume- and weight-based analysis in road transport and warehousing. Caution should be used when selecting a warehousing emission intensity factor.

Overall, the volume-based emission intensity factor for warehousing is 7.52 kg CO₂e/m³ of goods handled and stored.

Regarding product category contributions to total emissions, ’Normal’ products, which are stored within a temperature range of 15 °C to 25 °C, consistently account for the largest share throughout the observed period. Emissions attributed to ’Animal Health’ products, also stored within the same temperature range, showed increases in contributions from September 2023 to July 2024. ‘Fridge’ products and ‘other’ categories contributed minimally throughout the period. For a list of product storage types included in the ‘other’ category in Figure 16, refer to Table A2 in Appendix C.

The emission intensity factors derived in this section are novel since they allow for the calculation of a specific shipment’s emissions—both on a weight and volume basis. For example, 800 kg of pharmaceutical goods with a volume of 2.2 m³ are stored for an unknown period. The emission intensity factor for warehousing in terms of weight is 92.41 kgCO₂e/tonne of goods, while the volumetric emission intensity factor is 7.52 kgCO₂e/m³ of goods. Using the weight-based method results in emitting 73.93 kgCO2e, while the volume-based method results in 16.54 kgCO₂e. It is, therefore, evident that there is a significant difference between the two methods.

6. Conclusions

Pharmaceutical logistics is unique from other commodities due to several distinct characteristics of the products it handles, mandating special attention to this commodity group. Current frameworks and emission intensity factors in the literature are generic in nature and fail to account for volume constraints, highlighting the uniqueness of road transportation and warehousing activities in pharmaceutical distribution. Therefore, this article presents a methodological approach for collecting data and assessing the GHG emissions in pharmaceutical distribution, specifically focusing on road transportation and warehousing activities. Through the categorisation of variables that influence emission intensity factors in both logistical activities, the methodology offers a structured approach to address critical challenges in emissions reporting and provides a comprehensive tool for emission calculation designed for the pharmaceutical supply chain. The application of the methodology in a real-world scenario involving a nationally operating logistics service provider in South Africa demonstrated its practical utility and adaptability.

Findings from the road transport analysis reveal a consistent trend: as the load factor increases and empty running decreases, the emissions per unit of transported weight or volume decreases. Smaller vehicle sizes have higher emissions intensity at low load factors compared to larger vehicles, highlighting their limited efficiency under partial loads. At a 10% load factor, the 1-tonne vehicles emit 6 156.80 gCO₂e/t-km and 1 413.84 gCO₂e/m³-km, compared to the 8-tonne vehicles that emit 2 395.66 gCO₂e/t-km and 457.48 gCO₂e/m³-km. On the other hand, larger vehicles distribute emissions across larger loads, achieving greater fuel efficiency per unit transported and a lower emission intensity. At a 100% load factor, the 1-tonne vehicles emit 615.68 gCO₂e/t-km and 141.38 gCO₂e/m³-km, compared to the 8-tonne vehicles that emit 239.57 gCO₂e/t-km and 45.75 gCO₂e/m³-km. This relationship between load factor and emissions is observed consistently in both weight and volumetric analyses. The weight analysis shows higher absolute amounts of emission intensity compared to volume analysis, indicating that weight constraints may be more impactful than volume. The road transport analysis also iterates the sensitivity of emissions to loading parameters and empty running, the impact of vehicle choice on product carbon footprint due to transportation, and the importance of data collection—all of which are important managerial considerations for Company X. The road transport results also show the significant difference between calculated emission intensity factors and those reported in the literature.

For warehousing, the results highlight fluctuations in electricity consumption and generator diesel usage, with a stabilising effect on weight-based emission intensity following the end of load shedding in April 2024, emphasising the impact of reliable energy sources. The losses in pharmaceuticals were similar in terms of weight and volume. Of the total weight of goods in the facility, 99.58% is sold, while 0.42% is destroyed, compared to the total volume where 99.32% are sold, while 0.68% are destroyed. Weight-based emissions ranged from 81.70 kgCO₂e/t of goods to 104.42 kgCO₂e/t of goods and were significantly higher than those based on volume, which ranged from 6.07 kgCO₂e/m³ of goods to 8.85 kgCO₂e/m³ of goods. Ambient-stored ’Normal’ products contributed the most to overall emissions in both types of analysis. The warehousing analysis shows the emission intensity of simply handling and storing different pharmaceutical products and the importance of more detailed data collection for Company X.

These findings offer valuable insights for logistics providers like Company X, enabling them to identify high-emission activities, understand the emissions impact of their operations and decisions, and implement targeted decarbonisation strategies, contributing to the global shift toward sustainable supply chain management and aligning with broader SDGs such as 3 and 13. Future work by Company X should focus on expanding the granularity of energy consumption data for distinct storage conditions, allowing for even more precise emissions insights across diverse pharmaceutical categories. This will further support Company X and the pharmaceutical sector’s goals of transparency and carbon reduction, particularly as it strives to align with the ambitious targets set by international climate commitments.