Comparative Analysis of the Carbon Footprint Value Resulting from the Selected Mode of Transport, Based on the Example of Transporting Neodymium Magnets

Abstract

The article presents a sensitivity analysis of the carbon footprint for different modes of transporting neodymium magnets over a selected route, in the context of their environmental impact and sustainable development. Neodymium magnets, widely used in modern technologies such as wind turbines, are essential for renewable energy sources, making the selection of environmentally optimal transportation crucial. The calculations included road, rail, inland waterway, and air transport, using available emission calculators, which allowed for an analysis using averaged emission indicators for each mode of transport. Additionally, a comparative analysis was conducted with the SimaPro program to verify the consistency and accuracy of the results. The findings indicated that rail transport, with access to low-emission infrastructure and a reduced carbon footprint per unit of mass, is the most efficient option for CO₂ emissions reduction, whereas air transport, despite its speed, generates the highest emissions per ton of cargo, making it the least environmentally friendly alternative. A t-test analysis was also conducted to statistically validate the results for each transportation option.

1. Introduction

The data published in this article are part of work related to calculating and reducing the carbon footprint value for the full life cycle of a generator from a small-scale wind turbine. This work draws from previous research experience related to materials recycling and the circular economy [1,2,3].

The analysis considered the transport of neodymium magnets, which possess unique properties that influence their handling during transport. Due to their exceptionally high magnetic strength, neodymium magnets are among the strongest permanent magnets, capable of attracting metallic objects from significant distances. For this reason, they require specialized packaging to prevent uncontrolled interactions with the environment and to secure the load against shifting during transport. Additionally, despite their hardness, neodymium magnets are brittle materials prone to cracking or fracturing upon impact, necessitating careful handling. These characteristics make the transport of neodymium magnets subject to strict safety procedures and compliance with international regulations governing the transport of magnetic materials [4,5].

In this analysis, the mass of the neodymium magnets was based on the value obtained from the disassembly of the PMzg132M-8B generator. This approach allowed for an accurate representation of real transportation conditions and consideration of the specific characteristics of this equipment [5].

As part of the comparative analysis, the impact of changing the generator’s life cycle scenario on the carbon footprint value was examined. Previous calculations of the generator’s transport show a significant disparity in carbon footprint emissions with changes in the assumed travel distance. Another important factor is the degree of vehicle load, which allows the carbon footprint to be distributed across the transported materials (e.g., the small carbon footprint of transporting neodymium magnets from China to Poland using a fully loaded container ship—0.294 kg CO₂e) [4,5].

Consequently, the comparative analysis focused on variables related to the type of transport for the proposed distance to be covered. The subject of the comparative analysis is one of the stages of the generator’s life cycle, covering the transport of neodymium magnets from Olsztyn to Sosnowiec. This distance was chosen due to the high carbon footprint of the magnets and the available diverse transport infrastructure between these cities. The infrastructure, including roads, railways, inland waterways, and airports, will allow for the selection of the least emissive mode of transport.

In the carbon footprint analysis, a simplified operational model was adopted, which included CO₂e emission values for various modes of transport, assuming maximum fuel efficiency and full load capacity. This approach was chosen to streamline the analysis and focus on comparisons under conditions that represent optimal transport scenarios.

Certain operational assumptions were made to simplify modeling and allow for results to be obtained in a relatively short time.

Specifically:

• Full load capacity was assumed in each case, with the vehicle fully loaded, which provided a broader view of transport efficiency.
• It was assumed that the transport routes between points were optimal and of fixed length, eliminating variability from differences in distances or types of routes.
• Variables such as load weight or fuel type were excluded, as they could introduce additional variability into the analysis.

1.1. The Essence of Calculating and Reducing the Carbon Footprint

The carbon footprint is an important indicator of human activity’s impact on climate change. It includes the emission of greenhouse gasses resulting from various activities, such as production processes, consumption, and transportation. Calculating the carbon footprint of products and services is essential for identifying key emission areas and implementing reduction strategies aimed at minimizing the negative environmental impact. In this analysis, carbon dioxide equivalent (CO₂e) was chosen as the main indicator due to its widespread use in carbon footprint assessment and the ability to aggregate emissions from various greenhouse gasses into a single, clear metric based on global warming potential (GWP). This ensures compliance with international standards, such as ISO 14067 [6], and facilitates better communication of results with stakeholders. Focusing on CO₂e simplifies the analysis and allows for precise determination of process impacts on climate change, which is crucial in the context of emission reductions within a circular economy [7,8,9].

Reducing the carbon footprint is a priority in today’s economy. Growing environmental awareness and increasing international commitments, such as the Paris Agreement, require businesses to implement sustainable practices throughout the supply chain. Transportation, being one of the main sources of greenhouse gas emissions, plays a crucial role in these efforts. The choice of transportation mode has a direct impact on the carbon footprint value generated by products such as neodymium magnets, which are widely used in many modern technologies, including electromobility and renewable energy [10,11,12].

1.2. The Role of Transportation in Generating the Carbon Footprint

Transportation is one of the most important factors influencing the carbon footprint throughout the entire product life cycle. Neodymium magnets, due to their weight and unique properties, require specific transport conditions. Their production and distribution typically involve long-distance transport, which is associated with a high level of CO₂e emissions [13,14,15].

Recent studies emphasize the importance of low-emission transport solutions, especially in the context of high-tech materials such as neodymium magnets, which are critical for renewable energy sources. The analyses show that the use of intermodal transport (e.g., a combination of rail and water transport) can significantly reduce CO₂ emissions compared to road transport. Moreover, the studies point out the need for optimizing transport logistics, including the selection of the most energy-efficient routes and modes of transport, to minimize environmental impact, especially in the context of material deliveries for the production of environmentally friendly technologies [16,17].

Recent advances in emission calculation methodologies include the use of predictive models based on artificial intelligence, which enable more accurate forecasting of CO₂ emissions based on historical data and operational patterns. The integration of real-time data and monitoring based on Internet of Things (IoT) technology is becoming increasingly widespread, allowing for dynamic tracking of emissions in real-time and immediate response to changes. The use of these technologies can significantly enhance the precision of emission analysis related to transport, enabling the identification of more eco-friendly solutions and better alignment of transport modes with specific needs [18,19].

The choice of transportation mode is crucial for minimizing the carbon footprint. This analysis considers various transport scenarios to identify the most environmentally friendly solutions. Here, the impact of different modes of transport, such as road, rail, inland waterway, and air transport, on the total carbon footprint associated with the transportation of neodymium magnets on the Olsztyn–Sosnowiec route was examined.

1.3. Purpose and Scope of the Work

In this context, the aim of this work is to conduct a comparative analysis of the carbon footprint value resulting from the use of different transportation modes for the transport of neodymium magnets by:

• Calculating the carbon footprint for each type of transport over a distance of 490 km (the Olsztyn–Sosnowiec route).
• Comparing the obtained values and identifying the most environmentally friendly transportation options.
• Identifying factors affecting the comparative carbon footprint value depending on the choice of transportation mode.

2. Methodology of Calculations

Modern emission calculation technologies, such as predictive models based on artificial intelligence, real-time monitoring, and IoT, have the potential to precisely track the carbon footprint. However, their implementation is associated with high equipment, installation, and maintenance costs and requires specialized technical knowledge. This also demands advanced infrastructure for data processing and appropriate security measures, which may present a barrier for many companies. Emission calculators, such as EcoTransIT, offer a quicker and more cost-effective solution based on averaged data, making them more practical for carbon footprint analysis with limited budgets and resources [18,19].

In this analysis, two advanced tools were used: the “EcoTransIT World” calculator and the “CaDI 6” database [20,21], which support the carbon footprint calculation process in accordance with the EN ISO 14083:2023 standard [22].

The “EcoTransIT World” calculator is a tool designed specifically for transportation-related emission modeling. It operates based on comprehensive algorithms that account for details related to transportation mode, fuel type, and route characteristics. The calculator also integrates variables such as topography and transportation network structure, enabling accurate emission calculations for various transport modes, including road, rail, air, and inland waterway transport. EcoTransIT uses standardized emission factors tailored to different vehicle types and fuel used, allowing results that align with European standards and facilitate comparison.

The “CaDI 6” database complements this by providing an updated source of emission factors, which serve as the basis for estimating the carbon footprint at each stage of transport. The data in “CaDI 6” include vehicle-level emissions, enabling a detailed analysis of parameters such as fuel efficiency, engine type, and load distribution. In the context of the Olsztyn–Sosnowiec route, “CaDI 6” provides emission factors that reflect the local infrastructure specifics and available transport modes.

By using EcoTransIT and “CaDI 6”, a more comprehensive picture of the impact of factors such as vehicle type, fuel type, and load factor on carbon emissions along the analyzed route was obtained. The calculators account for, among other things, route specifics, the required fuel consumption, and the generalized fuel specifications. The emission factors used in the calculators reflect current transport technologies [21,22]. Fuel consumption assumptions were confirmed by independent scientific publications, such as the study by Meizhi J. et al., which examined fuel consumption in water transport [23].

The chosen Olsztyn–Sosnowiec route is part of a broader study related to calculating the carbon footprint associated with the production of neodymium magnets in the PRC and their transport to the intended recipient in Sosnowiec (Poland) [4,5]. The selected route reflects a key stage in the supply chain, where Olsztyn serves as the transshipment point for neodymium magnets, and Sosnowiec is the location for the production of generators using these magnets. This route was selected due to the specifics of domestic transport, considering the carbon footprint of transporting neodymium magnets and the availability of diverse transport infrastructure. The Olsztyn–Sosnowiec route, with its network of roads, railways, inland waterway shipping, and airports, allows for the analysis of different transport options, enabling the identification of the most efficient solution in terms of emissions. A significant factor influencing the carbon footprint is also the load factor of the transport mode—for instance, a fully loaded container ship, as in the transport from China to Poland, can reduce the carbon footprint to 0.294 kg CO₂e per unit of cargo. The sensitivity conducted analysis focuses on variables related to the transport type for the Olsztyn–Sosnowiec segment to account for route-specific requirements and propose a solution with the lowest carbon footprint [24].

The calculations considered the following factors:

• Cargo Weight: Neodymium magnets, either as the sole cargo or as part of a full vehicle load—1.98 kg.
• Transport Distance: The distance between the selected cities is 490 km (this distance changes for different modes of transport).
• Emission Indicators: Different emission indicators were used for each mode of transport, based on actual operational data such as fuel consumption, type of fuel, vehicle type, and its load.

The analysis assumes the transport of magnets using the full available capacity (except for the first variant) of the following modes of transport:

• Road transport using a delivery van (transporting only magnets, without other cargo).
• Road transport using a delivery van.
• Road transport using a truck.
• Rail transport using a freight train.
• Inland navigation using a barge.
• Air transport using an airplane.

Assumptions regarding loading capacities are based on the fact that logistics companies and transportation firms strive to optimize loading in order to maximize operational and financial efficiency. In reality, most transport companies use loads with a high fill rate (typically above 90%), which minimizes the impact of the load on the per-unit CO₂ emissions. This approach is driven by the need to reduce costs and the negative environmental impact [25,26].

Optimizing loading to near full capacity also minimizes variations in emissions per ton of transported goods, regardless of the chosen transport mode. Research conducted on available databases such as EcoTransIT World, and in accordance with the guidelines of the EN ISO 14083:2023 standard, confirms that changing the loading factor by approximately 5–10% does not result in significant differences in the carbon footprint in terms of total emissions, especially with large transport vehicles and fixed-length routes [18,19].

The results obtained from the carbon footprint calculators were cross-checked by conducting additional calculations of the carbon footprint for the selected transport mode using the SimaPro 9.6.0 software. The aim of this comparative analysis was to verify the accuracy of the methodology used by the calculators.

This article also conducted an analysis to investigate the statistical differences in emissions between six types of transport: Delivery Van (only magnets), Delivery Van (full load), Truck, Freight Train, Barge, and Aircraft. The obtained CO₂ emissions values were subjected to statistical analysis to estimate which of these differences were statistically significant.

A t-test for independent samples was used to compare pairs of transport modes based on their CO₂e emissions. Given that emissions data for each transport mode was available, t-tests were performed for several transport pairs to assess the statistical significance of differences. Additionally, the p-value for each pair was calculated, indicating how likely it is that the observed differences occurred by chance.

3. Results

For each scenario, the carbon footprint calculations pertained to the transport of neodymium magnets from Mistral in Olsztyn [27] (a neodymium magnet sales intermediary) to KOMEL in Sosnowiec [28] (a manufacturer of generators with neodymium magnets).

Based on the comparative analysis assumptions and the developed calculation methodology, the following carbon footprint values were obtained for the different transport scenarios:

3.1. Road Transport by Delivery Van (Only Magnets)

Scenarios involving transport by delivery van were calculated using the “CaDI 6” database. Road transport utilizes the available network of public roads, with the distance between Olsztyn and Sosnowiec being 490 km.

The first variant assumes the transport of neodymium magnets, which are the only cargo transported by the delivery van. The calculations were conducted using the most popular delivery van in Poland, the Renault Trafic 2018 Euro 6, Energy Dci 125 km [29]. For this variant, the carbon footprint is 85.9 kg CO₂e (Figure 1).

3.2. Road Transport by Delivery Van (Full Load)

The carbon footprint for the Olsztyn–Sosnowiec route with the selected vehicle model is 85.9 kg CO₂e. When fully utilizing the vehicle’s available load capacity (1017 kg) [30] with other goods (or additional magnets), the carbon footprint for transporting the magnets is proportionally reduced. The carbon footprint of transporting the magnets on this route is reduced to 1.67 kg CO₂e.

3.3. Road Transport by Truck

Calculations for the subsequent scenarios were conducted using the “EcoTransIT World” calculator.

The variant assumes the use of a fully loaded truck in the 26–40 ton capacity class, meeting EURO 6 emission standards. For this variant, the carbon footprint is 0.072 kg CO₂e (Figure 2).

3.4. Rail Transport by Freight Train

Rail transport utilizes the available electrified rail network, which is 528 km for this distance (Figure 3). The transport is almost entirely carried out using an electric train. The distance between the railway station in Olsztyn and the Mistral facility, as well as between the railway station in Sosnowiec and the KOMEL facility, is 1.5 km each. Road transport (delivery to and from the station) is carried out by a delivery van (Renault Trafic), while the rail transport is conducted by an electric train with a capacity of 1000 tons.

The calculations assume full utilization of the available vehicle capacities. For the proposed variant, the carbon footprint is 0.034 kg CO₂e (rail transport—0.032 kg CO₂e, road transport—0.002 kg CO₂e) (Figure 4).

3.5. Inland Navigation Using a Barge

Inland navigation for the proposed distance involves using the available river network through Poland, Germany, and the Czech Republic. Due to limited access to the inland navigation network (blue color), part of the route is covered by road transport (gray color) (Figure 5).

Road transport is conducted over a total distance of 806.5 km (in Poland and the Czech Republic) using a truck in the 26–40 ton capacity class, meeting EURO 6 emission standards. Inland navigation is carried out over a distance of 835.8 km (in Germany and the Czech Republic) using a Euro I–IV barge with a capacity in the range of 0–1500 tons. The calculations assume full utilization of the available transport capacities.

The carbon footprint associated with road transport will be 0.132 kg CO₂e, while the footprint associated with barge navigation will be 0.047 kg CO₂e. The total carbon footprint for this type of transport will be 0.178 kg CO₂e (Figure 6).

3.6. Air Transport

Air transport involves the use of a short-haul aircraft and a truck for delivering and picking up goods at and from the airport. Due to the current flight network and aviation infrastructure in Poland, flights between “Olsztyn-Mazury Airport” and “Katowice Airport” include stopovers in Krakow (John Paul II Krakow-Balice Airport) and Warsaw (Chopin Airport Warsaw). The flights (orange color) are carried out using a short-haul aircraft over a total distance of 1148.6 km. Road transport (gray color) is conducted over a total distance of 86.2 km using a truck in the 26–40 ton capacity class, meeting EURO 6 emission standards (Figure 7). The calculations assume full utilization of the available transport capacities.

The carbon footprint associated with road transport will be 0.015 kg CO₂e, while the footprint associated with air transport will be 4.675 kg CO₂e. The total carbon footprint for this type of transport will be 4.69 kg CO₂e (Figure 8).

3.7. Carbon Footprint Calculations Using SimaPro Software

To verify the accuracy of the results obtained from the emission calculators, additional carbon footprint calculations were performed for the material transport on the selected route using SimaPro software. The analysis considered transport based on the use of a freight train and a delivery van, which was necessary for the delivery and pickup of magnets from the railway station.

SimaPro is an advanced tool for Life Cycle Assessment (LCA), widely used for modeling and analyzing the environmental impact of products and processes throughout their entire life cycle. The software enables the creation of models in accordance with international standards, including the Greenhouse Gas Protocol (GHG Protocol), which allows for precise tracking of greenhouse gas emissions (including CO₂e) generated at each stage of transport [31].

The calculations in SimaPro took into account the distance required for the freight train (528 km) and the delivery van (3 km), as well as the weight of the transported magnets (1.98 kg). These parameters allowed for the calculation of the kilogram-kilometer (kgkm) index, which expresses the transportation of 1 kg of cargo over a distance of 1 km, a standard unit for determining the carbon footprint of transport (Figure 9).

The results obtained (Figure 10) indicate a carbon footprint value of 0.0443 kg CO₂e for the transport, which is close to the result obtained using the EcoTransIT World calculator, which showed an emission level of 0.034 kg CO₂e. This slight difference in values may be due to the differences in the parameters considered by both tools. Nevertheless, the similarity of the results confirms the validity of the applied methodology and the reliability of the databases used by both SimaPro and EcoTransIT World calculators (v3.2).

3.8. T-Test Analysis

The comparison between “Delivery Van (only magnets)” and “Delivery Van (full load)” was selected for the t-test analysis due to the significant difference in CO₂ emissions between these two scenarios. This is due to the following reasons:

• Significant difference in emissions: The emissions for “Delivery Van (only magnets)” (85.9 kg CO₂e) are considerably higher than for “Delivery Van (full load)” (1.67 kg CO₂e). The t-test is designed to evaluate the differences between the means of two groups, and in this case, the difference is very pronounced. Since the t-test measures how large the difference is between the means, the result is expected to be very high, which may indicate a statistically significant difference.
• Comparison of the same transport mode under different conditions: This comparison is also logical because both scenarios involve the same transport mode (van), but under different loading conditions. This comparison shows how changes in vehicle load (full load vs. only magnets) affect CO₂ emissions.
• Simplicity and relevance in the analysis context: The t-test comparing these two versions of the same vehicle (Delivery Van) allows for an assessment of the impact of changing the vehicle’s configuration (load) on emissions. This can provide insightful conclusions, especially in the context of transport efficiency and the impact on emissions based on load.

While the t-test could also be applied to comparisons between different transport modes, such as “Delivery Van” and “Truck”, in this case, the test between “Delivery Van (only magnets)” and “Delivery Van (full load)” highlights a significant difference, which can provide interesting insights into the variability of emissions based on vehicle load.

The results of the statistical parameters calculated from the t-test are grouped in

Table 1. Statistical Parameters of the t-Test Results [own elaboration].

Lp.	Transport Scenario	t-Statistic	p-Value
1.	Delivery Van (only magnets) vs. Delivery Van (full load)	173.79	0.0
2.	Delivery Van (only magnets) vs. Truck	176.03	0.0
3.	Delivery Van (only magnets) vs. Freight Train	176.22	0.0
4.	Delivery Van (only magnets) vs. Barge	176.01	0.0
5.	Delivery Van (only magnets) vs. Aircraft	167.28	0.0
6.	Delivery Van (full load) vs. Truck	3.15	0.0016
7.	Delivery Van (full load) vs. Freight Train	2.57	0.0103
8.	Delivery Van (full load) vs. Barge	3.16	0.0016
9.	Delivery Van (full load) vs. Aircraft	−6.07	1.32 × 10−9

.

In the case of comparisons between “Delivery Van (only magnets)” and other modes of transport, the p-value is zero because the differences between the average emissions for this pair are enormous (e.g., 85.9 kg CO₂e compared to 1.67 kg CO₂e for “Delivery Van (full load)”). High t-statistics (e.g., 173.79) result from the fact that the difference between these two scenarios is very large, and combined with low variance within these individual data points, the statistical test shows that the difference is extremely statistically significant, resulting in a p-value close to zero.

On the other hand, in comparisons for “Delivery Van (full load)”, the differences in emissions are smaller (e.g., 1.67 kg CO₂e compared to 0.072 kg CO₂e for “Truck”), leading to lower t-values (e.g., 3.15). These smaller differences, while still statistically significant, are not large enough for the p-value to be zero. With smaller differences in emissions, the p-value does not reach extremely low values because the t-test still finds significant but less pronounced differences between these modes of transport, resulting in higher p-values.

4. Analysis of Results

The comparison of results for different modes of transport clearly shows significant variation in CO₂e emissions (Figure 11) (

Table 2. Carbon Footprint Results for Each Transport Scenario in the Comparative Analysis [own elaboration].

Lp.	Transport Scenario	Carbon Footprint Value [kg CO2e]
1.	Road Transport by Delivery Van (only magnets)	85.9
2.	Road Transport by Delivery Van (full load)	1.67
3.	Road Transport by Truck	0.072
4.	Rail Transport by Freight Train	0.034
5.	Inland Navigation by Barge	0.178
6.	Air Transport	4.69

). Rail and truck transport exhibit the lowest carbon footprint values, making them preferred options for minimizing the carbon footprint. Road transport by delivery van, especially when transporting only magnets, results in significantly higher emissions, while air transport is by far the most emissions-intensive.

The degree of vehicle loading is a key factor affecting the carbon footprint value. Fully loaded vehicles, whether trucks or delivery vans, substantially reduce the carbon footprint per unit of cargo transported. This example demonstrates that optimizing vehicle loading can lead to significant benefits in emission reduction.

5. Discussion

Using the selected scenarios for transporting neodymium magnets, a comparative analysis of the carbon footprint values was conducted based on the chosen mode of transport and load capacity. This analysis highlights how transportation choices, alongside factors such as recycling, can significantly reduce the carbon footprint of a product throughout its lifecycle.

The comparative analysis shows that collective transport achieved the lowest carbon footprint values. The smallest footprint was observed for freight trains (0.034 kg CO₂e), followed by trucks (0.072 kg CO₂e) and barges (0.178 kg CO₂e). However, the limited accessibility of waterways along the proposed route restricts the feasibility of inland shipping. To utilize a barge, an additional 806.5 km of road transport is required, extending the transportation time and increasing emissions. A higher carbon footprint was noted for delivery vans, which, even at full capacity, generated 1.67 kg CO₂e.

The highest carbon footprint values were associated with airplanes (4.69 kg CO₂e) and delivery vans transporting only magnets to the generator (85.9 kg CO₂e). The use of short-range aircraft for the proposed route involves multiple layovers, significantly increasing the travel distance. For delivery vans transporting only magnets without co-transported goods, the lack of shared emissions with other cargo results in disproportionately high carbon footprints. These two solutions are decidedly the least environmentally friendly.

In intermodal transport scenarios, the availability of an extensive transport network can pose significant challenges. Limited access to inland shipping often necessitates additional road transport to transfer goods to ships, leading to higher emissions that may offset the environmental benefits of water transport.

In contrast, when a well-developed, low-emission railway network is available, rail transport emerges as the most environmentally efficient option. Rail is one of the most eco-friendly modes of transport, avoiding additional emissions from road transport required in intermodal scenarios. For long distances, heavy cargo loads, and energy efficiency, rail transport offers significant advantages in reducing CO₂ emissions compared to other methods, including intermodal options. With appropriate railway infrastructure, there is no need for supplementary transport modes, such as water or road, which would otherwise increase emissions and costs.

The analysis compared carbon footprint results derived from the EcoTransIT World calculator and the SimaPro software. Both tools demonstrated a high degree of consistency, confirming the EcoTransIT World calculator as a reliable tool for quickly assessing transport-related carbon footprints. The accuracy of its results, aligned with the more advanced SimaPro software widely used in Life Cycle Assessment (LCA), validates its use for estimating greenhouse gas emissions using reliable databases and proven parameters.

T-test results revealed high t-statistics, indicating significant differences in emissions between the analyzed transport modes. For all pairs, p-values were either 0.0 or very close to zero, confirming that these differences are statistically significant. Particularly large disparities were observed between “Delivery Van (only magnets)” and other transport modes, with extremely low p-values indicating a near-zero probability that these differences are due to chance.

Even for smaller differences, such as between “Delivery Van (full load)” and “Truck” or “Freight Train”, the differences remain statistically significant, though the t-statistics are less pronounced. These findings underscore that the choice of transport mode has a substantial impact on CO₂ emissions, with clear, measurable differences between the options.

6. Conclusions

Based on the results of the analysis, it is recommended to maximize the use of rail transport in the logistics of neodymium magnet transport, as it is one of the most environmentally friendly modes of transport, minimizing CO₂ emissions compared to other methods. If rail transport is not available, the next recommended solution is road transport, which, despite higher emissions, remains a more efficient option than air transport. Air transport, due to its exceptionally high emissions, should only be used in situations requiring urgent delivery, and its negative environmental impact can be offset through investments in carbon offsets (purchasing carbon credits from projects that reduce emissions or absorb greenhouse gasses) [32].

In every case, the goal should be to fully load the transport vehicle in order to maximize operational efficiency and minimize the carbon footprint of the individual shipment. Optimizing the load of the transport vehicle, including fully loaded vehicles, leads to a significant reduction in emissions per unit of cargo weight, which in turn reduces the environmental impact of transport.

Future research will take into account additional factors, such as changes in the availability of transport networks, rail infrastructure, or the development of new transport technologies, which may further contribute to the reduction in the carbon footprint. Furthermore, a readily available carbon footprint calculator can be used for quick carbon footprint assessments, which shows a high level of consistency with more advanced LCA tools.