A Framework for Integrating Carbon Accounting Standards into Decision Support Structures in Logistics

Abstract

This paper proposes a methodological framework for linking standardized carbon footprint reporting with structured decision support in logistics. The approach integrates the GHG Protocol framework and the ISO 14064 standard in order to formalize emissions inventories, reporting requirements, and verification constraints into a coherent, transparent, and auditable analytical structure. While existing standards provide robust guidance for the quantification and reporting of greenhouse gas emissions, their systematic integration into decision support representations remains limited. The main contribution of the paper consists of the formal operationalization of carbon accounting processes into decision variables, constraints, and performance indicators that preserve traceability, transparency, and compatibility with external verification requirements. A simplified linear programming formulation is employed as a standard-driven decision support abstraction, illustrating how emissions-related data derived from standardized reporting can be consistently translated into operational constraints and analytical indicators. The mathematical formulation is not intended to replace detailed logistics optimization models, but to demonstrate the methodological linkage between emissions reporting, verification requirements, and structured decision-oriented analysis. The proposed framework is illustrated through a logistics hub case study using average emission factors and estimated consumption data. The numerical results serve an illustrative purpose and highlight the functioning of the framework, rather than providing fully calibrated operational solutions. The methodology is designed to be reproducible and auditable and may be extended to other industrial sectors, as well as to more advanced modeling settings incorporating dynamic or stochastic elements.

1. Introduction

Industry 5.0 is often described as a new stage of industrial transformation, where technological progress is increasingly aligned with sustainability, resilience, and social responsibility objectives [1]. While Industry 4.0 focused primarily on digitalization and automation, Industry 5.0 emphasizes the need to reduce environmental impacts and improve the responsible use of resources, including in operational domains such as logistics [1]. This shift is particularly relevant in the context of growing regulatory pressure for the green transition and the transparent reporting of greenhouse gas emissions.

A key pillar of Industry 5.0 is sustainability and the promotion of the circular economy. Unlike previous industrial models, where environmental aspects were often treated as secondary, Industry 5.0 places environmental responsibility at the core of industrial activity, encouraging waste reduction, the reuse of materials, and more transparent resource management.

The European Commission underlines that this approach supports technologies and practices that contribute to the more effective and responsible use of resources, transforming sustainable development into an objective integrated into industrial policies. Within this perspective, industry is increasingly expected to demonstrate transparency, accountability, and consistency in addressing challenges such as climate change, resource depletion, and waste management.

Logistics operations are a key element of supply chains and also represent a significant source of greenhouse gas emissions. In recent years, companies have been confronted not only with the need to measure emissions but also with the requirement to demonstrate consistency, transparency, and credibility in their sustainability-related decisions. Established tools such as the GHG Protocol and seria of standards ISO 14064 provide robust frameworks for emissions inventory, reporting, and verification; however, they offer limited guidance on how such information can be systematically used within structured decision support contexts.

Starting from this methodological gap, the present article addresses the challenge within an international context, analyzing relevant standards and initiatives that support the transition towards a more sustainable and accountable logistics sector. The proposed framework integrates the requirements of the GHG Protocol [2] and ISO 14064-1:2018 [3] and ISO 14064-3:2019 [4] and formalizes them into a structured decision support representation. A simplified linear programming formulation is employed to illustrate how emissions accounting, reporting, and verification requirements can be consistently translated into decision variables and constraints, ensuring traceability and methodological coherence rather than operational optimization.

The main contribution of this paper consists of formalizing a reproducible and auditable framework that links emissions reporting and verification standards with structured decision support mechanisms. The proposed methodology is not limited to emissions assessment but provides a transparent structure through which reported emissions data can be systematically analyzed and interpreted in support of sustainability-oriented decision processes. The illustrative case study demonstrates the feasibility of the framework and its potential applicability in industrial contexts.

The research presents certain limitations. The applicability of the framework is illustrated through a single case study, the analysis is based on average emission factors, and the mathematical formulation is deterministic. These aspects limit the degree of generalization, and future research directions include extending the framework to other logistics contexts, integrating stochastic components, and validating the approach using real operational datasets.

The present study does not aim to develop a full-scale logistics optimization model. Rather, its contribution lies in the formalization of a transparent and auditable methodological framework that connects standardized carbon accounting and verification requirements with structured analytical representations. Within this framework, the linear programming formulation functions as an analytical abstraction that supports the systematic interpretation of emissions-related data in a decision support context, rather than as a prescriptive optimization instrument.

2. Literature Review

The Industry 5.0 concept has emerged in response to the limitations and implications of the fourth industrial revolution (Industry 4.0), focusing on balancing technological progress with societal and environmental protection needs. While Industry 4.0 was primarily oriented towards digitalization and automation, Industry 5.0 emphasizes the extension of industrial objectives beyond economic growth, encouraging active contributions to social welfare and environmental protection, including in the logistics sector.

The digital transformation supported by Industry 4.0 technologies has emerged as a promising approach to improving the efficiency and transparency of transportation and logistics systems. Technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and predictive maintenance have demonstrated their potential to support real-time monitoring and data-driven decision support in supply chain operations [5,6,7].

Within this analytical framework, logistics represents a critical component of supply chains and a significant source of greenhouse gas emissions. Transporting, storing, handling, and distributing goods involves intensive energy consumption and, in many cases, reliance on fossil fuels, resulting in a substantial carbon footprint. Consequently, the development of approaches aligned with green logistics and circular economy principles has become increasingly important.

Reverse logistics refers to the planning, implementation, and control of information and material flows along logistics processes [8]. In this context, green logistics involves the integration of environmental considerations into logistics-related decision processes, aiming to reduce ecological impacts while maintaining operational feasibility. Green logistics therefore encompasses not only technological solutions (e.g., electric vehicles, renewable energy sources) but also organizational strategies, governance mechanisms, and cooperative practices such as resource sharing and coordination.

Due to the growing pressure generated by climate change, companies are being forced to rethink the ways in which they perform their logistic operations. The transportation, storage, and distribution activities represent a significant contribution to the global emissions of greenhouse gases, which is why the green logistics concept has become essential in endeavors to achieve sustainability. Green logistics aims to integrate the principles of environmental protection into all logistic decisions, with one of the main goals being to reduce the carbon footprint.

The recent specialized literature highlights the fact that green logistics can significantly reduce the emissions of supply chains, both through technological modernization and via organizational strategies and adequate public policies [9,10]. At the same time, the transition to low-emissions logistics operations is not devoid of challenges, such as high costs, limited infrastructure, and a lack of specialized skills.

At the European Union level, sustainability-related policies are highly ambitious. Through the European Green Deal, the European Union aims to reduce greenhouse gas emissions from transport by 90% by 2050 [11]. In parallel, digital technologies are increasingly being used to improve the transparency, monitoring, and planning of logistics activities. Member states are encouraged to align logistics sector policies with sustainable development objectives and to allocate additional resources to green innovation and renewable energy in support of long-term sustainability [12].

The implementation of Industry 4.0 technologies in logistics has been associated with improved visibility, coordination, and analytical capabilities. These developments support better-informed planning and monitoring of logistics activities and facilitate the identification of opportunities for reducing energy consumption and emissions. Rather than guaranteeing specific performance improvements, digital tools enable the structured analysis of logistics processes and their environmental impacts [13,14].

Electric urban transport vehicles, short-distance electric lorries, and emerging hydrogen technologies are at the core of green logistics. According to the analysis performed [15], electrification may reduce CO₂ emissions by up to 40%, especially in the last-mile delivery sector.

Logistics also involves the improved energy efficiency of infrastructure. Storage facilities use large amounts of electricity for lighting, air conditioning, and mechanized and automated processes. Implementing efficient systems may reduce consumption by more than 50% in certain situations, according to the “Green Logistics 5.0” concept [16].

The recent literature also emphasizes the role of reverse logistics in waste reduction and the promotion of circular economy practices, including material recovery and product reconditioning [17,18,19]. Additionally, regulatory mechanisms such as carbon taxation and emissions trading systems influence logistics-related decisions by encouraging the adoption of less carbon-intensive solutions [20].

Emission regulations (carbon taxes, ETS systems) have a strong influence on the structures and decisions of logistics chains. The models proposed by these regulations indicate that introducing a tax on the quantity of carbon produced encourages companies to adopt cleaner transport, shorter routes, and greener technologies in order to minimize the total costs [21].

The outcomes of these initiatives are enhanced transparency and, often, the identification of further opportunities to increase efficiency. Practically, “what is measured is improved”, and digitalization allows for the measurement of almost all operational aspects, including environmental ones. In other words, introducing Industry 4.0 technologies in logistics helps to align business objectives with sustainability ones. Lowering emissions and waste is no longer solely aimed at abiding by legal regulations; rather, it becomes an essential element of efficiency.

From the perspective of carbon accounting, current practice reveals significant fragmentation in emissions reporting. While many organizations rely on the GHG Protocol to define organizational and operational emission boundaries, ISO 14064 provides a more standardized framework for verification. Although complementary, these instruments primarily focus on measurement and reporting and provide limited guidance on how emissions data can be systematically used within structured decision support contexts. This situation highlights the need for integrated approaches that connect standardized emissions accounting with transparent and reproducible analytical structures.

Recent European regulations, such as the Corporate Sustainability Reporting Directive (CSRD) [22], further emphasize the need for standardized, comparable, and verifiable sustainability reporting. In this regulatory context, logistics companies are increasingly required not only to quantify emissions but also to demonstrate consistency and traceability in how reported information supports sustainability-related decisions.

A methodological gap therefore becomes apparent: the absence of a coherent framework that systematically links emissions reporting standards, such as the GHG Protocol and ISO 14064, with structured decision support representations. Addressing this gap requires not necessarily increasingly complex optimization models but rather transparent, auditable mechanisms that enable the consistent interpretation of reported emissions data.

Within this research context, the present article contributes to the literature by proposing a methodological framework that connects emissions inventories derived from standardized reporting with a structured decision support representation illustrated through a simplified linear programming formulation. The framework provides an explicit linkage between reporting requirements and analytical structures, without aiming to deliver full-scale operational optimization.

The Simplex linear programming method is employed due to its widespread recognition and ease of implementation in analytical contexts [23]. In logistics research, linear programming approaches are commonly used to structure resource allocation and planning problems [20,21,23,24,25,26]. In the present study, Simplex serves as a demonstrative tool that supports the formalization of emissions-related constraints, rather than as a means of advanced logistics optimization.

Advanced optimization models addressing carbon emissions in logistics, such as carbon-constrained vehicle routing problems or mixed-integer linear programming formulations, typically incorporate detailed operational constraints, including vehicle capacity, time windows, and network structure [24,26]. While these models offer high operational fidelity, they are often developed independently of standardized emissions reporting and verification frameworks. The approach proposed in this paper does not seek to compete with such models but to complement them by providing a transparent and auditable bridge between emissions accounting standards and structured decision support. Achieving more transparent, efficient, and sustainable supply chains is a necessity in the current economic context [27].

Although the specialized literature reports numerous advanced modeling approaches addressing emissions in logistics, including vehicle routing problems and mixed-integer formulations, these contributions are primarily oriented towards detailed operational optimization under specific technical constraints [23,24,25,26]. The present study does not seek to replicate or extend such models; instead, they are referenced to provide an analytical context for the proposed framework, which operates at a different level of abstraction by integrating standardized carbon accounting and verification requirements into structured decision support representations.

3. Materials and Methods

3.1. General Considerations

The proposed mathematical formulation has an intentionally simplified structure, designed to support practical applicability and methodological transparency. The objective is not to reproduce the full operational complexity of a logistics hub but to illustrate how data derived from the application of the GHG Protocol and ISO 14064 can be systematically translated into decision variables, objective functions, and constraints within a linear programming framework. This formulation highlights the functional link between carbon emissions accounting and structured decision support, providing a reproducible methodological basis that can be extended in subsequent applications.

The development of the integrated framework is based on internationally recognized reference documents addressing the assessment of greenhouse gas emissions associated with economic activities, namely the GHG Protocol [2]; ISO 14064-1—Greenhouse gases—Part 1: Specification with guidance at the organizational level for quantification and reporting of greenhouse gas emissions and removals [3]; and ISO 14064-3—Greenhouse gases—Part 3: Specification with guidance for the verification and validation of greenhouse gas statements [4].

The reported quantities of greenhouse gas emissions may vary significantly depending on the methodological approach adopted by an organization, which can raise concerns from a sustainability and comparability perspective. However, the combined use of these reference documents enables the development of an integrated and coherent reporting structure. This need is further emphasized by the limited evidence in the literature regarding the systematic integration of these standards in practical decision support contexts.

According to the GHG Protocol, a company’s emissions of greenhouse gases fall into three domains or scopes. Domains/scopes 1 and 2 are compulsory for reporting, while domain/scope 3 is voluntary and the most difficult to monitor.

The scopes defined in the GHG Protocol are the following [2].

• Scope 1: Direct emissions generated from sources owned or controlled by the company.
• Scope 2: Indirect emissions generated by the energy purchased and used by the organization.
• Scope 3: Other indirect emissions (not included in Scope 2) generated by activities in the organization’s value chain, both upstream and downstream.

In the ISO14064-1 and ISO14064-3 standards, greenhouse gases are grouped into 6 categories [3,4]:

• Direct emissions and absorptions;
• Indirect emissions generated by the consumption of imported energy;
• Other indirect emissions within the value chain (both upstream and downstream);
• Indirect emissions associated with products used by the organization;
• Indirect emissions associated with the use of products belonging to the organization;
• Other indirect emissions generated by supplementary sources.

By grouping the above-mentioned categories in the GHG Protocol, we define with three types of emissions:

• Direct emissions;
• Indirect emissions from energy (associated with the purchased electric and thermic energy);
• “Other indirect emissions”.

3.2. Correlation of the GHG Protocol with the Series of ISO 14064 Standards

The GHG Protocol and the ISO 14064 standard share fundamental principles, such as relevance, completeness, consistency, transparency, and accuracy, which are essential in ensuring the quality of greenhouse gas inventories. Both frameworks aim to support comprehensive and comparable emissions reporting, although they differ in structure and application. Moreover, due to their distinct origins and development, the two differ in terms of approach. In practice, numerous organizations and stakeholders have asked the question of how the two reference documents can be integrated in order to benefit from the advantages that each of them has to offer. This integration involves aligning the quantification and reporting methodologies so that a GHG inventory complies with the requirements of both the GHG Protocol and ISO 14064.

Analyzing the specialized literature regarding the methodologies for integrating the GHG Protocol and the ISO 14064 standard, it can be found that there is a need for these two documents to be integrated. The first step in this direction consists of analyzing the similarities and the differences between the two approaches.

• Scope: The GHG Protocol focuses on corporate reporting and is accompanied by detailed guidelines and tools, including industry-specific methodologies. Its three scopes cover the whole spectrum of a company’s emissions, both direct and indirect, including the chain. ISO 14064-1, on the other hand, covers both the organization and, indirectly, the projects that the organization is involved in, and it focuses on general requirements that can be applied in many situations. Thus, the Protocol is in-depth (e.g., it offers calculus formulas, conversion factors, and illustrative case studies), while ISO 14064-1 remains at the specification level.
• Voluntary nature vs. external recommendations: Formally, both protocols are voluntary, but the GHG Protocol has earned a higher level of global recognition and is frequently adopted for voluntary public reporting. ISO 14064 is used especially in certification contexts, where independent verification is requested. In many cases, companies use the GHG Protocol for internal use and public communication and ISO 14064 for verification and external audit. The two approaches are not mutually exclusive, so they can be applied complementarily.
• Formality of external verification: A major practical difference is related to data verification. The GHG Protocol does not impose third-party verification, although it recommends transparency and internal controls. ISO 14064-1 was specifically designed for independent verification, offering detailed criteria that allow a certified auditor to assess the accuracy of the emissions inventory.
• Treatment of indirect emissions (value chain): Both documents recognize the importance of indirect emissions associated with the value chain, but the structuring and reporting differ, which justifies the need to map the methodological frameworks.

The practice of integrating the two documents is supported by recent studies. For example, in 2023, the carbon footprint of a university campus was evaluated, using concomitantly the ISO 14064 standard and the GHG Protocol [28]. The methodology combined the requirements of both documents and demonstrated the feasibility of integration, serving as a reference for other institutions. In general, the specialized literature is in favor of convergence and compatibility, and numerous organizations are applying elements from both frameworks simultaneously.

Thus, by analyzing the GHG Protocol and the series of ISO 14064 standards, common elements can be identified, as well as useful differences for the design of an integrated, clearly structured, and easily applicable methodology.

Table 1. Similarities/differences.

Similarities

Differences

Both tools aim at correctly quantifying and reporting emissions. The methodological principles are mostly identical. Both recognize the importance of transparency and accuracy.

Structure: The GHG Protocol analyzes emissions depending on their scope/application domain, while the ISO 14064 standards organize them into 6 categories. Applicability: The GHG Protocol is more often used for voluntary reporting, while the ISO 14064 standards are preferred for certification and verification. Compatibility: The GHG Protocol can be mapped to ISO 14064, but integration entails extra requirements.

Source: Own contribution.

synthesizes the main similarities and differences.

3.3. Developing an Integrated Methodology for Calculating the Carbon Footprint

The development of the proposed methodology aims to define objectives and limitations, to analyze the carbon footprint associated with logistics activities, and to support a structured analytical examination of emissions-related data within an operational context.

By relying on the GHG Protocol, the methodology provides a unitary and standardized approach to addressing greenhouse gas emissions generated by commercial activities. Given the inherent complexity of economic and logistics operations, the purpose of the methodology is not to deliver a detailed operational optimization model but to establish a coherent analytical framework that highlights the environmental impacts of energy and fuel consumption and enables the transparent interpretation of emissions-related indicators.

The objectives of the proposed model are oriented towards the following:

• Structuring and quantifying energy consumption associated with the activities carried out by a courier logistics hub;
• Enabling the transparent and consistent interpretation of emissions-related data within a structured decision support context.

The methodology developed on the basis of the GHG Protocol and the series of ISO 14064 standards presupposes the following stages.

Stage 1: Analyzing the organizational framework—identifying the company activities that generate emissions (transport, storage, equipment usage, energy consumption, etc.).

Stage 2: Mapping—correlating the data collected according to the GHG Protocol with the requirements of ISO 14064. Mapping is important for structuring the activities by scope/emission category and for ensuring compatibility between the two instruments.

Stage 3: Integrating the classifications—using the established correlations to bring together the domains in the GHG Protocol and the categories in ISO 14064, so that each activity is associated both with a certain type of emissions and with a set of reporting/verification requirements.

Stage 4: Collecting information on consumption—centralizing and verifying data related to fuel consumption, electricity use, and other resources with environmental impacts that are relevant to the organization.

Stage 5: Calculating the carbon footprint—estimating emissions associated with the identified activities using specific emission factors, in accordance with the GHG Protocol. Since the energy content of the fuel is not always known, emissions can be determined based on emission factors, using the formula

$${\mathrm{E}}_{\mathrm{c}}=\mathrm{C}\times {\mathrm{F}}_{\mathrm{e}}$$

(1)

where

$E_c$ represents emissions;

C is the quantity of fuel consumed (mass or volume);

Fe is the emission factor.

To calculate the emissions falling under scope 2 (indirect emissions from purchased energy), we use the following formula:

$${\mathrm{E}}_{\mathrm{c}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{C}}_{\mathrm{i}}\mathrm{x}{\mathrm{F}}_{\mathrm{e}\mathrm{i}}$$

(2)

where

$C_i$ represents energy or fuel consumption for activity i;

$F_{ei}$ is the corresponding emission factor i.

Emissions falling under scope 3 include indirect emissions generated upstream and downstream in the value chain. These emissions are not produced by assets owned or controlled by the organization but are associated with activities for which it bears indirect responsibility. The total quantity of such emissions is expressed as

$${ECO}_{2e}={\sum }_{i=1}^n{E}_{c_i}$$

(3)

where

${ECO}_{2e}—$CO₂-equivalent emissions;

$E_{ci}—$emissions associated with each activity or category.

Stage 6: Unitary reporting—drawing up an integrated report.

The outcome of applying the integrated approach is that of obtaining an annual sustainability report, where the data are presented according to the GHG Protocol but integrated with the compulsory elements from ISO 14064 regarding traceability, verification, and validation.

Stage 7: Illustrative decision support analysis based on emissions-related constraints.

Since cost and resource constraints represent a strategic consideration in logistics, structuring emissions-related data within a linear programming formulation allows for the analytical exploration of alternative configurations under predefined constraints.

Integrating the GHG Protocol with the ISO 14064 standard leads to a quantitative description of company activities by means of consumption indicators and emission factors. These indicators can be expressed as linear relations among activities, resources, and emissions (for example, “the total energy consumption cannot exceed a given threshold”, “emissions-related indicators must comply with predefined limits”, or “logistics flows must observe capacity constraints”).

In this context, the analytical problem concerns how logistics activities can be represented and compared within a linear programming structure that incorporates both energy-related indicators and basic operational constraints. To reflect a minimal but physically meaningful discrete operational dimension relevant to logistics activities, the analytical framework is formulated as a mixed-integer linear programming (MILP) model. In addition to continuous decision variables representing activity volumes, an integer variable is introduced to capture discrete capacity-related decisions, such as the number of active operational configurations (e.g., shifts or equipment sets), thereby strengthening the physical interpretability of the analytical representation.

Alongside the continuous decision variables that represent activity levels, an integer variable is included to capture capacity constraints, such as the number of transport units or operational cycles. A generic capacity constraint can be expressed as

$${\sum }_{i=1}^n{a}_i{x}_i\le C·y,y\in {\mathbb{Z}}^{+}$$

(4)

where

$x_i$ represents activity volumes;

$a_i$ denotes activity-specific capacity or consumption coefficients;

$C$ represents the average capacity per unit; and

$y$ is an integer variable reflecting the number of available capacity units.

The Simplex method is a widely used algorithm for solving linear programming problems and it allows for the following:

• The transformation of data resulting from the emissions inventory (according to GHG/ISO) into decision variables (volume of activity, level of equipment usage, structure of fluxes);
• Defining an objective function representing an aggregate energy or emissions-related indicator;
• Simultaneously observing constraints imposed by capacity, demand, environmental standards, and emissions-related limits.

Consequently, the use of the Simplex method is appropriate in the context of integrating the two instruments, as it connects the standardized reporting framework (GHG Protocol and ISO 14064) with a structured analytical representation of emissions-related constraints. The method is not arbitrarily introduced but is derived from the linear nature of the relationships among consumption, emissions, and resources in the proposed framework.

In logistics, issues such as transportation planning, allocation, and production mixes are classical applications of linear programming, and Simplex is a well-established method for addressing such analytical problems [29]. An important advantage of this approach lies in its capacity to manage models with multiple variables and constraints, characteristic of modern warehouses and logistics hubs.

Within this framework, operational efficiency is interpreted analytically as the structured allocation of activities and resources under predefined technical, economic, and environmental constraints. Accordingly, the proposed formulation is based on a linear programming problem in which the extreme of a linear objective function is determined while observing a set of restrictions reflecting both operational limitations and emissions-related considerations.

To illustrate the analytical formulation, we consider the standard minimizing form of a linear programming problem:

$$\{\begin{array}{c}[\mathit{min}] g=yb\\ yA\ge c\\ y\ge 0\end{array}$$

(5)

Subsequently, the corresponding dual problem is defined, leading to the formulation of the associated maximization problem used for analytical illustration.

The stages presented above can be synthesized as in Figure 1.

The model has a demonstrative role and aims to highlight the integration between an emissions inventory and structured decision support. It can be extended by adding additional constraints and analytical layers as part of future research.

3.4. Applying the Previous Algorithm to a Courier Logistics Hub

Since the logistics industry is increasingly being confronted with climate change challenges and stricter regulatory requirements, the need for a solid and transparent methodology to measure and interpret greenhouse gas emissions has emerged. Carbon emissions represent a global issue, while logistics operations often span across borders and continents, requiring a coherent and internationally compatible methodological approach.

Standardized documents such as the GHG Protocol and the ISO 14064 standards have emerged as global reference frameworks in this field, offering organizations a structured and internationally recognized approach to compiling emissions inventories in response to increasing regulatory and reporting requirements.

The limitations of the study stem from the fact that the analysis focuses exclusively on the energy consumption of equipment and transport means. Consequently, the estimated carbon footprint reflects only the energy- and fuel-related impacts of the analyzed operations. In addition, the calculations rely on emission factors specific to Romania, which may affect their international comparability. Other potential impact sources associated with the logistics hub, such as equipment manufacturing, technological upgrades, or waste flows, are not included and represent directions for future research.

The detailed presentation of the stages goes beyond a purely descriptive purpose, as it enables the transformation of energy consumption data into quantifiable variables within a structured analytical formulation. Each consumption category is represented as a variable or a constraint within a linear programming-based analytical structure, allowing for a transparent correlation between GHG/ISO reporting requirements and the analytical representation of operational activities.

Applying the previously described model to a logistics center entails the following.

Stage 1: Analyzing the organizational framework

Identifying the company activities that generate emissions, which are as follows:

• Own diesel lorries fleet (direct emissions—scope 1/ISO 14064-1);
• Warehouse electric energy consumption (indirect emissions—scope 2);
• Subcontractors’ activities (land transport)—other indirect emissions (scope 3).

Stage 2: Mapping

The data collected according to the GHG Protocol are correlated with the requirements of ISO 14064. To achieve this, the map of processes according to ISO 9001:2015 [30] is drawn up and the emissions are calculated by category.

Scope 1: quantity in tonnes of CO₂e/year (own fleet);

Scope 2: quantity in tonnes of CO₂e/year (electricity in warehouses);

Scope 3: quantity in tonnes of CO₂e/year (subcontractors, supply chain).

Stage 3: Integration of classifications

The methodological correlations are obtained between the two instruments, taking into consideration

• The use of standardized emission factors;
• Data uncertainty (±5%);
• Information traceability (fuel invoices, GPS reports, electricity consumption).

Stage 4: Collecting information about consumption

This stage involves centralizing and verifying the consumption of energy, fuel, and resources with an environmental impact.

Stage 5: Calculating the carbon footprint

The carbon footprint of the activities with an environmental impact is calculated.

The general formula used for direct emissions is formula no. 1. For the purchased energy (scope 2), the emissions are calculated as formula no. 2.

The emissions included in scope/domain 3 are the ones produced in the value chain of the reporting logistics center, both upstream and downstream.

Stage 6: Unitary reporting—drawing up an integrated report

The outcome of this integrated report consists of obtaining an annual sustainability report that may have the structure recommended in the ISO 9001:2015 standard [30]. This sustainability report will focus on the objectives, analysis domain, major results, emission sources, calculus basis for the emission factors, and improvement opportunities, as well as conclusions and recommendations.

Stage 7: Illustrative decision support analysis based on emissions-related constraints

In the final stage, data derived from the emissions inventory are introduced into the analytical model in order to support the structured assessment of energy consumption and emissions-related indicators under predefined constraints. The model is applied to a logistic courier hub that

• Manages around 250,000 parcels on a daily basis;
• Operates 24 h/day (3 shifts);
• Uses estimates without being influenced by operational peaks.

This section demonstrates how the integrated GHG Protocol–ISO 14064 framework can be operationalized and transposed into a structured analytical model that supports the examination of emissions-related data in a real-life logistics context.

The warehouse in the present case study has a total area of 2500 sqm and is divided into four operational areas: the buffer zone—725 sqm, the pickup/sorting area—875 sqm, the reception/shipping area—350 sqm, and the office area—300 sqm. The difference of 250 sqm is occupied by main circulation corridors, technical areas, access ways between the various areas, and operational safety—the so-called complementary area.

According to the mentioned standard, we took into consideration the existence of four different areas inside the warehouse of the logistics hub, each with its own lighting needs: the buffer zone—150–200 lux, the pickup/sorting area—300 lux, the reception/shipping area—200–300 lux, and the office area—500 lux.

The efficiency of the industrial LED electric appliances is 150–180 lm/W. For the calculations, an average value of 165 lm/W has been used.

The analysis of the air conditioning, ventilation, and lighting facilities inside the warehouse involved the following. Consumption for ventilation and air conditioning was calculated based on the analysis of the technical specifications of the air conditioning units provided by the manufacturers in the public technical data available online. For air conditioning and ventilation, we considered industrial equipment with average consumption of 2.85 kWh, which can cover an area of 70 sqm. The resulting consumption is summarized in

Table 2. Warehouse air conditioning consumption.

Type of Facility	Average Consumption Considered/Unit [kWh]	Nominal Area/ Unit [m2]	Warehouse Usable Area [m2]	Number of Units	Consumed Power with Air Conditioning [kWh]
Air conditioning + ventilation	2.85	70	2500	36	102.6

Source: Own contribution.

.

The EN 12464-1 standard [31] was used to calculate the consumption for lighting in the warehouse. In this way, the installed power was calculated using the following formula: power (W) = (lux × area)/efficiency.

A 1.3 correction factor has been applied for dark areas and uniformity.

The values, which were calculated and rounded up to the higher value for the four operational areas and the complementary area, are summarized in

Table 3. Consumption for warehouse lighting.

Warehouse Area	Area (m2)	Light Intensity [lux]	Used Power [KWh]	Power Used with Lighting [KWh]
Buffer	725	175	1	5.3
Pickup/sorting	875	300	2.1
Reception/shipping	350	250	0.7
Offices	300	500	1.2
Complementary	250	150	0.3

Source: Own contribution.

.

The lighting system and the air conditioning/ventilation system work 24 h per day.

In order to calculate the energy consumption of the equipment in the courier logistics hub, the consumption for the office and the sorting areas is considered separately.

Starting from the initial work hypothesis regarding the total number of parcels processed per day, we obtain an average of 10,420 parcels processed per hour. This value allows us to estimate the necessary equipment. Thus, for the specific operations of reception, transport, sorting, labeling, and packing, the following types of equipment are necessary:

• Feed conveyors (6 conveyors);
• Accumulation/deceleration conveyors (2 conveyors);
• Conveyors with sorting systems (2 conveyors);
• Outfeed conveyors (12 conveyors).

The picking of the parcels to be sorted is carried out by 6 conveyors. The average transport rate for each feed conveyor is approximately 1750 parcels per hour. According to the specifications provided by the manufacturer, the average consumption for a conveyor to correspond to the above-mentioned work hypotheses, including sensors and additional consumption, is 2 kWh.

The accumulation conveyors, used to distribute parcels to sorting systems on a regular basis, should each transport an average rate of 5210 parcels per hour. The average consumption for a feed conveyor to correspond to the work hypotheses, including sensors and additional consumption, is 3 kWh.

The conveyors with sorting systems distribute the parcels, which are identified by barcode scanners, to the 18 outfeed conveyors. According to the work hypotheses, these conveyors sort around 5210 parcels per hour. The average consumption for such a conveyor, including sensors, barcode scanners, and other additional consumption, is 3.5 kWh.

The outfeed conveyors transport the sorted parcels to the distribution area, where they are distributed by area. Each outfeed conveyor can take over an average of 580 parcels, according to the work hypotheses. The average consumption of an outfeed conveyor, including sensors and additional consumption, is 0.75 kWh.

The pallets are loaded into the lorries by two electric forklifts. The consumption of an electric forklift is 3.7 kWh and it operates on average for 6 h per day.

The energy consumption for each piece of equipment that is part of the sorting system can be seen in

Table 4. Warehouse equipment consumption.

Equipment Used	Number of Pieces of Equipment	Used/Consumed Power/ Equipment [KWh]	Total Consumed Power [KWh]	Total Consumed Power by Equipment [KWh]
Feed conveyor	6	2	12	39
Accumulation conveyor	2	3	6
Sorting conveyor	2	3.5	7
Outfeed conveyor	18	0.75	13.5

Source: Own contribution.

.

The estimated working time for the equipment in the operational area is 20 h/day.

The estimated consumption for the office area, including additional consumption, is approximately 1 kWh.

The estimated working time of the equipment in the office area is 24 h/day.

For the purpose of the analysis, it is essential to develop a process map. These tools allow one to understand the processes taking place within the logistics center and how to quantify them so that the emissions of each activity can then be calculated.

Starting from the definition of the process, the purpose of an organization is to transform, by means of coordinated activities, the input data into output data, while also creating added value. A general model of the process map is presented in Figure 2.

According to the process map generically described above, the operational processes in a courier logistics hub are as defined in Figure 3.

In logistics, these operational processes can be divided into subprocesses. For the present methodology, we restrict our analysis to the previously described processes (Figure 3).

This analysis seeks to illustrate, at an analytical level, how environmental impact indicators can be analytically assessed and compared within the proposed framework.

To analyze the environmental impact, the technical information regarding the various types of consumption is summarized in

Table 5. Warehouse consumption.

Type of Consumption	Used Power/ Equipment [KWh]	Working Time [h]	Total Used Power [KWh]
Facilities	107.9	24	2589.6
Transport/sorting technical systems	39	20	780
IT equipment	1	24	24
Electric forklifts	7.4	6	44.4

Source: Own contribution.

.

According to Table 5, the estimated total energy consumption is 3438 kWh per day.

The parcels are transported using ten 7.5 t lorries and 250 utility vehicles of 3.5 t each. Both the lorries and utility vehicles use diesel.

Table 6. Consumption of used vehicles.

Type of Vehicle	Number of Vehicles	Consumption [L/100 km]	Working Time [h]	Estimated Consumption [L/h]	Total Consumption per Day [L]
Lorries 7.5 tonnes	10	20	18	15	3000
Utility vehicles 3.5 tonnes	250	8	12	5	15,000

Source: Own contribution.

centralizes the consumption by hour, the number of working hours, and the total consumption for these means of transport.

According to Table 6, the total fuel consumption per day is 18,000 L diesel.

The total energy consumption for a month is calculated as the sum of the consumption values for each piece of equipment used. The following formula is applied:

$$C_t={\sum }_{i=1}^8{a}_j$$

(6)

where $a_j$ is the total monthly energy consumption for each piece of equipment used.

In order to calculate the carbon footprint using the previously described methodology, one can use both the emission and conversion factors, with the choice depending on the existing data.

The emission factor is applied when we start from the consumed quantities, while the conversion factor is used when we do not start from direct energy consumption but from another measurement, which must be transformed into emissions beforehand. The conversion factor is a-dimensional, and it is used to transform carbon content into a CO₂-equivalent quantity. The conversion factor is also applied when transforming the final energy into primary energy or emissions.

Next (

Table 7. Calculation of the emission factors for the fleet of vehicles used.

Activity	Fuel Quantity	Emission Factor	Total Emissions
Fuel used by the vehicle fleet (diesel)	18,000 L/day	2.64 kg CO2/L	47.520 kg CO2/day = 47.52 tonnes CO2 per day

Source: Own contribution.

), we calculate the total quantities of emissions generated by the fleet of vehicles used.

It is therefore found that the logistics center produces a total of 47520 kg CO₂e/day for transport activities.

From the viewpoint of the operational activities, the CO₂ emissions are as presented in

Table 8. Calculation of the emission factors for the operational processes.

Activity	Fuel Quantity	Emission Factor	Total Emissions
Energy consumption for support equipment	3.438 kWh	0.265 kg CO2 (emission factor for electricity in Romania)	0.91 kg CO2 = 0.00091 tonnes CO2

Source: Own contribution.

.

Electricity is a vital resource for the company’s operations. However, this resource presents risks related to the emission of greenhouse gasses (GHG). Reducing these emission factors can be achieved by changing the energy and fuel technology, as well as by establishing compromises between the objectives of a low-carbon emissions sector and other environmental objectives.

The subsequent stage consists of the preparation of the sustainability report, following the structure outlined above.

The analytical formulation is designed to support a structured examination of logistics-related activities within the organization. As previously indicated, the Simplex algorithm is employed as a well-established linear programming method for representing relationships among activities, resources, and constraints. Owing to its capacity to handle multiple variables and constraints, the Simplex method has been widely applied in analytical studies of logistics systems, including transport, routing, storage, and resource allocation.

Modern logistics involves an increasing number of operational and strategic decisions: which routes to choose, how the loads should be distributed among warehouses and clients, which transportation modes should be used, and how internal resources should be allocated. The Simplex algorithm can be applied in multiple scenarios.

By applying the Simplex algorithm to the previously described processes, the analytical model illustrates a feasible combination of activity volumes that respects the available time constraints and corresponds to the estimated daily energy consumption of 3438 kWh. Through mathematical modeling, the logistics center can accurately determine the consumption of energy based on the number of processed orders in each stage. The purpose of this analytical formulation is to illustrate how energy consumption can be represented and examined within a linear programming structure, without claiming direct operational optimization.

The consumption values introduced in the Simplex model are derived exclusively from the inventory calculated according to the GHG Protocol and ISO 14064, which ensures methodological coherence between emissions reporting and the analytical representation of operational activities.

For the illustrative analytical formulation, this paper considers the following aspects: the logistics center processes three fluxes of orders daily, which are called x₁, x₂, and x₃ for the present simulation. Each order undergoes three essential processes: pickup, parcel sorting, and parcel shipping. It can be seen that, although the team manages to be on time with all activities, the daily energy consumption is high, reaching 3438 kWh per day, and the company seeks to reduce both its costs and its carbon footprint.

The daily time allocated to each process is 8 h for pickup, 20 h for parcel sorting, and 8 h for parcel shipping. As a working hypothesis, we assume that a flux unit uses 0.5 h for pickup, 1.2 h for sorting, and 1 h for shipping. The x₂ flux needs 1.2 h for pickup, 0.5 h for sorting, and 0.3 h for shipping, while the x₃ flux uses 0.2 h for pickup, 0.1 h for sorting, and 0.3 h for shipping. In order to ensure an uninterrupted logistics operation, minimum required processing levels must be achieved for each activity (pickup, sorting, and shipping). As a result, we seek the combination of volumes x₁, x₂, x₃ that satisfies these minimum operational requirements while examining configurations associated with lower energy consumption.

For the activity to be uninterrupted, each flux should be included in the daily activity plan. From this perspective, we seek to find the combination of volumes x₁, x₂, x₃ that observes the available timeframes and minimizes the total energy consumption.

Each process is associated with a separate unit energy cost—1.9 kWh for process 1, 1.8 kWh for process 2, and 1.6 kWh for process 3—reflecting the differences in the specific energy consumption of each activity. The model is formulated as a minimizing problem with constraints, highlighting the methodology’s aim: reducing the total energy consumption without compromising the operational capacity. Consequently, analytically assessing energy consumption levels is equivalent with minimizing the GHG emissions associated with the daily processes.

The information above is summarized in

Table 9. Summary of information.

	Pickup (h)	Reception- Parcel Sorting (h)BA	Parcel Shipping (h)C	Minimum Value
X1	0.5 h	1.2 h	1 h	1.9
X2	1.2 h	0.5 h	0.3 h	1.8
X3	0.2 h	0.1 h	0.3 h	1.6

Source: Own contribution.

.

The mathematical expression of the previously described problem is the following:

$$\{\begin{array}{c}MINZ=1.9x_1+1.8x_2+1.6x_3\\ 0.5x_1+1.2x_2+1x_3\ge 3\\ 1{.2x}_1+0.5x_2+0.3x_3\ge 2\\ 0.2x_1+0.1x_2+0.3x_3\ge 0.5\\ 1.9x_1+1.8x_2+1.6x_3\le 5.5\\ x_1,x_2,x_3\ge 0\end{array}$$

In addition to time-related operational constraints, the analytical formulation explicitly incorporates a physical logistics capacity constraint in the form of a daily energy availability limit. The value of this constraint is derived directly from the emissions inventory calculated in accordance with the GHG Protocol and ISO 14064 standards, corresponding to estimated total daily energy consumption of 3438 kWh for the logistics hub. This constraint reflects the infrastructural and operational limits of the facility and ensures that the analytical model accounts for a fundamental logistics boundary without introducing detailed vehicle routing or fleet-level optimization.

To reinforce the physical interpretation of the analytical formulation and to explicitly account for a discrete operational decision, the energy availability constraint can be reformulated by introducing an integer decision variable y, representing the number of active operational configurations (e.g., shifts or equipment sets) available per day. The corresponding constraint is expressed as

1.9x₁ + 1.8x₂ + 1.6x₃ ≤ 5.5 · y, y ∈ ℤ⁺

For the illustrative case study presented in this paper, the value y = 1 is considered, corresponding to the current operational configuration of the logistics hub. This formulation preserves the simplicity of the numerical illustration while strengthening the methodological and physical grounding of the analytical model by explicitly linking the emissions-derived energy capacity to a discrete operational decision variable, without introducing detailed routing or fleet-level optimization.

The analytical solution obtained is x₁ = 0.82, x₂ = 1.70, and x₃ = 0.55, corresponding to the feasible allocation of activity volumes under the imposed constraints and the estimated daily energy consumption. The value of the objective function (Z) is 5.5, and it corresponds to the total daily energy consumption of 3438 kWh. This solution is presented as a feasible analytical configuration within the defined operational and energy capacity constraints; it does not aim to represent a fully optimized logistics plan.

The results obtained are also a balance point in the distribution of activities. In this optimal solution, all three constraints are active, which means that the minimum requirements are accurately fulfilled, without wasting any resources. From a logistics perspective, it is important that none of the processes have a value of zero: pickup, sorting, and shipping are all present in the daily plan, reflecting the operational reality of a courier hub.

In the developed methodology, this result is relevant because it allows us to clearly delineate the emissions sources. Through the structured allocation of volumes and time corresponding to each process, the framework allows for the identification of the relative contributions of each activity to the total carbon footprint, thereby enhancing the reporting transparency and supporting external verification.

4. Discussion

Prior to examining the numerical implications of the analytical formulation and the associated sensitivity analysis, the methodological positioning of the proposed approach within the broader literature on logistics decision support and analytical modeling is clarified.

While advanced optimization models such as vehicle routing problems or mixed-integer formulations provide high operational fidelity, they are typically developed independently of standardized carbon accounting and verification frameworks. The approach proposed in this paper deliberately adopts a higher level of abstraction in order to preserve traceability, auditability, and compatibility with the GHG Protocol and ISO 14064. By focusing on the analytical translation of standardized emissions data into decision support structures, the framework complements rather than competes with detailed logistics optimization models.

Considering that the analytical formulation is based on estimated input data, a sensitivity analysis was conducted to examine the stability of the solution structure under variations in selected key parameters. The analysis did not aim to recalibrate the model or to provide predictive accuracy but to illustrate how the proposed framework can be used to explore alternative analytical scenarios within a decision support context. A ±20% variation interval was adopted, consistent with common practice in exploratory logistics studies when initial data are estimated. This interval is wide enough to contain realistic fluctuations in costs and volumes. The sensitivity analysis refers to two scenarios.

• Scenario 1—Variation in energy unit prices

In the baseline formulation, the objective function is expressed as $MINZ=1.9x_1+1.8x_2+1.6x_3$, where the coefficients represent the unit energy consumption associated with the three core processes (pickup, sorting, shipping).

To simulate the increase in energy costs for a certain process, one of the coefficients—for example, coefficient 1.8—can be increased by 10–20%.

A proportional increase in all coefficients (e.g., +20% applied uniformly) does not alter the structure of the solution; it only rescales the objective function value. In this case, the total analytical energy indicator increases from approximately 5.5 to 6.6 units, while the relative values of $x_1,$ $x_2,x_3$ remain unchanged. This result is consistent with linear programming theory, where the uniform scaling of coefficients affects the objective value but not the solution structure.

On the other hand, a selective increase in the unit energy parameter associated with a single flow leads to a relative adjustment in the analytical weight of that flow within the solution structure, in favor of flows characterized by lower unit parameters, subject to the imposed time constraints. This behavior illustrates how the framework can be used to explore analytical trade-offs between alternative activity allocations and their associated energy-related indicators.

• Scenario 2—Variation in activity volume

In the baseline analytical formulation, time-related constraints for the three processes (pickup, sorting, and shipping) are defined to ensure a minimum daily level of activity (8 h for pickup, 20 h for sorting, and 8 h for shipping).

A relevant analytical scenario involves an increase in order volume—for example, by 20%. At the model level, this is represented by a proportional increase in the minimum constraint thresholds. Applying the formulation under these conditions results in higher values of $x_1,$ $x_2,x_3$ and, consequently, in an increased aggregate energy indicator. Importantly, all three flows remain active in the resulting analytical solution, indicating that the balanced representation of activities is preserved under moderate demand variations.

At the same time, the analytical formulation highlights that, for significant increases in activity volume, maintaining the same time constraints (8–20–8 h) becomes increasingly challenging without additional capacity. In this respect, the sensitivity analysis illustrates how the proposed framework can be used not only to examine energy-related indicators under given assumptions but also to support the analytical assessment of capacity requirements in scenarios of increased demand.

The two analyzed scenarios indicate the following:

• The analytical solution structure remains stable under moderate variations in the objective function coefficients;
• Increases in activity volume lead to proportional increases in the aggregate energy indicator, while preserving the inclusion of all activity flows;
• The integrated analytical framework can be used to explore alternative scenarios by linking emissions inventories with structured representations of operational constraints.

This analysis indicates that the proposed methodology is not critically dependent on a single input configuration but can be consistently applied to assess and compare multiple analytical scenarios within the defined modeling assumptions.

The results of the analytical formulation extend beyond purely numerical allocation. They provide a structured means of interpreting how different logistics activities contribute to overall energy consumption within the defined modeling framework. In line with observations reported in the specialized literature [32], the resulting representation illustrates how activity representations and analytical constraints can be examined in relation to energy-related indicators, without implying direct operational optimization.

Beyond the quantitative outcomes, the proposed analytical framework facilitates a transparent examination of the relative contributions of individual logistics activities to overall energy consumption and emissions. This interpretative capability is particularly relevant in decision support contexts, where the objective is not to prescribe operational solutions but to support structured comparison and scenario analysis in line with standardized carbon accounting practices.

As previously noted, within the presented analytical framework, the Z value of 5.5 corresponds to estimated daily energy consumption of 3438 kWh. A linear transformation indicates that one analytical unit corresponds to approximately 625.1 kWh. Accordingly, the objective function value can be consistently mapped to physical energy consumption and associated emissions, resulting in an estimated daily carbon footprint of 0.91 t CO₂e/day when applying the Romanian electricity emission factor of 0.265 kg CO₂/kWh. Similar energy-to-emissions mappings are recommended in recent studies [33,34,35] as part of structured decision support analyses.

These figures are consistent with the methodological logic developed on the basis of the GHG Protocol and ISO 14064. The principal advantage of the proposed analytical framework lies in its capacity to support the rapid examination of alternative scenarios within a transparent and reproducible structure, as suggested in studies focusing on linear programming applications in logistics [32]. In this sense, the presented formulation serves as an analytical instrument that links emissions reporting with structured representations of energy consumption and associated indicators.

When combined with emissions inventories developed in accordance with the GHG Protocol and the reporting and verification requirements of ISO 14064, the proposed framework functions as a methodological bridge between standardized carbon accounting and structured decision support. It enables the transparent and verifiable assessment of the ways in which pickup, sorting, and shipping activities contribute to overall energy consumption and emissions, without prescribing specific operational configurations.

5. Conclusions

Developing the methodological framework described above and integrating the two reference instruments is not only recommendable but increasingly necessary. The GHG Protocol provides a widely adopted basis for structuring emissions reporting, while ISO 14064 adds methodological rigor, standardization, and the possibility of external verification. In the logistics sector, where emissions are often dispersed, difficult to allocate, and highly dependent on routes, fleets, and subcontractors, this dual methodological anchoring is essential in ensuring consistency, comparability, and auditability.

However, accurate and verified reporting alone is not sufficient to support informed sustainability-oriented decision making. Logistics companies increasingly face the challenge of interpreting emissions data in a structured manner that allows for the systematic assessment and comparison of alternative operational configurations. In this context, mathematical programming methods can play an important analytical role by enabling the formal representation of relationships among activities, resources, and emissions within a transparent decision support framework. The Simplex algorithm, originally developed for linear programming problems, is widely used in analytical studies of logistics systems, where key variables such as distances, volumes, costs, and demand can be expressed in linear form.

In a logistics context, such analytical formulations can be used to examine alternative representations of activity allocation, resource use, and process structure under predefined assumptions and constraints. Rather than prescribing specific operational actions, the framework supports the analytical assessment of how different configurations influence energy-related indicators and emissions profiles. The resulting outputs can be consistently linked to sustainability reports prepared in accordance with the GHG Protocol and verified under ISO 14064, thereby strengthening the coherence between analytical assessment, reporting, and transparency.

The relevance of this approach becomes even clearer when considered in the context of evolving European and global regulatory frameworks, which increasingly require organizations not only to disclose emissions quantities but also to demonstrate methodological consistency, traceability, and transparency in how emissions-related information is used to support sustainability strategies.

At the same time, this paper acknowledges that the proposed framework has a demonstrative nature and is illustrated using data related to the activity of a single logistics hub. Consequently, validation using real-world datasets, the inclusion of additional operational constraints, and application in different logistics contexts represent important directions for future research.

Moreover, the paper does not propose a final or universally optimal solution for operating logistics hubs but rather a coherent, reproducible, and auditable framework that facilitates the transition from standardized emissions reporting to the structured assessment and comparison of emissions-related scenarios. This methodological contribution supports transparency and consistency while leaving room for further analytical development and contextual adaptation.