Synergy of Energy-Efficient and Low-Carbon Management of the Logistics Chains Within Developing Distributed Generation of Electric Power: The EU Evidence for Ukraine

Abstract

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system.

1. Introduction

Amid growing vulnerability to the threats and impacts of climate change, the need to achieve synergy between balanced resource use and the reduction of carbon dioxide emissions is becoming increasingly important. The European Union (EU) projects that transport-related greenhouse gas (GHG) emissions will fall below their 1990 levels by 2032 (Figure 1). The expansion of international trade—and, consequently, of freight transport—has led to a rising carbon footprint across EU member states.

According to Figure 2, “carbon emissions from international freight in the EU increased from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario” [1]. Furthermore, “emissions in the transport sector under the WEM scenario are expected to decrease by approximately 80.2% by 2050 compared with 2020” [2]. The persistent growth of carbon emissions from international freight underpins the continued development and strengthening of the EU Carbon Border Adjustment Mechanism (CBAM).

In the European Union (EU), the efficiency of carbon markets is determined by their classification into mandatory and voluntary systems, the operation of the Carbon Border Adjustment Mechanism (CBAM), and the categorisation of emissions according to their source of origin: Scope 1—direct emissions from a company’s operations; Scope 2—indirect emissions from purchased energy; and Scope 3—all other indirect emissions generated throughout a product’s entire life cycle. Taking this into account, the use of environmentally friendly vehicles for freight transport plays a crucial role in reducing the carbon footprint across the product life cycle.

In the field of road freight transport, achieving synergy between balanced resource use and the reduction of carbon dioxide emissions requires optimising logistics chains, modernising transport infrastructure, and implementing management decisions that promote the adoption of energy-efficient and climate-neutral technologies through the use of artificial intelligence (AI) and clean fuels [3]. Accordingly, the reform of urban transport logistics—based on safe, sustainable, smart, and clean mobility—necessitates consideration of trends in the development of climate-leading transport enterprises, the reorientation of public values towards conscious consumption, and, in particular, the wider adoption of environmentally friendly and energy-efficient vehicles, as well as the development of low-carbon transport networks [4].

Expanded access to charging infrastructure is of particular importance for transport decarbonisation. In this regard, distributed generation (DG) plays a key role in ensuring the operation of electric transport and enabling the use of electric vehicles (EVs) as auxiliary energy sources within microgrids through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies. This highlights the need to identify and scientifically substantiate a replicable paradigm for diversifying low-carbon fuels through renewable-based distributed electricity generation, particularly for resource-constrained and conflict-affected areas.

The aim of this study is to explore the synergy between energy-efficient and low-carbon management of logistics chains for road freight transportation in Ukraine under martial law, drawing on EU experience in distributed generation (DG) from renewable energy sources (RESs). In the context of wartime conditions, ensuring a stable energy supply is a critical priority for Ukraine’s economic resilience. Therefore, the EU’s experience in RES-based distributed generation, coupled with the use of EVs as auxiliary renewable energy sources for distributed electricity generation within microgrids (G2V and V2G technologies), provides valuable insights.

The scientific novelty of this research lies in identifying the conditions under which RES-based DG contributes to achieving energy-efficient and low-carbon outcomes in the formation of freight logistics chains, grounded in the principles of circular and inclusive economic development. The practical contribution lies in proposing a replicable model for diversifying low-carbon fuels through the development of renewable-based distributed electricity generation, offering a scalable framework for resource-limited and conflict-affected regions.

2. Literature Review

By utilising advanced technologies such as sensors, artificial intelligence (AI), and enhanced analytics, road authorities and logistics operators can more effectively anticipate and mitigate contemporary challenges [5,6]. This also applies to climate change, enabling the prediction of climate risks and the modelling of environmentally friendly project integration into business systems [7]. The application of machine learning (ML) and AI in supply chain planning supports the optimisation of charging networks based on distributed generation (DG). A distinctive feature of distributed networks is their capacity to generate electricity from local renewable energy sources (RESs), including biomass, hydrogen, solar, and wind power.

Moreover, the transport sector is currently undergoing three major revolutions: shared mobility, autonomous driving, and electrification. When designing charging infrastructure for electric vehicles (EVs), it is essential to consider the interactions and synergies among these three emerging systems [8]. In this context, improving urban spatiotemporal traffic flow forecasting is crucial—particularly through the development of models for daily activity patterns, anomaly detection, and assessments of the influence of weather conditions, weekends, and holidays. This process requires real-time traffic flow information derived from diverse sources such as mobile phone data, taxi trajectory data, metro and bus transit records, and bike-sharing systems [8].

To enhance the efficiency of road freight transport within the framework of sustainable development and a circular and inclusive economy, it is essential to monitor the environmental safety of traffic flows, develop reverse logistics systems, and assess their impact on the ecological, economic, and social performance indicators of transport enterprises [9]. Additionally, the application of Geographic Information System (GIS) technologies to optimise reverse logistics routes is critical for achieving these objectives [10]. Equally important are the development of multimodal and intermodal transport networks, the construction of modern multimodal logistics centres, the expansion of container connectivity [11], and the optimisation of freight routes through improved methods for determining the number and load capacity of rolling stock, thereby reducing vehicle idle time during cargo operations.

Given these trends, the reform of transport enterprises should be comprehensive, encompassing the advancement of environmentally responsible practices, the promotion of green energy, smart technologies, and sustainable urban planning. The overall development of transport enterprises is interconnected with broader systemic factors, including the progress of information technologies, the macroeconomic environment, demographic trends, socio-cultural conditions, and the legislative and policy framework. Consequently, “there is a need to apply a holistic ecological approach to reforming transport policy by involving all participants in transport logistics: government authorities, local administrations, organisations and enterprises, research institutions, educational establishments, the media, and the public as consumers of transport services and city residents” [12].

At the same time, one of the current challenges in logistics and startup development lies in the reorientation of venture capital towards the “last-mile” delivery sector—companies specialising in the final stage of the distribution process (Figure 3). “Last-mile” startups increased their share of total logistics funding by five percentage points between 2022 and 2023. Among those receiving the highest funding were Zipline, a U.S.-based on-demand drone delivery service, which secured USD 330 million; XpressBees, an Indian express logistics provider that raised USD 120 million; and Delhivery, another Indian company, which attracted USD 114 million [13,14].

With this in mind, it is becoming increasingly important to integrate charging stations based on distributed generation (DG) into the logistics chain management ecosystem, guided by the principles of energy efficiency, climate neutrality, inclusiveness, and circular resource use. Furthermore, there is growing recognition that achieving sustainable and environmentally responsible transport goals requires more than technological progress alone. It also demands the production and use of clean fuels and vehicles [15], the adoption of electric vehicles (EVs), and the diversification of energy sources for low-carbon fuel production [16,17].

Table 1. Comparative analysis of research in the field of distributed energy generation (DEG).

Subject of Research	Study Region	Scientific Value of Results	Future Scientific Perspectives
Impact of Integrating Variable Renewable Energy Sources (solar and wind) into Grid-Connected Power Systems [18]	EU, UK, USA, Asia, Australia	A seven-point conceptual framework involving all energy stakeholders for managing variability in power system networks and increasing variable renewable energy (VRE) integration into grids has been proposed. Key findings highlight the importance of long-duration energy storage, virtual power plants (VPPs), smart grid infrastructure, cross-border interconnections, Power-to-X systems, and grid flexibility in achieving reliable, resilient, and stable energy systems.	Exploring the application of artificial intelligence (AI) to optimise digital twins for autonomous energy management, forecasting VRE intermittency, and predictive maintenance in VRE-integrated grid systems.
Assessment of Power Quality in Grid-Connected Distributed Renewable Energy Generation (DREG) Systems [19]	EU, UK, USA, Asia, Australia	The study reviews power quality issues in distributed renewable energy generation (DREG) systems and mitigation techniques for grid-connected configurations.	Developing advanced control methods such as real-time self-tuning and AI-based systems to enhance inverter resilience to grid disturbances.
Decentralised and Distributed Power Generation [20]	Africa	The study identifies both the benefits and risks associated with distributed energy generation from renewable sources, particularly in regions with limited electricity supply.	Determining the optimal size and spatial placement of decentralised renewable generation units in distribution systems to maximise technical and economic efficiency.
Economic Analysis of Distributed Photovoltaic Generation Projects [21]	China	Key economic indicators for analysing the profitability of distributed photovoltaic (PV) generation projects were established, including the internal rate of return (IRR), net present value (NPV), and payback period. Sensitivity analyses were conducted for uncertainties such as construction costs, operational revenues, and expenditures.	Modelling investment decision-making for distributed PV generation projects under uncertainty.
Optimisation of Reactive Power Dispatch Using Metaheuristic Algorithms for Renewable-Based Distributed Generation [22]	Asia	The implementation of optimal reactive power dispatch (ORPD) on IEEE bus systems reduces power losses, minimises voltage deviations, and improves the voltage stability index (VSI) through optimised control variables, including generator voltages, transformer tap settings, and reactive power compensators.	Applying metaheuristic algorithms to manage uncertainty in renewable energy generation and enhance grid stability.
Power Sharing Enhancement through a Decentralised Droop-Based Control Strategy in Islanded Microgrids [23]	UK, Africa	A decentralised intelligent droop-based control strategy was developed to ensure balanced active and reactive power sharing in inverter-based islanded microgrids.	Further optimisation of AI-enhanced droop-based control algorithms using metaheuristics to improve microgrid performance.
Optimal Scheduling of Distributed Generation (DG) and Electric Vehicle (EV) Parking Lots [24]	EU	A day-ahead scheduling model for the simultaneous operation of DG units and EV energy storage systems is proposed, effectively minimising total operational costs under generation constraints and ensuring compliance with demand response (DR) mechanisms.	Analysing façade-integrated photovoltaic systems for green hydrogen storage and next-generation Vehicle-to-Grid (V2G) energy storage technologies.
Optimal Operation of Electric Vehicles and Distributed Generation Resources in Smart Grids Considering Load Management [25]	China	A two-stage planning framework for the coordinated management of distributed energy generated by multiple EVs is presented.	Expanding production and distribution capacities within smart grid infrastructures using AI-assisted predictive load management.

Source: Compiled by the authors based on [18,19,20,21,22,23,24,25].

provides an overview of research on distributed energy generation (DEG). Most studies focus on integrating renewable energy sources (RESs) into distributed generation networks. Alongside solar and wind energy, particular attention has been paid to the contribution of distributed power generated by electric vehicles.

Specifically, ref. [18] investigates the causes, types of variability, challenges, and mitigation strategies associated with distributed generation from solar and wind sources, which are crucial for minimising grid variability. In turn, ref. [19] explores approaches to improving power quality and increasing the penetration of renewable energy (solar and wind) within grid-connected systems. The economic dimension is discussed in [20,21], which presents an analysis of the profitability of distributed photovoltaic generation projects, while ref. [20] highlights the potential risks to distribution systems with high renewable energy penetration.

In this context, refs. [22,23] propose technological solutions to enhance power distribution, including: a decentralised control strategy based on voltage-drop compensation in islanded microgrids; and optimisation of reactive power dispatch using metaheuristic algorithms for integrating renewable-based distributed generation (solar and wind), while accounting for intermittency.

Beyond solar and wind energy, research has increasingly focused on distributed energy provided by electric vehicle operations through Vehicle-to-Grid (V2G) technology. In [24], an optimal day-ahead scheduling approach for the simultaneous operation of distributed generators and electric vehicle energy storage systems is proposed, enabling effective minimisation of total operational costs under production constraints in accordance with demand response (DR) mechanisms. The study also emphasises improving the efficiency of distributed energy management through multi-vehicle EV integration within DG networks.

In the scientific literature, “sustainable transportation” is defined as “transportation that meets current mobility needs without compromising the ability to meet future needs.” The criteria for sustainable transportation include the degree of satisfaction of transport demand and the technical and commercial viability of transport technologies (economic goals); production and regenerative functions (ecological goals); and cultural richness, institutional efficiency, and social equity (social goals) [26]. According to this framework, the sustainability of transport systems can be assessed through the integration of economic, environmental, and social dimensions that collectively determine their long-term viability.

This process aims to optimise the use of eco-friendly transport modes by maximising their deployment, identifying shared priorities in transport logistics development, and strengthening the synergy between energy-efficient and low-carbon management in logistics chains for road freight transport.

Furthermore, the integration of energy-efficient and low-carbon management solutions within the distributed generation (DG) of electric power in the transport sector is driving a growing demand for electric vehicles (EVs) and the adoption of environmentally friendly public transport, thereby transforming municipal transport infrastructure. The advancement of EVs underscores the need for reliable access to clean energy and the establishment of enterprises producing sustainable fuels such as biofuels, solar power, wind energy, and hydrogen. “Sources of electricity supply include households and service-sector entities such as government institutions, retail facilities, hospitals, and schools. For instance, in the Netherlands, the majority of vehicle charging takes place at private charging stations” [27].

One of the main challenges in using energy derived from distributed generation lies in the instability of renewable energy supply. To address this, the optimisation of distributed generation systems through the diversification of energy sources has been proposed as an effective solution. Oludolapo Olanrewaju and Moses Jeremiah Barasa Kabeyi [20] examine the potential for integrating renewable energy sources (RESs) into distributed power generation systems. Zhang Qian and Pan Yuwei [21] present an economic assessment of distributed generation projects based on renewable photovoltaic energy, while Tiku Fidelis Etanya, Pierre Tsafack, and Divine Khan Ngwashi [19] investigate methods for improving power quality and increasing the overall penetration of renewable energy in grid-connected distributed generation systems.

A review of the scientific literature highlights the importance of advancing sustainable and smart transport, ensuring accessibility, and promoting the effective use of low-carbon vehicles powered by biofuels, hydrogen, and solar energy. Consequently, a crucial role in reducing anthropogenic environmental impacts and mitigating climate change should be attributed to the transition towards innovative transport solutions based on the circular utilisation of renewable resources, facilitated by the integration of renewable-based distributed generation into the logistics chain management ecosystem.

At the same time, research on the diversification of renewable sources for distributed energy generation under specific conditions—such as military risks and limited access to energy resources—remains scarce. In particular, under martial law in Ukraine, distributed generation from renewable sources—supported by the use of electric vehicles as ancillary renewable energy resources within microgrids through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—has become both relevant and urgent.

3. Methodology

The methodological foundation of this research builds upon the authors’ previous findings, including the assessment of the economic impact of implementing low-carbon technologies in transportation using artificial intelligence (AI) tools [3]; the evaluation of the economic feasibility of employing low-carbon materials for the construction of transport infrastructure [4]; and the development of a communicative model linking energy companies, refuelling stations, and electric vehicle (EV) users, thereby fostering sustainable development and promoting environmentally responsible decision-making by transport enterprises and municipalities [5].

From a socio-ecological–economic perspective, the theoretical and methodological basis for researching the sustainable development of the transport system lies in harmonising economic interests with environmental interaction throughout the production process [6]. Sustainable development can only be achieved by decoupling economic growth from the exploitation of natural resources and its associated environmental impacts [7].

The strategic direction of transport decarbonisation is reinforced by the growing volume of climate finance directed towards the transport infrastructure sector. Considering the need to ensure sustainable socio-economic development and to strengthen the interrelations between balanced resource use, energy efficiency, and climate neutrality, this study applies a system-dynamics approach to analyse logistics chains in road freight transportation. This approach enables an examination of the interconnections among the elements of the socio-economic system—including objectives, instruments, methods, and processes of sustainable development—within the context of external environmental and geopolitical conditions. The application of the system-dynamics approach provides a means to study regional sustainable development as a complex socio-economic system, interpret its structural characteristics, and substantiate effective management strategies for enhancing regional sustainability [28].

Figure 4 illustrates the research methodology aimed at achieving synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation under martial law in Ukraine, drawing on EU experience in distributed generation (DG) based on renewable energy sources (RESs).

The theoretical and methodological framework of the study is grounded in the paradigm of sustainable development in Ukraine, which has evolved under the influence of the 17 Sustainable Development Goals (SDGs) and the European Green Deal. The core conceptual foundations encompass the co-evolution of humans and nature, the circular economy, the bioeconomy, the inclusive economy, the digital economy, and the low-carbon economy. The application of a climate-neutral approach to resource use necessitates a transition towards renewable energy sources, accompanied by the activation of economic, political, and environmental drivers through the establishment of cross-sectoral linkages between the energy and transport markets. These linkages promote the sustainable utilisation of resources, the development of carbon and clean-energy markets, and the advancement of Industry 5.0.

Russia’s armed aggression against Ukraine has further underscored the need to strengthen the resilience of energy networks and transport systems. In this context, the expansion of distributed electricity generation through the diversification of energy sources is of paramount importance. Under conditions of climate change and military risk, a feasible decarbonisation pathway for Ukraine involves managing distributed energy generated by electric vehicles, integrated into microgrids through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies.

Accordingly, the system-dynamics approach was applied to the analysis of freight transport logistics chains, incorporating a decoupling analysis of energy-efficient and low-carbon management, alongside modelling the use of electric vehicles as auxiliary sources of distributed electricity generation under conditions of military risk. The implementation of such a model seeks to achieve economic, social, and environmental justice as the fundamental dimensions of sustainable transport transformation.

To identify the features of integrating climate-neutral and energy-efficient vectors into the strategy for forming logistics chains for road freight transportation within the development of distributed generation (DG) of electric power, the phenomenon of “decoupling” has been applied [29]. This concept describes the separation of economic growth from environmental impact and the balanced utilisation of energy resources, such as reducing the consumption of fossil fuels for energy production.

The essence of analysing this separation lies in distinguishing economic growth from resource use and economic growth from environmental impact. Decoupling natural resource use from economic growth implies that:

–

The economy grows faster than the rate of natural-resource consumption, although in absolute terms the total volume of resources may still increase;

–

The economy grows while resource use remains stable or decreases.

These two forms are generally referred to as relative and absolute decoupling, respectively. Similarly, decoupling environmental impact from economic growth occurs when the economy expands at a faster rate than environmental impact (relative decoupling) or when environmental impact stabilises or decreases despite ongoing economic growth (absolute decoupling) [28].

Based on the analysis presented in [28,29], it has been determined that, depending on the factors under consideration, decoupling can be differentiated by environmental-impact factors and resource-use factors. The quantitative assessment of these factors can be performed using the following equations:

$$\begin{array}{c}F=1-\left(\sqrt[m]{{\displaystyle \frac{{\mathrm{EP}}_{\mathrm{Em}}}{{\mathrm{EP}}_{\mathrm{Bm}}}}}/{\displaystyle \frac{{\mathrm{DF}}_E}{{\mathrm{DF}}_B}}\right)=1-\left(\sqrt[m]{I_{\mathrm{EPm}}}/I_{\mathrm{DF}}\right)\\ E=1-\left(\sqrt[n]{{\displaystyle \frac{N_{\mathrm{En}}}{N_{\mathrm{Bn}}}}}/{\displaystyle \frac{{\mathrm{DF}}_E}{{\mathrm{DF}}_B}}\right)=1-\left(\sqrt[n]{I_{\mathrm{Nm}}}/I_{\mathrm{DF}}\right)\end{array}$$

(1)

(1)

Calculation of the decoupling factor based on environmental impact (F):

$$F=1-\left(\sqrt[m]{{\displaystyle \frac{{\mathrm{EP}}_{\mathrm{Em}}}{{\mathrm{EP}}_{\mathrm{Bm}}}}}/{\displaystyle \frac{{\mathrm{DF}}_E}{{\mathrm{DF}}_B}}\right)=1-\left(\sqrt[m]{I_{\mathrm{EPm}}}/I_{\mathrm{DF}}\right)$$

(2)

(2)

Calculation of decoupling based on resource consumption (E):

$$E=1-\left(\sqrt[n]{{\displaystyle \frac{N_{\mathrm{En}}}{N_{\mathrm{Bn}}}}}/{\displaystyle \frac{{\mathrm{DF}}_E}{{\mathrm{DF}}_B}}\right)=1-\left(\sqrt[n]{I_{\mathrm{Nm}}}/I_{\mathrm{DF}}\right)$$

(3)

where EP_E and EP_B (Environment Pressure) are the indicators of anthropogenic pressure in year E (End—final year of measurements) and year B (Basic—beginning of measurements); DF_E and DF_B (Driving Force) are the economic growth indicators (which can be reflected through macro indicators such as GDP or GNP, gross value added, national income, etc.) in year E (End—final year of measurements) and year B (Basic—beginning of measurements); I_EP is the growth rate, or the index of relative change in anthropogenic pressure on the environment in the final period compared to the base period (%); I_DF is the growth rate of the economic growth indicator, expressed through the physical volume index of GDP or GNP (%); m is the number of anthropogenic pressure factors—types of pollution; n is the number of types of consumed natural resources; 1, 2, …, m are specific types of anthropogenic pressure on the environment; and 1, 2, …, n are types of natural resources.

To assess the decoupling factors of energy-efficient and low-carbon management in logistics chains for road freight transport, the analysis focuses on the following indicators: Gross Domestic Product (GDP) in the transport, warehousing, and postal and courier sectors; Emissions from mobile sources; Final consumption of biofuels by transport equipment; and mileage of freight vehicles using diesel fuel (

Table 2. Indicators for calculating the decoupling factor for energy-efficient and low-carbon management of road freight transport chains in Ukraine, 2017–2023.

Indicator	2017	2018	2019	2020	2021	2022	2023
Gross domestic product (GDP) in transportation, warehousing, postal, and courier activities, million UAH	420,484	503,326	582,500	594,010	669,354	676,047	811,256
Emissions of pollutants and greenhouse gases from mobile sources, thousand tonnes	1645.7	1612.9	1648.8	1436.7	1546.8	1593.2	1525.6
Final consumption of biofuels by road transport, tonnes of oil equivalent (toe)	45	37	88	51	83	78	84
Distance travelled by freight trucks using diesel fuel, thousand km	4,641,891.5	5,161,244.9	5,241,061.2	5,914,915.3	6,321,342.7	6,510,983.0	6,966,751.8

Source: State Statistics Service of Ukraine [30].

). Statistical data from the State Statistics Service of Ukraine [30] were used in this analysis.

To determine the climate-neutral and energy-efficient decoupling of road freight transport, the authors applied a synergistic approach to analysing decoupling factors influencing the management of freight transport chains. This approach involves calculating the index of the pace of transition to climate neutrality in road freight transport through the use of low-carbon fuels (I_C).

Specifically, to calculate the decoupling factor for energy-efficient and low-carbon management of road freight transport chains (I_F), the following equation is used:

$$I_F=1-\left(I_C/I_E\right)$$

(4)

where I_C denotes the index of the transition rate towards climate neutrality in automotive freight transport through the use of low-carbon fuels, and I_E represents the index of economic growth.

To calculate both the index of transition rate to climate neutrality (I_C) and the index of economic growth (I_E), the following system of equations is applied:

$$I_C=\left(I_P/I_R\right)I_M$$

(5)

$$I_P=P_e/P_b$$

(6)

$$I_R=R_e/R_b$$

(7)

$$I_M=M_e/M_b$$

(8)

$$I_E=E_e/E_b$$

(9)

where I_P is the index of emissions of pollutants and greenhouse gases from mobile sources; I_R is the index of final consumption of biofuels by road transport; I_M is the index of mileage of freight trucks operated on diesel fuel; P_e is the volume of pollutant emissions and greenhouse gases from mobile sources in the E-year (End—final year of measurement); P_b is the volume of pollutant emissions and greenhouse gases from mobile sources in the B-year (Basic—beginning year of measurement); R_e is the volume of final consumption of biofuels by road transport in the E-year (End—final year of measurement); R_b is the volume of final consumption of biofuels by road transport in the B-year (Basic—beginning year of measurement); M_e is the volume of distance travelled by freight vehicles on diesel fuel in the E-year (End—final year of measurement); M_b is the volume of distance travelled by freight vehicles on diesel fuel in the B-year (Basic—beginning year of measurement); E_e is the Gross Domestic Product in the transport, warehousing, postal, and courier sectors in the E-year (End—final year of measurement); and E_b is the Gross Domestic Product in the transport, warehousing, postal, and courier sectors in the B-year (Basic—beginning year of measurement).

The results of the decoupling factor (I_F) for energy-efficient and low-carbon management of logistics chains in road freight transportation are interpreted according to the following criteria:

If I_F > 0 and if this indicator increases over time, a decoupling effect is observed. This indicates that the carbon footprint of road freight transportation decreases due to the transition to low-carbon fuels (increasing index of biofuel consumption), while maintaining positive GDP growth.

If I_F < 0 and if this indicator decreases over time, economic growth leads to a significant increase in the carbon footprint of road freight transportation as a result of continued reliance on conventional fuels (decreasing index of biofuel consumption).

I_F = 0 indicates equilibrium between the rates of economic growth, carbon footprint, and renewable fuel utilisation.

4. Results

Table 3. Results of the decoupling factor calculation for energy-efficient and low-carbon management of logistics chains in road freight transportation in Ukraine, 2018–2023 (%).

Indicator	2018 *	2019	2020	2021	2022	2023
Index of emissions of pollutants and greenhouse gases from mobile sources (IP)	0.98	1.02	0.87	1.08	1.03	0.96
Index of final consumption of biofuels by road transport (IR)	0.82	2.38	0.58	1.63	0.94	1.08
Index of mileage of freight trucks operated on diesel fuel (IM)	1.11	1.02	1.13	1.07	1.03	1.07
Index of the transition rate to climate neutrality in freight transport through the use of low-carbon fuels (IC)	1.33	0.44	1.70	0.70	1.13	0.95
Index of economic growth (IE)	1.20	1.16	1.02	1.13	1.01	1.20
Decoupling factor for energy-efficient and low-carbon management of logistics chains in road freight transport (IF)	−0.11	0.62	−0.67	0.38	−0.12	0.20

* Data are calculated using 2017 as the base year. Source: Calculated by the authors based on data from the State Statistics Service of Ukraine [30].

presents the results of the decoupling factor calculation, used to assess the synergy between energy-efficient and low-carbon management of logistics chains in road freight transportation in Ukraine.

In general, “five primary goals of the decoupling strategy are distinguished: reducing the impact of pollution factors, increasing production efficiency, improving consumption efficiency, shifting expenditure structures towards less resource-intensive options, and enhancing overall quality of life” [31].

The results indicate that the synergy between energy-efficient and low-carbon management in Ukraine’s road freight logistics chains remains unstable. During the analysed period (2018–2023), the maximum decoupling factor reached 0.62 in 2019. However, it turned negative in 2020 (−0.67) and again in 2022 (−0.12), coinciding with the onset and continuation of martial law and wartime disruptions in Ukraine.

This dynamic suggests that the transition towards energy-efficient and low-carbon management of road freight logistics chains in Ukraine is still at an early stage of development. The observed fluctuations are largely attributable to the low share of low-carbon fuel use among freight carriers, reflecting existing structural, financial, and technological barriers.

These findings underscore the necessity of fostering environmentally responsible behaviour, strengthening climate leadership, and promoting a shift towards conscious and sustainable consumption. Achieving these objectives requires systemic measures focused on the modernisation of transport infrastructure through decarbonisation, electrification, and the integration of low-carbon and energy-efficient solutions in the formation of logistics chains for road freight transport.

Under wartime conditions in Ukraine, an energy-efficient and low-carbon solution has been proposed—specifically, the use of electric vehicles (EVs) as an auxiliary source of renewable energy for distributed power generation within microgrids, based on Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies.

To evaluate the characteristics of distributed power generation used for charging EVs within V2G frameworks under conditions of military risk, the Laplace criterion is applied. This criterion relies on the principle of insufficient justification, which assumes that all possible system states S1, S2, …, Sm are equally probable, corresponding in this context to the number of charged electric vehicles per 100 km of road.

The modelling challenge arises from the uncertainty introduced by wartime disruptions—particularly limited accessibility to charged EVs serving as auxiliary renewable-energy sources within microgrids. The main sources of uncertainty include: the unknown distance to the nearest charged EV and the unknown level of its battery charge.

Given these uncertainties, the problem can be formulated as a risky decision-making problem, in which an alternative ai is selected that yields either:

–

The maximum expected gain $R_i^1$, when $V(a_i,S_j),i=\overline{1,n},j=\overline{1,m}$, models the maximum battery level;

–

The minimum expected loss $R_i^2$, when $V(a_i,S_j),i=\overline{1,n},j=\overline{1,m}$, models the minimum battery level, where n denotes the number of EV brands and m represents the discrete levels of battery charge.

Thus, the expected values $R_i^k,k=1, 2$ are determined as follows:

$$R_1=\underset{i}{max}\left\{{\displaystyle \frac{1}{m}}\sum _{j=1}^{m}V(a_i,S_j)\right\},i=\overline{1,n},$$

(10)

$$R_2=\underset{i}{min}\left\{{\displaystyle \frac{1}{m}}\sum _{j=1}^{m}V(a_i,S_j)\right\},i=\overline{1,n},$$

(11)

According to analytical data, “over the past two years, the electric vehicle fleet in Ukraine has tripled: if at the beginning of 2023 it comprised 45.7 thousand vehicles, by early 2025 it reached 135.6 thousand units (out of a total vehicle fleet of about 10 million, i.e., slightly over 1%)” [32].

Furthermore, “the total length of public roads of local importance in Ukraine (excluding those in the Autonomous Republic of Crimea) amounts to 118,155.325 km” [33].

However, the exact number of charged EVs that can simultaneously operate on these roads remains uncertain.

To approximate this uncertainty, five possible charging states are proposed—80%, 85%, 90%, 95%, and 100%—corresponding to 108,480, 115,260, 122,040, 128,820, and 135,600 charged electric vehicles, respectively.

Table 4. Number of electric vehicles on Ukrainian roads in 2024 (system states $S_1,S_2,\dots ,S_m$.

Indicator	S1	S2	S3	S4	S5
Percentage of electric vehicles on the road, %	80	85	90	95	100
Number of electric vehicles on the road, vehicles	108,480	115,260	122,040	128,820	135,600
Average number of electric vehicles per 100 km, vehicles	91	97	103	109	114
Average number of electric vehicles of the same brand per 100 km, vehicles	7	8	8	8	9

Source: Calculated by the authors based on [30].

presents the number of electric vehicles operating on Ukrainian roads in 2024, which represent the system states $S_1, S_2, \dots , S_m$ used in the modelling of distributed power generation under wartime conditions.

For each of these possible values, there exists an optimal electric vehicle (EV) charge level or best alternative a_i in terms of potential energy costs.

Deviations from this optimal level increase the likelihood that a vehicle will be unable to recharge another EV within the distributed generation system.

“The consumption of an electric vehicle is measured in kilowatt-hours (kWh) per 100 km. Under careful and economical driving, energy consumption per 100 km is approximately 15 kWh. However, under real driving conditions, average consumption typically ranges between 20 and 25 kWh. The energy consumption rates for 13 EV brands ($a_1, a_2, \dots , a_{13}$) are as follows, in kWh per 100 km: Dacia Spring (a₁)—15.8; Renault Twingo Electric, Citroën ë-C4 Electric (a₂)—16.4; Hyundai Kona Electric, FIAT 500e (a₃)—18.8; Audi Q4 e-tron 40 (a₄)—21.1; VW ID.4 Pro, Opel Mokka-e Ultimate (a₅)—21.9; FIAT 500e Icon (a₆)—22.0; Mazda MX-30 (a₇)—22.4; Hyundai Ioniq 5 (a₈)—23.8; Opel Combo-e Life, Cupra Born 170 kW (a₉)—24.5; BMW iX xDrive50 (a₁₀)—25.3; Mercedes EQS 580 4M (a₁₁)—26.9; Kia EV6 AWD (a₁₂)—27.2; and Audi RS e-tron GT (a₁₃)—28.8” [34].

To model the characteristics of distributed power generation for charging EVs from other EVs (within Vehicle-to-Grid, V2G, technologies) under military-risk conditions, it is assumed that an equal number of vehicles are operating on the roads for each model considered (

Table 5. Alternatives a_i of distances for an electric vehicle to travel when recharged from another electric vehicle by 13 brands under system states S₁–S₅, km.

	$\mathit{V}({\mathit{a}}_{\mathit{i}},{\mathit{S}}_1)$	$\mathit{V}\left({\mathit{a}}_{\mathit{i}},{\mathit{S}}_2\right)-$ $\mathit{V}({\mathit{a}}_{\mathit{i}},{\mathit{S}}_4)$	$\mathit{V}({\mathit{a}}_{\mathit{i}},{\mathit{S}}_5)$	Average Rate	$\underset{\mathit{i}}{\mathit{m}\mathit{a}\mathit{x}}$	$\underset{\mathit{i}}{\mathit{m}\mathit{i}\mathit{n}}$
a1	2215	2532	2848	2532	R1 = 2532	R2 = 1389
a2	2134	2439	2744	2439
a3	1861	2128	2394	2128
a4	1659	1896	2133	1896
a5	1598	1826	2055	1826
a6	1591	1818	2046	1818
a7	1563	1786	2009	1786
a8	1471	1681	1891	1681
a9	1429	1633	1837	1633
a10	1383	1581	1779	1581
a11	1301	1487	1673	1487
a12	1287	1471	1654	1471
a13	1215	1389	1563	1389

Source: Calculated by the authors based on [30].

). The average capacity of a fully charged EV battery is 50 kWh [35].

Taking into account the data presented in Table 4, Table 5 shows the alternatives a_i, corresponding to the distances that each EV model can travel when recharged by another vehicle, under the five system states S₁–S₅.

The highest expected gain (R₁) and the lowest expected loss (R₂) are calculated using the Laplace criterion, as defined in Equations (10) and (11).

Therefore, according to the Laplace criterion, the best alternative—the one with the highest expected gain (R₁)—is alternative a₁, corresponding to recharging from Dacia Spring vehicles, which demonstrate the lowest energy consumption per 100 km.

This finding confirms the synergistic relationship between energy-efficient and climate-neutral logistics chain management and the advancement of distributed electricity generation.

It emphasises that optimising vehicle energy efficiency directly enhances the resilience of low-carbon transport networks and strengthens the sustainability of decentralised power systems, particularly under conditions of limited energy resources and military risk.

5. Discussion

Building upon the empirical findings presented in the previous section, the following discussion contextualises these results within Ukraine’s evolving energy and transport systems. During both the pre-war period and the ongoing martial law, a positive trend has been observed in implementing international sustainable development practices in the transport sector, particularly in the field of transport electrification within Ukraine’s evolving legal framework. This progress aligns with the country’s long-term participation in the European Neighbourhood Policy, which has been in effect since 2004. Under the Association Agreement with the European Union, Ukraine has implemented measures to reduce the energy intensity of its economy, diversify energy sources and supply routes, and expand domestic production in line with the principles of sustainable development.

Ukraine’s Energy Strategy for the Period up to 2035, entitled Security, Energy Efficiency, Competitiveness, envisages ‘intensive investment in the renewable energy sector, the development of distributed generation, including the design and implementation of “smart” energy grids (Smart Grids), and the creation of comprehensive infrastructure for the advancement of electric transport’ [36]. It further specifies that ‘in the transport sector, a progressive shift away from hydrocarbon internal combustion engines is anticipated, with a substantial share of such vehicles being replaced by rolling stock powered by zero-emission electric motors and environmentally friendly hydrogen engines’ [36].

The Energy Strategy of Ukraine until 2050 expands this vision by prioritising the development of bioenergy and hydrogen energy, innovation in power networks, and the broader utilisation of electricity [37]. Similarly, the Concept of Ukraine’s “Green” Energy Transition until 2050 emphasises the transition towards low-carbon transport through the deployment of electric vehicles, hydrogen technologies for passenger and freight transport, and the optimisation of multimodal transport interactions. Nevertheless, a noticeable gap persists between strategic commitments and the practical transformation of Ukraine’s freight transport market. Ukraine’s fleet of freight electric vehicles remains relatively small, currently including models such as the Maxus EV30L (load capacity: 1050 kg; range: 250 km), the Howo EV (weight: 7.5 tonnes; load capacity: 5 tonnes; range: 200 km; 100 kWh battery), and vehicles manufactured by Foton, Renault, Nissan, Peugeot, and Mercedes-Benz. Domestic producers include MeGoElectric UA (load capacity: up to 1 tonne; range: 100 km) and CoolOn (40 kWh battery; range: 200–300 km) [38].

In the context of ongoing transport-system reform, assessing the prerequisites for applying ecological and energy-efficient approaches to the innovative development of urban transport enterprises is essential. This process aims to optimise the deployment of eco-oriented vehicles by aligning logistics priorities and identifying shared pathways for sustainable mobility. Analytical studies indicate a growing public demand in Ukraine for environmentally friendly vehicles, accompanied by a corresponding need to establish specialised refuelling and charging infrastructure for eco-transport users. These findings underline the importance of advancing sustainable transport infrastructure—particularly the expansion of EV charging networks, including those serving electric taxis [39].

In this regard, the model of EV charging behaviour proposed in [27] is particularly relevant. This model simulates both centrally controlled and decentralised systems of charging-point management. The results demonstrate that, in the absence of centralised control, users tend to charge at maximum power whenever surplus renewable energy becomes available [27].

In [15], scenarios for the transition to clean transport in Albania were developed based on the utilisation of alternative energy sources to enhance environmental security in the transport sector.

In the present study, decoupling analysis is applied to substantiate the integration of economic growth, carbon neutrality, energy efficiency, and balanced resource utilisation resulting from innovation in logistics chains aimed at decarbonising road freight transport. To quantify this relationship, an index of the transition pace toward carbon neutrality in road freight transport using low-carbon fuels was introduced. The analysis revealed that the decoupling effect occurs when the carbon footprint of road freight transport declines through the substitution of conventional fuels with biofuels, while GDP continues to grow.

The circular use of renewable energy sources—such as biomass, hydrogen, and solar power—for low-carbon fuel production was confirmed. The extent to which low-carbon technologies are applied in fuel production directly influences the eco-design of road-freight logistics chains.

Accordingly, diversification of energy sources through decentralisation and local network development is essential to ensure the sustainability of Ukraine’s wartime economy. This would help stabilise the transport system by employing distributed generation to charge vehicles.

The empirical results indicate that the synergy between energy-efficient and low-carbon management factors in Ukraine’s road-freight logistics chains remains unstable, primarily due to the limited adoption of low-carbon fuels by carriers. Therefore, promoting environmentally conscious behaviour, strengthening climate leadership, and modernising transport infrastructure through decarbonisation are crucial.

Under these conditions—exacerbated by missile attacks on Ukraine’s energy infrastructure—the utilisation of distributed electricity generation from local renewable sources becomes increasingly important.

This study proposes an energy-efficient and low-carbon solution suitable for wartime conditions: employing electric vehicles (EVs) as ancillary sources of renewable energy for distributed power generation within microgrids using Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies. This approach aligns with prior studies [40,41,42,43] that outline planning frameworks for managing distributed energy generated by EVs.

EVs are transforming power systems by functioning as distributed energy resources, offering flexibility and resilience while introducing challenges such as grid congestion and peak-demand stress [40]. When integrating renewable-based distributed generation (RDG) into power networks for EV charging, technical difficulties in distribution networks arise from insufficient coordination between RDG units and EV infrastructure, including misalignment between electricity tariffs and charging-demand profiles [41].

Ancillary services provided by EVs are particularly important [40]. EV batteries can act as additional energy sources within microgrids: storing electricity during periods of surplus (G2V) and supplying it back to the grid during demand peaks (V2G) [42].

An optimal day-ahead scheduling approach for the simultaneous operation of distributed generation units and EV energy-storage systems was proposed in [24], minimising total operational costs under generation constraints consistent with demand-response mechanisms. Similarly, a two-stage planning framework for managing distributed energy generated by different EV types was introduced in [25].

Under martial-law conditions in Ukraine, the use of smart charging (V2G) technologies that enable EV batteries to return electricity to the grid has become particularly relevant [43]. This bidirectional technology employs DC-DC and AC-DC converters linked by a common DC bus, allowing EVs to act as both power sources and mobile loads. Such vehicles support reactive-power compensation, regulation of active power, grid stability, and reliability [43]. However, V2G deployment still faces challenges—transitioning from centralised synchronous generation to inverter-based distributed energy resources, integrating diverse renewables and battery chemistries, and ensuring interoperability across EV models and charging stations [43].

Complementary research [44] emphasises the application of bidirectional chargers for microgrids within Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) frameworks. Synthesising the analytical results, several conceptual and methodological contributions of this study can now be outlined.

The novelty of this research lies in addressing military-risk conditions, wherein electric vehicles (EVs) are conceptualised as auxiliary renewable-energy sources for distributed generation within microgrids (G2V/V2G). The proposed model incorporates uncertainty factors such as the distance to the nearest charged EV and the state of battery charge. Applying the Laplace criterion, the optimal alternative was identified as recharging from Dacia Spring vehicles, which demonstrate the lowest energy consumption (15.8 kWh per 100 km). This result confirms the synergy between energy-efficient and climate-neutral logistics-chain management and the development of distributed electricity generation. Compared with previous studies, the findings illustrate the feasibility of employing EVs as auxiliary renewable-energy sources for distributed generation in microgrids, particularly in regions characterised by unstable energy supply, socio-economic crises, or military risks.

6. Conclusions

Building upon the analytical and modelling results, this study provides both theoretical and practical insights into the development of sustainable logistics systems. The main outcome of the research is the identification of conditions that ensure synergy between energy-efficient and low-carbon management in road freight transport logistics chains. A distinctive contribution of the study is the examination of distributed energy generation by electric vehicles under military conditions in Ukraine, while also drawing upon the European Union’s experience in promoting distributed generation through energy-source diversification.

The scientific novelty lies in defining the conditions required to achieve energy-efficient and low-carbon effects in the formation of logistics chains for freight transportation using renewable-based distributed generation, grounded in the principles of circular and inclusive economic development.

The methodological framework is based on the hypothesis of divergence between sustainable energy use and climate neutrality in transport when forming an ecosystem of road freight transport enterprises. A system-dynamics approach was employed to analyse logistics chains in Ukraine’s road freight sector, enabling the identification of decoupling factors that characterise low-carbon and energy-efficient management performance.

The decoupling analysis examined the separation of economic growth, resource consumption, and environmental impact. An index of the transition pace toward climate neutrality in road freight transport through the use of low-carbon fuels was calculated. The results indicated that the decoupling effect occurs when the carbon footprint of road freight transport decreases as a result of switching to low-carbon fuels (reflected in the increased final consumption of biofuels) while maintaining a positive GDP growth trend.

It was further established that the circular utilisation of renewable energy sources—biomass, hydrogen, solar, and wind—is inherently embedded in low-carbon fuel production processes. The application level of such technologies directly influences the eco-design of logistics chains in road freight transport.

Under military conditions in Ukraine, this study proposes an energy-efficient and low-carbon solution that involves using electric vehicles as ancillary sources of renewable energy for distributed power generation within microgrids, employing Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies. The Laplace criterion was applied to determine the characteristics of distributed generation for EV charging under military-risk conditions. The model presented 13 alternatives corresponding to distances achievable by different EV brands when recharged from another vehicle. Based on the Laplace calculations, the best alternative was identified as a₁—charging from Dacia Spring vehicles—which demonstrate the lowest energy consumption per 100 km. This finding confirms the synergy between energy-efficient and climate-neutral logistics-chain management and the development of distributed electricity generation.

The practical value of this research lies in the application and validation of the proposed model for diversifying low-carbon fuels through the development of distributed power generation based on renewable energy resources. This model provides a replicable paradigm for resource-limited and conflict-affected regions of Ukraine.

The study revealed that the formation of synergy between energy-efficient and low-carbon management factors in road freight logistics chains remains unstable, primarily due to the limited adoption of low-carbon fuels among freight carriers. This underscores the need to foster environmentally responsible behaviour, strengthen climate leadership, and promote conscious consumption through systemic measures aimed at modernising transport infrastructure on a decarbonisation basis.

The findings also highlight the necessity of adopting low-carbon and energy-efficient solutions in road freight logistics, particularly through the utilisation of electric vehicles as auxiliary renewable-energy sources for distributed electricity generation within microgrids, employing Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies. Such approaches are particularly relevant in regions with unstable energy supply or those affected by socio-economic, human-made, or military crises.

Translating these theoretical results into actionable recommendations, this study proposes the following strategic measures for Ukraine for the period 2025–2030:

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To expand research in the field of distributed energy generation;

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To develop innovative transport infrastructure based on the “Transport–Energy–AI Nexus” concept;

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To integrate Ukraine into international networks of low-carbon innovation and technology through the formation of academic and non-academic consortia aimed at securing EU and other grant funding;

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To engage businesses, academia, and local authorities in establishing co-financing mechanisms for the deployment of distributed energy systems;

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To broaden the scope of Ukraine’s Energy Strategy until 2050 by incorporating specific provisions on renewable-source diversification and the use of electric vehicles as ancillary renewable-energy units within microgrids (G2V and V2G technologies).

7. Limitations and Prospects

Within the framework of the Paris Agreement, Ukraine is among the countries implementing adaptation and mitigation measures to address climate change across its national economy. To this end, several strategic and regulatory documents have been developed and approved, including the Strategy for Low-Carbon Development of Ukraine until 2050, the Strategy for Environmental Safety and Adaptation to Climate Change until 2030 (aimed at assessing sectoral vulnerability to climate impacts by 2030), the Presidential Decree on the Sustainable Development Goals of Ukraine until 2030, and the Concept for the Implementation of State Climate Policy until 2030. Collectively, these instruments aim to achieve the overarching global goal of transitioning toward climate neutrality.

Among the priority measures, particular emphasis is placed on the decarbonisation of Ukraine’s transport sector through the transition to electric vehicles. This transformation necessitates the modernisation of the national transport infrastructure, including the optimisation of road-freight logistics chains based on responsible energy consumption and the principles of sustainable and smart urban development.

Achieving energy-efficient and low-carbon management of freight-transport logistics chains requires several complementary approaches: the use of clean fuels, the deployment of artificial intelligence-based systems for monitoring carbon-dioxide emissions, and the establishment of eco-oriented communication networks linking clean-fuel producers, transport companies, and end-users.

At the same time, given the ongoing military risks confronting Ukraine, maintaining the stability of energy networks through source diversification has become a matter of strategic importance. This study substantiates the potential of distributed energy generation by electric vehicles under wartime conditions in Ukraine.

Prospects for further research include the development of innovative logistics chains for road freight transport that integrate distributed power generation based on renewable energy resources. The utilisation of digital twins and machine learning algorithms for modelling and forecasting the impact of unpredictable risks—including military disruptions—on transport-system organisation and performance is especially relevant. Future studies could also incorporate GPS–IoT big data for real-time monitoring of vehicle emissions and energy feedback flows, as well as conduct comparative analyses with conflict-affected countries such as Moldova, Georgia, and Serbia to validate external applicability.

Additionally, further research should address current methodological limitations, particularly the reliance on official macro-level statistics without micro-level tracking of enterprise-level fuel-substitution decisions. Since the aforementioned neighbouring countries also depend heavily on diesel-powered freight fleets and have vulnerable electricity grids, such comparative analysis would enhance the international replicability and transferability of the proposed model.

While the present study focuses primarily on emissions from the transport segment, future research should develop a comprehensive life-cycle assessment (LCA) model. This model should encompass the environmental impacts of battery production and end-of-life treatment, along with an evaluation of the carbon intensity and renewable share of the electricity used for charging electric vehicles.