A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS
This study introduces a holistic approach to enhance the efficiency, speed, and scope of energy efficiency and sustainability investment initiatives aimed at reducing greenhouse gas emissions. By facilitating closer interaction between investment project coordinating centers (integrators) and initia...
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| author | Yevtukhova, T. Novoseltsev , O. Kuznietsov , M. Karpenko , D. |
| author_facet | Yevtukhova, T. Novoseltsev , O. Kuznietsov , M. Karpenko , D. |
| author_institution_txt_mv | [
{
"author": " T. Yevtukhova",
"institution": "General energy institute, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "O. Novoseltsev ",
"institution": "General energy institute, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "M. Kuznietsov ",
"institution": "Institute of renewable energy, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "D. Karpenko ",
"institution": "General energy institute, NAS of Ukraine, Kyiv, Ukraine"
}
] |
| author_sort | Yevtukhova, T. |
| baseUrl_str | https://ve.org.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T06:32:23Z |
| description | This study introduces a holistic approach to enhance the efficiency, speed, and scope of energy efficiency and sustainability investment initiatives aimed at reducing greenhouse gas emissions. By facilitating closer interaction between investment project coordinating centers (integrators) and initiators, such as companies and organizations, at all stages of renewable energy service projects development and implementation, the approach allows to optimize their cooperation. Energy service companies (ESCOs) typically execute these functions. A methodological platform for implementing the approach using a formalized simulation model and an algorithm to optimize coordination among centers, participants, implementers, and investors was developed. The model, grounded in Bellman’s principle of optimality, formalizes project selection through iterative procedures constrained by technical and economic conditions of renewable energy. The methodology enables testing strategies that enhance resource redistribution to maximize renewable-based project outcomes. The study results demonstrate significant impact; for example, improving the efficiency of the pilot project from 0.7 to 0.8 increases energy savings by a factor of 1.7, opening up new opportunities to enhance the sustainable benefits of renewable energy. |
| doi_str_mv | 10.36296/1819-8058.2025.4(83).112-125 |
| first_indexed | 2026-02-08T07:59:27Z |
| format | Article |
| fulltext |
112
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
УДК 621.311 https://doi.org/10.36296/1819-8058.2025.4(83).112-125
A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF
TERRITORIAL-ADMINISTRATIVE UNITS
Received Sept. 15, 2025; accepted Dec. 09, 2025
Available online Dec. 31, 2025
Yevtukhova T.1, Novoseltsev O.2,
Kuznietsov M.3, Karpenko D.4
Author for correspondence: Mykola Kuznietsov,
e-mail: renewable@ukr.net
Abstract. This study introduces a holistic approach to enhance
the efficiency, speed, and scope of energy efficiency and sus-
tainability investment initiatives aimed at reducing greenhouse
gas emissions. By facilitating closer interaction between invest-
ment project coordinating centers (integrators) and initiators,
such as companies and organizations, at all stages of renewa-
ble energy service projects development and implementation,
the approach allows to optimize their cooperation. Energy ser-
vice companies (ESCOs) typically execute these functions. A methodological platform for implementing the ap-
proach using a formalized simulation model and an algorithm to optimize coordination among centers, partici-
pants, implementers, and investors was developed. The model, grounded in Bellman’s principle of optimality,
formalizes project selection through iterative procedures constrained by technical and economic conditions of re-
newable energy. The methodology enables testing strategies that enhance resource redistribution to maximize
renewable-based project outcomes. The study results demonstrate significant impact; for example, improving the
efficiency of the pilot project from 0.7 to 0.8 increases energy savings by a factor of 1.7, opening up new opportu-
nities to enhance the sustainable benefits of renewable energy.
Key words: renewable energy; energy efficiency; energy sustainability; energy services market; business model;
case study.
ДВОСТОРОННЯ БІЗНЕС-МОДЕЛЬ ДЛЯ ПІДВИЩЕННЯ СТАЛОГО ЕКО-РОЗВИТКУ
ТЕРИТОРІАЛЬНО-АДМІНІСТРАТИВНИХ ОДИНИЦЬ
Отримано 15 вер. 2025 р.; рекомендовано до публікації 09 груд. 2025 р.
Доступно онлайн 31 груд. 2025 р.
Євтухова Т.¹, Новосельцев О.²,
Кузнєцов М.³, Карпенко Д.⁴
Автор для кореспонденції: Кузнєцов Микола,
e-mail: renewable@ukr.net
Анотація. Це дослідження пропонує цілісний підхід до під-
вищення ефективності, швидкості та масштабів інвес-
тиційних ініціатив у сфері енергоефективності та ста-
лого розвитку, спрямованих на скорочення викидів
парникових газів. Сприяючи тіснішій взаємодії між коор-
динаційними центрами інвестиційних проектів (інтегра-
торами) та ініціаторами, такими як компанії та органі-
зації, на всіх етапах розробки та впровадження проектів відновлюваної енергетики, цей підхід дозволяє
оптимізувати їхню співпрацю. Зазвичай ці функції виконують енергосервісні компанії (ЕСКО). Розроблено
методологічну платформу для реалізації підходу з використанням формалізованої імітаційної моделі
1 Cand. of Science (Tech.), Associate Professor,
https://orcid.org/0009-0000-9687-4631
2 Dr. of Science (Tech.), corresponding member
of NAS of Ukraine
https://orcid.org/0000-0001-9272-6789
3 Dr. of Science (Tech.), corresponding member
of NAS of Ukraine
https://orcid.org/0000-0002-0497-7439
4 Cand. of Science (Tech.),
https://orcid.org/0000-0002-8022-9782
1, 2, 4 General energy institute, NAS of Ukraine,
Kyiv, Ukraine
3 Institute of renewable energy, NAS of Ukraine,
Kyiv, Ukraine
1 канд. техн. наук, доцент
https://orcid.org/0009-0000-9687-4631
2 д-р. техн. наук, член-кор. НАН України
https://orcid.org/0000-0001-9272-6789
3 д-р. техн. наук, член-кор. НАН України
https://orcid.org/0000-0002-0497-7439
⁴ канд. техн. наук
https://orcid.org/0000-0002-8022-9782
1, 2, 4 Інститут загальної енергетики
НАН України, Київ, Україна
3 Інститут відновлюваної енергетики НАН Ук-
раїни, Київ, Україна
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
та алгоритму для оптимізації координації між центрами, учасниками, виконавцями та інвесторами.
Модель, заснована на принципі оптимальності Беллмана, формалізує вибір проектів за допомогою іте-
ративних процедур, обмежених технічними та економічними умовами відновлюваної енергетики. Ме-
тодологія дозволяє тестувати стратегії, що покращують перерозподіл ресурсів для максимізації ефе-
ктивності та продуктивності проектів відновлюваної енергетики. Результати дослідження
демонструють значний вплив; наприклад, підвищення ефективності пілотного проєкту з 0,7 до 0,8 збі-
льшує економію енергії у 1,7 рази, відкриваючи нові можливості для посилення сталих переваг віднов-
люваної енергетики.
Ключові слова: відновлювана енергетика; енергоефективність; енергетична стійкість; ринок енерге-
тичних послуг; бізнес-модель; кейс-стаді.
Symbols and abbreviations:
3-E – energy-economy-ecology
B2B – business-to-business interactions
BS – sets of products within the business subsystems
EAA – equivalent annual annuity
EE – energy efficiency
ES – energy sustainability
ESCO – energy services company
EU – European Union
IRR – internal rate of return
LED – light-emitting diode
MEPS – Minimum Energy Performance Standards
NPV – net present value
PB – payback period
PIA – investment attractiveness index
PS – sets of products within the eco subsystems
RE – renewable energy
SESs – sustainable energy services
SMEs – small and medium sized enterprises
TAU – territorial administrative unit
VES – volume of planned annual energy and resource savings
1. Introduction
In recent decades, energy efficiency (EE), energy sustaina-
bility (ES), and especially renewable energy (RE) have
gained increasing importance for the transition towards a
low-carbon economy [1, 2].
The diversity of participants in these activities is notable,
encompassing ESCOs, national and local policymakers, re-
newable energy producers and suppliers, energy managers,
engineers, architects, equipment manufacturers, and in-
vestors. Together they drive projects that combine EE
measures with large-scale deployment of solar, wind, bio-
mass, and other renewables. These projects provide clients
with comprehensive energy service packages – energy au-
dits, equipment procurement, construction and installa-
tion, commissioning, monitoring, and verification of im-
provements – while ensuring that renewable sources form
the foundation of long-term sustainability. Such approach
is fairly universal and can be applied to states, provinces,
territories and counties, each of which has its own govern-
ance structure and responsibilities. At the same time, alt-
hough the need for optimal combination and use of limited
energy, economic and financial resources in territorial ad-
ministrative units (TAU) is urgent and the benefits are obvi-
ous, their inefficient use and the resulting environmental
pollution continue to significantly hinder progress in imple-
menting the low-carbon transition.
The paper [3] outlines the policy instruments currently
available to enhance energy efficiency and overcome mar-
ket barriers that limit the adoption of high-performance
technologies. It reviews the progress achieved over the
past 40 years of EU product policy, covering key initiatives
such as Energy Labelling, Minimum Energy Performance
Standards (MEPS), the Eco-design Directive, and voluntary
schemes like the EU Ecolabel and Green Public Procure-
ment. Additionally, the paper identifies ongoing challenges
and offers policy recommendations to further unlock the
EU’s potential for energy savings through products. The ar-
ticle [4] analyses how and why EU legislation promotes en-
ergy efficiency in companies by using mandatory energy au-
dit provisions based on the negative experiences of
member states’ governments. The article [5] discusses in-
effective knowledge and information communication as an
important barrier to improving energy efficiency in small
and medium sized enterprises (SMEs) and considers how to
make functional communication an enabler of future SME
energy-efficiency programs. Sourcing energy services from
energy providers in the context of business-to-business
(B2B) interactions facing increasing pressure to use renew-
able energy and achieve energy efficiency is seen as a chal-
lenging task in [6]. Theoretical provisions on the role of re-
newable energy in economic development and potential
sources of energy efficiency improvements for further
growth while reducing greenhouse gas emissions are dis-
cussed in [7]. It has been shown that improving regulation
is more important for this than radically new technologies.
It can be argued that the use of the potential of EE, ES and
RE in TAU is a complex task, important for their sustainable
development. To implement this, we apply an integrated
approach based on the principles of a two-sided market,
also known as a two-sided network in which two distinct
groups of users are represented that mutually generate
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
network benefits. The entity that generates value by ena-
bling direct interaction between two or more different
types of affiliated customers (clients), referred to as project
initiators, we designate as a coordination center or project
integrator. The integrator’s responsibility is to oversee
most stages of the value creation process by managing all
resources and capabilities related to value generation,
thereby enhancing efficiency, realizing economies of scale,
and minimizing reliance on supplier influence to ensure
lower costs and greater consistency in value creation.
The purpose of the work is to develop a business-oriented
model for improving energy efficiency, sustainable devel-
opment and the use of renewable energy in national and
local TAUs through targeted management support for initi-
ators and participants at all stages of investment projects
implementation.
The hypothesis is to improve the results of implementing
the targeted energy services projects through the mutually
beneficial integration of participants’ resources and rele-
vant national and local programs, which will ensure in-
creased energy efficiency and sustainable eco-develop-
ment of participants if:
− the economic interest of each participant will be not
only in the provision of its own market products and ser-
vices, but also in the final results of others;
− participants will not only supply high-quality products
and services, but also guarantee the efficiency and ef-
fectiveness (energy, economic, environmental) of their
utilization within the time frames stipulated by the
goals of other participants.
EE is recognized globally as a critical strategic priority for
tackling the challenge of low-carbon transition, improving
the reliability, sustainability, competitiveness, and accessi-
bility of energy for consumers. EE is defined by the ratio of
the output of products, services, goods, or energy to the
energy input. Due to its market-oriented nature, EE is pres-
ently regarded by the scientific and business communities
as an indicator of service quality, which advances either
when a certain level of service is provided with less energy
or when services improve with a fixed amount of energy
[8]. EE indicators encompass nearly all facets of human ac-
tivity in both production and household domains, particu-
larly aspects such as reducing pollutant emissions, enhanc-
ing energy security, managing prices and tariffs for fuel and
energy, boosting labor productivity, creating new jobs, en-
suring equitable access to energy resources, and promoting
health and well-being [9, 10].
ES can be understood, among other aspects, as the delivery
of energy services in a sustainable way, ensuring they are
adequate to fulfill the essential needs of individuals and
their communities without causing environmental damage
now or in the future [11]. To achieve this, EE and effective-
ness need substantial enhancement through the use of sus-
tainable energy resources and beneficial energy carriers,
mainly relying on renewable energy [1, 2, 12, 13].
Recent studies demonstrate that energy services can be de-
ployed across all segments of the local energy system, in-
cluding end‑users, producers, network operators, and stor-
age facilities, to enhance overall system performance.
Publications [14-17] show that the integration of independ-
ent producers, smart consumers and renewable energy
sources can bring annual economic benefits and a market
efficiency, thus underscoring the value of consumer‑ori-
ented, competitive frameworks in network organizations.
Recent advancements in mathematical and computational
methodologies further reinforce the necessity of integrat-
ing dynamic modeling and optimization techniques within
energy service markets. Game-theoretic frameworks, such
as the one developed by [18], enable the simulation of stra-
tegic interactions between emission sources and regulators
via coupled differential equations, offering fee‑based
mechanisms that align economic incentives with environ-
mental targets. Building on this, structure‑optimization
methods for power systems with high penetrations of re-
newables, outlined by [19-21], demonstrate how
techno‑economic forecasting and the incorporation of en-
ergy storage and demand‑response strategies can enhance
system resilience and reduce greenhouse gas emissions.
The primary function of energy services is to offer a physical
advantage, utility, or benefit resulting from the integration
of renewable energy with EE technology and/or specific ac-
tions, which could encompass the operations, mainte-
nance, and oversight required to provide the service, and
under typical conditions has demonstrated the ability to
produce verifiable and measurable or estimable improve-
ments in EE and/or savings in primary energy [8, 22, 23].
Modern energy services include professional business
(commercial) activities such as scientific research, engi-
neering, construction, project management, and consulting
[22, 23].
Energy services are applied across all sectors of industry
and the social domain at every phase of acquiring (extract-
ing) energy resources, transforming them into usable
forms, transporting, distributing, storing, using, and utiliz-
ing them, thereby enhancing the efficiency of supplying
consumers with electricity, heat, cooling, lighting, and
more. To achieve this within the context of energy sustain-
ability, energy services across all these stages must be har-
monized, refined, and structured into a cohesive, unified
cycle, prioritizing processes for reducing, reusing, and recy-
cling energy and materials [24-27]. In the following discus-
sion, we will refer to such services as sustainable energy
services (SESs).
Let us remember that the category of energy services,
based on its objectives, includes measures, methods, and
resources (we define this service category as energy perfor-
mance services or energy-related services [23, 28]), essen-
tial for enhancing the efficiency (productivity, quality, etc.)
of processes involved in producing, transmitting, distrib-
uting, supplying, and ultimately consuming energy, while
also generating additional value (profitability, benefit, util-
ity, etc.) through the integration of energy with EE technol-
ogy and/or actions, including the maintenance and control
procedures required to deliver these services [8, 29-31].
Additional significant classification characteristics of energy
services are available, for instance, in the works [32-34].
ESCOs are commercial entities offering an extensive array
of comprehensive energy services, spanning the phases of
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
designing, executing, monitoring, and verifying outcomes
from innovative projects aimed at enhancing EE and energy
savings at industrial and social-household sites [31, 35, 36].
The operations of an ESCO rely on the foundations of en-
ergy management, outsourcing, performance contracting,
project financing, and risk management, utilizing both its
internal capabilities and external resources, which include
financial, legal, material, technical, energy, and labor com-
ponents [22, 23]. An ESCO assesses and identifies opportu-
nities for EE and energy-saving initiatives at a client’s prem-
ises, formulates a set of initial business proposals, and
suggests to the client the adoption of those that are self-
financing.
To achieve this, ESCO evaluates the practicality of employing
diverse energy types and technological equipment, such as
local, secondary, unconventional, and renewable resources,
alongside measures to conserve other natural resources in-
volved in the technological process and focused on environ-
mental protection. The key aspect is that ESCO ensures the
value of the fuel, energy, and material resources saved
through each proposed energy services project will surpass
the costs over the payback period of those projects.
The rationale for integrating all elements and aspects of re-
source use considered in our study relates to the require-
ments of transformation and adaptation that emerge when
participants in the energy service market express a desire
to collaborate for the sustainable growth of their busi-
nesses by expanding, enhancing, and refining their opera-
tions [37]. The service-oriented logic of integration, as a
broad strategy for addressing cross-functional challenges,
involves not only the cross-functional alignment of market
participants’ activities but also their coordination as the
true catalysts of service delivery [38]. Business logic guides
decision-making in systems involving intricate, intercon-
nected activities, determining the practical steps needed to
meet specific business objectives. In our context, it estab-
lishes a framework of rules and methods that regulate the
exchange of information and resources among energy ser-
vice market participants to evaluate their applicability and
devise approaches for implementing these rules and meth-
ods in particular instances of mutually advantageous re-
source and program integration, thereby facilitating deci-
sions that enhance their EE and sustainability.
Specialized ESCOs serve a vital role in coordinating and sta-
bilizing the operations of SESs. What sets these companies
apart from conventional firms is their direct engagement in
carrying out systematically aligned measures (projects) to
upgrade and enhance the 3-E (energy-economy-ecology)
efficiency of production processes at every phase of trans-
forming primary resources, materials, and products, en-
couraging the optimal utilization of both primary and sec-
ondary resources while proactively substituting
nonrenewable elements with renewable alternatives.
The main aim of the work is to develop and test a method-
ology for improving energy efficiency and sustainability of
the activities of participants (business entities) in the
renewable energy market by implementing investment en-
ergy service projects.
The main results of achieving the set aim are as follows:
basic terms, their content and scope of application in the
field of energy efficiency, energy sustainability and energy
service, adapted to the specific tasks of integration of re-
newable energy market participants;
generalized model of sustainable energy services market
organization and management, taking into account closed
cycles of service-oriented interaction of its participants;
model and algorithm for solving the problem of selection
and optimal allocation of limited resources between the
projects presented by the participants, developed on the
basis of dynamic programming;
scenario calculations and selection of the optimal solution
based on the comparative analysis of discounted indicators
confirmed the fairness of the application of the proposed
hypothesis.
To maintain a broad perspective in the research methodol-
ogy, we refrain from focusing on any particular national or
local programs and projects related to EE, ES and RE. Our
goal is to present a structured business logic that highlights
the advantages and possibilities for achieving a synergistic
effect resulting from the coordinated collaboration of en-
ergy service market participants within the context of im-
plementing such projects. To guarantee that this approach
can be effectively replicated and expanded upon based on
existing findings, we suggest a methodological framework
designed to enhance the efficiency of attracting, distrib-
uting, and using limited investment resources by aligning
the overarching (group) objective with the individual goals
of the participants in a mutually beneficial manner.
The practical significance of the work is to provide policy-
makers, project designers and engineers with a methodo-
logical tool to make more informed, performance-based
decisions in the field of improving the competitiveness of
customers of energy service projects (economic entities).
2. Materials and methods
2.1 Generalized model of the energy service market: infra-
structure, participants, relationships
Drawing from the definitions provided earlier, the infra-
structure of the energy service market can be represented
as a broad model of the organizational and technological
framework of an open business ecosystem, including a vir-
tual version, which continuously interacts (exchanges) with
its surroundings and promotes greater efficiency in energy
utilization across all phases of its transformation, from pro-
duction through final consumption and utilization. The pa-
per [28] outlines the design of such a structure, integrating
markets for energy resources (renewable energy sources,
natural gas, coal, electricity, heating, and cooling) and en-
ergy services, markets for producers (suppliers) of EE
equipment and renewable energy sources, and markets for
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
energy consumers with varying ownership types (private,
state, municipal, cooperative, etc.).
The generalized model of the energy service market is de-
scribed as an ecosystem where renewable energy sources
are fully integrated at every stage: production, distribution,
storage, and consumption. Markets for renewables are
treated as central, not peripheral, with ESCOs increasingly
focused on renewable-based performance contracting. We
will assess the outcomes of these efforts using components
of vector sums of weighted values of two core metrics: the
first capturing the material essence (stored energy, waste,
emissions, etc.), expressed in physical units, and the second
representing its economic (cost) aspect, quantified in mon-
etary units.
Building on the concepts of "value-in-use" and "value-in-ex-
change" prevalent in market relations theory, which guide
the calculation of cost-related components, it becomes fea-
sible to apply the aforementioned fundamental values
more thoroughly in the energy services market, defining
their essence as follows: (1) the value (cost) of energy in use
(energy value-in-use), determined by the cost of energy uti-
lization at customers’ (consumers’) facilities, and (2) the ex-
change value of energy (energy value-in-exchange),
assessed as the potential of stored energy to be traded for
other goods (services) in specific quantitative ratios.
The essence of the service-oriented logic, which enables
manufacturing (supplying, etc.) firms to engage their cus-
tomers (consumers) in value creation processes, thus
broadening the market’s scope, is thoroughly explored in
the paper [39].
Adopting this logic as the fundamental approach, every par-
ticipant in the service-oriented market should act as a co-
creator of value, which, in our view, necessitates consider-
ing at least two additional components of the value aspect,
defined as follows: (3) the value of cooperation (value-in-
cooperation), determined as the value generated through
the collaboration of market participants (refer to the com-
ponents of this value in the work [40]); (4) the value of ser-
vices (value-in-service), assessed as the value resulting
from the delivery of services. Fig. 1 illustrates the proposed
energy services market infrastructure in the context of in-
tegrating a value-based approach into the structure of ex-
isting energy systems. It is shown that such integration
should cover the core processes of energy production,
transportation, consumption, and storage, ensuring effi-
cient and sustainable energy management.
Fig. 1. Framework for energy services market infrastructure
The final two suggestions stem from the findings of the
work [39], which recommends identifying at least three el-
ements within the concept of service: as a form of activity,
as a business logic for value creation by the client (con-
sumer), and as a business logic for the producer (supplier).
Simultaneously, the producer may actively engage, provide
opportunities, or fulfill supportive roles in addressing cus-
tomer needs and generating value. It is emphasized that
the core of service-oriented logic lies in interaction, rather
than mere exchange centered solely on transactions and
value promotion, without involving co-creation with cus-
tomers.
The findings from the preceding analysis enable the devel-
opment of a comprehensive model for structuring the sus-
tainable energy services market, incorporating closed loops
of service-oriented interactions among its participants. This
model comprises several integrated structural subsystems
established within the following frameworks: the market
for primary resources, materials, and products (encom-
passing both renewable and nonrenewable types); the
market for service-oriented services; the market for sec-
ondary resources, materials, and products undergoing pro-
cessing required for reuse; and the production market,
which justifiably holds a central position in this model. The
proposed model is open, meaning it interacts with the ex-
ternal environment through permeable boundaries, ex-
changing energy, information, equipment, and materials,
while depending on legal, regulatory, sociopolitical, and
other external inputs, and releasing waste and pollutant
emissions into the environment that cannot currently be
recycled or repurposed given the present state of the ma-
terial and technical infrastructure for production and ser-
vice delivery.
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2.2. Role model of the coordinating center (integrator)
The coordination center (hereinafter referred to as the
Center) is responsible for aligning the mutually advanta-
geous business interests of energy services market partici-
pants, including specialized ESCOs.
The creation and effective operation of such a center ena-
ble the integration of all technological processes involved
in transforming primary and secondary resources into a
continuous sequence, spanning their journey to final con-
sumption and utilization (i.e., throughout the entire prod-
uct life cycle), while ensuring the highest possible 3-E effi-
ciency for both the system as a whole and each individual
participant.
From a system analysis perspective, all subsystems within
the proposed model are multilayered, encompassing the
supply of primary resources, the production of component
products, the creation of the main product, final consump-
tion, and the transformation and disposal of waste. We ex-
press the fundamental principle of organizing the environ-
mentally balanced operations of these technological
subsystems as an optimization problem:
𝑃𝑆(𝛽) ∩ 𝐵𝑆(𝛾) ⇒ 𝑜𝑝𝑡, (1)
where PS and BS represent the sets of components and
products within the eco and business subsystems, with β
and γ as their respective parameters, and ∩ symbolizing the
intersection of these sets, which depict the condition of the
naturally occurring ecosystem PS and the influence of tech-
nological business subsystems BS on its capacity for self-re-
covery. The proposed model minimizes environmental
harm from business ecosystem activities by establishing
closed resource cycles, promoting their restoration and re-
use through measures (investment projects) aimed at en-
hancing EE and resource conservation, thereby reducing
the consumption of natural resources (energy and materi-
als) across all product life cycle stages while protecting the
environment and maintaining, or even augmenting, the
volume of natural resources utilized.
Fig. 2 illustrates a coordinated system for environmentally
balanced energy service operations, centered around the
Center at the level of enterprises (companies). It describes
in detail the stages of energy consumption by enterprises
(companies), taking into account the life cycle stages of its
production processes, from the supply of primary resources
to final consumption and waste transformation, ensuring
closed resource cycles and optimal investment in energy ef-
ficiency.
Fig. 2. Structural scheme of the coordinated, environmentally balanced energy service system
In global practice, resource intensity indicators serve as a
widely accepted quantitative measure of the efficient use of
energy, material, and technical resources, calculated as the ra-
tio of resources consumed (in physical or monetary units) to
the volume of products generated over a specific timeframe
(in physical or monetary units). Distinct indicators such as en-
ergy intensity, material intensity, and carbon intensity are rec-
ognized within the resource intensity category [41, 42].
Variations in the quantitative values of resource intensity
indicators primarily result from shifts in the types of re-
sources utilized and products created. These shifts mainly
stem from optimizing production structures, adopting
more efficient and higher-quality resource types, revising
norms and standards, adjusting market operation rules,
and altering the sectoral composition of the economy,
among other factors. A significant yet underutilized compo-
nent of resource intensity indicators, particularly in devel-
oping nations, is the influence of the rebound effect (Jev-
ons’ paradox), which emerges when improved resource
efficiency reduces costs but increases consumption, or
when better resource intensity indicators reflect higher
economic product value rather than reduced resource use.
When leveraged effectively, this effect yields a substantial
positive multiplier impact [43, 44].
To capitalize on these opportunities (activities, projects,
etc.) for improving EE and resource conservation, we
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suggest establishing a mechanism for system-wide man-
agement of the environmentally balanced operation of the
SES market within closed cycles. As previously mentioned,
the primary responsibilities for implementing this mecha-
nism should fall to the Center’s participants, with managing
the attraction and repayment of investments in EE and re-
source-saving projects being of utmost importance.
Thus, within the outlined mechanism, the Center must eval-
uate diverse and extensive investment projects proposed
by enterprises, ensuring coordination and balance among
the activities of system participants from fundamentally
distinct technological subsystems, while treating them as
part of an integrated system. This involves aligning local ef-
ficiency, which reflects the benefits participants gain di-
rectly from individual project investments, with system-
wide efficiency, which accounts for the collective outcomes
of all participants. Simultaneously, the Center should facili-
tate the selection of optimal investment proposals (pro-
jects) without compromising any participant’s interests, as
a decline in any single participant’s efficiency directly im-
pacts the performance of all others in the system.
2.3. Methodological approach for estimating the effective-
ness (productivity) of investment projects
Overall, this highlights the necessity for the Center to assess
the effectiveness of the implementation of various invest-
ment projects at the level of enterprises, which differ consid-
erably in purpose, funding scale, and implementation time-
lines. Since the payback periods for EE and RE projects
typically span several years, such evaluations should account
for the time value of money by applying a discounting meth-
odology to their value. The challenge arises because widely
used indicators for measuring project effectiveness, such as
net present value (NPV), payback period (PB), internal rate of
return (IRR), and equivalent annual annuity (EAA), calculated
by each system participant primarily based on its own inter-
ests, do not enable the Center to accurately compare pro-
jects in terms of their system-wide efficiency and resource
savings [45].It is evident that using these indicators for
choosing the most suitable investment proposals from enter-
prises necessitates the creation of an appropriate methodo-
logical framework and tools to address these challenges, re-
lying on optimization algorithms to facilitate cyclic
procedures for sequentially selecting proposals (i.e., ranking
projects) based on 3-E efficiency criteria.
To achieve this, drawing on the principles of system analy-
sis, we structure the model of aforementioned mechanism
as a multilevel system S, comprising n integrated subsys-
tems 𝑆𝑖 , 𝑖 = 1, 𝑛̅̅ ̅̅̅ (enterprises) positioned at distinct yet in-
terconnected levels of the technological process for pro-
ducing and utilizing products, followed by waste disposal.
We model the behavior of this multilevel system by em-
ploying algorithms designed to solve multistep dynamic
programming problems, grounded in the Bellman optimal-
ity principle, which asserts: regardless of the system S state
prior to the next step (level), the control (management) u
must be selected at that step to maximize the gain at that
step combined with the optimal gain across all subsequent
steps [39].
Additionally, based on the theoretical foundations of this
principle, we represent incremental changes in the sys-
tem’s state through a collection of state variables ℎ𝑖, 𝑖 =
1, 𝑛̅̅ ̅̅̅, which reside within the state space H of their allowa-
ble values, constrained by technical, economic, energy, and
other conditions of the technological process.
We examine the dynamics of state changes in the system,
recognizing that these shifts occur due to corresponding
control actions (events, etc.) during transitions between
levels of the technological process, specifically in our case,
when moving from one subsystem to another.
We designate the influence during the shift from state ℎ𝑖−1
to state ℎ𝑖 as 𝑢𝑖, which is typically defined by a set m (vec-
tor) of permissible control parameters 𝑢𝑖𝑗 , 𝑗 = 1, 𝑚̅̅ ̅̅ ̅̅ . Natu-
rally, the state ℎ𝑖 at each system level will also be shaped
by the set of admissible state variables. ℎ𝑖𝑔, 𝑔 = 1, 𝑙̅̅ ̅̅
Thus, at each system level, control influences and state var-
iables can assume values from their respective sets: 𝑢𝑖 ∈
𝑈𝑖 , ℎ𝑖 ∈ 𝐻𝑖. Consequently, for each state transition in the
system, we can express ℎ𝑖 = φ𝑖(ℎ𝑖−1, 𝑢𝑖), where φ𝑖 repre-
sents the functional relationship of the state. These rela-
tionships indicate that the system states modeled by this
methodology exhibit no aftereffects, meaning each subse-
quent i-th state depends solely on the preceding (i-1)-th
state and the control 𝑢𝑖. In this case, when determining the
parameters of the system states and the functional rela-
tionships between them, we will not need to calculate the
time dependencies of transitions between states, since
these dependencies in our case are determined by the in-
dicators of investment projects. For their calculation, well-
known investment analysis programs were used, allowing
us to significantly simplify the utilize of the Bellman opti-
mality principle
As a result, we will be able to assess the effectiveness of
implementing EE measures (investment projects) across
the entire system using a system-wide objective function
(optimality function) 𝐹𝑠 = ∑ 𝐹𝑖(ℎ𝑖−1, 𝑢𝑖)𝑛
𝑖=1 , calculated as
the sum of local objective functions 𝐹𝑖. The optimization
challenge then becomes maximizing (or minimizing, de-
pending on the objective) this system-wide objective func-
tion, subject to constraints on available resources (material
and technical, fuel and energy, financial and economic, la-
bor, etc.). It is important to note that optimizing the sys-
tem-wide objective function does not simply amount to the
arithmetic total of independently optimized local objective
functions, which, in our context, correspond to the objec-
tive functions of the system participants.
For any k-th stage of the optimization process, the funda-
mental dynamic programming equation (Bellman’s equa-
tion) for maximum/minimum problems is provided as fol-
lows [46]:
Fk
*(hk-1)= max
uk
{Fk(hk-1,uk)+Fk+1
* (hk)}, (2)
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where Fk+1
* (hk)= max
uk+1
{Fk+1(hk,uk+1)} and ℎ𝑘 = 𝜑𝑘(ℎ𝑘−1, 𝑢𝑘)
characterize the efficiency of investment project imple-
mentation under optimal management from the k-th to the
n-th level of the system (this is for maximum problems; for
minimization, min
uk
is used). According to this representa-
tion, equation (2) is recurrent, in which changes in the index
occur in reverse order.
Utilizing the equations (1)-(2) enables us to simplify the
process of addressing the outlined n-dimensional dynamic
programming optimization problem by breaking it down
into the sequential resolution of n more manageable one-
dimensional problems. We approach this problem in two
phases. During the first phase, each system participant in-
dependently computes NPV, PB, IRR, VES, where VES rep-
resents the volume of planned annual energy and resource
savings, and other significant indicators related to their
own projects, such as those pertaining to social or environ-
mental outcomes. In the second phase, the Center tackles
the task of selecting and optimally distributing limited re-
sources among the proposed projects.
Fig. 3 presents a multilevel optimization model structured
around system S, composed of several integrated subsys-
tems.
Fig. 3. Multilevel optimization model for evaluating energy efficiency investment projects
According to the proposed methodology, at the initial step
we will evaluate the effectiveness of the implementation of
investment projects based on net present value:
𝑁𝑃𝑉 = ∑ 𝐶𝐹𝑡
𝑁
𝑡=0 /(1 + 𝑟)𝑡 = −𝐼𝐶 + ∑ 𝐶𝐹𝑡
𝑁
𝑡=1 /(1 + 𝑟)𝑡, (3)
where: CFt – the value of net cash flow from energy and re-
source saving for the period of time t (most often it is a
year); N – the number of time periods for which the invest-
ment project is calculated; r – the discount rate; IC – initial
investment.
The simple payback period is calculated as PB = IC/CF, and
the value of IRR is determined by the value of the discount
rate at which the NPV is equal to zero, i.e.:
{−𝐼𝐶 + ∑ 𝐶𝐹𝑡
𝑁
𝑡=1 /(1 + 𝐼𝑅𝑅)𝑡 } = 0 (4)
3. Results
The model presented in Fig. 3 is quite universal and can be
applied to optimize a system with n≥2 subsystems (the max-
imum number of subsystems is limited by software capabili-
ties). Let us, for example, consider a system with 5 subsys-
tems, which can be subsystems of the energy system shown
in Fig. 1, or 5 enterprises belonging to one administrative dis-
trict, or even 5 divisions (workshops) of one enterprise. Sup-
pose that each of the five subsystems S1, S2, S3, S4, and S5
within system S submit applications to the Center for the
execution of five investment projects, totaling 5×5=25 pro-
jects, and the Center can secure 400 million monetary units
of borrowed resources for their funding. The Center must al-
locate these resources among the subsystems and their pro-
posed energy service projects to achieve the highest overall
profitability (including energy and resource savings, etc.) for
the entire system, while not interfering in the operational ac-
tivities of subsystems (enterprises, organizations) regarding
the determination of quantitative parameters of the effi-
ciency indicators of projects submitted to the Center. At the
same time, the subsystems are responsible for the final re-
sults of the implementation of the submitted projects and
must take into account the availability of various technolo-
gies (as in the example given at the end of the article, where
there is wind, sun, batteries, cogeneration, hydrogen) and
modes of their use. The Center controls the reliability of the
information provided by the subsystems and manages the
efficiency of attracting, distributing and using investment re-
sources at all stages of project implementation.
The outcomes of the optimal resource distribution by the
Center among the subsystem projects, computed using
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formulas (2), (3) and (4), are illustrated in Fig. 4, where the
size and value of NPV for projects chosen for funding are
indicated by large dots. From the projects submitted by
subsystems (S1, S2, S3, S4, S5), the dynamic programming
optimization problem identified projects with funding lev-
els of (1, 2, 5, 1, 1), corresponding to allocations of (40, 80,
200, 40, 40) million monetary units. Consequently, the
maximum total NPV achievable from implementing these
selected projects at the system level amounts to 8.0 + 9.0 +
18.0 + 4.0 + 5.0 = 44 million monetary units.
Fig. 4. Dependence of NPV of projects represented by sub-
systems on the required investment volumes
To identify the critical variables that can most significantly
affect the feasibility and effectiveness of projects, a sensi-
tivity analysis of project effectiveness indicators to changes
in investment volumes (∆𝐸𝑓/∆𝐼𝑛𝑣) was conducted. The re-
sults of the sensitivity analysis of NPV, presented in Fig. 5,
show that for three out of the five subsystems, the highest
sensitivity levels occur at the lowest (40 million monetary
units) and highest (200 million monetary units) investment
volumes, a finding substantiated by the project selection
outcomes.
Fig. 5. Sensitivity of NPV to changes in the investment vol-
umes
Comparable optimization analyses of the investment ap-
peal of projects submitted by subsystems S1, S2, S3, S4, and
S5 indicate that, based on the IRR indicator, projects should
be chosen at (1, 1, 1 + 5, 1, 1) funding levels, which differ
from those selected using the NPV indicator.
To address the challenge of selecting an optimal project set
for investment (loan) financing amidst conflicting criteria,
various approaches have been suggested [47-49]. One such
approach involves considering three factors simultaneously
for project selection, NPV, investment volume, and imple-
mentation duration, integrated into a project investment
attractiveness index (PIA):
𝑃𝐼𝐴 = 𝑁𝑃𝑉/(𝐼0 × 𝑛), (5)
where I0 is the initial investment volume and n is the num-
ber of calculation periods of the project (years, months,
etc.) from the beginning of its implementation to the end
(completion of liquidation measures).
In this version, formula (5) is applied to evaluate projects
generating standard cash flows, where investments occur
solely at the outset (recorded as negative), and subsequent
returns, energy and resource savings, remain positive.
Among multiple competing projects, the one with the
highest PIA is preferred.
The outcomes of optimally distributing the Center’s
resources among the subsystems’ projects, determined
using formulas (1) and (3), are shown in Fig. 6, with the size
and value of PIA for funded projects highlighted by large
dots. As indicated, from the projects proposed by the
subsystems, selections were made at (1, 1, 1+5, 1, 1)
financing levels.
Fig. 6. Dependence of PIA of projects represented by sub-
systems on the required investment volumes
The evaluation of the PIA index’s sensitivity to shifts in
investment amounts, with findings displayed in Fig. 7,
indicates that for all five subsystems, peak sensitivity levels
occur at the minimum investment volume (40 million
monetary units), and for one subsystem, the third, at the
maximum volume (200 million monetary units), aligning
with the project selection results based on IRR.
Consequently, in this specific instance, the PIA investment
attractiveness index sways managerial decisions toward
IRR. Nonetheless, this outcome is not universal, and the
Center retains the authority to make the final decision
among the three evaluated scenarios.
Addressing the challenges of EE, RE and resource conserva-
tion within complex organizational and technological sys-
tems encounters an additional issue that cannot be re-
solved solely through a technical and technological lens.
This issue arises because enhancing the efficiency of energy
resource utilization in any segment (subsystem) of the
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energy conversion (transmission, etc.) technological chain
typically involves investing internal or borrowed financial
resources, which reduces resource consumption not only in
that specific segment but also in all preceding technological
segments of the system, thereby delivering energy and re-
source-saving benefits to them. However, given the organ-
izational (legal, administrative, etc.) autonomy of subsys-
tems, the costs (and often losses) incurred by the investing
subsystem are typically not reimbursed.
Fig. 7. Sensitivity of PIA to changes in the investment
volumes
Resolving this issue, namely, ensuring a fair reallocation of
saved resources resulting from measures taken in
individual subsystems to boost EE and resource conserva-
tion, demands a holistic, integrated approach to their exe-
cution [31]. Drawing on the conceptual principles of this ap-
proach as outlined in these studies, we quantify the energy
and resource savings achieved in the production subsys-
tems of the service-oriented organizational-and-technolog-
ical system. To ensure an accurate comparison of condi-
tions "before" and "after" implementing energy and
resource-saving measures (projects), we evaluate two sce-
narios. In the first scenario, we assume that the output vol-
umes of goods (products, resources, services, etc.) remain
constant following these measures due to a proportional
decrease in input resource consumption, while in the sec-
ond scenario, input resource consumption levels stay fixed,
allowing an increase in the volume of resources produced
at the system output.
We analyze the system’s behavior under the first scenario
using the developed model, which depicts the technologi-
cal chain of energy conversion (transfer, etc.) as a series of
interconnected "black boxes".
The system’s behavior in the first scenario is examined us-
ing the model, which portrays the technological chain of
energy conversion (transmission, etc.) within the system as
a sequentially linked set of "black boxes", where the con-
nection between input and output variables is facilitated by
corresponding efficiency indicators of energy conversion
(transmission, etc.). We have:
𝐸𝑆𝑆 = ∑ 𝐸𝑆𝑖
𝑛
𝑖=1 , 𝐸𝑆𝑖 = (∆𝑖 − ∆𝑖𝐸
′ ), ∆𝑖= 𝑄0(1 − 𝜂𝑖)(∏ 𝜂𝑘−1)𝑖
𝑘=1 ,
∆𝑖𝐸
′ = 𝑄0(𝜀𝑖 − 𝜂𝑖)(∏ 𝜀𝑘+1
𝑛−𝑖
𝑘=1 )(∏ 𝜂𝑘−1
𝑖
𝑘=1 ), (6)
where 𝐸𝑆𝑠 – is the volume of energy saving in system S,
consisting of the volumes of energy saving 𝐸𝑆𝑖 , 𝑖 = 1, 𝑛̅̅ ̅̅̅ in
subsystems 𝑆𝑖 of the system; ∆𝑖 – are losses in the i-th sub-
system "before", and ∆𝑖𝐸
′ – "after" the implementation of
energy saving measures; 𝑄0 – is the volume of consumed
energy resources at the input of the system "before" and
𝑄0𝐸 – "after" the implementation of energy and resource
saving measures; 𝜂𝑖 – indicator of efficiency of energy con-
version (transmission, etc.) in the i-th subsystem "before",
and 𝜂𝑖𝐸 – "after" implementation of energy and resource
saving measures; 𝜀𝑖 = 𝜂𝑖/𝜂𝑖𝐸; 𝜂0 = 1.
To obtain a quantitative assessment of the results of the
system functioning under the first scenario (formula (6)),
we accept 𝑄0 = 100 conventional units; 𝜂𝑖 = 0,7, а 𝜂𝑖𝐸 =
0,8. Then, energy losses in subsystems 𝑆𝑖 and in the system
as a whole, "before" the implementation of energy-saving
measures, will be ∆𝑖 = (30.00 + 21.00 + 14.70 + 10.29 + 7.20)
= 83.19 units out of 100, i.e., 83.19 %. After the implemen-
tation of these measures, energy losses will decrease by 2.4
times: ∆𝑖𝐸
′ = (10.26 + 8.21 + 6.57 + 5.25 + 4.20) = 34.48 units.
New in the research on this model is also the possibility of
identifying and quantifying the effect of "redistribution" (re-
allocation) of saved energy (resources) between series-con-
nected subsystems, allowing for achieving planned system
performance indicators, which, however, has received virtu-
ally no attention from researchers. Thus, the volume of en-
ergy savings in the system as a whole as a result of the
implementation of energy and resource saving measures un-
der the considered scenario under the condition
ηiE=0,8, i=1,5̅̅ ̅̅ , reaches (19.74 + 12.79 + 8.13 + 5.04 + 3.00) =
48.71 units.
The results of a more complex comparison of the results of
implementation under the first scenario of measures to im-
prove the efficiency of energy conversion (transmission,
etc.) from 𝜂𝑖 = 0.7 to 𝜂𝑖𝐸 = 0.8 in the 1st and 5th subsystems
simultaneously are presented in Fig. 8. One can see the
changes in energy losses ∆𝑖 і ∆𝑖𝐸
′ measured on the left scale,
as well as energy savings in all five subsystems (right scale).
Fig. 8. Distribution of energy losses and energy saving in
subsystems under the first scenario
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Similar results on the implementation of measures to im-
prove the efficiency of energy conversion (transmission,
etc.) obtained under the second scenario are presented in
Fig. 9. Here, the picture changes fundamentally. There are
even conditionally "negative" volumes of energy savings in
the 3rd, 4th and 5th subsystems, where no energy saving
measures were taken. This phenomenon is explained by
the fact that in these subsystems, the energy losses "after"
the implementation of energy saving measures exceed the
corresponding losses "before" their implementation (see
Fig. 9). In general, the effect of increasing the efficiency of
energy conversion (transmission, etc.) in these subsystems,
as well as in the system as a whole, will be undoubtedly
positive since the growth of losses occurs against the back-
ground of the growth of the volumes of the initial energy
resources of the subsystems.
Fig. 9. Distribution of energy losses and energy savings in
subsystems under the second scenario
It is clear that the abovementioned features (advantages
and disadvantages) of implementing measures to increase
the efficiency of energy conversion (transmission, etc.) in
subsystems should be taken into account in the procedures
for the selection and implementation of investment pro-
jects submitted by participants to the Center. First, this will
apply to those participants who will require additional
funding to achieve the planned volumes and quality of the
resulting effect. As a methodological basis for solving this
issue, we propose to use, for example, the algorithm of re-
wards and penalties.
The results of the model application were verified using en-
ergy audit data from Ukraine and several Central Asian
countries. The key stakeholders included national energy
efficiency authorities (integrators), local governments,
schools and utilities (project initiators), local energy service
companies (implementers) and investors (international
technical assistance programs, private equity). The project
portfolio included public buildings (thermal renovation of
schools, LED lighting), district heating (boiler moderniza-
tion, pipe insulation), street lighting (smart LED infrastruc-
ture), water supply (improving the efficiency of pumping
stations), industry (waste heat recovery, compressor
station modernization, cogeneration, distributed air condi-
tioning).
As an example of the complexity of our projects, we present
the results of one of our latest proposals, – an assessment
of the implementation of a 1 MW microgrid in the Khmel-
nytskyi region of Ukraine, which includes:
− solar PV (400 kW) – panels on the roofs of university
buildings (e.g. 1,000 m² at 400 W/m²), which generate
480–560 MWh/year (1,200–1,400 kWh/kWp, based on
the solar potential of Khmelnytskyi) and cover critical
loads during the daytime (e.g. 100 kW for university de-
mand);
− wind turbine (200 kW) – a single small turbine on city
outskirts (5-6 m/s wind speed), which generates 400-
500 MWh/year (2,000-2,500 full-load hours) supple-
menting solar, especially at night or in winter;
− battery storage (400 kWh) – lithium-ion unit co-located
with renewables, is designed to run for 4 hours at 100
kW, to store excess daytime electricity and compensate
for short-term outages;
− hydrogen fuel cell backup (100 kW) – paired with a small
electrolyzer (10 kW) and 200 kg hydrogen storage (48
hours at 100 kW) for long-duration backup under multi-
day grid failures;
− cogeneration unit (300 kW) – gas-piston engine (scaled
from existing 6.4 MW CGUs) that produces 1200 MWh
of electricity + 1200 MWh of heat per year (8000 hours),
providing a base level of electricity and heat that can be
offset by renewable energy sources;
− SCADA-based controls system to monitor, optimize, and
prioritize energy flows in "island" mode, rerouting
power to critical facilities during attacks and providing
grid integration, demand response and power trading.
Conclusion
Improving the efficiency, speed and scale of renewable en-
ergy services projects is critical to sustainable economic
growth in the global economy, especially during the shift
toward eco-friendly products and services. Holistically inte-
grated measures that promote energy efficiency and sus-
tainable development at regional, national and local levels
are essential tools to achieve this goal. However, a key con-
straint, in our view, is the lack of sufficient mutually bene-
ficial collaboration among the initiators, participants and
end users of projects, which should satisfy the needs of all
involved parties. To overcome this, we propose a business-
oriented model that promotes interaction between all par-
ticipants in the energy services market. The interaction
logic outlined in this study encourages them to unite their
efforts and resources, boosting efficiency and profitability
by prioritizing critical projects. This is accomplished
through better attraction, allocation, and use of limited re-
sources from participants and investors. Improved coordi-
nation between the Center (integrator) and participants
ensures timely, cost-effective project completion with
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
reduced risks, ultimately enhancing EE and sustainability.
This requires a significant shift in the methodology and
tools for executing program activities as a cohesive, service-
oriented business system, with all stages aligned toward
the final outcome.
This shift also demands updated expectations for the Cen-
ter and primary implementers (ESCOs), who hold central
roles. Our proposal’s uniqueness lies in the need for mutu-
ally beneficial involvement of the Center, participants, im-
plementers, and investors across all phases, from invest-
ment attraction and optimal allocation during project
selection to improving implementation efficiency through
coordinated resolution of discrepancies.
A key strength of this study is the creation of a formalized
business model and algorithm that optimizes the coordina-
tion and balance of efforts among all of them. This enables
testing various strategies via iterative project selection pro-
cesses. The model, rooted in the Bellman optimality princi-
ple, supports management decisions for selecting the most
effective participants, projects, implementers, and inves-
tors. We incorporated this principle’s theoretical frame-
work, defining gradual selection changes through state var-
iables constrained by technical, economic, and energy
conditions of project processes.
Using the Bellman equation, we simplified the multidimen-
sional optimization problem into sequential one-dimen-
sional solutions. To assess the model numerically, we ap-
plied discounting techniques, calculating indicators like
NPV, PB, IRR, VES, and PIA. We analyzed the distribution of
400 million monetary units across 25 projects to maximize
system profitability, including energy and resource savings,
examining NPV-PIA relationships, investment needs, and
sensitivity to investment changes.
Expanding the dynamic programming model, we developed
two main scenarios: one maintaining output levels with re-
duced input resources, and another keeping input levels
steady while increasing output. Other scenarios lie be-
tween these extremes. A novel contribution is the model’s
ability to identify and measure the “redistribution” effect
of saved resources (energy, economic, financial) across pro-
jects, an understudied area. For example, raising the effi-
ciency indicator from 0.7 to 0.8 increased energy savings by
up to 1.7 times.
The methodological approach’s validity is supported by
practice-tested models, algorithms, and case study results,
confirming the hypothesis’s conditions are met. Economic
incentives (“carrot and stick”) motivate participants and
the Center to deliver high-quality, efficient goods and ser-
vices, proving the hypothesis’s practicality and applicability
for addressing EE and sustainability challenges.
The authors recognize risk minimization as a vital business
concern, especially for energy services projects. Although
this topic is not covered here due to its mathematical com-
plexity and uncertainty, it remains the focus of our ongoing
research, and a solution will be presented in our next study.
Funding: The authors did not receive support from any or-
ganization for the submitted work.
Conflicts of Interest: The authors have no conflicts of inter-
est to declare that are relevant to the content of this article.
Consent: The authors affirm that human research partici-
pants provided informed consent for publication of the im-
ages and consented to the submission of the case report to
the journal.
Ethical responsibilities: The submitted work should be orig-
inal and should not have been published elsewhere in any
form or language.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data availability statement: Not applicable.
Author Contributions: Tetyana Yevtukhova: Methodology
& modelling, Formal analysis and investigation, Writing - re-
view & editing; Oleksandr Novoseltsev: Conceptualization,
Writing - review & editing, Supervision; Mykola Kuznietsov:
Conceptualization, Review & editing, Supervision; Dmytro
Karpenko: Modelling, Formal analysis and investigation,
Writing - review & editing.
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| spelling | veorgua-article-5782026-07-18T06:32:23Z A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS ДВОСТОРОННЯ БІЗНЕС-МОДЕЛЬ ДЛЯ ПІДВИЩЕННЯ СТАЛОГО ЕКО-РОЗВИТКУ ТЕРИТОРІАЛЬНО-АДМІНІСТРАТИВНИХ ОДИНИЦЬ Yevtukhova, T. Novoseltsev , O. Kuznietsov , M. Karpenko , D. renewable energy; energy efficiency; energy sustainability; energy services market; business model; case study. відновлювана енергетика; енергоефективність; енергетична стійкість; ринок енергетичних послуг; бізнес-модель; кейс-стаді. This study introduces a holistic approach to enhance the efficiency, speed, and scope of energy efficiency and sustainability investment initiatives aimed at reducing greenhouse gas emissions. By facilitating closer interaction between investment project coordinating centers (integrators) and initiators, such as companies and organizations, at all stages of renewable energy service projects development and implementation, the approach allows to optimize their cooperation. Energy service companies (ESCOs) typically execute these functions. A methodological platform for implementing the approach using a formalized simulation model and an algorithm to optimize coordination among centers, participants, implementers, and investors was developed. The model, grounded in Bellman’s principle of optimality, formalizes project selection through iterative procedures constrained by technical and economic conditions of renewable energy. The methodology enables testing strategies that enhance resource redistribution to maximize renewable-based project outcomes. The study results demonstrate significant impact; for example, improving the efficiency of the pilot project from 0.7 to 0.8 increases energy savings by a factor of 1.7, opening up new opportunities to enhance the sustainable benefits of renewable energy. Це дослідження пропонує цілісний підхід до підвищення ефективності, швидкості та масштабів інвестиційних ініціатив у сфері енергоефективності та сталого розвитку, спрямованих на скорочення викидів парникових газів. Сприяючи тіснішій взаємодії між координаційними центрами інвестиційних проектів (інтеграторами) та ініціаторами, такими як компанії та організації, на всіх етапах розробки та впровадження проектів відновлюваної енергетики, цей підхід дозволяє оптимізувати їхню співпрацю. Зазвичай ці функції виконують енергосервісні компанії (ЕСКО). Розроблено методологічну платформу для реалізації підходу з використанням формалізованої імітаційної моделі та алгоритму для оптимізації координації між центрами, учасниками, виконавцями та інвесторами. Модель, заснована на принципі оптимальності Беллмана, формалізує вибір проектів за допомогою ітеративних процедур, обмежених технічними та економічними умовами відновлюваної енергетики. Методологія дозволяє тестувати стратегії, що покращують перерозподіл ресурсів для максимізації ефективності та продуктивності проектів відновлюваної енергетики. Результати дослідження демонструють значний вплив; наприклад, підвищення ефективності пілотного проєкту з 0,7 до 0,8 збільшує економію енергії у 1,7 рази, відкриваючи нові можливості для посилення сталих переваг відновлюваної енергетики. Institute of Renewable Energy National Academy of Sciences of Ukraine 2025-12-27 Article Article application/pdf https://ve.org.ua/index.php/journal/article/view/578 10.36296/1819-8058.2025.4(83).112-125 Vidnovluvana energetika ; No. 4(83) (2025): Scientific and applied Journal renewable energy ; 112-125 Возобновляемая энергетика; ##issue.no## 4(83) (2025): Scientific and applied Journal renewable energy ; 112-125 Відновлювана енергетика; № 4(83) (2025): Науково-прикладний журнал Відновлювана енергетика; 112-125 2664-8172 1819-8058 10.36296/1819-8058.2025.4(83) en https://ve.org.ua/index.php/journal/article/view/578/489 Copyright (c) 2025 T. Yevtukhova, O. Novoseltsev , M. Kuznietsov , D. Karpenko https://creativecommons.org/licenses/by-nc-nd/4.0 |
| spellingShingle | renewable energy energy efficiency energy sustainability energy services market business model case study. Yevtukhova, T. Novoseltsev , O. Kuznietsov , M. Karpenko , D. A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title | A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title_alt | ДВОСТОРОННЯ БІЗНЕС-МОДЕЛЬ ДЛЯ ПІДВИЩЕННЯ СТАЛОГО ЕКО-РОЗВИТКУ ТЕРИТОРІАЛЬНО-АДМІНІСТРАТИВНИХ ОДИНИЦЬ |
| title_full | A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title_fullStr | A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title_full_unstemmed | A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title_short | A TWO-SIDED BUSINESS MODEL FOR IMPROVING SUSTAINABLE ECO-DEVELOPMENT OF TERRITORIAL-ADMINISTRATIVE UNITS |
| title_sort | two-sided business model for improving sustainable eco-development of territorial-administrative units |
| topic | renewable energy energy efficiency energy sustainability energy services market business model case study. |
| topic_facet | renewable energy energy efficiency energy sustainability energy services market business model case study. відновлювана енергетика енергоефективність енергетична стійкість ринок енергетичних послуг бізнес-модель кейс-стаді. |
| url | https://ve.org.ua/index.php/journal/article/view/578 |
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