Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition
Background. The intensification of climate, energy and geopolitical risks contributes to the formation of a fundamentally new paradigm of strategic economic planning. For countries characterised by institutional fragility and increased geopolitical vulnerability, postponing the green energy transiti...
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2026
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|---|---|
| author | Mikhno, Inesa Penev, Nikolay Dudka, Tetyana Sabev, Ivan Nikolaev |
| author_facet | Mikhno, Inesa Penev, Nikolay Dudka, Tetyana Sabev, Ivan Nikolaev |
| author_institution_txt_mv | [
{
"author": "Inesa Mikhno",
"institution": "National University “Kyiv Aviation Institute”, Kyiv, Ukraine"
},
{
"author": "Nikolay Penev",
"institution": "Trakia University, Stara Zagora, Bulgaria"
},
{
"author": "Tetyana Dudka",
"institution": "National University “Kyiv Aviation Institute”, Kyiv, Ukraine"
},
{
"author": "Ivan Nikolaev Sabev",
"institution": "Trakia University, Stara Zagora, Bulgaria"
}
] |
| author_sort | Mikhno, Inesa |
| baseUrl_str | https://ees-journal.com/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-06-30T15:48:29Z |
| description | Background. The intensification of climate, energy and geopolitical risks contributes to the formation of a fundamentally new paradigm of strategic economic planning. For countries characterised by institutional fragility and increased geopolitical vulnerability, postponing the green energy transition is associated with a potentially non-linear escalation of economic losses due to the cumulative and mutually reinforcing impact of systemic risks.
Purpose. The study aims to conduct an analysis of green energy transition, with an emphasis on the economic interpretation of the “cost of choice”, and on identifying barriers that may hinder the large-scale implementation of renewable energy sources.
Findings. The methodological basis is a discounted expected cost model that accounts for direct investment, probabilistic estimates of energy shocks, and related macroeconomic losses. The analytical horizon was 10 years, with a 5% discount rate. The cumulative discounted “opportunity cost” of the active green transition scenario in Ukraine is USD 28.12 billion, while the status quo scenario reaches USD 75.06 billion. The total costs, adjusted for energy shocks, under the green transition scenario are estimated at USD 1.7 billion, compared to USD 9.2 billion under the status quo scenario. The risk of disruption in the energy sector under the green transition scenario is estimated at approximately 15%. However, the current energy balance relies heavily on traditional energy sources, significantly increasing risks and raising the probability of system failure to 40%. The cumulative costs of inaction are projected to reach a staggering USD 47 billion over the next decade of planning. Consequently, the initial capital outlays required for green initiatives are effectively offset by the mitigation of these long-term financial liabilities.
Implications. Delaying the green transition leads to a non-linear increase in macroeconomic losses, with the “cost of inaction” significantly exceeding the costs of active transformation. In this context, accelerating decarbonisation is crucial not only as a goal to combat climate change but also as a key strategy to reduce systemic risks and enhance the resilience of the energy sector. Furthermore, the opportunity cost methodology proposed in this paper represents an effective tool for addressing the challenges of modern energy management and strategic planning. |
| doi_str_mv | 10.61954/2616-7107/2026.10.2-2 |
| first_indexed | 2026-07-01T01:00:38Z |
| format | Article |
| fulltext |
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
23
Research Article
UDC 338.2:620.9
JEL: G31, Q42, Q48, Q54
ANALYSIS OF INVESTMENT TRADE-OFFS AND
THE COST OF POLICY INACTION IN THE GREEN
ENERGY TRANSITION
Inesa Mikhno *
National University “Kyiv Aviation
Institute”,
Kyiv, Ukraine
ORCID iD: 0000-0003-3661-1965
Nikolay Penev
Trakia University,
Stara Zagora, Bulgaria
ORCID iD: 0009-0003-7359-8357
Tetyana Dudka
National University “Kyiv Aviation
Institute”,
Kyiv, Ukraine
ORCID iD: 0000-0003-4216-1083
Ivan Nikolaev Sabev
Trakia University,
Stara Zagora, Bulgaria
ORCID iD: 0000-0001-9815-147X
*Corresponding author
E-mail: inessa.mihno@npp.nau.edu.ua
Background. The intensification of climate, energy and
geopolitical risks contributes to the formation of a
fundamentally new paradigm of strategic economic planning.
For countries characterised by institutional fragility and
increased geopolitical vulnerability, postponing the green
energy transition is associated with a potentially non-linear
escalation of economic losses due to the cumulative and
mutually reinforcing impact of systemic risks.
Purpose. The study aims to conduct an analysis of green
energy transition, with an emphasis on the economic
interpretation of the “cost of choice”, and on identifying
barriers that may hinder the large-scale implementation of
renewable energy sources.
Findings. The methodological basis is a discounted
expected cost model that accounts for direct investment,
probabilistic estimates of energy shocks, and related
macroeconomic losses. The analytical horizon was 10 years,
with a 5% discount rate. The cumulative discounted
“opportunity cost” of the active green transition scenario in
Ukraine is USD 28.12 billion, while the status quo scenario
reaches USD 75.06 billion. The total costs, adjusted for energy
shocks, under the green transition scenario are estimated at
USD 1.7 billion, compared to USD 9.2 billion under the status
quo scenario. The risk of disruption in the energy sector under
the green transition scenario is estimated at approximately
15%. However, the current energy balance relies heavily on
traditional energy sources, significantly increasing risks and
raising the probability of system failure to 40%. The
cumulative costs of inaction are projected to reach a staggering
USD 47 billion over the next decade of planning.
Consequently, the initial capital outlays required for green
initiatives are effectively offset by the mitigation of these long-
term financial liabilities.
Implications. Delaying the green transition leads to a
non-linear increase in macroeconomic losses, with the “cost of
inaction” significantly exceeding the costs of active
transformation. In this context, accelerating decarbonisation is
crucial not only as a goal to combat climate change but also as
a key strategy to reduce systemic risks and enhance the
resilience of the energy sector. Furthermore, the opportunity
cost methodology proposed in this paper represents an
effective tool for addressing the challenges of modern energy
management and strategic planning.
Keywords: Cost of Inaction, Economic Risks, Energy
Policy, Green Transition, Renewable Energy.
Received: 10/02/2026
Revised: 27/04/2026
Accepted: 16/05/2026
Published: 30/06/2026
DOI: 10.61954/2616-7107/2026.10.2-2
© Economics Ecology Socium, 2026
CC BY-NC 4.0 license
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
24
1. Introduction.
The global economy operates in a highly
volatile and uncertain environment. Climate
change, energy price volatility, and escalating
geopolitical tensions are interconnected and
overlapping. In this context, the development of
green technologies has ceased to be simply a
matter of environmental protection; it has
become a critical factor in energy security, the
transition to carbon neutrality, and long-term
economic sustainability. This study takes a
broader look at the concept of “costs of inaction”
examining not only the direct financial costs of
additional carbon reductions but also the future
economic losses countries will incur if energy
reforms are delayed. This study focuses on the
balance between short-term benefits and long-
term stability, as well as the relationship
between risk reduction, increased energy
independence, and sustainable economic growth.
Investments in renewable energy, energy
efficiency, and eco-innovation are inherently
capital-intensive and require not only
institutional stability but also the availability of
long-term financial instruments (Halldén et al.,
2025; Beloeva et al., 2025). In contrast,
continued reliance on traditional energy sources
contributes to the accumulation of
macroeconomic imbalances, amplifies the impact
of external shocks, and exacerbates the negative
consequences of climate change (Iorember et al.,
2025; Wen et al., 2025).
This study examines the need for a
systematic, integrated assessment of the
determinants shaping the investment
environment for green technologies in Ukraine
amid economic, energy, and political
uncertainty.
Regarding its scholarly novelty, this study
establishes the “cost of inaction” (CoI) as a
precise, quantifiable derivative of systemic risk.
Unlike traditional models, our approach
integrates the probability of energy-sector
disruptions into the fiscal architecture of
investment modelling. Furthermore, a non-linear
scenario-based methodology is deployed to
adjudicate the inherent tensions between
immediate liquidity constraints and long-term
macroeconomic durability. This analytical lens
is particularly relevant to emerging or threatened
economies, where geopolitical instability is a
primary determinant of energy security.
2. Literature Review.
2.1. Drivers of Green Technology
Development.
The literature has demonstrated that
“green” technologies and investments are
shaped by complex combinations of economic,
environmental, and social factors (Niu et al.,
2023). Green investment activities include
projects in renewable energy, energy
efficiency, clean technologies, and related
areas (Adamowicz, 2021).
There is lack of a single, universally
accepted definition due to the diversity of
approaches and application contexts (Milindi &
Inglesi-Lotz, 2022).
Green investment, as a broader concept,
encompasses ESG investments, socially
responsible investments, and sustainable
finance instruments, highlighting its dynamic
and context-dependent nature (Eyraud et al.,
2013; Iliev et al., 2023; Shopova et al., 2023).
Recent studies have identified several
key drivers of green technology
implementation and investment.
1. Regulatory and policy pressures
(Young et al., 2011). Environmental regulations
and government incentives (tax benefits,
subsidies, and grants) strongly encourage
enterprises to pursue green innovation, thereby
supporting the transformation of production
processes and technology.
2. Market factors and consumer demand.
The formation of a “clean” technology market
and stakeholder requirements force companies
to adapt to environmental standards, stimulating
innovation (Fu et al., 2020).
3. Financial instruments and financing
innovations. The development of sustainable
finance is considered an important driver that
facilitates access to capital and reduces barriers
to the implementation of technological
innovations (Kumar et al., 2023).
The literature confirms that the
development of green technologies is influenced
by regulatory, market, and financial factors. For
Ukraine, an active green transition is
strategically important, as it helps reduce
systemic economic and energy risks, increase
energy resilience, and integrate into global clean
technology markets.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
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2.2. Theoretical Perspectives on
Barriers to the Green Transition.
The set of barriers and constraints that
hinder the widespread implementation of green
technologies and investments is widely
represented in the literature.
1. Financial constraints related to the high
capital intensity of green projects, limited access
to financing for small businesses, and immature
financial markets significantly restrain the
development of clean technologies (Ozcelik et
al., 2025).
2. Policy uncertainty and regulatory risks
determine the instability of environmental
legislation, and uncertainty regarding subsidies
and regulatory frameworks reduces the
attractiveness of investments in green projects
(Kodua et al., 2022).
3. Organisational and technological
constraints include low levels of IT and
innovation competencies, and high R&D costs,
which are significant barriers in certain sectors
and the economy as a whole (Mojumder et al.,
2022).
This study proposes an approach to
analysing the “cost of choice”, which combines
scenario modelling of economic and financial
costs with an assessment of national
institutional and geopolitical risks. It considers
the investment trade-off between short-term
costs and long-term benefits for both the state
and businesses. The literature proposes various
theoretical approaches to understanding
investments in green technologies.
Drivers–barriers approach within
innovation diffusion theory, combining
institutional theory, resource-based view, and
diffusion of innovation theory to explain how
internal and external factors influence the
adoption of green technologies (Tan et al.,
2022). In this framework, drivers reduce long-
term costs, while barriers increase the “cost of
inaction”.
Behavioural factors approach, where
the implementation of green technologies
depends not only on economic incentives but
also on the behavioural characteristics of
agents (firms and investors), which may
explain slow adoption even under favourable
economic conditions (Zacher et al., 2023).
Du et al. (2021) highlighted the positive
long-term impact of green technologies on
structural economic transformation, while
Mikhno et al. (2021) argued that green
technology-led growth has a revolutionary
impact on reducing carbon intensity, especially
in high-income countries.
Demirel et al. (2025) highlighted the
significant financial and institutional
constraints that may slow the implementation
of green growth policies, even within the EU’s
Green Deal. Similarly, Angelakis et al. (2025)
emphasise that regulatory instability and
insufficient institutional capacity can offset the
potential economic benefits of green
investments. Furthermore, Fathoni et al. (2025)
analyse the green transition within the
framework of innovation diffusion theory,
highlighting the role of internal organisational
resources and strategic management.
Koval et al. (2021) focused on
behavioural aspects, showing that even
economically beneficial green solutions may
not be implemented because of cognitive and
psychological barriers.
However, previous studies have not
formally considered the risks associated with
investment decisions, assessed the “costs of
choice” or compared the economic
consequences of deciding not to make one. To
support future strategic investments in
Ukraine’s green technology sector, a model is
needed that integrates economic assessments,
risks, and country-specific factors. A literature
review shows that, although numerous factors
facilitate the adoption of green technologies,
many barriers remain that must be overcome
through institutional innovation, financial
mechanisms, and specific regulations.
2.3. Literature Gap and Research
Questions.
An analysis of the academic literature
indicates a large number of studies on the
drivers and barriers to green investment. The
issue of a comprehensive quantitative
assessment of the “cost of choice” between an
active green transition and maintaining the
status quo remains underdeveloped, especially
in economies with heightened geopolitical
vulnerability.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
26
The identified gaps in “cost of choice”
research highlight the importance of studying
the strategic green transition in Ukraine's
energy sector. In this regard, the following
research questions are posed:
RQ1. Does the total discounted expected
cost of inaction exceed the cost of an active
green transition in a risk-oriented assessment?
RQ2. To what extent do energy, political,
and institutional risks affect the formation of
the “cost of choice” in the medium term?
RQ3. What is the magnitude of the "cost
of inaction" in an economy with increased
geopolitical vulnerability?
RQ4. What investment trade-offs are
critical for the formation of effective public
policies for the green transition?
Thus, the formulated research questions
define the logic for further modelling and serve
as the basis for a quantitative scenario analysis.
3. Methodology.
3.1. Research Design and
Methodological Approach.
The methodological basis of the study is
the concepts of sustainable development, green
economy, and investment theory, which allow
considering the development of green
technologies as the result of interaction
between economic incentives, institutional
conditions, and external risks. Particular
attention is paid to the concept of the “cost of
choice”, which is defined as the combination of
short-term costs and long-term benefits
associated with the adoption or postponement
of decisions regarding the green transition in
the energy sector.
Within the study, a model for evaluating
investment trade-offs and the “cost of choice”
is proposed, combining scenario modelling,
comparison of alternative investment
trajectories and risk-oriented cost assessment.
3.2. Scenario Framework of the Green
Transition.
In this study, a scenario-based approach
was used, involving the development of two
alternative trajectories for the energy sector and
the economy as a whole.
1. Scenario G (Green Transition).
This scenario involves large-scale
investments in renewable energy sources,
modernisation of energy networks, development
of energy storage systems, improvement in
energy efficiency, and gradual diversification of
energy consumption. High initial capital
investments and a reduction in the probability of
energy and macroeconomic shocks in the
medium term characterise it.
2. Scenario S (Status Quo).
This scenario is characterised by
maintaining the existing energy balance
structure, limited investment in renewable
energy sources, and high dependence on
traditional energy resources. Short-term costs
are lower; however, the probability of systemic
risks and the scale of potential economic losses
increase accordingly.
The modelling horizon is 10 years, which
allows for capturing the medium-term effects of
investments in green technologies while
maintaining the relative predictability of
macroeconomic and political parameters. A ten-
year period is sufficient to assess the cumulative
effects of investments, structural changes in
energy consumption, and the impact of risk on
economic resilience.
The comparison was based on calculating
the total discounted expected cost for each
scenario. The cost structure includes the
following:
Initial investment costs.
Annual operating costs.
Expected losses from potential energy
and political shocks (adjusted by probability).
Discounted value of future payments.
The “cost of choice” is defined as the
difference between the total discounted costs of
scenarios “S” and “G”. If the discounted cost of
scenario “S” exceeds that of scenario “G”,
postponing the green transition is considered
economically irrational.
This approach formalises the trade-off
between the short-term financial burden and
long-term macroeconomic stability, accounting
for the non-linear increase in the “cost of
inaction” under conditions of elevated systemic
risk.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
27
Effective scenario modelling should focus
on contrasting yet internally consistent
development paths to enable the identification
of key decision points.
In the context of energy transformation,
the use of a binary structure, “transition vs
inaction”, allows for the clear isolation of the
effects of investment decisions and assessment
of differences in long-term expected costs. This
approach is consistent with scenario analysis
practices in the energy sector, particularly those
used by the International Energy Agency
(2025), which compares a baseline scenario
with a transformation scenario.
Furthermore, the risk-oriented model
accounts for asymmetry in outcomes. In
vulnerable economies, potential losses from
negative shocks may be non-linear and
significantly exceed short-term savings from
reduced investment activity.
Therefore, integrating shock probabilities
into discounted cost calculations is
methodologically justified and is consistent with
modern approaches to strategic planning under
uncertainty.
This approach treats uncertainty as an
integral structural element of the model, rather
than as an external factor. Special attention is
given to economies with high geopolitical
vulnerability, where risks tend to accumulate
and grow non-linearly, significantly affecting
the long-term “cost of inaction”.
Scenario modelling enables the
quantitative assessment of the economic, social,
and environmental outcomes of each
development option.
The “cost of choice” is expressed as the
minimisation of discounted total costs under
two alternative scenarios (Table 1): G – an
active green transition; and S – the status quo.
Table 1. Key Model Parameters and Assumptions.
Parameter Symbol Value Description
Time Horizon T 10 years Evaluation period for the scenario analysis
Discount Rate r 5%
Social discount rate applied to infrastructure
investments
Probability of Energy Shock
(Green Scenario)
pG 15%
Estimated probability of systemic energy
disruptions under the green transition scenario
Probability of Energy Shock
(Status Quo Scenario)
pS 40%
Higher probability due to continued reliance on
energy imports
Loss per Shock (Green
Scenario)
LG
USD 6
billion
Economic losses associated with localised
disruptions in the energy system
Loss per Shock (Status Quo
Scenario)
LS
USD 18
billion
Economic losses from large-scale disruptions,
including significant GDP impacts
Table 2 presents a comparative cost
structure for two alternative energy system
development scenarios: an active green
transition scenario (G) and a business-as-usual
scenario (S). The cost structure is organised into
four key categories, allowing for a
comprehensive assessment of differences not
only in direct financial costs but also in hidden
economic consequences, the level of
vulnerability to energy shocks, and the long-
term sustainability of the energy system in each
scenario.
The estimation of losses from an energy
shock (L) represents another limitation of the
model. In the proposed scenarios, losses are
assumed at USD 6 billion for the green
transition scenario (L₍G₎) and USD 18 billion for
the status quo scenario (L₍S₎). However, the
methodology for deriving these values is not
fully specified. The threefold difference reflects
the assumption that a more resilient and
decentralised energy system significantly
mitigates the scale of economic damage;
however, this proportionality may raise
questions regarding its empirical justification.
The estimation is based on a modelled
area of 1,000 km², using data on the number of
enterprises located in the Kyiv region, their
economic performance indicators, and
environmental impact metrics.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
28
Table 2. Comparative Cost Structure of Energy Transition Scenarios.
Cost Category Green Transition Scenario (G) Status Quo Scenario (S)
Initial and Capital
Costs
Investments in renewable energy (solar, wind,
bioenergy); grid modernisation and
digitalisation (smart grids); energy storage
systems; decentralisation of generation; R&D
in green technologies
Limited capital investments;
maintenance of existing
infrastructure; emergency repairs;
partial and delayed modernisation
Operational and
Current Costs
Operation and maintenance of renewable
capacities; integration of renewables into the
energy system; administrative costs of policy
implementation
Fossil fuel imports; price
compensation mechanisms; tariff
subsidies; increasing operating costs
of outdated infrastructure
Institutional and
Macroeconomic
Costs
Workforce retraining programs; policy
incentives (subsidies, tax benefits);
educational campaigns; improved energy
independence and investment attractiveness
Losses from energy dependence;
price volatility; reduced investment
attractiveness; capital outflows;
weak institutional support
Risk-Related Losses
and System
Outcomes
Local disruptions in early stages; delays in
technology deployment; regulatory risks;
reduced probability of systemic shocks; long-
term price stabilisation and resilience
Large-scale energy shocks;
infrastructure destruction; high price
volatility; GDP losses due to supply
disruptions; persistent systemic
vulnerability
In this context, it should be clarified that
the estimated losses include GDP decline, costs
of emergency response and recovery, as well as
losses associated with industrial disruptions.
While this approach allows for an approximate
macro-level assessment, it involves
simplifications and may not fully capture
regional heterogeneity across the country. A
more precise estimation would require detailed
modelling and empirical validation, including
references to statistical data or prior studies,
which can be considered as a direction for
further research.
A key feature of scenario S is the higher
probability of systemic shocks and larger scale
of losses, leading to non-linear growth of
expected costs.
Taking into account uncertainty, the
model of expected costs can be expressed as:
𝐸 𝑇𝐶 𝐼 , ∑ , , (1)
where:
TCi – total discounted “cost of choice” of
scenario i;
T – time horizon;
Ci,t – direct costs in period t;
I0,i – initial investment expenditures for
scenario 𝑖;
pt – probability of an energy shock in
period t;
r – discount rate;
Li,t – economic losses associated with a
shock in period t.
The decision criterion is based on the
difference between the total costs of the status
quo scenario (S) and the green transition
scenario (G):
P0 = TCS−TCG (2)
where:
P0 – economic effect of postponing the
green transition.
P0 > 0 indicates that delaying the green
transition is economically irrational, as the total
discounted costs of maintaining the status quo
exceed those of the green transition.
The annual costs (Cᵢ) used in the model
for the 10-year analysis horizon include both
operational, institutional, and risk-related
expenditures as well as initial capital
investments. Specifically, for the green
transition scenario (G), annual operational and
institutional costs are estimated at USD 0.8
billion per year, while initial investment
expenditures total USD 15–28 billion (covering
renewable energy capacities, grid
modernisation, energy storage, decentralisation,
and R&D). For the status quo scenario (S),
annual operational and institutional costs are
USD 2 billion per year, with initial investments
of USD 4 billion.
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Therefore, extending the analytical
horizon further strengthens the relative
effectiveness of the green scenario. The model
uses a standard discount rate of 5% without
fully exploring alternative methodological
approaches. While the sensitivity analysis
includes variations of 3% and 7%, the choice of
the base rate may require additional
justification, especially in the context of post-
conflict economies characterised by uncertainty
and risk. A higher discount rate should be used.
However, the 5% discount rate chosen here
reflects generally accepted methods for
evaluating public infrastructure projects.
Finally, the probabilities of energy shocks
used in the model (pG2 = 15% for the green
transition scenario, pS2 = 40% for the status quo
scenario) are based on expert assessment and
reflect the current vulnerability profile of
Ukraine’s energy transport system under
different development scenarios.
The high probability of the status quo
reflects reliance on imported energy, obstacles
to infrastructure development, and ongoing
geopolitical risks. In contrast, the low
probability under a "green" scenario reflects
energy decentralisation, diversification of
energy sources, and increased reliance on
imports. However, the lack of formal statistical
assessment is a significant limitation and a key
area for future research.
4. Results.
The recent data from the International
Renewable Energy Agency (IRENA, 2025)
indicate that global investment in renewable
energy has exceeded USD 500 billion, with
clean energy now accounting for 30% of the
global energy mix. This reflects a profound
structural transformation of national energy
systems, with green capital becoming a
fundamental economic driver.
In this study, "choice cost" is considered a
complex economic prism encompassing direct
costs, opportunity costs, and delayed effects.
Moving away from traditional models that focus
solely on implementation costs this framework
pits two realities against each other: an
accelerated green shift versus the maintenance
of the status quo.
While the transition undoubtedly carries a
heavy price tag in terms of initial CAPEX and
fiscal strain, the alternative is a deceptive “cost
of inaction”.
Traditionally, the main barriers to the
development of green technologies in the
academic literature are financial constraints and
technological immaturity. However, under
modern instability, institutional and behavioural
factors are gaining importance. Policy
inconsistency, frequent regulatory rule changes,
and the absence of long-term signals for
investors significantly reduce the effectiveness
of financially attractive projects. Particular
attention should be paid to the behavioural
aspects of decision-making.
Even when economic incentives are
present, economic agents may avoid investing in
green technologies due to high uncertainty,
information asymmetry, and a tendency to
preserve existing business models. This
suggests that barriers to the green transition are
not only economic, but also institutional and
psychological. To visually present the risk-
oriented assessment of the “cost of choice”, a
flowchart is proposed that captures the main
modelling stages and interconnections among
scenarios, risks, and expected outcomes. First,
the initial data are formed, including alternative
investment trajectories (active green transition
and status quo scenarios), initial capital
investments, and socio-economic parameters.
At the next stage, a risk-adjusted
evaluation is performed, incorporating
technological, energy-related, and socio-
institutional uncertainties, which allows for the
correction of expected cost estimates.
Subsequently, the “cost of choice” framework is
applied to formalise the intertemporal trade-off
between immediate expenditures and
prospective benefits, with explicit consideration
of the identified risk factors.
By synthesizing these variables, the model
generates a set of outputs that include projected
costs, a quantified "cost of inaction," and
sensitivity analyses tied to specific risk
parameters. This provides more than just raw
data; it establishes a rigorous analytical
foundation for strategic planning in green
technology deployment (Figure 2).
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Fig. 2. Block Diagram of the Risk-Oriented Model for Assessing the “Cost of Choice” of the
Green Transition.
Despite the growing risks, the current
transition undoubtedly requires significant
initial capital investments, but it is characterised
by the emergence of new factors in the
development of “green” technologies.
At the same time, the gradual decline in
the cost of some green technologies has reduced
the "choice costs" of a green transition. Table 3
shows that the "choice costs" of a green
transition extend beyond initial investments.
Table 3. “Cost of Choice” of the Green Transition: Comparison of Alternative Development
Scenarios of a Country.
Dimension of
analysis
Active development of green technologies Delay / status quo preservation
Capital expenditures
(short-term)
High initial investments in renewable energy sources
(RES), grids, energy storage, and energy efficiency
Lower initial costs, absence of large-
scale modernisation
Fiscal burden
Temporary increase in budget expenditures (subsidies,
investment support)
Hidden fiscal risks due to price
shocks and energy crises
Energy security
Reduced dependence on energy imports,
diversification of sources
Persistence of high external
dependence and vulnerability
Price stability Gradual stabilisation of energy prices in the long term High volatility of energy prices
Investment
attractiveness
Increased interest from international and private
investors
Limited access to green finance
Technological
development
Formation of new economic sectors and innovation
clusters
Risk of technological lag
Social implications
Redistribution of employment, need for workforce
reskilling
Preservation of traditional jobs, but
without long-term prospects
Climate and
environmental costs
Reduction of emissions and environmental pressure
Increase in adaptation costs to
climate change
Long-term economic
benefits
Enhanced economic resilience and competitiveness
Accumulation of structural and
macroeconomic risks
Overall “cost of
choice”
High short-term cost → lower long-term risks
Low short-term cost → high cost of
inaction
Input Data
• Defines the scenarios of
an active transition and
the status quo, initial
capital investments, and
socio-economic
parameters
Risk-Oriented
Assessment
• Integrates technological,
energy, and socio-
regulatory risks to adjust
expected costs
“Cost of Choice”
Model
• Formalises the trade-off
between short-term
costs and long-term
benefits, taking into
account all risks
Output Results
• Expected costs, the
“cost of inaction,” and
the sensitivity of results
to key risks, enabling
well-informed
investment decisions
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Lagging in green technology development
results in long-term hidden costs, including
increased energy dependence. This model uses a
10-year calculation period and a 5% discount
rate. A 10-year period was chosen as the
optimal time frame for assessing green
technology investments because it allows
consideration for the medium-term impacts of
renewable energy deployment and energy
system modernisation to be examined. The 5%
discount rate balances reducing the long-term
risks of public and private investments with the
current value of money in the context of the
national economy.
Table 4 presents the results of the scenario
analysis, summarising the expected costs of
transitioning to green technologies compared to
maintaining the status quo.
Table 4. Assessment of the “Cost of Choice” of the Green Transition for Ukraine Based on
Scenario Calculations.
Indicator Active Green Transition (G) Status Quo / Deferral (S)
Initial Investments
(t = 0)
USD 15 billion *
USD 4 billion (maintenance and
support of the current power system)
Operating Expenses (OPEX),
USD billion/year
0.8 (maintenance, system support, RES
integration)
2.0 (fuel imports, emergency repairs,
compensation mechanisms)
Probability of Energy Shock,
p
15% (lower due to decentralisation, renewable
energy expansion, and reduced import
dependence)
40% (higher due to external shocks,
import dependence, and price
volatility)
Loss per Shock, L
USD 6 billion (localised disruptions of the
energy system)
USD 18 billion (large-scale
blackouts, import disruptions, and
GDP losses)
Expected Risk Losses (pL),
USD billion/year
0.9 7.2
Total Annual Costs (C + pL),
USD billion/year
1.7 9.2
Discounted Operating and
Risk Costs (10 years), USD
billion
13.12 71.06
Total Expected Cost of
Choice, E(TC), USD billion
28.12 75.06
Cost of Inaction (S − G),
USD billion
≈ 47
Note:* Baseline calculation uses USD 15 billion (phased transition). Full investment range: USD
15–28 billion; upper bound (USD 28 billion) tested in sensitivity analysis.
The calculation is based on the expected
total cost formula (Formula 1), where I₀
denotes the initial investment, C denotes the
annual cost, pL denotes the expected annual
risk-related losses, and r denotes the discount
rate.
The estimated “cost of inaction” of
approximately USD 47 billion, equivalent to
25–30% of Ukraine’s GDP, underscores the
substantial macroeconomic scale of potential
losses and the critical importance of a timely
green transition.
The values of USD 13.12 billion and
USD 71.06 billion are obtained as the
discounted sum of annual costs (C + pL) over a
10-year period at r = 5%, after which the initial
investments are added.
The difference between the scenarios (≈
USD 47 billion) is interpreted as the “cost of
inaction”. The green scenario (G) demonstrates
a significant reduction in expected losses from
energy shocks: the pL indicator decreases from
USD 7.2 billion to USD 0.9 billion per year,
that is, by approximately 87.5%, indicating a
sharp increase in the resilience of both the
energy system and economy to crisis events.
The total annual costs, including risk (C
+ pꞏL), amount to USD 1.7 billion in the green
scenario versus USD 9.2 billion in the baseline,
representing a reduction of approximately
81.5%. This indicates that, even when
accounting for the investment component, the
green transition is economically more efficient
in the medium term.
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The discounted difference in costs
between the scenarios amounts to
approximately USD 47 billion, which is
interpreted as the “cost of inaction”, the
additional costs the economy will incur if the
energy transition is delayed. Maintaining the
status quo incurs a significant “cost of
inaction” that exceeds USD 47 billion and is
driven by the high probability of large-scale
energy shocks. To assess the impact of the key
directions on the success of the green
transition, an expert analytical approach with
weighted evaluation was applied. The
methodology is based on determining the
relative importance of each direction and the
balance between positive factors and barriers
(Table 5).
In the first stage, the main directions
determining the effectiveness of the green
transition were identified, including policy and
regulation, investment and finance,
technologies and infrastructure, the social
dimension, risks and system resilience, and
external political influence.
In the second stage, a qualitative
assessment was performed for each direction.
• The level of potential positive impact of
the proposed recommendations
• The strength and scale of existing
barriers that may hinder implementation.
These assessments were aggregated into
an integrated impact indicator that reflected the
net effect of each direction, accounting for both
enabling and constraining factors.
The indicator “Approximate impact on
success (%)” presented in the table is
interpreted as the relative weight of each
direction in shaping the overall outcome of the
green transition.
These values were expertly determined,
considering the following: scale of economic
effects; speed of implementation of changes;
the impact of reducing systemic risks;
criticality for energy security.
The sum of all weights equals 100%,
allowing them to be considered as the
contribution structure of different factors in
achieving a successful transition.
Table 5. Recommendations and Potential Barriers to the Development of the Green Transition
in Ukraine.
Pillar / Direction Policy Recommendations Potential Barriers
Estimated
Impact on
Success (%)
Policy and
Regulation
Implementation of a national green
transition strategy; ensuring a stable
legislative framework and tax incentives
Frequent legislative changes;
insufficient enforcement of
policy measures
25% (Critical)
Investment and
Financing
Establishment of dedicated funds and
credit lines for renewable energy sources
(RES); attraction of private investment
through guarantees and incentives
High initial capital
requirements; lack of long-
term financial instruments
20% (High)
Technology and
Infrastructure
Modernisation of energy grids for RES
integration; support for research in energy
storage and smart energy management
Legacy infrastructure
incompatible with RES;
insufficient technological
and scientific capacity
20% (High)
Social Dimension
and Education
Workforce upskilling for green
technologies; public awareness campaigns
on energy efficiency and sustainable
consumption
Resistance to change in
traditional sectors; low level
of public awareness
15% (Medium)
Risk Management
and System
Resilience
Integration of risk assessment into energy
planning; development of rapid response
mechanisms to energy shocks
High exposure to energy and
climate-related risks
20% (High)
External Political
Factors
Diversification of funding sources;
strengthening energy independence and
international partnerships
External political influence
and geopolitical instability
potentially delaying
implementation
10%
(Moderate)
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The analysis in Table 5 shows that the
successful implementation of the green
transition in Ukraine depends on a
comprehensive approach to political,
economic, technological, and social aspects.
The most critical barriers include
instability of legislation and insufficient
enforcement of policies (≈25% impact on
success), high initial capital investments and
limited access to long-term financing (≈20%),
outdated infrastructure and an insufficient
scientific and technical base for integrating
renewable energy sources (≈20%), resistance
to change among the workforce and low public
awareness (≈15%), and high risks of energy
and climate change (≈20%).
Overcoming these barriers requires a
coherent state strategy, a stable regulatory
environment, investment support and,
modernisation of energy infrastructure. If most
of these measures are implemented, the share
of successful integration of renewable energy
sources into the country’s energy balance could
reach 35–45% by 2030, significantly reducing
economic risks and lowering the “cost of
inaction”. To assess the robustness of the
economic efficiency of the green transition, a
sensitivity analysis of the key parameters of the
“cost of choice” model was conducted. The
main parameters affecting the expected costs
and the “cost of inaction” are presented in
Table 6.
Table 6. Key Parameters Influencing Expected Costs and the “Cost of Inaction”.
Parameter
Base
Value
Tested
Variants
Result
Discount Rate 5% 3%, 7%
Reducing the rate to 3% decreases the expected costs of the
Green Scenario by ≈8%; increasing it to 7% raises costs by
≈10%. Long-term benefits of the green transition are highly
sensitive to financial parameters.
Probability of
Energy Shocks
(External Risks)
15% 10%, 20%
Reducing the shock probability to 10% lowers expected
additional costs of the Status Quo by ≈5%; increasing it to
20% raises them by ≈12%. This underscores the importance
of integrating risk assessment into strategic planning.
Initial Capital
Expenditures
(CAPEX) in RES
USD 28
billion
±20%
A 20% increase in CAPEX raises expected costs by ≈15%; a
20% decrease lowers them by ≈12%. Despite these
fluctuations, the Green Scenario remains more economically
viable than the Status Quo.
Impact of Social
and Regulatory
Barriers
– –
Lack of incentives and slow workforce adaptation could
reduce the overall efficiency of the green transition by ≈10–
15%.
A comparison of Ukraine with Central
and Eastern European countries (Poland, the
Czech Republic, Romania, and Hungary)
reveals significant differences in the pace of
integrating renewable energy sources into the
electricity grid and in the expected economic
costs of inaction. Table 7 shows that the level
of renewable energy integration in Ukraine is
moderate. Ukraine faces high costs of inaction
due to dependence on existing energy sources
and inadequate infrastructure. In contrast, the
experience of neighbouring countries shows
that even before the share of renewable energy
sources peaks, investment in technology and
political stability can mitigate economic risks.
It is worth noting that, in addition to
domestic political factors, Ukraine’s “green”
development path is significantly influenced by
foreign policy factors, which hinder the
achievement of long-term strategic goals and
reduce the effectiveness of renewable energy
projects. In this tense environment, geopolitical
instability is expected to increase the “cost of
inaction” by 5-10% and exacerbate economic
risks.
Decarbonisation has moved from a
moral choice to a strategic advantage for
energy importers, while fossil fuel producers
face the growing threat of "stranded assets"
(Mercure et al., 2021).
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Table 7. Comparison Countries on Renewable Energy Integration and the “Cost of Inaction”.
Country
RES Share in
Electricity
Generation
Key Features / Commentary
Expected “Cost of
Inaction” (USD
billion)
Cost of
Inaction (% of
GDP)
Ukraine 15%
Low RES share; high dependence
on traditional energy sources
47
29%
Poland 20%
High dependence on coal; slow
energy transition
55 6.9%
Czech
Republic
22%
Significant investments in solar
and wind energy
38.5 11.7%
Romania 25%
Active state support for
renewable energy investments
33–35 11–12%
Hungary 18%
Low energy autonomy; high
vulnerability to external shocks
44
24%
As the environmental component plays
an increasingly important role, the energy
transition is now a means to ensure energy
independence and resilience against external
shocks (Genc & Kosempel, 2023).
In developing countries, renewable
energy packages often produce neutral or even
negative results in the short term, with
significant gains achieved in the medium term
(Galeazzi et al., 2024). Moreover, this shift is
reflected in public support not for climate
initiatives but for the possibility of achieving
energy security (Ahonen et al., 2025).
5. Discussion.
In economies suffering from institutional
weaknesses and geopolitical instability, the
costs of inaction do not grow linearly; rather,
they accumulate as systemic risks intensify.
Therefore, postponing structural reforms makes
no economic sense.
Future research should delve deeper into
the sensitivity analysis of variables and gain a
more complete understanding of the
interactions among discount rates, specific
probability distributions of energy shocks, and
capital demand volume. Furthermore, a logical
next step would be to direct macroeconomic
analysis towards geospatial mapping of
Ukraine’s renewable energy potential.
Complementary research directions may
include comparative analyses of the integration
of advanced technologies into energy system
management, storage infrastructure, and
digitalisation processes.
These findings are broadly consistent
with contemporary contributions to the energy
transition literature, which emphasises that the
primary effect of decarbonisation extends
beyond emission reduction to encompass a
substantial decrease in systemic economic
risks. In particular, Mercure et al. (2021) and
Genc and Kosempel (2023) demonstrated that
the transition to renewable energy sources is
associated with reduced macroeconomic
vulnerability, largely due to lower dependence
on imported energy resources and a diminished
price volatility. These findings indicate
significantly lower expected risk-related losses
in the green transition scenario. A comparison
of modelling approaches within the EU Green
Deal further confirms the conceptual
consistency of these results.
Large-scale European models, such as
PRIMES (Price-Induced Market Equilibrium
System) and GEM-E3 (General Equilibrium
Model), explicitly incorporate the cost of
inaction, which typically manifests as higher
energy import costs, increased climate
adaptation costs, and measurable losses in
gross domestic product. However, in contrast
to the relatively stable EU economies, this
effect is considerably more pronounced in
Ukraine owing to the compounded influence of
war-related risks, infrastructure degradation,
and persistent energy dependence. Compared
with the economies of Central and Eastern
Europe, the estimated cost of inaction for
Ukraine (approximately USD 47 billion) is
comparable or even lower in absolute terms.
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However, relative to GDP, particularly
as a share of GDP, it is substantially higher.
These findings indicate that postponing the
green transition in Ukraine entails
disproportionately high economic costs,
thereby strengthening the strategic case for a
more rapid transformation of the energy
sector.
The results support the existence of an
“insurance investment effect”, in which a
significant decline in long-term costs and
systemic risks offsets the higher upfront
investments required for a green transition.
This interpretation is consistent with analytical
approaches that conceptualise infrastructure
investment as a form of risk management.
Moreover, the green transition generates
a range of additional macroeconomic effects
that are not fully captured within the model,
including the following:
– A reduction in the balance deficit
owing to decreased reliance on energy imports.
– Stimulation of investment activity and
employment growth within the renewable
energy sector.
– An increase in energy autonomy,
accompanied by enhanced geopolitical
resilience.
Thus, even under relatively conservative
modelling assumptions, the results robustly
confirm that delaying the green transition is
economically inefficient. Simultaneously, the
active transformation of the energy sector is a
key determinant of long-term macroeconomic
stability and national economic security.
6. Conclusions.
This study provides substantial,
methodologically sound evidence that the
transition to green energy in Ukraine should be
viewed not only as an ecological necessity but
also as an economically rational and
strategically inevitable path that fully meets the
stated goal of assessing the relative efficiency
of alternative development trajectories in the
national energy sector. In particular, the
analysis of the so-called “choice cost” clearly
demonstrates that any delay in integrating
renewable energy sources imposes significant
long-term economic burdens.
It manifests through a combination of
increased external energy dependence, greater
macroeconomic instability, and the systematic
loss of potentially profitable investment
opportunities that could otherwise support
structural modernisation.
Regarding the research objective, the
green transition scenario is characterised by a
significantly higher level of economic
efficiency than maintaining the status quo.
Over 10-years, an effective transition to a
green economy would cost USD 1.7 billion per
year, compared to USD 9.2 billion per year for
the status quo. However, the “cost of inaction”
is USD 47 billion, as delaying decisions only
increases risks rather than generating savings.
When compared to its Central and
Eastern European peers, Ukraine shows a
moderate level of RES integration, yet the
fiscal penalty for delaying this transition is
disproportionately severe. This is largely
because deep-seated structural rigidities
continue to stifle the country’s adaptive
capacity.
Sensitivity analysis confirms that,
regardless of whether we reset the discount
rates or adjust the expected probability of
energy shocks, the green energy transition
trajectory consistently outperforms the baseline
scenario. The green energy transition lays the
foundation for long-term sustainable growth by
promoting energy autonomy and mitigating the
impact of external shocks.
Conflict of Interest Statement.
The authors have declared no conflict of
interest.
Funding Disclosure.
This research received no external
funding.
AI Use Statement.
During the preparation of this work the
author(s) used Grammarly in order to improve
the readability and language of the manuscript.
After using this tool/service, the authors
reviewed the content and take full
responsibility for the content of the article.
Economics Ecology Socium e-ISSN 2786-8958
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37
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| id | oai:ojs2.www.ees-journal.com:article-341 |
| institution | Economics Ecology Socium |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-01T01:00:38Z |
| publishDate | 2026 |
| publisher | Dr. Viktor Koval |
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| resource_txt_mv | ees-journalcom/15/52c536dcaa1f609cca4fb9257095f015.pdf |
| spelling | oai:ojs2.www.ees-journal.com:article-3412026-06-30T15:48:29Z Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition Mikhno, Inesa Penev, Nikolay Dudka, Tetyana Sabev, Ivan Nikolaev Cost of Inaction, Economic Risks, Energy Policy, Green Transition, Renewable Energy. Cost of Inaction, Economic Risks, Energy Policy, Green Transition, Renewable Energy. Background. The intensification of climate, energy and geopolitical risks contributes to the formation of a fundamentally new paradigm of strategic economic planning. For countries characterised by institutional fragility and increased geopolitical vulnerability, postponing the green energy transition is associated with a potentially non-linear escalation of economic losses due to the cumulative and mutually reinforcing impact of systemic risks. Purpose. The study aims to conduct an analysis of green energy transition, with an emphasis on the economic interpretation of the “cost of choice”, and on identifying barriers that may hinder the large-scale implementation of renewable energy sources. Findings. The methodological basis is a discounted expected cost model that accounts for direct investment, probabilistic estimates of energy shocks, and related macroeconomic losses. The analytical horizon was 10 years, with a 5% discount rate. The cumulative discounted “opportunity cost” of the active green transition scenario in Ukraine is USD 28.12 billion, while the status quo scenario reaches USD 75.06 billion. The total costs, adjusted for energy shocks, under the green transition scenario are estimated at USD 1.7 billion, compared to USD 9.2 billion under the status quo scenario. The risk of disruption in the energy sector under the green transition scenario is estimated at approximately 15%. However, the current energy balance relies heavily on traditional energy sources, significantly increasing risks and raising the probability of system failure to 40%. The cumulative costs of inaction are projected to reach a staggering USD 47 billion over the next decade of planning. Consequently, the initial capital outlays required for green initiatives are effectively offset by the mitigation of these long-term financial liabilities. Implications. Delaying the green transition leads to a non-linear increase in macroeconomic losses, with the “cost of inaction” significantly exceeding the costs of active transformation. In this context, accelerating decarbonisation is crucial not only as a goal to combat climate change but also as a key strategy to reduce systemic risks and enhance the resilience of the energy sector. Furthermore, the opportunity cost methodology proposed in this paper represents an effective tool for addressing the challenges of modern energy management and strategic planning. Background. The intensification of climate, energy and geopolitical risks contributes to the formation of a fundamentally new paradigm of strategic economic planning. For countries characterised by institutional fragility and increased geopolitical vulnerability, postponing the green energy transition is associated with a potentially non-linear escalation of economic losses due to the cumulative and mutually reinforcing impact of systemic risks. Purpose. The study aims to conduct an analysis of green energy transition, with an emphasis on the economic interpretation of the “cost of choice”, and on identifying barriers that may hinder the large-scale implementation of renewable energy sources. Findings. The methodological basis is a discounted expected cost model that accounts for direct investment, probabilistic estimates of energy shocks, and related macroeconomic losses. The analytical horizon was 10 years, with a 5% discount rate. The cumulative discounted “opportunity cost” of the active green transition scenario in Ukraine is USD 28.12 billion, while the status quo scenario reaches USD 75.06 billion. The total costs, adjusted for energy shocks, under the green transition scenario are estimated at USD 1.7 billion, compared to USD 9.2 billion under the status quo scenario. The risk of disruption in the energy sector under the green transition scenario is estimated at approximately 15%. However, the current energy balance relies heavily on traditional energy sources, significantly increasing risks and raising the probability of system failure to 40%. The cumulative costs of inaction are projected to reach a staggering USD 47 billion over the next decade of planning. Consequently, the initial capital outlays required for green initiatives are effectively offset by the mitigation of these long-term financial liabilities. Implications. Delaying the green transition leads to a non-linear increase in macroeconomic losses, with the “cost of inaction” significantly exceeding the costs of active transformation. In this context, accelerating decarbonisation is crucial not only as a goal to combat climate change but also as a key strategy to reduce systemic risks and enhance the resilience of the energy sector. Furthermore, the opportunity cost methodology proposed in this paper represents an effective tool for addressing the challenges of modern energy management and strategic planning. Dr. Viktor Koval 2026-06-30 Article Article Peer-reviewed Article application/pdf https://ees-journal.com/index.php/journal/article/view/341 10.61954/2616-7107/2026.10.2-2 Economics Ecology Socium; Vol. 10 No. 2 (2026): Economics Ecology Socium; 23-38 Економіка Екологія Соціум; Том 10 № 2 (2026): Economics Ecology Socium; 23-38 2616-7107 2616-7107 10.61954/2616-7107/2026.10.2 en https://ees-journal.com/index.php/journal/article/view/341/293 Copyright (c) 2026 Economics Ecology Socium https://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | Cost of Inaction Economic Risks Energy Policy Green Transition Renewable Energy. Mikhno, Inesa Penev, Nikolay Dudka, Tetyana Sabev, Ivan Nikolaev Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_alt | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_full | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_fullStr | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_full_unstemmed | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_short | Analysis of Investment Trade-Offs and the Cost of Policy Inaction in the Green Energy Transition |
| title_sort | analysis of investment trade-offs and the cost of policy inaction in the green energy transition |
| topic | Cost of Inaction Economic Risks Energy Policy Green Transition Renewable Energy. |
| topic_facet | Cost of Inaction Economic Risks Energy Policy Green Transition Renewable Energy. Cost of Inaction Economic Risks Energy Policy Green Transition Renewable Energy. |
| url | https://ees-journal.com/index.php/journal/article/view/341 |
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