International Journal of Energy Studies,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 20, 2024
This
research
investigates
the
convergence
of
nascent
hydrogen
market
and
blockchain
technology.
Driven
by
renewable
energy
policies,
demand
has
surged
across
traditional
industries,
expanding
overall
market.
However,
trading
remains
a
relatively
new
sector
in
need
growth
investment.
By
examining
interplay
between
trade
mechanisms
within
existing
international
regulatory
landscape,
including
World
Trade
Organization
frameworks,
this
study
explores
feasibility
integration.
Stakeholder
analysis
highlights
government
as
pivotal
actor
emerging
ecosystem.
At
same
time,
to
enhance
efficacy
smart
contracts,
integration
artificial
intelligence
is
proposed.
International Journal of Energy Research,
Год журнала:
2024,
Номер
2024(1)
Опубликована: Янв. 1, 2024
This
study
aims
to
identify
the
key
predictors
of
multidimensional
energy
poverty
index
(MEPI)
by
employing
advanced
machine
learning
(ML)
ensemble
methods.
Traditional
research
often
relies
on
conventional
statistical
techniques,
which
limits
understanding
complex
socioeconomic
factors.
To
address
this
gap,
we
propose
an
approach
using
three
distinct
ML
models:
extreme
gradient
boosting
(XGBoost)‐random
forest
(RF),
XGBoost‐multiple
linear
regression
(MLR),
and
XGBoost‐artificial
neural
network
(ANN).
These
models
are
applied
a
comprehensive
dataset
encompassing
various
indicators.
The
findings
demonstrate
that
XGBoost‐RF
achieves
exceptional
accuracy
reliability,
with
root
mean
squared
error
(RMSE)
0.041,
R
‐squared
(
2
)
0.975,
Pearson
correlation
coefficient
0.992.
XGBoost‐MLR
shows
superior
generalizability,
maintaining
consistent
0.845
across
both
testing
training
phases.
XGBoost‐ANN
model
balances
complexity
predictive
capability,
achieving
RMSE
0.056,
0.954
in
phase,
0.799
training.
Significantly,
identifies
“Education,”
“Food
Consumption
Score
(FCS),”
“Household
Food
Insecurity
Access
Scale
(HFIA),”
“Dietary
Diversity
(DDS)”
as
critical
MEPI.
results
highlight
intricate
relationship
between
factors
related
food
security
education.
By
integrating
insights
from
these
policy
initiatives,
offers
promising
new
addressing
poverty.
It
highlights
importance
education,
security,
crafting
effective
interventions.
Blockchain Research and Applications,
Год журнала:
2024,
Номер
unknown, С. 100223 - 100223
Опубликована: Июль 1, 2024
In
the
networked
enlarged
electric
vehicle
(EV)
charging
infrastructures,
security
and
authenticity
of
stakeholders
involved
in
EV
energy
market
pool
are
prime
important.
This
paper
proposes
an
network
hub
(EVNH)
comprising
vehicles,
aggregators
(EVAs),
nodes
pool.
The
various
EVAs
implement
different
heterogeneous
blockchains.
facilitates
blockchain-based
secure
resilient
trading
under
grid
to
grid.
emphasizes
interoperability
challenges
involving
blockchains
communicate
transfer
assets
or
data
between
them.
We
suggest
trustworthy
across
using
multiple
tokens
for
through
cross-chain
communications.
consider
a
Nash
equilibrium-seeking
strategy
find
equilibrium
non-cooperative
game
EVAs.
effectiveness
proposed
is
tested
MATLAB,
Solidity,
Python
software.
Studia Universitatis „Vasile Goldis” Arad – Economics Series,
Год журнала:
2025,
Номер
35(2), С. 113 - 139
Опубликована: Апрель 17, 2025
Abstract
The
most
essential
factors
should
be
defined
to
increase
the
effectiveness
of
sustainable
energy
financing.
Otherwise,
businesses
may
face
some
financial
and
operational
problems
due
not
using
resources
effectively.
However,
only
a
limited
number
studies
in
literature
have
identified
these
important
factors.
This
situation
shows
need
for
new
study
determine
variables
that
greatest
impact
on
Thus,
purpose
this
is
identify
significant
determinants
affect
For
situation,
3-stage
model
constructed
reach
purpose.
first
stage
prioritizes
experts
with
help
artificial
intelligence
(AI).
second
weights
assessment
criteria
financing
by
quantum
spherical
fuzzy
M-SWARA.
Finally,
balanced
scorecard-based
project
priorities
are
ranked
WASPAS.
main
contribution
detailed
evaluation
performed
understand
strategies
improvements
novel
model.
Calculation
expert
AI
increases
quality
originality
Similarly,
considering
M-SWARA,
WASPAS,
theory,
sets
also
because
managing
uncertainties
more
technical
competence
enterprise
Funding
diversification
found
as
items
increasing
Additionally,
according
ranking
results,
it
determined
issues
customer
needs
alternatives.
This
study
aims
to
identify
the
key
predictors
of
Multidimensional
Energy
Poverty
Index
(MEPI)
by
employing
advanced
Machine
Learning
(ML)
ensemble
methods.
Traditional
energy
poverty
research
often
relies
on
conventional
statistical
techniques,
which
limits
understanding
complex
socioeconomic
factors.
To
address
this
gap,
we
propose
an
approach
using
three
distinct
ML
models:
XGBoost-Random
Forest
(RF),
XGBoost-Multiple
Linear
Regression
(MLR),
and
XGBoost-Artificial
Neural
Network
(ANN).
These
models
are
applied
a
comprehensive
dataset
encompassing
various
indicators.
The
findings
demonstrate
that
XGBoost-RF
achieves
exceptional
accuracy
reliability,
with
RMSE
0.041,
R²
0.975,
PCC
0.992.
XGBoost-MLR
shows
superior
generalizability,
maintaining
consistent
0.845
across
both
testing
training
phases.
XGBoost-ANN
model
balances
complexity
predictive
capability,
achieving
0.056,
0.954
in
phase,
0.799
training.
Significantly,
identifies
'Education',
'Food
Consumption
Score
(FCS)',
'Household
Food
Insecurity
Access
Scale
(HFIA)',
'Dietary
Diversity
(DDS)'
as
critical
MEPI.
results
highlight
intricate
relationship
between
factors
related
food
security
education.
By
integrating
insights
from
these
policy
initiatives,
offers
promising
new
addressing
poverty.
It
highlights
importance
education,
security,
crafting
effective
interventions.
