Data-Driven Insights into Greener Technologies for Waste Valorization: Advancing Circular Economy Practices
Abstract
The
advancement
of
circular
economies
requires
efficient
waste
management,
yet
dozens
countries
still
face
difficulties
with
landfills
as
well
sorting
problems
and
inadequate
resource
extraction.
A
data-based
analysis
municipal
solid
(MSW)
management
uses
economic
demographic
indicators
the
main
components
this
study.
data
processing
was
conducted
utilizing
Python
in
Google
Colab,
after
which
exploratory
evaluation
construction
regression
model
to
find
valorization
inefficiencies
ensued.
Sankey
diagram
provided
evidence
patterns
movements
along
prime
locations
recycling
recovery
process
inefficiencies.
results
demonstrate
that
produces
60%
waste,
landfill
consumption
consumes
20%
energy
production
receives
support
from
50%
recovered
waste.
Regression
established
predictive
power
both
linear
polynomial
models
unsatisfactory
(R-squared:
0.102)
because
policy
enforcement,
technological
integration
public
participation,
steered
efficiency.
Geographical
differences
output
ability
demand
specific
policies
need
development
across
regions.
This
research
endorses
AI-driven
together
blockchain-based
tracking
public-private
collaborations
strategies
boost
efforts.
Extended
Producer
Responsibility
(EPR)
must
be
strengthened
while
waste-to-energy
technology
should
receive
financial
for
improving
sustainability
through
rates
on
disposals.
Effective
approaches
are
available
Germany,
Japan
Sweden
can
adopted
globally.
offers
a
comprehensive
optimization
guide
brings
advancements
legislative
needs
community
engagement
foster
Sustainable
Development
Goals
(SDGs)
enhance
economy.
Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown
Published: April 24, 2025
Language: Английский