The c-ECO Framework is grounded in the convergence of multiple
established scientific, institutional and legal research traditions.
Its foundations include complex-systems science,
critical-transition research, resilience theory,
Earth-system science, planetary boundaries,
social–ecological systems, network science,
critical-infrastructure interdependency,
financial systemic-risk research,
artificial-intelligence governance,
Science and Technology Studies,
institutional theory, Earth-system governance,
Earth System Law and selected posthumanist
and more-than-human perspectives.
The framework brings these traditions together around one central question:
How can institutions identify, interpret and respond to systemic deterioration
while meaningful reversibility still exists?
The c-ECO Framework distinguishes between
established scientific or scholarly foundations
and framework-specific hypotheses, methods and legal mechanisms.
Research on critical transitions, resilience, Earth-system dynamics,
planetary boundaries, network cascades,
infrastructure interdependency and systemic financial risk
provides established scientific foundations.
Institutional theory, Science and Technology Studies,
Earth System Governance, Earth System Law,
Indigenous and local knowledge research
and selected philosophical traditions provide
additional institutional, epistemological and interpretive foundations.
By contrast, constructs including Reversibility Liquidity (Lr),
the SIL 19×19 architecture,
the integrated P / ΔV / σ / Lr methodology,
SPS / TRS / RLS,
Trajectory Illegality
and the legal mechanisms proposed by c-ECO
remain framework-specific propositions requiring validation.
Established knowledge provides the foundations.
c-ECO provides an integrative architecture and a research agenda.
This distinction is central to the methodological credibility
of the framework.
Existing literature should not be represented as validating
a c-ECO-specific construct unless that construct
has itself been independently tested.
Complex systems may exhibit non-linear behavior,
feedback effects, regime shifts,
critical transitions and abrupt changes in state.
A system that remains apparently functional
may nevertheless be moving toward a condition
in which recovery becomes increasingly difficult.
Research on critical transitions has demonstrated that
some systems may display measurable changes
before major state shifts occur,
including patterns associated with declining resilience.
These signals do not provide universal or deterministic prediction.
They provide a scientific basis for investigating
whether deteriorating dynamics can sometimes be detected
before a critical transition becomes irreversible.
The scientific problem is not simply to identify collapse.
It is to understand the dynamics that precede it.
Scientific foundations:
Holling (1973);
Scheffer et al. (2009);
Lenton et al. (2019).
Resilience research examines the capacity of systems
to absorb disturbance, adapt, reorganize
and maintain essential functions under changing conditions.
This perspective is central to c-ECO because
current performance and remaining corrective capacity
are not equivalent.
A system may continue operating while its ability
to recover, adapt or redirect itself
is progressively being depleted.
The c-ECO concept of Reversibility Liquidity (Lr)
builds on this broader resilience tradition
but is not itself an established resilience metric.
Lr is proposed as a way to estimate
the practical capacity still available
to stabilize, redirect, repair or restore
a deteriorating system trajectory.
Research requirement:
Reversibility Liquidity must be empirically calibrated
for different systems and cannot be assumed
to have the same meaning across all nineteen SIL domains.
Scientific foundations:
Holling (1973);
Folke (2006).
Earth-system science demonstrates that climate,
biosphere integrity, freshwater,
biogeochemical cycles and other planetary processes
operate through interacting feedbacks
rather than as isolated environmental variables.
The planetary-boundaries literature introduced
the concept of a safe operating space
for humanity based on biophysical processes
whose destabilization may increase
the risk of large-scale Earth-system change.
The framework was initially proposed in 2009,
substantially updated in 2015
and reassessed again in 2023.
The 2023 assessment reported that six of nine
planetary boundaries were transgressed,
reinforcing the importance of examining
not merely static boundary positions
but the direction and evolution of system conditions.
A boundary is not only a location.
Its governance significance depends on trajectory.
Scientific foundations:
Rockström et al. (2009);
Steffen et al. (2015);
Richardson et al. (2023).
Conventional risk analysis often emphasizes
probability, exposure and consequence.
c-ECO adds an explicitly dynamic dimension:
the evolution of the system through time.
Two systems at the same measured position
may imply radically different governance conditions
if one is stabilizing
while the other is rapidly accelerating
toward a critical boundary.
P
Position
Where is the system relative to
the applicable reference condition or boundary?
ΔV
Velocity
In which direction,
and at what rate,
is the system changing?
σ
Uncertainty
How reliable is the evidence
and the resulting representation of the system?
Lr
Reversibility Liquidity
How much practical corrective capacity remains
to redirect or restore the trajectory?
The combined P / ΔV / σ / Lr architecture
is a c-ECO methodological synthesis
and therefore requires domain-specific calibration
and validation.
Ecological systems cannot be separated cleanly
from institutions, economies, technology,
infrastructure and human behavior.
Research on social–ecological systems emphasizes
adaptation, institutional fit,
scale, learning and interaction
between environmental dynamics and social organization.
Oran Young's work on fit, interplay and scale
is especially relevant to the c-ECO proposition
that governance structures must correspond
to the scale and dynamics of the systems being governed.
This provides part of the intellectual basis
for using Living Labs as real-world environments
in which technical dynamics and institutional responses
can be examined together.
Scientific and institutional foundations:
Folke (2006);
Young (2002).
Modern societies depend on interconnected ecological,
technological, financial and institutional systems.
Electricity depends on water and digital infrastructure;
telecommunications depend on electricity;
health systems depend on energy, logistics and communications;
finance depends on functioning infrastructure and supply chains.
Research in network science and infrastructure resilience
shows that interdependence can amplify disturbances
and produce cascading failures across systems
that might otherwise appear individually resilient.
The unit of systemic analysis is therefore
not only the individual sector.
It is the sector embedded within a network of dependencies.
Scientific foundations:
Rinaldi, Peerenboom & Kelly (2001);
Buldyrev et al. (2010);
Helbing (2013).
The Systemic Intelligence Layer / Library (SIL)
is the c-ECO architecture designed to translate
cross-domain interdependence into a structured,
machine-readable and analytically usable representation.
The framework currently organizes systemic intelligence
across nineteen major domains.
Each domain may interact with every other domain,
creating a 19×19 interdependency architecture.
19 domains × 19 domains = systemic interdependency,
not nineteen isolated risk assessments.
19
System Domains
Ecological, industrial, financial,
digital, institutional and strategic systems.
19×19
Dependency Architecture
Cross-domain relationships
and propagation pathways.
9
Intelligence Parts per Domain
Standardized analytical architecture.
The SIL architecture is intended to support analysis
of direct dependencies,
indirect amplification,
cascade pathways,
monitoring gaps,
institutional relationships
and restoration dependencies.
SIL is a c-ECO integrative methodology.
Network science and infrastructure-interdependency research
support the need for this type of analysis,
but they do not independently validate
the specific 19×19 structure.
Each SIL sector follows a standardized
nine-part intelligence architecture.
This allows sector-specific calibration
while preserving comparability
and enabling cross-sector cascade analysis.
| Part |
Architecture |
Purpose |
| I |
Domain Intelligence Profile |
Sector definition, historical evolution
and institutional landscape.
|
| II |
Systemic Risk Intelligence |
Primary risks, amplification mechanisms,
emerging risks and cross-domain cascades.
|
| III |
Systemic Response Intelligence |
Pre-positioned response protocols,
escalation and contingency architecture.
|
| IV |
Governance & Legal Architecture |
Treaties, regulation, liability,
data sovereignty and jurisdiction.
|
| V |
TFP Calibration Intelligence |
P, ΔV, σ, Lr,
thresholds and data sources.
|
| VI |
Scenario & Simulation Intelligence |
Stress tests, simulations
and recovery scenarios.
|
| VII |
Restoration & Recovery Architecture |
Recovery sequencing,
provider capacity and restoration requirements.
|
| VIII |
Authoritative Source Library |
Evidence classification
and variable-linked sources.
|
| IX |
Source Matrices & Historical Intelligence |
Dependency matrices,
monitoring gaps
and historical calibration evidence.
|
Environmental and infrastructural disturbances
can propagate through credit,
insurance, portfolios,
guarantees, asset values
and financial markets.
Financial-system research has increasingly used
concepts from ecology, network theory
and complexity science
to understand contagion
and systemic instability.
Haldane and May explicitly examined banking systems
through an ecological-system analogy,
while Battiston and collaborators argued
for greater use of complexity theory
in financial regulation.
These traditions support the c-ECO decision
to treat finance as a system embedded
within ecological, infrastructural
and institutional networks.
Scientific foundations:
Haldane & May (2011);
Battiston et al. (2016).
Systemic governance depends not only
on the quantity of available data,
but also on how knowledge is produced,
validated, interpreted
and incorporated into institutions.
Research in Science and Technology Studies (STS)
has shown that scientific knowledge
and institutional order are not entirely independent.
The production, interpretation and use of evidence
are shaped by measurement systems,
institutional practices,
expert communities,
legal mandates
and governance arrangements.
For c-ECO, this means that no single observation system
should be assumed to exhaust all knowledge
relevant to a complex systemic condition.
Epistemic plurality does not mean
that all knowledge claims carry equal evidentiary weight.
It means that relevant knowledge may originate
from different scientific, institutional
and territorial sources,
and that those sources require explicit rules
for provenance, validation,
uncertainty and authorized use.
Primary foundations:
Jasanoff (2004);
Berkes (2018);
IPBES (2022).
The concept of co-production provides
an important foundation for understanding
why scientific evidence cannot be separated entirely
from the institutional structures
through which it becomes actionable.
Measurement standards,
data classifications,
regulatory thresholds,
expert procedures
and decision rules shape the way
systemic conditions are represented.
Scientific findings simultaneously influence
the design of institutions
and governance mechanisms.
Institutional epistemology:
Jasanoff (2004).
In many territories,
ecological and systemic change is observed
through knowledge traditions extending
far beyond the duration of modern instrumental monitoring.
Traditional ecological knowledge
and Indigenous knowledge systems
may contain long-duration observations
of ecological cycles,
species behavior,
hydrology,
seasonal variability,
landscape change
and disturbance patterns.
Research in resource management
and international biodiversity assessment
has demonstrated that such knowledge
can contribute meaningfully to environmental understanding
when incorporated through appropriate methods,
consent, provenance and validation procedures.
The c-ECO framework does not treat Indigenous
or local knowledge as an informal substitute
for scientific evidence.
It treats it as a distinct knowledge system
whose relevance, context,
ownership, uncertainty
and permitted use must be governed explicitly.
The IPBES methodological architecture is especially relevant
because it provides an intergovernmental precedent
for recognizing multiple values
and knowledge systems
within formal scientific assessment processes.
Knowledge-system foundations:
Berkes (2018);
IPBES (2022).
The broader question of whether governance frameworks
are excessively human-centered
has also been explored in posthumanist,
more-than-human and relational scholarship.
These traditions are relevant to c-ECO
at the level of epistemic orientation.
They challenge the assumption
that only explicitly human representations
of a system should count
as meaningful sources of systemic knowledge.
They are not used by c-ECO
as empirical validation
of TFP variables,
SIL architecture,
legal triggers
or operational thresholds.
Posthumanist scholarship informs
the framework's epistemic posture.
It does not constitute empirical validation
of its operational mechanisms.
Within the c-ECO architecture,
this perspective supports attention
to ecological relationships,
material feedbacks,
technological systems
and non-human processes
without assigning them autonomous legal sovereignty.
Interpretive context:
Haraway (2016);
Latour (2005);
Braidotti (2013).
For operational purposes,
c-ECO distinguishes three domains
through which systemically relevant information
enters, is processed
and becomes institutionally actionable.
The term intelligence
is used functionally.
It does not imply that ecological systems,
computational systems
and human beings possess
the same form of cognition,
agency or legal status.
P
Planetary / Ecological Intelligence
The information-bearing dynamics
of material and ecological systems:
feedbacks, constraints,
thresholds, flows,
responses and changing conditions.
A
Algorithmic Intelligence
Computational capacity
to identify patterns,
integrate evidence,
model dependencies,
simulate scenarios
and support classification.
H
Human Intelligence
Interpretive judgment,
scientific responsibility,
ethical reasoning,
institutional legitimacy
and legally accountable decision-making.
Planetary intelligence constrains.
Algorithmic intelligence computes.
Human intelligence governs.
The three domains are complementary
rather than interchangeable.
Artificial intelligence,
statistical models,
digital twins
and computational systems
can expand the capacity
to identify patterns
that are difficult for individual human observers
to detect across large and interconnected datasets.
Within c-ECO,
these systems may support:
- measurement and aggregation;
- pattern recognition;
- dependency mapping;
- trajectory analysis;
- scenario and cascade simulation;
- forecasting;
- SPS, TRS and RLS calculation;
- alert generation;
- decision-support systems.
They do not independently:
- create legal obligations;
- exercise public authority;
- determine normative priorities;
- bind the c-ECO Institute contractually;
- issue regulatory permissions;
- override authorized human decision-makers.
Machine intelligence expands observability.
It does not eliminate human accountability.
Governance foundation:
NIST (2023), Artificial Intelligence Risk Management Framework.
Scientific recognition and institutional action
are different processes.
A scientifically valid signal
does not automatically produce
a legally legitimate decision.
The purpose of the framework
is therefore not to automate sovereignty.
It is to reduce the distance
between scientific recognition
and legitimate institutional response.
Once risk is understood dynamically,
an important legal problem appears.
What happens when an activity remains formally lawful today,
but its monitored and reasonably foreseeable trajectory
is progressively destroying the conditions
required for continued systemic viability?
The c-ECO Framework describes
this proposed condition
as Trajectory Illegality.
Trajectory Illegality is not presented
as an established doctrine
of contemporary law.
It is a c-ECO legal research proposition
arising from the interaction
between dynamic systems science
and legal governance.
A scientific model may identify
trajectory and reversibility.
It cannot by itself determine illegality.
That transition requires legal theory,
competent authority,
due process
and appropriately designed
legal or contractual mechanisms.
The governance problem addressed by c-ECO
is part of a broader scholarly debate
concerning whether institutions and law
are adequately configured
for interconnected Earth-system change.
Earth System Governance research examines
governance across scales,
institutions and planetary processes.
Earth System Law develops
the corresponding legal question:
how should law respond
when ecological and Earth-system dynamics
cannot be meaningfully governed
through isolated sectors
or conventional jurisdictional boundaries?
This literature does not validate
c-ECO's specific legal mechanisms.
It establishes a broader intellectual foundation
for investigating them.
Institutional and legal foundations:
Biermann (2014);
Kotzé & Kim (2019);
Kotzé et al. (2022).
Legal scholarship has previously questioned
whether nature must remain merely
an object of human legal relations.
Christopher Stone's classic argument
on legal standing for natural objects
opened an important line of inquiry
concerning legal subjectivity,
natural systems
and institutional representation.
c-ECO takes a different
but related operational position:
planetary dynamics do not necessarily
need to acquire legal personhood
before becoming legally relevant.
c-ECO does not require nature
to become a legal person
before planetary dynamics
become legally relevant.
The research question is whether
certified changes in material system conditions
may legitimately influence
the continued enforceability,
authorization
or governance
of human-created legal relationships.
Legal foundation:
Stone (1972).
The scientific architecture creates legal questions
for which conventional ex-post mechanisms
may be insufficient.
c-ECO therefore proposes
a family of legal research mechanisms.
These should be understood as
propositions for doctrinal,
comparative, contractual,
judicial and institutional development
,
not as universally established legal doctrines.
| Proposed Mechanism |
Research Function |
|
Pre-Threshold Principle
|
Investigates whether legal relevance
can arise when measurable reversibility
begins contracting,
before irreversible damage occurs.
|
|
Trajectory Illegality
|
Investigates whether formally lawful conduct
can become systemically incompatible
through the foreseeable trajectory
of continued execution.
|
|
Rule of Systemic Compatibility
|
Tests whether continued legal
or economic execution
can remain conditioned
upon compatibility
with relevant systemic limits.
|
|
Ex-Ante Unenforceability — IEX
|
Investigates circumstances
in which an obligation
may become unenforceable
before its continued execution
creates irreversible systemic consequences.
|
|
Systemic Perclusion
|
Explores preventive limitation
of a legal pathway
once predefined systemic conditions
are satisfied.
|
|
Conditioned Immediate Object
|
Examines whether contractual performance
depends on continued existence
of the material conditions
that make execution viable.
|
|
Systemic Proof
|
Explores the evidentiary status
of certified monitoring,
continuous data
and auditable computational records.
|
|
Functional Conversion
|
Investigates conversion
of incompatible obligations
into restoration
or alternative system-preserving duties.
|
|
Restoration First
|
Examines when restoring systemic capacity
should precede ordinary financial
or contractual performance.
|
|
Algorithmic Subordination of Execution
|
Investigates legally predefined adjustment
of execution when verified trajectories
materially change,
without transferring normative authority
to the algorithm itself.
|
|
Automatic Asset Affection for Restoration
|
Investigates whether predefined guarantees,
reserves or ring-fenced assets
may become automatically dedicated
to containment,
restoration
or safe decommissioning
when contractually defined systemic
trigger conditions are satisfied.
|
Such asset-affection mechanisms
may be implemented through conventional contractual,
escrow, guarantee,
insurance or regulated financial structures.
Automated execution through smart-contract infrastructure
would constitute a separate technological pathway
requiring its own legal,
cybersecurity,
financial-regulatory
and governance assessment.
Pre-threshold intervention also creates risks.
A governance system that acts earlier
can potentially intervene
on incomplete evidence,
generate false positives,
restrict legitimate activity
or transfer excessive authority
to technical institutions.
For that reason,
c-ECO's legal research
cannot rely exclusively
on arguments favoring precaution.
It must engage directly
with critiques of precautionary governance.
Cass Sunstein's analysis is relevant
because it demonstrates how precautionary rules
can become internally inconsistent,
overinclusive
or incapable of distinguishing competing risks.
Earlier intervention requires stronger evidence governance,
not weaker procedural safeguards.
Pre-threshold governance must therefore develop together
with uncertainty treatment,
proportionality,
review,
appeal mechanisms,
transparency
and institutional accountability.
Critical legal counterpoint:
Sunstein (2005).
Critical infrastructure offers
one of the clearest examples
of why interdependency
must be treated scientifically.
Energy, water,
communications,
transport,
digital infrastructure,
public health
and emergency systems
can fail through dependency relationships
rather than through isolated component failure.
Rinaldi, Peerenboom and Kelly
established a foundational framework
for analysing critical-infrastructure interdependencies.
Later network research demonstrated
how interconnected systems
can experience cascading failures.
These foundations directly inform
the logic underlying
SIL cross-sector dependency mapping.
Scientific foundations:
Rinaldi, Peerenboom & Kelly (2001);
Buldyrev et al. (2010).
Systemic governance is not determined
only by scientific capability.
Institutions operate through mandates,
jurisdictions,
budgets,
legal authority,
political incentives
and different decision horizons.
Young's work on fit,
interplay and scale
shows that governance effectiveness
depends partly on whether institutional structures
correspond to the characteristics
and scale of the systems
they seek to govern.
This is directly relevant to c-ECO
because systemic trajectories
can cross sectors,
territories
and institutional mandates
faster than conventional governance structures
can coordinate.
Institutional foundation:
Young (2002).
No single discipline can validate
the complete c-ECO Framework.
Its development therefore requires
structured interdisciplinary
and, where appropriate,
transdisciplinary research.
| Field |
Contribution |
| Complex Systems Science |
Non-linear dynamics,
tipping behavior,
emergence
and transition processes.
|
| Earth-System Science |
Planetary processes,
feedbacks
and biophysical constraints.
|
| Ecology & Resilience Science |
Adaptive capacity,
regime shifts
and recovery dynamics.
|
| Network Science |
Interdependency,
cascade propagation
and systemic topology.
|
| Engineering |
Infrastructure reliability,
redundancy,
failure
and restoration.
|
| Finance & Economics |
Risk transmission,
contagion,
pricing
and capital allocation.
|
| Computer Science & AI |
Pattern detection,
simulation,
knowledge representation
and decision support.
|
| Law |
Authority,
enforceability,
evidence,
liability,
due process
and institutional competence.
|
| Earth System Governance |
Governance across scales,
institutions
and planetary processes.
|
| Science and Technology Studies |
Co-production of knowledge,
expertise,
institutions
and evidence governance.
|
| Political Science |
Institutions,
legitimacy,
collective action
and decision-making.
|
| Posthumanist Studies |
Interpretive perspectives
on distributed agency,
human–nonhuman relations
and more-than-human epistemology.
|
| Indigenous & Local Knowledge Studies |
Territorial knowledge,
epistemic pluralism
and long-duration system observation.
|
| Ethics |
Normative limits,
justice,
responsibility
and algorithmic legitimacy.
|
| Public Health |
Population vulnerability,
exposure
and health-system continuity.
|
The c-ECO Framework requires
several distinct forms of validation.
Success in one layer
does not automatically validate
the others.
01
Scientific Validation
Are the proposed signals,
relationships and thresholds
empirically defensible?
02
Methodological Validation
Do P, ΔV, σ, Lr,
SPS, TRS and RLS
behave reliably?
03
Cross-Domain Validation
Does SIL correctly represent
important system dependencies
and cascades?
04
Operational Validation
Do triggers and protocols
improve decision timing
and coordination?
05
Legal Validation
Can the proposed mechanisms
operate within legitimate legal systems
while preserving due process?
06
Institutional Validation
Can real organizations implement
the architecture
under practical constraints?
Many of these questions
cannot be resolved by theory,
simulation
or literature alone.
Living Labs and authorized Case Study Nodes
provide environments
in which scientific,
computational,
institutional
and legal hypotheses
can be tested against observed conditions.
The Fellowship Program provides
the human-capital pathway
through which researchers
and practitioners
can engage with
the interdisciplinary structure
of the framework.
Fellows may contribute
to supervised research,
SIL domain intelligence,
legal and governance studies,
Living Labs,
Case Studies
and methodological development
subject to their status
and applicable authorization.
Fellowship recognition
is an institutional credential,
not an operational license.
Independent monitoring,
Data Custodianship,
enterprise deployment,
formal calibration authority
and institutional representation
remain separately governed.
-
Which measurable signals reliably precede systemic transitions?
-
How can structural deterioration
be distinguished from normal variation?
-
How should velocity modify interpretation
of a system's current position?
-
How should uncertainty influence
prudential classification?
-
Can Reversibility Liquidity
be measured consistently across domains?
-
Which interdependencies create
the most important cascade pathways?
-
How should planetary,
algorithmic
and human intelligence interact
without one replacing another?
-
How should Indigenous,
local and institutional knowledge
enter a certified evidence architecture?
-
At what point does
a formally lawful trajectory
become systemically incompatible?
-
What evidentiary standards
would be required
for Systemic Proof?
-
How should preventive governance
distinguish true signals
from false positives?
-
Which legal mechanisms
can legitimately act
before irreversible damage?
-
Which institutional structures
are capable of implementing
pre-threshold governance?
- TDR theoretical framework;
- threshold and trajectory taxonomy;
- cascade and interdependency taxonomy;
- SIL 19×19 systemic intelligence architecture;
- sector-specific intelligence libraries;
- P / ΔV / σ / Lr calibration methods;
- SPS / TRS / RLS validation studies;
- Safe Operating Space methodologies;
- reversibility and restoration metrics;
- Systemic Proof evidentiary methodologies;
- Trajectory Illegality interdisciplinary research;
- pre-threshold legal mechanism studies;
- Living Lab comparative studies;
- AI and distributed-intelligence governance protocols;
- peer-reviewed scientific and interdisciplinary research;
- technical standards and replication protocols.
| Research Area |
Type of Foundation |
Status in c-ECO |
|
Complex systems,
critical transitions
and resilience
|
Scientific / empirical
|
Established research foundation.
|
|
Earth-system science
and planetary boundaries
|
Scientific / empirical
|
Established and actively evolving research foundation.
|
|
Infrastructure interdependency
and network cascades
|
Scientific / engineering
|
Established research foundation.
|
|
Financial systemic risk
and network contagion
|
Scientific / economic
|
Established research foundation.
|
|
Co-production,
epistemic pluralism
and knowledge-system governance
|
STS / institutional / empirical
|
Established interdisciplinary foundation
informing evidence governance.
|
|
Indigenous and local ecological knowledge
|
Empirical / ethnographic / institutional
|
Established field informing
plural knowledge integration.
|
|
Posthumanism
and more-than-human perspectives
|
Philosophical / interpretive
|
Established intellectual tradition
informing the framework's epistemic posture;
not an empirical validation basis
for operational mechanisms.
|
|
Earth System Governance
and Earth System Law
|
Institutional / legal scholarship
|
Established but developing
interdisciplinary research fields.
|
|
SIL 19×19
|
c-ECO methodology
|
Integrative architecture requiring
empirical and cross-domain validation.
|
|
P / ΔV / σ / Lr
|
c-ECO methodology
|
Methodological synthesis requiring
domain-specific calibration.
|
|
SPS / TRS / RLS
|
c-ECO methodology
|
Operational scoring architecture
requiring validation.
|
|
Trajectory Illegality,
IEX,
Systemic Perclusion
and related mechanisms
|
c-ECO legal research
|
Proposed legal mechanisms requiring
doctrinal, comparative,
judicial, contractual
and institutional development.
|
The references below identify
major scientific,
institutional,
legal
and epistemological traditions
relevant to the c-ECO research architecture.
Their inclusion does not imply
endorsement or independent validation
of c-ECO-specific constructs.
Holling, C. S. (1973).
Resilience and Stability of Ecological Systems.
Annual Review of Ecology and Systematics, 4, 1–23.
DOI: 10.1146/annurev.es.04.110173.000245.
Scheffer, M., Bascompte, J., Brock, W. A., et al. (2009).
Early-warning signals for critical transitions.
Nature, 461, 53–59.
DOI: 10.1038/nature08227.
Folke, C. (2006).
Resilience: The emergence of a perspective
for social–ecological systems analyses.
Global Environmental Change, 16(3), 253–267.
DOI: 10.1016/j.gloenvcha.2006.04.002.
Rockström, J., Steffen, W., Noone, K., et al. (2009).
A safe operating space for humanity.
Nature, 461, 472–475.
DOI: 10.1038/461472a.
Steffen, W., Richardson, K., Rockström, J., et al. (2015).
Planetary boundaries:
Guiding human development on a changing planet.
Science, 347(6223), 1259855.
DOI: 10.1126/science.1259855.
Richardson, K., Steffen, W., Lucht, W., et al. (2023).
Earth beyond six of nine planetary boundaries.
Science Advances, 9(37), eadh2458.
DOI: 10.1126/sciadv.adh2458.
Lenton, T. M., Rockström, J., Gaffney, O., et al. (2019).
Climate tipping points — too risky to bet against.
Nature, 575, 592–595.
DOI: 10.1038/d41586-019-03595-0.
Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001).
Identifying, understanding, and analyzing
critical infrastructure interdependencies.
IEEE Control Systems Magazine, 21(6), 11–25.
DOI: 10.1109/37.969131.
Buldyrev, S. V., Parshani, R., Paul, G.,
Stanley, H. E., & Havlin, S. (2010).
Catastrophic cascade of failures in interdependent networks.
Nature, 464, 1025–1028.
DOI: 10.1038/nature08932.
Helbing, D. (2013).
Globally networked risks and how to respond.
Nature, 497, 51–59.
DOI: 10.1038/nature12047.
Haldane, A. G., & May, R. M. (2011).
Systemic risk in banking ecosystems.
Nature, 469, 351–355.
DOI: 10.1038/nature09659.
Battiston, S., Farmer, J. D., Flache, A., et al. (2016).
Complexity theory and financial regulation.
Science, 351(6275), 818–819.
DOI: 10.1126/science.aad0299.
IPCC (2022).
Climate Change 2022:
Impacts, Adaptation and Vulnerability.
Contribution of Working Group II
to the Sixth Assessment Report of the IPCC.
Young, O. R. (2002).
The Institutional Dimensions of Environmental Change:
Fit, Interplay, and Scale.
MIT Press.
Jasanoff, S. (ed.) (2004).
States of Knowledge:
The Co-Production of Science and the Social Order.
Routledge.
Berkes, F. (2018).
Sacred Ecology.
4th ed.
Routledge.
IPBES (2022).
Methodological Assessment Report on the Diverse Values
and Valuation of Nature of the Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services.
Balvanera, P., Pascual, U., Christie, M., Baptiste, B., &
González-Jiménez, D. (eds.).
IPBES secretariat, Bonn, Germany.
DOI: 10.5281/zenodo.6522522.
Biermann, F. (2014).
Earth System Governance:
World Politics in the Anthropocene.
MIT Press.
Kotzé, L. J. & Kim, R. E. (2019).
Earth system law:
The juridical dimensions of Earth system governance.
Earth System Governance, 1, 100003.
DOI: 10.1016/j.esg.2019.100003.
Kotzé, L. J. et al. (2022).
Earth system law:
Exploring new frontiers in legal science.
Earth System Governance, 11, 100126.
DOI: 10.1016/j.esg.2021.100126.
Stone, C. D. (1972).
Should trees have standing?
Toward legal rights for natural objects.
Southern California Law Review, 45, 450–501.
Sunstein, C. R. (2005).
Laws of Fear:
Beyond the Precautionary Principle.
Cambridge University Press.
NIST (2023).
Artificial Intelligence Risk Management Framework
(AI RMF 1.0).
NIST AI 100-1.
DOI: 10.6028/NIST.AI.100-1.
The references in this subsection inform the framework's
epistemic orientation and do not constitute empirical validation
of c-ECO operational mechanisms.
Braidotti, R. (2013).
The Posthuman.
Polity Press.
Haraway, D. J. (2016).
Staying with the Trouble:
Making Kin in the Chthulucene.
Duke University Press.
Latour, B. (2005).
Reassembling the Social:
An Introduction to Actor-Network-Theory.
Oxford University Press.
The scientific ambition of c-ECO
is not to replace existing disciplines.
It is to create an operational interface
among disciplines that already understand
different parts of systemic risk,
and to investigate what new scientific,
computational,
institutional
and legal mechanisms become necessary
when those parts are treated
as one interconnected system.
The framework therefore carries
two simultaneous obligations:
to remain anchored in established evidence
where established evidence exists;
and to clearly identify,
test and validate
the hypotheses and mechanisms
that c-ECO itself introduces.
