seeking sustainability within complex regional nrm systems

Seeking sustainability within complex regional NRM systems

Graham Harris

Seeking sustainability within complex regional NRM systems

Graham Harris

Rapid change on Earth

• The world is changing as issues become more pressing – need for systems thinking– Interactions between energy, carbon,

climate, water, water, soils, biodiversity, food security, population, animal disease

• John Beddington, UK: “The perfect storm”– Tipping point in 25-50 years? – Poor assessments of risk: Dan Gardner

• Urgent need for new regional approaches

Multiple capitals

• World is overlapping set of stocks and flows with non-linear, adaptive interactions– Biodiversity: genes, populations, species– Biogeochemistry: water, energy, nutrients– Capitals: natural, physical, human, financial

• Complexity, emergence, thresholds, tipping points, surprises (inc. financial crashes)

• So the natural world is not just complicated it is formally complex: uncertain, unpredictable

What is sustainability?

• “development that meets the needs of the present without compromising the ability of future generations to meet their needs”– Strong sustainability – more than just

economic welfare and “choice” - there are absolutes, so “the capacity to endure”

• Act here and now so that the environment and quality of life later and elsewhere will not be eroded

The flip side of sustainability

• The (inverse) flip side is risk...– Seeking sustainability means minimising risk

amidst complexity and uncertainty– Risk is about reality, beliefs and culture

• So we require analytical tools to understand the behaviour of interacting systems and...

• Participatory tools to deal with beliefs and values, debate options, communicate risk and act

Biosphere Anthroposphere

ComplexMiddleground

Biophysicalconstraints

ThermodynamicsEvolution

RealistScientificApproach

Analytical tools Participatory tools

SociologyEconomics

ValuesBeliefsRelativismPostmodernism

Worldviews and semiotics

UncertaintyIncompleteknowledge

NarrativeEngagement

DecisionsRisk

EmergenceThresholdsRegime shifts

Expertise?

Key slide 1

Here be monsters!

We are not rational beings!

Cause and effect

• Need to understand relationships between parts and wholes, wholes and parts– Local <-> regional <-> global– Scaling, fractals, emergence

• BMPs to catchment outcomes – EU WFD– Risk, load apportionment: DEFRA, EA– Local actions to regional outcomes

• Cause and effect across scales is a problem– Global CO2 reductions: national jurisdictions

The science “framing issue”

• Usual scientific debate framed around balance and equilibrium – has very old roots– Theory, data collection and analysis issues

• Philosophical basis is idealised (Wimsatt)– Not appropriate for complex systems

• Analysis tools – monitoring and assessment generally about stocks not flows

• NRM institutions, bureaucracy, policy only focussing on the participation tools

The Complexity “turn” (sociologists!)

• Adaptive interactions between capitals– agents, institutions, systems evolve

• Resilience and tipping points– Precariousness and thresholds

• Uncertainty: knowledge and models partial– Emergence, surprises will occur

• Multiple stressors – “causal thickets”– Predict-act frameworks unreliable

• Many players, institutions, governance

Make no mistake: “complexity” is a major shift in world view which requires changes in culture and

practice

Business as usual is not an option!

More is different – things don’t scale well

The uniqueness of place

• The concept of place arises from complexity– Nested spatial and temporal heterogeneity,

contingent history, stocks and flows

• Requires complexity of governance: decision theory, robustness and resilience – No universal Best Management Practices

• Perhaps there never will be a simple theory of place – so just how much is predictable?– We are “waiting for Carnot”......

then now

things

stuff

STOCKS

FLOWS

PAST PRESENTComplex systems

EcosystemsHuman systems

We cannot ignore the flows between human and natural systems

contingency

interactions

description

Small scale processSpatially discretePatterned Temporally evolving

2

Not Gaia; MedeaNo homeostasis

Incentives and restoration

• Targets, reference sites, valuation techniques and MBIs at risk from contingency, uncertainty and emergence

• Complexity makes restoration difficult– Change leads to new “non-homologous”

novel ecosystems (Hobbs et al.) Base lines??

• Focus on inputs rather than outcomes reflects complexity of situation and difficulties with “programs of measures”

Inability to detect effects of management interventions:also there are multiple stressors

and surprises!!

Billions invested: no apparent result?

New models for self organising systems

• Urgent search for new models for complex (fractal, SO) landscape systems– Agent Based, CA, emulation (Young) or high

level analytical (Kirchner, Rodriguez-Iturbe)

• Search for techniques to predict thresholds– critical slowing down (Scheffer, Carpenter)

• But will the warnings be timely or sufficient?

• GRID models of everything everywhere –including uncertainty (Beven)

Death of Red Gum and Black Box forests

Clearly a tipping point has been reached!

The evolution of modelling

• From “mean field” simulations, to Neural Networks, to Genetic Algorithms, to Agent Based, to Adaptive Cellular Automata– populations –> individuals -> information

• Discrete, spatial, adaptive, self-organised properties (no “equilibrium” solutions)

• Landscapes as spatially heterogeneous, information processing, self-organising, uncertain, temporally evolving entities– New approaches to industrial ecology

Hierarchical (nested) dynamics

• The small and fast are really important– Emergence and non-linearity

• Both bottom up and top down causation– Philosophers have real problems with this!

• Modelling from the middle-out: emulation– Systems biology idea attributed to Sydney

Brenner but actually a very old concept

• Capturing the essence whilst recognising uncertainty (Unknown Unknowns again)

µ scale

Macro-scale

Big, slow driversBiophysical constraintsClimate changeExtreme events

Small scale “hot spots”Spatially discreteBehaviour, PhysiologyEvolution

Meso-scale world

The non-equilibrium hierarchical patch dynamics view

models

ResilienceMultiple statesHysteresis

Diverse emergentcomponentsInteractionsStocks and flows

Localdrivers

management

3

New data – spatial and temporal

• New data from web enabled sensors and systems: “everything, everywhere”– High resolution DEMs, GIS, time series– Stocks and flows, history, development

• Insights into small scale pattern and process– The “high frequency” wave of the future– “Beethoven symphonies” with orchestration

• Use of personal devices: GPS, mobile phones with on-board cameras and other sensors

New theories of risk management

• Need new risk management tools: Scenarios for future likely paths– Decision frameworks with “minimum regret”

to manage unpredictable events– Lempert et al – Robust Decision Making

• “predict-act” oversold: need adaptive mgmt– Therefore more likely “observe-reflect-act”– Data, models, uncertainty, robust options

• The past is no guide to the future

Approaching the undefinable

• If “sustainability” is a complex goal and the uncertainty is great – Then how to proceed?

• One option is to reduce unsustainable practices and apply biophysical limits– Moving in the right direction

• The other is Robust (‘minimum regrets’) Decision Making – data and models– Risk management under uncertainty

The Environment Institute