Grappling with uncertainty in collaboration: a New Zealand case study - - PowerPoint PPT Presentation

Grappling with uncertainty in collaboration: a New Zealand case study

Ronlyn Duncan, PhD Senior Lecturer in Water Management Department of Environmental Management, Lincoln University Christchurch, New Zealand Ronlyn.Duncan@lincoln.ac.nz

IAIA2017 Le Centre Sheraton Montreal Hotel Montreal, Canada, 4‐7 April, 2017

Duncan, R. (2017) Rescaling knowledge and governance and enrolling the future in New Zealand: a co‐production analysis of Canterbury’s water management reforms to regulate diffuse pollution Society & Natural Resources 30(4): 436‐452 Special Issue: Water crises and institutions: governance challenges in an era of uncertainty

The case of New Zealand illustrates:

–how community collaboration, as a new venue of impact assessment, is changing conceptions of uncertainty; and –how uncertainty is influencing how collaboration is conducted

Water politics in New Zealand

A water quality problem: too many nutrients in water

Photo: Ronlyn Duncan

Key question: how to bring visibility and

governability to diffuse agricultural pollution?

Policy reforms

• National Policy Statement for Freshwater

Management 2011 & 2014 – enforceable water quality and quantity limits

• Extends regulatory reach
• Catchment‐based
• Community‐derived

Critical for achieving vision

• Predictive computer modelling
• An accounting system

Modelling a catchment:

• Land cover, land

use and soils data

• Climatic data
• Hydrological data

(surface water and ground water)

• Lake dynamics
• Water quality

monitoring data

• Economic data
• 1. Land and climate
• 2. Groundwater
• 3. Surface water
• 4. Lake

Source: Environment Canterbury

Catchment modelling

The policy vision

• Quantify diffuse pollution and cumulative

effects at catchment scale

• Set catchment load limits for nutrients
• Allocate loads to individual farms/irrigation

schemes

• Identify necessary sector reductions to

address over‐allocation or create headroom

• Manage within limits while expanding

production

Limit = 100 tonnes nitrogen/p.a. Existing farmers leaching 90 tonnes of nitrogen/pa New irrigation scheme needs 50 tonnes/pa Existing farmers have to come down by 40 tonnes/pa

The National Policy Statement vision for setting limits and facilitating new irrigation

Accounting vision gets translated into land use rules

The problems with models …

• Simplifications
• Assumptions and limited data inputs
• Multiple models
• Uncertainty everywhere
• Difficult to defend in court

Problem solved …

• Take away merit appeal rights to

Environment Court

–No more ‘uncertainty exploitation’ –Models and limits protected from deconstruction

But …

• With Environment Court banished
• Authority of science difficult to

substantiate …

• How is legitimacy for models and limits

to be established?

Community as ‘decision‐maker’

“collaboration; empowering communities

to make their own decisions to meet agreed region wide and local targets”.

Environment Canterbury Regional Council, 2015

Source: Environment Canterbury Regional Council

Uncertainty: from nowhere to everywhere!

In community processes:

• uncertainty openly acknowledged
• not a reason not to make decisions
• community told more science won’t help
• r resolve uncertainty
• get on with making decisions

“There are many sources of uncertainty in a limit setting process …. There is uncertainty both in the input sources

• f information and the numeric models and assessment

techniques used to make predictions”.

(Robson for ECRC, 2014, p. 16)

Uncertainty disclosure

Scientists maintain they are supplying information that is “sufficient, relevant and credible” that has been: “legitimately gathered, analysed and presented to a community in a way for them to understand the connections and make recommendations in the knowledge of the likely consequences – i.e. to make an informed value judgment.

(Robson for ECRC, 2014, p. 16).

Fostering legitimacy

“The key role for the technical team … is one of informing

those decisions, by making consequences transparent, rather than making the decisions themselves. This shifts the role of [scientists and the regional council] from knowledge ‘arbiter’ to one of knowledge ‘broker’, exploring the implications of different management options with the community”.

(Robson for ECRC, 2014, p. 16, emphasis in original).

Changed identities

Returning to arguments

The case of New Zealand shows how:

–community collaboration is changing conceptions of uncertainty –With uncertainty exploitation averted, and community as decision‐maker, uncertainty now conceived as inevitable and everywhere

Returning to arguments

The case of New Zealand shows how:

–uncertainty is influencing how collaboration is conducted –community as decision‐maker legitimizing models and limits; scientists and regional council as knowledge broker

Implications for impact assessment?

With uncertainty acknowledged as everywhere: –at front end of process, communities and planners relying heavily on models to negotiate and resolve conflict –models as heuristics (uncertain nature) and ‘truth machines’ (predictable nature), e.g. catchment load 4,830 tonnes by 2037

New Zealand’s hybrid framework of collaboration and statutory force

Non‐statutory collaborative process e.g. where should water quantity and quality limits be set? Statutory Planning Process e.g. limits translated into land use rules + public hearings

Regional Plan limits, rules, provisions for water use

Uncertain nature Predictable nature

Implications for impact assessment?

• a focus on uncertainty opens questions about

the role of models in impact assessment

– how are models and their uncertainties to be disclosed and understood – how are models, the outputs and policy they substantiate to be verified?

Implications for impact assessment?

• modelling well beyond informing and

facilitating decision‐making to constituting the identities, objects and spaces of governance

Implications for impact assessment?

• if this is the case, what influence does

modelling have on policy implementation?, e.g. faith in accounting, false sense of security in assessing impacts, unknowable assumed doable …

Implications for impact assessment?

models as sedatives?

• transporting decision‐makers to a parallel

universe where anything is possible?

Thank you!

Ronlyn.Duncan@lincoln.ac.nz

Duncan, R. (2014) ‘Regulating agricultural land use to manage water quality: the challenges for science and policy in enforcing limits on non‐point source pollution’. Land Use Policy, 41, 378‐387. Duncan, R. (2013) ‘Converting community knowledge into catchment nutrient limits: a constructivist analysis of a New Zealand collaborative approach to water management’, Nature and Culture, 8(2), 205‐225. Duncan, R. (2013) ‘Opening new institutional spaces for grappling with uncertainty: a constructivist perspective’. Environmental Impact Assessment Review 38,151‐154. Duncan, R. (2008). Problematic practice in integrated impact assessment: the role of consultants and predictive computer models in burying uncertainty. Impact Assessment and Project Appraisal 26 (1), 53–66.