IAIA2017 Le Centre Sheraton Montreal Hotel Montreal, Canada, 4 ‐ 7 April, 2017 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
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
Catchment modelling Modelling a catchment: • Land cover, land use and soils data • Climatic data • Hydrological data (surface water and ground water) • Lake dynamics • Water quality 1. Land and climate monitoring data 2. Groundwater • Economic data 3. Surface water 4. Lake Source: Environment Canterbury
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
New irrigation Existing farmers scheme needs have to come down 50 tonnes/pa by Limit = 40 tonnes/pa 100 tonnes nitrogen/p.a. Existing farmers Accounting leaching vision gets 90 tonnes of translated into nitrogen/pa land use rules The National Policy Statement vision for setting limits and facilitating new irrigation
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 or resolve uncertainty • get on with making decisions
Uncertainty disclosure “There are many sources of uncertainty in a limit setting process …. There is uncertainty both in the input sources of information and the numeric models and assessment techniques used to make predictions”. (Robson for ECRC, 2014, p. 16)
Fostering legitimacy 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).
Changed identities “ 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).
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 Regional Statutory Non ‐ statutory Planning Process Plan collaborative process e.g. limits limits, rules, e.g. where should translated into water quantity and provisions for land use rules + quality limits be set? public hearings 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.
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