AN SEA PROCESS INCORPORATING

SUSTAINABILITY CRITERIA

Lisa White

Ph.D. Candidate

School of Environment and Sustainability

University of Saskatchewan

May 30th, 2012

Research objectives Determine how sustainability principles &

criteria can be integrated into the development of energy futures using an SEA methodology

Through applying SEA & sustainability to an electricity sector case study

SEA Case Study of Electricity Futures in SK

Apply a structured SEA framework using an expert-based assessment of alternative future scenarios for electricity development in SK

Generalized SEA framework:Reference frameworkVECs (assessment criteria)AlternativesImpact assessmentPreferred alternative

3 overall goals, to:Determine a preferred path for Saskatchewan Demonstrate an SEA process that operationalizes

sustainability criteriaExamine the methodological implications

Reference framework 3,840 MW capacity in

2009 Additional 4,100 MW

capacity required by 2030

Requires a long-term strategic plan

No formal SEA requirement in SK

Coal44%

Hydro22%

Wind 4%

Natural Gas29%

Development of Sustainability Criteria

Preliminary criteria based on case studies and academic literature

Final list refined with 7 electricity experts 8 criteria developed, including:

C1: Adaptive capacityC2: Emissions ManagementC3: Employment & Income SufficiencyC4: Ecological IntegrityC5: Security of SupplyC6: Energy Production & Transmission EfficiencyC7: Aboriginal RightsC8: Public Health & Safety

5 Alternatives for next 30 yrs.

2009 Mix

A1

A2

A3

A4

A5

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110%

Conv. Coal Hydro Wind NuclearCCS Coal Small Scale Biomass Natural Gas

Alternative assessment 44 member expert panel participated

17 from government, 15 from private sector, 13 from NGOs and ENGOs

Panel assessed the alternatives based on Saaty’s Analytical Hierarchy Process (AHP)Quantification of subjective judgements

Online Expert Choice Comparion Suite software utilized for the assessment Criteria importance weighted using a paired

comparison approach (9 point scale)Alternative preference ranked pairwise based on

each criterion

Weighting the criteria

Evaluating the alternatives

Data analysis Individual assessment values were

compiled into matrices (in Expert Choice) MCA used to calculate the eigenvectors of

the matrices (in Expert Choice)Individual criterion weight scores & alternative

preference scores Results aggregated & analyzed using

exploratory data & non-parametric statistical analysis (using SPSS software)

Data analysis cont.

RobustnessBased on a concordance analysis Used to look for rank reversal issues

Sensitivity analysisChanging criterion weights based on ‘what if’

conditions & removing inconsistent responsesUsed to determine if alternative ranking would

changeBased on the interval ranking of alternatives

(Euclidean distance from 0 to 1)

The results Criteria weights:

Health & Safety > Security of Supply > Ecological Integrity > Production & Transmission Efficiency > Employment & Income Sufficiency > Aboriginal Rights

Alternative rankings: A3 > A5 I A2 I A4 > A1

Sensitivity tests:Removal of inconsistent responses has no effect

on alternative rankingsC7, Aboriginal rights shifts most preferable

alternative to A5, natural gas and least A2, nuclear

Implications

For electricity planning in SK○ Implementation issues○ Infrastructure, cost, environment

For SEA methodology○ Structure & flexibility○ Quantitative impact assessment methodologies○ Sustainability

Questions?

I would like to thank:

Wayne Clifton, Clifton Associates Ltd., for his generous support of my academic endeavours

Dr. Bram Noble, School of Environment and Sustainability, for his support and advice throughout my studies