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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
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
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