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
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Apply a structured SEA framework using an
expert-based assessment of alternative future
scenarios for electricity development in SK
Generalized SEA framework:
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Reference framework
VECs (assessment criteria)
Alternatives
Impact assessment
Preferred alternative
3 overall goals, to:
 Determine a preferred path for Saskatchewan
 Demonstrate an SEA process that operationalizes
sustainability criteria
 Examine 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

Natural Gas
29%
Wind
5%
Hydro
22%
Coal
44%
Development of Sustainability
Criteria
Preliminary criteria based on case studies
and academic literature
 Final list refined with 7 electricity experts
 8 criteria developed, including:
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C1: Adaptive capacity
C2: Emissions Management
C3: Employment & Income Sufficiency
C4: Ecological Integrity
C5: Security of Supply
C6: Energy Production & Transmission Efficiency
C7: Aboriginal Rights
C8: Public Health & Safety
5 Alternatives for next 30 yrs.
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110%
2009 Mix
A1
A2
A3
A4
A5
Conv. Coal
CCS Coal
Hydro
Small Scale
Wind
Biomass
Nuclear
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)
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 Individual criterion weight scores & alternative
preference scores
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Results aggregated & analyzed using
exploratory data & non-parametric
statistical analysis (using SPSS software)
Data analysis cont.
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Robustness
 Based on a concordance analysis
 Used to look for rank reversal issues
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Sensitivity analysis
 Changing criterion weights based on ‘what if’
conditions & removing inconsistent responses
 Used to determine if alternative ranking would
change
 Based on the interval ranking of alternatives
(Euclidean distance from 0 to 1)
The results
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Criteria weights:
 Health & Safety > Security of Supply > Ecological
Integrity > Production & Transmission Efficiency >
Employment & Income Sufficiency > Aboriginal
Rights
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Alternative rankings:
 A3 > A5 I A2 I A4 > A1
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Sensitivity tests:
 Removal of inconsistent responses has no effect
on alternative rankings
 C7, Aboriginal rights shifts most preferable
alternative to A5, natural gas and least A2,
nuclear
Implications
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For electricity planning in SK
○ Implementation issues
○ Infrastructure, cost, environment
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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