Lisa White Ph.D. Candidate School of Environment and Sustainability University of Saskatchewan May 30 th, 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 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

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 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.

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.  Robustness Based on a concordance analysis Used to look for rank reversal issues  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  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 rankings C7, 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