Al & Sustainability webinar
Al & Sustainability: Promise, Power, and Responsibility
February 13 at 1-3 p.m. EST
Presented by: Dr. Sui Sui and Dr. Vik Singh, INSPIRE
This webinar brings together scholars and industry leaders to examine Al as both a driver of sustainability innovation and a source of new environmental and ethical challenges. Through applied cases-from materials science and ESG systems to sustainable aviation and Indigenous-centered Al governance the discussion explores how Al can be designed, governed, and deployed responsibly in an era of rising energy use, climate urgency, and global inequities.
Moderator: Vice President
Business Development & Member Experience Canadian Council for Indigenous Business
CEO, Kanario Ltd
Associate Professor & Canada Research Chair, Trent University
Sustainable Aviation Fuel Council (SAF Council)
INSPIRE hosted a timely and interdisciplinary webinar examining Artificial Intelligence as both a catalyst for sustainability innovation and a source of new environmental, ethical, and governance challenges. Bringing together academic researchers and industry leaders, the discussion explored how AI can be designed, scaled, and governed responsibly in an era defined by climate urgency, geopolitical uncertainty, and accelerating technological change.
With approximately 30 attendees, including Indigenous business participants and representatives from industry and academia, the session fostered a rich cross-sector dialogue aligned with INSPIRE’s commitment to inclusive and collaborative knowledge exchange.
Moderated by Paul-Emile McNab (Canadian Council for Indigenous Business), the session featured four complementary perspectives spanning infrastructure, policy, aviation, enterprise governance, and Indigenous-centered AI design.
Peyman Sadrimajd (Kanario Ltd.)
Peyman framed AI as “the new electricity”—a general-purpose infrastructure technology capable of transforming nearly every sector. Drawing on economic and systems thinking concepts such as the Pareto frontier and Doughnut Economics, he argued that AI enables organizations to manage complexity at unprecedented scale.
He highlighted examples ranging from optimization in supply chains and production scheduling to scientific discovery (e.g., protein folding and climate modeling). AI, in this view, is not merely a productivity tool but a foundational capability that can shift sustainability trade-offs by accelerating search, optimization, and learning across interconnected systems.
At the same time, he acknowledged the environmental costs of compute and emphasized the need to improve impact density—delivering greater functionality per unit of energy consumed.
Dr. Anne Pasek (Trent University)
Dr. Pasek offered a critical environmental perspective, arguing that “there is no such thing as sustainable AI—only harm reduction.” Rather than focusing on individual consumer use or narrow efficiency metrics, she directed attention to the infrastructure layer: data centers.
Her analysis examined how AI-driven demand contributes to:
- Rising electricity consumption and grid strain
- Higher consumer energy bills
- Delays in fossil fuel phase-outs
- Water-intensive cooling systems
- Local land-use and community impacts
She emphasized that current governance frameworks are not equipped to measure or regulate these impacts effectively, particularly given limited transparency from technology companies.
Dr. Pasek outlined harm-reduction strategies including renewable energy matching requirements, grid load flexibility, noise standards, tariff protections for ratepayers, strategic siting of data centers, and stronger public policy coordination. Her conclusion underscored the importance of democratic oversight and long-term planning in Canada’s AI development trajectory.
Aaron Dalal (Sustainable Aviation Fuel Council)
Aaron examined AI through the lens of sustainable aviation fuel (SAF), a critical decarbonization pathway for an industry responsible for approximately 2–3% of global emissions. While SAF can reduce lifecycle emissions by up to 80%, scaling production to meet 2050 net-zero targets requires massive industrial coordination and investment.
He identified key barriers—including high production costs, feedstock availability, certification complexity, and infrastructure financing—and positioned AI as a force multiplier across the SAF value chain.
Applications include:
- Feedstock optimization and supply chain modeling
- Predictive maintenance and refinery simulation
- Quality control and certification processes
- Route optimization and emissions tracking
AI, in this context, enhances operational intelligence and trust mechanisms (monitoring, reporting, verification), accelerating both efficiency and transparency.
Brian Ritchie (Kama.AI)
Brian introduced a human-centered and Indigenous-informed framework for AI development, focusing on reliability, governance, and ethical safeguards. He presented a “hybrid AI” architecture combining deterministic knowledge-graph systems with generative models to mitigate hallucination risks and reduce energy intensity.
His approach emphasizes:
- Human-in-the-loop oversight
- Value alignment grounded in community-defined principles
- Transparent governance structures
- Reduced per-inquiry energy consumption
- Enterprise-level guardrails for autonomous agents
Brian underscored the importance of Indigenous values such as stewardship, responsibility, and long-term thinking in shaping AI systems that serve humanity rather than displace or undermine it.
He also raised broader questions about sovereignty, geopolitical risk, and the need for global cooperation in managing advanced AI systems.
Several themes emerged across the discussion:
- AI as critical infrastructure rather than a discrete product
- The tension between scaling and sustainability
- Energy consumption and rebound effects
- Governance gaps in measurement and transparency
- Indigenous knowledge and community-centered design
- Sovereign AI and geopolitical fragmentation
- The need to balance innovation, responsibility, and long-term resilience
The conversation made clear that AI’s trajectory will not be determined by technical capability alone, but by policy frameworks, institutional incentives, and societal values.
Looking Ahead
INSPIRE’s mission is to foster interdisciplinary collaboration across research, industry, policy, and community sectors. This webinar reaffirmed the importance of sustained dialogue at the intersection of AI and sustainability—where innovation must be matched by governance, and power must be balanced with responsibility.
We welcome continued partnerships and industry collaborations as we advance research and action in responsible AI and sustainable enterprise.