After the Sustainability Recession: It’s time to expand our focus from responsible business to regenerative markets

John Elkington

John Elkington, Founder of Volans and an ISSP Sustainability Hall of Fame Honoree, is widely recognized as one of the founders of the global sustainability movement. In this piece, he signals that if sustainability is to deliver, our understanding of what it is likely to involve must also expand. No longer simply about transforming businesses, sustainability must be about transforming markets. His most recent book — Tickling Sharks: How We Sold Business on Sustainability — was published in June 2024 by Fast Company Press. 


I began mapping the ups and downs in the sustainability agenda thirty years ago. There have been a series of upwaves since, with ever-taller peaks truncated by sustainability recessions — though the intervals between the peaks are shrinking.


This time around, the ESG agenda was hit soonest and hardest, but the wider sustainability agenda will be under pressure through the second Trump administration. Weirdly, though, I feel more optimistic than I have for years — because experience suggests that the critical work is done in downwave, recessionary periods.


So, while sustainability champions — including thousands of newly-minted chief sustainability officers (CSOs) — spotlight the need to move beyond incrementalism to systemic solutions, it is not clear that they yet know what this will involve.


One implication is that truly effective solutions will be structural. This means restructuring not just individual businesses but also reconfiguring the markets they serve.


Take the Ford Motor Company. It split itself into two operating units — one (“Ford Blue”) focuses on the company’s legacy business anchored around the internal combustion engine, while the second (“Ford Model E”) is configured to become a nimbler player in the electric vehicle market. Model E may be struggling, but the increasing urgency of such solutions to fundamentally structural challenges will be increasingly obvious.


Nor is Ford alone. Solvay, a Belgian multinational chemical company, is another pioneer that  has gone structural. While its core business has doubled down on established product lines like soda ash, peroxides, and specialty chemicals, Solvay has spun out a new venture, Syensqo, to focus on a range of “breakthrough” opportunities in such areas as renewable materials and green hydrogen.


The interesting thing here is that the new venture already boasts revenues well ahead of the legacy business. At this stage, such examples reflect corporate restructurings in response to emerging market trends. Yet they also raise the question of how soon tomorrow’s business success stories will be based on conscious market restructurings, driven as much by policymakers (as in the case of the EU’s Green Deal and the US Inflation Reduction Act) as by incumbent corporates and insurgent entrepreneurs.

 


Markets Wobble As Politicization Grows


The more exposed a company is to financial markets, the less independence of thought it is often allowed when it comes to sustainability-directed transformations. Where people might once have blamed the gods for their mishaps, today’s business leaders blame market realities (specifically macroeconomic, political, and financial factors) for their failures to deliver on publicly announced commitments in the sustainability space.


Think of Shell CEO Wael Sawan, with his announcement of a lower ambition for his energy company’s climate targets; of Mercedes-Benz CEO Ola Kalenius throttling back on his company’s target of 100 percent electric vehicles by 2030; and of Unilever CEO Hein Schumacher declaring that the FMCG giant’s long-vaunted sustainability goals had failed to deliver sufficient shareholder value — and dialling back on the speed and scale of change in some areas.


The market travails of BlackRock CEO Larry Fink, who found himself embroiled in a furious anti-ESG storm, will feature in many future business school case studies. Standing back, it is evident that, even if Fink and BlackRock were correct in their analysis of the longer-term market trajectories, they misjudged the political consequences of the recent boom in market interest in ESG.


Progress always triggers counter-measures from those economically or ideologically trapped in the old paradigm. So, as the transition builds, expect growing tensions — alongside climbing casualty rates, both for leaders and businesses. Once again, whether or not they care to embrace it, there is a critical role for governments in ensuring that most of the actual and potential victims of such transformations are compensated, or reskilled and re-employed.

 


Sustainability is our biggest market failure


If sustainability is to deliver, our understanding of what it is likely to involve must also expand. It is no longer simply about transforming businesses, critical though that may be. Increasingly, too, it must be about transforming markets — to the point where necessary outcomes are secured by new market default settings.


Talk to many CSOs meanwhile, though, and while they are increasingly happy to talk about business models, they may be significantly less comfortable when it comes to discussing wider economic models. For that, it can be more productive to turn to the smaller number of Chief Economists in business. They include some of the most interesting — and provocative — thinkers in today’s private sector.


As Dow Chief Economist Rafael Cayuela told me, “sustainability is our biggest market failure — but solving these challenges is our biggest-ever market opportunity. We are seeing a phase change in key markets, where sustainability shifts from being considered simply as a cost, an additional set of constraints, to an increasingly powerful set of market drivers.”

 


At best, tariffs are short-term remedy


Anyone wanting a better sense of how all this might play out should consider taking a learning journey to China in 2025 — a country that is clearly committed to dominating the commanding heights of tomorrow’s economy.


China’s pro-green-growth mindset may not be driven by sustainability priorities, at least as we understand them, but their undeniable success means that the EU is now having to slap massive tariffs on imported Chinese cars.


Tariffs , of course, will be in the limelight throughout 2025. But there really is a limit to how long you can use such instruments to hold back the future. Expect to see more market initiatives aggregating sustainability-oriented demand at scale. Consider the Climate Group’s RE100 initiative, with over 400 corporate members committed to consuming 100 percent renewable electricity. Collectively they consume more power than France — and are closing the gap with Germany.


Finally, while it’s understandable why Jeff Bezos might want to keep his Washington Post empire above the electoral fray, it’s time for business leaders to align their wider market ecosystems with their declared commitments.


One angle Volans is pursuing involves encouraging companies to review their memberships of industry federations — to test the alignment of the federations’ relevant lobbying activities against a given company’s commitments. Our recent study with InfluenceMap for Unilever is a case in point. We found that around a quarter of the federations and associations with which Unilever is currently affiliated are lobbying in contrary directions. The logical question then is what such companies should do next: stay in and fight — or publicly resign, explaining why they have done so?


In headlines, the current sustainability recession is likely to drive our agenda in more market-oriented directions, dictating increasingly structural responses from corporations, and — as a result — a continuing politicization of issues that by the 2040s will nonetheless be taken for granted.

About the Author:

John Elkington, Founder of Volans and an ISSP Sustainability Hall of Fame Honoree and author of the newly released

Tickling Sharks: How We Sold Business on Sustainability.


PHOTO: China takes on the world: BYD cars on display in Munich, Germany | Matti Blume | BYD booth, IAA Summit 2023 | Munich, Germany

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Can sustainability be saved by tackling loneliness, not just CO₂ emissions?

Can sustainability be saved by tackling loneliness, not just CO₂ emissions?

By Raz Godelnik, Associate Professor • November 20, 2025
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Toward Appropriate and Responsible AI: Pathways to Sustainable Adoption and Infrastructure

By Nicole Cacal, MSc, • October 30, 2025
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People want to explore AI's potential, but they're acutely aware of the energy consumption, the water usage, the carbon footprint. It's become almost a reflex: mention AI, and someone immediately raises the environmental cost. I get it. The data centers, the training runs, and the resource demands. They're real and they're significant. But here's what I've come to believe: if we shift the narrative from focusing solely on AI's detriment to the environment and instead ask how much good it can create, what role we can play in driving data centers to go greener, and how we can generate recursive sustainability, we unlock better questions. We start thinking forward rather than just defensively. As sustainability professionals, our job isn't to reject technology wholesale. It's to shape its evolution. And right now, we have an opportunity to influence how AI develops and deploys in ways that align with planetary boundaries and social equity. But to do that, we need to move beyond binary thinking. Right-Sizing AI: Why Bigger Isn't Always Better One of the most overlooked levers we have for sustainable AI is also one of the simplest: choosing the right model for the job. The AI industry has been caught in a "bigger is better" arms race for years now. Every new model release touts more parameters, more capabilities, more everything. And sure, these massive general-purpose models are impressive. But they've created a dangerous assumption: that every task requires maximum firepower. This is where my strategic design training from Parsons kicks in. Good design isn't about having the biggest toolkit. It's about matching the tool to the task. It's about elegance through constraint. The same principle applies to AI deployment. The emerging concept of "Small is Sufficient " is gaining traction for good reason. Research shows that selecting smaller, purpose-fit AI models for specific tasks can achieve nearly the same accuracy as their larger counterparts while reducing global energy demand by up to 28% . Twenty-eight percent. That's not marginal; that's transformational. Think about what your organization actually needs. Are you processing customer service inquiries? Analyzing spreadsheet data? Generating product descriptions? Most of these tasks don't require a frontier model. A fine-tuned, task-specific model will do the job with a fraction of the computational overhead. The shift we need is cultural as much as technical. We need to move from asking "what's the most powerful AI we can deploy?" to "what's the most appropriate AI for this specific use case?" That question changes everything, from procurement decisions to vendor relationships, internal training, and infrastructure planning. AI as Infrastructure Manager: The Self-Optimizing Data Center Here's an irony that doesn't get enough attention: AI might be energy-intensive, but it's also one of our best tools for managing energy systems efficiently. When we only think of AI as a consumer of data center resources, we miss part of the story. AI can also be the conductor of efficiency, orchestrating complex systems in real-time to minimize waste and maximize renewable integration. Consider three optimization domains where AI is already making measurable impact: Cooling systems: Data centers generate enormous heat, and cooling accounts for a massive portion of their energy use. AI can continuously adjust cooling based on workload patterns, outside temperature, humidity, and dozens of other variables, optimizing in ways that static systems simply can't match. Workload scheduling: Not all computing tasks need to happen immediately. AI can intelligently schedule batch processing, model training, and background tasks for times when renewable energy is abundant or when grid demand is lowest. This isn't just theory. Companies are already doing this. Renewable energy integration: This one hits close to home in Hawaii, where we're working toward aggressive renewable energy targets but face unique challenges with grid stability and storage. AI-managed facilities can modulate demand in response to solar and wind availability, essentially turning data centers into flexible grid assets rather than inflexible burdens. When organizations approach their operations as integrated systems rather than collections of independent components, they achieve results that surprise even them. AI-orchestrated data centers represent this systems thinking at its most sophisticated. The technology optimizes itself recursively, reducing the footprint of AI through AI. That's the kind of elegant solution we should be scaling. Measuring What Matters: Beyond Energy to Net Benefit But here's the challenge: if we only measure AI's direct energy consumption, we miss the full picture. We need frameworks that capture both the operational cost and the systemic benefit. This is where life cycle assessment combined with comparative modeling becomes essential. We need to ask: compared to what? And over what timeframe? The sectoral success stories are compelling when you run the numbers: Building automation systems powered by AI are consistently achieving energy savings in the range of 20-30% across diverse building types. One documented case study of a commercial office building in the United States showed a 32% reduction in overall energy consumption with a 2.4-year return on investment (a $2.1 million system investment generating $875,000 in annual savings). In Stockholm, the SISAB school building portfolio achieved similar results with a two-year payback period. 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It requires companies to demand more of their technology providers and deploy their systems sustainably when greener options become available. But (and this is crucial) these benefits only materialize when we pair the right AI with the right infrastructure and the right deployment strategy. Which brings us to governance. The Path Forward: Governance, Transparency, and Adaptive Thinking The sustainability community, including organizations like ISSP, is actively developing shared frameworks for assessing AI's net impact. These emerging approaches include system-level energy auditing, selective task deployment protocols, and strategies for minimizing "dark data" (the vast amounts of stored data that's never used but still requires energy to maintain). Multi-stakeholder governance initiatives are bringing together technologists, policymakers, environmental scientists, and business leaders to create adaptive standards. This isn't about creating rigid regulations that will be obsolete in two years. It's about establishing principles and processes that evolve with the technology. Those with a technology management background know that the most successful systems are those designed for adaptation. We need governance structures that can respond to new information, course-correct quickly, and remain grounded in measurable outcomes. Transparency is non-negotiable. Organizations deploying AI need to measure and report not just their energy consumption but their net impact. What problems are you solving? What resources are you saving? What would the alternative approach have cost? These aren't easy questions, but they're the right ones. As sustainability professionals, this is our arena. We have the frameworks: life cycle thinking, systems analysis, stakeholder engagement, and metrics development, to name a few. We need to apply these tools to AI with the same rigor we've applied to supply chains, built environments, and industrial processes. So here's my invitation: What are you seeing in your sector? How is your organization approaching the AI sustainability question? Are you finding innovative ways to ensure deployment is appropriate and responsible? Because ultimately, appropriate AI isn't about choosing between progress and sustainability. It's about insisting that progress is sustainable. It's about right-sizing models, optimizing infrastructure, measuring net benefit, and building governance systems worthy of the challenge. The technology itself is neutral. Our choices determine whether AI becomes a driver of sustainability or another extractive burden. Let's choose wisely.
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