This article analyzes the effects of artificial intelligence (AI) on jobs, skills, and work organization at Veolia, in a context of rapid business transformation. A prospective study highlights three major dynamics: the evolution of professional practices, the accelerated automation of support functions, and the gradual transformation of industrial environments.In response to these changes, Veolia has launched an ambitious training strategy targeting 100% of employees by 2027. This strategy is based on gradual deployment, a network of champions, and strengthened governance. The approach combines acculturation, empowerment, and the adoption of concrete use cases. It aims to put AI at the service of collective performance and ecological transformation.
The advancements and integration of artificial intelligence (AI) present a transformative opportunity to address pressing global environmental challenges, including climate change, biodiversity loss, and pollution. However, realizing AI’s full potential in environmental services is contingent upon a robust and ethically sound data ecosystem. This article synthesizes existing literature to explore critical data considerations across four key dimensions: access, governance, quality, and ethics. Analysis reveals that while AI offers unprecedented capabilities for environmental monitoring, prediction, and management, significant challenges persist in data fragmentation, proprietary barriers, quality inconsistencies, and the inherent ethical complexities of AI deployment. Overarching opportunities lie in fostering open data initiatives, developing adaptive governance frameworks, implementing inclusive data curation and preprocessing, and prioritizing human-centered, sustainable AI development. The article concludes with strategic recommendations aimed at cultivating a collaborative, transparent, and equitable data infrastructure essential for harnessing AI effectively for a healthier planet.
Many recent assessments of the effects of artificial intelligence (AI) systems lack rigor. The electricity use and emissions of AI operations are often viewed as the most salient issues, but use of AI systems can have important effects when they are deployed, and such deployments can lead to complicated systemic interactions between AI systems, the broader energy system, and the economy as a whole. All effects of AI deployment are subject to deep uncertainty, but analyzing the effects of AI operations is usually the most feasible. Human understanding of the effects of AI deployments on specific domains and on interactions with the broader economy is in its infancy, but we know that these effects could either increase societal energy use (e.g., by making fossil fuel or geothermal extraction cheaper, or fueling increased consumer consumption by more targeted advertising) or decrease societal energy use (e.g., by enabling deployment of batteries to increase renewable energy adoption, which is more efficient than thermal plants on a primary energy basis, or improving efficiency throughout the broader economy). It is impossible to know in advance the sign of the net effect over the long term.For these less well understood effects, researchers should design consistent test cases, focusing on measuring economic, energetic, and environmental parameters before and after the deployment of new AI systems. For testing interactions, new kinds of large-scale economic models may be needed, as current models do not represent the effects of technology changes in a sufficiently detailed and systematic way.
Decarbonizing the energy sector while meeting increasing energy demand is one of the central challenges in the global response to climate change. This article discusses the ways in which artificial intelligence (AI) can help to decarbonize the energy sector, and the risks and challenges that can arise from AI-led innovation. We provide evidence-based examples of AI applications in energy supply, generation, and consumption that support decarbonization pathways. Additionally, future potential trajectories for AI in the energy sector are explored and discussed. Challenges to the uptake and scalability of these AI solutions include existing societal inequalities, financial and infrastructure gaps, and the uncertainty around rebound effects of AI. While AI technologies show much promise, realizing their potential to help achieve decarbonization targets will require addressing current inequalities and unsustainable political, societal, and economic behaviors.
As global focus intensifies on reducing emissions and improving energy efficiency, buildings have emerged as critical targets due to their significant share of energy consumption and carbon output. This article explores how artificial intelligence (AI) is revolutionizing building energy management by addressing inefficiencies, enhancing automation, and supporting sustainability goals. AI-powered Energy Management Systems (EMS), when combined with IoT devices, digital twins, and clean technologies like solar PV and battery energy storage systems, allow real-time monitoring, predictive maintenance, and intelligent control. These solutions optimize HVAC systems, lighting, load distribution, and occupant comfort while significantly reducing operational costs and energy waste. Despite challenges such as aging infrastructure, data fragmentation, and emerging cybersecurity concerns, the opportunities for improvement remain substantial. Real-world applications demonstrate how AI supports smarter heating, ventilation, and air conditioning (HVAC) and lighting systems, demand response, predictive maintenance, digital twin and smart energy management with solar photovoltaic (PV) and Battery Energy Storage System (BESS), contributing to energy savings and cost reduction. AI stands as a key enabler in the shift toward resilient, adaptive, and low-carbon buildings that meet both current demands and long-term climate objectives.The article also discusses how policy support, new business models, and collaboration between stakeholders are helping accelerate adoption. AI is increasingly recognized not just as a technological upgrade but as a catalyst for creating low-carbon, resilient buildings prepared for future demands.
The rapid advancement and adoption of artificial intelligence (AI) is transforming global production and consumption systems. AI offers opportunities to improve efficiency, reduce costs, and unlock transformations across sectors. At the same time, its expansion raises socio-environmental concerns, including energy demand, resource impacts, and potential inequalities across value chains.AI is evolving from a technical tool into a socio-cultural force that shapes how knowledge is created, owned, and shared, influencing how societies work, communicate, and make decisions. In the context of sustainability and climate, this shift creates both risks and significant opportunities to support more resilient and equitable development pathways. The central question is how to intentionally guide AI so that it contributes to planetary stability. This paper examines the concept of Earth alignment as a framework to promote AI development and deployment in ways that protect Earth systems, ensure equitable access to benefits, and strengthen social cohesion. Finally, it outlines plausible scenarios for aligning AI development with Earth-centric objectives, offering insights into potential pathways forward.
Excitement and concerns alike animate discussions on the impacts of artificial intelligence (AI) in Africa. Some see potential for AI to unlock new economic prospects, while others fear that its developments risk creating an AI divide, with Africans left behind. Meanwhile, the present dynamics and future aspirations for the continent’s socioeconomic development are converging on the significant role that digital transformation will play in realizing them. Policy deliberations are generating demand for contextual insights on how Africa’s youthful demographic can be leveraged as a strength, the role of (in)formal education, how relevant skills are imparted and the type of jobs available to the majority population. This essay analyzes some of the prevailing narratives on skilling and jobs for Africa’s present and future workforce, to contextualize the role AI can play in delivering on Africa’s overarching public policy and development goals of harnessing the continent’s demographic dividend and fostering sustainable economic growth.
As artificial intelligence (AI) becomes increasingly central to environmental services, its own environmental footprint – particularly water use – remains poorly understood. This paper examines the water footprint of AI through the lens of data centers, considering three key dimensions: direct water consumption from cooling systems, indirect water use embedded in the electricity that powers these facilities, and the indirect water embodied in hardware production. Training and deploying large-scale AI models demand immense computational power, which drives high energy use and substantial heat generation. To dissipate this heat, many data center facilities rely on water-intensive cooling systems that draw heavily on freshwater sources. While data centers’ global water use is modest compared to major sectors such as agriculture, manufacturing, and energy, concerns are mounting about their rapid expansion and concentrated local impacts. Hyperscale facilities, in particular, can intensify localized water stress, especially in arid or water-scarce regions, creating competition with residential, industrial, and agricultural users and placing additional pressure on already fragile water systems. These risks highlight the urgency of aligning AI growth and data center development with local and regional water resource planning, environmental policy, and infrastructure resilience. This paper identifies technological and system-level barriers that hinder sustainable AI deployment and proposes strategies to align AI development and deployment with sustainable, water-conscious growth. Key priorities include fostering technological innovation and optimization (e.g., advancing cooling technologies, improving efficiency through model compression, chip design, algorithmic improvement, and operational and network optimization), strengthening water resource management practices (e.g., applying circular water economy principles such as wastewater reuse, water recovery, and closed-loop cooling, while better integrating data center planning into local and regional water management), and enhancing transparency through standardized water-use reporting at both the infrastructure and AI model levels. Ultimately, the paper calls for a multidisciplinary, systems-level approach to AI governance that aligns technological advancement with sustainable water stewardship, ensuring innovation supports – rather than undermines – long-term water security and resilience.
This article focuses on sustainability and AI as a twin transition, and how the two are interacting to evolve both workforces. We discuss the role of AI for sustainability that is supporting the further development of the green economy, exploring how AI is enabling green workers to advance in energy efficiency, climate solutions, and resource management. We also examine workforce trends in sustainable AI, that is, AI’s contributions to climate change, emphasizing the importance of developing green skills among AI professionals and the workforce that supports AI’s growth.By prioritizing these competencies, we address the need for a workforce that is both technically proficient and skilled in sustainability. Throughout, our analysis centers on the workforce perspective, considering how targeted training and upskilling are essential to bridging these two transitions. In doing so, we aim to produce an overview of these workforces and contribute actionable insights on preparing workers to both develop and apply AI technologies for a sustainable future.
Escalating global climate change requires an urgent and coordinated response, for which artificial intelligence (AI) presents an unprecedented opportunity. By leveraging its advanced data analysis, predictive modeling, and optimization capabilities, AI can accelerate efforts in both climate mitigation and adaptation. This transformative potential, however, remains unrealized mainly due to technical, ethical, and institutional challenges in collecting, managing, and utilizing data. Data governance must be re-envisioned as a strategic enabler, foundational to the successful and responsible deployment of AI for climate action.This article explores the symbiotic relationship between data governance and climate-focused AI, highlighting both the considerable opportunities and significant challenges. While AI is critically reliant on high-quality, standardized, and comprehensive data, its development is hindered by data fragmentation and a persistent lack of interoperability. The immense promise of AI is also a double-edged sword, as its computational demands and energy consumption can exacerbate the very climate problem it aims to solve. The outputs of AI could risk perpetuating existing societal inequities, particularly the digital divide, if data collection and model training are not intentionally designed to be inclusive and representative.The path forward requires a multi-pronged approach that addresses not only technical fixes but also institutional arrangements for data governance. The development of harmonized data standards and the adoption of trust-based models for data sharing are crucial to fostering cross-sector collaboration needed to address a complex problem like climate change. Global institutions of responsible AI must be grounded in principles of equity, fairness, and transparency. By implementing these principles, organizations and governments can ensure that AI serves as a reliable, just, and powerful tool in building a more resilient and sustainable future.
As the global economy confronts the dual imperatives of ecological preservation and technological advancement, we at Veolia, are executing a comprehensive strategy that places Artificial Intelligence (AI) at the core of our operations and future growth. In this article, I provide an in-depth analysis of how we are leveraging AI not merely for incremental efficiency gains, but to fundamentally re-architect our approach to water, waste, and energy management. Our AI integration follows a multi-layered strategy. It is driven by our corporate vision and enabled by a dual-platform architecture. We have proven its effectiveness through a diverse portfolio of solutions, all guided by a forward-thinking governance framework.The strategic impetus for this transformation is our GreenUp program (2024-2027), an ambitious corporate program backed by a €4 billion investment to achieve aggressive targets in decarbonization, depollution, and resource regeneration. This approach reframes sustainability, not as a regulatory burden or a cost center, but as a core driver of competitive advantage and profitability. We have explicitly identified AI as one of the catalysts for achieving these goals, creating a powerful synergy between environmental outcomes and financial performance.The architectural foundation of our strategy rests on two complementary platforms. The first, Talk to My Plant (TTMP), is our industrial-scale AI system, powered by a strategic alliance with Mistral AI, designed to create smart facilities that can be co-piloted through natural language and eventually perform autonomous actions. The second, VeoliaSecureGPT, is a democratized, internal AI tool that we have developed to empower all of our 180,000 connected employees, fostering a culture of bottom-up innovation and creating a workforce of augmented employees. This strategic and architectural framework is substantiated by a growing portfolio of tangible AI solutions across all our business lines. From Digital Twin systems in wastewater treatment that reduce carbon footprints by up to 60%, to computer vision on our waste collection trucks that improves recycling purity, we are deploying a range of AI technologies to solve real-world environmental challenges. Finally, our strategy is underpinned by a commitment to responsible AI. Through our engagement with the Coalition for Sustainable AI, we are proactively shaping the ethical and regulatory landscape, building the crucial social license to operate for AI in critical infrastructure. I believe that our AI strategy sets a new standard for the environmental services industry. It shows how we are using intelligent technology not only to strengthen commercial performance, but also to drive continuous ecological gains ‒ redefining what sustainable success looks like for an industrial leader.
This paper examines the critical role of data quality and digital infrastructure in enabling effective AI deployment for environmental services across water, energy, and waste management systems. As cities worldwide grapple with mounting environmental challenges, artificial intelligence (AI) emerges as a transformative solution, but only when supported by robust data ecosystems and scalable digital infrastructure. Through analysis of real-world implementations in smart cities from New York to Barcelona, this paper explores how data accessibility, quality standards, and infrastructure readiness directly influence AI performance in environmental applications. This paper reveals that AI environmental services attracted £3.4 billion in funding during 2024, a 156% increase from the previous year. Success in unlocking AI’s potential for environmental sustainability and achieving global climate goals depends fundamentally on addressing data quality inconsistencies, infrastructure limitations, and equity considerations that can either unlock or constrain AI’s environmental potential.
The rise of generative AI has reignited the debate about the energy cost of AI. However, so far, the potential for AI to help optimize resource use, or to facilitate firms’ transition from dirty to green production technologies, has been largely overlooked. No serious attempt has been made at computing the overall energy cost and/or benefit of AI. Traditional AI models require significantly less energy-intensive training and inference and could enable companies to improve the energy efficiency of their technologies and help them meet their environmental objectives for the coming years. Machine and deep learning models used in industry and deployed using digital twins can control complex processes and optimize their resource use, in energy-intensive industries, and achieve significant energy-saving potential. Wastewater treatment is emblematic in this respect: aeration during secondary treatment typically accounts for roughly half of a plant’s electricity consumption. Our focus in this article is on the comparison between the direct energy cost of AI operations and the energy saving AI induces when implemented by Veolia. Veolia, in partnership with PureControl, has rolled out one of the first large-scale deployments of AI for climate-relevant efficiency, covering about 200 plants. PureControl’s system ingests high-frequency data (≈15 minutes intervals) on electricity prices, weather, sensor streams, and laboratory quality samples to maintain a live digital replica of the plant. The AI then schedules and doses aeration to minimize cost and consumption while assuring effluent quality and regulatory thresholds.We evaluate AI’s net effect using plant-level operational data, natural experiments (unplanned interruptions), and a full accounting of the AI layer’s own electricity use. Preliminary results from ~15 plants indicate a nearly 10% reduction in electricity consumption and GHG emissions, while AI’s direct electricity use accounts for less than 1% of the gross energy savings. Even under conservative assumptions about additional required hardware installations, the maximum lifecycle carbon cost of AI remains well below the emissions abatement, thereby pointing to a robust net-positive climate contribution.
Artificial intelligence (AI) is rapidly transforming the field of materials science, offering a new paradigm to accelerate the discovery and design of sustainable materials. This article explores how AI-driven innovations are enabling breakthroughs across the material spectrum, from recyclable polymers, coolants, to low-carbon cement and crystalline materials for energy applications ‒ advancing goals across material circularity, clean energy transition, and climate resilience. Additionally, it examines current key bottlenecks in AI for materials design, including computational limitations and data gaps. Finally, the article highlights emerging opportunities to close the loop between theory and practice through agentic AI systems and automation, emphasizing the importance of sustained investment and thoughtful deployment to catalyze a more sustainable future.
The urban population in Indonesia is characterized by rapid growth, while urban microclimates are developing in the context of climate change, increasingly having a detrimental impact on human health, especially due to significant heat-related challenges. BeCool's solar reflective paint allows for a cooler surface on painted roofing materials, similar to the effect of wearing white clothes during the day. This innovative technology helps make a concrete contribution to global warming mitigation efforts in Indonesian cities, as it combats the urban heat island effect and its neg impacts, thereby preserving the health of urban dwellers. By reflecting sunlight back into the atmosphere, applying paint can reduce outer surface temperatures by 15°C and inner space temperatures by 3°C, resulting in an 80% reduction in ozone warming.BeCool’s commitment to mitigating global warming extends beyond developing innovative products. The company actively participates in various programs, including painting public buildings, residences, worship and educational buildings. Additionally, BeCool provides education to students across different regions in Indonesia and builds solar reflective houses. These initiatives aim to foster sustainable environmental improvements, ultimately enhancing the health and quality of life within the community.
Protecting the ocean, the planet’s number one climate regulator, is the sine qua non for maintaining life on earth. The ocean’s resources are vast, diverse and essential, particularly in ensuring our supply of food. Our trade and communication both depend on the ocean. It is indispensable to our physical and social lives. It ensures that the planet remains habitable for all life, including humankind.But human activities pose threats to the health of the ocean and its resources. If we fail to take appropriate, concerted and ambitious action, this tendency may quickly trigger famines, major population movements, and socio-economic inequalities, hampering our societies’ aspirations for fair and sustainable development. How will the ocean behave tomorrow in the face of the pressures placed on it by humans and the uses we make of it? If we fail to remember this key element of our life on earth, we will alter how it functions and its health. This will inevitably affect our well-being, as our habitat depends on the ocean's vitality. How can we ensure the long-term future of this natural capital on an international scale? The ocean is a complex, dynamic and interconnected system, making it difficult to turn warnings from the scientific community into firm, actionable policy decisions. The Intergovernmental Panel for Ocean Sustainability (IPOS) is a project initiated by scientists that proposes the creation of an international body under the aegis of the United Nations to facilitate concerted reflections on the future of a sustainable ocean, and to take the necessary action.
Although climate change is affecting the entire planet, local effects differ in both nature and severity according to geography. Some geographic differences are readily apparent – such as the inherent vulnerability of coastal areas to sea level rise – whereas others are emerging. Geographic vulnerabilities are modified by the built environment and by disparities in the ability to adapt to climate change, further complicating the risk across the globe. The complex interactions of climate threats, local geographic and social vulnerabilities, and adaptation can best be explored at the regional level through examples relevant to other regions facing similar issues. This article will describe general principles of geography and climate change risk and explore how these play out using four examples: harmful algal blooms in Alaska, loss of glaciers in Peru, sea level rise causing increased drinking water salinity in Bangladesh, and HIV and food insecurity in Kenya related to extreme weather. The magnitude of the threat to humans from climate change will be significant, and geographic vulnerability is in many ways immutable, but much can still be done by humans to either increase or reduce risk. Lessons from one region can inform strategies in areas across the globe that share similar geographic vulnerabilities.
While the ecological crisis has gradually moved to the top of everybody's agenda – driven in large part by the realization of the health impacts – ecological transition seems to have become a new source of social division. There are two opposing blocs in many countries: advocates of an environmental pause on the one hand, adding fuel to the ecological backlash*, and on the other hand those who argue in favor of speeding up the rate of transition.However, it would be wrong to view the ecological crisis simply through the prism of this conflict. The truth is that, alongside differences that are sometimes genuine and often overinterpreted, there are also areas of real convergence. As shown in the second edition of the Ecological Transformation Barometer, conducted by ELABE for Veolia, the severity of the health threat posed by the ecological crisis is now a universal certainty and an individual fear. 75% of people around the world believe that “climate change is the greatest health threat facing humanity.” 64% feel exposed and vulnerable to risks to their physical or mental health. This feeling of vulnerability to health risks is not the sort of fear that prevents people from acting. Quite the opposite! The prospect of living in better health is the number one driver for the acceptability, and even desirability, of ecological transformation.*. A term referring to people’s resistance to environmental initiatives and policies.
Climate change, driven primarily by human activities, leads to persistent shifts in global temperatures and weather patterns. These changes trigger more frequent and intense weather events, which impact populations through both direct and indirect consequences. In particular, populations in Sub-Saharan Africa face heightened risks from climate change due in part to inadequate infrastructure and limited climate resilience. Climate change has significant implications for waterborne diseases, such as cholera, typhoid fever, schistosomiasis and hepatitis A, which affect populations throughout Sub-Saharan Africa. Limited access to clean water, sanitation, and hygiene infrastructure constitute significant risk factors for such diseases. Extreme climate-related events exacerbate these risks. For example, flooding can lead to contaminated water sources, while droughts compromise water quantity and quality. Additionally, extreme weather events can cause malnutrition, population displacement and disrupt livelihoods, further increasing vulnerability to diseases. Mitigating climate change involves reducing greenhouse gas emissions and transitioning to cleaner energy sources. However, short- to medium-term prevention and preparedness can minimize the impacts of waterborne diseases. To prevent waterborne diseases in Sub-Saharan Africa, it is crucial to improve access to safe drinking water, sanitation and hygiene infrastructure.
