The Digital Footprint of Transparency: A Critical Assessment of the Environmental Impact of Blockchain and AI Infrastructures for Environmental Data Verification

In recent years, digital notarization technologies, blockchain systems, and artificial intelligence (AI) models have been increasingly proposed and adopted as tools to enhance traceability, immutability, and verifiability of environmental data. In particular, their application in climate and environmental monitoring systems is often justified by the need to improve data reliability, automate verification processes, and reduce risks of manipulation or human error. However, this technological enthusiasm frequently overlooks a fundamental dimension: the direct and indirect environmental impact of the digital infrastructures required to support these systems. This paper offers a critical assessment of the environmental footprint of blockchain technologies, AI-based verification systems, and digital notarization infrastructures when applied to environmental data governance. The aim is to highlight the paradox of deploying computationally intensive technologies for sustainability purposes that may, in turn, generate significant environmental burdens across their lifecycle. The analysis is structured along three main dimensions. The first concerns the energy consumption and associated emissions of different computational architectures, including blockchain consensus mechanisms (such as proof-of-work and proof-of-stake) and AI model training and inference processes. Particular attention is given to the role of large-scale data centers, which constitute the shared physical infrastructure underpinning both distributed ledger systems and contemporary AI applications. These facilities represent a critical yet often under-acknowledged source of electricity demand, cooling requirements, and embodied carbon emissions linked to hardware production and renewal cycles. The second dimension addresses the indirect environmental impact related to the diffusion and scalability of blockchain- and AI-based solutions in environmental data governance. The uncritical adoption of such technologies can lead to the proliferation of overlapping verification systems, redundant computations, and continuously expanding data pipelines. This multiplication of digital infrastructures increases overall system-wide energy demand, often without proportional gains in environmental monitoring efficiency or decision-making effectiveness. The third dimension explores the risk of technological rebound effects, whereby tools designed to support sustainability objectives inadvertently stimulate increased data generation, processing, storage, and verification activities. AI-driven analytics and automated verification systems, in particular, can contribute to an acceleration of data-intensive practices, further amplifying the environmental footprint of digital ecosystems, even when individual algorithms or systems are optimized for efficiency. Adopting a systemic and socio-technical perspective, the paper argues that blockchain and AI in environmental contexts should not be evaluated solely in terms of technical performance, automation capacity, or data integrity improvements. Instead, they must be assessed through comprehensive sustainability criteria that incorporate lifecycle environmental costs, infrastructural dependencies, and system-level interactions between computation, energy use, and governance practices. The contribution engages with broader debates on digital technologies for sustainability and environmental governance, emphasizing that digital infrastructures are not environmentally neutral. Rather, they actively shape the ecological consequences of the systems they support. As such, blockchain and AI should be understood not only as tools for environmental accountability, but also as significant components of contemporary environmental impact.