Digital extractivism(s) and digitally-facilitated extractivism(s): Towards conceptual expansion and clarity

Abstract

Spanish

The rapid proliferation of digital technologies has increased interest in extractivism beyond traditional resource sectors. This article provides a review of the Anglophone literature on digital extractivism(s) and advances a new term – digitally-facilitated extractivism(s) – to improve conceptual clarity. It traces the expansion of extractivism from its roots in large-scale mineral and hydrocarbon exploitation to data, finance and ‘green’ energy systems. It argues that digital extractivism applies only when inherently digital resources – such as personal data or cryptocurrencies – can be extracted at an intensity and scale to be extractivism through digital technologies. The article also addresses misuses of the term digital extractivism, and the misclassification of any form of extractivism that involves digital technologies as digital extractivism. To address the issue of misclassification, the article introduces digitally-facilitated extractivism(s) to describe cases where digital systems mediate or exacerbate existing extractivisms without transforming them into digital extractivisms. Examples include fintech-enabled financial extractivism and green extractivisms using sensors, AI and blockchain technology. The article concludes by identifying emerging avenues of research and calls for more nuanced analyses of how digital infrastructures reshape socioecological relations and extraction frontiers.

Keywords

Digital extractivism extractivism data extractivism cryptocurrency Fintech data centres green-digital transition

Introduction

The invasion, mediation, embeddedness and impacts of digital technologies have become a major area of research in many fields, from geography to law and medical research. This has been especially true since the COVID-19 pandemic rapidly forced a large part of daily life to be mediated through digital technologies. While critical research on impacts of digital technologies well predates the pandemic, the way the pandemic ramped up ‘datafication’ of all aspects of life forced people around the world to viscerally feel, embrace or confront these impacts. The use of the term ‘digital extractivism’ has also proliferated in recent years. Google Scholar searches for articles using the term ‘digital extractivism’ until 2020 only generates six articles, while this search in 2025 returns 154 hits.

While the term itself has proliferated, this does not necessarily mean that the term is always used appropriately. Eduardo Gudynas, a major figure in the conceptualization of extractivism(s), has been a vocal critic of authors expanding the lens of extractivism beyond areas of mining, hydrocarbons, agriculture and agroforestry. Gudynas (2018: 63, 2021: 10) contends that expanding the term can lead to it becoming increasingly diffuse and confusing, leading to it losing descriptive and analytical value, and ultimately undermining the search for alternatives. While analytical value certainly emerges with the idea of digital extractivism, Gudynas’ (2018; 2021) concern is justified and deserves careful consideration.

The purpose of this article is twofold. First, to review the (primarily English-language) work that has been published on digital extractivism(s). Second, to propose the term ‘digitally-facilitated extractivism(s)’ to describe extractivist projects that are embedded with digital technologies and automation. This includes further delving into financial markets and so-called ‘green’ energy projects to demonstrate how digitally-facilitated extractivisms are already underway. This term can help enhance conceptual and analytical clarity for researchers and the public, and more broadly help the search for alternatives.

The remainder of the article begins by sharing methodology considerations, which is followed by a brief overview that illuminates the defining features of extractivism(s) and digital extractivism(s). The next section traces the concept of digital extractivism, reviewing influential works on the topic. Following that, there is an overview of research on previously-established types of digital extractivisms and related concepts. The Digitally-facilitated extractivism(s) section introduces the concept along with a review of work that falls under it in relation to financial extractivism and green extractivism. The article concludes by reflecting on the state of the literature and proposes avenues for future research.

Methodology

Drawing on Bahl (2023), I made an initial list of search terms related to digital extractivism, such as ‘data extractivism’,¹ gold farming and crytpocurrencies. I then expanded the search to include some emergent areas, including fintech, data center and data centre. I searched in Google Scholar and Scopus for all articles up to the point of writing in late 2025. The combined searches yielded 372 results. I went through each English-language result that I could access, skipping duplicates. I searched within the documents for the term ‘extractivism’ to confirm that some form of digital extractivism was addressed. This filtering resulted in 172 unique documents that were primarily articles and book chapters, but also theses, reports and dissertations. The documents were then analyzed, categorized and coded. The articles mentioned in this review were selected based on how frequently they are cited in discussions of digital extractivism(s), appropriate use of the concept and novel expansions of the concept.

A significant limitation of this review is that it is primarily focused on English-language sources, due to personal linguistic limitations. The concept of Extractivism grew out of the Latin American context and there is a wealth of research in Spanish and Portuguese, as well as a growing body of work done in other languages. While there is significant work on the topic done in/translated into English, this article should not be treated as a universal overview of all approaches to the topic, but only as addressing the Anglophone literature and translations.

From extractivism to digital extractivism(s)

The term ‘extractivism’ (from the Spanish extractivismo) was initially focused on hydrocarbons and minerals (Gudynas, 2021). However, not all extraction is extractivism (Durante et al., 2021). Eduardo Gudynas (2009, 2021), a seminal scholar of extractivism, presented three core features of extractivism: (1) the high-volume, high-intensity extraction of raw resources with more than 50% being exported, profits being concentrated in national/international business classes (or, for neo-extractivism, in governments), and the breakdown of social and ecological relationships, as well as pre-existing economic sectors; (2) severe environmental degradation; and (3) limitation of labour opportunities and degradation of working conditions. Extraction/appropriation and severe socioecological damage are essential features of extractivism, which retain wider applicability. Alberto Acosta (2013, 2011) extended the lens of extractivism to include aquaculture, agriculture and forestry. In this vein, Gómez-Barris (2017) discusses the ‘extractive gaze’ in which all life and nature can be transformed into profit, frequently through colonialism and extractivism. This gaze is blind to epistemologies and ontologies that value harmony and deeper connections among life and nature.

Extractivism gained greater visibility and popularity as an analytical lens in the late 2000s with the global commodity boom embraced by left-wing Latin American governments to further exploit resources as ‘neo-extractivism’ (Bebbington, 2012; Lang and Mokrani, 2013, 2011; Veltmeyer and Petras, 2014). Neo-extractivism was first major categorical expansion of extractivism. According to Lang and Mokrani (2013: 192), neo-extractivism is defined by the ‘active participation of the State, with a view to increase state income to fund social policies through state owned companies or by imposing increased taxes or fees on private companies’, Ramón Grosfoguel (2019) expanded extractivism to ‘immaterial’ resources with his concept of ‘epistemic extractivism’. According to Grosfoguel (2019: 208), ‘The aim of epistemic extractivism is to plunder ideas in order to promote and transform them into economic capital, or appropriate them into the Western academic machinery to earn symbolic capital. In both cases, this involves decontextualizing them in order to remove the radical content and may depoliticize them in order to make them more commercially attractive’. The pharmaceutical industry also perpetrates epistemic extractivism, exploiting indigenous knowledge of plants used for healing in biodiverse areas to create and patent new medicines for sale (Alum, 2024). Epistemic extractivism remains controversial among scholars such as Gudynas.

‘Green extractivism’ was another major conceptual expansion, first proposed by Alexander Dunlap and Andrea Brock in a series of presentations in 2017 (Dunlap et al., 2024). Dunlap and colleagues (2024) present four key features of green extractivism: (1) it employs ‘socioecological, weather and climate crises to generate new or reinforce existing “green” markets or profit-generating activities’ (447); (2) it uses ‘claims of ecological sustainability, “carbon neutrality” and combating climate change in order to legitimize and rationalize extraction. Such claims include relying on lower-carbon, or “renewable” energy systems, contributing to biodiversity conservation, promoting enhanced energy efficiency, producing electric vehicle systems, using digital (“smart”) application systems and engaging in carbon offsetting or restoration’ (447–448); (3) ‘it is fundamentally entwined with conventional extractivism’ (448); and (4) ‘it rests on false assumptions regarding the renewability of “resources” or existences’ (449). Dunlap and Jakobsen (2020) put forward the concept of total extractivism to describe how different forms of extractivisms connect, reinforce and exacerbate each other. These works provide a contextualization and precedent for expanding extractivism into digital realms.

Digital extractivism(s)

Digital extractivism is a category of extractivisms defined by cases in which the resource being extracted can only be harvested at extractivist scale in a digital form with the use of digital technologies. For example, personal data has been valuable for thousands of years, but can only be harvested with digital technologies in a high enough volume and intensity to be considered extractivism (Chagnon and Hagolani-Albov, 2023). It is important to recall that extractivism requires appropriation and severe socioecological damage. Two issues emerge within many works on digital extractivisms: (1) Either they do not demonstrate damages from extraction or (2) they not disentangling damages of extraction from damages of (potential) use. For example, social media platforms are designed to harvest as much personal data as possible. This design causes social damages, such as addiction, dependency, and facilitating the spread of polarization, among others (Chagnon and Hagolani-Albov, 2023). After the data is harvested it can be used for harmful purposes, such as state surveillance. Even if these damages are closely intertwined, they are distinct. All the works below struggle with the latter issue.

Gago and Mezzadra (2015), writing in Spanish, briefly suggested the possibility of extractivism in digital realms as part of a broader argument to expand the concept of extractivisms. Mezzadra and Neilson (2017) built on this to argue for an expanded concept of extractivism beyond literal extractive frontiers, though they do not use the term ‘digital extractivism’. They highlight some dynamics of ‘literal’ extractivisms (mining, hydrocarbons, agriculture and aquaculture), and then discuss possible types of extractivism ‘beyond literal extraction’: cryptocurrency, gold farming and data harvesting (193). They also discuss financialization and posit DNA and data from biological experiments as a form of data harvesting that could be associated with extractivism.

Crawford and Joler (2018) map out the energy-use, data harvesting, data use, and the origins and processing of materials of an Amazon Alexa, framing such AI systems as extractivist projects. The piece is frequently cited in uses of the term ‘digital extractivism’, however, the authors do not use the term. They provide an excellent outline of processes that merge to form data extractivism; the ways digital extractivisms connect to other forms of extractivisms; and how data is used. A significant issue, however, is that they define extractivism as ‘the relationship between different forms of extractive operations in contemporary capitalism’, and misattribute this definition to Mezzadra and Neilson (2017). Crawford (2021) repeats this definition and misattribution. These two pieces are frequently cited in works that misuse the term extractivism. Joler (2020) discusses ‘new extractivism’ as an assemblage of concepts and allegories. These include digital colonialism, connections to types of labour, allegories with biology and physics, and impacts.

Iyer and colleagues (2021) wrote an influential report on digital extractivism in the African context. They define digital extractivism as ‘a form of exploitation based on the virtualization or digitization of commodities and services through a borderless digital capitalism that perpetuates pre-existing colonial practices of value grabbing and wealth accumulation. In this case, the resources may be mineral (e.g. Coltan), labor, data and, even culture’ (Iyer et al., 2021: 6). They put forward nine ‘methods’ of digital extractivism. While the report is insightful, it raises numerous issues. First, it is mostly about data extractivism rather than digital extractivisms more broadly. Data extractivism is a variety of digital extractivism that is focused on harvesting data, especially personal data. Second, they do not engage with extractivism literature in defining digital extractivism, and only engage with a few sources in broad strokes overall.

Further, most of the ‘methods’ they describe are not actually methods (i.e. ways of extracting digital resources). The first is ‘digital labour’, as labour exploitation for creating digital systems and via digital systems. In some circumstances this could be a method, in others it could be digitally-facilitated labour exploitation, but is generally a structural feature of data extractivism. Second is ‘illicit financial flows’, technology companies not paying taxes to the countries they operate in if they do not have a legal presence. This is contextually important information, but not a method of digital extractivism. Third is ‘data extraction’, the harvesting and usage of personal data. This is effectively a rephrasing of data extractivism. Fourth is ‘natural resource mining’, the extraction of minerals used in digital systems and infrastructures. This highlights how digital extractivism drives other forms of extractivism, not a method of it. Fifth is ‘infrastructure monopolies’, when large foreign companies build digital infrastructure. This is contextually important, but not a method of digital extractivism. Sixth is ‘digital lending’, apps providing exploitative microcredit loans. This is better described as digitally-facilitated financial extractivism. Seventh is ‘funding structures’, highlighting the influence funders from the global North have on technology developed by African startups, and the tendency of all funders to prefer funding startups founded by white people in African countries. This is contextually important, but not a method of digital extractivism. Eighth is ‘beta testing’, the testing of new technologies in an unethical way, especially on vulnerable populations. This can be seen as a method of digital extractivism, and possibly a specific type of data extractivism. Finally, ‘platform governance’ describes the impacts of content moderation, or the lack thereof. This is a structural feature of data extractivism, not a method of digital extractivism.

Chagnon and colleagues (2021) explicitly use the term ‘digital extractivism(s)’ and attempt to make connections to extractivism literature. They map a web of how digital extractivisms drive other forms of extractivism through the materiality (the resources required to build devices, infrastructures, produce energy, etc.), energy (required to run devices, infrastructure, etc.), infrastructure (devices, servers, cables, etc.) and transport (physical movement of devices, infrastructures, materials, etc.) required for digital extractivisms to operate. A key conceptual contribution is its recognition of digital extractivism as a category of extractivism with different varieties. It draws on Mezzadra and Neilson (2017) to delineate data, gold farming and cryptocurrency as potential varieties, while advocating for conceptual expansions.

Varieties of digital extractivisms

Mezzadra and Neilson (2017) identified three types of digital extractivisms: data extractivism, cryptocurrency and gold farming. Data extractivism is by far the most discussed form of digital extractivism, returning 724 results in a Google Scholar search (as of 2025). This article only discusses key works that are frequently cited and/or focus on theoretically grounding data extractivism in extractivism literature. It would not be possible to more fully review the variety of works here without limiting other sections.

Ricaurte (2019) and Couldry and Mejias (2019) both discuss data extractivism in general terms and engage with some extractivism literature, but as a process and dimension of the broader concept of data colonialism. Igor Calzada (2023: 2) has used the term ‘data extractivism’ in many publications, always defined as ‘the practice of collecting, analyzing, and commodifying large amounts of personal data from digital citizens without their explicit consent or control, often for commercial or political purpose’. Calzada's (2023) definition (and sometimes usage) conflates the damages of data use with data extraction, and misses out on key aspects and damages. He does not generally engage with extractivism literature and misattributes his definition to Sadowski (2019), which does not use the term ‘extractivism’ nor engage substantially with extractivism literature.

Chagnon and Hagolani-Albov (2023) focus on conceptualizing damages of data extractivism, termed as ‘social pollution’. These are broken into broad categories of toxifying the social environment (e.g., addiction, facilitating polarization), homogenization (e.g., loss of language and culture) and inequalities (e.g., security, access to benefits). Chagnon and Hagolani-Albov (Forthcoming) focuses on tying broadly accepted definitive features and structures of extractivism to data extractivism. This includes discussing data as a resource, how data extractivism is dependent upon and exacerbates inequalities, and the intertwinement of technology companies and the state.

Most works focus on personal data, but the term is applied to disparate types of data. Some (mis)use the term in reference to research data rather than ‘epistemic extractivism’ (Landström, 2024). Others have used it to discuss DNA and pheno-data (Lee et al., 2024).

Cryptocurrency

Despite many articles briefly mentioning cryptocurrencies and extractivism, surprisingly few theoretically flesh out cryptocurrencies as extractivism or analyze case studies of cryptocurrency mining explicitly through extractivism. Tarnowski (2023), drawing on Kröger (2022), explicitly connects cryptocurrencies to features and outcomes of extractivism. They highlight the steep inequalities that are required to monopolize energy production for cryptocurrencies and the way this exacerbates inequalities. They also briefly discuss environmental impacts from the mining, energy production and infrastructure expansion needed for cryptocurrency mining systems. Zimmer (2017) compares the motivations, methods and impacts of Potosí, Bolivia silver mining during the colonial period to cryptocurrencies. They posit that cryptocurrency mining represents a constellation of technology, resources, ecological impacts, labour and global trade.

Existing research predominantly focuses on cryptocurrency mining carried out by formal companies (sometimes with state engagement) and associated socioenvironmental impacts from the energy usage and landgrabs. Reinke (2022) discusses environmental damages related to electricity usage, global examples of power outages and increased energy prices for local communities caused by cryptocurrency mining. They feature a case study of perceptions and impacts of cryptocurrency mining operations that move from town to town in Washington state, USA. Atkins and colleagues (2021) focus of cryptocurrency mining operations in Quebec, Canada. The cool atmosphere, inexpensive hydropower and incentives from postindustrial towns meant to attract job-creating industries made Quebec a popular destination for cryptocurrency mining. They discuss the colonial connections of hydropower in Quebec, the impacts on local environments, lack of job creation, and the impacts of higher electricity prices on local people. Howson and colleagues (2024) look at landgrabs for planned projects meant to attract cryptocurrency mining around the world and the impacts on local environments and communities. They see patterns of extractivism in how these projects appropriate local resources, enable virtual land grabs, and how they inspire anarcho-capitalist crypto-utopian ‘exit zones’, generally in the global South.

Rosales and colleagues (2023) demonstrate there are more angles to assessing the impacts of cryptocurrency mining, focusing on cases in El Salvador and Venezuela. In El Salvador, they focus on negative social impacts of state-sponsored cryptocurrency uptake. The Salvadoran government rolled out a cryptocurrency wallet for citizens, claiming it would ease remittance transfers and expand financial networks to underserviced, impoverished populations. However, security flaws and lack of cybersecurity education led to a rise of scams that defrauded many out of their savings. In Venezuela, they look at small-scale informal cryptocurrency miners. Many Venezuelans see cryptocurrencies as a hedge against hyperinflation and currency restrictions, and the country has some of the highest cryptocurrency usage in the world. Some individuals have illegally set up mining operations in abandoned homes and homes of family members to take advantage of cheap electricity and evade government detection. However, they see little reward for the extra pressure they put on a decrepit power grid that suffers frequent blackouts. This novel research gives a glimpse of the potential variety of approaches to cryptocurrencies as extractivism.

Gold farming

Gold farming is the practice of people playing massively multiplayer online games for long periods to level up a character and/or collect rare items and in-game currency to sell for real money (Chagnon et al., 2021). This can be done by individuals or by ‘studios’ employing people frequently in poor working conditions, for long hours and little pay (Tai and Hu, 2017). While gold farming has been approached from many angles, no works currently provide case studies of gold farming as extractivism. This is possibly because it does not seem to fit extractivism as well as labour exploitation, which has its own rich body of literature. Environmental impacts are likely similar to those of net cafes, as these games are designed for consumer electronics. Gago and Mezzadra (2015) included both gold farming and labour exploitation as forms of extractivism. If one considers labour as an object of extractivism, it could be considered digitally-facilitated labour extractivism. However, that debate is outside the scope of this article.

Expansions and related concepts

Calvão and Archer (2021: 1), although not using the term ‘digital extractivism’, put forward a concept of ‘digital extraction’ to describe ‘the collection, analysis, and instrumentalization of digital data generated under the banner of blockchain-based due diligence, chain of custody certifications and various transparency mechanisms, situated alongside and in support of mineral extraction’. Focusing on diamonds and cobalt, they find that blockchain tracing programmes might ease the conscience of consumers, investors and regulators, but also consolidate power in the hands of the companies that create and control the blockchains. This increasingly obscures power relations, disempowers local actors and marginalizes information that does not easily translate to spreadsheets. They suggest this approach can apply well to agriculture and forestry.

Arboleda's (2020) concepts of the planetary mine and fourth machine age are relevant and complimentary to digital and digitally-facilitated extractivisms, recognizing the role that digital technologies play in the expansion of extraction and valorization in many areas. However, they also include using technologies such as gene sequencing and biotechnology to facilitate extraction of resources and value. This broader scope of technologies and the areas of application open up interesting symmetries and possibly complications for digital extractivisms.

Madianou (2019) put forward ‘technocolonialism’. They briefly engage with extractivism literature and the core dynamics are effectively data extractivism in humanitarian contexts, carried out by international organizations, frequently in collaboration with technology companies. Sandvik's (2023) ‘humanitarian extractivism’ is very similar, but expands to include testing emerging technology systems (such as automated drones) in humanitarian contexts. In both instances, there is effectively no consent in these systems, as users are dependent on them to receive assistance and are forced to give up their personal data. In the testing of new systems, the damages from failures impact these marginalized users.

Loder (2021) introduced the concept of ‘photogrammetric extractivism’, to describe how companies such as Meta and AirBnB have harvested user photos and videos without true consent, as users are forced to agree in order to use the systems. These are then used to create VR or digital recreations of the interior environments in the photos and videos invading user privacy and prompting security concerns.

Liu (2025) discusses new technologies in digital finance expanding financialization and reinforcing global financial hegemony. Beyond fintech and cryptocurrencies, they mention high-frequency trading – automated stock trading that performs a high number of trades in fractions of a second to take advantage of small discrepancies in prices. This is an interesting new direction for future research in digital extractivisms, because while stocks were historically traded with physical slips of paper and in-person, this type of trading is only reasonably possible with digital technologies. Beyond environmental damages from energy use and materiality, high-frequency trading exacerbates financial inequalities.

A significant expansion related to digital extractivism(s) is the conception of data centres not only as vital infrastructure of digital extractivisms, but as cases of extractivism themselves. Brodie (2020, 2023, 2024) has written about the growing importance of low carbon energy sources for powering data centres and the subsequent socioecological impacts. The negative environmental and social impacts of data centres on communities have been proliferating and gaining significant public attention (Murphy and Feng, 2025; Zhao, 2025; Tironi and Albornoz, 2025).

Inappropriate uses of digital extractivism

With the popularity of the term ‘extractivism’ there has been a growing issue of authors inappropriately using the concept of digital/data extractivism(s) to describe cases of exploitative or damaging data use, but not significant damages from data extraction. Even excellent work can fall into this trap. For example, Fairbairn and Kish (2023) discuss how the Global Open Data for Agriculture and Nutrition initiative disadvantages smallholder farmers and advances corporate control by giving corporations access to a wealth of data that smallholder farmers cannot utilize. They claim this is an example of data extractivism and data colonialism. However, beyond obliquely mentioning privacy considerations in collecting data, they do not discuss damages from data extraction. They also state that collectives that only share such data among smallholder farmers benefit those farmers, demonstrating that the issue is fundamentally one of use. As such, while the article is excellent overall, and the case is interesting and fits well into data colonialism, it is not digital/data extractivism. This is a pervasive issue that manifests in more ways than can be covered here. This article was selected to demonstrate that even high-quality work can still have this issue and is not meant as a castigation of the authors.

Beyond misusing digital extractivism for inappropriate cases, some authors discuss cases of extractivisms, but mislabel them as digital extractivism because they feature some digital technology. Understanding digital extractivisms is important, as is understanding the growing impacts of digital technologies on other forms of extractivism. However, these are not the same. The following section presents an argument for a new term to facilitate conceptual clarity between these two related but distinct concepts.

Digitally-facilitated extractivism(s)

The defining feature of digital extractivism(s) as a category is that what is extracted can only reasonably be extracted to a point of extractivism (volume, intensity, etc.) in a digital form with digital technologies. If another form of extractivism migrates to a digital medium, but the damages and dynamics are largely unchanged, this is not digital extractivism. For example, financial extractivism and financial technology/fintech (digital technologies used to enable or support financial services or banking) (Bateman, 2020a). Financial extractivism can manifest in forms of exploitative microcredit that can take place via fintech apps, leading to high indebtedness and financial ruin (Bateman, 2020a). The damages are largely the same as if they happened offline – that is financial extractivism, not digital extractivism. At the same time, being on a digital medium has impacts, making exploitative microcredit easily accessible at all hours. The digital medium does not change the fundamental nature of financial extractivism, but it does facilitate it. It is also important to note that digital extractivisms can happen in parallel. Microcredit apps are frequently also data extractivist, harvesting personal data for exploitation. These are two intertwined but separate issues.

The term ‘digitally-facilitated extractivism(s)’² describes when other forms of extractivisms, such as financial extractivism or epistemic extractivism, move to digital technologies or have systems that become heavily embedded with digital technologies (e.g., green extractivism). Digital systems (and especially AI) embedded in extractivist projects can exacerbate damages and expand frontiers, while increasing profitability for owners. For example, embedding sensors in windmills for automation and atmospheric data collection (and the associated materiality of the required data centres), or the use of drones, sensors and AI to map out underground deposits in remote areas. This intertwinement is important to study and understand for extractivisms and other areas. Walker and Winders (2023) provide an excellent look at how AI impacts areas of labour, surveillance, and activism. The term ‘digitally-facilitated extractivism(s)’ provides greater conceptual and linguistic clarity versus lumping everything into digital extractivism. The following provides a brief overview of digitally-facilitated financial extractivism and digitally-facilitated green extractivism.

Digitally-facilitated financial extractivism

Although Gago and Mezzadra (2015) briefly discuss expanding extractivism to digital realms, they focus more on linking financialization with financial extractivism. The rise of fintech has brought some attention to the topic through the lens of extractivism. Gago (2025) interweaves fintech with a broader discussion of financial extractivism and social reproduction. Bateman (2020a) conceptually connects fintech to extractivism. Bateman (2020b) focuses on the damages of fintech and digitally-facilitated financial extractivism in the context of Cambodia, using the framing to contest the neoliberal lionization of microcredit as a poverty reduction model. Jain and Gabor (2020) discuss how fintech systems in India allow companies to both extract money and data (which they then sell) from marginalized groups. Bateman and Teixeira (2022) discuss fintech as both a form of extractivism and alternative. They include brief case studies in a variety of contexts. They discuss how M-Pesa, launched in Kenya, pioneered fintech and microfinance in the global South and as a form of extractivism. They also discuss how the rapid expansion of microfinance in South Africa pushes impoverished people into debt by offering quick high-interest loans using social grants as collateral. Turning to China, they present ways business lending fintech has undermined local development and rapidly increased fraud. The term ‘digitally-facilitated’ is warranted, as the fundamental damages of financial extractivism are exacerbated and facilitated by digital technologies.

Digitally-facilitated green extractivism

Dunlap and colleagues (2024) directly mention how green extractivism is intertwined with digital technologies. They also crucially introduce the concept of ‘indirect green extractivism’ to describe other extractive operations that enable green extractivism. The ‘digitally-facilitated’ terminology complements this. While digital extractivisms can certainly be indirect green extractivism, not all digital systems are necessarily extractivist in and of themselves.

‘Green’ energy and datafication have become deeply intertwined conceptually. Bringel and Svampa (2023) discuss a new ‘decarbonization consensus’ of governments, international organizations and corporations to push a corporate-led energy transition based on green and digital technologies. This is an evolution of the ‘commodities consensus’ (focused on mass export of raw materials), which evolved from the Washington consensus (focused on financial valorization) (Svampa, 2015). Governments and international organizations now talk about a ‘green-digital’ or ‘twin’ transition, promoting the co-development of green energy and automated digital systems in the name of efficiency and carbon reduction. Embedding digital systems to collect atmospheric data in green energy production has become more the rule than the exception. While companies could get equally useful data in non-extractivist ways, it is easier and cheaper to embed it in the manufacturing/construction process, harvesting more data than is strictly needed. However, the data extraction does not largely change the immediate major damages. For example, not every windmill in a large ‘park’ strictly needs to have sensors to collect atmospheric data for effective adjustments and climate modelling, but they are still there and do not change impacts on wind flows and local environment.

Patrick Brodie demonstrates how digital systems and lower-carbon energy extraction are intertwined. Brodie (2020) shows how digital wind and other energy systems are part of a broader discussion of what he terms ‘climate extractivism’, which he relates to data centres flocking to Ireland to take advantage of the climate and government policies. Brodie (2023) makes this more explicit in discussing the pairing of ‘smart’ and green technology infrastructures, with data centres preventing local communities from accessing low-carbon energy by contracting directly with producers. Brodie (2024) highlights that solar, wind and geothermal energy require real-time processing via digitalized infrastructures to produce energy. Steinhoff and Brodie (2025) build on this, discussing the growing role of AI and data centres in the production of synthetic data and the planning/operation of green energy sites. Bresnihan and Brodie (2024) discuss how industry interests are increasingly presenting data centres as integral to the flexibility of expanding green energy systems, as they have large batteries for backup energy that can be used to store energy from intermittent energy sources like solar and wind. This exemplifies digitally-facilitated green extractivism in practice.

Ruelas Espinosa and Dunlap (2023) show how digital technologies can facilitate green extractivism at the intersection of carbon credit schemes and conservation. Their work discusses a case in the Yucatán Peninsula, Mexico, where a local company has developed a carbon credits meta register for a protected forest inhabited by jaguars. They use cameras in the forest to capture images of jaguars, then combine those with carbon credits in a blockchain token to make the tokens more attractive to buyers. This serves to greenwash controversial local development projects and commodify the forest and jaguars without any guarantees of conservation. Beyond this, Özden-Schilling (2023) has looked at individuals in Canada using digital maps to conduct remote prospecting for potential mining sites. This intersection is ripe for research and expansion. The work of Dunlap and colleagues (2025), although focusing on the intertwinement of green extractivism and mining, demonstrate the importance and potential of interrogating intertwined systems.

Conclusion

This article has provided an overview of research on digital extractivism(s), proposed the concept of ‘digitally-facilitated’ extractivism(s), and given an overview of works relevant to that concept. Digital extractivism(s) is defined by the digital nature of what is being extracted and that it is only possible to extract it to a point of extractivism in a digital format with digital technologies. There are multiple varieties of digital extractivisms. Data extractitivsm, focusing on personal data, is the most popularly used, and causes damages such as addiction, mental health problems and increasing the spread of polarization. Cryptocurrencies are another variety, causing environmental damage through their energy use and materiality, as well as a variety of impacts on local communities, users and informal miners. High-frequency trading is an interesting expansion, exploiting financial markets for the wealthy and powerful. It was encouraging to see some interesting expansions related to digital extractivism, particularly data centres. Brodie (2020, 2023, 2024) makes compelling arguments for considering them as extractivist projects, highlighting their environmental damages and social impacts. Examining data centres in relation to digital extractivism is a fruitful area, especially considering potentially unique dynamics in different global contexts.

Digitally-facilitated extractivism(s) describes when a form of extractivism takes place via digital technologies or has systems that become heavily embedded with digital technologies. It can help provide greater clarity and push back on the mislabelling of any form of extractivism that includes digital technologies as digital extractivism. Cases of financial extractivism in relation to microfinance fintech as well as a variety of green extractivisms can already fit into this concept. Beyond these mining is a natural expansion, given the proliferation of emerging technologies (such as AI, drones and satellites) in prospecting for minerals and the policy push to expand mining in the name of the green-digital transition. Agriculture and forestry are also fruitful areas of application. There are many potential angles, such as interrogating if projected benefits of digitalization manifest, exploring environmental impacts of energy usage and e-waste, and impacts of these technologies on the volume and intensity of extraction.

In some ways the proliferation of research on extractivisms can be seen as a testament to its analytical utility and how it taps into the zeitgeist of a world in polycrisis. However, with this growing popularity, Gudynas’ (2018: 63, 2021: 10) concerns about overuse and misuse to the point of losing the analytical utility of extractivism have begun to manifest, and the possibility of undermining the search for alternatives is becoming more likely. This article has helped facilitate greater by providing a needed overview of digital extractivisms in the English-language corpus as well proposing the term digitally-facilitated extractivism(s) to provide an alternative to misuse of digital extractivism(s). As digital systems are expanding into new areas, research using and evolving these concepts is urgently needed.

Footnotes

Acknowledgements

The author would like to thank the editors for suggesting this topic and the opportunity to write this article, as well as the reviewers for their helpful comments.

ORCID iD

Christopher W Chagnon

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical consideration

The author(s) declared that ethical approval was not necessary for this study.

Data availability statement

All data comes from the articles cited.

Notes

Author biography

Christopher W Chagnon is a doctoral researcher in Global Development Studies at the University of Helsinki. His area of research is digital extractivisms and social impacts of technology, focusing on impacts of data extractivism in Zambia.

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