Definitions

“Smart city” is a byword meaning different things. The Wikipedia definition reads: “A smart city is an urban area that uses different types of electronic methods and sensors to collect data”. To define it, some scholars focus on its technical constituents (Goodman 2020, 823; Drahos 2021, 159–60). Others hone in on its socio-economic potentials and promises and view it as a way of addressing complex urban issues by means of data-intensive technologies (Caragliu, Bo, and Nijkamp 2011). Other social scientists underscore its cultural implications, signalling a shift towards an “ideal urban future which has rapidly become significant in policy and industry parlance, something more than its cables, sensors, and servers, or a combination of these” (Cardullo 2021, 15). Although there is no unique description, smart city cheerleaders typically treat the label as a “floating signifier that can change referents whenever needed, allowing for a flexible, dynamic space in which to plug a variety of products, practices, and policies” (Sadowski and Pasquale 2015). Public local authorities tend to use the term “smart city” more as a branding effort to attract million-dollar investments, seeking to swell urban coffers (Goodman 2020, 825). In doing so, some cities turn into testing apparatuses to pilot novel data technologies and appeal to the “creative classes” (Cardullo 2019, 816).

Applications

The intricacies of employing data-driven technologies in the urban context reflect a myriad of contextual applications. The smart city is not one single thing, but it actualises by virtue of various data-driven technologies. The bad and the good of things come from how one uses them, rather than how they look like by scratching their surface. One cannot label the smart city as the root of all evil without engaging in the analysis of its technological components.

There exist relatively “unoffensive” applications of the smart city. One illustration in this respect is the smart “adaptive” traffic signals connecting to autonomous vehicles in real time and regulating the traffic flow accordingly (Astarita et al. 2020). Other examples can be smart waste management and smart lighting apparatuses. The former systems collect large-scale sets of data on waste production. The sensors of the smart rubbish skips can gauge filling level and anomalies, providing efficient collection routing based on the filling level, and therefore less citizens’ inconvenience because of full or faulty bins. These systems have been implemented in Rotterdam, for instance¹. Smart lightning systems rest on interconnected lampposts transmitting and analysing traffic data and environmental information (e.g. urban bustle, air quality, and so forth). Major European cities such as Munich, Barcelona, Rotterdam, and Copenhagen have already implemented these systems. For example, Barcelona uses smart urban lighting to govern crowds in public areas (e.g. beaches) (Diran et al. 2021, 4). Other instances of this typology of data-driven urban technologies are smart irrigation systems that automatically gauge the amount and time of watering based on the outside weather conditions; and smart urban mobility systems, as will be analysed in the subsequent section.

The other end of the spectrum comprises more “worrying” technology applications, to say the least. Some smart city applications pose ominous risks, grounding avant-garde technological forms. This is the case of CCTV systems equipped with facial recognition technologies that some municipalities have installed in bustling spaces for law enforcement purposes (Financial Times 2021).

In between the two ends of the spectrum, we can single out applications in which drawing a line between the good and the bad is more controversial. Some smart city applications can direct and nudge individuals into prearranged activities and behaviours (Ranchordás 2020, 254; Yeung 2017, 118). Nudging practices frequently aim to prompt urban citizens into sustainable and eco-friendly attitudes. Smart navigation devices, amongst other things, can suggest the least congested route to prevent traffic jams or recommend using less polluting transport services (Franke and Gailhofer 2021, 2). For example, in Durham (North Carolina), commuters are nudged into preferring public transport means over their private vehicles. The municipal authorities deliver personalised route indications from houses to work by email. Collaboration with local companies has made this real (Ranchordás 2020, 264). Similarly, in the Netherlands, the Enschede municipality contracted a private provider to develop an app (Enschede Fietst) to nudge citizens into cycling in town and reward them for such behaviour². Smart nudging practices can nevertheless pose risks for citizens’ autonomy (Galič 2019, 255–61). For instance, in Eindhoven (Netherlands) some smart lightning systems technologies have been installed to “de-escalate” aggressive behaviours of pedestrians in one of the most popular nightlife areas of the city. This system was at the core of De-escalate, one of the initiatives of the umbrella project Stratumseind 2.0, which aimed to create a “living lab” (Stratumseind Living Lab, SLL) of smart technology experimentation. Psychology scholarship has shown that spotting colour lights can influence personal emotions and mitigate people’s reaction when they lose control (Galič and Gellert 2021, 5). For example, faint and warmer colours come with lower excitation; by the same token, people tend to slow down their breath when they are exposed to low-frequency flickering orange light. Direct bright light, moreover, can enhance self-awareness. Using lighting tools in real-time can thus prove crucial to influence citizens’ behaviour in the streets. Nudging by means of smart lighting systems, albeit “easily resistible” (Galič 2019, 6), steers people’s conducts for a benign purpose.

The smart city regulation between privatisation and data commercialisation

Cities have implemented smart city projects through various legal tools. Local regulatory efforts are illustrative instances of “regulatory capitalism” in miniature: the privatisation of urban services is not pure deregulation, but a way for “different kinds of actors (to become) important national, regional and global regulators” (Braithwaite 2008, 29). In this sense, cities have accessed the political arena as autonomous global actors in an effort to inspire and shape international agendas, either by means of soft law tools³ or in transnational networks⁴ (Swiney 2020, 229; Voorwinden and Ranchordás 2022).

Municipal institutions routinely come to terms with private actors to implement smart city technologies. Just to give a taste of the business growth, the smart city market is meant to reach $3 trillion by 2025, exceeding the size of all traditional business sectors (Morozov and Bria 2018, 6). The public and private actors’ interactions in the smart city implementation mostly rest on public-private partnerships (Halpern et al. 2013). A striking illustration of the trend is the IBM “Smarter Cities” project, which puts together government technologies for city management. Amongst other things, IBM smart package encompasses water and energy management solutions, data-driven transportation systems, and financial designs⁵ (Albanese 2020, 185). All in all, private conglomerations’ role in the smart city signals a shift from public infrastructures built for the common good to corporate arrangements of urban service provision, firmly anchored in public-private partnerships (Cardullo 2021, 44; Sadowski and Pasquale 2015). Quite unexpectedly, “such a shift in the ownership of what were public assets (privatisation) and provisioning of services (marketisation) has been driven by arguments concerning efficiency, competitiveness, and value-for-money that paved the way to strong austerity policies” (Cardullo and Kitchin 2019, 816). As Ranchordas maintains (2018, 155), citizens are therefore increasingly considered as consumers or end users of data-driven services, or even the objects of data collection practices, rather than politically engaged actors⁶.

Viewed through the lens of the “conventional” private initiatives and the PPPs, novel challenges related to the ways in which returns and gains from data production are allocated ensue. More specifically, data produced in the smart city do not always contribute to “collectively participated” democratic forms of urban management (Zoonen 2020 ; Viljoen 2021). Citizens are usually unaware of the fact that a private corporate partner collects data (Ranchordás 2020, 265). Private actors contributing to developing smart city services, moreover, frequently commercialise citizens’ personal data.

Some examples are illustrative in this respect. For example, smart waste management providers, such as the WasteHero App, commercialise the personal data of users ⁷. By the same token, the Italian smart grid provider EnelX has designed an app, YoUrban, that allows citizens to report failures (e.g. breakdowns) of the smart street lighting system. According to the privacy policy, EnelX is entitled to use (and transfer to other businesses upon payment) the personal data of the app users for marketing purposes, market research, and profiling. Similar considerations apply in respect to tools hoarding urban mobility data (UMD) as well. UMD plays a prominent role in the smart city since it connects together micro-mobility and small-sized electric vehicles, such as electric scooters, mopeds, skateboards, bicycles, or even cars (Frosio 2020, 166). In the smart city, mobility has gradually turned into Mobility-as-a-Service (MaaS), which is anchored in putting together various forms of transport services in just one single mobility solution accessible on demand. The business model works as follows: a MaaS operator enables a broad range of options, be they public transport, ride, car or bike-sharing, taxi or car rental/lease, or a combination thereof. MaaS assembles and “platformises” various services. Users benefit from the MaaS inventory as they can use an app to access mobility by means of an individual payment channel instead of a myriad of ticketing and payment operations. As signalled by the public-private partnership MaaS Alliance, “for its users, MaaS should be the best value proposition, by helping them meet their mobility needs and solve the inconvenient parts of individual journeys as well as the entire system of mobility services”. An illustration of MaaS solution is the Nugo app, which gathers diverse Italian transport services⁸. As other smart city applications, MaaS’s success rests on data harvesting by a great deal. In particular, MaaS operators collect data from different sources, these being governments, public authorities, communities and individuals themselves (Cottrill 2020, 51; Frosio 2020, 174). All in all, MaaS providers monetise their practices by processing data for purposes even very dissimilar to simple urban service provision. The most used processing purposes include service optimisation, marketing communications, automated profiling, as the privacy policies of the apps Nugo⁹ and Moovit demonstrate.

As signalled above, to be efficient and gainful, smart city applications work on the back of large-scale data extraction, collection and (re-)use. Thus, adopting a viable data governance system plays a critical role in how a data holder can allocate benefits and risks of the most strategic asset of the smart city, i.e. data. In the subsequent section I scrutinise the implications of two macro-categories of data governance mechanisms on which the literature has spilled much ink now. One is based on substantial access barriers resulting in “data ownership”, the other is grounded in widespread data availability and sharing practices amongst diverse actors (“commons-based data governance”).

Data ownership

The legal literature has investigated how “data ownership” takes place in empirics and pragmatics by means of legal, behavioural, and technological barriers. In another article, I have already sketched out the main features of the proprietorial phenomenology in practice (Fia 2021, 185–87). Legal barriers have to do with IP rights (copyright law, database rights, trade secrets) and data protection provisions restricting data access. Behavioural barriers are grounded in contractual limitations (e.g. terms and conditions) routinely encapsulating exclusionary practices of private actors holding unequal bargaining power. Technological barriers pertain to implementing technical protection measures (TPMs) and using non-interoperable formats preventing effective data exchanges (Hoffmann and Gonzalez Otero 2020). All in all, private vendors creating and installing sensors and data processing systems in smart cities are under no obligation or not willing to freely share data they collect with others (Kitchin 2018, 51). Such a behaviour stems from the privatisation of public services in urban environments. Data about public transport, energy and water is usually black-boxed and access thereto is restricted (Kitchin 2018, 51). Public authorities frequently restrict access to urban data as well by siloing it within departments. Thus, data is not shared “with other units within the organisation, or open for other institutions or the public to use” (Kitchin 2018, 51). Identifying who controls data resulting from the aggregation and combination of manifold data sources can nevertheless be challenging (Löfgren and Webster 2020, 10).

Commons-based data governance

Urban scientists, activists and legal scholars have advocated for a data governance system anchored in open data access approaches in the smart city (Morozov and Bria 2018). The reasons for putting forth access-widening solutions lie in the relational features of data (Viljoen 2021). Urban data stems from the activities of multiple actors involved in a web of voluntary and involuntary cooperative relations. This is the case of many aspects of human life (Benkler 2011). More specifically, urban data amounts to depictions of interactions and cooperation webs without which data analytics and other reuses would not be that valuable. There are many instructive examples in this respect. For instance, urban public transit equipped with sensors produces real-time information on traffic which would not come into existence without passengers using an app. By the same token, data on urban congestions illustrates the activities of citizens and people passing through cities that drive around the streets. Waste management aggregated information is just about how much rubbish citizens produce. Illustrations of urban data at work are countless, but they all tend to show how citizens’ cooperation is a necessary condition for smart city technologies to work. Hence, the conclusion that recognising data ownership amounts to tolerating dominance of smart city contractors over datafication of citizens and local communities. Furthermore, acknowledging control over data access means accepting private appropriation of information in the public domain. Accordingly, advocates for data access in the smart city have shaped novel forms of commons-based data governance. They aim to form the UDC, meaning that they emanate from urban life itself, “in mundane and very material practices of urban-dwelling, social encounters and social reproduction” (Cardullo 2019, 90; Lange 2019). Even more fundamentally, commons-based approaches of data governance propose ways of fostering data use and access opportunities as opposed to the shortcomings of data ownership.

Commons-based data governance in the smart city revolves around four strands of research and approaches. First, one may examine whether and to what extent the statements of principle in some local by-laws on “urban commons” (e.g. in Italy) can also apply to UDC where they refer to digital goods. Second, other projects of commons-based data governance have specifically aimed to regulate data flows in the smart city. Some municipalities have implemented thoroughgoing public-led regulatory projects, such as the Decode Project in Amsterdam and Barcelona. In other cities, private conglomerations have taken up the reins of regulation by winning bids and implementing large programmes of data governance. This is the case of Sidewalk Toronto in Canada. Lastly, there have been cases of bottom-up informal approaches, whereby groups of citizens and (small) local communities have taken the lead on grassroots initiatives opening up data access.

The little relevance of unjust enrichment

Unjust enrichment was acknowledged as a distinct branch of law, together with contract and tort, in the 16th and 17th centuries by late scholastic philosophers. The subject area of unjust enrichment, early theorists maintained, is any breach of corrective (or commutative) justice¹⁶ whereby someone “unjustly enriched has used resources that belong to another person to obtain a benefit to which that person was entitled” (Gordley, Jiang, and Taylor von Mehren 2021, 526). Late scholastics argued that any case in which the defendant is enriched at the plaintiff’s detriment suffices to ground relief.

The Aristotelians have inspired today’s laws of unjust enrichment. Modern unjust enrichment, however, “deals with a favourable deviation from the status quo” (Zimmermann 1995, 404) due to a predetermined legal basis in civilian countries¹⁷ or “unjust factor” in common law jurisdictions¹⁸. Given the prevalence of contract, tort, and property in most legal traditions, it is frequently seen as a residual category with little application range. Moreover, matters of distributional justice are left out from the objectives that unjust enrichment laws pursue (Vercellone 2020, 2).

Thus conceptualised, unjust enrichment can hardly help to redress positive externalities in the smart city. First, matters of value redistribution in the smart city are related to distributive justice, and not much to commutative justice. There is no question smart city contractors’ profits are wealth shares anchored in an utterly legitimate service provision activity. It is difficult to rearrange unjust enrichment relief in such a way as to grant a share of smart city contractors’ accumulation to the urban collectivity (represented by the municipality entities). Second, expanding unjust enrichment rules by means of extensive interpretation and analogy does not seem viable since its prerequisites (i.e. the legal basis or the unjust factor) are stringent. Drawing recommendations in this sense that municipal actors can follow in public procurement and negotiations with private contractors, therefore, poses implementation problems.

Schumacher’s critique of ownership of large-sized companies: taking redistribution seriously for big data ownership

A theoretical model into which it is worth looking is Fritz Schumacher’s critique of capitalist ownership and the consistent normative proposal in Small is beautiful: Economics as if people mattered (1973, 262 ff.), a collection of essays which the author had found it hard to publish (Leonard 2018, 260). In the last two chapters of the book, the Austrian economist advances a radical rethinking of property relations. His basic assumption is the distinction “between (a) property that is an aid to creative work and (b) property that is an alternative to it” (E. F. Schumacher 1973, 263). The former rests on “something natural and healthy”, that is “the private property of the working proprietor”. The latter, meanwhile, is “unnatural and unhealthy” as the corporate owner “lives parasitically on the work of others”. The dividing line between entrepreneurial models comes from the scale of a private company. Hence, different features apply depending on how large an enterprise is. In Schumacher’s words:

it is immediately apparent that in this matter of private ownership the question of scale is critical. When we switch from small-scale to medium-scale, the connection between ownership and work already becomes attenuated; private enterprise tends to become impersonal and also a significant social factor in the locality; it may even assume more than local significance. The very idea of private property becomes increasingly misleading (1973, 264).

In large-sized companies, private ownership turns into a “fiction for the purpose of enabling functionless owners to live parasitically on the labour of others” (E. F. Schumacher 1973, 267). The bulk of capitalists’ success, Schumacher drives home, does not come only from their own virtues and merits, but arises with public authorities’ actions towards building up an infrastructure that fosters companies’ competition. Hence, the conclusion that it would be desirable that “the contribution of public expenditure to the profits of private enterprise is recognised in the structure of ownership of the means of production” (E. F. Schumacher 1973, 274). Schumacher scrutinises two poignant examples in which the property relations in question differ, to some degree at least, from the extractive ownership model. The first illustration is the Scott Bader Commonwealth. Dora Scott and Ernst Bader founded the Scott Bader Ltd., a company producing synthetic resins and composite materials, in 1921 in London. After converting to Quakerism, they brought “revolutionary changes” in the firm in the way in which they combined “freedom, happiness and human dignity (…) without loss of profitability” (E. F. Schumacher 1973, 275). In 1951, they established the Scott Bader Commonwealth, made up by the companies’ employees, and transferred Scott Bader Ltd.’s ownership to them. Relying on decentralised power, the novel corporate governance mechanism has been successful for many years now (D. Schumacher 2011, 103–12).

Most importantly for present purposes, the second instance that Schumacher sketches out is what he calls “new methods of socialisation” (E. F. Schumacher 1973, 283). In this respect, drawing from the fact that private enterprise flourishes on the back of public expenditure on infrastructure-creating activities, public authorities should participate in the private profits. Specifically, they should be entitled to receive one-half of the distributed profits of large-sized private corporation by means of a fifty per cent ownership of the equity of such enterprises (E. F. Schumacher 1973, 285). Thus, such an ownership scheme would not amount to expropriation sensu stricto, since it would be “a direct public participation in the economic assets from the use of which taxable profits are obtained”. Accordingly, this would embed the general public contribution and “non-capitalist social forces” (E. F. Schumacher 1973, 286) into creating private wealth.

Schumacher goes on to address some possible critiques of the proposed theory. First, he elucidates the rights associated with ownership of the “public hand”. The public participation in one-half of the profits would encompass two groups of rights: managerial rights and pecuniary rights. The former has to do with corporate management. In this respect, the author has a penchant for linking public hand’s participation to a right to information and observation of the business activities. Conversely, the public owners would not preserve voting rights in shareholder meetings. The main objective is to ensure public accountability and transparency of corporations. As for pecuniary rights, the distributed profits would automatically go to the “public hand” holding the shares which ought to be, in principle, non-waivable (E. F. Schumacher 1973, 288). The equity share would replace other profit taxes (E. F. Schumacher 1973, 291). Second, Schumacher analyses what “public hand” is in empirics. In his view, it corresponds to the “local body in the district where the enterprise in question is located” (E. F. Schumacher 1973, 286). The local entity, however, is not necessarily a political elected authority, but a “Social Council” made up of local trade unions members, local employers’ organisations, local professional associations and local residents. The Social Council would have transparency and accountability obligations as well (E. F. Schumacher 1973, 289). All in all, the aforementioned proposal would be a “constitution-making” exercise that private actors can introduce in an experimental and evolutionary manner (E. F. Schumacher 1973, 292).

Mainstream neoclassical economists have never considered Schumacher’s bold proposal. This accounts for the nearly utter lack of critiques and studies of his ambitious redesign of corporate ownership. Admittedly, Schumacher’s radical view may not find justification through the lens of efficiency claims and welfare maximisation. Yet, it can offer some motivating rudiments of novel ways of approaching data governance in the smart city. It is striking to see how Schumacher’s proposal fits large contractors’ operations in the smart city. His account tells a story of appropriation of publicly created infrastructure. As the case of Sidewalk Lab demonstrates, smart city contractors seize on the urban interactions’ surplus to conduct their businesses. Their business model would simply not work without appropriation and commercial deployment of urban needs, activities, and relations. Data ownership’s most distinctive feature is immateriality, meaning that datafication processes occur “discreetly”, as a result of installing and utilising data-driven technologies in the smart city (Bibri 2019 ; Sadowski 2019). Most citizens do not even realise that data production and harvesting take place in the public space. Datafication of “non-capitalist” urban forces, such as citizens’ cooperation webs and daily activities, nonetheless contribute to making smart city contractors’ fortune by a great deal.

Several aspects of Schumacher’s “new methods of socialisation” may help to form data governance policies for smart city applications. First, Schumacher focuses on how the public infrastructural urban components play a prominent role in building capitalists’ success. This is exactly the case for the urban locales and situations where citizens’ data collection takes place. Second, he proposes a redistribution scheme (50% of shares to the “public hand”) aiming to address the positive externalities arising in large-sized companies. He views the public constituent participating to the private gains as a local body in the district where an enterprise is placed. More than the formal nitty-gritty of his proposal, it is noteworthy how Schumacher sets out to link the production to a locally circumscribed environment. By the same token, multiple smart city applications heavily draw on technologies installed in local public spaces. One may think of smart traffic lights, CCTV cameras, and smart parking sensors. Even “moving” smart technologies, such as SMU, relate to productive “non-capitalist” forces occurring in discrete urban environments (e.g. people in a bus, a tram, traffic congestion at a roundabout and so forth).

Thus viewed, data production in the smart city would empower municipal authorities in negotiations to impose, say, a flat fee on smart city vendors based on various criteria, some instances of these being the urban place where a smart city technology is installed, the number of citizens involved, the neighbourhood size, and so forth. As for Schumacher’s “managerial rights”, the representative municipal authorities could be entitled to have a say when defining the rationale and the purposes of the data processing practices in the smart city. For instance, a veto rule of the local authorities over data commercialisation would improve citizens’ control over personal data¹⁹.

IP benefit sharing and (indigenous) communities: a new role in the smart city

The general thrust of Schumacher’s argument on which we can heavily draw to develop an alternative is the stress on establishing redistributive obligations. As briefly signalled above, distributive justice as a goal of private law has never taken much root in the legal literature. The reason is that taxes-and-transfer payments are viewed as the most efficient and proper tool to attain redistribution considerations (Lewinsohn-Zamir 2006). There is, however, a growing trend in IP scholarship (Yanisky-Ravid 2017), reflecting property scholars’ investigations (Alexander 2018), arguing in favour of pursuing reallocation by means of rules of private law. By the same token, the scholarly focus shifts from intellectual property rights to IP owners’ obligations towards the general public (Drahos 2016, 260 ff.; Li 2021, 9 ff.). Solely honing in on rights means justifying “the deceiving concept of absolute ownership and entitlement” and neglecting other values (Li 2021, 20). Thus, obligations of right-holders should aim to complement and counterbalance IP owners’ privileges inhibiting others’ rights and liberties.

The grounds for the distributive obligations can be the benefit-sharing schemes in international treaties. First, the benefit-sharing principle creates viable obligatory patterns in the way in which it imposes duties of profit redistribution on IP owners. In the 1960s, developing countries viewed the benefit-sharing concept as a strategic sovereignty-safeguarding means to secure the benefits of colonial peoples’ exploitation of natural resources (Cabrera Medaglia and Perron-Welch 2019, 63). Later on, it was incorporated in international treaties, such as the 1992 Convention on Biological Diversity (CBD) and the 2010 Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Resulting from their Utilization (the Nagoya Protocol) (Cabrera Medaglia and Perron-Welch 2019, 64) as a bulwark against intellectual property monopolies and, more fundamentally, extractive approaches to indigenous knowledge (Drahos 2014, 72 ff.). As a remuneration scheme, benefit-sharing revolves around the concept of sustainable development applied to traditional knowledge, genetic resources, and biological biodiversity. The recipients of the benefit-sharing programmes can be either States or non-state actors, such as indigenous communities. As for intrastate relationship, the benefit-sharing principle mandates States “to find an equitable balance between the interests of the countries of origin and those of States that have the technical and technological means to use GR develop and use technologies stemming there from” (Cabrera Medaglia and Perron-Welch 2019, 65). On a similar note, the Nagoya Protocol provides that shared benefits can be monetary or non-monetary in the Article 5(4) (United Nations - Convention on Biological Diversity 2011). Amongst monetary benefits, the Annex to the Nagoya Protocol mentions, amongst other items, access fees per sample collected, up-front payments, milestone payments, payment of royalties, licence fees in case of commercialisation, special fees to be paid to trust funds supporting conservation and sustainable use of biodiversity. Apart from international law tools, several constitutional texts enshrine the benefit-sharing principle as well, an example being the Ecuador Constitution. The article 57(7) of the latter reads as follows:

indigenous communes, communities, peoples and nations are recognised and guaranteed (…) the following collective rights: (…) participate in the profits earned from the plans and programs for prospecting, producing and marketing non-renewable resources located on their lands and which could have an environmental or cultural impact on them (República del Ecuador 2008).

Thus, a duty to benefit sharing mandates in principle remuneration of local communities as a result of commercialising local biomaterials and knowledge (Li 2021, 28). Notwithstanding its potential, the benefit-sharing principle frequently does not turn into effective rules in domestic legal systems. More specifically, principles such as the benefit-sharing “are important to design of property standards for indigenous knowledge”, but they do not deliver binding systems for the protection of indigenous knowledge (e.g. proprietary-like regimes) (Drahos 2014, 77). In fact, some states (e.g. the United States) tend not to engage in reforms of the IP systems, feeling strongly that benefit-sharing is best effected through freedom of contract (Drahos 2014, 148).

Like Schumacher’s ownership, benefit-sharing schemes can nevertheless bring some inspiration in the negotiations between municipal authorities and the smart city vendors. Analogies in the value formation are evident: a community creates a knowledge apparatus that a private conglomeration turns into exchange value. In one case, we are presented with an indigenous community, in the other, there is an urban community that is represented and mediated by local municipal authorities. Thus conceptualised, the benefit-sharing concept can then ground model clauses in the negotiations between municipalities and smart city vendors. For example, it may mean incorporating value redistribution in the form of differential fees to the advantage of local municipal authorities for commercialising citizens’ personal data, as similarly established in the Annex to the Nagoya Protocol. Recent developments in the governance of non-personal data in India are a promising starting point in this respect. In 2020, a committee of experts appointed by the Indian government released a Report on Non-Personal Data Governance Framework (Indian Ministry of Electronics and Information Technology 2020), hereinafter the “NPD Report”, that deals with the relationship between communities and non-personal data. Amongst the proposed model rules, the report mentions the right of the community “to derive economic and other value and maximising data’s benefits for the community” (Indian Ministry of Electronics and Information Technology 2020, 16). The datasets beneficial to the community form “high-value datasets” (HVD) and deserve special governance mechanisms. The management of non-personal datasets can be assigned to some data trustees that would “manage the non-personal data of a specific community and would have the ability to recommend soft obligations for “data custodians” processing such data” (Indian Ministry of Electronics and Information Technology 2020, 16). This aspect evokes the managerial rights as proposed by Schumacher. The community rights in non-personal data conjure up the ownership issues and community rights over natural resources (Nagaraj, Rao, and Shukla 2021, 20 ff.). Singling out a community when it comes to data production, however, can prove difficult due to its nebulous boundaries (Nagaraj, Rao, and Shukla 2021, 22). In urban applications, this reflection a fortiori holds true: it is challenging to circumscribe a discreet part of an urban community and its relevant distributional prerogatives. Moreover, the NPD Report stipulates that the data trustees must convey the “best interest” of the community to the data custodians by means of pieces of advice and guidelines. To partially redress these imbalances, the NPD Report allows (local) government authorities to act as the appropriate “data trustee” (2020, 18).