This study employs a scoping review methodology, appropriate for mapping concepts and evidence in emerging fields where comprehensive causal synthesis is premature (Petticrew and Roberts, 2006). The review adheres to the five-stage framework developed by Arksey and O’Malley (2005) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist (Tricco et al., 2018). Methodological rigor was enhanced using the recommendations of Levac et al. (2010) and the iterative Population-Concept-Context (PCC) framework to define eligibility, as advised by Peters et al. (2020).

A comprehensive literature search was executed on 1 May 2025in two sources: Scopus and Google Scholar. Boolean operators and wildcards were used. The DePIN query included “Decentralized Physical Infrastructure Network*,” DePIN, “Token Incentivized Physical Infrastructure Network*,” TIPIN, “Proof of Physical Work,” PoPW, and MachineFi. After limiting to relevant subject areas and English, Scopus returned 42 records (Supplementary Appendix B). Google Scholar queries (Supplementary Appendix B) returned 19 records. Backward and forward citation chasing supplemented database searching. Backward and forward citation chasing supplemented database searching. Furthermore, to capture essential grey literature, 17 records were identified from targeted searches of industry websites and project repositories (see Figure 1).

The study selection was conducted in a two-stage process by the author. First, following deduplication of all retrieved records, titles and abstracts were screened against the predefined eligibility criteria. Second, the full texts of all potentially relevant records from the initial stage were retrieved and evaluated in detail. This process yielded a final sample of 46 sources. Recognizing that a substantial portion of the discourse in this nascent field occurs outside traditional academic publishing, the final sample deliberately included 24 academic articles (conferences, pre-prints, and articles) and 22 grey-literature sources (e.g., news articles, blog posts, white papers, reports), following Joanna Briggs Institute guidance on scoping-review evidence inclusion.

Studies were required to meet eligibility criteria defined by the PCC framework and publication type. The Population of interest was (DePIN), including specific projects, their protocols, or the sector as a whole. The Concept centered on tokenomics and crypto-economic phenomena within DePIN, such as economic models, token design, incentive mechanisms, and risk analyses. The Context was restricted to global literature published in English between 1 January 2021, and 1 May 2025. Included sources were peer-reviewed journal articles, conference proceedings, and substantive working papers or preprints. Conversely, studies were excluded if they were purely technical, focused on general blockchain or DeFi without a DePIN application, were brief market commentaries or promotional materials, were not in English, or fell outside the specified date range.

A standardized data-charting form was developed to extract relevant information from all included studies. Key data points extracted included: bibliographic details; the DePIN sector(s) and specific project(s) analyzed; described tokenomics frameworks and incentive mechanisms; token utilities, sustainability features, and governance arrangements; identified economic and financial risks; any quantitative metrics reported; and stated research gaps or directions for future research. The author completed the extraction for all studies no formal pilot of the charting form was conducted. Instead, the template was iteratively refined during early extraction, and all corrections were retro-applied to previously charted records to ensure consistency.

The extracted data were collated and summarized using a narrative approach (Popay et al., 2006) supplemented by descriptive statistics on study characteristics. A thematic analysis was then conducted (Thomas and Harden, 2008), which involved systematically coding the charted information, grouping codes into descriptive themes, and developing higher-order analytical themes that collectively map the DePIN tokenomics landscape. Consistent with Joanna Briggs Institute (JBI) guidance for scoping reviews, we did not undertake a formal risk-of-bias assessment; instead, we narratively reflected on methodological transparency and the depth of economic analysis to contextualize findings. Industry-funded and project-authored documents were flagged during charting; claims drawn from such sources were treated as contextual and triangulated with peer-reviewed evidence where possible and were not used to make inferential claims about effect sizes or performance.

This review is subject to several limitations. First, the restriction to English-language publications may have introduced a language bias, resulting in the exclusion of relevant research published in other languages. Second, the analysis is confined to publicly available data, meaning proprietary or unpublished performance metrics from DePIN projects were not captured. Third, the necessary inclusion of grey literature—while enhancing coverage in a nascent field—introduces risks of bias associated with non-peer-reviewed sources such as industry reports; we mitigated this by flagging funding and authorship, triangulating with peer-reviewed sources, and using such materials primarily for background and context. Finally, our use of Google Scholar was intentionally supplementary and limited to the top-ranked 100 results per query to enhance reproducibility; although this cap may miss lower-ranked items, evidence shows that Google Scholar is unsuitable as a sole source for systematic searching and is best paired with curated databases, which we did. Finally, the DePIN landscape is vast, with hundreds of active projects. The objective of this scoping review is to map the core concepts and dominant economic architectures documented in the accessible academic and substantive grey literature, not to conduct a comprehensive, quantitative comparison of all extant projects, which represents a distinct methodological undertaking beyond the scope of this review.

The included literature (N = 46) displayed characteristics typical of an emerging field. A significant portion consisted of academic conference papers (n = 15), reflecting the rapid dissemination of ideas, while peer-reviewed journal articles (n = 5) and working papers/preprints (n = 6) provided more developed contributions. Grey literature (n = 22) offered timely industry perspectives. The publication dates were heavily skewed towards recent years (2023-early 2025), indicating accelerating research interest. While many sources adopted a global perspective, case studies frequently centered on prominent North American or European projects in the Wireless, Storage, and Compute sectors. The predominant methodological approaches in academic sources were conceptual analysis, descriptive case studies, and taxonomy development. Rigorous, quantitative empirical studies focusing specifically on the financial or economic outcomes of tokenomics designs were notably unavailable in the selected literature.

The DePIN flywheel emerges as a widely used, usage-driven feedback loop for bootstrapping and sustaining physical-infrastructure networks (Messari Research, 2025). Think of it like a heavy merry-go-round which requires a significant effort to start, but once it is spinning, its own momentum helps it continue with minimal force, the DePIN model applies this concept to network growth. This framework highlights how token incentives are designed to drive network growth and sustainability in DePINs. In what follows, the core mechanics, verification requirements, and recurrent challenges are synthesized.

The operational logic of the DePIN Flywheel is driven by a complex interdependency between three primary actors, whose roles, incentives, and value flows are systematically decomposed in the following table (see Table 1).

The tokenomic designs of DePIN projects, while sharing core principles, exhibit significant diversity in their implementation across different sectors. Table 2 provides a systematic comparison of the economic models of four leading projects, illustrating the variations in their verification, demand-side, and sustainability mechanisms.

The literature confirms that tokens in DePINs play multiple essential roles beyond simple payment and that the structural design of these token systems varies significantly across projects. Understanding these different functions and architectural choices is key to evaluating the mechanics and potential sustainability of any DePIN. This theme outlines the core functions of tokens, the architectural models employed, and how implementations differ across sectors.

Recognizing the limitations of purely inflationary reward models, the literature describes various mechanisms designed to foster long-term economic sustainability by linking token value to network activity and managing supply. This theme details the specific strategies for usage-driven value capture, supply control, and staking, which together form the pathways for sustainable value accrual.

Literature extensively documents the significant hurdles and various risks inherent in designing and operating sustainable DePIN tokenomic systems. These challenges can be categorized into five distinct domains: economic, operational, governance, legal, and market related.

This review’s findings provide a clear map of the DePIN tokenomics field. Answering RQ1 and RQ2, the analysis observes a prevailing “DePIN flywheel” pattern in leading deployments (Messari Research, 2025), in which end-user demand is priced in prepaid, USD-denominated credits while on-chain burns and governed issuance finance supply. In Helium, for example, one Data Credit equals $0.00001 and is minted by burning HNT; governance has recently consolidated rewards back to HNT under HIP-138 (approved late-2024; phased-in early-2025), simplifying value routing. Hivemapper enacted MIP-19 to raise Map Credit prices, effective 1 January 2025, to increase on-chain accrual; Render Network formalizes a similar burn-and-mint equilibrium (BME) for job settlement and emissions. The review also mapped the diverse functions of DePIN tokens—primarily utility, governance, and staking—and the variety of architectural choices, such as single versus multi-token models. In response to RQ3, key sustainability mechanisms were identified, including token burns and fee distributions designed to link token value directly to network usage and control inflation.

However, a crucial insight emerging from the mapping (addressing RQ4) is the inherent tension and design trade-offs within DePIN economic models. The literature reveals two dominant approaches to monetizing demand, each with distinct trade-offs. The first, the Fiat-Credit Model (BME), is utilized by projects like Helium, Render, and Hivemapper. This model uses fiat-denominated, non-transferable credits (e.g., Data Credits) minted by burning the native token. The primary advantage of this design is its ability to insulate end-users from token price volatility, thereby offering stable, predictable service pricing crucial for attracting non-speculative, enterprise demand. However, this benefit comes at the cost of greater system complexity and a heavy reliance on active governance to calibrate the BME parameters, which is necessary to ensure provider rewards remain sufficient during market downturns. In contrast, the Native Asset and Stablecoin Model—utilized by projects like Filecoin (paying in FIL) and Akash (supporting native AKT or stablecoins like USDC)—offers notable simplicity and a direct, uncensorable utility link for the native token. The significant drawback of this alternative, however, is that it exposes both users and providers to the token’s price volatility, a factor that can create significant friction for user adoption and severely complicate provider ROI calculations.

The ‘lessons learned’ from existing projects, visible in their governance proposals, demonstrate a continuous effort to manage these trade-offs. For instance, Helium’s HIP-138 (consolidating rewards back to HNT) represents a move to reduce complexity and strengthen the HNT asset’s value accrual. Similarly, Hivemapper’s MIP-19 (raising Map Credit prices) is a direct governance intervention to calibrate the BME for long-term sustainability. These actions confirm that tokenomic design is not static but a dynamic process of balancing stakeholder incentives.

Regarding token performance, the scoping review’s findings confirm that a principal gap in the literature is the lack of rigorous, independent empirical studies on performance. Therefore, while this review synthesizes design principles and governance actions, the quantitative validation of token performance remains a critical direction for future research, as detailed in Section 4.4.

A critical assessment of the underlying project documentation (whitepapers and official network specifications) reveals that while the conceptual clarity of the actor-action model and value routing (e.g., the BME) is high, the analytical transparency is often insufficient. Specifically, documentation frequently functions as a design rationale rather than a rigorous economic proof, often prioritizing descriptive explanations over explicit mathematical models of stability, long-term equilibrium, or the specific cost-benefit analysis faced by providers. Furthermore, while tokenomics parameters are technically transparent on-chain, the lack of published independent performance metrics (e.g., quality-adjusted provider ROI or empirically measured price elasticity of demand) in public documentation makes rigorous due diligence challenging for financial analysts, reinforcing the need for empirical validation.

A second insight is that “decentralization” is spectrum-based rather than binary: development, treasury, and parameter updates often remain concentrated, a point echoed in analyses of decentralized governance for cyber-physical systems (Nabben et al., 2024). While mechanisms like token burns aim to counter inflationary pressures, their long-term effectiveness remains an open question requiring empirical validation. The literature strongly suggests that success hinges on transitioning from models subsidized by token inflation towards systems where real economic value, generated through user fees, sustains the network.

Verification stringency is a third cross-cutting insight. Wireless networks rely on (PoC) with a move toward oracle-assisted plausibility checks; storage networks separate sealing-time Proof-of-Replication from ongoing Proof-of-Spacetime audits. The economic salience is direct: weaker attestations propagate noise into payouts; stronger proofs constrain gaming but raise verification costs.

The reviewed literature offers rich, descriptive accounts of DePIN projects and a robust conceptual framework for tokenomic principles, drawing heavily on comprehensive industry reports and pioneering academic analyses (e.g., Ballandies et al., 2023). These studies excel at mapping intended tokenomic designs and articulating potential challenges.

However, the evidence base exhibits several limitations. First, rigorous, independent quantitative evaluations of tokenomic performance are scarce; many claims derive from project materials or industry reports. Second, methodological heterogeneity—spanning technical specifications, design essays, and qualitative case studies—complicates synthesis. Third, source bias is non-trivial in grey literature and in case studies concentrated on prominent projects. Finally, protocols iterate quickly; time-to-obsolescence is short, especially when governance changes alter emissions, fee splits, or pricing (e.g., HIP-138; MIP-19; BME deployments). These limitations justify our scoping-review choice and motivate a forward research agenda grounded in on-chain and governance data.

The findings carry important implications for practitioners, policymakers, and theorists.

For Practitioners (Developers and Investors): The review underscores the imperative of rigorous due diligence centered on tokenomic sustainability and demand-generation potential. For developers, this highlights the need to design explicit mechanisms for demand-driven value accrual and to mitigate operational risks like incentive gaming. For investors and financial analysts, it emphasizes that analysis must extend beyond technological novelty to include a rigorous assessment of a project’s demand-side viability, governance robustness, and the current lack of standardized valuation metrics.

For Policymakers: The pronounced regulatory ambiguity surrounding DePINs demands the swift development of technology-nuanced frameworks. Policymakers must engage with industry stakeholders to craft regulations that stimulate innovation while safeguarding financial stability and consumer protection. Given the transnational nature of these networks, international coordination is vital to achieve harmonized standards.

For Academic Theorists: DePIN tokenomic models offer fertile ground for advancing scholarship. They present real-world case studies for mechanism design, challenge traditional bootstrapping theories in network economics, raise novel questions for financial economics regarding the valuation of hybrid utility-governance assets, and test the limits of DAO-based governance in agency theory.

Addressing RQ5, this review delineates several critical areas derived directly from the limitations and findings identified in the synthesized literature (Themes 1-4 and Section 4.2) warranting further investigation to build a more robust, empirically grounded understanding of DePIN tokenomics.

First, Empirical Adjudication of Competing Models. The literature presents a clear theoretical debate between BME and Native Asset models, yet it lacks empirical evidence to validate the performance of either. Rigorous quantitative analyses using on-chain and market data are urgently needed to resolve this. Future research should conduct event-study analyses on the impact of major governance proposals, such as Helium’s HIP-138 or Hivemapper’s MIP-19, on token price volatility, trading volume, and on-chain metrics like daily token burns and provider growth. This approach would enable causal inference regarding the economic effects of protocol parameter changes, rigorously assessing how governance actions influence financial stability.

Second, Sophisticated Provider Economics and Demand-Side Analysis. Research should employ sophisticated financial modeling to assess provider profitability under realistic volatility conditions. Beyond Monte Carlo simulations, future work should apply real options analysis to model the investment decisions of DePIN providers. This would explicitly account for the value of waiting or abandoning participation given the high uncertainty of token-denominated revenue streams, offering a more realistic assessment of network sustainability. Concurrently, studies must investigate the demand bottleneck by analyzing adoption drivers and measuring the price elasticity of demand, particularly how consumption (via token burns) responds to changes in the fiat-denominated price of usage credits.

Third, Advanced Valuation Frameworks and Governance Effectiveness. Researchers should develop and test valuation frameworks tailored to DePIN tokens. These models must move beyond simple metrics to integrate discounted cash flow analysis (based on network fees and burns), the option value of governance rights, and appropriate risk adjustments for technological and regulatory uncertainty. Furthermore, empirical research is needed to examine how DAO characteristics—such as voter participation rates and token distribution—affect network performance, adaptability, and financial stability.

Fourth, Comparative and Longitudinal Studies. Comparative sectoral analysis is needed to empirically investigate why specific tokenomic models, such as the fiat-credit system, have prevailed in certain DePIN sectors (e.g., storage versus compute).

Finally, longitudinal studies tracking a cohort of projects over several years will be invaluable for identifying common failure modes, resilience factors, and the adaptive strategies that determine long-term viability.