Will the global RWA policy reverse? The political variables behind the midterm elections

Abstract

This article systematically evaluates the impact of midterm election variables on the elasticity of RWA policies using major global jurisdictions as research samples. By constructing four core indicators: Policy Continuity Index (PCI), Policy Transparency Index (PTI), Policy Execution Efficiency Index (PEI), and Policy Reversal Risk Index (PRI), the report quantifies the disruptive mechanisms of election cycles on policy formulation, execution, and regulatory direction, and conducts cross-sectional comparisons in North America, Europe, Asia-Pacific, and emerging markets. The study shows that the U.S. is significantly affected by midterm elections, with low policy continuity and implementation efficiency, and high reversal risk; the EU and Singapore exhibit strong institutional stability and lower policy uncertainty; the UK and some emerging markets are at moderate risk levels. Based on the RWA policy elasticity risk rating model (RWA-PERM), this article proposes response strategies for investment institutions, project parties, and platform parties, including policy sensitivity monitoring, multi-institutional arbitrage, compliance process flexibility design, and scenario simulation plans. The conclusion indicates that policy elasticity not only determines the feasibility of RWA projects but also influences the strategic design of global RWA layouts, and election cycles should become an important consideration for institutional investment and project structural planning.

Keywords: RWA, policy elasticity, midterm elections, risk rating, compliance strategies

Midterm Election Variables and RWA Policy Environment

1.1 Research Background and Significance

1.1.1 Overview of RWA Market Development

Real World Assets (RWA) are forms of assets that digitize and tokenize tangible or intangible assets from the real world through blockchain technology, represented on-chain. These assets include, but are not limited to, real estate, bonds, private credit, commodities, and other traditional asset classes. The core logic of RWA is to enhance asset liquidity, divisibility, and trading transparency through Tokenization, linking traditional finance (TradFi) and decentralized finance (DeFi) systems, becoming an important bridge in the future financial ecosystem.

According to recent data from RWA.xyz, as of March 26, 2026, the on-chain RWA market size (excluding stablecoins) is approximately $26.67 billion, up from $1.18 billion three years ago, growing nearly 2160%, indicating a rapid evolution from experimental pilots to institutional adoption. Market forecasts suggest that the global RWA market size could reach tens of trillions to hundreds of trillions of dollars in the next decade. For example, institutions like RedStone and Standard Chartered predict the market could reach about $30 trillion by 2034, while consulting firms like BCG expect the scale to be around $16 trillion by 2030.

The tokenization of RWA is not limited to financial products (such as bonds, funds, etc.), but also extends to assets like real estate, commodities, and intellectual property. Once assets are on-chain, they can achieve Fractional Ownership, all-weather trading, and global accessibility, greatly unlocking the value of off-chain assets.

The growth potential of the RWA market is reflected not only in scale expansion but also in the participation of multiple stakeholders, from traditional financial institutions (such as asset management companies, banks, and funds) to blockchain-native protocols, marking the gradual entry of RWA from the fringes of innovation into mainstream financial focus.

This section aims to showcase the real profile of the RWA market through quantitative and qualitative data, providing a realistic basis for subsequent discussions on policy elasticity and election impacts.

1.1.2 Midterm Election Cycles and Policy Path Dependency Theory

Midterm election variables refer to the dynamic influence of relevant political forces on the governing party’s agenda, regulatory framework, and policy priorities during the midterm period (non-presidential legislative elections in parliamentary systems or midterm congressional elections in presidential systems) in countries or regions with regular election systems. This phenomenon falls under the category of policy path dependency theory in political science.

The policy path dependency theory posits that established policy frameworks and institutional arrangements from the past create a locking effect on future policy choices, meaning that policies evolve along existing trajectories rather than being completely restructured. Additionally, political games and power restructuring during election cycles interfere with regulatory agendas, making the policy formulation process constrained by historical trajectories while exposing it to changing risks related to election outcomes.

Specifically, in the policy environment of the RWA market, midterm election variables are primarily reflected in the following aspects:

(1) Adjustment of policy priorities: Election-oriented regulatory bodies may focus more on short-term economic performance, such as employment and monetary stability, while delaying or reassessing the regulatory agenda for emerging financial technologies.

(2) Increased regulatory volatility risk: As elections approach or results remain uncertain, regulatory policies often adopt a cautious or wait-and-see attitude, leading to potential delays in rule issuance or significant changes in the RWA sector.

(3) Challenges to policy continuity: Different parties/alliances may have varying attitudes towards financial innovation and blockchain technology, and election outcomes may lead to the redefinition of existing regulatory roadmaps, thus affecting the strategic planning of market participants.

Therefore, when assessing the elasticity of RWA policies in a country or region, it is necessary to incorporate election cycle factors into the policy continuity evaluation model, which can provide market participants with forward-looking regulatory risk predictions.

1.1.3 The Disruption Mechanism of Election Variables on Policy Formulation

The disruption mechanism of election variables on the policy formulation process can primarily be understood from three aspects: legislation, execution, and supervision.

In the intersection of financial technology that is RWA, the complexity of policy rules itself, which often involves coordination among multiple departments, exacerbates regulatory uncertainty brought about by these mechanisms of elections, forming a policy ecosystem where short-term volatility coexists with long-term path dependence.

1.2 Report Research Framework and Methodology: Variable Definitions and Policy Elasticity Indicator System Construction

To ensure a precise understanding of the ambiguous yet critical variable of policy elasticity, we have constructed a rigorous and quantifiable assessment system and conducted a multidimensional analysis using major global jurisdictions as samples.

This study defines policy elasticity as the dynamic adaptability and volatility of a regulatory environment in response to external political variables (such as midterm elections), concerning its policy direction, stability, and execution efficiency. To quantitatively assess policy elasticity, the following indicator system is constructed:

The indicator system is built using a combination of conventional database records, public regulatory texts, and institutional evaluation scores, providing a quantitative basis for subsequent cross-national comparisons and ratings.

International RWA Policy Elasticity Assessment Indicator System and Overall Trends

2.1 Construction of RWA Policy Elasticity Assessment System

In existing research and market discussions, the RWA regulatory environment is often simplified into three categories: friendly, neutral, and strict. This classification has certain reference value at the macro level, but its explanatory power is significantly insufficient in practical operations. Especially in cross-cycle observations, it can be found that the regulatory attitude, enforcement intensity, and even policy direction regarding RWA in the same jurisdiction often exhibit phase shifts, even creating inconsistent regulatory signals. This indicates that a single regulatory tightness cannot accurately depict the true institutional environment. Further, for RWA projects and institutional investors, what truly influences decision-making is not a regulatory stance at a specific point in time, but whether that stance maintains stability and predictability throughout a complete political cycle. In other words, compared to whether a policy is friendly, the market is more concerned with: whether the policy will change, when it will change, and whether the magnitude of change is controllable. Based on this realistic constraint, this article introduces policy elasticity as a core analytical variable. Its focus is not to evaluate regulation itself, but to measure the probability, direction, and magnitude of policy adjustments under external shocks (especially midterm elections). This perspective shift allows comparisons between different countries to move from static judgments to dynamic stability characterizations. Methodologically, to avoid subjective issues arising from abstract discussions, this article further breaks down policy elasticity into several observable and quantifiable dimensions and constructs a unified indicator system to support cross-country and cross-cycle horizontal comparisons. It is important to emphasize that this system is not intended to provide simple rankings, but to provide a foundational analytical framework for risk identification, structural design, and global layout decisions in subsequent chapters. On this basis, the definitions and measurement logic of each core indicator will be introduced in sequence.

2.1.1 Policy Continuity Index (PCI): Frequency and Direction of Policy Changes

The policy continuity depicted here is not about whether regulation is strict, but rather a more practical question: within a relatively complete time window, whether the jurisdiction advances RWA systems along a consistent regulatory path or presents repeated adjustments or directional swings. This indicator directly relates to whether project structures can operate stably across cycles and is one of the core variables that institutional investors focus on when assessing long-term risks.

The higher the PCI score, the greater the instability in terms of the frequency of policy changes and consistency of direction in that jurisdiction, indicating that policy paths are more prone to phase adjustments or reversals, corresponding to higher risks of institutional uncertainty.

2.1.2 Policy Transparency Index (PTI): Clarity of Regulations and Predictability

In the previous discussion on policy continuity (PCI), it can be seen that even if a jurisdiction maintains relative stability in regulatory direction, it does not mean that it is operationally feasible for RWA projects. The more common situation in practice is: while the policy path has not undergone significant reversals, due to unclear expressions of rules or excessive interpretation space, project parties still find it difficult to determine compliance boundaries. This is also one of the important reasons why many RWA projects have repeatedly adjusted their structures or been forced to pause during their advancement in recent years. In other words, PCI addresses whether policies will change, but for practical implementation, another equally critical question must also be answered: whether the market can understand and predict the specific applicability of regulatory rules. If policy continuity determines the temporal stability of the system, then rule transparency directly affects the executability of the system. Based on this consideration, this article introduces the Policy Transparency Index (PTI) as the second core dimension, in addition to PCI. This indicator does not focus on the regulatory attitude itself, but rather concentrates on a more practical issue: whether regulatory rules are communicated to the market in a clear, interpretable, and verifiable manner. For RWAs, which involve legal qualifications, asset rights confirmation, and cross-border circulation, this aspect is often more decisive than whether innovation is encouraged.

In specific evaluation methods, PTI is mainly broken down from several aspects such as the rule formation process, textual clarity, and supporting explanatory mechanisms, to ensure that this indicator can reflect both the level of institutional design and cover the key judgment criteria when projects are practically implemented. The relevant evaluation dimensions and scoring standards are as follows.

From actual market feedback, the higher the PTI score, the stronger the uncertainty in terms of regulatory expression, regulatory communication, and execution standards in that jurisdiction, leading to higher understanding costs and compliance misjudgment risks for market participants.

2.1.3 Policy Execution Efficiency Index (PEI): Speed of Implementation and Supporting Mechanisms

In the previous analysis of policy transparency (PTI), it can be seen that even if a jurisdiction has a high level of rule clarity, allowing the market to understand regulatory boundaries, it does not guarantee that RWA projects can proceed smoothly. In practical operations, another equally common but more hidden constraint begins to emerge: although the system is visible, it may not be executable.

This issue has been particularly prominent in the RWA practice over the past two years. Some countries have provided relatively clear directions and principles at the policy document level, but at the execution stage, due to the lack of supporting details, lengthy approval processes, or untimely regulatory feedback, the project advancement period has been significantly extended, and even stalled at critical junctures. For RWA projects that rely on financing rhythms and market windows, this uncertainty at the execution level often has a more direct impact than the lack of clarity in rules themselves. Therefore, PCI addresses whether policies are stable, PTI answers whether rules are clear, but beyond these, it is also necessary to answer a more practical question: whether the regulatory system has the ability to translate rules into executable processes. Without this capability, even the clearest institutional design is difficult to convert into real market supply.

Based on the above observations, this article introduces the Policy Execution Efficiency Index (PEI) to characterize the efficiency of regulatory transformation from rule issuance to actual execution. This indicator focuses not on the policy content itself, but on the rhythm of institutional operations, response mechanisms, and execution consistency, thus reflecting a jurisdiction’s ability to support the implementation of RWA in practical terms. In specific evaluation methods, PEI is structured by breaking down key elements such as the policy effectiveness cycle, completeness of supporting rules, regulatory response speed, and execution uniformity. The relevant measurement dimensions are as follows.

From cross-market comparisons, a higher PEI score means there are more noticeable delays, uncertainties, or execution deviations in the process from policy issuance to implementation, reflecting higher friction costs and uncertainty risks in institutional execution.

2.1.4 Policy Reversal Risk Index (PRI): Fluctuation of Policies Before and After Midterm Elections

In the previously analyzed framework, whether it is policy continuity (PCI), rule transparency (PTI), or implementation efficiency (PEI), they fundamentally revolve around the stability and execution capability of the existing regulatory system itself. This set of indicators can more comprehensively characterize the institutional quality of a jurisdiction under normal circumstances, but in cross-cycle observations, it becomes evident that relying solely on these static dimensions still fails to explain a key phenomenon: why some markets experience significant shifts in regulatory attitudes in a short period, even causing substantial impacts on existing compliance paths. Further dissection of this issue reveals that the root cause lies not in regulatory capability itself, but in external political cycles, especially the institutional disruptions brought about by midterm elections. Unlike the aforementioned indicators, these effects do not manifest in rule texts or execution processes, but instead act on the RWA market indirectly through agenda setting, regulatory orientation, and changes in enforcement rhythms. Thus, if evaluations are conducted solely from the institutional perspective, it is easy to underestimate the real risk exposure.

Based on this understanding, this article introduces the Policy Reversal Risk Index (PRI), specifically to characterize the probability and intensity of directional adjustments in regulatory policies under the disturbances of election cycles. Unlike indicators like PCI, PRI does not describe the current state of the institution but incorporates the time dimension and political variables into the analytical framework, serving as an important supplement and extension of the aforementioned assessment system.

In specific methods, PRI does not attempt to predict specific policy content, but identifies potential risk accumulation and release paths by observing the characteristics of regulatory behaviors at different stages during the election cycle. In other words, its core concern is not how policies change, but when changes are more likely to occur, and how these changes impact the market. Based on this logic, the election cycle can be divided into several key observation phases to assist in judging the level of institutional uncertainty faced by RWA projects at different time windows.

From an overall framework perspective, PRI is not a fourth indicator parallel to PCI, PTI, and PEI, but is more akin to a cross-cycle risk amplifier: once the institutional foundation depicted by the current three indicators overlaps with a high PRI environment, the originally controllable compliance paths may face reconstruction pressure in a short time. Therefore, in practical applications, the significance of PRI lies not in a standalone high or low assessment but in its combination with other indicators to judge the sustainability of an RWA project over a complete political cycle.

To ensure the comparability between indicators and the consistency of overall scoring, this article adopts a unified scoring direction for all core indicators: 0 indicates the highest policy stability and lowest risk, while 10 indicates the highest policy volatility and risk.

Under this standard, each indicator is characterized from the perspective of policy elasticity risk, meaning that the higher the score, the more susceptible that dimension’s policies are to disturbances from external variables (especially election cycles), thus increasing institutional uncertainty. This unified approach is applicable to both individual indicator scoring and the subsequent comprehensive scoring model (RWA-PERM).

2.2 Overall Comparison of Policy Elasticity Among Sample Countries

Under the unified scoring approach, the aforementioned sub-indicators have all been transformed into risk-oriented expressions, meaning that a higher score indicates greater policy uncertainty in that dimension. Based on this, this article constructs a comprehensive policy elasticity risk score (RWA-PERM Score) using a weighted method to characterize the overall policy volatility risk level of each jurisdiction under the influence of election cycles.

When scoring each jurisdiction, this article does not rely on subjective judgment but integrates three types of information sources: first, public regulatory documents and legislative progress (such as legal texts, consultation drafts, and regulatory guidelines); second, regulatory practice data (including the number of enforcement cases, approval cycles, and market feedback); third, political cycle factors (especially election time windows and policy adjustment rhythms). On this basis, the four indicators of PCI, PTI, PEI, and PRI are scored on a scale of 0–10, where 0 indicates the lowest risk / highest stability and 10 indicates the highest risk / strongest uncertainty. It is important to emphasize that this scoring is not an absolute evaluation but is used to support relative comparisons and structural judgments between different jurisdictions. Within this methodological framework, the following analysis will be conducted regionally on major areas.

2.2.1 Comparison of Policy Elasticity in North America

The core differentiation in the North American market lies in whether there is a regulatory model that substitutes enforcement for legislation. This directly impacts policy elasticity and election sensitivity.

The combination of a high PRI and a low PEI in the U.S. indicates that it is not that it cannot be done, but rather that it is more suitable for phase-based, structurally transferable RWA projects; while Canada is closer to a stable regulatory environment.

2.2.2 Comparison of Policy Elasticity in Europe

The core feature of Europe is: through unified legislation (MiCA) to hedge against the political cycle differences of member states, thereby reducing policy elasticity from an institutional level.

The EU significantly lowers the PRI through institutional design, but the PEI is affected by the execution differences of member states; the UK is in an intermediate state, suitable for observation but not for heavy long-term structures.

2.2.3 Comparison of Policy Elasticity in Asia-Pacific

The Asia-Pacific region exhibits a clear stratification: a stable regulatory environment represented by Singapore coexists with an adjustment and transitional regulatory environment represented by Hong Kong and Japan.

Singapore is a typical low-elasticity, high-stability market, suitable for long-term RWA infrastructure; Hong Kong is in a policy upcycle, with opportunities and uncertainties coexisting.

2.2.4 Emerging Markets and Developing Economies

The core issue in emerging markets is not regulatory strictness, but institutional instability and execution inconsistency.

Emerging markets do not lack opportunities but must participate through structural isolation and risk hedging, and are not suitable as core issuance locations.

2.3 Classification of Global RWA Compliance Systems

In the previous section, we quantitatively compared the policy elasticity of different jurisdictions based on the four indicators: PCI, PTI, PEI, and PRI. From the results, the differences between countries are not merely reflected in score levels but present a more structural differentiation: some countries exhibit high consistency across the four indicators, while others show significant combination discrepancies (e.g., low PCI + high PRI, or high transparency but low execution efficiency). This phenomenon implies that the global RWA regulatory environment is not gradually converging along a single direction but is evolving into several relatively stable combination patterns under different institutional constraints. In other words, what truly impacts the landing of RWA projects is not a single indicator but the structural relationships among multiple indicators.

Further dissection of these combination differences reveals that they are not randomly distributed but are primarily determined by three deep-seated factors:

First, differences in legal systems (codified law vs. case law) affect the clarity and interpretive space of regulatory rules;

Second, the maturity of financial regulation determines whether institutional design comes first with rules or follows pilots with standards;

Third, the intensity of political cycle interference, especially the extent to which election factors impact regulatory continuity.

Under the interplay of these factors, different countries gradually form several internally consistent institutional paths in RWA regulatory practice. Based on the matching relationship between indicator performance and institutional characteristics, this report categorizes the compliance environment of major global jurisdictions into four types. This classification is not based on subjective generalizations but rather stems from the convergence results of indicator combinations.

2.3.1 Clear Regulation Type: Low Elasticity, High Certainty Institutional Anchor

From the indicator combination perspective, this type typically presents: low PCI (high continuity), low PRI (low reversal risk), and a relatively high PTI. This means that its regulatory system is centered around codified laws and has strong cross-cycle stability. Represented by the EU MiCA framework, the formation path of this type of system is relatively clear: first, establish regulatory boundaries through legislation, and then embed RWA-related arrangements within the existing legal framework. Thus, RWA does not exist as experimental assets but is directly incorporated into the traditional financial regulatory logic. The immediate result of this institutional arrangement is clear rules and stable expectations, but innovation space is constrained and compliance costs are relatively high. Therefore, it is more suitable for long-term sustainable, large-scale, and institutionally led RWA projects.

From a logical argument perspective, this type is not because the EU is this way, but because its indicator combination naturally points to a low-elasticity but high-certainty institutional equilibrium state.

2.3.2 Guideline-type Flexible Regulation: Dynamic Equilibrium under Undetermined Rules

Compared to the clear regulation type, the typical characteristics of this type in indicators are: moderate PCI + high PRI, while PTI fluctuates significantly. This means that the rules themselves have not yet fully solidified and are easily influenced by political cycles.

This combination is more common in Anglo-American legal systems, where regulatory logic does not rely on complete legislation but progressively defines boundaries through regulatory statements, enforcement cases, and market practices. In other words, rules are defined in use rather than pre-defined.

This model has certain advantages in the early stages: it can accommodate innovation and lower entry barriers. However, from the indicator results, its instability is also significant—especially in environments with high PRI, where policy direction may experience phase adjustments according to election cycles.

Therefore, the institutional characteristics of this type can be understood as a dynamic equilibrium: oscillating between innovation and risk rather than forming stable anchor points. For RWA projects, it is more suitable for adjustable and transferable structural designs.

2.3.3 Iterative Pilot Type (Sandbox): Institutional Path of Experimentation Instead of Formalization

Quantitatively, this type often presents: low PCI (insufficient continuity) + moderate PTI + unstable PEI, while PRI significantly impacts the results. This indicates that its system has not yet entered a stable stage and is in a continuous process of experimentation and correction.

The core of this model lies not in the rules themselves but in how rules are generated: through regulatory sandboxes or special pilots, testing risks on a small scale, and then gradually adjusting institutional arrangements based on results. It should be noted that from an indicator perspective, this high elasticity does not equate to low risk. On the contrary, due to the rules not being solidified, its uncertainty is often higher, especially when PRI rises (such as close to election cycles), when pilot policies may be interrupted or reset.

Therefore, this type should be understood as a stage of institutional generation rather than a mature regulatory environment. The RWA scenarios it fits are relatively clear: verification projects, small-scale issuances, and structural designs with explicit exit mechanisms.

2.3.4 Closed or Contraction-type Regulation: Institutional Exclusion under High-risk Combinations

In the indicator system, this type exhibits the most consistent characteristics: high PCI (low continuity) + low PTI + low PEI, while PRI is at a high level. This combination means that regulation is both unstable and opaque, highly influenced by political and external events. In this environment, the issue is no longer how to comply but whether a compliance path exists at all. Even when policy windows arise in certain phases, their sustainability is weak, making it difficult to support institutional-level capital entry. From a logical argument perspective, this type is not defined as poor regulation subjectively but is an extreme combination that the indicator results naturally point to: high uncertainty + low executability, resulting in structural exclusion of RWA.

Overall, the core of this section is not to present the four types themselves but to clarify a key methodology: institutional types are not a priori classifications but are derived from the structural combinations of indicators. In other words, PCI, PTI, and PEI determine whether the institution is currently usable; PRI determines whether the institution will change in the future; the combination of the four determines whether RWA can stably exist for more than one cycle. Therefore, instead of simply judging which country is friendlier, it is more important to identify its institutional type and further assess: whether that type can traverse election cycles, whether it supports the long-term existence of assets, and whether it needs to structurally reserve migration or exit paths. This classification framework will be directly applied in the subsequent sections for global layout strategies and investment committee decision-making models, serving as the underlying basis for country selection and risk pricing.

Practical Case Analysis of RWA Policy Elasticity Before and After Midterm Elections in Typical Countries

3.1 United States: SEC and RWA Policy Path Under Midterm Elections

3.1.1 Overview of the U.S. Midterm Election System and Regulatory Structure

Institutional Background: The U.S. Congress holds midterm elections every two years, marking an important node for political power reshaping; at the same time, fiscal and financial regulatory issues (especially technology and new asset classes) are often incorporated into electoral contests, leading to cyclic volatility in financial regulatory policies. There is currently no specific legislation for RWA in the U.S.; their classification is often placed under existing frameworks such as securities law, investment company law, commodity law, and state blue sky laws. The high uncertainty of policies and strong enforcement orientation are core governance features of the U.S. RWA market.

Regulatory Agencies: These mainly include the U.S. Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC), state financial regulatory agencies, etc. The classification, compliance paths, and judgments on the securities attributes of RWA by these agencies directly impact project implementation.

3.1.2 Timeline of RWA Policy Progress (Including Key Regulations from 2022 to 2026)

To reflect the impact of election variables on regulatory paths, the following chart summarizes significant policy or regulatory nodes related to RWA in the U.S. in recent years (including future predicted trends).

3.1.3 Empirical Analysis of Policy Elasticity and Market Response

In the previous section, we have analyzed how midterm elections influence U.S. RWA policy through regulatory paths from a mechanistic perspective. However, merely qualitative judgments still struggle to capture the phased changing characteristics of this impact. Especially under different election cycles, policy continuity and reversal risks do not change linearly but exhibit significant volatility structures.

Therefore, it is necessary to convert the above mechanisms into comparable quantitative indicators to dissect the policy elasticity of the U.S. over different time periods. To ensure consistency with the analytical framework in Chapter Two, this section continues to use the four indicators of PCI (Policy Continuity), PTI (Transparency), PEI (Execution Efficiency), and PRI (Policy Reversal Risk), uniformly adopting a 0–10 scoring system, where 0 indicates extremely high stability / extremely low risk and 10 indicates high uncertainty / strong policy volatility. Based on this, the structural changes in the U.S. regulatory environment before and after the midterm elections can be observed more clearly.

Empirical Market Response Data (2025–2026):

(1) In the first half of 2025, the global RWA market saw significant growth, with some reports indicating that the RWA tokenization market grew over 260% in 2025 (despite a low base), surpassing approximately $23 billion; this growth reflects institutional actions seeking regulatory clarity[6].

(2) As of February 9, 2026, the total on-chain market value of RWA reached approximately $23.87 billion, and the number of asset holders rapidly increased to about 835,000, a month-on-month surge of 34.45%[2].

Despite the high uncertainty in the regulatory environment, the above data shows that market participants remain actively searching for compliant paths, reflecting the U.S. market’s adaptability to regulatory elasticity and structural innovation demands.

3.1.4 Typical RWA Asset Issuance Shock Case

• Case: Tokenization project of a large infrastructure revenue right in the U.S. from 2025 to 2026

• Asset Type: Large infrastructure (transportation/energy revenue rights)

• Issuance Structure: SPV + restricted security tokens

• Core Issue: The SEC’s ambiguous determination of securities attributes led to multiple delays in offers

• Result: Issuance delayed by 8–12 months, with the company ultimately shifting part of the issuance to the Singapore and EU market frameworks

• Real Challenges: The SEC’s uncertainty in applying the Howey Test, with the project needing to continually invest in legal opinions and regulatory communication costs (significantly increasing)

• Trend Analysis (2026–2030): Before clear legislation is established, large-scale RWA issuances in the U.S. are more likely to adopt a gradual path of compliance first, followed by promotion.

3.2 European Union: Parliamentary Changes and RWA Regulatory Coordination Mechanisms

3.2.1 European Parliament Election Mechanism and Regulatory Framework

The EU parliamentary elections are held every five years, and their periodicity has a relatively stable overall impact on financial regulation, as most financial regulations are uniformly established at the EU level and gradually implemented by member states. This mechanism is reflected in the RWA compliance field by lowering policy reversal risks through unified rules.

3.2.2 Policy Evolution of MiCA and RWA Boundary Specifications

The core legislation for digital asset regulation in the EU is MiCA (Markets in Crypto-Assets Regulation). Although MiCA primarily targets crypto asset categories, it provides a foundational institutional framework for RWA-related assets (such as asset-referenced tokens and electronic money tokens). Unlike the enforcement-driven approach in the U.S., the EU demonstrates a regulatory style that is proactive and coordinated. EU RWA market outlook: Some analyses suggest that the European RWA tokenization market is expected to become a significant growth pole globally by 2030, with the value potential of promoting RWA as an institutional-grade liquidity tool being enormous[7].

3.2.3 Comparison of Policy Elasticity Among Member States

As analyzed previously, the EU has moved the regulatory rules to a unified legislative level through MiCA, making it difficult for the political cycles at the member state level to directly alter the RWA regulatory path. The practical effect of this institutional arrangement is reflected not only in macro-level stability judgments but can also be quantitatively presented through specific indicators. To more intuitively portray the execution differences among different member states under the unified framework, this article scores major countries based on PCI (Policy Continuity), PTI (Transparency), PEI (Execution Efficiency), and PRI (Policy Reversal Risk). The scoring adopts a 0–10 system, where 0 indicates extremely low risk / high stability and 10 indicates high uncertainty / strong volatility. Under the constraints of unified legislation, the differences among countries primarily manifest at the execution level rather than the policy direction itself.

Relevant Practical Feedback:

(1) Multiple institutions have pointed out that the EU’s unified regulations reduce the uncertainty of compliance boundaries;

(2) However, due to differences in the execution details of member states, at least a 10–14 month implementation time is the norm[8].

3.2.4 Case Analysis of Failure and Delayed Implementation

• Case: A private market fixed-income RWA project in France

• Issue: Although the MiCA framework is in place, French regulatory agencies take a long time to review relevant issuance details;

• Result: Project implementation was delayed by 14 months;

• Core Reason: Regulatory execution details have not been fully unified, and approval standards differ among member states.

• Trend Prediction: As ESMA is expected to release clearer DeFi supplementary regulatory guidelines in 2026–2027, this will help reduce cross-border execution discrepancies[8].

3.3 United Kingdom: Mild Policy Fluctuations Driven by Elections—An Intermediate Form Between the U.S. and EU

3.3.1 How Election Variables Enter the UK Regulatory System

Unlike the enforcement-driven regulatory path of the U.S., the UK has formed a dual-track driven structure centered around the Treasury (HM Treasury) and FCA after Brexit: on one hand, establishing frameworks through legislation; on the other hand, dynamically adjusting through regulatory details. However, it is important to note that the key variable in the UK is not whether regulation exists but the cyclical oscillation between tightening regulation and innovation orientation.

This oscillation is triggered by parliamentary elections and fiscal policy cycles:

First, Conservative Party Cycle: emphasizes financial innovation and the status of London as an international financial center.

Second, Labour Party Cycle: emphasizes consumer protection and financial stability.

This means that the UK will not experience the same path reversals as the U.S., but will witness shifts in regulatory focus.

3.3.2 Policy Evolution and Adjustment Nodes (2024–2026)

Under the above mechanism, UK RWA-related policies show a characteristic of continuous advancement and partial adjustment. To more clearly depict its rhythm changes, the key policy nodes are summarized as follows:

From the time series, it can be seen that the policy direction has not reversed, with adjustments concentrated at the detail level rather than the institutional level.

3.3.3 Quantifying Policy Elasticity: Moderate Fluctuation Structure under Election Influence

In the previous analysis, it was noted that UK RWA regulation does not exhibit path reversals similar to the U.S., but its policy evolution is also not completely linear and may experience phase shifts between innovation orientation and prudent regulation according to election cycles. Merely using stable or unstable for qualitative descriptions fails to accurately capture this intermediate state. Therefore, it is necessary to incorporate this mild fluctuation structure into a unified quantitative framework for comparative analysis with the U.S. and Singapore. Methodologically, we will continue to utilize the consistent four-dimensional indicator system to characterize the policy elasticity of the UK, including PCI (Policy Continuity), PTI (Transparency), PEI (Execution Efficiency), and PRI (Policy Reversal Risk).

To ensure cross-country and cross-cycle comparability, all indicators uniformly adopt a 0–10 scoring system, with specific meanings as follows: 0 = highly stable policy / almost no uncertainty (optimal state), 10 = highly volatile policy / extremely strong uncertainty (highest risk). It is important to emphasize that this scoring system measures policy volatility, not regulatory intensity or friendliness.

In other words, a higher score does not indicate strict regulation but rather unstable paths; a lower score does not mean lenient regulation but indicates controllable expectations. Under this unified standard, as a sample with moderate election influences, the policy elasticity structure of the UK can be quantified as follows:

Conclusion: From the quantitative results, the UK’s PRI has long been in the range of 4–5, significantly lower than that of the U.S. (around 8+). This indicates that its policy system does not exhibit directional reversal risks but may display some degree of fluctuation across different political cycles. The core feature of the UK is not policy instability but predictable cyclical oscillations. This structure distinguishes it from the high-elasticity market of the U.S.

3.4 Singapore: A Policy Anchor-type Market with Low Election Interference (Control Group)

3.4.1 Why Singapore Must Be Used as a Control Sample

After analyzing the U.S. and the UK, a key question arises: does policy instability stem from regulation itself or from the political cycle? To answer this question, it is essential to introduce a control sample with low election interference.

Singapore serves as this benchmark: a single ruling system, high-level technocracy of the Monetary Authority of Singapore (MAS), and long-term stability in policy objectives (financial center + risk control). Therefore, the significance of Singapore is not in being superior but in stripping away the election variables to reveal the pure regulatory form.

3.4.2 RWA Policy Continuity Assessment (2026)

Singapore’s policy advancement in the RWA and digital asset sectors exhibits typical characteristics:

(1) Continuous issuance of guidelines (rather than repeated revisions)

(2) Emphasis on risk layering and licensing mechanisms

(3) Promotion of cross-border standardization

Essentially, it replaces cyclical adjustments with gradual improvements.

3.4.3 Comparative Conclusions with High Elasticity Countries

In the previous sections, we have argued from an institutional structure perspective that Singapore’s RWA regulatory system is largely unaffected by election cycles, presenting clear characteristics of dominant continuity in policy evolution. However, mere qualitative judgments are still insufficient for comparative analysis with the U.S. and the UK. Therefore, it is necessary to further translate this low political interference characteristic into quantitative indicators to be included in the unified policy elasticity assessment system throughout this article. Following the four-dimensional indicator framework, we will quantitatively characterize Singapore, including PCI (Policy Continuity), PTI (Transparency), PEI (Execution Efficiency), and PRI (Policy Reversal Risk).

To ensure consistency in cross-country comparisons, all indicators uniformly adopt a 0–10 scoring system, where 0 = extremely high stability / extremely low risk (optimal state) and 10 = high uncertainty / strong policy volatility (most unstable state).

It should be specifically noted that this scoring system measures policy volatility, not regulatory quality. In other words, a lower score does not imply looser regulation but indicates a more stable policy path and more controllable market expectations.

Under this standard, Singapore, as a low-election-interference sample, can be clearly quantified as follows:

Conclusion: From the quantitative results, Singapore maintains low volatility across all four indicators, especially with the PRI consistently staying below 2. This result further verifies that when election variables are weakened, the RWA policy system tends to stabilize rather than exhibit cyclical volatility structures.

RWA Policy Elasticity Risk Rating and Response Strategies

4.1 National/Regional RWA Policy Elasticity Risk Rating Model and Results

4.1.1 Explanation of Risk Rating Dimensions

The RWA policy elasticity risk rating model (RWA-PERM, Policy Elasticity Risk Model) constructed in this article is not a traditional regulatory friendliness ranking but focuses on measuring the probability and magnitude of directional shifts or execution fractures in policies under the impact of midterm election variables. The model sets four primary dimensions, with weights determined by commonly used risk factor weighting methods in institutional investment committees, and incorporates quantitative scoring (0–5) to ensure comparability across countries and cycles: 0 = extremely low risk / highly stable policies, 5 = extremely high risk / policies prone to significant directional shifts or execution fractures, using weighted averages to form comprehensive risk scores. Data sources include government public documents, regulatory enforcement statistics, election system research, and market practical feedback.

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