The review, authored by Juanjuan Wan of Guangdong Polytechnic Normal University, uses the DNV and Menon Economics assessment framework as its benchmark. That framework has shifted decisively from hard infrastructure toward institutional quality, scoring cities on dimensions such as maritime finance and law and attractiveness and competitiveness. The 2024 edition placed Singapore first overall, Shanghai fourth and rising, while Hong Kong—which ranked fourth in 2019—had fallen to twelfth. The report cautions that its 2024 methodology changed, so rankings cannot be compared directly with earlier editions. Even so, the pattern is striking: Shenzhen and Guangzhou rank among the world’s leaders in container throughput, with Shenzhen handling 33.39 million TEUs in 2024, yet neither entered the top five in the composite port ranking, which also weighs governance, connectivity and sustainability.

The GBA’s configuration is unique among major bay areas: one country, two systems, three jurisdictions, three separate customs territories and three currencies. The nine Mainland cities operate under China’s socialist legal system, Hong Kong retains its common-law tradition, and Macao preserves a Portuguese-derived civil-law system. Wan is careful to distinguish legal pluralism itself, which is constitutionally established under Article 31 of the Chinese Constitution and the two Basic Laws, from what she calls institutional fragmentation—gaps in choice-of-law rules, cross-boundary enforcement, professional mutual recognition and regulatory standards. Hong Kong’s common-law system, she notes, is among the region’s principal maritime assets. The problem is connectivity, not plurality.

The review identifies a split profile of maritime strengths. Shenzhen and Guangzhou possess world-class port and logistics capacity, while Hong Kong concentrates common-law expertise, international arbitration and maritime finance. In an integrated single-jurisdiction city such as Singapore or Shanghai, these strengths reinforce one another within one institutional environment. In the GBA, their combination depends on rules crossing jurisdictional boundaries. The sub-indicator data make the split vivid: Hong Kong ranks second globally for maritime legal experts and third for the market capitalization of listed maritime companies, while Shenzhen and Guangzhou rank between 27th and 44th on the legal indicators or are not ranked at all. London, by contrast, is first in the world for maritime finance and law, scoring first in five of ten indicators.

Three mechanisms link these connectivity gaps to maritime soft power. The first is uncertainty in the application of law. A single ship-finance or marine-insurance transaction may need to comply simultaneously with three sets of contract rules and registration requirements across the Mainland, Hong Kong and Macao, multiplying compliance costs. Before the recent revision of China’s Maritime Law, rules on ship mortgages were dispersed across three statutes, creating an intricate order of priority. A ship-finance lease might designate Hong Kong law while the mortgaged ship is registered in the Mainland, forcing enforcement to confront differences between Mainland maritime rules and Hong Kong security law. Marine insurance faces similar difficulties: China’s insurance regulator and its judicial authorities have both considered that shipowners’ mutual associations do not constitute commercial insurance activities under the Insurance Law, leaving protection and indemnity contracts regulated only by general contract-law rules.

The second mechanism is regulatory arbitrage and competition in standards. Because the three jurisdictions apply different marine environmental standards, emission limits and enforcement procedures, highly polluting vessels may register and operate where standards are lower, creating a de facto pollution haven. The GBA’s cooperation agreements on environmental protection are administrative instruments without the normative rank of legislation, so rules on information sharing and joint monitoring lack legally binding force. The pressure is visible in the Pearl River Estuary, which crosses all three jurisdictions: Guangdong’s 2024 environment bulletin reports that the proportion of coastal waters with excellent or good water quality fell by 1.7 percentage points year on year, while waters classified worse than Grade IV rose by 2.2 points. Comparative experience from the North Sea shows that ship-source pollution declines only when governance measures are implemented consistently across jurisdictions—precisely what the GBA’s fragmented enforcement landscape makes structurally difficult.

The third mechanism is the cost of judicial coordination. Cross-boundary enforcement of arbitral awards among the Mainland and the two SARs rests on interregional arrangements under the Basic Laws rather than the New York Convention, which applies only to awards made in another State. The 2019 Mainland-Hong Kong judgment arrangement, effective in 2024, is the most significant recent advance, yet its Article 3 excludes judgments on marine pollution, limitation of liability for maritime claims, general average, salvage, maritime liens and passenger carriage. Court judgments in precisely the categories most relevant to Pearl River Estuary environmental disputes therefore still lack a reciprocal enforcement channel. Evidentiary differences compound the costs: Mainland procedure emphasizes a hierarchy of documentary evidence, while Hong Kong’s common-law tradition emphasizes oral testimony and cross-examination, so parties to a cross-border maritime dispute may need to prepare two evidentiary packages for the same facts.

The arbitration caseload data illustrate the concentration of maritime dispute resolution elsewhere. In 2024, an estimated 1,733 new references were made on London Maritime Arbitrators Association terms, with roughly 1,818 new maritime arbitrations seated in London in total. Singapore’s maritime caseload was about nine percent of London’s. The China Maritime Arbitration Commission recorded 239 filings, 79 percent of them maritime, of which 93 were foreign-related, involving parties from forty jurisdictions. The figures are indicative rather than strictly comparable, since the LMAA does not administer arbitrations directly, but the order-of-magnitude difference is clear. Meanwhile China’s maritime courts accepted 6,823 foreign-related cases from 2022 to 2024, and the foreign-related share of CMAC’s caseload rose sharply from about three percent in 2023 to 38.9 percent in 2024—signs of growing demand that the review argues are magnified by enforcement uncertainty in the three-jurisdictional setting.

In response, the review proposes three institutional arrangements. A rule-of-law coordination pilot zone, building on the Qianhai, Nansha and Hengqin cooperation platforms, would extend mutual recognition of contractual choice of law—a step already partly taken by the Qianhai Regulations and a 2025 Supreme People’s Court reply supporting Hong Kong or Macao law choices by SAR-invested enterprises in Shenzhen and Zhuhai. A marine environmental dispute resolution center would use a three-tier jurisdiction defined by arbitrability: commercial disputes such as P&I contract claims would be arbitrable, cleanup-cost claims would proceed through mediation and judicial confirmation, and public-interest litigation would remain with the courts. The review prefers arbitration to a new court for a decisive legal reason: arbitral awards can move among the three jurisdictions under existing interregional arrangements and abroad under the New York Convention, whereas court judgments on marine pollution remain outside the Mainland-Hong Kong arrangement. A cross-border data interoperability mechanism would harmonize the statistical definitions on which international assessments depend, since ship-finance volumes and insurance payout ratios are currently dispersed across incompatible systems.

The review’s most consequential proposal is conceptual: that rule-of-law coordination—the efficiency of cross-jurisdictional legal alignment, encompassing mutual recognition of rules, convergence of enforcement standards and the speed of coordinated dispute resolution—should become a regional indicator in international maritime-city assessments. All five existing dimensions use a single jurisdiction as the analytical unit, so they cannot capture this variable. Functional comparisons with the Rhine-Scheldt basin, where the 1815 Central Commission for Navigation of the Rhine unified technical shipping standards, and with the Port Authority of New York and New Jersey, whose bi-state governance has not prevented courts from applying different state-law tests, show that the problem is structural, not unique to the GBA. Even the Scheldt Estuary, with two centuries of bilateral treaties, has seen courts repeatedly suspend Antwerp’s port expansion over divergent applications of EU environmental directives. Wan is explicit about the limits of her analysis: it identifies mechanisms consistent with observed ranking patterns rather than estimating causal effects, and rankings are also shaped by the pandemic, geopolitics and methodology changes. But if the GBA can make an early breakthrough in cross-jurisdictional rule-of-law coordination, its experience could provide an operational framework for every multi-jurisdictional port region in the world seeking to convert institutional diversity into competitive advantage.

Subject of Research: Marine Affairs and Policy

Article Title: Institutional fragmentation and rule-of-law coordination in the Guangdong-Hong Kong-Macao Greater Bay Area: a maritime soft power perspective

Article References: Wan, J. (2026). Institutional fragmentation and rule-of-law coordination in the Guangdong-Hong Kong-Macao Greater Bay Area: a maritime soft power perspective. Frontiers in Marine Science, 13, Article 1977131. https://doi.org/10.3389/fmars.2026.1977131

Image Credits: AI Generated

DOI: 10.3389/fmars.2026.1977131

Keywords: maritime soft power, Greater Bay Area, rule of law, maritime arbitration, port governance, institutional fragmentation, Institutional, fragmentation, rule-of-law, coordination, Guangdong-Hong, Kong-Macao

The research, conducted by Domenico Cangemi and Livia De Giovanni of LUISS Università di Roma together with Pierpaolo D’Urso and Vincenzina Vitale of Sapienza Università di Roma, applies an enhanced form of Exploratory Graph Analysis, or EGA, to the Varieties of Democracy dataset, the most ambitious expert-based measurement project in political science. V-Dem covers more than 600 indicators annually from 1789 to the present, drawing on evaluations from a pool of more than 4,200 country specialists who assess concepts that no official statistic can capture, such as academic freedom, judicial independence and the deliberative quality of public reasoning. For each country-year observation, the project typically collects assessments from five experts and feeds them into a Bayesian measurement model that estimates the reliability of each coder, corrects for systematic differences in how experts interpret response scales, and produces interval-level latent estimates together with credible regions that express measurement uncertainty.

From this vast apparatus the researchers selected 37 interval-valued indicators covering 179 countries in 2024, indicators that V-Dem’s theory organizes into five high-level principles of democracy: electoral, liberal, participatory, deliberative and egalitarian. These principles are operationalized through indices such as the Electoral Democracy Index, which combines clean elections with freedom of expression, association and universal suffrage, the Liberal Component Index, which captures judicial and legislative constraints on executive power, the Participatory Component Index, which measures civil society engagement and direct democracy, the Deliberative Component Index, which gauges whether decisions emerge from public reasoning oriented toward the common good, and the Egalitarian Component Index, which tracks equal access to political power and resources across social groups.

The core question was simple to state and hard to answer: does the empirically observed dependence among these 37 indicators reproduce the theoretically defined five-dimension structure, or does the data tell a different story? To find out, the team turned to EGA, a network psychometrics method in which variables appear as nodes and their conditional associations as edges, so that latent dimensions emerge as communities of densely connected nodes. The standard pipeline estimates a Gaussian Graphical Model using the graphical LASSO, a regularization technique that shrinks small partial correlations to zero, and then applies the Walktrap community detection algorithm, which finds clusters through random walks that preferentially traverse strong edges. The trouble is that this machinery quietly assumes multivariate normality, and political indicators are anything but normal. Measures of democracy remain substantially skewed or heavy-tailed across countries, and under such conditions the Pearson correlation matrix, the workhorse input of the standard method, can distort the estimated network and with it the recovered dimensions.

The study’s first methodological innovation addresses exactly this weakness. The authors replace the Pearson matrix with a nonparanormal estimator, a semiparametric device that treats each observed variable as a monotone transformation of a latent Gaussian variable, thereby preserving a Gaussian copula while allowing the marginals to take almost any shape. Under this model, the latent Gaussian correlation can be recovered directly from Spearman rank correlations through a trigonometric transformation, and this identity survives any strictly increasing rescaling of the data. In simulation experiments with three true latent dimensions and deliberately skewed, heavy-tailed marginals, the contrast was striking. At sample sizes of 50 and 100, the Pearson-based routine repeatedly failed to recover the correct number of dimensions, over-extracting four or more communities in 46 and 38 of 100 replications respectively, while the nonparanormal approach recovered the true three-dimensional structure nearly perfectly. Even at 500 observations the Pearson method still misclassified a variable, whereas the rank-based method did not.

Robustness to non-normality, however, solves only half the problem. A single EGA solution delivers a crisp partition of variables into communities, but both the estimated network and the resulting community assignments can wobble under small perturbations of the data, a concern that has shadowed network psychometrics since studies showed that psychopathology symptom networks can have limited replicability. The second innovation therefore wraps the analysis in a bootstrap. The team generated 500 synthetic datasets from a multivariate normal distribution built on the estimated nonparanormal correlation matrix, ran EGA on each, and then summarized the ensemble of 500 partitions through a co-association matrix, whose entries record the proportion of replications in which each pair of indicators landed in the same community. This consensus matrix is invariant to arbitrary relabeling of communities across runs, preserving the full ensemble information rather than reducing it to per-item stability scores.

Instead of forcing the consensus structure back into a crisp partition, the researchers applied Non-Euclidean Fuzzy Relational Clustering, an algorithm that works directly on the dissimilarity matrix derived from the co-association counts and produces fuzzy memberships, allowing each indicator to belong partially to several latent dimensions. Indicators with one dominant membership can be read as stable core components of a dimension, while those with distributed memberships flag genuine ambiguity or conceptual overlap. The authors are careful to stress that fuzziness is not necessarily a sign of model failure; it can reveal systematic cross-dimensional association that a binary assignment would simply erase.

When the full pipeline ran on the 2024 V-Dem data, the nonparanormal version identified five latent dimensions, exactly matching the theoretical classification, while the Pearson-based routine produced six and scored lower against the theory-based reference partition on the Adjusted Rand Index. The fuzzy pipeline built on the nonparanormal consensus achieved the highest fuzzy agreement of all tested variants. Most indicators behaved as theory predicted: all nine freedom of expression indicators clustered together, all five deliberative component indicators stayed intact, and most judicial constraint indicators formed a coherent group. But two findings stood out. First, freedom of religion, which V-Dem classifies under equality before the law, attached itself empirically to the freedom of expression cluster, consistent with the idea that countries suppressing religious practice tend also to suppress media and civil liberties. Second, and more substantively, the algorithm split V-Dem’s Equality before the Law and Individual Liberty index, which theory treats as a single construct, into two sharply distinct dimensions: one gathering institutional constraints on state behavior, including access to justice, freedom from torture, freedom from political killings, impartial administration and transparent law enforcement, and the other gathering individual freedoms with a pronounced gender component, including property rights, freedom of domestic movement and freedom from forced labor for men and women, alongside women’s civic participation.

That split carries a provocative implication: the rule-of-law securities a state provides and the personal freedoms its citizens actually experience do not necessarily move together. A regime may protect institutional legality while individual and gender-specific liberties lag behind, and the conventional index aggregates these divergent profiles into a single number, conflating regimes that are empirically quite different. Four indicators also emerged as fuzzy, including freedom of foreign movement, whose nearly uniform membership across all dimensions reflects how exit restrictions intertwine civil liberty, judicial transparency and political control at once. The authors propose practical consequences: index users could weight ambiguous indicators by their membership values, treat near-uniform ones as bridging indicators to be monitored separately, and compare regimes at the level of empirically recovered dimensions rather than relying solely on theoretical aggregation. The recovered structure survived sensitivity checks, holding when the community detection algorithm was swapped for Louvain or Leiden, when the fuzziness parameter varied, and largely when an alternative fuzzy clustering method was used. The team frames the rule-of-law split as an empirical suggestion worthy of dedicated investigation rather than an established property of governance, but the message for the growing industry of democracy monitoring is clear: behind every headline number lies a dimensional structure, and that structure, examined with the right statistical tools, can tell stories the headline number conceals.

Subject of Research: Robust network psychometric methods for recovering latent dimensions in cross-national democracy measurements

Article Title: Recovering Latent Dimensions in Cross-National Democracy Measurements Through Robust Exploratory Graph Analysis

Article References: Cangemi, D., D’Urso, P., De Giovanni, L., & Vitale, V. (2026). Recovering Latent Dimensions in Cross-National Democracy Measurements Through Robust Exploratory Graph Analysis. Social Indicators Research, 184(2), Article 41. https://doi.org/10.1007/s11205-026-03929-2

Image Credits: AI Generated

DOI: 10.1007/s11205-026-03929-2

Keywords: Exploratory Graph Analysis, V-Dem, democracy indices, nonparanormal model, network psychometrics, fuzzy clustering, Gaussian graphical models, rule of law, Spearman correlation, bootstrap stability, latent dimensions, Social Indicators Research

Public sector AI experimentation inverts that division of roles. When a tax authority pilots an algorithm to detect suspected fraud, or a welfare agency deploys machine learning to prioritize benefit claims, the state is simultaneously the innovator, the regulator, the evaluator, and often the sole affected counterparty for citizens. There is no external firm whose market entry must be facilitated, and no conventional consumer making a voluntary purchase decision. Instead, the people affected are frequently captive audiences: taxpayers, benefit claimants, asylum seekers, and patients who cannot opt out of interacting with the state. This means that the consumer protection rationale at the heart of the traditional sandbox translates only imperfectly. Protecting a consumer from a faulty financial product is meaningfully different from protecting a claimant from a biased eligibility algorithm, because the claimant’s interaction with the state implicates constitutional rights to due process, equal treatment, and administrative justice rather than market fairness alone.

The experimentalist governance tradition offers a useful lens for understanding what sandboxes are meant to accomplish, and also where they fall short. Experimentalist frameworks, as developed in the scholarship the article engages with, are characterized by a recursive loop: broad framework goals are set, local actors are given discretion to pursue those goals through experimentation, results are monitored and reported upward, and the framework goals are then revised in light of what was learned. Peer review, benchmarking, and iterative revision are central. A sandbox fits this logic naturally, because it generates evidence about an innovation under controlled conditions and feeds that evidence back into regulation. But experimentalism also presupposes a degree of independence between the experimenting unit and the monitoring unit. When the state experiments on its own administrative processes, the monitoring function risks becoming self-review, which is precisely the structural weakness that administrative law doctrines such as impartial decision-making and independent appeals were designed to counteract.

The pacing problem that motivates sandbox adoption is particularly acute for AI in government. Legal scholarship has long observed that innovation outpaces regulation, but AI compresses development cycles to a degree that reactive, statute-by-statute lawmaking cannot match. An algorithmic system can be retrained, redeployed, and materially altered in behavior within weeks, while legislative amendment takes years. Moreover, the technical properties of AI systems strain established legal categories. Opacity complicates the duty to give reasons for administrative decisions, a cornerstone of administrative law across many jurisdictions. Statistical bias complicates equality guarantees, because discrimination may emerge from training data rather than from any identifiable discriminatory intent. Distributed development pipelines complicate liability attribution, since a government agency, a commercial vendor, and an open-source model developer may each contribute to a harmful outcome. These are not merely compliance hurdles; they are challenges to the conceptual architecture of public law itself.

The EU AI Act adds an important institutional dimension to this landscape. By mandating that member states establish AI regulatory sandboxes, the Act embeds experimentalist governance into binding European law, and it explicitly contemplates testing before market entry or operational deployment. This is significant because it signals legislative recognition that supervised experimentation can serve both innovation policy and regulatory learning simultaneously. Yet the Act’s sandbox provisions, like the national sandboxes in the United Kingdom, Norway, and Finland that preceded them, were largely conceived with private developers in mind: companies seeking to bring AI products to European markets under conditions of legal uncertainty. The extension of sandbox logic to public sector deployment, where the state itself is the deployer, stretches a framework built around market entry toward institutional contexts it was never designed to govern.

One way to see the mismatch clearly is to compare the seven parameters along which the article distinguishes public sector sandboxes from their private sector predecessors. The primary purpose of a conventional sandbox is innovation promotion balanced against consumer protection; a public sector sandbox must instead balance administrative improvement against legality, fundamental rights, and democratic legitimacy. The legal basis differs because public authorities cannot simply be granted waivers from the constitutional and statutory obligations that bind them; a waiver that suspends due process in the name of experimentation would itself be unlawful in most legal systems. The risk model differs because the relevant harms are not market harms but rights harms, including wrongful denial of benefits, discriminatory enforcement, and erosion of procedural fairness. Each of these parameters requires deliberate redesign rather than straightforward transplantation.

Accountability structures illustrate the redesign problem concretely. In a private sector sandbox, remedies typically include compensation for affected consumers, withdrawal of the product, and enforcement action against the firm. In a public sector sandbox, the affected population may be an entire category of benefit recipients, and the remedy may require not merely withdrawing a tool but unwinding thousands of individual decisions made with its assistance. The Dutch childcare benefits scandal, frequently cited in the literature on algorithmic government, demonstrated how algorithmic decision-making at scale can generate mass injustice that ordinary complaint mechanisms were never equipped to remediate. A public sector sandbox must therefore build remediation capacity into the experimental design itself, including mechanisms for identifying affected individuals, reversing erroneous decisions, and providing redress, rather than treating remedies as an afterthought to be addressed at exit.

Transparency and participation raise parallel difficulties. Conventional sandboxes involve confidentiality arrangements that protect the commercial interests of participating firms, on the theory that firms will not disclose proprietary innovations to a regulator without assurance that trade secrets will be safeguarded. Public sector experimentation cannot rest on the same premise, because the public has a democratic interest in knowing how its government makes decisions about it. Meaningful participation requires more than publication of a final evaluation report; it requires engaging affected communities, civil society organizations, and independent experts at the design stage, when the objectives and risk tolerances of the experiment are being set. Without such participation, a public sector sandbox risks becoming a mechanism by which the state legitimizes decisions it has already made, rather than a genuine forum for democratic deliberation about the proper role of AI in governance.

Data governance is a further parameter where public sector sandboxes demand distinct treatment. Private sector sandboxes typically involve firms processing consumer data under relaxed regulatory supervision, with data protection law operating as one of the rule sets that may be flexibly interpreted. Public sector AI systems, by contrast, often depend on administrative datasets, such as tax records, social security files, and immigration data, that were collected for purposes unrelated to the proposed AI application. Linking such datasets for experimental purposes raises questions of purpose limitation, data minimization, and the legality of secondary use that go well beyond consumer privacy. A well-designed public sector sandbox must specify what data may be used, under what legal authority, with what safeguards against re-identification and function creep, and with what arrangements for deleting or archiving data once the experiment concludes.

Evaluation design and transfer pathways complete the picture. The point of a sandbox is not experimentation for its own sake but the generation of transferable knowledge: either the innovation is institutionalized into ordinary administration, or it is abandoned, and in either case the regulatory framework should be updated in light of what was learned. For private sector sandboxes, the transfer pathway is market entry followed by standard regulatory oversight. For public sector AI, the transfer pathway is institutionalization into administrative practice, which raises questions about whether the safeguards that applied during the experiment, such as human review of algorithmic outputs, enhanced documentation, and periodic audits, will persist after the sandbox closes. Experience with government algorithm deployments suggests that safeguards often erode after pilots end, as budget pressures and operational demands mount. A credible public sector sandbox framework must therefore specify binding conditions for institutionalization, not merely criteria for entry into testing.

Taken together, these considerations support the article’s central claim that adaptation, not adoption, is the appropriate posture toward sandboxes for public sector AI. The sandbox remains an attractive instrument because it preserves what experimentalist governance does best: structured, time-bound, evidence-generating experimentation under supervision, with feedback into the regulatory framework. But the normative foundations must be rebuilt around public law values rather than market values. This entails anchoring public sector sandboxes in explicit statutory authority, defining rights-protective risk thresholds that cannot be traded away for efficiency gains, establishing independent oversight that is institutionally separate from the experimenting agency, guaranteeing transparency and participation rights for affected populations, imposing strict data governance conditions, and designing evaluation and transfer mechanisms that carry safeguards forward into institutionalized deployment. Where these conditions are met, the sandbox can serve as a legitimate bridge between the pace of AI innovation and the stability that legality and democratic accountability require. Where they are not, the same instrument risks becoming a vehicle for normalizing practices that would not survive ordinary administrative law scrutiny.

Subject of Research: Adapting regulatory sandboxes as experimentalist governance frameworks for public sector artificial intelligence experimentation

Article Title: Adapting regulatory sandboxes as experimentalist governance frameworks for public sector artificial intelligence experimentation

Article References: Okonjo, J. (2026). Adapting regulatory sandboxes as experimentalist governance frameworks for public sector artificial intelligence experimentation. Global Public Policy and Governance, 6(2), 259-281. https://doi.org/10.1007/s43508-026-00147-x

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00147-x

Keywords: artificial intelligence, regulatory sandboxes, public sector experimentation, experimentalist governance, publicness theory, administrative law, democratic accountability, rule of law, EU AI Act, algorithmic governance, transparency, innovation policy