The research, published in Environmental and Sustainability Indicators, was conducted by Herfita Rizki Hasanah Gurning of IPB University and colleagues, who set out to answer a question that has largely been overlooked in previous work: do provinces with strong marine economies also possess the economic, social and environmental capacities needed to sustain them, and are comparatively developed provinces actually making full use of their marine potential? Earlier studies had tended to treat regional development simply as economic growth or environmental carrying capacity, rather than as a distinct, multidimensional system in its own right.

To build a comparable sample, the team first used K-medoids clustering on data from 34 Indonesian provinces covering 2019 to 2023, drawing on indicators of marine and brackishwater fisheries intensity, marine transport intensity, marine tourism intensity and the share of coastal villages. The two-cluster solution proved statistically strongest, with the highest Silhouette Coefficient of 0.448 and the lowest Davies-Bouldin Index of 0.761, and it kept a balanced minimum cluster size of 14 provinces. Twenty provinces emerged with comparatively high marine economic intensity, and these formed the analytical sample. Notably, the researchers measured marine subsector contributions against non-mining gross regional domestic product, a deliberate choice to avoid distorting results in provinces where extractive industries dominate headline output figures.

The study then applied Grey Relational Analysis, a technique well suited to settings where comprehensive provincial marine data are scarce, to construct a Marine Economy Performance Index and a Regional Development Performance Index for each province in 2019 and 2023. The marine economy index captured economic scale, economic structure and economic efficiency, while the regional development index combined economic indicators such as non-mining growth and unemployment, social indicators including poverty, the Human Development Index and the Gini ratio, and environmental measures comprising a composite environmental quality index and a disaster risk index from Indonesia’s National Disaster Management Authority.

The results reveal a striking divergence between the two systems. Average marine economic performance barely moved, slipping from 0.55 in 2019 to 0.54 in 2023, while average regional development performance rose from 0.56 to 0.62. Within the marine economy, the structure dimension improved markedly, from 0.60 to 0.65, reflecting diversification away from primary fisheries dependence and stronger marine tourism, but the efficiency dimension fell from 0.53 to 0.44 as capture fisheries and aquaculture productivity weakened across many provinces. Bali made the most dramatic climb, rising from twelfth to second place in marine economic performance as its structure score reached a perfect 1.00, while North Maluku held first place with its efficiency dimension hitting the ideal value. At the other extreme, DKI Jakarta plummeted from sixth to twentieth as its productivity scores collapsed.

Regional development told a different story. Bali rose from third to first place with an index of 0.77, Riau Islands held second, and West Nusa Tenggara and North Sulawesi posted some of the largest gains. The economic dimension improved most, from 0.59 to 0.67, and the environmental dimension rose from 0.54 to 0.61, but the social dimension advanced only modestly, from 0.54 to 0.58. That slower social progress matters, the authors argue, because economic and environmental gains in coastal regions do not automatically translate into improvements in welfare, inclusion or human development, and unequal benefit distribution remains a persistent challenge in marine-dependent communities.

The core of the analysis lies in the coupling coordination degree, a measure that combines the proportional balance between the two indices with their overall development level. Average coordination improved only slightly, from 0.74 in 2019 to 0.76 in 2023, leaving most provinces in the intermediate coupling range of 0.7 to 0.8. No province reached the high-quality coupling category, defined as 0.9 or above. Bali and North Maluku entered the good coupling range, at 0.85, after improving both subsystems simultaneously, while Aceh moved up from primary to intermediate coupling. By contrast, North Kalimantan slipped from good to intermediate coordination, and DKI Jakarta showed the sharpest imbalance, with regional development improving even as marine economic performance declined sharply. No province recorded the reverse pattern of rising marine performance alongside falling regional development.

To pinpoint what was holding coordination back, the team deployed an Obstacle Degree Model using entropy weights, which quantifies how far each indicator sits from its ideal state and how much it constrains the system overall. The findings were unambiguous. The marine economy subsystem accounted for 65.43 percent of the total obstacle degree in 2019, rising to 68.25 percent in 2023. Within it, the productivity of marine and brackishwater aquaculture was the single largest obstacle, growing from 28.93 percent to 30.27 percent, followed by the contribution of marine transportation to regional output, which climbed from 14.66 percent to 17.34 percent. Together these two indicators accounted for nearly half of all constraints by 2023, and their dominance intensified over the study period.

The provincial heatmaps add important nuance. Aquaculture productivity was especially problematic in North Kalimantan and the Riau Islands, where its obstacle degree exceeded 40 percent, while marine transportation was the dominant constraint in Bali despite the island’s overall gains. In North Maluku, disaster risk emerged as the leading obstacle in 2023, showing that even provinces with strong coordination can carry acute vulnerabilities in a single dimension. Prior research suggests aquaculture performance is often limited by capital constraints, high feed costs, disease outbreaks, declining water quality and unreliable electricity, while Indonesia’s maritime transport challenges include limited port capacity, weak intermodal connectivity and insufficient institutional coordination, though the study itself is diagnostic rather than causal and does not identify which mechanisms operate in each province.

The policy implications are concrete. The authors argue that provincial governments should prioritize aquaculture productivity through better production technology, water-quality and disease management, infrastructure, and access to capital; strengthen the economic role of marine transportation through port capacity, logistics and intermodal connectivity; and embed disaster-risk reduction more systematically into coastal planning. Just as importantly, because the obstacle structure differs from province to province, a uniform national approach is unlikely to work. Provinces where both systems improved need policies that consolidate gains, while those with stagnant or declining marine performance need targeted interventions. As Indonesia pushes its blue economy agenda across a fragmented archipelago, the study suggests that converting marine potential into broad-based regional benefit depends less on expanding production than on fixing the specific, measurable bottlenecks — above all pond productivity and maritime connectivity — that keep the sea and the shore from developing together.

Subject of Research: Coupling coordination between marine economy performance and regional development across coastal Indonesian provinces

Article Title: Coupling coordination and obstacle analysis of the marine economy and regional development: Evidence from coastal Indonesia

Article References: Gurning, H. R. H., Fauzi, A., Rustiadi, E., & Pravitasari, A. E. (2026). Coupling coordination and obstacle analysis of the marine economy and regional development: Evidence from coastal Indonesia. Environmental and Sustainability Indicators, 32, Article 101513. https://doi.org/10.1016/j.indic.2026.101513

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101513

Keywords: Indonesia, marine economy, regional development, aquaculture productivity, coupling coordination degree, marine transportation, coastal provinces, blue economy, Grey Relational Analysis, obstacle degree model, disaster risk, sustainable development

The central premise of the study is deceptively simple but consequential: cities do not function as islands. Labor markets, innovation networks, infrastructure systems, and digital services routinely spill over municipal borders, tying together core cities, suburbs, and smaller towns into integrated functional regions. If smart city performance—measured through indicators of digital infrastructure, technological innovation, human capital, and connected urban services—is produced within these wider regional systems, then assessments that stop at the city limit risk misreading both the sources and the consequences of urban smartness. The authors argue that regional scaling, the way performance changes with the size of a regional system, provides a crucial test of whether smart urban capabilities are driven by local assets or by broader structural dynamics.

To carry out this analysis, the research assembles regional-level data across European territories, combining indicators of smart urban development with measures of regional population, economic output, and spatial structure. The methodological logic draws on scaling analysis, an approach borrowed from complexity science that has previously been used to show how many socioeconomic outputs—from patents to wages to the incidence of certain social phenomena—tend to increase superlinearly with city size. Applied at the regional scale, the question becomes whether smart performance grows proportionally, sublinearly, or superlinearly as regions become larger and more densely connected, and whether that scaling behavior differs systematically across parts of Europe.

The study’s mapping of spatial distribution reveals a sharply uneven geography. Smart urban capabilities cluster in a limited set of regions, typically those anchored by large metropolitan areas with strong research institutions, dense producer-service sectors, and well-developed digital infrastructure. This pattern echoes long-standing observations about European spatial economics, including the persistence of a core-periphery gradient running roughly from the so-called blue banana of urbanization through southern Germany, the Low Countries, and parts of northern France, toward more sparsely provisioned peripheral regions in southern and eastern Europe. But the regional analysis adds an important nuance: proximity to a high-performing core does not automatically translate into high regional performance, and some regions without globally famous capitals nonetheless demonstrate robust smart urban profiles built on secondary cities and coordinated regional networks.

The scaling results carry particular significance for urban policy. When the researchers examine how smart performance varies with regional size, they find evidence that the relationship is not uniform across Europe. In some regional contexts, performance rises more than proportionally with size, consistent with agglomeration effects in which larger, denser regional systems generate disproportionate returns on digital and innovative activity. In others, the relationship flattens or weakens, suggesting diminishing returns or structural constraints—such as fragmented governance, uneven infrastructure investment, or dependency on a single urban core—that prevent large regions from converting size into smart performance. This heterogeneity undermines the idea that there is a single universal law of smart urban growth and instead points to regionally specific development regimes.

One of the study’s most provocative implications concerns the proliferation of smart city rankings and benchmarks. Municipal league tables, the authors suggest, can be misleading in two directions. A small city may appear exceptionally smart on a per-capita basis while depending heavily on regional universities, transport systems, and firms headquartered elsewhere; conversely, a sprawling polycentric region may look mediocre when judged by its largest municipality alone while its distributed network of towns collectively delivers strong digital and innovative capacity. By evaluating performance at the regional scale, the study provides a corrective that better reflects how the production of smart urban outcomes is actually organized in functional space.

The findings also speak to a central debate in regional science: the tension between concentration and dispersion of high-value activities. If smart capabilities reward agglomeration with superlinear returns, then market forces and even some policy interventions will tend to deepen regional inequality, funneling talent, investment, and infrastructure toward already advantaged metropolitan areas. The study’s evidence of heterogeneous scaling offers a more hopeful reading. Where regional systems—particularly polycentric ones with multiple cooperating cities—achieve strong performance, the lesson is that scale advantages are not the exclusive property of single dominant metropolises. Connectivity, complementarity, and coordinated governance among smaller cities can substitute, at least in part, for raw metropolitan size.

For European policymakers, the research lands at a moment when cohesion policy, digital transition funding, and smart city programs are being recalibrated. The European Union’s digital and green transitions explicitly target territorial balance, yet the study suggests that policy instruments designed around individual municipalities may systematically miss the regional systems in which smart performance is actually produced. Investment in broadband or innovation vouchers allocated city by city may underperform relative to instruments that reward inter-municipal cooperation, shared regional data platforms, and integrated transport-and-digital planning. The scaling evidence implies that the effectiveness of such interventions will differ by regional context, arguing for territorially differentiated strategies rather than one-size-fits-all smart city templates.

The study is candid about the limits of its evidence base. Regional indicators of smart urban performance remain imperfect proxies for the underlying phenomena they seek to capture, and data availability varies across countries, complicating pan-European comparison. Scaling relationships, moreover, are correlational: demonstrating that performance rises with regional size does not by itself identify the mechanisms—labor pooling, knowledge spillovers, infrastructure economies, or network effects—that drive the pattern. The authors call for finer-grained, longitudinal work that can trace how regional smart performance evolves over time and how specific policies alter scaling behavior. Still, the analysis marks a meaningful step in relocating the smart city debate from the showcase municipality to the regional systems in which urban intelligence is embedded.

In the end, the research reframes a familiar question with fresh analytical force. The smart city narrative promised that technology would make urban life more efficient, sustainable, and responsive; this study suggests that whether that promise is fulfilled depends less on any single city’s gadgetry than on the scale, structure, and connectivity of the regions those cities inhabit. As Europe confronts the twin challenges of digital transformation and territorial cohesion, the message is clear: the future of smart urbanism will be decided not city by city, but region by region, and policies that recognize this spatial reality are far more likely to deliver smartness that is both high-performing and broadly shared.

Subject of Research: Spatial distribution and regional scaling of smart city performance across European regions

Article Title: Beyond smart cities: spatial distribution and regional scaling performance in European regions

Article References: Dai, Y., Hasanefendic, S., & Bossink, B. (2026). Beyond smart cities: spatial distribution and regional scaling performance in European regions. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00474-2

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00474-2

Keywords: smart cities, European regions, urban scaling, regional development, urban sustainability, digital infrastructure, spatial distribution, agglomeration, territorial cohesion, polycentricity, regional policy, complexity science