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

Investment casting is one of metallurgy’s oldest processes, with roots stretching back to early weapons, jewelry, and religious art. It remains prized today for aerospace turbine blades and biomedical components because it delivers exceptional surface finish, dimensional accuracy, and the ability to reproduce complex shapes. In Nepal, the technique has been used since at least the sixth century A.D. to produce the bronze and gilded deities that fill temples and monasteries across the Himalayas. The process is intensely manual: a sculptor first carves a detailed wax master pattern, which is encased in a rubber mold from which multiple wax replicas can be made. These replicas are assembled with gating systems, dipped in a traditional slurry of cow dung and clay, reinforced with metal wires, dewaxed, preheated, and finally filled with molten metal. After cooling, each piece demands hours of filing, chiseling, and hand-applied gold plating.

That artisanal character comes at a cost. Defect rates in Nepali sculpture casting hover around thirty percent, and sculptures can take anywhere from one month to two years to complete depending on size. Because global economic shifts and advancing technology are squeezing demand for Nepali cast sculptures, improving casting design, modeling, and production efficiency has become essential for the industry’s survival. The research team, led by Zenisha Shrestha with Abhishek Pandey and Bijendra Prajapati, set out to determine whether systematic parameter optimization, never before applied to this traditional setting, could meaningfully improve dimensional accuracy in gilding metal, an alloy of ninety percent copper and ten percent zinc that is the most widely used sculpture material in Nepal.

The researchers chose a sword as their test specimen, selected for its cultural relevance in Nepali society and its relatively simple geometry, which makes dimensional analysis tractable. The design was modeled in SOLIDWORKS, with the gating system developed through the modulus method to reflect designs typical of sculpture manufacturing. From an Ishikawa cause-and-effect analysis of casting stability, the team identified three controllable parameters: the number of slurry coating layers, the mold preheat temperature, and the metal pouring temperature. Factors such as wax composition, alloy composition, slurry composition, cooling methods, and environmental conditions were treated as noise parameters that could not be easily controlled in the workshop.

The experimental design followed a Taguchi L9 orthogonal array, allowing nine carefully chosen experiments to explore the parameter space efficiently. Slurry coatings ranged from two to four layers, preheat temperatures spanned 500 to 600 degrees Celsius, and pouring temperatures covered the 1150 to 1200 degree Celsius range typical of Nepali sculpture foundries. Each coating choice involves a trade-off: thin shells save material and time but risk bulging and leaking under the metallostatic pressure of pouring, while thicker coatings prevent defects and improve heat retention at the cost of longer processing. Preheating the mold reduces thermal shock and premature freezing of the melt, improving fill of thin sections, though excessive preheat can accelerate mold-metal reactions and increase surface-connected porosity. Higher pouring temperatures superheat the metal above its melting point, preventing unfilled sections in intricate features.

Four response variables were measured for each casting: weight, length, breadth, and thickness. Before optimization, the team verified data quality through normality testing with probability plots and the Anderson-Darling test, confirming that all responses followed normal distributions. Pareto tests and analysis of variance at a 95 percent confidence level then established that all three process parameters significantly influenced every response variable. The number of coatings emerged as the dominant factor, contributing 44.42 percent of the variance in weight, 48.01 percent in length, 58.81 percent in breadth, and 41.82 percent in thickness. Pouring temperature exerted its greatest influence on weight at 39.31 percent and length at 31.38 percent, while preheat temperature most strongly affected breadth and thickness. Residual plots showed randomly distributed errors, confirming the reliability of the statistical model.

Because dimensional accuracy depends on optimizing all four responses simultaneously, the researchers turned to multi-response optimization. Grey relational analysis, a technique designed for systems with limited information, converts multiple responses into a single grey relational grade by normalizing the data and calculating correlation coefficients, using an identification coefficient of 0.5 consistent with prior studies. The innovation here was the coupling of grey relational analysis with principal component analysis, which uses eigenvectors to derive objective weights for each response rather than assuming they matter equally. The first principal component captured 81.9 percent of the data’s variance and identified length as the most significant response. Experiment 3, combining a 500 degree Celsius preheat, a 1200 degree Celsius pour, and four coating layers, ranked highest under both the standard and PCA-weighted grades, and both methods converged on the same optimal setting, strengthening confidence in the result.

The confirmatory experiment delivered striking improvements. The weighted grey relational grade rose from 0.357 under initial conditions to 0.966 under optimal conditions, closely matching the predicted value of 0.9677. Weight deficit fell from 3.160 percent to 0.682 percent, length shrinkage dropped from 4.860 percent to 0.545 percent, breadth shrinkage plummeted from 6.156 percent to 1.067 percent, and thickness shrinkage declined from 4.200 percent to 1.800 percent. In practical terms, a cast sword produced under the optimized parameters now deviates from its wax pattern by barely one percent in its principal dimensions, meaning far less manual filing and correction before gold plating. Given that finishing work is among the most labor-intensive stages of sculpture production, even modest dimensional improvements translate into significant reductions in lead time and cost.

The implications extend well beyond Nepal’s foundries. The authors note that the GRA-PCA methodology can be adapted to any complex manufacturing process where multiple conflicting objectives must be balanced, including precision casting of aerospace turbine blades, biomedical device manufacturing where dimensional control is critical, and automotive component casting where strength, weight, and tolerances compete. For Nepal, the study represents the first application of advanced multi-response optimization to traditional sculpture casting, and it arrives at a pivotal moment for an industry whose economic viability depends on competing with industrialized producers. Future work, the researchers suggest, could examine surface roughness, mechanical properties, and additional process parameters. But the central message is already clear: fourteen centuries of artisanal wisdom and twenty-first-century statistical rigor are not adversaries. When the number of coatings, the preheat temperature, and the pouring temperature are tuned together, the ancient art of Himalayan metal sculpture can achieve a precision its original masters could scarcely have imagined, preserving both a cultural heritage and the livelihoods of the craftsmen who sustain it.

Subject of Research: Multi-response optimization of investment casting parameters to improve the dimensional accuracy of gilding metal sculptures in traditional Nepali manufacturing.

Article Title: Improvement of dimensional accuracy in gilding metal sculpture manufacturing using grey relational analysis coupled with principal component analysis

Article References: Shrestha, Z., Pandey, A., & Prajapati, B. (2026). Improvement of dimensional accuracy in gilding metal sculpture manufacturing using grey relational analysis coupled with principal component analysis. Heliyon, 12(14), Article e45412. https://doi.org/10.1016/j.heliyon.2026.e45412

Image Credits: AI Generated

DOI: Not provided

Keywords: investment casting, grey relational analysis, principal component analysis, Taguchi method, dimensional accuracy, gilding metal, Nepal sculpture manufacturing, shrinkage reduction, ANOVA, multi-response optimization, Kathmandu Valley, metal casting