The study’s ecological focus reflects the distinctive properties of blue carbon habitats, including mangroves, tidal marshes and seagrass beds. These systems occupy less than 0.5% of the ocean surface but sequester carbon at rates that dwarf those of most terrestrial forests. Their stored carbon can also be unusually resistant to rapid policy repair, with losses from mature, carbon-dense salt-marsh stands not necessarily compensated by restoration on policy-relevant timescales. Blue carbon is therefore not merely another environmental outcome in the assessment. It provides the ecological foundation against which political, economic, industrial and environmental risks were ranked. The procedure was grounded in polycentric governance theory, which examines coordination problems when international, regional, national and local institutions share responsibility for marine resources, and in ecosystem-services economics, which addresses why public ecological goods may receive insufficient weight in national policy calculations.

Construction began with a literature search covering maritime politics, maritime economics, marine industry, and marine culture and environment. A parallel search in the China National Knowledge Infrastructure returned 698 marine-policy articles distributed across those four thematic domains, providing a fixed literature base for indicator development. The authors then applied a four-stage Nominal Group Technique involving specialists in marine policy, environmental economics, blue carbon ecology and risk governance. Individual idea generation was followed by structured clarification, anonymous voting and consensus-based closure, a sequence intended to reduce conformity pressures and authority differences. This process produced four principal dimensions, each divided into three sub-dimensions containing three criteria, for 36 criteria in total. The environmental hierarchy was extended with indicators for carbon sequestration capacity, blue carbon habitat integrity, coastal erosion and disaster risk, and pollution affecting sedimentary carbon dynamics.

Weights were estimated with Fuzzy AHP, a method that replaces single numerical judgments in pairwise comparisons with triangular fuzzy numbers. A narrow interval represents greater confidence, while a broader interval preserves uncertainty, making the method suitable for contested questions that exceed any one expert’s knowledge. A nine-level linguistic scale was used to compare criteria at the dimension, sub-dimension and individual-criterion levels. Questionnaires were distributed in March 2023 to 120 government officials, marine-industry operators and members of the public with documented coastal engagement; 114 usable responses produced a 95% effective response rate and 702 pairwise-comparison matrices. Within each stakeholder group, responses were aggregated before the groups were combined with a geometric mean. Local weights were multiplied through the hierarchy and normalized to produce global weights for all 36 criteria. Yager’s index converted the fuzzy numbers into scalar values, giving greater authority to the modal judgment while retaining sensitivity to uncertainty.

Beyond environmental pollution and strategic disputes, the upper end of the ranking was dominated by risks closely connected to blue carbon. Ecosystem pollution ranked third with a weight of 0.128, disputes over inter-country marine governance organizations fourth at 0.123, coastal erosion and disaster fifth at 0.081, biodiversity loss sixth at 0.078 and carbon sink degradation seventh at 0.068. Five of the seven highest-ranked criteria were therefore blue-carbon-relevant. Two—marine environmental pollution and ecosystem pollution—had direct counterparts in standard environmental assessment, while three were blue-carbon-specific additions: coastal erosion and disaster, biodiversity loss and carbon sink degradation. Under an explicitly hypothetical comparison, a parsimonious generic framework retaining only the two standard analogues would have omitted three of the five highest-severity blue-carbon-relevant criteria, or 60%. The paper stresses that this is an expository counterfactual, not an empirical estimate of improved detection: the stronger claim would require applying blue-carbon-calibrated and non-calibrated FAHP models to the same data with transparent indicator mapping.

At the dimension level, Marine Culture and Environment received the highest weight, followed by Maritime Politics, Marine Industry and Maritime Economics. The pattern does not show that economic effects involving seafood markets, aquaculture, tourism or supply chains are unimportant. Rather, it suggests that respondents considered economic risks comparatively manageable through tools such as market-access adjustment, trade compensation, import restrictions and subsidy reorientation. Environmental and political risks, by contrast, involve potentially irreversible ecological damage and fragmented authority across jurisdictions. The proximity of environmental contamination and strategic contestation is particularly important because no single state internalizes all of the ecological costs generated by a unilateral marine decision. Political disputes can determine whether monitoring, precaution and coordinated responses are available, while contamination and habitat disruption can simultaneously deepen political conflict. The authors interpret the two high weights as consistent with mutually reinforcing political and ecological risks, rather than treating them as sequential events.

The researchers compared three policy bundles by calculating the aggregate absolute deviation between the 36 risk-severity weights and corresponding policy-capacity weights. Marine environmental policy included contamination monitoring, radionuclide surveillance, discharge standards, sediment remediation, habitat-protection designations and marine protected-area networks. Marine spatial policy included exclusive economic zone management, maritime-boundary clarification, zoning separating industrial and conservation uses, and strategic blue carbon habitat designation. Marine economic policy encompassed trade and market-access measures, fishery and aquaculture subsidy reorientation, import restrictions, compensation mechanisms and blue-economy investment incentives. The preferred option was the one with the smallest aggregate deviation, evaluated under three decision rules: optimistic, which assumes favorable implementation; pessimistic, which emphasizes protection against worst-case governance failure; and equal-probabilities, which assigns no prior probability to favorable or unfavorable conditions.

No instrument dominated in all six combinations of decision rule and analytical level. Under the optimistic rule, environmental policy ranked first at the dimension level, with a deviation of 1.188, while economic policy ranked first at the criterion level with a deviation of 1.201. The divergence shows that environmental policy’s advantage at the broad dimension level was concentrated in its alignment with the high-severity blue-carbon criteria, whereas economic policy offered broader coverage across individual criteria in best-case conditions. Under the pessimistic rule, spatial policy performed best at both levels, reaching deviations of 1.010 at dimension level and 1.063 at criterion level. Its relative advantage was associated with tools that receiving states can use within their own jurisdictions, including zoning, boundary management and habitat designation, when international cooperation and multilateral enforcement are assumed to be weak. Under equal probabilities, environmental policy ranked first at both levels and produced the study’s lowest deviation: 0.856 at dimension level, compared with 1.204 for spatial policy and 1.098 for economic policy.

The changing rankings are consistent with complementary roles for different instruments. Environmental measures directly address the criteria associated with sediment contamination, ecosystem pollution and carbon sink degradation; spatial tools can establish jurisdictional perimeters when international cooperation is weak; and economic instruments can facilitate responses through trade, market access and investment. However, the analysis evaluated the three alternatives independently and did not test a combined policy package, so complementarity remains an interpretation of the results rather than a demonstrated outcome. The sensitivity to analytical level also matters: conclusions based only on dimension-level analysis can obscure criterion-level heterogeneity, while criterion-level rankings can be influenced by numerous lower-weight indicators. For that reason, the authors treated comparisons at both levels as necessary rather than selecting one aggregation level as decisive.

Several limits constrain how far the findings can travel. The analysis covers one illustrative case rather than a set of transboundary events, so the weight pattern cannot yet be assumed to be structural. The study did not establish a case-specific chain linking the discharge to measured ecological outcomes in any blue carbon habitat, although the authors drew on general evidence concerning radionuclide pathways, thermal stress, sediment contamination and trophic amplification. The response records also do not permit exact reconciliation of which pairwise-comparison matrices each respondent completed. Results depend on Yager’s defuzzification method, geometric-mean aggregation and equal prior weighting of the three stakeholder groups; no formal sensitivity analysis varied those choices or applied bootstrap resampling. Several criteria are also conceptually adjacent, including environmental pollution, biodiversity loss, habitat destruction and carbon sink degradation, and their discriminant validity was not tested. The framework is therefore best understood as a structured way to make expert judgments visible and comparable when bilateral ecological data are unavailable, not as a substitute for biophysical monitoring, measurement, reporting and verification. Its central result is that transboundary blue carbon protection requires governments to confront political instability and ecological contamination together, while recognizing that no single policy instrument covers every dimension of the risk.

Subject of Research: Expert assessment of transboundary nuclear wastewater policy risks to blue carbon ecosystems

Article Title: Cross-domain risk governance of transboundary marine policies for blue carbon ecosystem protection: a fuzzy AHP evaluation framework applied to the nuclear wastewater discharge case

Article References: Huang, W. (2026). Cross-domain risk governance of transboundary marine policies for blue carbon ecosystem protection: a fuzzy AHP evaluation framework applied to the nuclear wastewater discharge case. Frontiers in Marine Science, 13, Article 1928781. https://doi.org/10.3389/fmars.2026.1928781

Image Credits: AI Generated

DOI: 10.3389/fmars.2026.1928781

Keywords: blue carbon, Fuzzy AHP, nuclear wastewater, transboundary governance, marine policy, coastal ecosystems, Fukushima Daiichi, risk assessment, ecosystem services, Cross-domain, risk, governance

Groundwater is the invisible lifeline of India. It supplies roughly 62 percent of the country’s irrigation water, about 85 percent of rural drinking water and around half of urban drinking supplies. Yet in regions underlain by the Deccan Traps, the vast flood-basalt province that covers much of peninsular India, water does not sit in convenient underground reservoirs. Instead, it occupies weathered, fractured and jointed zones within otherwise impermeable rock, making its distribution patchy, unpredictable and notoriously difficult to assess. Climate change is intensifying the hydrological cycle, and the widening gap between water demand and natural recharge makes scientific planning of this resource increasingly urgent.

The research team, led by Nayan D. Zagade of Prof. Ramkrishna More College in Pune with colleagues from Savitribai Phule Pune University, focused on Baramati tehsil, an administrative unit of about 1,382 square kilometres in the southern part of Pune district. The area lies in a dry to semi-arid zone, receives a modest annual rainfall of roughly 333 to 417 millimetres, and is drained by the Karha River, which flows into the Nira River along the tehsil’s southern boundary. With a population of nearly 430,000 people and an economy anchored in agriculture, the region’s dependence on groundwater extracted through dug wells and bore wells is intense and growing.

To map where water is most likely to be found, the researchers assembled eight thematic layers in a geographic information system: geomorphology, slope, lineament density, vadose-zone thickness, soil, drainage density, rainfall and land use/land cover. Each layer captures a different piece of the hydrogeological puzzle. Lineaments, the linear fractures and joints visible in terrain analysis, create secondary porosity through which water can infiltrate and travel. The vadose zone, the unsaturated thickness between the land surface and the water table, was reconstructed from water-level measurements in 38 field wells. The land-cover map was derived from 2024 Sentinel-2 satellite imagery using supervised classification, achieving an overall accuracy of 92 percent and a kappa coefficient of 0.89.

The technical heart of the study lies in how these layers were weighted and combined. The team first applied the Analytical Hierarchy Process, or AHP, a multi-criteria decision-making method introduced by Thomas Saaty in 1980. In AHP, factors are compared pairwise on a one-to-nine scale, and criterion weights are extracted from the principal eigenvector of the resulting comparison matrix. Under this scheme, geomorphology received the highest weight at 0.22, followed by slope and lineament density at 0.17 each, with vadose zone and soil at 0.12, drainage density at 0.08, land use at 0.07 and rainfall at 0.05. The consistency ratio of 0.06 confirmed that the expert judgments were logically coherent.

Conventional AHP, however, forces experts to commit to crisp, single-value judgments even when their confidence is uncertain. To address this, the researchers also applied the Fuzzy Analytical Hierarchy Process, in which each pairwise comparison is expressed as a triangular fuzzy number with lower, most-probable and upper bounds. After defuzzification using the centroid method, the fuzzy model reshuffled the priorities: lineament density rose to the top with a weight of 0.15, followed by vadose zone at 0.14, slope and rainfall at 0.13 each, and geomorphology at 0.12. This shift suggests that when uncertainty is explicitly accounted for, structural controls on groundwater, the fractures that channel water through hard basalt, gain greater prominence. The fuzzy model’s consistency ratio of 0.04 was even better than the crisp version’s.

After weighted overlay of the eight reclassified layers, both models painted Baramati as a region of predominantly moderate groundwater potential, but with instructive differences. The AHP map assigned 67.72 percent of the area to the moderate class, 19.35 percent to high potential and 12.76 percent to low potential, with very high and very low zones occupying only marginal slivers of land. The F-AHP map was more conservative: the moderate class expanded to 78.40 percent, while high and low zones shrank to 14.42 percent and 7.12 percent respectively, and no area at all fell into the very low category. The fuzzy approach, by smoothing out abrupt transitions between suitability classes, produced a more cautious and arguably more realistic delineation.

To test how well the maps performed, the team used Receiver Operating Characteristic analysis, comparing predicted groundwater potential values at the 38 well locations against background values across the study area. The AHP model achieved an Area Under the Curve of 0.772, while the F-AHP model reached 0.835, a clear comparative edge. The authors are careful to note an important caveat: because the same 38 wells used for validation also informed the vadose-zone input layer, and because the ROC analysis was a presence/background comparison without independent true-negative data, these AUC values should be read as internal consistency measures rather than fully independent predictive validation. This kind of transparency about circularity is refreshing in a field where validation statistics are sometimes presented with more confidence than they deserve.

The hydrogeological interpretation behind the maps is grounded in the realities of basaltic aquifers. Favorable zones correspond to flat terrain, fractured rock, permeable clayey soils that still allow some infiltration, lower drainage density and agricultural land use that supports recharge. Unfavorable zones coincide with residual hills, exposed bedrock and steep slopes in the rugged north of the tehsil, where runoff dominates and water has little chance to percolate. The combined influence of geomorphology and lineament density, the two heaviest-weighted factors across both models, explains most of the spatial pattern of moderate-to-high potential.

For the people of Baramati, the practical implications are direct. The maps can guide where to prioritize well drilling and recharge interventions, and where abstraction should be restrained in favor of water conservation. The authors emphasize that the outputs are decision-support tools, not absolute portraits of the subsurface, since expert weighting, static rainfall surfaces and limited well data all introduce uncertainty. Still, the study offers a compelling demonstration that embracing uncertainty, rather than suppressing it, can make environmental models both more honest and more accurate. In a semi-arid world where every drop counts, that lesson travels far beyond one tehsil in Maharashtra.

Subject of Research: Geospatial mapping of groundwater potential zones in a semi-arid hard-rock region of India using AHP and fuzzy AHP methods

Article Title: Geospatial modelling of groundwater potential zones in Baramati tehsil, India using AHP and F-AHP approaches

Article References: Zagade, N. D., Pardeshi, S. S., Umrikar, B. N., & Badhe, Y. P. (2026). Geospatial modelling of groundwater potential zones in Baramati tehsil, India using AHP and F-AHP approaches. Discover Geoscience, 4(1), Article 330. https://doi.org/10.1007/s44288-026-00701-4

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00701-4

Keywords: groundwater, geospatial modelling, AHP, fuzzy AHP, GIS, remote sensing, Deccan Traps, Maharashtra, hydrogeology, water resources, multi-criteria decision making, ROC analysis