Shoreline change strongly affects ecosystem conditions and livelihood security in deltaic coasts. However, long-term and spatially explicit baselines remain limited for many rapidly changing coastal systems. This study quantified multi-decadal shoreline dynamics in Ujung Pangkah, Indonesia, to identify persistent patterns of accretion and erosion and to assess their implications for sustainable coastal management. Multi-epoch satellite images from 1973 to 2021 were used to extract shoreline positions through water-index-based classification. The extracted shorelines were analyzed using standardized Digital Shoreline Analysis System (DSAS) metrics to estimate net shoreline movement (NSM) and end point rate (EPR) across segmented coastal areas. The results indicate a segment-structured shoreline mosaic rather than a uniform coast-wide trend. Most sectors were accretion-dominated, with the accretion component reaching approximately +12 to +15 m$\cdot$yr$^{-1}$, particularly in Area C. In contrast, Area D formed the main erosional hotspot, with an erosion component of -8.68 m$\cdot$yr$^{-1}$ and an NSM erosion value of -416.53 m, while its net EPR and net NSM were -0.66 m$\cdot$yr$^{-1}$ and -31.77 m, respectively. These findings show that shoreline change in Ujung Pangkah is spatially concentrated in localized reaches. Therefore, coast-wide averages may obscure areas where erosion risk persists and where accretion gains are sustained. This study provides a quantitative long-term baseline and a reproducible remote-sensing and GIS-based workflow to support hotspot identification, segment-scale monitoring, and the prioritization of coastal protection and rehabilitation measures in dynamic deltaic environments.

Coastlines host dense human activity that concentrates combustion and elevates carbon monoxide (CO) and nitrogen dioxide (NO₂) burdens. Yet complex coastal meteorology often limits ground monitoring. We address this gap with a multi-year, dual-pollutant, jurisdiction-scale analysis using a transparent Sentinel-5P column-burden workflow. We employ the workflow on Canada’s Nova Scotia (NS), a cool and relatively stable North Atlantic coast, and the US state of Louisiana (LA), a warm-humid Gulf coast with one of the densest refining hubs, providing two contrasting coastal domains. We analyse 2019–2024 tropospheric column CO and NO₂, apply uniform quality-assured screening, generate time series composites at native resolution, classify spatial fields with Jenks Natural Breaks, and examine temporal trends. Columns are compared with inventories and ground networks as consistency checks. Six-year means highlight persistent contrasts: NS’s column CO is slightly higher than LA’s (0.0338 vs. 0.0321 mol m⁻²), and NS’s NO₂ is ≈ 2.5× LA’s (6.09×10⁻⁵ vs. 2.39×10⁻⁵ mol m⁻²). In NS, NO₂ peaks in summer, while CO reaches its highest seasonal mean in spring; in LA, NO₂ peaks in winter and CO peaks in spring. Recurring hotspots appear over Halifax-Dartmouth and North Sydney, and along the Baton Rouge-New Orleans corridor and northern parishes. These patterns may reflect a combined influence of coastal setting, seasonal atmospheric structure, and local activity, although direct meteorological attribution was not performed. By integrating satellite archives with ground networks, the study provides a reproducible, auditable approach that translates seasonal column dynamics into jurisdiction-ready evidence for evaluation calendars and corridor-focused siting, improving the timing and targeting of coastal air-quality management, and supporting United Nations Sustainable Development Goals (SDGs) 3 and 11.

This study aims to develop a model for predicting daily sea wave heights in the Makassar Strait to support shipping safety in tropical waters. Observation data were obtained from the Makassar station of the Meteorology, Climatology, and Geophysical Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG) (January 2018–December 2023), covering wind speed, wind direction, sea surface temperature, and rainfall. Feature selection was performed using Frequent Pattern Growth (FP-Growth), which was chosen because it efficiently finds association patterns between variables with only two database scans, making it more economical than other techniques such as Recursive Feature Elimination or Principal Component Analysis. The selected features were used to build a Support Vector Regression (SVR) model optimised with the Fruit Fly Optimisation Algorithm (FOA). The evaluation was conducted with zonal validation in three sub-regions of the Makassar Strait (north, central, south) using a lead time of one day ahead. The results show that the SVR-FOA model produces an average root mean square error (RMSE) of 0.4938 m (95% confidence interval (CI): 0.472–0.516), mean absolute percentage error (MAPE) of 0.00208 (95% CI: 0.00195–0.00221), and a correlation of 0.935. SVR-FOA reduced the RMSE by 16.8% compared to the default SVR, while compared to the grid search SVR, there was a 6.7% reduction. The model’s performance is comparable to similar studies in the literature, although the RMSE is still higher than Long-Short Term Memory (LSTM) and XGBoost; however, SVR-FOA excels in stability between zones. In conclusion, SVR-FOA with FP-Growth feature selection effectively predicts daily sea wave height in the Makassar Strait. Further research is needed to test shorter time scale predictions, real-time data integration, and field validation with stakeholders.

This study aims to evaluate and rank regional agricultural technology competitiveness in East Java, Indonesia, using a structured multi-criteria decision-making approach. Specifically, it addresses four key objectives: (1) to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method to assess and rank regional competitiveness across multiple technological dimensions; (2) to examine whether agricultural technology adoption levels differ significantly across regions using one-way Analysis of Variance (ANOVA); (3) to evaluate the sensitivity and robustness of the ranking results under alternative weighting scenarios through sensitivity analysis and rank correlation measures (Spearman’s ρ and Kendall’s τ); and (4) to derive policy-relevant and system-oriented implications for enhancing competitiveness and reducing regional disparities. The study employs a quantitative approach based on primary survey data collected from 210 farmers across seven regions in East Java. Four key dimensions are considered, namely environmental, irrigation, marketing, and production technologies. The PROMETHEE method is used to generate regional rankings, while ANOVA is applied to test for statistically significant differences in technology adoption. Robustness is further assessed through systematic weight variations and rank correlation analysis. The results reveal substantial regional disparities in relative technological competitiveness, with leading regions demonstrating more balanced, integrated adoption across multiple technological dimensions. ANOVA results confirm that differences in technology adoption across regions are statistically significant (p < 0.01), thereby providing complementary statistical evidence for inter-regional variation in the underlying technology adoption indicators used in the PROMETHEE analysis. The robustness analysis shows that the ranking results are highly stable across most weighting scenarios, with only minor variations observed when marketing-related criteria are emphasized. This study contributes methodologically by integrating multi-criteria decision-making with statistical validation and robustness testing in a unified framework. From a policy perspective, the findings highlight the importance of strengthening market access, improving technological integration, and implementing region-specific interventions to enhance agricultural competitiveness and reduce disparities.

The research analyzes whether monitoring system design, calibration management, timestamp consistency, data traceability, and verification procedures relate to the risk control of the financial aspects of methane-abatement engineering projects. An analytical case study based on a single project and involving a before-and-after comparison of the implementation of an monitoring, reporting, and verification (MRV) regime was conducted under fixed engineering and accounting conditions. This design allows the comparison to focus on differences in MRV evidence management conditions rather than on changes in physical mitigation technology. Conservative issuability was estimated using the low-confidence adjustment metric (LCAM). This analytical metric scales engineering emission reductions by evidence-related factors without supplanting registry rules or verifier judgment. With the enhanced MRV regime, the conservatively supportable fraction was 77.0% to 91.3%, while the realized price wedge declined from 0.30 to 0.12. The monitoring-to-issuance period was also shortened by 50 days.

Railway timetable planning plays a central role in the coordination and operational performance of transportation systems. Efficient timetable development remains essential for balancing infrastructure constraints, service quality, operational efficiency, and economic objectives in railway operations. The interaction among these dimensions makes timetable planning a complex decision problem for infrastructure managers and transport operators. This study aims to evaluate the relative importance of the principal criteria influencing railway timetable planning and to provide quantitative support for transportation system decision-making. A structured evaluation framework was developed using the fuzzy PIvot Pairwise RElative Criteria Importance Assessment (fuzzy PIPRECIA) method. Forty decision-makers with professional experience in railway operation, infrastructure management, engineering practice, and academia participated in the assessment process. Five main criteria were examined: railway line capacity, railway station capacity, number of passed trains, quality of train operations, and revenues of the planned timetable. The results showed that revenues of the planned timetable received the highest importance weight, followed by quality of train operations, number of passed trains, railway line capacity, and railway station capacity. The findings further showed that operational and economic dimensions exerted greater influence on timetable planning decisions than infrastructure-capacity factors. The results indicate that railway timetable planning should be approached as a system-level coordination problem rather than a capacity allocation exercise alone. This study provides a structured decision-support perspective for evaluating competing planning priorities and offers a practical basis for improving timetable development and operational performance in railway transportation systems.