Plastic pollution in the ocean is an emerging environmental concern in the Philippines. This study was conducted to investigate the prevalence of macroplastics, composition of plastic litter, and the clean-coast index (CCI) of urban and rural beaches in Sarangani Bay. Plastic litter was collected by delineating a 100-m transecting line with three 4 m × 4 m quadrats. The density of macroplastic litter in urban areas (0.66 items m⁻²) was significantly higher than in rural areas (0.29 items m⁻²). The plastics sampled were predominantly food packaging, such as polyethylene bags, which are locally known as sando bags. The accumulation rate of macroplastic litter ranged from 0.07 items d⁻¹ m⁻² to 0.40 items d⁻¹ m⁻², in which urban beaches (0.25 items d⁻¹ m⁻²) have a significantly higher accumulation rate than rural beaches (0.11 items d⁻¹ m⁻²). Overall, the calculated CCI of the beaches of Sarangani Bay was categorized as clean to moderately clean for rural beaches and moderately clean to extremely dirty for urban beaches.

In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.

Iligan City has been the heart of major industries in steel, cement, and hydropower and is one of the highly urbanized coastal cities in Mindanao, Philippines. Due to its geological state, environmental challenges like flooding, natural disasters, and environmental degradation have been experienced by the city. With the climate crisis, these risks are being magnified. Recently, nature-based solutions (NBS) have gained significant attention worldwide, viewed as a key solution to the consequences of climate change. Yet implementation of strategies and policies regarding NBS, especially in developing countries, has received poor attention. Hence, this study aims to assess the awareness and perceptions of stakeholders in Iligan City regarding climate change, NBS, and willingness to engage in actions involving NBS. Stakeholder mapping and surveys were conducted through purposive sampling. A total of 187 respondents were interviewed from different sectors (LGUs, NGOs, the private sector, and academia) in Iligan City. Results revealed significant differences in awareness and perceptions across the distinct socio-economic backgrounds of stakeholders, where respondents with higher education and income have greater levels of awareness, perception, and willingness. The study recommends future targeted approaches and increased campaigns for climate change and sustainable solutions such as integrating NBS in adaptation strategies and mitigation plans to foster multi-level stakeholder collaboration.

The 2030 global agenda for sustainable development integrates social, economic, and environmental dimensions, emphasizing peace, human rights, gender equality, and women’s empowerment. The SDG framework, consisting of 17 goals, 169 targets (SDTs), and 231 indicators (SDIs), forms a complex, interconnected network that necessitates extensive research. Despite prior studies on SDG interlinkages, the integration of Climate Change (CC), Sustainable Forest Management (SFM), and Information and Communication Technology (ICT), collectively known as CSI Nexus, remains underexplored. This study addresses this gap by identifying SDTs aligned with CC, SFM, and ICT (CSI) and analyzing their linkages within the SDG framework using a systems thinking approach. The objectives are to (1) investigate and identify SDTs connected with the CSI Nexus and (2) assess the significant relationship between and among CC, SFM, and ICT. The primary method involves a simplified meta-analysis and systems thinking approach incorporating content analysis, network visualization, affiliation matrix mapping, frequency distributions, and Spearman’s rho correlation. Results reveal 56 SDTs directly connected within CC + SFM + ICT, 16 within CC + SFM, one within SFM + ICT, and 51 within ICT + CC. The analysis indicates CC is significantly associated with SFM, while ICT has no significant association with CC and SFM, asserting minimal influence of ICT and SFM on the SDG 2030 framework. This research provides significant insights for decision-makers and stakeholders, contributing as a science-informed guide for priority-setting, policy coherence, and decision-making supporting the 2030 Sustainable Development Goals across sectors.

The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability.