This review presents a comprehensive overview of recent advances in TiO₂-based photoelectrocatalysis (PEC), with an emphasis on material design strategies to enhance visible-light responsiveness and charge carrier dynamics. Key approaches—including elemental doping, defect engineering, heterojunction construction, and plasmonic enhancement—are systematically discussed in relation to their roles in modulating energy band structures and promoting charge separation. Beyond fundamental mechanisms, the review highlights the broad environmental and energy-related applications of TiO₂-driven PEC systems, encompassing the degradation of persistent organic pollutants, microbial disinfection, heavy metal removal, photoelectrochemical water splitting for hydrogen production, and CO₂ reduction. Recent progress in integrating PEC systems with energy harvesting modules to construct self-powered platforms is critically examined. Current limitations and future directions are also outlined to guide the rational development of next-generation TiO₂-based photoelectrocatalytic systems for sustainable environmental remediation and solar fuel conversion.

Forests play a pivotal role in the global carbon cycle, making accurate estimation of forest carbon stocks essential for climate change mitigation efforts. However, the diverse methods available for assessing forest carbon yield varying results and have different limitations. This study provides a comprehensive review of current methods for estimating forest carbon stocks, including field-based measurements, remote sensing techniques, and integrated approaches. We systematically collected and analyzed recent studies (2010–2025) on forest carbon estimation across various ecosystems. Our review indicates that field-based methods, such as forest inventories and allometric equations, offer high accuracy at local scales but are labor-intensive. Remote sensing methods (e.g., LiDAR and satellite imagery) enable large-scale carbon assessment with moderate accuracy and efficiency. Integrated approaches that combine ground measurements with remote sensing data can improve accuracy while expanding spatial coverage. We discuss the strengths and weaknesses of each method category in terms of accuracy, cost, and scalability. Based on the synthesis of findings, we recommend a balanced approach that leverages both ground and remote sensing techniques for reliable forest carbon monitoring. This review also identifies knowledge gaps and suggests directions for future research to enhance the precision and applicability of forest carbon estimation methods.