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.

Forest carbon monitoring represents a cornerstone of global climate mitigation strategies, particularly under the accelerating push toward carbon neutrality. Recent advances in remote sensing technologies, geographic information systems (GIS), and artificial intelligence (AI) have transformed the capacity to quantify forest carbon dynamics across scales. This review synthesizes developments from 2020 to 2025, focusing on the integration of multi-source remote sensing data—including optical, SAR, and LiDAR—into scalable, GIS-based carbon monitoring systems. We systematically evaluate modeling frameworks ranging from empirical regression to machine learning, deep learning, and process-based ecological models, emphasizing their performance, scalability, and uncertainty management. Furthermore, we examine the operationalization of these technologies in national carbon accounting systems, REDD+ monitoring frameworks, and voluntary carbon markets. Case studies from Brazil and the Congo Basin illustrate diverse implementation pathways. Key challenges—including data standardization, model transferability, and high-resolution cost barriers—are critically assessed. Finally, future directions highlight the need for AI-augmented modeling, cloud-native workflows, and integrated sky-to-ground monitoring networks to enable real-time, policy-relevant carbon assessments. Together, these elements frame a next-generation forest carbon monitoring paradigm that is scientifically robust, operationally scalable, and aligned with global carbon neutrality goals.

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.

This investigation reports on the efficacy of a photoelectrocatalysis (PEC) system enhanced by a nitrogen-doped TiO₂ nanocrystal-modified TiO₂ nanotube array (N-TiO₂ NCs/TNTAs) anode paired with a graphene oxide/activated carbon (GO/AC) photocathode for diclofenac removal from effluent. The FE-SEM and EDX analyses validated the elemental composition of the anode—27.56% C, 30.81% N, 6.03% O, and 26.49% Ti. The XRD results confirmed the anatase phase and nitrogen integration, essential for photocatalytic activity enhancement. Quantum chemical simulations provided a comprehensive understanding of the red-shifted absorption bands in N-TiO₂, and UV-vis DRS demonstrated a red-shift in absorption to the visible spectrum, indicating improved light utilization. The PEC configuration achieved a photocurrent density of 9.8 mA/dm², significantly higher than the unmodified and solely nitrogen-doped counterparts at 4.8 mA/dm² and 6.1 mA/dm², respectively. Notably, this system reduced diclofenac concentrations by 58% within 75 min, outperforming standard photocatalytic setups. These findings underscore the potential of N-TiO₂ NCs/TNTAs-AC-GO/PTFE composite material for advanced environmental photoelectrocatalytic applications.