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The development of valid and reliable health questionnaires relies on respondents' accurate comprehension of items. The thinking-aloud (TA) technique, where participants verbalize their thought processes while answering survey questions, offers a powerful means to explore cognitive mechanisms underlying response behaviour. Despite its wide use, there is no consensus on how TA protocols should be designed, conducted, and analysed. This scoping review aimed to map methodological approaches applied to TA in health-related questionnaire development and validation, describe their variability, and critically examine current methodological practices to inform future research. PubMed, Scopus, and Embase were searched on July 17, 2025, without time limits. Eligible studies were original articles in English applying TA for questionnaire validation, including assessment of content, face, construct validity, or comprehensibility. Two reviewers independently screened records and extracted data using a standardized charting form. An operational criterion was used to judge the completeness of methodological reporting. Data were synthesized narratively and tabulated to describe study purposes, TA typologies, analytical strategies, and theoretical frameworks. From 1,678 retrieved records, 210 studies met inclusion criteria, of which 84 provided complete methodological descriptions. Most of the 84 studies involved patients (48%) and used concurrent TA (48%), with frequent prompting (55%) and probing (48%). Sessions were mainly face-to-face (71%), audio recorded (64%), and transcribed verbatim. Thematic (38%) and content (26%) analyses predominated, often supported by NVivo. The Survey Response Model was the most cited theoretical framework (26%), though 22% of studies lacked any theoretical reference. Coding strategies were mixed (36%), and about half of the studies reported questionnaire revisions after analysis. TA remains a versatile but methodologically fragmented approach in questionnaire validation. The lack of standardization limits comparability and interpretability across studies. Greater transparency in reporting and explicit theoretical framing are recommended to improve methodological rigor. The review followed the PRISMA-ScR framework and was prospectively registered on OSF (DOI: https://doi.org/10.17605/OSF.IO/AZY3).

The main aim of this article is to significantly expand the frame model in order to analyze scientific theories by means of what I call theory frame modules (TFMs). I will illustrate the notion of TFMs through three scientific theories (Aim 1), each employing different scales of measurement that will be analyzed by corresponding TFMs (Aim 2). To this end, I will propose a frame-based definition of theoretical concepts (Aim 3). In the next step, I will focus on intertheoretical relations between TFMs and provide frame-based definitions of the relations of specialization and theoretization (Aim 4). Frames offer a means of reconstructing recursive structures. To provide a more differentiated perspective on recursion within frames, I will distinguish between recursion in the narrow sense and recursion in the broad sense (Aim 5). Finally, I will propose a frame-based distinction between an operationalist and a unificationist approach to scientific concepts, the latter of which allows for the introduction of theoretical concepts that designate a common cause of different empirical phenomena (Aim 6).

This study explored the relationship between Artificial Intelligence (AI) literacy and decision-making quality, with employee-AI collaboration serving as the mediating variable, while also examining the moderating role of AI trust in this process. The research is based on the conservation of resources theory. A questionnaire survey was conducted among 435 employees of high-end manufacturing enterprises in the Yangtze River Delta and Pearl River Delta regions of China. Hierarchical regression analysis was employed to test the theoretical model. The research results indicate that AI literacy was positively related to decision-making quality. Employee-AI collaboration played a mediating role in the relationship between AI literacy and decision-making quality. Further analysis of the moderating effects also indicates that different modes of AI trust have different moderating roles. The relationship between employee-AI collaboration and decision-making quality was weaker for employees with high initial trust; however, the relationship between employee-AI collaboration and decision-making quality was stronger for employees with high post-task trust. This study provides a new theoretical perspective for understanding the relationship between AI literacy and decision-making quality, and offers empirical evidence for optimizing the design and management practices in human-AI collaborative systems.

Dental materials science, a foundational discipline in stomatology, faces persistent challenges in traditional pedagogy: voluminous content, outdated textbooks, and a disconnect between theoretical knowledge and clinical application. Students often memorize material parameters passively without developing the capacity to translate this knowledge into real-world clinical decision-making. Blended learning platforms such as Moodle offer a promising avenue to bridge this gap through clinically oriented supplementary instruction. This study aimed to construct and evaluate a Moodle-based, clinically oriented online supplementary course in dental materials, and to examine its differential effects on students at different educational stages. A prospective quasi-experimental design with pre- and post-test comparisons was employed. A total of 60 undergraduate dental students were enrolled, comprising 30 fourth-year students (pre-clinical, G4) and 30 fifth-year students (clinical internship, G5). All participants completed a 4-week Moodle-based online supplementary course featuring H5P interactive videos, virtual case decision-making modules, and structured peer-review exercises. Learning outcomes were assessed via a self-developed Clinical Case Test (CCT) scored independently by two clinical teachers (ICC > 0.8), a modified Dundee Ready Education Environment Measure (DREEM) inventory (Cronbach's α = 0.947), and a course satisfaction questionnaire (Cronbach's α = 0.927). Statistical analysis was performed using paired and independent samples t-tests with α = 0.05. Both groups perceived the educational environment positively (overall DREEM score rate 82.35%). After the intervention, G4 students demonstrated a significant improvement in case analysis scores (24.30 ± 4.34 to 28.33 ± 6.76, P = 0.0083), while total CCT scores showed marginal significance (70.83 ± 7.79 to 75.73 ± 11.46, P = 0.0583). G5 students showed no significant change in total scores (75.77 ± 8.17 to 77.53 ± 9.86, P = 0.4532) but significant improvement in case analysis (29.57 ± 4.80 to 33.87 ± 4.97, P = 0.0012). Post-intervention, G4 CCT scores were statistically indistinguishable from G5 baseline scores (P = 0.5170), indicating that the intervention elevated pre-clinical students to near-internship levels. DREEM subscale comparisons revealed G4 scored significantly higher than G5 in Students' Perception of Learning (SPL: 47.40 ± 6.66 vs. 43.30 ± 9.00, P = 0.050) and Social Self-Perception (SSP: 13.03 ± 1.99 vs. 11.80 ± 2.55, P = 0.042). Course satisfaction was high overall (G4: 87.77 ± 11.74; G5: 83.20 ± 13.33), with 71.67% of students affirming that blended teaching most helped with "knowledge integration and clinical transformation." Moodle-based clinically oriented blended teaching enhances dental students' clinical case analysis competence, particularly for students at the pre-clinical stage. The platform effectively bridges the gap between theoretical knowledge and clinical application, positioning it as a valuable supplementary tool for clinical pre-education. Platform design should be dynamically adapted to students' clinical experience levels.

Simultaneous trajectory tracking and obstacle avoidance are critical capabilities for unmanned underwater vehicles (UUVs). However, implementing these tasks on digital processors inevitably introduces discretization errors and time delays. Most existing continuous-time methods suffer from significant performance degradation when directly applied to discrete-time systems. Therefore, achieving high-precision control in the discrete-time domain remains a considerable challenge. To address this issue, a robust discrete-time dual-loop control architecture is proposed in this paper. First, a discrete-time distributed model predictive control (DMPC) scheme serves as the outer-loop kinematic controller, solving an online optimization problem to generate collision-free velocity commands. To guarantee closed-loop stability, discrete LQR-based terminal constraints are strictly incorporated. Furthermore, slack variables are introduced to dynamically relax these constraints during obstacle evasion maneuvers. This strategy effectively resolves the inherent conflict between theoretical stability and recursive feasibility in cluttered environments. Second, a discrete-time sliding mode controller (DSMC) is developed for the inner dynamic loop, where a specialized reaching law is designed to strictly confine state fluctuations within a minimal quasi-sliding mode band. In addition, a discrete disturbance observer (DDOB) is introduced to estimate and compensate for the effects of time-varying ocean current disturbances. Extensive simulation results demonstrate that the proposed framework achieves superior tracking accuracy, high computational efficiency, and reliable obstacle avoidance. Furthermore, rigorous theoretical analysis ensures global system stability under discrete-time implementation constraints.

Type 1 diabetes poses substantial self-management challenges for children. Gamified interventions are a promising strategy to support this population, yet evidence on their design, implementation, and reported outcomes remains scattered. This scoping review aims to systematically map the international evidence on gamified interventions for children with T1DM, focusing on their characteristics, delivery, and outcomes. Following the Arksey and O'Malley framework, this scoping review systematically searched six databases (PubMed, CINAHL, Embase, Scopus, Cochrane Library, Web of Science) from January 1, 2010, to January 25, 2026. We also examined reference lists and performed citation tracking. Twenty-three of 762 retrieved articles were included. Interventions were primarily delivered via mobile applications (52%) and websites (17%). The most common gamification elements were goal setting, challenges, and fun (each 96%); social features were less frequent (35%). Most studies (70%) lacked an explicit theoretical framework, and intervention durations varied widely. The interventions demonstrated benefits for glycemic control, self-management, knowledge, and psychological distress, but inconsistent effects on quality of life. They were generally feasible, usable, and acceptable. Gamified interventions represent a promising approach to T1DM management in children, aligning well with their developmental needs. However, current studies often lack a theoretical foundation and evidence of sustained benefits. Future work should prioritize theory-driven design and rigorous long-term evaluation. This review protocol is registered on the Open Science Framework (OSF) and accessible via the following link: https://doi.org/10.17605/OSF.IO/MN6AE.

Although health messaging by social media influencers (SMIs) may have positive effects such as promoting awareness and decreasing stigma, concerns have been raised that influencer narratives may lack comprehensive or balanced information and may evidence a disconnect with accepted clinical guidelines, especially when it comes to mental health. The purpose of this scoping review was to describe the state of research on both content of and audience response to SMI mental health communication. A total of 2,746 records were retrieved from searches, among which 23 studies met the inclusion criteria. Studies have investigated a range of influencer types, mental illnesses, and social media platforms. Research on this topic is nascent, with the majority of studies having been published in 2024 or later. Across methodologies, the focus was on mapping the existing situation, analyzing SMI personal narratives of mental illness, identifying dialectical tensions for both SMI content creators and followers, and describing ethical dilemmas of SMIs. Few studies were theoretically based, and few of the concerns raised in the previous literature regarding either problematic content or negative behavioral impacts of SMI mental health messaging were empirically investigated. Thoughtfully designed longitudinal designs of key issues of concern articulated in the previous literature, as well as testing and development of appropriate theories, are needed going forward.

Chimeric antigen receptor T-cell (CAR-T) therapy represents a major advance in cellular immunotherapy and has demonstrated substantial clinical benefit in relapsed or refractory B-cell malignancies. However, autologous CAR-T therapy remains constrained by manufacturing complexity, high cost, variability in product quality, and treatment delays that may compromise outcomes in rapidly progressing disease. Allogeneic "off-the-shelf" CAR-T cell approaches have emerged as a potential strategy to address these limitations by enabling standardized manufacturing, rapid availability, and scalable production. Nevertheless, these theoretical advantages must be carefully balanced against significant challenges, including alloreactivity, immune rejection, complex genome engineering requirements, and regulatory constraints. This review provides a critical and balanced overview of the advantages and limitations of allogeneic CAR-T cell therapy, with a particular focus on applications in solid tumors. We discuss key biological barriers, including tumor microenvironment-mediated immunosuppression, and evaluate current engineering strategies aimed at enhancing efficacy, along with emerging clinical data. Collectively, while allogeneic CAR-T therapies hold considerable promise, substantial scientific, technical, and regulatory challenges must be addressed before their widespread clinical implementation.

Starbursts are the light-intensity patterns seen when small bright sources are observed at low illumination levels, typically stars at night. Starburst patterns are formed because the eye's wave aberrations generate caustics at the retina. However, a fascinating yet unexplained fact about starbursts is that they usually exhibit p-fold symmetry. Moreover, the number of peaks, related to the symmetry perceived by the subject, is not always the same. The main aim of this study is to explain these visual optics phenomena. For this purpose, we provide a theoretical framework based on the geometric and algebraic properties of the wave aberration function expressed as a Zernike polynomial expansion. Specifically, we investigated the number and distribution of the fertile cusps of Gauss of the wave aberration function. We also established the connections between these points with the symmetries and the number of starburst peaks. We found that starbursts are likely generated by wave aberrations dominated by axially symmetric polynomials combined with a certain amount of non-axially symmetric ones. For instance, whereas a wave aberration with a dominant spherical aberration (Zernike polynomial [Formula: see text]) plus [Formula: see text] may induce a 3-peaks starburst with a 3-fold symmetry, a wave aberration combining [Formula: see text] and [Formula: see text] may induce a 4-fold symmetry starburst with four or eight peaks. In addition to providing a comprehensive explanation of starburst symmetries, our theory has other promising applications; for instance, we could infer some basic properties of an eye's wave aberration function from a measurement (subjective or objective) of the starburst pattern.

Systemic inflammation is increasingly recognised as a key modulator of brain function, yet its impact on large-scale brain network topology remains incompletely understood. In particular, it is unclear whether inflammation alters the balance between functional segregation and integration, as captured by small-world organisation, and whether such effects are better detected using dynamic rather than static connectivity analyses. In this study, we conducted a secondary analysis of a previously collected dataset to examine the effects of experimentally induced inflammation on static and dynamic small-world topology using resting-state fMRI and graph-theoretical methods. Eighteen healthy male participants completed a double-blind, placebo-controlled, randomised crossover trial involving typhoid vaccination, a well-established model of low-grade systemic inflammation. Small-worldness was quantified across a fixed density range for both static and dynamic functional connectivity. No significant differences were observed in static small-worldness between conditions. In contrast, dynamic analyses revealed a significant reduction in mean small-worldness following vaccination. This effect was primarily driven by reduced clustering coefficient, with no change in characteristic path length, indicating a selective alteration in local segregation while preserving global integration. Dynamic state analysis identified two recurring connectivity states with distinct topological profiles. Although differences in fractional occupancy and dwell time were not statistically significant, participants showed a consistent tendency to spend less time in the high small-worldness state following inflammation. These preliminary findings indicate that inflammation modulates the temporal organisation of brain networks in ways not detectable using static approaches and are consistent with a metabolically efficient reconfiguration of network topology under inflammatory challenge.

Curved molecular carbons have attracted considerable interest owing to their distinctive properties relative to planar analogues and their structural relevance to diverse nonplanar carbon allotropes. Aromatic ribbons with continuously and directionally curved π-surfaces, predicted as key building blocks for circular carbon architectures, remain challenging synthetic targets, with only a few examples reported to date. Herein, we report the synthesis and properties of arch-shaped nitrogen-bordered nanoribbons featuring rylene backbones fused with five-membered rings at the armchair edges, achieved through a programmable curved π-extension strategy. X-ray crystallography reveals well-defined arched topologies and continuously curved π-surfaces, while variable-temperature proton nuclear magnetic resonance experiments and theoretical calculations demonstrate high arch-to-arch inversion barriers. The resulting quaterrylene arch exhibits an enhanced fluorescence quantum yield in solution and forms host-guest complexes with fullerene C60 in both solution and the solid state. This work provides a foundation for the synthesis of long-sought-after carbon nanobelts.

Salinity fluctuations represent a major environmental stressor for freshwater bivalves; however, the regulatory interplay among osmoregulation, energy metabolism, and immune responses in Hyriopsis cumingii remains poorly understood. In this study, physiological and biochemical assays, histopathological examinations, and transcriptomic analyses were integrated to comprehensively characterize the responses of H. cumingii to increasing salinity stress. Elevated salinity significantly disrupted osmotic homeostasis, as evidenced by increased hemolymph osmolality, tissue dehydration, enhanced Na+/K+-ATPase activity, and the accumulation of alanine and glutamate. Salinity stress also induced oxidative stress, as indicated by increased catalase activity and salinity-dependent alterations in superoxide dismutase activity, accompanied by progressive histological damage to the gills and hepatopancreas. Transcriptomic analyses further revealed extensive molecular reprogramming in response to salinity stress, including the enrichment of pathways associated with energy metabolism, amino acid and lipid utilization, and innate immune regulation. Notably, immune-related signaling pathways, including MAPK and NF-κB, were significantly activated, suggesting that osmotic imbalance and metabolic adjustments were closely linked to inflammatory and stress-response signaling. Collectively, these findings indicate that the response of H. cumingii to elevated salinity involves short-term compensatory mechanisms, including osmotic adjustment, metabolic remodeling, antioxidant defense, and immune activation. However, the concurrent occurrence of tissue injury and stress-associated immune signaling suggests that these responses may not constitute stable adaptation to high-salinity conditions. This study provides species-specific insights into the integrated physiological and transcriptional mechanisms underlying salinity stress responses in freshwater pearl mussels and offers a theoretical foundation for stress assessment and aquaculture management.

To investigate the immune functions and evolutionary features of complement genes in Mastacembelus armatus, three complement genes (MaC4B, MaC6 and MaC7) were cloned and characterized for the first time, and their expression profiles upon Aeromonas veronii infection as well as their positively selected sites were analyzed. The full-length coding sequences (CDSs) of MaC4B, MaC6 and MaC7 were successfully obtained. The encoded proteins consist of 1708, 936 and 840 amino acids, respectively, and possess conserved protein domains identical to those of other teleost fish. Tissue expression analysis revealed that the three genes were constitutively expressed in nine tested tissues, with relatively high transcript levels in the liver, muscle, fins and gills. Upon challenge with A. veronii, the three genes displayed distinct tissue-specific expression patterns. Their transcript levels in the spleen and kidney decreased initially and then increased, whereas expression in the liver was persistently downregulated. Phylogenetic analysis showed that M. armatus clustered closely with other Perciformes species. Positive selection analysis demonstrated that the three genes were mainly subjected to purifying selection at the whole-gene level, while several sites were identified to be under significant positive selection. These findings indicated that MaC4B, MaC6 and MaC7 participate in the antibacterial immune response of M. armatus and have undergone adaptive evolution under pathogen stress. This study provides a theoretical basis for exploring the immune defense mechanisms of the complement system in teleosts.

Death anxiety is a multidimensional construct encompassing emotional, cognitive, and behavioral responses to mortality. In societies like Türkiye, where cultural and spiritual values significantly shape the perception of death, valid measurement tools are essential for understanding how individuals experience these concerns. The Death Anxiety Beliefs and Behaviors Scale (DABBS) is a comprehensive instrument capturing these dimensions; however, no validated Turkish version has been available. This study aimed to adapt the DABBS into Turkish and evaluate its structural validity and reliability among adults in Türkiye. A methodological and cross-sectional study design was employed with data collected from 195 adults across different regions of Türkiye. Participants completed an online survey including a Descriptive Form and the 18-item Turkish version of the scale (DABBS-T). Translation and cultural adaptation followed international guidelines. Construct validity was assessed through confirmatory factor analysis (CFA), and reliability was evaluated using Cronbach's alpha and test-retest stability. CFA supported the original three-factor structure of the scale-emotion, belief, and behavior-with factor loadings ranging from 0.530 to 0.940. The model demonstrated acceptable fit indices (χ2/df = 2.00, RMSEA = 0.072, CFI = 0.97, TLI = 0.97). Internal consistency was high across all subscales (Cronbach's alpha = 0.849-0.932). Test-retest reliability coefficients ranged from 0.903 to 0.947, indicating strong temporal stability. No item removal or structural modification was required, suggesting that the Turkish version retained the theoretical integrity of the original instrument. The DABBS-T is a valid and reliable tool for assessing death-related beliefs and behaviors among Turkish adults. Given the scale's strong psychometric performance, it is highly suitable for research and clinical settings, particularly in exploring the intersection of spiritual beliefs and psychological health. It facilitates cross-cultural comparisons and contributes to a broader understanding of death anxiety within diverse cultural frameworks.

Social anxiety (SA) is characterised by heightened vigilance to socially evaluative threat, yet the extent to which this vigilance operates outside conscious awareness remains understudied. This review synthesises evidence from 11 studies investigating threat processing in individuals with SA or elevated SA traits, using masking and suppression paradigms to manipulate visual awareness. Across paradigms, samples, and contexts, findings on threat processing under constrained awareness in SA do not converge on a consistent effect, varying instead with paradigm-specific features, awareness level, stimulus type, and task context. Conceptual and methodological heterogeneity, particularly in the definition and assessment of awareness, limits cross-study integration. SA-related effects can be better characterised as reflecting preconscious vigilance to socially evaluative cues rather than unconscious threat processing. Masking paradigms most consistently reveal early vigilance under restricted awareness, whereas interocular suppression paradigms capture later attentional dynamics (avoidance or regulatory) as stimuli compete for conscious access. These divergences highlight the need for clearer theoretical distinctions between levels of awareness, standardised awareness checks, and attention to socially evaluative context. A refined framework conceptualising SA-related threat processing along a continuum, from preconscious vigilance to conscious avoidance, may better guide future research and improve interpretability.

The study aimed to develop a content-valid conceptual model to capture patients' perceptions of therapy goal achievement within interdisciplinary multimodal pain treatment (IMPT) programs. This model serves as the theoretical foundation for a new patient-reported outcome measure (PROM) designed to evaluate IMPT effectiveness from the patient perspective. Concept elicitation followed the concept mapping approach, combining qualitative and quantitative techniques. 61 patients with primary chronic pain participated in focus groups and online surveys. Patient-generated treatment goals were collected, categorized, analyzed by the multidimensional scaling method and a subsequent hierarchical cluster analysis. Finally, the data were validated by an interdisciplinary expert panel to ensure conceptual accuracy and relevance for the conceptual model. 210 therapy goals were collected. 127 were retained for the final model after a reduction process. Fifteen coherent clusters emerged from analysis, representing three overarching domains: functional restoration, therapy-related competence development, and psychological growth. This multidimensional structure reflects the biopsychosocial complexity of chronic pain and emphasizes that successful therapy is perceived as enhanced autonomy, regained participation, and self-empowerment rather than mere pain reduction. The developed conceptual model provides a content-valid, patient-centered basis for assessing treatment goal achievement in IMPT. By integrating functional, cognitive, and emotional dimensions, it enables a differentiated assessment of patient-relevant treatment outcomes. Future work should focus on developing a conceptual framework and item generation, cognitive debriefing and validation, psychometric evaluation in larger and more diverse samples ensuring reliability, validity, and applicability of the final PROM across clinical and research settings.

The long-term ecological impact of total phosphorus (TP) in fluvial systems is well-established, prompting its inclusion in water quality regulations worldwide. Due to the strong affinity between sediment and phosphorus, certain regulatory protocols recommend pretreating water samples using methods such as shaking or clarification. Recent studies have noted discrepancies between these methods, but their comparisons were experimental and did not consider sediment conditions altered by dam construction. This study aims to investigate how the shaking and clarification methods differ under varying sediment conditions associated with inflow-outflow scenarios. Using the globally representative, dam-regulated Yellow and Yangtze rivers as case studies, we conducted comparative analyses of TP determined by both methods, based on consecutive field measurements and long-term hydrological data. The shaking method indicated substantial reductions in TP (up to 63.75%) from inflow to outflow sites. In contrast, the clarification method yielded much smaller reductions, and during flood seasons it even showed an increase (up to 35.89%). These discrepancies in methodology may lead to substantial underestimation of reservoir TP retention from the perspective of reservoir-induced sediment regime shifts. By integrating theoretical analysis with field data, we demonstrate that this discrepancy is inevitable: the clarification method significantly underestimates TP under inflow condition characterized by coarse sediment or high sediment concentration (S), whereas this underestimation becomes negligible under outflow condition featuring fine sediment or low S. In order to keep a consistent measurement in TP under varying sediment conditions and thereby evaluate the TP retention effect of dams more objectively, we recommend adopting the shaking method for complete TP quantification. This work provides fundamental insights into TP measurement that are crucial for managing river ecosystems.

This study proposes an integrated multi-model coupling framework for ex-ante carbon assessment in new urban district planning. The framework combines investment allocation, land-use carbon estimation, and traffic emission models within a unified analytical environment to simulate spatial and economic feedbacks that drive urban carbon emissions. Compared with conventional carbon assessment approaches, which often evaluate land use and transport as independent modules, the proposed framework introduces a rule-based coupling mechanism that dynamically links investment intensity, spatial configuration, and carbon output through iterative feedback. Each sub-model exchanges parameters via a shared data layer, enabling recursive interactions among economic input, land-use change, and mobility patterns. The framework advances urban carbon modeling through three key innovations. First, it establishes an investment-carbon elasticity mechanism that quantifies how capital concentration influences spatial emission patterns. Second, it formulates a rule-based symbolic coupling algorithm that enables cross-model parameter updating during iteration. Third, it develops a feedback-controlled carbon evaluation process that transforms traditional PSS from descriptive visualization tools into predictive decision-support frameworks. Applied to a representative new urban district, the framework demonstrates effectiveness in identifying low-carbon planning strategies under varying investment-intensity scenarios. By integrating economic, spatial, and environmental dimensions into a unified analytical logic, this research provides a scalable foundation for quantitative decision-making in sustainable urban transitions and contributes a theoretical model applicable to broader regional and national carbon-neutral planning frameworks.