This study applied a qualitative approach to investigate the underlying influences on consumers’ green food consumption from the intention generation phase to intention execution phase in the perspectives of purchase intention and the intention–behaviour gap (IBG). Additionally, the impact of the “Coronavirus Disease 2019” (COVID-19) pandemic on consumers’ green food purchases was explored. Research data were derived from semi-structured in-depth interviews with 28 consumers and analyzed using grounded theory. The findings identified factors that influenced intentions and the IBG in the process of consumers’ green food purchases. Specifically, these findings reported that health consciousness, perceived attributes, environmental consciousness, social influence, family structure, and enjoyable shopping experiences were identified as major drivers for generating consumers’ green food purchase intentions. High prices of green food, unavailability issues, mistrust issues, and limited knowledge were factors triggering the gap between green food purchase intentions and behaviours. In addition, the results revealed that the COVID-19 crisis increased consumers’ green food purchase intentions, whereas the IBG widens as a result of issues of unavailability, price, and panic. These findings will help stakeholders build future policy and suitable strategies to better promote green food consumption in the Chinese context.

The rising sea levels due to climate change are a significant concern, particularly for vulnerable, low-lying coastal regions like the Gulf of Guinea (GoG). To effectively address this issue, it is crucial to gain a comprehensive understanding of historical sea level variability, and the influencing factors, and develop a reliable modeling system for future projections. This knowledge is essential for informed planning and mitigation strategies aimed at protecting coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020, covering three distinct periods (1993–2002, 2003–2012, and 2013–2020). It investigates the connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Furthermore, the study evaluates the performance of supervised machine learning techniques to optimize sea level modeling. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the northern region, with a total linear trend of 88 mm over the entire period. The highest decadal trend (38.7 mm) emerged during 2013–2020, with the most substantial percentage increment (100%) occurring in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. The evaluation of supervised learning modeling methods indicates that Random Forest Regression and Gradient Boosting Machines are the most accurate, reproducing interannual sea level patterns in the GoG with 97% and 96% accuracy. These models could be used to derive regional sea level projections via downscaling of climate models. These findings provide essential insights for effective coastal management and climate adaptation strategies in the GoG.

Since the launch of CFOSAT on 29 October 2018, more than three years of observational data of ocean wave spectra with a frequency range of 0.02–0.26 Hz and a horizontal resolution of 70–90 km have been obtained. This study compares wave spectra retrieved from 6°, 8°, and 10° incidence angle beams and their combination provided by CFOSAT with corresponding data from 98 buoys from the National Data Buoy Center (NDBC) in order to validate the remote sensing wave spectral accuracy from 1 January 2020 to 31 December 2022. The correlation coefficient of frequency spectra (Rs) between CFOSAT and buoys is used to represent the accuracy of the spectral form; the root mean square (RMS) of the significant wave height (SWH) is used to represent the accuracy of the total energy. The results indicate that CFOSAT can retrieve reliable wave frequency spectral forms with a high significant wave height (Rs > 0.8 when SWH > 3 m; RS < 0.4 when SWH < 1 m). The low-frequency noise in the swell part causes the main error, the RMS of the swell height is 0.4 m whereas the RMS of wind wave height is 0.24 m, and the mask filter used for spectral partitioned provided by CFOSAT can eliminate the low-frequency noise and improve the Rs of 10° beam wave spectra from 0.59 to 0.64. For the wind wave spectra, the correct spectra have been achieved and the mask filter cannot improve the accuracy. The wave spectra from the 10° beam without mask filtering provides the best estimation of total energy, the RMS of SWH is 0.23 m, after the mask filtering, the best estimation of spectral form can be achieved, the Rs is 0.64. The novelty of this study is that we found the strong correlation between SWH and Rs, where the scatter of SWH and Rs can be fitted as: Rs = 1 − exp(−0.89·SWH + 0.20); according to this approximate formula, we can estimate the reliability of wave spectra provided by CFOSAT according to the SWH in any region, which is important for wave spectral assimilation in the numerical model. The validation of wave direction indicates that the accuracy of wave spectra in the directional component is poor; further research is needed on the causes of directional errors. Generally, this study is not only an evaluation of the quality of the CFOSAT spectral data, but also an important reference for a series of research requiring the CFOSAT spectral data.