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In view of a lack of evidence on the effectiveness of climate change education (CCE) in China, this study aims to evaluate if a CCE course newly designed based on research recommendations and implemented with established pedagogy was effective in changing the beliefs and attitudes of first-year science students.

This study took a simple longitudinal approach with surveys administered at the beginning and the end of the course and the differences in the responses analyzed with nonparametric statistical analyses.

The results showed that the course produced significant changes in the beliefs concerning the anthropogenic causes of climate change and vulnerability to its impacts, which results in inequality of the impacts received. However, the course did not produce significant attitudinal changes among the students. Spearman’s correlation, which affirmed the belief–attitude association, revealed that the students already had desirable attitudes toward climate change and these attitudes had not been reinforced. The students’ perception of the importance of local government in climate action increased by the end of the course. Multimedia-aid learning, debates and discussions were useful in conveying the concepts of responsibility, ethics and vulnerability, but the addition of student-led community projects will increase personal significance of the course.

This study is instrumental for the development of a regional model of CCE in the mainstreaming of education for sustainable development in China, knowing that the regional approach is crucial to address the nuances in climate change knowledge, hence conceptions and beliefs across regions and, even, between different sectors of a region. This regional experience could also serve as a reference for other similar settings, particularly those of the developing countries.

This study presents one of the very few studies dedicated to gauging the effects of CCE in China, particularly of a newly developed climate change course, on the beliefs and attitudes of students. This permits pedagogical development and continuous improvement of CCE in China.

This study aims to analyze the perceptions of smallholder farmers on climate change and events and further explores climate change adaptation strategies and associated challenges. The findings provide useful information for enhancing the adaptive capacity of smallholder farmers to adjust to climate-related hazards and improve their resilience and disaster preparedness in northern Ghana.

This study uses a multistage sampling procedure and sample size of 150 farmers, the Binary Probit Model (BPM), to identify and examine the determinants of climate change adaptation strategies adopted by smallholder farmers. Also, the constraints of adaptation were analyzed using Kendall’s coefficient of concordance.

The results from the BPM and statistics of Kendall’s coefficient revealed that the farm risk level, ability to adapt, farmer’s income, age, farming experience, climate change awareness and extension visits were factors that significantly influenced the adaptation strategies of smallholder farmers (in order of importance). The majority (60%) of the farmers ranked farm risk level as the major constraint to adopting climate change strategies.

The findings of this study enhance understanding on access to relevant and timely climate change adaptation information such as an early warning to farmers during the start of the farming/rainy season to support their adaptive responses to climate change.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.