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Overweight and obesity are rapidly growing threats in China. Improvement in dietary knowledge can potentially prevent overweight and obesity, conditions which are receiving substantial attention from international organizations and governments. The purpose of this study was to investigate the impact of changes in dietary knowledge on adult overweight and obesity, using a balanced panel data consisting of 10,401 samples from the 2006, 2009, and 2011 iterations of the China Health and Nutrition Survey. Results indicate that overweight and obesity are becoming increasingly problematic in China, and the level of dietary knowledge among Chinese adults needs improvement. Moreover, the empirical results indicate that changes in dietary knowledge among adults has no significant influence on adult overweight and obesity, a likely result of lacking systematic dietary knowledge and having inadequate guidance on overweight/obesity-related behaviors.

Purpose -The purpose of this paper is to separate households into several types based on their features, and then to further investigate determinants of household fish consumption in China by figuring out consumption preference divergences between types of households under the effects of economic and socio-demographics factors. Design/methodology/approach -This paper first applies Multiple Correspondence Analysis to separate the modalities of variables and households according to their features, with health knowledge and time constraint of a spouse highlighted. Then, the transcribed principal information of both variables and households has been added into Marshallian demand function with fish price, income, child effect, and health status for identification of factors on household fish demand. The robust fixed effect and robust random effect GLS regression has been conducted.

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Background Insufficient nutrition intake has negatively influenced the health of the elderly in rural China where the problem of population aging is serious. The present study aims to explore whether the medical system, called the New Rural Cooperative Medical System (NRCMS), can improve the rural elderly’s nutrition intake and the mechanism behind it. Methods The difference in differences (DID) model and the propensity score matching-difference in differences (PSM-DID) model are both performed to investigate the impact of the medical system on nutrition improvement for the rural elderly. Two thousand seven hundred eighty rural elderly samples tracked in 2000 and 2006 from the China Health and Nutrition Survey are analyzed. Indices for the elderly’s nutrition intake includes daily average intake of energy, fat, protein, and carbohydrate. Results The results show that participation in the NRCMS can significantly increase the rural elderly’s total energy intake, carbohydrate intake, and protein intake by 206.688 kcal, 36.379 g, and 6.979 g, respectively. A more significant impact of the NRCMS on nutrition intake is observed in the central and near-western where economic development is lagging behind. Also, compared to people of 18–60 age group, such impact is statistically more significant in the elderly for the carbohydrate intake. Conclusions The NRCMS can improve the rural elderly’s nutrition intake in China. As the population ages rapidly in rural China, the present study provides recommendations on how to improve nutrition and health status of the elderly from the aspect of the medical system.

This article examines the impact of the stability of the management rights of transferred land (TLMR) on the adoption of technologies aiming to reduce the use of chemical fertilizers (ARFTs) based on the survey data of large-scale grain growing households in Anhui, China. Using the IV-Probit model, the present paper defines the stability of TLMR and the results estimated by IV-Probit model shows that a one-year extension of land lease period can increase the probability of using organic fertilizer and soil-testing formula fertilizer by 3.16% and 4.92%, respectively, while contract breaching in the lease period can reduce the probability of using organic fertilizer and soil-testing formula fertilizer by 46.9% and 51.38%, respectively. However, the land-lease period and land transfer contract breaching in the lease period have no significant effect on the use of farmyard manure by large-scale grain growing households. The main conclusion is that improving the stability of TLMR is conducive to prompting large-scale grain growing households to adopt ARFTs, especially the adoption of organic fertilizer and soil-testing formula fertilizer. The government should improve the stability of TLMR by standardizing the form and content associated with land transfer contracts and setting the minimum land-lease term.

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PurposeDevelopment of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.Design/methodology/approachThe study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.FindingsThe study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.Practical implicationsThe machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.Originality/valueThis is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.