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Development 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.

The 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.

The 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.

The 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.

This 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.

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.

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.

The paper provides empirical insights about what and how factors affect household fish consumption. It suggests that, for all households, pork is still a main substitution of fish, fish consumption regarding to each household should be constant, and fish consumption differs a lot between provinces. For households with higher dietary knowledge, the authors found that increase of income, the existence of adolescent would cause an increase in fish consumption, while illness of household member makes a decrease in fish consumption. For households with working women who have higher opportunity cost of time pursue much more convenience, then consume less fish at home than their counterparts.

The increasing variety in consumer’s dietary need makes the understanding of which becoming much more difficult than before. This paper uses three-wave panel data with households spread over nine provinces in China, but the results still has its limitation since china is the one with vast in territory and residents. In the future, the difference between urban and rural area in fish consumption need further research.

The paper reveals the common determinants of fish consumption in China, and makes a further clear answer by a further discussion on different household types. The results have rather high implications for making targeted policy or precisely forecasting a future fish demand in China, which will rather be helpful for fishery industry development in China.

This paper fulfills an identified need to study the divergence of determinants or the impact degree of different factors on fish consumption in China by household types. An increasing trend of food away from home has significant effect on how to count household size in food consumption studies, and the identification of persday in this study shows its advantages in dealing with this issue, which makes a contribution on resolve the overestimation of household size issue.

In recent years, fresh produce (fresh vegetables and fruit) has been circulated widely via e-commerce in Chinese large cities in the form of fresh produce portfolios (FPPs). The purpose of this paper is to analyze the preferences of Chinese consumers for specific FPP attributes.

A choice experiment approach was used to explore consumer preferences. The authors conducted a means-end-chains evaluation to select the attributes for the choice experiment. The authors used a fractional factorial design and finally obtained 18 choice scenarios. The authors collected 166 effective consumer questionnaires in Beijing.

The authors found that among the four attributes considered, certification and the diversity of the FPP had significant effects on the willingness to pay (WTP) among consumers. Residents had heterogeneous preferences for FPP diversity and certification, but certification was the major concern when considering fresh produce in the FPP. With regard to the WTP for attributes in the portfolio, the WTP values for “green” and “organic” attributes were high, but the WTP for the diversity of FPPs was low.

This study is the first attempt to explore the preferences of Chinese consumers regarding the attributes of FPP in an e-commerce environment.