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For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

Micro-regeneration can effectively enhance a neighborhood’s commercial vitality and serve as a viable approach to boost economic benefits. However, the small scale of micro-regeneration efforts and the fragmented nature of information currently limit the availability of strong empirical evidence demonstrating its impact on neighborhood commercial vitality. The aim of the study was to examine the link between micro-regeneration and neighborhood commercial vitality, focusing on the average, time-lag, spatial spillover, and spatial heterogeneity effects.

Using the panel data set of 1,755 neighborhoods in Chongqing from 2016 to 2021 as the research sample, the difference-in-differences (DID) method was employed in this study to explore the impact micro-regeneration has on neighborhood commercial vitality.

The results illustrate that: (1) micro-regeneration can promote neighborhood commercial vitality in terms of the number and types of local consumption amenities by 27.76 and 5.89%, respectively, with no time-lag effect; (2) the positive spillovers can exist within the range of 5,000 meters–5,500 meters of regenerated neighborhoods; and (3) the effect of micro-regeneration on neighborhood commercial vitality can be greater in peripheral areas than in core areas of the city.

The findings fill the knowledge gap on the relationship between micro-regeneration and neighborhood commercial vitality. Additionally, the results on the time-lag effect, spatial spillover effects, and spatial heterogeneity provide practical implications that can support the government and private sector in developing temporal and spatial arrangements for micro-regeneration projects.

We investigate the interconnectedness between the financial sectors and new energy companies in China from the perspective of the multilayer network, and analyze the static and time-varying characteristics of the multilayer network at system and company levels, respectively.

We employ the multilayer network containing the realized volatility (RV here after) layer, the realized skewness (RS here after) layer and the realized kurtosis (RK here after) layer. The three realized indicators adopted to construct the multilayer network are generated by the intraday trading data from 2012 to 2022.

(1) Different layers have different characteristics, and can provide supplementary information. (2) Banks tend to play the role of risk transmitters on the whole, while the insurances and new energy companies tend to play the role of risk receivers on average. (3) The connectedness strength of financial sectors and new energy companies varies over time, and climbs sharply during the major crisis events. The roles of financial sectors and new energy companies may change from risk transmitters to risk receivers, and vice versa.

We adopt three realized indicators to construct the three-layer network, which provides a more comprehensive perspective for understanding the connectedness between the financial sectors and new energy companies in China.