Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

With the intensification of the aging population trend, China is facing an increasingly growing demand for older adult care services. As an important field that meets the needs of the older adult, the development of the older adult care service industry is of significant importance for social stability and the well-being of the older adult. This paper examines the trends and optimization paths of the older adult care service industry in China. It aims to analyze the current situation, problems, and causes of the industry, and propose corresponding policy adjustment recommendations. Through comparative analysis of scholars’ viewpoints, the paper redefines the connotation and scope of the older adult care service industry, emphasizing the characteristics of its compound industrial system. The analysis reveals that the current Chinese older adult care service industry is characterized by a small scale, single functionality, narrow coverage, short industrial chain, and a lack of policy support and rational resource allocation. Policy adjustment recommendations are proposed, including top-level institutional design, improvement of the social security system, and the formation of a comprehensive industrial system, in order to promote the development of the older adult care service industry. These recommendations not only promote the expansion of industry scale and the expansion of functionality, but also enhance the quality and effectiveness of older adult care services to meet the diverse needs of the older adult. The value of this paper lies in its in-depth analysis of the current situation of the older adult care service industry in China and the proposal of specific and feasible policy adjustment recommendations, providing important guidance for government departments and practitioners. The research findings can provide beneficial references for the sustainable development of the older adult care service industry, further promoting the progress of the social economy and the healthy development of an aging society.

This paper aims to segment online consumers based on their attitude toward self-interest and ethical attitudes and explore the impact of these attitudes on the purchasing behavior of agricultural products online in China. The study was conducted using 633 online survey responses from consumers who have purchased agricultural products online in China. First, to validate the relationship between attitude and behavior by structural equation modeling. Next, the number of segments was determined using K-means. Finally, Pearson Chi-square difference tests were performed to analyze demographic and behavioral variables and identify each segment’s characteristics. The results of this study provide a segmentation analysis of the online market for agricultural products in China. The four segments identified are pure ethical consumers, information communicators, brand-quality pursuers, and well-heeled shoppers. Additionally, this study reveals the characteristics of each segment based on demographic and behavioral variables. This study provides a novel approach to segmenting Chinese consumers who purchase agricultural products online based on their attitudes toward self-interest and ethical attitudes, aiming to understand the impact of these attitudes on their purchasing behavior. Moreover, from an ethical consumerism perspective, it explores the effect of ethical information on purchasing agricultural products online, highlighting its significant implications for online marketing strategies.

This article attempts to use public sphere theory as a starting point to compare the behavior of the British government in protecting the interests of British business in China in the 19th century with the Chinese government’s neglect of the interests of Chinese business in the Philippines. Mill’s method of finding identities will be used. This article uses the Shanghai Branch of the China Association and the Philippine Chinese Charitable Association Inc. as representatives of civil business groups in the UK and China. For the UK, due to the public sphere of competition with the central government within the UK, civil business groups must consult with relevant civil business groups when implementing any economic foreign policy. This process promotes consensus between the British government and society to promotes the British government’s determination to maintain British business in China. However, for China, due to the absence of a public sphere where the central government interacts with society, even though Chinese civil business groups have huge interests overseas, the state and society have always been unable to form a positive interaction. At the same time, this situation also results in Chinese business in the Philippines having to rely on their abilities to maintain their local interests.