With the world’s rapidly growing urbanization, urban sustainability is now expected for urban life. Due to this rapid growth, meeting the emerging challenges for urban management and sustainability worldwide is challenging. Big data-driven technologies can be an excellent solution to address these upcoming challenges. Therefore, this study explores the potential of big data technologies for ensuring sustainability in urban management. The study conducted a systematic literature review guided by PRISMA (preferred reporting items for systematic review and meta-analysis) on publications over the last 21 years. The study argues that urban management is an integrated function of public and private agencies to address the significant challenges of urban life and to develop the city as more competitive, habitable, and sustainable. Urban management can utilize big data analytics (BDA) for digital instrumentation, data-informed policy decisions, governance, real-time management, and evidence-based decisions. Urban sustainability can ensure the smooth operation of urban affairs through strategic planning under three major dimensions: social, economic, and environmental. Big data technologies can ensure smart transport, traffic, waste management, energy, environment, infrastructure, safety, healthcare, planning, and citizen participation in regular urban affairs to provide a better urban life. This study develops several indicators that will be helpful for concerned stakeholders in policy, planning, designing, and implementing sustainable urban development.

Data are playing an increasingly important role in the development of industry–education cooperation strategies in vocational education and training. The objective of this study was to promote the comprehensive progress of an industry–education cooperation system and improve the effect of the application of big data technology in this system. First, we designed of a big data technology application in an intelligent management platform system for industry–education cooperation. Second, we analyzed the synthetical design of the system. Finally, we optimized and designed a support vector machine (SVM) data mining (DM) algorithm model based on big data, and evaluated the model. The results revealed that the designed algorithm model provides outstanding advantages compared with similar algorithm models. In general, the highest average computation time of the designed SVM algorithm model is about 95 ms. The overall average calculation time linearly decreases around 200 iterations and tends to be stable, and the lowest overall average computation time is about 20 ms. In the DM process, the highest accuracy rate of the model is about 97%, and the lowest is about 92%. The DM accuracy rate is always stable as the number of iterations of the model continues to increase. The designed model slowly increases the occupancy rate of the system in the process of increasing computing time. At about 60 min, the system occupancy rate of the model tends to be stable, and the highest is maintained at about 23%. This study not only provides technical support for the optimization of DM algorithms with big data technology, but also contributes to the integrated development of industry–education cooperation systems.

According to the theory that the degree of education of workers in educational economics has a certain positive relationship with social labor productivity, the fuzzy system and neural network modeling mechanism are used to establish the fuzziness of laborers’ education level to social productivity (per capita national income). This article combines fuzzy theory and neural network theory to construct an empirical model for the analysis of the contribution of education economy and conduct an empirical analysis of statistical data from 2010 to 2020. Analysis shows that there is a great correlation between per capita years of education and per capita GDP, especially the number of college students per million people has a greater correlation with per capita GDP. This fully confirms that economic growth is increasingly dependent on education, especially higher education. The main body of this article is the improvement of the measurement model and the calculation of the contribution of our country’s education to economic growth using the fuzzy neural network measurement model. The final empirical conclusion shows that education has a significant role in promoting the development of our country’s economy.

The role of social responsibility in kindergartens is critical for fostering parental loyalty, especially amid declining enrollment rates in China. However, the relationship between kindergarten social responsibility, brand trust, perceived value, and parental loyalty is not well understood. This study investigates the influence of kindergarten social responsibility on parental loyalty, focusing on the mediating roles of brand trust and perceived value. A nationwide survey was conducted, collecting 745 valid responses from parents across 27 provinces in China. Data were analyzed using the PROCESS macro, with mediation effects tested via the bias-corrected nonparametric percentile bootstrap method. The findings reveal that kindergarten social responsibility significantly enhances parental loyalty both directly and indirectly through brand trust and perceived value. Brand trust was identified as the strongest mediator, particularly in non-inclusive kindergartens, where its effect on loyalty was more pronounced. The study also found that parents with higher education levels and higher income tend to have lower perceptions of social responsibility and perceived value, affecting their loyalty. These results suggest that kindergartens must tailor their social responsibility strategies to different parent demographics and kindergarten types to maximize parental loyalty. The study emphasizes the importance of social responsibility in strengthening parental loyalty, with specific implications for inclusive and non-inclusive kindergartens. By understanding the mediating roles of brand trust and perceived value, kindergartens can develop targeted strategies to improve competitiveness and parental engagement.

The adoption of agricultural technologies is paramount for enhancing global agricultural productivity and sustainability. However, widespread implementation faces significant challenges, particularly in developing regions. Using data from the China Land Economic Survey (CLES), this study examines how land certification reform affects farmers’ technology adoption behavior from an institutional perspective. Results demonstrate that land certification significantly increases agricultural technology adoption rates, with more pronounced effects observed among households possessing greater human and physical capital. A mechanistic analysis reveals that land certification facilitates technology adoption through three pathways: (1) improving credit accessibility, (2) strengthening long-term investment incentives, and (3) expanding the production and operational scale. These findings highlight land tenure security as a fundamental institutional driver of agricultural modernization, deepen the understanding of the interaction between institutions and innovation in agriculture, and offer actionable insights for integrating property rights reforms with technological advancements.

With the development of China’s economy and the deepening of urbanization, the number of migrants whose parents have migrant work experience continues to rise. However, what is the long-term impact of parental migrant work experience on migrant children? Existing literature has not yet adequately answered. Based on the data from the China Migrants Dynamic Survey in 2016–2017, this article uses a multiple linear regression model to examine the impact of parental migrant work experience on the income of rural-urban migrants and its impact mechanism and heterogeneity empirically. We find that parental migrant work experience has a positive impact on the monthly income of second-generation rural-urban migrants. Specifically, compared with those whose parents had no such experience, the average monthly income of those whose parents had such experience increased significantly by 3.08% (approximately 124 yuan), and this effect was more apparent when fathers had migrant work experience. The main influencing channel comes from the significant increase in the probability of rural-urban migrants choosing self-employment. The results of the heterogeneity analysis showed that this effect was more significant in the sample of males and those with high school education and below. After a series of robustness tests, these conclusions remain valid. This work enriches the corresponding research literature and provides empirical evidence for studying the long-term effects of parents’ early experiences on their children.