Food crop breeding technology innovation is the cornerstone of national food security and a critical pathway to building a strong agricultural nation. Despite significant progress, China still lags behind developed countries in unit yield of major food crops, with the gap in breeding innovation and market application translating into substantial productivity disparities. While existing literature has explored crop breeding innovation from various dimensions, comprehensive evaluation frameworks based on the innovation chain perspective remain underdeveloped, limiting the comparability and policy relevance of research findings.This study constructs a food crop breeding technology innovation evaluation system from the innovation chain perspective, encompassing four interconnected stages: basic research, applied research, new variety development, and market application. Using panel data from 31 Chinese provinces (excluding Hong Kong, Macao, and Taiwan) from 2013 to 2023, we employ the entropy weight method to measure innovation levels, the Dagum Gini coefficient to decompose regional disparities, Kernel density estimation to analyze distribution dynamics, and Markov chain analysis to examine dynamic evolution characteristics with and without spatial effects.China's food crop breeding technology innovation demonstrates an upward trend amidst fluctuations, with main sales areas outperforming main production areas and balanced production-marketing areas. Regional disparities are significant but narrowing, with inter-regional differences constituting the primary source of overall inequality. The distribution of innovation levels shows increasing dispersion with gradually diminishing polarization. Traditional Markov chain analysis reveals a gradual, path-dependent development process with pronounced Matthew effects. Spatial Markov chain analysis confirms significant spatial spillover effects that intensify over time, where proximity to high-level regions enhances upward mobility probability.

How does digitalization affect the urban-rural income gap? A study using panel fsQCA and NCA in developing economies

How can we capture the value of rail transit to improve spatial equity across income groups?

ABSTRACT Tourism, as an important component of sustainable consumption, plays a significant role in stimulating market vitality, promoting cleaner patterns of development, and supporting broader sustainability goals. Understanding tourism growth within the context of environmental governance is therefore essential for advancing sustainable development. This study investigates the impact of the Ecological Garden City (EGC) initiative, an environmental incentive policy aimed at improving urban ecological quality, on tourism development. Using a time-varying difference-in-differences (DID) model, we find that the EGC policy significantly promotes local tourism development, with stronger effects observed in cities with higher ecological quality, better air conditions, and more developed transportation systems. Beyond stimulating tourism growth, the policy also appears to strengthen sustainable consumption through enhanced public environmental attention and stronger government environmental commitment. At the same time, the results do not support the existence of a universal ‘Matthew effect.’ Rather than systematically widening regional inequality, the spillover effect appears limited and conditional, depending on specific urban and spatial contexts. Overall, the EGC initiative provides useful policy evidence for aligning environmental incentives with sustainable tourism development.

The rapid penetration of artificial intelligence in education has triggered unprecedented ethical risk perception and response challenges for educators, but existing research lacks systematic exploration of deep ethical cognitive mechanisms. To address this, this study operationalizes macro level sociotechnical imaginaries into micro level psychological perceptions to construct and test a theoretical model that integrates dual-dimensional risk perception, AI Literacy, and situational factors. PLS-SEM analysis of 502 Chinese educators reveals that: both Inhibitory Ethical Risk and Promotional Professional Risk are significantly and positively associated with Defensive and Constructive Coping strategies simultaneously, challenging the traditional one dimensional risk hypothesis; AI Literacy significantly attenuated the positive association between Inhibitory Ethical Risk and Constructive Coping, supporting the hypothesis of the Literacy Sedative Effect; The strength of the positive association between Promotional Professional Risk and Constructive Coping in lower tier schools is 2.78 times stronger than that in first tier schools, providing empirical evidence for the Reverse Matthew Effect. This study proposes a dual-dimensional risk-coping framework, revealing the cognitive decoupling mechanism of AI Literacy and challenging the classical Matthew Effect, providing a new perspective for understanding individual behavior in disruptive technological change; at the same time, it also provides practical guidance for cultivating cautious and proactive adopters and developing differentiated AI Literacy training strategies.

ABSTRACT Research shows the lifelong benefits of social-emotional skills while also raising concerns about their uneven distribution. Drawing on Bourdieu’s sociology, this study investigates how family resources shape the cultivation of children’s social-emotional skills in the Chinese context of rural-urban disparity. Using hierarchical and quantile regression on a sample of 1,453 eighth-grade children, we find a pronounced Matthew effect: compared to their urban peers, rural and rural-to-urban migrant children exhibit a significant disadvantage in social-emotional skills across the skills distribution, particularly at higher quantiles. Crucially, the study uncovers gendered transmission mechanisms. Paternal emotional and cultural capital amplify the advantage for those at the higher end of the skills distribution. Maternal emotional capital serves as a compensatory anchor for children at the lower end of the skills distribution. The non-significance of economic capital indicates that the intergenerational transmission of social-emotional skills relies less on financial capacity, but more on the gendered cultural and emotional labor undertaken in the domestic milieu. These findings offer international implications for understanding how ‘soft skills’ function as embodied capital, perpetuating inequality through invisible, gendered family dynamics.

ABSTRACT Proposals for the adoption of artificial intelligence (AI) in education are often accompanied by claims that it promotes ‘personalization’. However, the concept of personalization has been narrowly and ambiguously framed. This paper traces the genealogy of the notion of personalization in education, highlighting its roots in Anglo‐Saxon political‐economic frameworks that redefined citizens as ‘consumers’ of services, contrasting it with the Ibero‐American personalist tradition that emphasizes autonomy, dignity and integral human flourishing. While early 21st‐century policy initiatives and contemporary AI‐driven platforms promote what we term ‘Personalization 4.0’, they reproduce the limitations of modern instrumental rationality: reducing cognition to computation and freedom to choice‐optimization. Building on phenomenological and hermeneutic critiques of modern epistemology, the paper examines how AI personalization may foster depersonalization rather than deep personalization. We identify five paradoxes that reveal the tension between its promises and outcomes: (1) expanding horizons while narrowing knowledge through correlational standardization; (2) simulating uniqueness while fostering homogenization; (3) enabling personalization at the cost of consented surveillance; (4) serving as both oar and crutch, enhancing autonomy for the skilled while undermining development for novices; and (5) promising equity while deepening divides through the Matthew Effect. To address these tensions, we propose distinguishing between extrinsic personalization (externally provided adaptive conditions and tools) and intrinsic personalization (learner‐driven processes of agency, metacognition and self‐regulation). While AI contributes effectively to extrinsic personalization, it cannot substitute the intrinsic dimension, which remains the foundation of deep personalization. Genuine educational personalization requires their integration, yet current trends risk undermining intrinsic processes. The conclusion is that we need to depart from rationalist/modernist notion of rationality in order to fully understand how AI can contribute to deep personalization. Otherwise, there's a chance that current narratives, even when they claim to foster personalization, might actually hinder its true development.

ABSTRACT In times of fast‐changing social needs and limited public resources, private welfare provision plays an increasingly prominent role in the welfare mix. This article explores two dimensions of this trend, which have often been examined separately: occupational welfare (OW) and employment in the social economy. We investigate their interplay through a case study of cooperative enterprises in Italy. While cooperatives are central actors in Italy's social economy and play a vital role in delivering welfare services, existing literature points to low wages and precarious working conditions. Does OW improve employment conditions in a sector that, despite its social mission, often reproduces precarious forms of work? To address this question, the article draws on a mixed‐methods approach, combining data from two original surveys with qualitative insights from 25 interviews. The findings reveal that OW appears to be relatively widespread among cooperatives. However, especially in those offering publicly procured welfare services, their provision is severely constrained by tight economic margins. Where present, OW is likely to be used as an indirect supplement to (low) wages rather than for genuine social purposes. Moreover, a clear Matthew effect emerges: higher‐skilled workers in managerial positions benefit the most from OW, which often fails to reach the most vulnerable workers.

In response to global climate change and China’s “Dual Carbon” goals, the transition to low-carbon manufacturing has become a crucial step for achieving high-quality regional development. This study focuses on the Chengdu–Chongqing Economic Circle and constructs a comprehensive evaluation system spanning economic, technological, energy, carbon emission, and environmental dimensions. By applying a dynamic Set Pair Analysis (SPA) model coupled with a state transition matrix, we assess the low-carbon manufacturing performance of eight core cities from 2016 to 2023. The results indicate the following: (1) Strong path dependence characterizes regional low-carbon development, revealing a “Matthew effect” in which leading cities continue to advance while lagging ones face persistent barriers. (2) Cities are evolving into distinct equilibrium patterns: Chongqing is progressing toward full optimization, and Chengdu and Mianyang remain in a high-level equilibrium, whereas Suining and Zigong show signs of long-term low-level lock-in. (3) A three-tier regional structure emerges: Chongqing and Chengdu represent a high-level steady state; Deyang, Mianyang, Yibin, and Luzhou form an intermediate fluctuating tier; and Suining and Zigong constitute a low-level locked tier. (4) The low-carbon transition of manufacturing within the region remains markedly unstable, with cities such as Deyang and Yibin yet to establish steady low-carbon trajectories and remaining susceptible to regression. These findings provide a robust, evidence-based foundation for policymaking aimed at fostering coordinated and sustainable low-carbon development in the Chengdu–Chongqing region’s manufacturing sector.

ABSTRACT This study examines the spatial correlation structure and driving mechanisms of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Provincial emissions are estimated using reclassified energy consumption data. A spatial correlation network is constructed using a modified gravity model and analyzed with Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP). Results show rapid growth followed by gradual decline, with a spatial gradient of ‘East > West > Central > Northeast’ and expanding high-emission zones. The network exhibits significant spillover effects but remains structurally sparse, characterized by a core–periphery structure and a ‘Matthew effect’ dominated by core provinces. Four functional roles—net spillover, intermediary, bidirectional spillover, and net beneficiary—indicate heterogeneous interprovincial interactions. QAP results indicate that technological innovation, transportation intensity, and economic development promote network linkages, whereas geographic distance, industrial structure differences, and population density disparities hinder the formation of such linkages.

ABSTRACT The rapid development of digital technologies has reshaped traditional financial service models and created new avenues for reducing inequality. However, existing studies have largely overlooked the potential intergenerational income mobility (IIM) effects of digital finance. Using data from the China Family Panel Studies (CFPS), this paper systematically examines how digital finance influences IIM and the direction of such mobility. The results show that: (1) digital finance enhances IIM, particularly upward mobility, through three mechanisms: promoting household educational investment, improving health-related investment, and restructuring financing networks; (2) the mobility-enhancing effect mainly arises from deeper digital finance usage and higher levels of digitalization, and is stronger among households with proficient internet users and among urban families; (3) digital finance exerts a more pronounced upward mobility effect on low-income households and households receiving social assistance, thereby extending the concept of ‘inclusive growth’ to the intergenerational dimension and offering a new pathway to mitigating the Matthew effect. Additionally, digital finance and traditional credit are found to be complementary in promoting intergenerational mobility. This study broadens the research frontier on new technologies and income distribution and provides important implications for regulating digital finance and advancing social equity.

In e-commerce, the lack of physical inspection creates valuation uncertainty, which renders information transparency critical to consumer decisions and firm performance. This study examines the impact of information transparency across direct-selling and live-streaming channels. In the live-streaming channel, the streamer can enhance transparency through information facilitation effort. An analytical framework is developed where consumers with uncertain valuations depend on external signals. The results show that as initial information transparency increases, optimal retail prices in both channels follow a non-monotonic pattern; they first decrease and then increase. When initial information transparency is relatively high, the market demand in each channel may increase or decrease, driven by the interplay between the value-enhancement and uncertainty-mitigation effects. Interestingly, when consumers are moderately sensitive to information deficiency, the firm's optimal channel strategy shifts from direct selling to live streaming and subsequently returns to direct selling as transparency increases. From a welfare perspective, even when consumers are relatively insensitive to information deficiency, higher transparency in both channels may still enhance consumer surplus. Furthermore, when the cost efficiency of information facilitation effort is moderate, the streamer's effort exhibits a Matthew effect in which higher transparency induces high effort, whereas lower transparency suppresses it.

This study focuses on town and township primary schools in Zhejiang Province to explore the implementation of the “Science Education Plus” policy. First, it analyzes the policy’s evolutionary traits and reveals academic research trends and urban-rural disparities via bibliometric methods. Second, it assesses implementation from policy promotion, curriculum innovation, resource allocation, and social coordination, identifying issues like “campaign-style” promotion lacking long-term mechanisms, superficial curriculum reform, the Matthew effect in resource distribution, and fragmented social collaboration.Field research at Chisong Town’s H Primary School highlights four core pain points: structural mismatches between curriculum resources and content, inefficient teaching methods and faculty, weak science education atmosphere, and insufficient educational informatization and intelligence. Accordingly, it proposes optimizations: building a “localized” science service system, innovating implementation models, creating resource aggregation mechanisms, and establishing a UGSHE collaborative education paradigm. These offer a replicable solution to break the urban-rural dual structure, advance rural education revitalization, nurture early-stage scientific talents, and uphold educational equity and the national innovation-driven development strategy.

There is a growing consensus that we must look beyond IQ to understand the mechanisms of talent development. Grounded in the Actiotope Model of Giftedness, this study adopts a resource-based approach and examines the incremental and interactive contributions of educational and learning capital to STEM achievement beyond IQ. Data were collected from 318 German secondary school students (grades 6-10; Mage = 12.08; 50.3% male) using domain-specific measures of educational and learning capital, a nonverbal matrix intelligence test, and STEM grades. Robust regression and mediation analyses showed that learning capital significantly predicted STEM achievement beyond general intelligence, whereas educational capital exerted no direct effect. Instead, the relationship between educational capital and achievement was fully mediated by learning capital. Moreover, the interaction term of educational and learning capital predicted achievement. A further interaction indicated that the positive effect of learning capital on STEM achievement was stronger for students with higher intelligence, consistent with an intelligence utilization (Matthew) effect. These findings support a systemic interpretation of achievement in which intelligence reflects prior resource utilization and functions as a catalyst, while current learning resources constitute the proximal determinants of STEM performance.

Summary Conservation science is constrained by conditional visibility, an epistemic trap whereby recognition depends on alignment with Global North priorities. This phenomenon represents the operational intersection of the Matthew and Matilda effects, compounded by the feminization of poverty in the Global South. By marginalizing Indigenous and local knowledge, this structural filter severs global mandates from grounded ecological realities, leading to maladaptive conservation interventions. To bridge this gap, we propose specific interventions targeting the financial, administrative and evaluative pillars of the discipline. By matching structural reform with internal empowerment, we move beyond rhetoric to ensure effective planetary stewardship.

Abstract Population health is shaped not only by global and national processes but equally by decisions taken at the local level. Yet comparatively little is known about why municipal public health policies differ. This study examines how structural conditions, previously found to influence local policymaking, combine with the presence or absence of universal targeting in public health policies across Poland’s 23 largest cities. Using fuzzy-set Qualitative Comparative Analysis and building on the Local Welfare Regimes perspective, the analysis consistently links universal targeting in municipal public health policy to a combination of strong civil society, substantial representation of women in city councils, and the absence of an unhealthy population. As one of the first explicit applications of the LWR theory with fsQCA, the study contributes to debates on universal targeting, confirming that it can be implemented in a range of ways, while simultaneously highlighting the risk of Matthew Effects.

To explore the benefit of soil improvement and restoration models in typical counties of Shandong Province，we constructed a multi-dimensional system of "soil condition-ecological environment-economic society" comprehensive benefit evaluation index system and utilized the entropy-weighted TOPSIS comprehensive evaluation model，Kernel density estimation，coupling coordination model，and obstacle factor diagnostic model to fully analyze the comprehensive benefits of soil improvement and restoration model in typical counties of Shandong Province from 2019 to 2022. The results showed that：① The comprehensive benefit level of soil improvement and restoration model in typical counties showed a fluctuating upward trend，the gap between counties was narrowing，and there was a non-synchronization in the development of subsystems. ② The coupling coordination degree of soil improvement and restoration model in various counties showed a fluctuating increasing trend，which revealed a strong "Matthew effect." ③ The influence of each system layer on the level of comprehensive benefits，in descending order，was the economic and social system，the soil condition system，and the ecological environment system，and the main obstacles were the use of agricultural plastic film，the use of agricultural fertilizers，the effective phosphorus of the soil，and the total power of agricultural machinery. The results of the study have important reference value for enhancing the comprehensive benefits of soil ecological remediation，promoting the safe utilization of contaminated arable land，and contributing to the guarantee of national food security.

Hydrological period conversions induce micro-eukaryotic community homogenization along the eastern route of China's South-to-North Water Diversion Project.

Water environment governance (WEG) plays an important role in absorbing pollutants and reducing carbon emissions. This study focuses on the synergistic pollution reduction and carbon mitigation effect achieved by implementing water environmental governance through process mechanisms and policy measures, and explores the focus point aimed at achieving the co-benefits of pollution reduction and carbon mitigation effectively at a local level within the basin. After selecting synergy indicators of comprehensive environmental pollution and carbon emissions and conducting modeling tests, the synergistic pollution reduction and carbon mitigation effect of WEG in the Yellow River Basin is examined. The analysis results show that WEG is characterized by the “Matthew effect” (i.e., the phenomenon of ‘the stronger get stronger’), and it can generate a synergistic pollution reduction and carbon mitigation effect with a lag, though the extent of reduction differs between pollutants and carbon emissions. The process mechanisms of source decarbonization (i.e., industrial structure transformation) and process emission reduction (i.e., green technology innovation) can achieve overall co-benefits, while end sink enhancement (i.e., environmental regulation) is only effective in reducing the pollution. The mixed nature of policy measures influences the overall co-benefits: homogeneous policy measures (e.g., river chief system policy and water ecological civilization construction pilot) contribute positively, while the opposite is true for heterogeneous policy measures (e.g., integrating WEG and energy utilization policies). These findings provide useful operational recommendations for decision-makers to efficiently reduce pollutants and carbon emissions and promote low-carbon sustainability in China’s watersheds.

Purpose This study aims to explore the direct impact, asymmetric characteristics and mechanism of top management teams’ environmental attention (EA) on breakthrough green innovation boundaries (GIB) in energy enterprises, and to address the deficiencies in existing research regarding GIB and the role of executive decision-making subjects. Design/methodology/approach Patent information is used to define GIB from three dimensions: breadth, depth and width. Text analysis is employed to measure EA. The study examines the relationship, asymmetric characteristics, mechanism and moderating effect through empirical analysis. Findings EA significantly expands the breadth, depth and width of GIB. The impact of EA on GIB has asymmetric characteristics: EA exerts a weaker promoting effect on GIB for enterprises at the lower end of the conditional distribution, while it strengthens more prominently for those at the upper end, showing an increasing trend. Meanwhile, EA exacerbates the differentiation of GIB among enterprises, leading to more obvious discrete and right-skewed characteristics in the distribution of GIB among enterprises, and intensifying the “Matthew effect” where the strong get stronger and the weak get weaker. Mechanism analysis shows that EA can expand GIB through two paths: alleviating external environmental public opinion pressure and reducing internal green agency costs. CEOs’ academic experience plays a positive moderating role. Originality/value This study reveals the micro-cognitive mechanism of firms’ green innovation, enriches research related to green innovation and top management characteristics, provides theoretical support and practical suggestions for firms to improve their green innovation capabilities and makes up for the deficiencies of existing research in terms of GIB and the role of top management decision-making subjects.