Search Results (1,505)

Digital rural construction is a key component of China’s agricultural and rural transformation, as well as a means to improve rural productivity and strengthen the coordinated development of urban and rural areas in the Yangtze River Delta, which requires the backing of an efficient policy framework. Based on this, the research object is the 145 digital village policies issued by the Yangtze River Delta, and 16 of them are selected for evaluation after content mining. The findings are as follows: (1) Sixteen policies have an average PMC index of 8.42; four policies are excellent, 12 are good, and the quality of policies is generally good. (2) At the administrative level, there is a characteristic that the quality of provincial policies is superior to that of municipal policies. Among regions, the policy quality advantages represented by Zhejiang are obvious, presenting a pattern of “Zhejiang leading, Jiangsu steady, Anhui catching up, and Shanghai waiting for improvement”. (3) Except for the “policy function”, although the absolute scores of some indicators (such as policy field, policy content, policy evaluation) are at a high level, there is still a significant gap compared to the outstanding performance of the policy function (0.98). Moreover, from the perspective of the requirement for comprehensive and coordinated development of policies, the attention and investment in these dimensions are slightly insufficient, resulting in policies not fully exerting their expected comprehensive effectiveness, which is the main reason restricting the overall quality of policies. (4) It is recommended to increase the regulatory and advisory nature of the policy; Expand the scope of policy audiences; Take into account the forward-looking and long-term nature of policy formulation; Refine the execution plan to ensure the implementation of policies; The content of the construction of fiscal taxation, laws and regulations will be increased to provide a scientific basis for the rural transformation; Encourage local exploration and innovation, combine with local conditions, and form replicable and promotable development models. Full article

Whether corporate digital spending can produce sustainability-related outcomes is still not well understood. Much of the existing discussion treats digital transformation as a broad strategic process, but firms make sustainability decisions through more concrete resource allocations, including investment in information systems, data infrastructure, and IT-based management tools. This study therefore focuses on IT investment and examines its relationship with corporate environmental, social, and governance (ESG) performance. Based on panel data for Chinese A-share listed firms from 2009 to 2024, we estimate fixed-effects models and further test the roles of technological spillovers, market competition, and ownership structure. The evidence indicates that firms with higher IT investment tend to report better ESG performance. This association remains significant when lagged IT investment is used, suggesting that the result is not merely driven by same-period correlation. Technological spillovers explain part of the relationship: IT investment appears to support ESG performance partly by improving technological learning, innovation diffusion, and knowledge accumulation. By contrast, market competition does not significantly change the IT investment–ESG relationship. Additional analysis shows that the positive effect is stronger in state-owned enterprises than in non-state-owned enterprises. The findings imply that IT investment can be viewed not only as a source of operational efficiency, but also as a digital resource that may strengthen firms’ capacity for sustainable governance. Full article

The rapid advancement of laboratory automation, robotics, and digital technologies has significantly transformed laboratory medicine worldwide, improving efficiency, diagnostic accuracy, and quality management. However, the adoption of these technologies in developing countries such as Indonesia remains uneven and is influenced by infrastructural, financial, regulatory, and workforce-related challenges. This structured narrative review aimed to critically examine the current landscape of laboratory automation and robotics in Indonesia, with particular emphasis on implementation challenges, workforce transformation among medical laboratory scientists (Ahli Teknologi Laboratorium Medik, ATLM), and pathways toward equitable integration. Studies published between 2015 and 2025 were identified through PubMed, Scopus, and Google Scholar, complemented by Indonesian regulatory documents, professional guidelines, and relevant grey literature. The review was informed by PRISMA principles and synthesized narratively to explore technological developments, operational impacts, policy contexts, and implementation barriers relevant to Indonesian laboratory systems. The findings indicate that automation and robotics offer substantial benefits, including improved turnaround time, enhanced quality assurance, reduced laboratory errors, and greater operational efficiency. Nevertheless, significant barriers persist, particularly disparities in digital infrastructure, financial constraints, limited workforce readiness, and the absence of comprehensive implementation frameworks. The review further highlights that automation is reshaping rather than replacing the role of ATLM, shifting professional responsibilities toward digital competency, automation oversight, data interpretation, and quality management. Achieving sustainable laboratory automation in Indonesia therefore requires an equity-centered and systems-oriented approach involving regulatory strengthening, workforce development, infrastructure investment, and multi-stakeholder collaboration. With strategic planning and policy alignment, laboratory automation and robotics hold considerable potential to modernize laboratory services and support Indonesia’s broader healthcare transformation agenda. Full article

Against the backdrop of geopolitical restructuring and rapid digital transformation, foreign investment security reviews in host countries have become an increasingly important institutional constraint on cross-border business activities. From a broader organizational perspective, such regulatory mechanisms shape the external environment in which emerging market multinational enterprises (EMNEs) make international investment decisions and conduct cross-border acquisitions. This paper explores the moderating effects of EMNE network and internal agency problems on the duration of reverse cross-border M&A (CBMA) under host country security reviews. Utilizing a Negative Binomial regression model, we empirically analyze 503 reverse CBMAs undertaken by Chinese firms in developed economies from 2003 to 2022. The findings reveal that host country security reviews significantly prolong M&A duration. Notably, business group affiliation weakens this positive relationship, whereas political networking strengthens it. Regarding internal governance factors, Type I agency problems reinforce the delaying effect of foreign investment security reviews, while Type II agency problems weaken it. This study provides practical implications for managers and policymakers seeking to improve firms’ responses to increasingly complex regulatory environments. Full article

Artificial intelligence (AI) is increasingly viewed as a key driver of sustainable development, yet evidence on its role in corporate sustainable green innovation remains limited. Drawing on Resource Orchestration Theory and Signaling Theory, this study examines the impact of AI capability on sustainable green innovation using panel data from Chinese A-share listed companies during 2014–2023. The results show that stronger AI capability significantly promotes sustainable green innovation. AI investment serves as a mediating mechanism. However, the estimated indirect effect is negative, suggesting that the process of AI implementation may involve resource reallocation and organizational adjustment costs before innovation benefits can be fully realized. Environmental investment strengthens the positive impact of AI capability, whereas digital transformation weakens it. Robustness tests confirm the reliability of the findings. Further analyses indicate that the positive effect of AI capability is more pronounced in non-state-owned enterprises, low-technology firms, and firms in the growth stage. By revealing the mechanisms and boundary conditions through which AI capability influences sustainable green innovation, this study enriches the literature on AI-enabled sustainability and offers practical insights for firms pursuing long-term sustainable development. Full article

This study examines whether ESG rating disagreement is a leading indicator of corporate greenwashing and how digital transformation (DTI) moderates this relationship through disclosure and performance channels. Using 8111 firm-year observations from Chinese A-share companies (2012–2022), we employ two-way fixed-effects panel regression complemented by Bayesian-optimised machine learning models interpreted through SHAP. Aggregate rating disagreement is a strong and robust predictor of greenwashing. Channel decomposition reveals asymmetric DTI moderation: the disclosure channel amplifies greenwashing risk as digitally advanced firms expand reporting capacity to widen the gap between disclosed and actual ESG performance (bloom_DTI: β = +0.2471, p < 0.01), while the performance channel attenuates greenwashing risk as digital operational monitoring translates substantive performance into a measurable reduction (hua_DTI: β = −0.2804, p < 0.01). This pattern is robust across ownership structure, pollution intensity, and region. Machine learning analysis confirms the econometric findings and reveals nonlinear threshold effects invisible to panel regression. This asymmetric channel mechanism contributes to the ESG rating divergence literature and has implications for disclosure regulation and ESG-based investment screening. Full article

Industrial process systems must reduce energy use and carbon emissions while maintaining productivity under binding constraints. Energy-use rights trading (EURT), also referred to as energy-consuming rights trading, energy quota trading, or energy-consumption permit trading, converts administrative energy-consumption control into tradable entitlements. This narrative and integrative review uses a transparent search-and-screening audit and reframes EURT primarily through the Chinese pilot experience, while using international energy-efficiency certificate and obligation schemes as comparative context. The review examines how quota scarcity, quota prices, monitoring, reporting and verification, trading liquidity and policy coordination may influence process-level energy management, production scheduling, heat integration, waste-heat recovery, equipment renewal, fuel substitution, electrification, digital monitoring and low-carbon retrofit decisions. It compares EURT with carbon-emissions trading, pollution-permit trading, white-certificate or energy-efficiency-obligation schemes, water-rights trading and renewable-energy certificates. Evidence suggests that EURT can support pollution reduction, carbon mitigation, and green productivity improvement when quota scarcity is binding, markets are liquid, monitoring is reliable, and policy coordination is credible, but findings remain heterogeneous and vulnerable to contamination from overlapping policies. A stylized process-system illustration shows how quota prices can alter the ranking of retrofit investments. Future research should integrate transaction records, equipment-level energy data, process simulation and multi-policy identification strategies. Full article

Industrial modernization is widely regarded as an important pathway toward high-quality and sustainable economic development. Using panel data from 30 provinces in mainland China from 2012 to 2023, this study examines the relationship between artificial intelligence (AI) and industrial modernization. AI is proxied by industrial robot density, while industrial modernization is evaluated using a composite index covering supportiveness, substantiveness, innovativeness, greenness, openness, and integration. Fixed-effects models are employed, alongside a series of robustness tests and instrumental variable estimation. The results indicate that AI, as captured by industrial robot density, is positively associated with industrial modernization. This relationship remains robust after adopting alternative measures, introducing lagged explanatory variables and additional controls, applying winsorization, adjusting the sample, and addressing potential endogeneity. Heterogeneity analysis shows that the association is stronger in eastern provinces and in regions with higher levels of AI infrastructure and technical talent. Mechanism analysis suggests that green technology innovation is an important channel through which AI is associated with industrial modernization. In addition, software industry development strengthens the positive association between AI and industrial modernization, highlighting the importance of complementary digital capabilities in supporting industrial transformation. These findings contribute to understanding how AI adoption, represented by industrial robot deployment, is related to industrial modernization and suggest that policies promoting AI-driven industrial transformation should be accompanied by investments in green innovation, software industry development, digital infrastructure, and technical talent cultivation. Full article

This article investigates how structural transformations—growth, development, digitalisation, renewable energy consumption, education, and economic freedom—could influence public investment in education and public debt in the EU27. This objective was achieved by taking into consideration suggestive secondary data for the period 2009–2021 and applying the Structural Equation Model (SEM), dynamic panel data (ARDL), and, for robustness, fixed effects (FE) models. Education shows the ability, in structural terms, to relax the public budget, even if, in the very short term, the mandatory education is associated with additional public investment. Growth may create conditions, in the short term, to diminish the public investment in education and to be favourable for less dependence on financial markets, regardless of the period. In contrast, human development is associated with increased investment in education in the very short and long term and with increased public debt in the short and very short term. Education can adjust investment in the sector, especially in the long term, even if tertiary education depresses public investment in the very short term. However, education, regardless of level, has a significant contribution to the economy and society, which qualifies investment in education as being strategic, and education as a sector which must be protected even in complicated economic periods. Economic freedom, in the very short term, through deregulations, helps the state to become less indebted; in the long term, the accumulation of vulnerabilities and dependencies changes this dynamic. The energy transition, despite an immediate favourable effect, in the short term implies state intervention, including through debt, so that European decarbonisation objectives can be achieved as soon as possible. The results complete the literature, especially as there are significant gaps, and inspire decision-makers in establishing educational policy measures regarding public investment and in resolving tensions among European economic policy objectives. The results are not strictly causal relationships; the situation of each member state may differ due to the heterogeneity in the European space. Full article

The rapid expansion of e-commerce into rural areas has emerged as a prominent policy initiative aimed at promoting digital economic development and facilitating structural transformation in developing countries. However, empirical evidence on whether and how such policies affect rural labor markets remains limited. This paper investigates the impact of China’s E-commerce into Rural Areas Policy on non-agricultural employment among rural residents. We develop a theoretical framework in which an individual’s utility derives from commodity consumption, leisure, and social recognition associated with e-commerce participation. The model predicts that reducing the investment price of e-commerce activities—the primary intervention of the policy—increases non-agricultural labor supply through both direct and indirect channels. Using panel data from the China Family Panel Studies (CFPS) spanning 2010 to 2022 and exploiting the staggered rollout of the Rural E-commerce Demonstration Pilot as a quasi-natural experiment, we employ a difference-in-differences (DID) approach to estimate the policy’s causal effects. The results show that the policy marginally significantly increases the probability of non-agricultural employment by approximately 1.1 percentage points, an effect that remains robust after parallel trend tests, placebo tests, and controlling for concurrent policies. Mechanism analysis reveals that the policy operates through two distinct channels: promoting regional industrial development, particularly the growth of the tertiary sector, and generating village-level peer economic incentives that encourage participation through social networks and information spillovers. Heterogeneity analysis indicates that the policy’s effects are larger for males, younger individuals, those with higher educational attainment, and residents of poor counties or regions with e-commerce potential. These findings contribute to the literature on digital development and rural labor markets by providing rigorous causal evidence and identifying the mechanisms underlying the policy’s effectiveness. The results also offer practical insights for policymakers seeking to leverage digital technologies for rural employment generation and structural transformation. Full article

Frequent introductions and revisions of climate policy instruments constitute a salient exogenous shock to firms’ strategic decisions. From a socio-technical systems perspective, climate policy uncertainty (CPU) represents an external institutional disturbance that reshapes the interaction between firms’ technological upgrading and organizational governance. Using panel data on 1783 Chinese listed firms from 2011–2024, we examined how CPU affects firms’ industrial intelligence. We employed fixed-effects models, mediation, and moderation analyses, supplemented by robustness tests. We found that, first, CPU significantly promotes firms’ industrial intelligence transformation. Second, board governance plays a key moderating role: higher board educational attainment, stronger innovation orientation, and an environmental or risk committee significantly strengthen CPU’s positive effect on industrial intelligence. Third, CPU promotes industrial intelligence mainly through two channels: an opportunity effect via increased R&D investment, and a pressure effect via reduced total factor productivity, pushing firms to adopt intelligent transformation to address productivity pressure. Moreover, this effect is stronger for firms in high-pollution industries, larger firms, and long-established firms. These findings suggest that corporate industrial intelligence is not merely a technological response, but a socio-technical adaptation process shaped by climate policy uncertainty, board governance, and resource reconfiguration. This study provides evidence on firms’ digital, intelligent, and green transformation under climate policy uncertainty and offers implications for board governance and sustainable adaptation. Full article

Background and Purpose: Contemporary digital government initiatives in China face a well-documented real-world paradox: massive fiscal outlays on digital governance coexist with marked disequilibrium in regional innovation returns. Two structural mismatches define this paradox. First, local governments overwhelmingly prioritize high-visibility hardware investments such as data centers and large AI models, while neglecting deep-seated institutional reforms including cross-departmental business process reengineering and factor market liberalization. The pervasive phenomenon of “aggregated but non-interoperable data, and interoperable data left unused” reflects a severe asynchrony between rapid technological deployment and lagging institutional restructuring. Second, comparable digital investments yield vastly divergent innovation dividends across eastern, central, and western regions, with regional divergence entrenching into a rigid “higher in the east lower in the west, higher in the south lower in the north” pattern. Extant literature, largely confined to the lens of “instrumental rationality,” reduces digital government to an exogenous technological variable, leaving it unable to explain this core practical puzzle of “homogeneous inputs generating heterogeneous returns.” Moving beyond the narrow “technology-enabled governance” narrative, this study draws on the Digital-Era Governance (DEG) paradigm to investigate the actual impact of institutional restructuring on regional scientific and technological innovation capacity, aiming to provide empirical evidence to unlock the inefficiency lock-in prevalent in digital governance practices. Research Methods: This study uses 280 prefecture-level cities and above in China from 2018 to 2023 as the research sample and constructs a two-way fixed-effects model for benchmark regression analysis. To address endogeneity, the average level of digital government development in other cities within the same province is used as an instrumental variable, and the 2SLS method is employed to identify the causal effect. On this basis, a series of robustness checks are conducted, including excluding the special impact of the COVID-19 pandemic, substituting core variable measures, and decomposing the dimensions of the core explanatory variables, to ensure the reliability of the research conclusions. For mechanism identification, the Bootstrap sampling method is used to test the dual mediating effects of “digital industry agglomeration” and “resource misallocation alleviation”; furthermore, moderating effects and heterogeneity analysis models are introduced to reveal the boundary constraints of regional economic development levels and city types on the empowerment effect. Main Findings: Empirical results show that: (1) Digital government construction significantly improves regional scientific and technological innovation capacity, and this conclusion remains valid after endogeneity treatment and robustness checks. (2) Mechanism analysis demonstrates that digital government drives innovation through the dual paths of “promoting digital industry agglomeration” and “alleviating resource misallocation,” with the marginal contribution of alleviating resource misallocation being significantly higher than that of industrial agglomeration. This suggests that, in transitional economies, eliminating institutional frictions in factor mobility brings greater innovation dividends than simply building physical spatial clusters. (3) Moderating effects indicate that the higher the level of regional economic development, the stronger the innovation empowerment effect of digital government. (4) Heterogeneity analysis further reveals that the innovation dividends of digital government are significant only in non-resource-based cities, non-central cities, and large and medium-sized cities, while in resource-based cities, central cities, and small cities, the effects are systematically absorbed and not significant. Research Conclusions and Contributions: This study breaks through the ontological limitations of existing research that views digital government as a technological tool, grounding it within the DEG theoretical framework and confirming that digital government is an institutional force in the reconstruction of regional innovation ecosystems. The findings suggest to policymakers that digital government construction should promote a shift from a “technology-oriented” to an “institution-oriented” approach. The policy focus should shift from mere infrastructure expansion to the elimination of deep-seated institutional frictions, the improvement of factor allocation efficiency, and the advancement of gradients and the implementation of classified governance, all guided by regional economic foundations and heterogeneity characteristics. Full article

Objectives: Inadequate access to safe water, sanitation, and hygiene (WASH) continues to drive infectious diseases, malnutrition, and educational disparities, particularly among vulnerable populations. This systematic review examined WASH intervention strategies implemented in Malaysia between 2014 and 2025, focusing on shifts in hygiene-related knowledge, attitudes and practices (KAP), health outcomes, infrastructure improvements, and implementation challenges. Methods: A comprehensive search across five databases (Science Direct, PubMed, Scopus, Web of Science, and Google Scholar) identified twelve eligible studies targeting schools, healthcare settings, and rural or Indigenous communities. Results: Education-based interventions predominated (n = 10), often employing participatory and theory-driven approaches grounded in the Health Belief Model or Information–Motivation–Behavioural Skills framework. Evidence revealed significant improvements in KAP, particularly when digital, gamified, or storytelling elements were integrated. Community-led and caregiver-inclusive models demonstrated greater behavioural adoption and retention. Thematic analysis identified several implementation challenges, which include (i) sole reliance on self-reported outcomes with limited use of objective indicators, (ii) short intervention durations (<2 months) that limit long-term impact, and (iii) lack of policy and curriculum integration. Conclusions: Findings underscore the need for culturally tailored, longitudinal, and system-embedded interventions that combine behavioural theory with infrastructure investment. Integrating WASH initiatives into Malaysia’s health and education frameworks could advance Sustainable Development Goal 6, ensuring scalable and equitable improvements in hygiene literacy, community resilience, and public health outcomes. Full article

Effectively harnessing the productive potential of local government debt while mitigating financial risks is critical for urban sustainable development. Focusing on the standardized development phase of local government debt in China, this study examines how local government debt affects green technological innovation using Chinese A-share listed firms from 2015 to 2021. We find that regulated explicit debt significantly promotes firms’ green innovation through three channels: green bond issuance, digital infrastructure investment, and improved firm profitability. In contrast, implicit debt exerts a nonlinear inhibitory effect, with significant negative impacts only after a threshold is exceeded. Heterogeneity analysis shows stronger effects in developed regions, areas with more optimized industrial structures, and state-owned, large, and high-ESG firms. Our findings suggest that optimizing debt structure and directing funds to productive green and digital sectors can effectively drive urban green innovation. Full article

Robo-advisors have expanded access to automated investment services, but many platforms continue to rely on relatively static onboarding procedures and limited forms of user interaction. This study examines how participants with investment experience respond to two next-generation robo-advisory design features: financial digital twins, understood as dynamic investor profiles that integrate goals, risk tolerance, cash-flow patterns, and anticipated life events, and conversational artificial intelligence (AI), understood as an interactive interface for explaining recommendations. Using a scenario-based randomized 2 × 2 online experiment, 336 adult respondents with self-reported investment experience, recruited through professional and academic networks, were assigned to one of four robo-advisor scenarios that varied the personalization architecture, standard profile versus digital twin, and the interface style, plain dashboard versus conversational AI, while holding the portfolio recommendation constant. The results show that digital-twin personalization increases perceived personalization and privacy concern, indicating that more adaptive advisory architectures may be viewed as both more relevant and more data-intensive. Conversational AI increases the perceived interactive quality of the advisory experience, while selected willingness-related patterns, especially in the combined digital-twin and conversational-AI condition, are treated as exploratory because several secondary composites displayed limited internal consistency. The strongest confirmatory emphasis is therefore placed on perceived personalization and privacy concern, and the remaining findings are best interpreted as scenario-based investor responses rather than evidence of actual adoption behavior or confirmed psychological mechanisms. The study contributes to behavioral FinTech research by clarifying the personalization–privacy tension in AI-enabled robo-advisory services and by offering design implications for more transparent, interactive, and responsibly personalized digital wealth-management systems. Full article