Search Results (1,215)

The synergistic transition toward digital transformation and green development has been widely regarded as a core pathway to achieving sustainable development in knowledge production. Using balanced panel data from 39 economies covering 2018–2024, this study employed a two-way fixed-effects model to examine the associations of the energy efficiency of digital infrastructure and the energy structure with carbon intensity (CI). The findings showed that: (1) Reductions in Power Usage Effectiveness (PUE) values were significantly associated with higher macro-level CI (coefficient = −2.1564, p < 0.05), which is consistent with the possibility of a rebound effect in the digital sector. Further, time-series discontinuity tests further suggested that the surge in AI computing power, especially in 2023–2024, may have coincided with a structural shift in this relationship (Chow test, p < 0.05). (2) A Panel Threshold Regression (PTR) identified an optimal renewable energy threshold at 59.82%. Crucially, the carbon rebound effect remained highly significant across both high and low green power regimes, demonstrating that supply-side energy transition alone cannot fully absorb the exponential carbon footprint of digital expansion. Furthermore, Instrumental Variable (IV-2SLS) and Placebo Break Tests confirmed the strict validity of these findings. (3) The emission-reduction benefits related to digital knowledge spillovers appeared to be subject to time lags and a possible energy lock in effect, while current environmental policies and carbon pricing mechanisms appear to impose insufficient constraints. This study provides a crucial quantitative framework for monitoring and evaluating the environmental sustainability of the ICT sector. By highlighting the limitations of pure supply-side greening and the necessity of absolute carbon caps, our findings offer integrated policy approaches to align the exponential growth of Generative AI with global sustainable development goals. Full article

In recent years, artificial intelligence (AI) has been increasingly used in criminal justice systems across the world. To achieve objectives set out through Sustainable Development Goals (SDGs), adoption of technology is inevitable and undeniable. The press release dated 25 February 2025 from India’s Ministry of Law and Justice, quoting Prime Minister of India Narendra Modi to make a “justice system that will be fully future-ready”, confirmed that the Indian law enforcement agencies are integrating AI into policing and law enforcement to enhance crime detection, criminal investigation, etc. It is intended to enhance their capabilities in solving criminal cases and delivering justice speedily and more efficiently. However, the usage of AI tools in such contexts presents a double-edged sword, as evidenced by their application in a number of cases across the world like Christopher Gatlin, Nijeer Parks, the Harm Assessment Risk Tool (HART), and in India during the 2020 Delhi riots cases. As reported by the Washington Post, in Christopher Gatlin’s case it was found that the police arrested him on the basis of the facial recognition programme matching his face with the captured video footage. He spent 17 months in jail before his release by the court, observing that the police failed to conduct fair investigation. A similar incident was reported by NJ.com and CNN Business. In the investigations following the 2020 Delhi riots, Delhi Police effected over 1900 arrests in 758 riot-related cases, relying predominantly on AI-driven facial recognition matches. Subsequent court scrutiny in decided cases raised questions about reliability, leading to widespread acquittals and discharges of the accused in 82% of decided cases as of early 2025. In certain cases, AI-driven solutions have failed, leading to criminal prosecutions of innocent people based on AI-generated evidence. This study examines the reliability, validity, and ethics of AI technology in the criminal justice system in India’s unique socio-legal and political environment. The researchers analyse three interrelated axes. First, a comprehensive review of the international algorithmic policing literature to identify successes and failures. In addition, cases of AI-assisted investigations during the Delhi riots show how facial recognition systems and other AI techniques were used for inquiry. Finally, stakeholders’ perspectives, including a preliminary survey of 27 legal experts showing strong consensus on classifying AI-FRT outputs strictly as corroborative evidence and highlighting BSA insufficiencies for addressing opacity and explainability, help identify practical, procedural, and normative fault lines. Researchers noted that while AI has the potential to revolutionise resource-constrained investigative agencies, its unquestioning and uncritical adoption risks amplify pre-existing biases, undermine presumptions of innocence, and shift the burden of refuting algorithmic inference onto the accused. Independent algorithmic audits, transparent documentation of error rates and confidence thresholds, statutory guidelines on AI tool use and admissibility, and sustained capacity-building throughout the justice delivery chain are needed to integrate it into the Indian criminal justice system. Without such measures, the very tools designed and introduced to enhance accuracy threaten to undermine the fundamental norms of the criminal justice system such as fairness and due process. This fills a gap in doctrinal analysis of AI-specific evidentiary admissibility in non-Western contexts like India. This study aims to propose policy reforms, enhance judicial discourse, and promote a more circumspect trajectory for AI adoption in Indian law enforcement by mapping the potential and risks of algorithmic evidence in a non-Western legal order. Full article

This study investigates the relative impact of utilizing various forms of work flexibility on mothers’ retention in full-time jobs. Drawing from a model of work reentry for new mothers and Human Ecology Theory (HET), we theorize that mothers’ flexibility needs influence retention through flexibility use and that top management support strengthens this process. Using a cross-sectional Qualtrics online survey, we recruited a diverse sample of 213 women across the United States who stayed with or left their full-time jobs after childbirth or adoption. Using relative weights and path analysis, we compared six forms of flexibility. Schedule, career, and leave flexibility emerged as stronger predictors of retention than other forms of flexibility, with schedule flexibility explaining the largest proportion of variance. For most flexibility types, the need for flexibility increased usage, which, in turn, raised the likelihood of staying in a full-time position. Strong top management work–life support further strengthened the relationship between need and use for certain forms of flexibility. We extend the work reentry framework beyond early motherhood by including mothers in later career and parenting stages. Our results also extend HET by demonstrating that top management support is a critical environmental factor influencing whether flexibility need translates into actual use by working mothers. This suggests that working mothers use flexibility to shape their work environment to meet personal needs, and top management support signals that the use of flexibility effectively addresses these needs. From a practical standpoint, organizations can use these insights to design flexibility policies that more effectively support working mothers in their full-time jobs. Full article

With the rapid development of e-commerce, data-driven models have significantly enhanced service experience. We can obtain the optimal values for the price but have also intensified consumer privacy concerns. Among various privacy protection policies, which are more effective? Is there a governance framework that balances commercial efficiency with privacy safety? To address this, we develop a duopoly game-theory model that analyzes consumer behavior characterized by heterogeneous privacy costs and preferences, aiming to evaluate the impact of differentiated privacy protection policies within digital ecosystems. We analyze whether opt-in requirement or inference regulation is more advantageous for consumer and firm competition. We find that, in a competitive environment, imposing opt-in requirement on one party can yield competitive advantages and profit increases, whereas imposing inference regulation on the other may result in a competitive disadvantage. Such differentiated policies create an asymmetric competitive landscape, effectively avoiding a prisoner’s dilemma and, under certain conditions, increasing both consumer and total surplus. Furthermore, our study reveals significant differences in the impact of these policies on data-driven and usage-driven firms. Based on these findings, we recommend that regulators carefully tailor privacy protection policies according to industry-specific data characteristics, adopting differentiated regulatory strategies when appropriate and providing compensation mechanisms for disadvantaged firms to optimize total welfare. Full article

As digital technology has become increasingly integrated into rural governance and daily life in China, Internet use among rural residents exerts a multifaceted influence on their mental health. A key mechanism lies in its restructuring of the neighborhood social environment. Uncovering this mechanism is essential for understanding the theoretical and practical connections between rural social transformation and individual well-being in the digital age. This study applied a binary probit model to data from the 2020 China Family Panel Studies (CFPS) to examine the impact of Internet use on the mental health of rural residents. Mediation analysis was used to examine the role of the neighborhood social environment, and the conditional mixed process method was applied to address potential endogeneity issues. Empirical results demonstrate that access to the Internet, along with the breadth and depth of its use all significantly improve the mental health of rural residents. Internet use promotes mental health by strengthening neighborhood relationship and trust, whereas it also negatively affects mental health by suppressing neighborhood identity. Heterogeneity analyses reveal three key dimensions of variation. (1) By usage type: Activities such as gaming, short-video consumption, and WeChat communication show positive associations with mental health, whereas online shopping and learning exhibit non-significant effects. (2) By user group: The mental health benefits are more pronounced among women, less-educated individuals, and middle-aged to older adults. (3) By region: Positive associations are observed in central and western China, with the most substantial effect in the central region. This study elucidates the mechanism through which Internet use affects mental health: the restructuring of traditional, place-based social capital in rural neighborhoods. These findings offer robust empirical support for policies that integrate digital initiatives with the nurturing of local community bonds to improve rural mental health and foster livable and harmonious villages. Full article

Background: Health disparities in individuals with type 2 diabetes result in an enormous amount of healthcare usage and are difficult to address. We examine how Dutch private, not-for profit health insurers, responsible for accessibility of care, use their claims data in the commissioning process to reduce health disparities. Objective: To identify factors influencing the possibility to reduce health disparities in commissioning based on data-driven insights. Methods: Key informant group interview data was analyzed using a hybrid deductive–inductive approach following the 6SQIuD framework to identify factors, their relationships, and potential for change, with results validated using a saturation, member and expert check. Results: From the 79 factors found, three were data capability-related and 76 were decision context-related. Fifteen main factors were found on socio-cultural, system, organizational and interpersonal levels. The factors in the decision context can be divided into the themes equality, quality and organizational sustainability. Conclusions: This study explored the factors influencing commissioners’ ability to reduce disparities and the role of data. Interestingly, no main factors related to data capabilities were identified. Three paradoxes were seen after interpreting our data: equal access leads to unfavorable unequal outcomes; a focus on evidence-based healthcare interventions limits effectiveness in reducing health disparities; and conservative organizational behavior threatens long-term viability. Further policy analyses are needed to better understand which systemic and organizational conditions facilitate commissioners in addressing health disparities. Full article

Employees are often encouraged to use artificial intelligence (AI) in the workplace, and industry experts tend to promote the idea that AI is here to stay. Extravagant events are consistently held to showcase the magic of AI, yet companies seem to have limited policies and mixed signals concerning AI’s adoption. This is creating a conflicting narrative, as the practical applications of such evolving technologies remain fragmented—particularly, the synergies of the numerous variables at play, which are often explored in isolation or underexplored altogether. As such, this research uses a three-construct approach, which includes ‘Institutional Policies’ and ‘Stakeholder Sentiments’, to explore their individual and collective implications on ‘AI Usage’ in Kuwait from a multi-stakeholder point of view. This is the first research to use a triple-lens framework, stemming from Institutional Theory, Technology Acceptance Model, and Human Capital Theory, to argue that AI usage cannot be assessed from one perspective due to its subjectivity. The methodology includes a quantitative assessment administered to 153 participants (n = 153) using a closed survey. The findings confirm that higher positive views of AI usage are connected to higher Stakeholder Sentiments as well as ‘Institutional Policies’. Also, a moderate relationship was found between ‘Stakeholder Sentiments’ and ‘Institutional Policies’, meaning that better policies are connected to better stakeholder sentiments. The research contributes to the broader literature on AI usage from a practical perspective with a multi-lens framework which takes several constructs into consideration collectively rather than in isolation. Full article

This study examines the risk of accidental subscription among elderly consumers in micro-drama platforms, addressing a critical gap in digital consumer behavior research as aging populations increasingly engage with subscription-based digital services. Using a quantitative approach, data were collected from 780 Thai respondents aged 60 and above through a structured online questionnaire. The data were analyzed using binary logistic regression to assess the effects of demographic factors (age, gender, education, and income) and behavioral factors (platform usage frequency, time spent per session, prior subscription experience, and impulse clicking behavior) on the likelihood of accidental subscription. The findings reveal that age, gender, platform usage frequency, time spent per session, and prior subscription experience significantly influence accidental subscription, while education, income, and impulse clicking behavior do not. Notably, frequent platform use and prior experience increase risk, whereas longer session duration reduces it, suggesting nuanced engagement effects. These results confirm that accidental subscription is a systematic and predictable outcome shaped by user characteristics and interaction patterns. The study contributes by extending consumer behavior research to unintended outcomes and offers practical implications for user-centered platform design, consumer protection policies, and targeted digital literacy initiatives, particularly in emerging digital economies. Full article

As a typical model of urban green and sustainable micro-transportation, shared electric bicycles play a crucial role in short and medium-distance travel as well as in connecting with public transportation. To respond to the national concept of low-carbon travel, this study takes users of urban shared electric bicycles in Kunming, Yunnan Province as the research sample and distributes questionnaires online through the Wenjuanxing platform to conduct an investigation into the factors influencing residents’ short-term and long-term continuance usage intention of shared electric bicycles. The results of the ordered logit model show that: at the level of personal attributes, the number of family-owned electric bicycles exerts a negative impact on residents’ short-term and long-term willingness to continue using shared electric bicycles. In terms of travel attributes, travel frequency has a positive impact on residents’ long-term continuance usage intention of shared electric bicycles, while exerting little influence on their short-term continuance usage intention. As for the original travel modes, groups with the habit of walking show a rejection of shared electric bicycles. From the perspective of attitudinal perceptions, independent variables reflecting instantaneity characteristics such as riding speed, unlocking speed and easy electric bicycle returning have a promoting effect on residents’ short-term continuance usage intention; independent variables reflecting sustainability characteristics such as good endurance capacity contribute to residents’ long-term continuance usage intention, while the overall travel comfort has a positive effect on the continuance usage intention across all time periods. Based on the model results, this paper puts forward policy recommendations from four aspects to promote urban residents’ continuance usage intention of shared electric bicycles. Full article

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community. Full article

Income inequality remains a central impediment to inclusive growth, yet whether government-led digital infrastructure programs mitigate or exacerbate distributional disparities is empirically contested. Exploiting the staggered rollout of China’s “Broadband China” (BBC) demonstration cities as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework on a panel of 281 prefecture-level cities spanning 2009–2022 to estimate the designation effects of national digital infrastructure policy under the DID identifying assumptions. After parallel-trends validation, permutation-based placebo tests, and propensity score matching, the baseline estimates indicate a dual distributional pattern: BBC designation is associated with a wider urban-rural income gap and lower within-prefecture nighttime-light-based spatial income inequality. Candidate-channel analysis provides evidence consistent with financial deepening and factor mobility as plausible pathways: expanded financial coverage and cross-regional labor reallocation are associated with spatial convergence, whereas asymmetric usage depth and selective labor mobility reinforce urban-rural divergence. Exploratory heterogeneity analysis across four institutional dimensions, officials’ political promotion incentives, local fiscal capacity, traditional infrastructure endowments, and urban hierarchy, further shows that this distributional pattern varies across local contexts. Furthermore, this study extends the analytical lens to the spatial dimension by employing a spatial DID framework. The results identify significant cross-border externalities characterized by cross-prefecture spillovers associated with lower within-prefecture nighttime-light-based spatial income inequality in neighboring cities. These findings provide an integrated policy-evaluation framework that disentangles the complex, multidimensional distributional consequences of digital infrastructure investment, offering actionable insights for designing more equitable digital public policies in developing economies. Full article

Energy-harvesting (EH) AIoT systems enable long-term autonomous operation but suffer from time-varying energy availability, which makes stable learning difficult. In such environments, federated learning (FL) is prone to energy depletion (blackout), while continual learning is required to handle evolving data distributions, leading to a trade-off between energy stability and catastrophic forgetting. In this paper, we propose a stability-controlled continual federated learning framework that jointly regulates local training intensity and rehearsal usage based on the residual energy state. The proposed method is derived from a Lyapunov drift-plus-penalty formulation and implemented as a lightweight mode-based control policy. Simulation results using real solar energy traces show that the proposed method significantly reduces blackout while improving accuracy and mitigating forgetting compared to existing approaches. These results demonstrate the effectiveness of energy-aware joint control for stable continual federated learning in EH-AIoT systems. Full article

The digital transformation of agriculture has generated vast heterogeneous datasets from sensors, machinery, and administrative systems; however, interoperability and data sovereignty remain critical challenges. This study presents an IDS-compliant Agricultural Data Space tailored to the Italian context, integrating regulatory frameworks (General Data Protection Regulation, Data Governance Act and Data Act) with the International Data Spaces (IDS) Reference Architecture Model. The study addresses key barriers to data sharing, including technical fragmentation, governance gaps, and economic incentives, by mapping Italian agricultural data flows onto the five-layer IDS model. Policy-based usage control is implemented through machine-enforceable Open Digital Rights Language policies, enabling farmer-centric data sovereignty. Three use cases, namely administrative Common Agricultural Policy (CAP) declarations, machine-generated data portability, and agri-food supply-chain traceability, demonstrate how structured and interoperable data exchange can reduce redundancy, mitigate vendor lock-in, and support sustainable agri-food systems. The findings highlight the feasibility of IDS-driven solutions in real-world agricultural ecosystems, emphasizing the need for sector-specific policy templates and scalable governance mechanisms. This work contributes to the development of the Common European Agricultural Data Space by bridging institutional, technical, and regulatory gaps. Full article

The digital economy (DE) is increasingly associated with higher carbon productivity (CP) and is widely regarded as an important factor in efforts to achieve the dual-carbon goals. However, the formulation of differentiated policies is constrained by a limited understanding of the multi-factor collaborative mechanisms and their asymmetric configurational pathways. This study combines the GMDH algorithm with the fsQCA approach to explore the multiple sufficient paths for high carbon productivity. Through feature selection and nonlinear modeling, the GMDH algorithm identifies five key variables associated with CP: the industrial robot permeability, software business development, digital innovation input, the usage depth of digital finance, and mobile communication facilities. The fsQCA method reveals that three configurational pathways consistent with higher levels of CP: the “innovation and finance-driven model” represented by Sichuan and Hunan, the “innovation-assisted digital industrialization model” represented by Henan and Hebei, and the “industry digitalization first developing model” represented by Jiangxi, Guangdong, Zhejiang, and Shanghai. Considering the uneven regional development across China, this study further categorizes provinces into four regional development types: innovation and finance-driven, digital industry empowerment, industrial digitalization leadership, and potential cultivation. Correspondingly, tailored policy recommendations are proposed for each region, providing practical insights consistent with the observed configurational patterns for improving CP in the context of DE development. Full article

This study examines the impact of energy risk on environmental quality in BRICS economies (Brazil, Russia, India, China, and South Africa) from 2000 to 2024, including economic growth, renewable energy, institutional quality, urbanization and energy usage. Specifically, this study uses Fully Modified Ordinary Least Squares (FMOLS) under the Environmental Kuznets Curve (EKC) hypothesis to estimate long-run relationships in countries, assessing robustness through Driscoll–Kraay Standard Errors to address heteroskedasticity, serial correlation, and cross-sectional dependence. The empirical findings provide strong support for the EKC hypothesis, as evidenced by the positive and significant coefficient of economic growth and the negative and significant coefficient of its squared term. Energy consumption and urbanization are found to significantly increase environmental degradation, indicating their substantial contribution to emissions. In contrast, renewable energy consumption significantly reduces emissions, highlighting its role in improving environmental sustainability. Importantly, energy risk does not exhibit a statistically significant impact on environmental quality, suggesting that energy security vulnerabilities have not directly translated into measurable environmental effects in the long run across BRICS countries. Institutional quality shows a positive and significant relationship with emissions, implying that governance improvements alone have not yet effectively supported environmental sustainability and decarbonization efforts. Overall, the findings underscore the need for integrated policy frameworks that promote renewable energy adoption, manage urban expansion, and enhance the effectiveness of institutional mechanisms to achieve sustainable environmental outcomes in BRICS economies. Full article