Search Results (769)

We develop a stochastic Stackelberg model of a subscription market with cancellation costs. A representative consumer chooses when to subscribe to and cancel a service as the utility derived from the subscription evolves according to a diffusion process, while the firm selects the subscription fee and cancellation cost to maximize its expected payoff. The consumer’s problem is equivalent to the classical real-options model of entry and exit under uncertainty with adjustment costs and exhibits a two-threshold policy with an inaction band and hysteresis. Unlike the standard formulation, in which the optimal thresholds are characterized implicitly through a system of nonlinear equations, we derive an explicit parametric solution in closed form. This solution reduces the firm’s optimization problem to a two-dimensional unconstrained problem and yields a detailed characterization of the optimal pricing policy. We show that the firm’s strategy exhibits three qualitatively distinct regimes depending on the initial utility level. For small utility levels, the optimal cancellation cost is zero. In an intermediate regime, the firm’s optimal policy induces the consumer to set the entry threshold equal to the initial utility level, resulting in immediate subscription. For sufficiently large utility levels, the firm induces permanent lock-in by setting a high cancellation cost and a low subscription fee: the consumer subscribes immediately and never subsequently unsubscribes. The transition between the latter two regimes is discontinuous and results from competition between two local maxima of the firm’s payoff function. We then extend the model to a heterogeneous population of consumers. The superposition of individual two-threshold subscription strategies generates a Preisach hysteresis operator describing the aggregate dependence of the firm’s revenue on the utility dynamics. The discontinuous regime transition persists under heterogeneity, demonstrating the robustness of the underlying mechanism. The Preisach representation predicts complex history dependence and long-term effects of temporary utility shocks. For a gamma distribution of consumer preferences, the firm’s expected payoff is obtained in closed form in terms of incomplete gamma functions. Full article

Legal transplants have consolidated as a core issue of contemporary comparative law, although their study remains marked by significant theoretical and methodological tensions. This article presents a PRISMA-informed structured systematic review of 25 studies published between 2008 and 2025 and retrieved through SciSpace, Google Scholar, PubMed, and Web of Science-supported searching, with the aim of identifying conceptual frameworks, recurrent conditions of validity, and practical limitations in recent legal-transplant scholarship. The search yielded 960 exported records; after deduplication and screening, 30 articles were assessed in full text, of which 25 were available and included. The revised article identifies the 25 studies individually and links the descriptive claims to a study-by-study coding table. The findings show five recurrent theoretical lenses: positivist transfer models, culturalist critiques, diffusion mechanisms and multicausal models, communicative metaphors, and mixed transplant concepts. The corpus covers studies in Europe, China, Asia-Pacific, Vietnam, Japan, India, Afghanistan, Pakistan, Hungary, Brazil, Africa, Latin America, and transnational settings. The evidence does not support universal causal claims, but it consistently suggests that contextual compatibility, institutional capacity, local legitimacy, and interpretive adaptation shape the effectiveness of legal transplants, while linguistic barriers, interpretive mismatches, coercive imposition, and weak implementation capacity constrain them. The corpus remains concentrated in corporate law, intellectual property, constitutional law, criminal law, and drug policy, with a predominance of comparative, doctrinal, case-study, and conceptual methodologies. Full article

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Dexterous grasping with multi-finger robotic hands is essential for general-purpose robotic manipulation, but remains challenging due to high-dimensional hand configurations, multimodal grasp distributions, and contact-rich execution dynamics. Existing methods often decouple grasp target generation from execution policy learning, which limits the consistency between generated grasp goals and downstream control. To address this problem, we propose DexGraspDiffuser, a target-coupled grasp and action diffusion framework for dexterous grasping. The first stage, GraspDiffusion, generates diverse and physically plausible target grasps from object point clouds using a compact representation of hand root translation, continuous rotation, and finger joint configuration. The second stage, a Goal-Conditioned Diffusion Policy, predicts temporally coherent action sequences conditioned on the selected target grasp and current observation. During inference, receding-horizon execution enables action-prefix execution and online replanning for improved robustness. Experiments demonstrate that DexGraspDiffuser achieves success rates of 0.76, 0.72, and 0.68 on training objects, unseen objects from seen categories, and objects from unseen categories, respectively. These results correspond to a three-split average success rate of 0.72 and a train-to-unseen generalization gap of 0.08. Compared with the reproduced UniDexGrasp-T baseline under the same object split and evaluation protocol, DexGraspDiffuser improves the three-split average success rate by 3.3 percentage points and reduces the average mean position error by 0.53 cm. This indicates that target-coupled grasp and action diffusion contribute to improved grasp quality, execution accuracy, and closed-loop stability. Full article

This article examines the maturation of the Astana Hub technology park and its association with Kazakhstan’s national digital competitiveness during the country’s transition from resource dependency toward a higher value-added digital economy. As Central Asia’s largest international technology park, Astana Hub provides a policy-relevant case of a state-initiated open-innovation ecosystem. To proxy the national enabling environment, we construct the Kazakhstan Digital Performance Index (KDPI), a composite measure that normalizes and aggregates three publicly available global indices: the UN E-Government Development Index (EGDI), the Global Innovation Index (GII, WIPO), and the Network Readiness Index (NRI, Portulans Institute). Using an Interrupted Time Series (ITS) design over 2010–2024, we estimate the change in the level and slope of national IT service exports associated with the establishment of the Hub in 2018. The analysis identifies a statistically significant post-2018 increase in the export-growth slope of approximately $112.8 million per year relative to the pre-intervention trend, alongside a near 20-fold rise in IT service exports between 2020 and 2024. By contrast, the composite KDPI shows no comparable acceleration, and an exploratory correlation analysis over the short post-2018 window indicates a positive association between Hub growth and e-government infrastructure but a negative association with the GII. We interpret these patterns cautiously as descriptive evidence consistent with a temporary “island of efficiency,” in which commercial scaling has outpaced economy-wide innovation diffusion, rather than as confirmation of a bidirectional feedback loop or of causal national-level human-capital effects, which the single-country quasi-experimental design cannot establish. The paper discusses policy options for diffusing Hub capabilities into the wider economy and sets out the limitations of the design. Full article

This study examines public perceptions of electric vehicle (EV) adoption in Kuwait, a high-income petroleum-dependent country characterized by highly subsidized electricity, low fuel prices, limited charging infrastructure, and extreme climatic conditions. Using a structured survey of 1753 licensed drivers, the study evaluates how economic incentives, practical constraints, environmental perceptions, technological confidence, and safety concerns shape expectations of future EV diffusion. Descriptive statistics, principal component analysis, and ordinal logistic regression were used to examine the factors associated with respondents’ expectation of widespread EV adoption in Kuwait over the next ten years. The regression results show that low-tariff/delayed-bill perception was the strongest positive predictor of expected EV adoption, indicating that Kuwait’s low-cost electricity environment may strengthen expectations of EV diffusion. However, the findings also demonstrate that electricity tariffs alone do not explain public expectations. EV performance perception, environmental benefit perception, workplace charging, battery warranty, prior passenger experience in an EV, and higher weekly fuel expenditure were also positively associated with stronger expectations of EV adoption. In contrast, perceived complexity was negatively associated with expected adoption, highlighting the importance of consumer familiarity and ease of use. Safety-related perceptions, particularly concerns regarding EV fire-extinguishing difficulty and lower perceived safety compared with conventional vehicles, were also significant, suggesting that fire safety remains a salient issue in the Kuwaiti context. The findings contribute to the literature on sustainable transportation adoption in petroleum-based economies and extreme climates by showing that EV diffusion depends on a combination of economic, infrastructural, technological, environmental, and safety-related factors. Policy efforts in Kuwait should therefore combine charging-infrastructure development, workplace charging expansion, consumer education, battery-warranty assurance, and EV-specific safety and emergency-response measures. Full article

While digitalization is expected to increase the demand for continuing workforce training in European economies, large-scale, cross-country panel data on macro-level relationships between participation in adult learning and digitalization remain insufficient. This study examines how digital diffusion, employment in the information and communication technologies sector, research and development intensity, and institutional quality are associated with adult learning participation across EU-27 member states. An unbalanced panel dataset covering the 27 EU member states for the period 2015–2023 was created from Eurostat and Sustainable Development Goals monitoring indicators. The empirical strategy includes cross-sectional dependence diagnostics, unit root and cointegration tests, fixed-effects estimation with Driscoll–Kraay standard errors, moderation analysis, and robustness checks based on alternative covariance specifications and first differences. The level-fixed-effects results show positive associations between individual internet use, as a proxy for economy-wide digital diffusion, and research and development intensity with adult learning participation (β = 0.110, p < 0.01; β = 2.451, p < 0.01, respectively). Employment in the information and communication technologies sector is not statistically significant, and institutional quality does not significantly moderate the association between individual internet use and adult learning participation. In hypothesis terms, H1 (digital diffusion) and H3 (research and development intensity) are supported in the level specification, whereas H2 (information and communication technologies employment) and H4 (institutional quality moderation) are not supported. When standard errors are clustered at the country level, the key coefficients lose significance, so these level estimates are descriptive structural associations rather than causal effects. The first-difference estimator does not reproduce the level relationships, indicating that the findings primarily reflect cross-country structural associations rather than within-country dynamic effects. Country-group analysis reveals a fourfold difference in mean adult learning participation between the lowest and highest digital diffusion quartiles (5.3% vs. 20.3%). The findings inform EU digital and skills policy by suggesting that the expansion of digital infrastructure should be coordinated with adult learning targets within the 2030 Digital Compass framework. Full article

In this paper, we propose simDP, a sim-to-real transfer framework that enables diffusion policies trained in simulations for the efficient deployment on real-world robots. The key idea is to reduce the sim-to-real gap by aligning the action and observation spaces between simulation and reality. Specifically, we reformulate the action space using end-effector pose and binary gripper state, which can be shared between simulated and physical robots. In addition, we use camera-based visual observations as the primary sensing modality in both domains and train a real-world observation encoder to align with the latent representation learned in simulation. This design allows the action decoder trained in simulation to be reused in the real-world with minimal modification. We evaluated simDP on object manipulation tasks derived from the MimicGen benchmark and show that a simulation-trained diffusion decoder, when combined with a real-world adapted observation encoder, achieves task completion performance similar to and in some cases better than diffusion policies trained only on limited real-world data. These results, obtained across four manipulation tasks in a calibrated real-world transfer setting, suggest that reusing a simulation-trained action decoder with lightweight real-world encoder adaptation provides an effective strategy for controlled sim-to-real transfer, while broader evaluation across diverse tasks, environments, and robot embodiments remains an important direction for future work. Full article

Rural policy is pivotal to achieving the UN 2030 Agenda amid rapid global urbanization. This study integrates bibliometric analysis, stage-based comparative policy analysis, and quantitative SDG alignment modeling across six economies (USA, Germany, Japan, China, India, South Africa) spanning 79 rural policy documents from 1913 to 2025. Each document was scored against all 17 SDGs using a three-point ordinal scale, with AI-assisted coding validated through independent human review (inter-coder reliability: Cohen’s κ = 0.763, indicating substantial agreement prior to reconciliation). Bibliometric results document a post-2015 shift from sectoral silos to integrated sustainability frameworks. Generalized Linear Models (GLMs) identify a pattern of “aggregate convergence with structural divergence”: the year of policy enactment is the sole significant predictor of overall SDG alignment (p < 0.01), while income stage and development status show no independent effect on total scores, indicating that global discourse diffusion drives the universal rise in SDG coverage. However, per-SDG regressions demonstrate that income stage and the developed–developing divide significantly shape which specific SDGs receive attention: “late-emergence” goals scale with income, while “development-imperative” goals are systematically prioritized in developing countries. Three distinct evolutionary trajectories are proposed as interpretive constructs derived from comparative analysis: a U-shaped remedial path in developed economies, a J-shaped leapfrogging path in developing economies, and China’s unique Compressed Checkmark trajectory. A Research–Policy–Development nexus model suggests that economic stages act as a “filter” channeling governance capacity toward goals aligned with prevailing social needs. The findings suggest that developing countries may benefit from a “late-comer discursive advantage” in policy-text alignment; however, policy-text alignment does not imply implementation capacity, and realizing SDGs depends fundamentally on developmental resources to bridge vision and reality. Full article

Against the backdrop of global warming and the “dual carbon” goals, scientifically assessing the spatiotemporal patterns of regional carbon emissions is of great significance for formulating differentiated emission reduction policies. Taking counties of Jiangsu Province as the basic analytical unit, this study integrates NPP-VIIRS nighttime light data and energy statistical yearbook data from 2000 to 2020. An IPCC carbon emission coefficient method was adopted to construct a county-level carbon emission estimation model. Spatial autocorrelation analysis, hot spot detection, Theil–Sen trend estimation, and the Mann–Kendall significance test were comprehensively applied to systematically reveal the spatiotemporal evolution characteristics of energy consumption carbon emissions in Jiangsu Province. The results indicate that county-level carbon emissions in Jiangsu Province exhibit a stable spatial pattern of “higher in the south, lower in the north, and agglomeration along the Yangtze River,” and the total carbon emissions in the southern core area show a statistically significant increasing trend. The spatial pattern of carbon emissions has transformed from “unipolar high-intensity agglomeration” to “zonal diffusion coexisting with multi-point agglomeration.” High per capita carbon emission areas persistently cluster along the Yangtze River, whereas high-carbon-emission-intensity areas have shifted to certain counties in northern Jiangsu. Full article

Rural household energy-saving behavior is central to low-carbon development in ecologically fragile plateau regions. This study explores whether climate risk perception promotes household energy-saving behavior, through which livelihood capital mechanisms this effect operates, and which livelihood capital configurations support high levels of such behavior. Drawing on survey data from 315 rural households in Xining, China, a sustainable livelihood framework is integrated with the pressure–state–response model, and PLS-SEM, an ANN, and fsQCA are applied. The integrated framework regards climate risk perception as external pressure, livelihood capital as the household livelihood state, and energy-saving behavior as the behavioral response. The sustainable livelihood framework identifies the multidimensional resource conditions of rural households, whereas the pressure–state–response model specifies the causal sequence through which perceived climate pressure affects livelihood states and induces behavioral responses. The results show that climate risk perception significantly promotes energy-saving behavior. Physical, human, and social capital exert positive effects, whereas natural and financial capital exert negative effects. Moreover, natural, financial, and social capital significantly mediate the link between climate risk perception and energy-saving behavior. Multi-group analysis shows that physical capital matters more for agriculture-dominated households than non-farm households. The ANN results identify social and human capital as the strongest predictors, and the fsQCA results show that high levels of energy-saving behavior arise not from any single condition but from multiple capital configurations, in which social capital is consistently central. Energy conservation under climate risk is therefore best understood as a multidimensional, nonlinear adaptation process embedded in household livelihood structures rather than a response to any single factor. These findings extend rural energy-saving research by linking climate pressure, livelihood conditions, and configurational decision logic in a plateau socio-ecological context. Policy interventions should combine energy-efficient infrastructure, targeted financial incentives, community-based diffusion, and livelihood-sensitive support for rural households. Full article

The construction sector contributes substantially to global greenhouse gas emissions, making material substitutions a key strategy for advancing sustainability transitions. Mass timber has emerged as a low-carbon alternative to mineral-based construction materials, offering biogenic carbon storage and compatibility with prefabricated and industrialized building systems. This study aims to systematically synthesize the economic, cost, and market evidence on mass timber construction by reviewing 143 peer-reviewed publications, with the objective of clarifying what is empirically known and where uncertainties remain. The reviewed literature reveals three core findings. First, economic outcomes are mixed: while several studies report regional value creation, supply-chain upgrading, and alignment with circular-economy principles, others highlight persistent constraints such as limited manufacturing capacity and uneven policy support. Second, construction cost findings vary substantially, ranging from cost parity or modest savings relative to conventional systems to premiums of approximately 10–15%, shaped by regional pricing, labor availability, transportation distance, regulatory conditions, and supply-chain maturity. Third, market-oriented studies consistently identify slow diffusion, limited practitioner experience, and risk-averse investment environments as key barriers to adoption. Overall, the review shows that economic performance is not yet consistently established and underscores the need for more standardized, context-sensitive, and methodologically consistent evaluation frameworks to support informed decision-making and the sustainable scaling of mass timber construction. Full article

This paper asks whether the development of national artificial intelligence ecosystems contributes to greener economic performance, and whether public governance shapes that relationship. The analysis covers a balanced panel of 36 advanced and emerging economies from 2017 to 2023. We capture general national artificial intelligence ecosystem development through a multidimensional index built on five pillars (innovation, economic diffusion, skills, policy, computing infrastructure) aggregated by within-pillar principal component analysis, and estimate the model by two-step System-GMM, with instrumentation anchored in Wooldridge endogeneity tests robust to heteroscedasticity. Green growth is highly path-dependent, with an autoregressive coefficient close to 0.96 that corresponds to an annual convergence speed of 4.5 percent. Government effectiveness contributes positively and significantly. The artificial intelligence ecosystem index displays no detectable independent effect once persistence and endogeneity are addressed, and its interaction with government effectiveness is similarly indistinguishable from zero, a result that calls for caution in narratives expecting artificial intelligence to deliver sustainability gains on its own. Full article

This paper examines how technology blockade affects leader and follower firms’ research and development (R&D) incentives and their cooperation decisions under asymmetric knowledge spillovers, while also exploring the role of government subsidies in mitigating market failures and restoring cooperation incentives. Motivated by the increasing restrictions on knowledge diffusion in high-technology industries, we develop a two-stage game in which firms first choose R&D investment and then compete in quantities under both non-cooperative and cooperative regimes. Our analysis shows that the impact of technology blockade on firms’ R&D investment and profit distribution depends on R&D efficiency and the presence of asymmetric knowledge spillovers. Specifically, under non-cooperative behavior, the interaction between asymmetric spillovers and R&D efficiency generates nonlinear effects on both R&D efforts and profit allocation. Under cooperative regimes, although firms can internalize spillovers, technology blockade reduces coordination benefits and leads to asymmetric profits, resulting in the absence of a self-enforcing cooperation region. Furthermore, our results indicate that government subsidies can partially or fully restore cooperation incentives, thereby increasing R&D investment and enhancing social welfare in most cases. These findings highlight a substitution effect between policy intervention and external technological constraints, emphasizing the importance of targeted subsidies in mitigating the adverse effects of technology blockade on innovation and collaboration. Full article

Freshwater aquaculture in China has expanded rapidly in recent decades, raising growing concerns about its environmental sustainability. However, the relationship between industrial agglomeration and environmental efficiency in freshwater aquaculture remains insufficiently understood. Using panel data from 18 major freshwater aquaculture provinces in China from 2009 to 2023, this study investigates the nonlinear effects of industrial agglomeration on environmental efficiency. Environmental efficiency is evaluated using a Global Super-SBM model incorporating undesirable outputs, while industrial agglomeration is measured by the location quotient index. A two-way fixed-effects model is employed for empirical estimation. The results reveal a significant inverted U-shaped relationship between industrial agglomeration and environmental efficiency, with a turning point at an agglomeration level of 2.519. Moderate agglomeration improves environmental efficiency through economies of scale and technology diffusion, whereas excessive agglomeration generates crowding effects that reduce efficiency. Further mechanism analysis shows that technology diffusion, proxied by the number of trained fishermen, plays a significant mediating role in this relationship. This study provides new empirical evidence on the nonlinear environmental effects of industrial agglomeration in freshwater aquaculture and offers policy implications for optimizing industrial spatial layout and developing differentiated environmental regulations to support the green and sustainable development of the sector. Full article