Search Results (48)

Mixing a proppant-laden base fluid with high-pressure CO₂ can reduce its effective sand volume fraction, thereby impairing proppant placement and fracture conductivity. This study performs a numerical structural screening of the helical guide groove in a centrifugal–filtration sand concentrator intended for the pre-foaming base-fluid line upstream of the foam generator and operating against a 105 MPa outlet backpressure. The full-scale base geometry—an inner diameter of 500 mm, a concentration-zone length of 3500 mm, and 18 slotted-screen openings—was retained, while groove depth (10–40 mm), groove width (110–150 mm), and pitch (300–700 mm) were varied sequentially in 14 factor-level evaluations (12 unique geometries). Steady-state calculations were conducted in ANSYS Fluent 2022 R1 using an Eulerian–Eulerian two-fluid framework, the RNG k–ε turbulence closure, a constant apparent liquid viscosity of 0.040 Pa·s, and a compiled UDF-based phase-selective screen condition. CO₂ was not included as a separate phase. Within the investigated ranges, increasing groove depth and width increased the sand volume fraction at the concentrated-fluid outlet, whereas pitch produced a pronounced inverted-U-shaped response. The best tested geometry had a pitch of 500 mm, a width of 150 mm, and a depth of 40 mm, yielding an outlet sand volume fraction of 54.05%. This value is 3.80 percentage points higher than the lowest-performing tested configuration (50.25%), corresponding to a relative increase of 7.56%; it is also 14.05 percentage points above the inlet value of 40.00% and 4.05 percentage points above the engineering target of 50.00%. The results provide comparative numerical evidence for groove-parameter selection; independent experimental validation remains required before field application. Full article

Continuous inter-firm collaboration gives rise to technological innovation networks (TIN), which increasingly shape firms’ stakeholder perceptions and reputation. This study examines how TIN characteristics influence corporate reputation using Chinese A-share listed firms from 2018 to 2022. The results show that TIN scale, heterogeneity, and openness significantly enhance corporate reputation, while tie strength has no significant effect. Further analyses reveal that the employee income gap mediates the relationship between TIN scale and reputation with an inverted U-shaped effect. In addition, TIN heterogeneity improves reputation by enhancing investment efficiency, and TIN openness contributes to reputation enhancement by reducing customer concentration risk. These findings advance understanding of the reputational value of innovation networks and provide implications for network strategy and stakeholder management. Full article

This article looks at how trade, energy transition indicators, and economic prosperity are connected in the ASEAN-7 countries (Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam) from 1995 to 2021. Using dynamic panel data and panel threshold regression analysis, the research shows that trade openness has a significant effect on energy transition and economic prosperity. The key findings include: first, there is a strong conditional relationship between trade openness and energy transition indicators that affect economic prosperity. Second, the study finds different patterns for the indicators: an inverted U-shaped relationship for renewable energy share, peaking at about 34.7% before declining, and a U-shaped relationship for total energy use, which first drops to a low of 50.9 kgoe per capita before rising again. This indicates that economic prosperity first benefits from an increase in renewable capacity but later shifts toward total energy consumption. Third, the research identifies a trade threshold of 5.40, connected to a trade share of GDP of 221.41%, necessary to maximize the benefits of energy transitions in the region. The article highlights the complexity of the relationship between trade dynamics and energy policies. It suggests that well-designed trade strategies can improve the benefits of energy transitions. However, it also points out limitations, such as its focus on ASEAN-7 due to data constraints, possible biases from endogeneity in the model, and the complex effects of CO₂ emissions on economic prosperity. Future research could help policymakers find a balance between economic prosperity and sustainable energy use. Future research should explore more fundamental trade liberalization issues involving the US and other global regions. Including a broader array of topics, especially relevant research on the sustainable development of countries in the Asia-Pacific region, could enhance future studies. Full article

Breakthrough innovation—central to industrial competitiveness and the ongoing clean-energy transition—remains persistently constrained by information homogenization and weak cross-domain integration in single-layer innovation networks. Technology Innovation Composite Networks (TICNs) have therefore been advocated as dual-layer platforms coupling knowledge and collaboration networks, yet the cross-layer mechanism through which they generate breakthrough outputs has not been specified. This paper specifies and tests how knowledge-layer structural holes open access to heterogeneous information that must cross into the collaboration layer to be recombined into breakthroughs. Two distinct boundaries shape the outcome. Inventors’ finite cognitive processing capacity makes integration returns decay along an inverted U-shape; separately, excessive degree and closeness centrality drive the collaboration layer into homogenization and localization, narrowing the range of structural holes it can productively absorb and shifting the breakthrough peak toward lower structural-hole levels. Together, they delineate an optimal cross-layer integration zone. Using panel data on 10,681 patents, 948 inventors, and 5631 inventor-year observations from new energy (2004–2018), a fixed-effects negative binomial model confirms the inverted U-shape and the steepening, peak-shifting moderations of degree and closeness centrality; a Lind–Mehlum test places the turning point inside the observed data range, and negative binomial (robust SE), Poisson and zero-inflated Poisson specifications—together with a stricter top-1% breakthrough threshold—yield consistent results. The study moves multilayer network research from structural description toward mechanism-level identification and offers actionable network-design guidance. Full article

Reconciling economic growth with environmental sustainability and energy security is a defining challenge for resource-constrained emerging economies. This study examines whether Jordan follows the Environmental Kuznets Curve (EKC) and the Energy Kuznets Curve (EnKC)—two hypotheses positing that as an economy grows, its environmental degradation and energy consumption follow an inverted U-shaped curve in relation to per capita GDP—as counterparts to the original Kuznets curve. While these relationships have been investigated in cross-country settings, little attention has been given to individual emerging economies such as Jordan, where energy and environmental issues are among the most pressing challenges of the new century. The existence of EKC and EnKC curves is tested using a “time-varying parametric (TVP) framework”—specifically, the unobserved components model (UCM), utilizing annual data from 1980 to 2024. Further tests are carried out to validate the nonlinearity hypothesis using the variable-addition test and non-nested model selection tests. Moreover, we augment the EKC and EnKC by incorporating trade openness and urbanization as control variables. For robustness, we support the UCM results with the Dynamic OLS (DOLS) long-run estimator. The results support the EnKC across the entire battery of tests, with a turning point of roughly USD 4000–4650 depending on specification. For the EKC, the OLS quadratic estimation does not exhibit a clear inverted-U; however, once a stochastic trend (UCM) or appropriate covariates (Trade, Urban) are introduced, the inverted-U re-emerges with a turning point near USD 4149–4874. This study contributes novel empirical evidence on the EKC and EnKC for Jordan using a TVP framework. Whereas prior studies have explored the EKC in Jordan, this study systematically validates both the energy and environmental variants of the Kuznets curve using robust econometric strategies. The results offer valuable policy insights for sustainable development in Jordan and other resource-constrained emerging economies facing analogous development–environment trade-offs within international climate transition frameworks. Full article

Under the dual-carbon goals and energy transition backdrop, the photovoltaic cell has become a crucial pillar for optimizing China’s energy structure and promoting green development. From the perspective of patent intelligence, this study systematically investigates the spatiotemporal evolution paths, coupling characteristics, and driving mechanisms of China’s photovoltaic cell industry and innovation chains, using nationwide photovoltaic cell enterprise and patent data from 2005 to 2024 and integrating spatial gravity center modeling, location quotient analysis, and spatial Durbin models. The findings reveal the following: (1) the spatiotemporal evolution of the dual chains exhibits distinct phases, with a notable developmental leap after 2015. The industry chain shows a pattern of “westward shift and eastern optimization,” while the innovation chain evolves from eastern dominance toward a nationally coordinated, multipolar network. (2) At the macro level, the dual chains demonstrate a coupling trend characterized by “coordinated gravity center migration and spatial distance convergence,” yet significant spatial heterogeneity and mismatch persist at the city scale. (3) Industrial agglomeration has an inverted U-shaped effect on innovation, with regional heterogeneity in its impact, driven synergistically by multidimensional factors such as economic foundation, the innovation environment, and openness. Based on these insights, this study proposes recommendations for optimizing the spatial layout of these dual chains, strengthening multifactor synergy, and implementing regionally differentiated policies, aiming to provide decision-making references for achieving sustainable and high-quality development in the photovoltaic cell. Full article

The rapid advancement of artificial intelligence (AI) has reshaped corporate innovation, yet the existing literature has largely overlooked the non-linear boundary conditions of AI’s innovation effects. This study asks: what is the functional form of the AI–innovation relationship, and through which mechanisms does it operate? Using a sample of 25,204 firm-year observations from Chinese A-share manufacturing companies (2010–2023), we employ fixed-effects models, U-tests, bootstrap mediation, and text similarity analysis. The findings reveal an inverted-U-shaped relationship with a turning point at 2.948. Absorptive capacity partially mediates this relationship, while industry concentration negatively moderates it. Patent text similarity analysis confirms the “homogenization trap.” Heterogeneity analysis shows AI’s enabling effect is more sustainable in non-state-owned and high-tech firms. This study extends the TOE framework by identifying the optimal AI adoption range and empirically validating the homogenization trap, offering guidance for firms to invest in proprietary AI models and for governments to promote open data initiatives. Future research should test these findings across different institutional contexts, particularly European economies. Full article

Against the backdrop of the digital economy reshaping the global competitive landscape and the urgent demand for sustainable development, how data factors drive startups to break through resource constraints, achieve a niche leap, and realize long-term sustainable growth has become a critical issue of common concern in academia and policy circles. Drawing on resource orchestration theory and the dynamic capability view, this study constructs a theoretical framework of “Data Factor Allocation → Digital Capability Integration → Niche Leap → Sustainable Growth” and conducts an empirical test, using 412 technology-based startups as samples. The findings are as follows: (1) Data factor allocation (encompassing scenario-based access, lightweight tool penetration, and ecological sharing) exerts a significant inverted U-shaped relationship impact on both digital capability integration and the startup niche leap (range of quadratic term coefficients for core dimensions: −0.165~−0.203, p < 0.01), with turning points between 3.41 and 3.72 on a 5-point scale. Excessive data investment may trigger risks of capability hollowing and niche lock-in, hindering sustainable growth. (2) Digital capability integration (including technology application, resource coordination, and dynamic adaptation capabilities) plays a non-linear mediating role, with mediation proportions ranging from 18.7% to 32.4%. Among them, the technology application capability exhibits the highest transmission efficiency between lightweight tool penetration and the niche leap (32.4%), thereby promoting sustainable value creation. (3) The moderating effect of data governance maturity is heterogeneous: governance adaptability significantly strengthens the mediating path of the technology application capability (β = 0.187, p < 0.01) and security compliance enhances the transmission efficiency of the resource coordination capability (β = 0.165, p < 0.01), while the moderating effect of open sharing is insignificant. These findings provide a dynamic framework for the non-linear and sustainable leap of startups by integrating two core theories. They offer a decision-making basis for enterprises to optimize data allocation strategies (e.g., controlling allocation thresholds to avoid resource waste) and for governments to improve governance policies (e.g., data vouchers, trusted data spaces), thereby facilitating the implementation of the “Data Factor × Innovation and Entrepreneurship × Sustainable Development” initiative and promoting the sustainable growth of the digital economy ecosystem. Full article

Open public data is a vital institutional arrangement for overcoming data constraints in corporate low-carbon technological innovation. Using a panel dataset of China’s Shanghai and Shenzhen A-share listed firms over the 2007–2023 period, this study employs a difference-in-differences (DID) approach to examine the impact of open public data on corporate low-carbon technological innovation. The results show that open public data has a significant positive effect on corporate low-carbon technological innovation, and the results remain robust across multiple validation tests. Mechanism tests point out that government transparency negatively moderates the promotional effect of public data openness on corporate low-carbon technological innovation, while barriers to factor mobility positively moderate this effect. The heterogeneity analysis indicates that the positive impact of open public data is more pronounced among firms characterized by higher R&D investment, lower financial constraints, and greater digitalization. Further analysis indicates that open public data also exhibits significant geographic and industry spillover effects, with the geographic spillover following an inverted U-shaped pattern of decay and the industry spillover driven by peer imitation. This study provides evidence on leveraging open public data to stimulate low-carbon innovation and facilitate green economic transformation, offering valuable insights for advancing data-driven sustainable development globally. Full article

In the context of China’s accelerated “dual transition” towards industrial intelligence and green development, this paper investigates how local government decarbonization pressures affect urban industrial intelligence in China. Using the Low-Carbon City Pilot policy as a quasi-natural experiment, a staggered difference-in-differences approach and Causal Forest model reveal the following findings: (1) Local government decarbonization pressures significantly boost urban industrial intelligence. (2) Local government decarbonization pressures foster intelligent development by encouraging the introduction of intelligent policies, which motivate enterprises to adopt proactive strategies. Meanwhile, the pressures compel enterprises to engage in source-based environmental governance, resulting in a passive intelligent response. Together, these approaches enhance urban industrial intelligence. (3) Fiscal pressure negatively moderates the relationship between local government decarbonization pressures and urban industrial intelligence. (4) There is an inverted U-shaped relationship between openness to foreign trade and the Conditional Average Treatment Effect (CATE), while CATE is higher for cities with higher urban labor costs. (5) Finally, urban industrial intelligence effectively channels local government decarbonization pressures into measurable emission reductions. These findings have significant policy relevance for building a low-carbon, intelligent society. Full article

This study focuses on the effect of financial development, natural resource rent, human development, and technological innovation on the ecological and carbon footprints of the G-10 countries between 1990 and 2022. This study also considers the impact of globalization, trade openness, urbanization, and renewable energy on environmental degradation. The study uses Kao and Westerlund DH cointegration tests, FMOLS and DOLS estimators, and panel Fisher and Hatemi-J asymmetric causality tests to provide reliable results. Long-run estimates confirm an inverted U-shaped linkage between financial development and ecological and carbon footprints. Natural resource rent and technological innovation increase ecological and carbon footprints, while human development decreases them. Furthermore, globalization, trade openness, and renewable energy contribute to environmental quality, while urbanization increases environmental degradation. The Fisher test findings reveal that financial development, natural resource rent, human development, and technological innovation have a causal link with the ecological and carbon footprint. The results of the Hatemi-J test show that the negative shocks observed in the ecological and carbon footprint are affected by both negative and positive shocks in financial development, natural resource rent, and technological innovation. Moreover, positive and negative shocks in human development are the main drivers of negative shocks in the carbon footprint, while positive shocks in human development lead to negative shocks in the ecological footprint. Full article

This study examines the intricate relationship between economic growth and European environmental degradation via the Environmental Kuznets Curve (EKC). Our results contest the traditional inverted U-shape model of the Environmental Kuznets Curve, indicating that the theory may not be consistently applicable across European countries. Utilizing CS-ARDL and MMQR modelling, we reveal substantial regional disparities. Western European nations demonstrate a typical Environmental Kuznets Curve (EKC) pattern in the short term, characterized by an initial increase in emissions alongside GDP development, followed by a subsequent fall. Conversely, Eastern and Balkan nations exhibit a U-shaped connection, described by an early decline in emissions followed by a subsequent increase as their development levels increase. The influence of renewable energy differs, as it decreases emissions in the short term in Western Europe. However, its long-term impacts are variable, especially when contrasted with its more pronounced effect on emissions in Eastern and Balkan countries. Furthermore, trade openness intensifies environmental degradation in the short-term across all regions, although its long-term impact diminishes, particularly concerning greenhouse gases (GHG). The relationship between renewable energy and trade openness is substantial for the short-term reduction of carbon dioxide emissions, but this effect declines with time. The results indicate that a uniform environmental policy throughout Europe may lack efficacy. Customized strategies to expedite the transition in Western Europe and more specific interventions in Eastern Europe are essential to harmonize economic progress with environmental sustainability. Future research should examine the determinants of the diminishing long-term effects of renewable energy and the interplay between trade and environmental policies. Full article

Sci-Tech innovation enterprises (STIEs) in China are responsible for improving the quality of national innovation (IQ). Because of their inherent innovation openness (IO), STIEs are facing constantly changing external cooperation channels and gradually optimizing their openness. However, existing research considers external cooperation relationships as established network environments, which may not apply to STIEs’ network relationships that are still under construction. Hence, this study investigates the impact of STIEs’ IO on IQ by exploring the role of structure embeddedness (SE). Empirical findings from 362 sample enterprises suggest that openness breadth and depth have an inverted U-shaped relationship with IQ, while openness balance impacts IQ positively. Moreover, network centrality plays a partial mediation role between openness depth and IQ, and network reach fully mediates the relationship between openness balance and IQ. The results indicate the influence of three openness factors on IQ and further expand the research on the SE of STIEs in the dynamic development stage. These can support STIEs to improve IQ through the adjustment of network centrality and reach by changing their openness depth and balance. Full article

Part-time farming is a widespread phenomenon associated with the long-term global trend of urbanization, especially in China since its reform and opening-up in 1978. The shift of agricultural labor to non-agricultural sectors has significantly impacted cultivated land use activities, yet the connection between part-time farming and cultivated land use sustainability (CLS) remains understudied. Here, we construct an index system for assessing CLS that integrates ecological, economic, and social sustainability. Using survey data from seven Chinese villages across three provinces, we analyze the impact pattern and mechanism of part-time farming on CLS. We find the following: (1) The impact of part-time farming on CLS presents an inverted U-shape, peaking negatively at a 45% inflection point; (2) Spatial heterogeneity exists in the effect of part-time farming on CLS; (3) A household’s non-agricultural workforce size and the gender of the household head significantly moderate the link between part-time farming and CLS; (4) CLS strongly hinges on various factors including the household head’s health, other family members’ education levels, commercial insurance, and agricultural skills training. Our findings provide empirical insights into governing part-time farming for sustainable cultivated land use and, eventually, rural human–land system sustainability. Full article

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments. Full article