Search Results (138)

This study investigates the coupling coordination between carbon emission efficiency (CEE) and carbon balance (CB) in the Yellow River Basin (YRB), aiming to support high-quality regional development and the realization of China’s “dual carbon” goals. Based on panel data from 74 cities in the YRB between 2006 and 2022, the Super-SBM model, Ecological Support Coefficient (ESC), and coupling coordination degree (CCD) model are applied to evaluate the synergy between CEE and CB. Spatiotemporal patterns and driving mechanisms are analyzed using kernel density estimation, Moran’s I index, the Dagum Gini coefficient, Markov chains, and the XGBoost algorithm. The results reveal a generally low and declining level of CCD, with the upstream and midstream regions performing better than the downstream. Spatial clustering is evident, characterized by significant positive autocorrelation and high-high or low-low clusters. Although regional disparities in CCD have narrowed slightly over time, interregional differences remain the primary source of variation. The likelihood of leapfrog development in CCD is limited, and high-CCD regions exhibit weak spillover effects. Forest coverage is identified as the most critical driver, significantly promoting CCD. Conversely, population density, urbanization, energy structure, and energy intensity negatively affect coordination. Economic development demonstrates a U-shaped relationship with CCD. Moreover, nonlinear interactions among forest coverage, population density, energy structure, and industrial enterprise scale further intensify the complexity of CCD. These findings provide important implications for enhancing regional carbon governance and achieving balanced ecological-economic development in the YRB. Full article

International industrial transfer has driven rapid construction land expansion in emerging metropolitan areas, posing challenges for sustainable land management. However, existing research has largely overlooked the spatiotemporal patterns and driving mechanisms of this expansion, particularly in Southeast Asian metropolitan regions. To address this gap, we focused on the Ho Chi Minh City metropolitan area, utilizing construction land data from GLC_FCS30D to analyze the dynamics of construction land expansion during this period. Findings indicated that: (1) Continuous expansion of construction land, with the expansion rate during 2010–2020 being five times that of 2000–2010; (2) The spatial pattern evolved from initial infilling development in urban cores to subsequent leapfrogging and edge expansion toward peripheral counties and transportation corridors; (3) The expansion of construction land occurred alongside substantial losses of wetland and cultivated land. Between 2000 and 2020, the conversion of cultivated land to construction land increased significantly, particularly during 2010–2020 when cultivated land conversion accounted for 93.76% of newly developed construction land. Wetland conversion also showed notable growth during this period, comprising 3.86% of total newly added construction land; (4) Foreign direct investment (FDI) served as the primary catalyst, while industrial park development and transport infrastructure projects functioned as secondary accelerants. This study constructed a framework to systematically analyze the global and local driving mechanisms of metropolitan land expansion. The findings deepen the understanding of land-use transitions in emerging countries and provide both theoretical support and policy references for sustainable land management. Full article

As globalization deepens, concerns over global fragmentation have intensified, accompanied by rising expectations that the Global South will emerge as a key driver of innovation, competitiveness, advanced markets, and high-quality employment. The widespread diffusion of the Internet and smartphones across developing countries suggests the possibility of leapfrog growth, highlighting the informal economy as a potential source of innovation. Recent developments in generative artificial intelligence (GAI) have further underscored the opportunity for collaborative engagement between developed and developing countries to awaken and harness sleeping innovation resources. This study investigates the dynamism of such international collaboration, focusing on digitalization-related challenges and its contributions to leapfrog growth. The interconnections among Internet usage, smartphone penetration, and economic development are examined, revealing the formation of a self-propagating cycle facilitated by GAI. A mathematical model is constructed to demonstrate the dependency of growth on sleeping resources inherent in the informal economy, which is empirically validated through data from nine African countries. Using the coevolutionary dynamics of Amazon and AWS as a conceptual reference, a novel framework is proposed for international collaborative utilization of sleeping innovation resources, offering new insights into GAI-driven innovation rooted in the informal economy. Full article

Recently, new and nontrivial analytical solutions that contain the Kummer functions have been found for an equation system of two diffusion–reaction equations. The equations are coupled by two different types of linear reaction terms which have explicit time-dependence. We first make some corrections to these solutions in the case of two different reaction terms. Then, we collect eight efficient explicit numerical schemes which are unconditionally stable if the reaction terms are missing, and apply them to the system of equations. We show that they severely outperform the standard explicit methods when low or medium accuracy is required. Using parameter sweeps, we thoroughly investigate how the performance of the methods depends on the coefficients and parameters such as the length of the examined time interval. We obtained that, similarly to the single-equation case, the leapfrog–hopscotch method is usually the most efficient to solve these problems. Full article

Global efforts to reduce climate change have increased, necessitating more comprehensive research. However, empirical evidence of the implication of synergizing foreign direct investment (FDI) and renewable energy consumption (REC) to reduce environmental pollution, specifically with nitrous oxide (N₂O) and methane (CH₄) emissions, is missing in the literature. This research investigates the impact of FDI, REC and their synergy in facilitating technological leapfrogging, analyzing their linear, non-linear and indirect effects on environmental pollution (CO₂, N₂O and CH₄ emissions). The analysis focuses on 81 developing countries, analyzing them at both the general level and by income groups—low-income countries (LICs), middle-income countries (MICs) and high-income countries (HICs), with government effectiveness and economic growth serving as mediating variables. Using Canonical Correlation Regression (CCR), Fully Modified Ordinary Least Squares (FMOLS) and clustered Pooled Least Square (PLS) techniques, the analysis covers data from 2003 to 2023. The results indicate that at the general level, FDI and REC increase N₂O and CH₄ emissions individually. However, their integration mitigates N₂O and CH₄ emissions. Additionally, the relationships remain consistent even when government effectiveness and economic growth are considered mediators. However, economic growth is more pronounced than government effectiveness in reducing environmental pollution. The non-linear analysis also reveals that FDI and REC have a significant U-shaped effect on CO₂ emissions. However, their synergy demonstrates an inverted U-shaped nexus with CO₂ emissions. At the income group levels, the interplay of FDI and REC reduces N₂O and CH₄ emissions in MICs; however, in LICs and HICs, it increases N₂O and CH₄ emissions. Full article

This paper argues that investing in artificial intelligence (AI) in developing economies involves significant trade-offs requiring ethical, financial, and geopolitical scrutiny. While AI is increasingly seen as a vehicle for technological leapfrogging, such ambitions often mask structural constraints, including weak infrastructure, limited institutional capacity, and external dependency. Using the economic theory of opportunity cost—extended through the political economy and digital governance perspectives—this study critically examines AI policy strategies in Ghana, Kenya, and Rwanda. A qualitative design grounded in secondary data and a thematic analysis reveal how AI investment may reallocate scarce resources away from essential services, exacerbate inequality, and entrench strategic technological dependency. This paper proposes a public policy framework built on four principles—sequential readiness, strategic alignment, ethical governance, and capacity building—to guide equitable AI deployment. It argues for establishing a digital social compact between states, citizens, and technology actors to safeguard public interest in AI-driven development. Finally, this paper outlines a future research agenda emphasizing the mixed-method evaluation of AI’s long-term social impacts, including employment, inclusion, and public service delivery. Full article

Shenzhen is a pilot pioneer in China. Developing the photovoltaic industry is an important area for Shenzhen to address climate change; thus, the Shenzhen’s government issued a series of support policies. However, does the released policy promote the development of the Shenzhen photovoltaic industry? Starting from the guiding mechanism of industrial policy on the development of the industrial chain, this paper discusses the compatibility between industrial policy and the development of the industrial chain. Through the analysis of Shenzhen photovoltaic industry data, it is found that the total factor productivity of the Shenzhen photovoltaic industry is twice that of the Yangtze River Delta and the Pearl River Delta, and the life cycle of the industrial chain is lower than the national average. However, the concentration of Shenzhen’s photovoltaic industry in 2021 was less than two-thirds of that in 2013, and it is still declining. At the same time, Shenzhen has obvious advantages in the photovoltaic industry market, but the compatibility of industrial policies is insufficient. Therefore, the overall policy suitability of Shenzhen’s photovoltaic industry is poor. The policy adjustment should be based on improving the concentration of the regional photovoltaic industry and realizing the leapfrog development of the industry by encouraging photovoltaic enterprises to extend to both ends of the industrial chain. Full article

This study focuses on the intrinsic mechanisms and sustainable value of artificial intelligence (AI)-driven green collaborative innovation in enterprises amid the global green low-carbon transition, revealing new pathways for digital technology-enabled green development. Based on the data of China’s A-share listed companies jointly applying for green patents with other entities from 2010 to 2023, this study used a high-dimensional fixed effect model to empirically find that artificial intelligence significantly promotes green collaborative innovation. This promoting effect proved more pronounced in the case of high macroeconomic uncertainty, large enterprises and SOEs. A mechanism test revealed that artificial intelligence drives green collaborative innovation primarily by reducing transaction costs and optimizing the labor structure. A moderating effect analysis showed that green investor entry and CEO openness can strengthen the facilitating effect of artificial intelligence on green collaborative innovation. In addition, the facilitating effect of artificial intelligence on green collaborative innovation helps companies reduce carbon emissions and improve ESG performance, driving the transformation of business ecosystems toward environmental sustainability. From a technology–organization–environment co-evolution perspective, this research clarifies the micro-level operational chain of AI-enabled green innovation, providing theoretical support for developing countries to achieve leapfrog low-carbon transitions through digital technologies. Practically, it offers actionable insights for advancing AI-enabled green industries, constructing collaborative green innovation ecosystems, and supporting the realization of the United Nations Sustainable Development Goals (SDGs). Full article

This article presents a comparative analysis of rock mass fracturing at the Karasu gold deposit, located approximately 400 km northwest of Karaganda, Kazakhstan. The analysis is based on core drilling data and measurements from underground workings, including an old mine that was explored and investigated to gather missing information. The spatial characteristics of fractures and their relationship with tectonic faults are identified. The feasibility of using the Rock Quality Designation (RQD) index for classifying fracture systems is assessed. Engineering and geological studies include the identification of major fracture systems and their characteristics using Leapfrog and Rocscience software, chosen for their ease of use and extensive functionality. The stability parameters of open-pit slopes are calculated, considering the physical and mechanical properties of rocks, the degree of fracturing, and the influence of groundwater. Key engineering and geological elements of the rock mass are identified, emphasizing the necessity of integrating fracture data from various sources to improve the accuracy of mine design and ensure the safe operation of open pits. These studies are part of the exploration phase to assess the geological situation and the physico-mechanical properties of these rocks for further quarry design. Full article

As a precious non-renewable resource, the rational utilization of land resources is crucial for global sustainable development, with urban land development scenario prediction and analysis serving as key methodologies to achieve this goal. Although previous studies have extensively explored urban land expansion simulation and scenario forecasting, further investigation is still required to simultaneously address spatial functional zoning differentiation and urban expansion mode diversity while simulating development trends under various expansion modes. In this study, we integrated major functional zones and ecological redlines to delineate urban spatial functional units and define development coefficients for construction land within each unit. Based on the spatial heterogeneity of expansion modes, the scopes of infill, sprawl, and leapfrog expansion modes were determined. Combining functional zoning and expansion mode zoning, we employed cellular automata model principles to design land conversion rules and simulate the evolution of land use under different expansion modes. Using Jiangyin City, China, as a case study, the model achieved a high simulation accuracy (kappa coefficient of 0.959), significantly outperforming comparative models. By predicting land-use patterns under different expansion scenarios and aligning with Jiangyin’s territorial planning goals, we recommend implementing infill–sprawl–leapfrog and infill–leapfrog–sprawl expansion modes. The results demonstrate that the model effectively supports the refined simulation of urban land expansion, providing a scientific basis for optimizing land resource allocation and balancing ecological protection with urban development. Future research could integrate multiple types of territorial control elements, refine land-use categories, and optimize prediction scenarios to enhance the model’s practicality and applicability. Full article

This paper aims to assess the impact of the New Energy Vehicle (NEV) mandate policy on the passenger vehicle market in China, with a focus on its effectiveness in promoting NEV adoption. In response to global climate goals and energy security concerns, China has implemented various NEV policies, including the phase-out of direct subsidies and the introduction of the NEV mandate policy (dual-credits policy). This policy, which combines NEV credits and Corporate Average Fuel Consumption (CAFC) credits, aims not only to promote NEV adoption but also to support industrial policy objectives by helping the auto industry leapfrog traditional internal combustion engines and become globally competitive. In this study, a System Dynamics (SD) model was developed using Vensim software (10.2.2) to simulate interactions between automakers, government policies, and consumer behaviors. The results show that the NEV mandate policy significantly boosts NEV sales, with projections indicating that NEV sales will reach 15 million units by 2030, accounting for 55% of the passenger vehicle market. Additionally, the study finds that tightening NEV credits standards and increasing the NEV credit proportion requirements can further enhance market growth, with stricter measures post-2023 being crucial to achieving a 50% market share. In contrast, under a scenario where the dual-credits policy ends in 2024, the NEV market share would still grow but would fall short of the 50% target by 2030. The findings suggest that stronger policy measures will be essential to maintain long-term market momentum. Full article

Vast rural populations squeezed into cities, leaving small townships hollowed out. Even so, some townships’ lands are still expanding. The dilemma of land expansion with a shrinking population raises various challenges including farmland reduction. Much of the current research on the impervious expansion has focused on urban areas, while townships were often neglected. Based on high-resolution satellite data and statistic data in 1993–2018, this study explored long-term township impervious land expansion dynamics and explored the real-world relationship with their population for the 17 first-batch-of-strong-tourism counties in China. The results showed that over the past 26 years, there had been an increasing trend in the impervious areas in 17 counties. There were diseconomies of scale for impervious land expansion, i.e., the township’s land expansion became less efficient with the shrinking population. The impervious area was predominantly converted from cropland (ranging from 16.40% to 71.96%). The expansion in highlands was also increasing, although most of the growth occurred in the lowlands. The expansion patterns were mainly dominated by infilling and edge-expansion during the early stage, after which leapfrogging occurred, and infilling increased again in recent years. Townships with a “closer” accessibility to tourist attractions had the largest and fastest rate of impervious land expansion and an increasing influence of townships. These counties needed customized development with its unique natural conditions. This study could provide data-based evidence for better planning and governing to promote sustainable development worldwide. Full article

It is well established that China’s rapid urban expansion has led to a substantial loss of cropland. However, few studies have examined how different urban expansion patterns contribute to cropland consumption, which has hindered the formulation of sustainable urban development and cropland protection policies. To fill this gap, we analyzed the occupation of cropland under three urban expansion patterns (leap-frogging, edge-spreading, and interior filling) in China from 1990 to 2020, using long-term land use data. The dominant driving forces of cropland loss were then explored using the XGBoost model and SHAP values. Our findings indicate that urban expansion in China from 1990 to 2020 resulted in a 6.3% reduction in cropland, with edge-spreading (4.0%) contributing the most, followed by leap-frogging (2.1%) and interior filling (0.2%). Change in urban intensity (CUI) proved to be the most critical driver of cropland loss, with SHAP values of 0.38, 0.28, and 0.37 for edge-spreading, leap-frogging, and interior filling, respectively. Over time, the driving forces evolved from a single demographic-economic dominance to a more diversified and integrated set of drivers. Based on these findings, we propose tailored planning and policies for different urban expansion patterns; for regions dominated by edge-spreading, stricter controls on urban boundaries and stronger land use planning constraints are required. For regions with prominent interior filling expansion, efforts should be made to improve internal land use efficiency while preserving existing cropland spaces. In regions characterized by leap-frogging expansion, further optimization of construction land allocation is needed to reduce the occupation of productive suburban cropland. These findings not only offer new empirical evidence for understanding the interplay between urban expansion and cropland conservation but also provide transferable insights that can inform sustainable land-use planning and cropland protection strategies in other rapidly urbanizing regions facing similar challenges. Full article

With the urgent demand for net-zero emissions, renewable energy is taking the lead and wind power is becoming increasingly important. Among the most promising sources, offshore wind energy located in deep water has gained significant attention. This review focuses on the experimental methods, simulation approaches, and wake characteristics of floating offshore wind turbines (FOWTs). The hydrodynamics and aerodynamics of FOWTs are not isolated and they interact with each other. Under the environmental load and mooring force, the floating platform has six degrees of freedom motions, which bring the changes in the relative wind speed to the turbine rotor, and furthermore, to the turbine aerodynamics. Then, the platform’s movements lead to a complex FOWT wake evolution, including wake recovery acceleration, velocity deficit fluctuations, wake deformation and wake meandering. In scale FOWT tests, it is challenging to simultaneously satisfy Reynolds number and Froude number similarity, resulting in gaps between scale model experiments and field measurements. Recently, progress has been made in scale model experiments; furthermore, a “Hardware in the loop” technique has been developed as an effective solution to the above contradiction. In numerical simulations, the coupling of hydrodynamics and aerodynamics is the concern and a typical numerical simulation of multi-body and multi-physical coupling is reviewed in this paper. Furthermore, recent advancements have been made in the analysis of wake characteristics, such as the application of instability theory and modal decomposition techniques in the study of FOWT wake evolution. These studies have revealed the formation of vortex rings and leapfrogging behavior in adjacent helical vortices, which deepens the understanding of the FOWT wake. Overall, this paper provides a comprehensive review of recent research on FOWT wake dynamics. Full article

As a prominent forest pest on international quarantine lists, pine wilt disease (PWD) is characterized by its ease of transmission, rapid onset, high mortality rate, and the complexity of its prevention and control. The disease inflicts devastating damage on pine forest ecosystems and biodiversity in affected regions, resulting in substantial losses of ecological and economic value. This study uses 40 years of county-level data on PWD occurrences in China to investigate the historical spatiotemporal distribution patterns, the spreading process, and the impact of PWD on forest ecosystems. We divided the spread of PWD in China into three stages based on the changes in the number of affected areas. We used SaTScan spatial scanning to analyze the spatiotemporal distribution patterns and regional characteristics of the disease in each stage. Based on the spatial relationships of the affected areas, we identified two types of spread, namely continuous spread and leapfrogging spread, and conducted ecological models of the two spreading processes to describe the spread of PWD over the past 40 years. The results indicate that PWD has two major expansion periods in China. They show a diffusion pattern spreading from points to areas, ultimately forming four clusters with regional characteristics. Driving factors were selected for model construction based on the biological characteristics and spatiotemporal distribution patterns of PWD. The Susceptible (SIS) model and Random Forest (RF) model achieve good results in simulating continuous and leapfrog spread. By integrating the models of the two spreading processes, we can clearly quantify the 40-year spread of PWD in China. The long-term dynamic ecological modeling of PWD, based on historical dissemination characteristics, facilitates the development of disaster prediction models and the maintenance of forest ecosystems while also providing case studies for the invasion and spread of forest pests and pathogens. Full article