Search Results (91)

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights. Full article

Within China’s “Central China Rising” strategy, urban tourismification operates as a production mode that reconfigures spatial, economic, and ecological systems—mirroring global overtourism challenges seen in Barcelona and Venice, where rapid infrastructure development often prioritizes economic gains over ecological resilience (cf. Lines 43–46). This study examines 80 central Chinese cities (2010–2021), proposing the Urban Tourismification–Transportation Quality–Ecological Resilience System (UTTES) framework. Using entropy weighting, improved coupling coordination degree (CCD), GM (1,1) forecasting, and spatial Durbin models, we analyze coordination relationships, driving factors, and mechanisms. Key findings reveal the following: (1) UTTES coordination peaked in 2019 (pre-COVID), showing a spatial “center-periphery” gradient with provincial capitals leading. (2) Projections indicate transportation efficiency as a critical bottleneck—most cities will achieve good coordination post-2026. (3) Economic activity, social restructuring, and policy support drive the system, with spatial spillovers creating dual-path mechanisms (economic growth vs. manufacturing/environmental barriers). The UTTES framework advances a replicable methodology for diagnosing Tourism–Transportation–Ecology synergies in rapidly developing regions, integrating multidimensional indicators to balance environmental governance and tourism dynamics. Full article

Amid China’s “dual carbon” goals of achieving carbon peaking and carbon neutrality, understanding the spatial dynamics of carbon emissions is essential for promoting coordinated regional decarbonization. This study takes a systems perspective to investigate the drivers and network structures of carbon emissions across 31 Chinese provinces from 2000 to 2022. Utilizing a Spatial Durbin Model (SDM) alongside social network analysis (SNA), it examines both the spatial spillover effects of key economic and innovation-related factors and the structural characteristics of interprovincial carbon transmission networks. The main findings include the following: (1) a significant spatial autocorrelation in provincial carbon emissions, indicating strong cross-regional spillover effects; (2) a nonlinear, inverted U-shaped relationship between green innovation and carbon emissions, where emissions initially rise before declining as innovation matures; (3) a dual impact of human capital, which increases local emissions but reduces emissions in neighboring regions through knowledge diffusion; and (4) the identification of key provinces such as Shaanxi, Henan and Hubei as central nodes within the carbon emission network, acting as influential hubs in the transmission of carbon emissions. This study highlights the importance of differentiated policy design based on regional network centrality and advocates for a systemic governance framework that promotes technology diffusion, talent mobility, and collaborative emission control across provinces. The integrated SDM-SNA approach provides a novel perspective for understanding the complexity of carbon governance in large economies and offers a flexible framework that can be adapted to other national or subnational settings. Full article

To support China’s “3060” dual carbon targets, this study quantitatively evaluates the spatial–temporal characteristics and influencing factors of carbon emission performance (CEP) across administrative levels. While prior research has examined CEP patterns, a systematic comparison of factor contributions at different levels—particularly from global and local perspectives—is lacking. This study addresses this gap by analyzing CEP in 31 provinces and 333 prefecture-level cities (2003–2020) using a coupling coordination degree model to measure CEP, spatial autocorrelation indices (Moran’s I) to assess global/local dependence, static/dynamic Spatial Durbin model (SDM/DSDM) with two-way fixed effects to compare global impacts, and geographically and temporally weighted regression (GTWR) to quantify spatiotemporal heterogeneity. The results show the following: (1) CEP showed consistent growth at both levels with positive spatial autocorrelation, revealing significantly richer clustering patterns at the prefectural rather than provincial level. (2) From a global perspective, influencing factors’ contributions to CEP vary significantly between levels. Provincially, dominant factors rank as time-lagged CEP(CEP_lag)> proportion of built-up land(P_built) > spatial lag of CEP(W×CEP) > fractional vegetation coverage (lnFVC); while prefecturally, CEP_lag > spatial error coefficient(rho) > W×CEP > P_built, with the proportion of secondary industry in GDP (GDP2)/proportion of tertiary industry in GDP (GDP3) gaining greater significance. (3) Local regression results reveal significant spatiotemporal heterogeneity in CEP influencing factors. lnFVC and W×CEP show the most distinct differences between levels, while land-use factors like P_built and nighttime light index (NTL) exhibit unstable spatiotemporal effects. The study underscores the need for scale-specific policies addressing spatial spillovers and local heterogeneity, providing actionable insights for China’s carbon mitigation strategies. Full article

In alignment with China’s carbon peak and carbon neutrality commitments, digital inclusive finance (DIF) has emerged as a strategic instrument for carbon emission mitigation, facilitated by coordinated policy interventions and market-driven innovations. This study conducted an original multi-dimensional investigation into DIF’s carbon intensity reduction effects through an integrated analytical framework. Employing two-way fixed effects and mediation analysis models, we systematically evaluated both direct impacts and green-credit-mediated pathways using panel data across 247 Chinese cities from 2011 to 2020. A dynamic Spatial Durbin model further elucidated the spatiotemporal evolution of DIF’s spatial spillover effects. It was found that DIF development can reduce the carbon intensity of cities, and in particular, this phenomenon shows different effects in different types of cities. Green credit mechanisms effectively mediate their effects in the decarbonization process of DIF, confirming their key role in financial intermediation. In addition, DIF has a strong cross-regional spatial spillover effect, and its carbon emission reduction impact transcends local administrative jurisdictions. The results of this study will provide valuable insights and practical recommendations for policymakers and stakeholders to develop effective carbon reduction strategies that contribute to sustainable development in China and globally. Full article

This study investigated the spatiotemporal dynamics, regional disparities, and spatial spillover effects of carbon emissions in China’s livestock sector from 2003 to 2022. By integrating carbon accounting, decoupling elasticity analysis, kernel density estimation, Theil index decomposition, and the Spatial Durbin Model, the research revealed a 6.5% reduction in national livestock carbon emissions alongside intensified spatial polarization. The decoupling relationship evolved dynamically, with strong decoupling dominating but regional fluctuations persisting, particularly in resource-dependent areas. The distribution of emission intensity shifted from unimodal right-skewness to bimodal concentration, indicating technological diffusion barriers and structural divergence across regions. Spatial econometric analysis confirmed significant emission interdependence (ρ = 0.214, p < 0.01), where neighboring economic growth increased local emission intensity. These findings highlighted the limitations of uniform policy approaches and emphasized the need for region-specific governance, market-based incentives, and localized technological innovation. The study provided empirical evidence and a policy framework to address cross-regional coordination and sustainable low-carbon transitions in agriculture. Full article

While existing research has focused on logistics carbon emissions, understanding spatiotemporal emission cost dynamics and drivers remains limited. This study bridges three gaps through methodological advances: (1) Applying the Non-Radial Directional Distance Function (NDDF) to measure Marginal Carbon Abatement Costs (MCAC), overcoming traditional Data Envelopment Analysis (DEA) model’s proportional adjustment constraints for provincial heterogeneity; (2) Pioneering dual-dimensional MCAC analysis integrating temporal trends (2013–2022) with spatial autocorrelation; and (3) Developing a spatial Durbin error model with time-fixed effects capturing direct/indirect impacts of innovation and infrastructure. Based on provincial data from 2013–2022, our findings demonstrate a U-shaped temporal trajectory of MCAC with the index fluctuating between 0.3483 and 0.4655, alongside significant spatial heterogeneity following an Eastern > Central > Northeastern > Western pattern. The identification of persistent high-high/low-low clusters through local Moran’s I analysis provides new evidence of spatial dependence in emission reduction costs, with these polarized clusters consistently comprising 70% of Chinese cities throughout the study period. Notably, the spatial econometric results reveal that foreign investment and logistics infrastructure exert competitive spillover effects, paradoxically increasing neighboring regions’ MCAC, a previously undocumented phenomenon in sustainability literature. These methodological advancements and empirical insights establish a novel framework for spatial cost allocation in emission reduction planning. Full article

This study focuses on the coupling and coordination between China’s new-type urbanization (NU) and transportation carbon emission efficiency (CET), revealing its spatial and temporal evolution patterns and driving factors. In recent years, the rapid rise of the digital economy has profoundly reshaped traditional industrial structures. It has catalyzed new forms of production and consumption and opened up new pathways for carbon reduction. This makes synergies between NU and CET increasingly important for realizing a low-carbon transition. In addition, digital infrastructures such as 5G networks and big data platforms promote energy efficiency and facilitate industrial upgrading. It also promotes the integration of low-carbon goals into urban governance, thus strengthening the linkages between NU and CET. The study aims to provide a scientific basis for regional synergistic development and green transformation for the goal of “dual carbon”. Based on the panel data of 30 provinces in China from 2004 to 2021, the study adopts the entropy weight method and the super-efficiency SBM model to quantify NU and CET, and then analyzes their spatial and temporal interactions and spatial spillovers by combining the coupled coordination degree model and the spatial Durbin model. The following is found: (1) NU and CET show a spatial pattern of “leading in the east and lagging in the west”, and are optimized over time, but with significant regional differences; (2) the degree of coupling coordination jumps from “basic disorder” to “basic coordination”, but has not yet reached the level of advanced coordination, with significant spatial clustering characteristics (Moran’s I index between 0.244 and 0.461); (3) labor force structure, transportation and energy intensity, industrial structure and scientific and technological innovation are the core factors driving the coupled coordination, and have significant spatial spillover effects, while government intervention and per capita income have limited roles. This paper innovatively reveals the two-way synergistic mechanism of NU and CET, breaks through the traditional unidirectional research framework, and systematically analyzes the two-way feedback effect of the two. A multidimensional NU evaluation system is constructed to overcome the limitations of the previous single economic or demographic dimension, and comprehensively portray the comprehensive effect of new urbanization. A multi-dimensional coupled coordination measurement framework is proposed to quantify the synergistic evolution law of NU and CET from the perspective of spatio-temporal dynamics and spatial correlation. The spatial spillover paths of key factors are finally quantified. The findings provide decision-making references for optimizing low-carbon policies, promoting green transformation of transportation, and taking advantage of the digital economy. Full article

Entering the new development stage, empowering the modern tourism system by upgrading it with new quality productive forces (NQPF) is of great significance in promoting the high-quality development of China’s tourism industry. Based on the panel data of 30 provinces in China between 2018 and 2022, the two-way fixed effects model, the mediated-effects model, and the spatial Durbin model SDM were constructed using STATA 16 for empirical analysis. Results indicated that NQPF have a significant facilitating effect on upgrading the modern tourism system, which is reflected in four aspects: industrial efficiency upgrading, industrial technology upgrading, industrial structure upgrading, and open sharing upgrading. The results of the mechanism test show that the dynamic capacity of the industry plays an important intermediary role in the process of NQPF promoting the upgrading of the modern tourism system. In addition, NQPF has a spatial spillover effect on upgrading the modern tourism system. Based on the above conclusions, strengthening the cultivation and development of NQPF, optimizing the industry dynamic capacity, promoting coordinated regional development, and optimizing the policy environment are proposed in order to further enhance the overall level of the modern tourism system and to realize the high-quality development of tourism. Full article

Green technology innovation (GTI) is crucial for sustainable economic development and achieving “peak carbon” and “carbon neutrality” goals. While fiscal vertical imbalance (FVI) may exert an inhibiting effect on regional GTI, the existing literature has paid insufficient attention to investigating the underlying mechanisms and potential mitigation strategies for such impacts. Using provincial data from China (2005–2019), this study explores the impact of FVI on GTI through theoretical analysis and empirical testing. The results indicate that FVI significantly inhibits GTI, as validated by the dynamic system Generalized Method of Moments (GMM) and spatial Durbin model analyses. Mechanistically, FVI hinders GTI by altering government innovation preferences and reducing investments in environmental pollution control. Moreover, financial decentralization and fiscal transparency positively moderate this relationship, with nonlinear moderating effects. These findings suggest that enhancing regional financial decentralization and fiscal transparency can mitigate the negative effects of FVI on GTI, offering practical insights for harmonizing fiscal policies and green economic transitions. Full article

Global warming is projected to intensify the impact of droughts. Although numerous studies have examined carbon emissions and droughts, few have explored their interactive effects or the spatial spillover effects of carbon emissions on droughts. To address this gap, we use panel data from 2012 to 2021 for China’s arid, semi-arid, and potentially semi-arid regions in the future. First, we estimate city-level carbon emissions data for the study areas based on nighttime light data. We then apply a Random Intercept Cross-Lagged Panel Model to investigate the temporal causal relationship between carbon emissions and droughts. Finally, we employ a dynamic spatial Durbin model with spatial and temporal fixed effects, incorporating one-period-lagged carbon emissions to assess both direct and spatial spillover effects on droughts. The results show that carbon emissions have a statistically significant cross-temporal and spatial impact on droughts, with both current and one-period-lagged carbon emissions exhibiting substantial spatial spillover effects on drought conditions. This research offers valuable insights for cities seeking collaborative approaches to mitigate both carbon emissions and drought risks. Full article

Technological innovation is essential to promoting sustainable development in emerging economies as it drives regional coordination and industry upgrading. In order to address the understudied connection between regional coordination and industrial structural transformation, this study examines the spatial dynamics of technological innovation efficiency (TIE) in China’s advanced manufacturing industry (AMI) along the Yangtze River Economic Belt (YREB) from 2007 to 2022. Through a Data Envelopment Analysis (DEA) and Spatial Durbin Model (SDM), we systematically evaluated TIE patterns using panel data from 11 provinces. Our empirical analysis reveals three key findings. (1) The temporal distribution of TIE in AMI in the YREB showed an annual increasing trend. The spatial distribution characteristics showed a gradient distribution disparity between the eastern, central, and western regions, but the regional gap of TIE in AMI is gradually closing. (2) Through the examination of Moran’s I, the spatial spillover effect of TIE in AMI was observed, that is, the TIE is spreading from high-performance provinces to other regions, suggesting that interregional collaboration and knowledge exchange may be beneficial. (3) According to the factor identification study, the main factors affecting the spatial distribution of TIE in AMI are industrialization, human capital, and innovation capability. Interestingly, the effects of information technology and economic progress are not statistically significant, suggesting that cautious government actions are required. By optimizing technological innovation processes and spatial arrangements, this study adds to the expanding body of knowledge on the spatial aspects of technological innovation and provides valuable insights for policymakers looking to enhance global competitiveness and foster sustainable economic growth in the AMI. The findings advance our knowledge of how to support sustainable economic development in emerging nations by highlighting the critical role that innovation and technology management play in removing regional development obstacles and encouraging the modernization of industrial structures. Full article

In recent years, China’s transport logistics industry has experienced rapid development, driven by the technological advancements. But the coupling mechanism between transport logistics and technology is currently unclear, and there are likely regional differences. This study uses the entropy weight method, coupling coordination models and 20-year provincial panel data to measure the coupling coordinated development level of transport logistics and technology across 31 Chinese provinces (districts, cities). The spatial–temporal distribution, dynamic evolution, and regional differences in the coupling coordination development were analysed using kernel density estimation and the Moran index. Through the application of the Spatial Durbin Model (SDM), the mechanisms and spatial effects of selected influencing factors on the development levels are revealed. The results of this study revealed the following findings. (1) The levels of development in transport logistics and technology have consistently shown a positive upward trend with regional disparities. (2) Most provinces demonstrated a positive upward trend in the coupling coordinated development with a multi-polarised state. The overall level of coupling coordination is decreasing from east to west. In 2022, the difference between the highest and lowest coupling coordination degree between provinces is 0.78. (3) The national economy, industrial structure, urbanisation level, and consumption intensity have positive impacts on the coupling coordinated development of local regions. The findings of this study, which reveal positive trends and significant regional disparities, underscore the importance of formulating strategic plans tailored to local conditions to promote the coupled development of transport logistics and technology. Full article

In the context of sustainable development, breaking free from resource endowment constraints and promoting energy transformation are long-term goals of concern. The digital economy empowers the development of the energy industry and provides a feasible path for improving energy efficiency. This article selects interprovincial panel data from China to analyze the direct and indirect impacts of China’s digital economy on green total factor energy efficiency (GTFEE), as well as spatial spillover effects. Based on the calculation of green total factor energy efficiency, static and dynamic panel models are used to analyze the direct impact of the digital economy on green total factor energy efficiency through index decomposition and threshold models, as well as the indirect impact of digital economy technology effects on it. The research results indicate that the direct impact of the digital economy on GTFEE exhibits a positive U-shaped effect. Indirect impact analysis shows that technological innovation has a significant dual threshold effect on the variables of green total factor energy technology efficiency index and green total factor energy technology progress index. Further analysis using the spatial Durbin model shows that the digital economy has nonlinear spatial spillover effects on GTFEE, with regional heterogeneity and resource endowment differences. Studying the impact of digital economy development on green all-factor energy efficiency is of great practical significance in order to propose suggestions for promoting green and sustainable development. Full article