Search Results (29)

Achieving sustainable urban development and optimizing the urban scale structure are central priorities in global governance. However, the relentless population agglomeration in Chinese megacities, despite astronomical living costs, presents a prominent “China Paradox” that seemingly defies classical spatial equilibrium theories. This study decodes this paradox by endogenizing the strategic land supply behaviors of local governments. Utilizing a comprehensive panel dataset of 287 Chinese prefecture-level cities from 2006 to 2020, we construct a multi-dimensional mediation framework and a panel threshold model to investigate how the structural misallocation of land—specifically, the pro-industrial and anti-residential bias—reshapes urban migration dynamics. Empirical results reveal that this land allocation bias acts as the fundamental institutional engine driving urban polarization. Analysis of the transmission pathways reveals a complex push–pull dynamic at the core of this paradox. The artificial restriction of residential land drives up housing prices, generating a profound centrifugal “push” force. However, this dispersion effect is entirely neutralized by two formidable centripetal “pull” forces: industrial co-agglomeration fueled by subsidized manufacturing land, and premium public service capitalization financed through lucrative land revenues. Furthermore, this demographic pull effect exhibits a pronounced inverted U-shaped dynamic, peaking during the rapid growth phase but diminishing precipitously once cities cross the threshold into highly developed megacities (LnGDP > 11.525). These findings highlight the ultimate unsustainability of the land-driven urbanization model. We propose a paradigm shift towards sustainable urban governance, advocating for stage-specific land supply reforms and the transition from monopolistic land finance to a sustainable property tax system to foster a spatially just and resilient urban hierarchy. Full article

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy. Full article

China’s urban size distribution has increasingly shifted toward concentration in large cities amid global urbanization and the restructuring of urban development patterns. This trend has intensified governance and spatial pressures in major cities while exposing weaker growth momentum in small and medium-sized cities and reducing overall urban system coordination. Existing studies mainly explain this pattern through market forces such as agglomeration economies, housing prices, and migration, while others examine the consequences of local land practices from the perspectives of land finance, local competition, and institutional change. However, there is still no systematic explanation of why centrally imposed aggregate land constraints, operating through heterogeneous local land allocation, generate uneven urban outcomes. Against the background of the 2004 strict land management reform, this paper develops a theory-oriented conceptual framework linking central land constraints, local land allocation, and urban size structure. It clarifies how uniform central constraints may be translated into uneven urban outcomes through differentiated local land-allocation practices. Local land allocation is identified as the key transmission mechanism through which development opportunities are reshaped across cities and, under specific institutional conditions, the upper tiers of the urban hierarchy are reinforced. This paper therefore offers a bounded explanation of how central–local land governance shapes China’s urban size structure, while also underscoring the relevance of land governance to more balanced, resource-efficient, and sustainable urban development. Full article

Following China’s completion of the removal of provincial boundary toll stations and expressway network integration reform, a large number of electronic toll collection (ETC) gantries were deployed along expressway mainlines nationwide, transforming these facilities from dedicated toll terminals into pervasive traffic-sensing infrastructure covering the entire road network. However, the data value and technological potential embedded in this major infrastructure transformation have not yet been systematically reviewed. This paper adopts a narrative review methodology, incorporating 71 publications identified through multi-database systematic searches. The review is organized along the functional upgrade path of ETC gantries, covering the progression from toll terminals to traffic sensing nodes, multi-source fusion hubs, and finally vehicle–road–cloud cooperative control nodes, and synthesizes research progress in expressway traffic sensing, multi-source data fusion, safety operations, and emerging applications. The review reveals that ETC data have enabled a diverse methodological repertoire spanning travel time estimation, traffic flow prediction, origin–destination (OD) matrix inference, toll plaza safety analysis, dynamic pricing strategies, and environmental impact assessment. Nevertheless, a single ETC data source suffers from inherent limitations: spatial–temporal resolution constrained by gantry spacing and real-time capability limited by transmission latency. This fundamental contradiction constitutes the core driving force behind multi-source data fusion and vehicle–road–cloud integration technologies. The paper further argues that establishing a closed-loop pipeline integrating sensing, fusion, decision, and control and anchored on ETC gantry nodes represents the key direction for realizing intelligent expressway transformation. Full article

This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and persistent transmission congestion. Using publicly available hourly operational data for 2024, multiple machine learning regressors including Linear Regression (base case), Bayesian Ridge, Automatic Relevance Determination, Decision Trees, Random Forests, and Support Vector Regression are implemented under a node-specific modeling strategy. Two alternative approaches for predictor selection are compared: a system-wide methodology that exploits lagged SMP information from all network nodes; and a spatially filtered methodology that restricts SMP inputs to correlated subsystems identified through nodal correlation analysis. Model robustness is explicitly assessed by reserving January and July as out-of-sample test periods, capturing contrasting summer and winter operating conditions. Forecasting performance is analyzed for representative nodes located in the northern, central, and southern zones of the NES, which exhibit markedly different congestion levels and generation mixes. Results indicate that non-linear and ensemble models, particularly Random Forest and Support Vector Regression, provide the most accurate forecasts in well-connected areas, achieving mean absolute errors close to 10 USD/MWh. In contrast, forecast errors increase substantially in highly congested southern zones, reflecting the structural influence of transmission constraints on price formation. While average performance differences between M1 and M2 are modest, a paired Wilcoxon signed-rank test reveals statistically significant improvements with M2 in highly congested zones, where M2 yields lower absolute errors for most models, despite relying on fewer inputs. These findings highlight the importance of congestion-aware feature selection for reliable price forecasting in renewable-intensive systems. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

This study investigates how housing prices influence urban–rural income inequality (URG) in mainland China by explicitly incorporating spatial interdependence and nonlinear adjustment mechanisms, features often neglected in previous research. Using a balanced panel of 31 provinces from 2005 to 2023, we develop a dynamic spatial panel threshold model that jointly accounts for temporal persistence, spatial spillovers, and regime-dependent estimation. This framework enables a full decomposition of housing price effects into direct, indirect (spillover), and total impacts across distinct market regimes. The results reveal three major insights. First, URG in mainland China displays strong temporal persistence, suggesting that income disparities evolve gradually over time. Second, rising housing prices significantly widen the urban–rural income gap, both within provinces and through interprovincial transmission, underscoring the amplifying role of spatial spillovers. Third, threshold estimation identifies a critical housing price level of ln(HP) = 8.4843 (approximately 4838.21 RMB/m²), revealing that the inequality-enhancing effect of housing prices is stronger in low-price regions but diminishes as markets mature and affordability constraints intensify. These findings provide new empirical evidence that the housing market functions as a nonlinear and asymmetric driver of regional inequality in mainland China, with implications for housing policy and inclusive growth. Full article

Potatoes, possessing the characteristics of being suitable for food crop, vegetable, and fodder use, have become an important supplementary product for ensuring food security and vegetable supply. Their price fluctuations play a significant role in regulating production and consumption. Against the backdrop of establishing a unified national market, studying potato price fluctuations from a spatial perspective is crucial for scientifically and systematically understanding the patterns of China’s potato market. This study employs Ensemble Empirical Mode Decomposition, Spatial autocorrelation and Vector Autoregression models to analyse spatial variations and inter-regional transmission mechanisms in China’s potato price fluctuations, utilising wholesale market price data from January 2014 to December 2024 across diverse regions. Findings indicate distinct spatial patterns in potato price dynamics with significant inter-regional interactions. The Northern Crop Region exhibits predominantly short-term, high-frequency fluctuations, whereas the Central Crop Region, Southern Crop Region, and Southwestern Crop Region are characterized by long-term, low-frequency fluctuations. Potato prices in China exhibit significant spatial heterogeneity, and potato price fluctuations at both national and regional levels are primarily influenced negatively by those in other regions. The degree of interactive influence between potato prices across regions exhibits considerable variation, with the Central China crop region holding a certain degree of dominance in the national market. Based on these findings, policy recommendations are proposed, including strengthening tiered and regional monitoring and analysis of potato prices, standardizing inter-regional transmission pathways for potato prices, and guiding the formation of a complementary regional structure for potato production. Full article

In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context. Full article

Food price volatility is a persistent challenge in Indonesia, where agriculture is central to food security and rural livelihoods. While price transmission has been studied, little is known about how volatility spreads sub-nationally in archipelagic economies with fragmented infrastructure. This study applies a Dynamic Conditional Correlation GARCH (DCC-GARCH) model to monthly rural producer price data from 2009 to 2022 for six commodities: rice, chicken, eggs, chili, cayenne, and shallots. Results show that Java functions as the core volatility transmitter, with long-run conditional correlations exceeding 0.92 in Sumatra, 0.91 in Kalimantan, and 0.90 in Papua, reflecting strong and persistent co-movements. Even in low-production regions such as Maluku, significant volatility linkages reveal structural dependence on Java. Volatility clustering is particularly intense for perishables like chili and shallots. The findings highlight the need for spatially differentiated stabilization policies, including upstream interventions in Java and cooperative-based storage systems in outer islands. This study is the first to apply a DCC-GARCH framework to rural producer price data in an archipelagic context, capturing volatility transmission across regions. Its novelty lies in linking these spillovers with regional market dependence, offering new empirical evidence and actionable insights for designing inclusive and geographically responsive food security strategies. Full article

With the continuous integration of globalization and financial markets, the linkage of global financial risks has increased significantly. This study examines the risk spillover effects and transmission dynamics among the financial markets in G20 countries, which together represent over 80% of global GDP. With increasing globalization and the interconnectedness of financial markets, understanding risk transmission mechanisms has become critical for effective risk management. Previous research has primarily focused on price volatility to measure financial risks, often overlooking other critical dimensions such as liquidity, credit, and operational risks. This paper addresses this gap by utilizing the vector autoregressive (VAR) model to explore the spillover effects and the temporal and spatial characteristics of risk transmission. Specifically, we employ global and local Moran indices to analyze spatial dependencies across markets. Our findings reveal that the risk linkages among the G20 financial markets exhibit significant time-varying characteristics, with spatial risk distribution showing weaker dispersion. By constructing a comprehensive financial risk index system and applying a network-based spillover analysis, this study enhances the measurement of financial market risk and uncovers the complex transmission pathways between sub-markets and countries. These results not only deepen our understanding of global financial market dynamics but also provide valuable insights for the design of effective cross-border financial regulatory policies. The study’s contributions lie in enriching the empirical literature on multi-dimensional financial risks, advancing policy formulation by identifying key risk transmission channels, and supporting international risk management strategies through the detection and mitigation of potential contagion effects. Full article

This paper constructs a theoretical framework integrating health risk transmission, remote work constraints, and spatial equilibrium to analyze the impact mechanisms of major public health events on the real estate market. This study finds that pandemics affect market equilibrium through multiple channels, including changes in residents’ utility functions, the reshaping of labor market structures, and adjustments in location choices. The model combines the SIR model from epidemiology with spatial economics to depict the endogenous decision mechanism of health risks. By constructing a two-sector general equilibrium model that includes remote work sectors, this study reveals the impact of technological change on the spatial structure of the real estate market. Based on the mobility preference theory, an asset pricing framework incorporating health risk premiums is established. Comparative static analysis shows that the health risk transmission coefficient influences housing prices through two channels: directly lowering willingness to pay and indirectly affecting the distribution of population density. Dynamic analysis indicates that, under specific parameter conditions, the market exhibits asymptotic stability. Policy simulation results show that the transmission effects of monetary and fiscal policies exhibit significant spatial heterogeneity, requiring policymakers to pay more attention to regional differences. This study not only enriches the analytical tools of real estate economics but also provides theoretical support for relevant policy formulation. Full article

Climate change manifests itself in rising temperatures across the continent and affects the El Niño–Southern Oscillation (ENSO) by changing sea surface temperatures and atmospheric circulation. This affects precipitation and temperature patterns, with South Africa normally experiencing drier conditions during El Niño events. These weather anomalies influence crop yields and food prices. Spatial price transmission indicates the extent to which prices of agricultural goods are linked across different geographical areas and how quickly price signals from one area are passed on to another. Although numerous studies explore spatial price transmission between various countries, there is a gap in the literature on price transmission between the US and South African maize markets during ENSO events. Therefore, we investigate how ENSO-related events impacted maize price transmission between the Chicago Mercantile Exchange and the Johannesburg Stock Exchange from 1997 to 2024. The empirical analysis starts with a correlation analysis, followed by tests for cointegration and error correction models. The results confirm the dominating impact of US maize prices on South African prices, but also how this relationship changes based on the nature of the ENSO event. There is some indication of lower levels of integration and higher levels of price diversion during El Niño periods. Full article

The global food market’s escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level. Full article

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators. Full article