Search Results (100)

Grounded in the theory of new economic geography, this research develops a comprehensive theoretical framework to examine the spatial interaction mechanisms between the Green Finance Index and carbon emissions. Employing a range of econometric techniques—including three-dimensional kernel density estimation, spatial quantile regression, bivariate spatial autocorrelation analysis, and the spatial linkage equation model—the dynamic evolution, spatial pattern shifts, and mutual influences of green finance and carbon emissions in the middle and lower reaches of the Yellow River from 2003 to 2022 are systematically assessed. The findings indicate that (1) both carbon emissions and the Green Finance Index have experienced a trajectory of continuous growth, phased decline, and structural optimization, accompanied by a gradual shift in the regional center of gravity from coastal economic zones towards resource-intensive and traditional industry-concentrated areas; (2) significant spatial clustering is evident for both green finance and carbon emissions, demonstrating a strong spatial correlation and regional synergy effects; (3) a persistent negative spatial correlation exists between green finance and carbon emissions; and (4) green finance exerts a stable negative spatial spillover effect on carbon emissions, suggesting that the influence of green finance extends beyond localities to adjacent regions through spatial externalities, manifesting pronounced spatial transmission and linkage characteristics. By unveiling the bidirectional spatial association between green finance and carbon emissions, this study highlights the pivotal role of green finance in driving regional low-carbon transitions. The results provide theoretical insights for optimizing green finance policies within the Yellow River Basin and offer valuable international references for similar regional low-carbon development initiatives. Full article

In the new paradigm where the digital economy is profoundly reshaping urban spatial organization, how the platform economy transcends traditional geographical constraints to restructure the urban system has become a strategic issue in urban geography and regional economics. This study develops an innovative measurement framework based on Business-to-Business (B2B) e-commerce enterprises to analyze platform-driven urban systems across 337 Chinese cities. Using spatial autocorrelation, rank-size distributions, and urban scaling laws, we reveal spatial differentiation patterns of cities’ B2B platforms. Combining Ordinary Least Squares (OLS) and random forest models with Partial Dependence Plots (PDP), Individual Conditional Expectations (ICE), and Locally Weighted Scatterplot Smoothing (LOWESS), we uncover non-linear mechanisms between platform development and urban attributes. Results indicate that (1) B2B platforms exhibit “superliner agglomeration” and “gradient locking”, reinforcing advantages in top-tier cities; (2) platform effects are non-linear, with Gross Domestic Product (GDP), Information Technology (IT) employment, and service sector shares showing threshold-enhanced marginal effects, while manufacturing bases display saturation effects; and (3) regional divergence exists, with eastern consumer-oriented platforms forming digital synergies, while western manufacturing platforms face path dependence. The findings highlight that platform economy evolution is shaped by a “threshold–adaptation–differentiation” mechanism rather than neutral diffusion. This study provides new insights into urban system restructuring under digital transformation. Full article

In the dual-carbon context, forestry green total factor productivity (FGTFP) serves as a key indicator of the quality and efficiency of forestry development. Based on New Economic Geography Theory, this study explores FGTFP and its spatial divergence under the constraint of carbon emissions. We analyzed panel data from 30 Chinese provinces between 2004 and 2022. The Directional Distance Function (DDF) model was applied to measure FGTFP, and the Global Malmquist–Luenberger (GML) model was applied to measure FGTFP’s decomposition index. The Dagum Gini coefficient was employed to analyze the degree of spatial divergence of FGTFP and identify its sources. Using Porter’s model and Sustainable Development Theory, the geo-detector was applied to examine the driving factors of FGTFP and its decomposition index. The study’s findings indicate that (1) FGTFP in China generally trended upward from 2004 to 2022, with significant heterogeneity observed at both interprovincial and regional levels; (2) Technological Improvement (TI) was the primary driver of FGTFP growth in the eastern, northeastern and central regions, while Efficiency Change (EC) was the key driver in the western region; (3) FGTFP exhibited distinct spatial divergence patterns in China, with hypervariable density as the primary source, followed by interregional differentiation, and regional differentiation contributing the least; and (4) green energy transition factors consistently showed a significant “two-factor enhancement effect” and a “non-linear enhancement trend”, while external environmental factors exhibited strong interaction effects but demonstrated a “non-linear weakening trend”. Therefore, it is essential to promote the green transformation of production modes, facilitate structural adjustments and upgrades in the forestry industry, enhance regional collaboration, and advance the “dual enhancement” of technological progress and efficiency. Additionally, leveraging regional comparative advantages will promote coordinated development. Full article

The expansion of construction land is a key feature of urbanization. Understanding its spatiotemporal evolution in Yunnan’s plateau basins is crucial for minimizing resource waste and promoting coordinated regional development. This study employs land use and nighttime light data to analyze the spatiotemporal dynamics of construction land expansion and its decoupling from economic growth, using various indices and the Tapio decoupling model. The results reveal a steady rise in urban construction land from 1990 to 2020, characterized by significant spatial variations in expansion speed and intensity. Edge expansion predominated throughout all periods, accounting for over 50% in most regions. After 2010, expansion spread into smaller basins, markedly increasing the number of areas experiencing new expansion. The decoupling between construction land expansion and economic growth in these basins remains primarily weak and unstable, indicating a strong reliance on land for economic development. Factors such as socioeconomic conditions, geography, ecology, and policy influence both land expansion and economic growth, highlighting the interdependence between the two. These findings provide a foundation for sustainable basin development and offer valuable insights for planning and policy-making. Full article

This study leverages advanced analytical tools such as ArcGIS spatial analysis and Geographical Detectors to conduct a comprehensive analysis of the spatial distribution characteristics, equilibrium, hotspot areas, and geographic associations of 164 district-level agribusiness brands in Guangxi, highlighting the unique insights these tools provide into the spatial heterogeneity of agricultural enterprise brands. Our findings reveal a significant concentration of brands in the northern region, particularly in Nanning, Liuzhou, and Guilin, with a dense northeast and sparse southwest distribution pattern. We identify a positive correlation between the number of regional brands and GDP and a negative correlation with distance from major highways. This suggests that regional economic development and transportation infrastructure significantly impact brand distribution. To enhance brand development, we recommend focusing on regional brand cultivation, innovation, and leveraging digital marketing strategies. This study provides actionable insights for policymakers and practitioners aiming to promote agricultural brand growth and rural revitalization in Guangxi. This pattern suggests that regions with abundant natural resources and higher economic development levels are more conducive to the formation and growth of agricultural enterprise brands, highlighting the importance of regional economic foundations and resource endowments in agricultural branding. Specifically, brands are primarily concentrated in the northern region, with Nanning, Liuzhou, and Guilin having a particularly high number of such brands. This pattern aligns with the core–periphery theory in economic geography, which suggests that economic activities tend to cluster around central cities due to agglomeration economies. The findings challenge the assumption that agricultural brands are evenly distributed across regions, highlighting the importance of regional economic foundations and infrastructure in brand development. Additionally, the significant positive correlation between the number of regional brands and Gross Domestic Product supports the idea that economic strength fosters brand development. Conversely, the significant negative correlation between the number of agricultural brand enterprises and the distance from major highways underscores the critical role of transportation infrastructure in facilitating market access and brand growth. Therefore, this study highlights the importance of cultivating regional brands, enhancing innovation capabilities, and advancing new marketing methods to promote the spatial equilibrium and sustainable development of agricultural enterprise brands, contributing to rural revitalization in Guangxi. Full article

In China, with the increasing emphasis on the concept of green sustainable development, polluting industries characterized by pollution and high energy consumption are facing unprecedented challenges. The development of the intermediate demand-type characteristics of polluting industries should be more reasonably laid out and regulated. In this paper, environmental regulation and environmental quality are introduced into the new economic geography model. On the basis of theoretical analysis, the IV regression method was used to study the interaction between polluting industry agglomeration, environmental regulation, and their effects on urban air quality with key cities as research objects. The results show that an increase in the agglomeration of polluting industries leads to significant deterioration in urban air quality and that this effect is linear, whereas an increase in the intensity of environmental regulation significantly dampens this effect. Each 1% increase in the intensity of environmental regulation results in a 1.17% reduction in air pollution. Therefore, to effectively protect the environment, the development of polluting industries should be relatively decentralized. Additionally, city governments should fully consider their urban eco-geographical characteristics, directly reduce and indirectly inhibit the degree of agglomeration of polluting industries and simultaneously strengthen the intensity of environmental regulation. Full article

This paper investigates the influence of expanding free compulsory education on regional entrepreneurial activities, exploring both theoretical and empirical aspects. Our research comprises two main parts. Initially, we employ a novel economic geography model to theoretically assess the effect of extending free compulsory education on regional entrepreneurial endeavors, proposing four hypotheses for further examination. Subsequently, we empirically test these hypotheses using balanced panel data collected from 2528 districts and counties in China spanning from 2000 to 2021. The findings indicate that the expansion of free compulsory education indeed stimulates regional entrepreneurial activity. However, this policy’s impact is influenced by the local industrial structure and the knowledge level of residents. Notably, the effect becomes visible only after the third year of the policy’s enactment, and it demonstrates a certain spatial spillover. Our conclusions remain consistent even after accounting for various endogenous factors. Our research has the potential to offer invaluable countermeasures for other developing countries, enabling them to advance sustainable development by enhancing their obligations. Full article

From the color geography perspective, a field investigation was conducted in the Minjiang River Basin, constructing a color index system of traditional villages. In Python, a random forest model was constructed to screen out important color indexes for traditional village color classification and explore its influence mechanism. Among eight color indexes, the important indexes are wall form and building face form, accounting for 30.50% and 19.40%, respectively. Based on this, the basin was divided into four color zones presenting color characteristics and eight color subzones presenting architectural features. The influence mechanism concerns dialect divisions that have shaped traditional villages of different color types, and the interconnection of water systems has promoted the connections among them. The application of traditional village colors in the new urban and rural planning can enhance local characteristics. Integrating the color resources of traditional villages contributes to the regional protection of culture and economic development. Full article

In recent years, several studies have evaluated the potential of renewable energy sources in response to climate change and high energy demand. Due to its equatorial location and significant solar and wind potential, Brazil has incorporated alternative sources into its energy matrix, driven by more efficient and economical technologies for solar energy. However, the availability of observed data is still limited, and many studies rely on satellite estimates or extrapolations of in situ observations from other regions, compromising the efficiency of new technologies. This study uses NASA MERRA-2 reanalysis data to evaluate the influence of aerosols and cloudiness on the estimate of solar irradiance in Brazil. INMET stations were chosen in regions representative of the Brazilian climate and geography, with more than 12 years of observational data. MERRA-2 includes aerosol fields that interact with the model’s radiation fields, with a spatial resolution of 0.5° and hourly temporal resolution. Variables used include shortwave radiation fluxes and aerosol optical depth. Statistical indices used in performance analysis include mean bias, mean squared error, and Pearson correlation coefficient. The stations’ diurnal solar irradiance cycles were compared with MERRA-2 reanalysis data, considering different scenarios of aerosol and cloudiness effects. The reanalysis data represented the Bauru and Santa Maria stations well, while others, such as Barreiras and Goiânia, showed underestimation. Monthly cycling was also analyzed, highlighting seasonality, with greater amplitude in Santa Maria and lower in Caicó. In some locations, such as Campo Grande, the influence of aerosols is more significant, especially during the dry months, when forest fires, mainly in the Amazon region, increase the aerosol optical depth. The results show that reanalysis estimates can be used to evaluate the temporal variability of solar irradiation in regions without observational data. In conclusion, the study was able to evaluate the temporal variability of solar irradiation in Brazil using MERRA-2 atmospheric reanalysis data, demonstrating that, although there are differences with observational data, reanalysis estimates are useful in areas without observed data, with values correlation values above 0.8 and reaching values close to 0.95. However, although small, the differences observed between measured and estimated solar irradiation are generally caused by the inability of models to adequately represent the fraction of clouds and aerosols in the atmosphere. Full article

Sinceits introduction in the 19th century to address geometric problems, topology as a methodology has undergone a series of evolutions, encompassing branches of geometric topology, point-set topology (analytic topology), algebraic topology, and differential topology, gradually permeating into various interdisciplinary applied fields. Starting from disciplines with typical geometric characteristics such as geography, physics, biology, and computer science, topology has found its way to economic fields in the 20th century. Given that the introduction of topology to economics is relatively new and presents features of being fragmented and non-systematic, this review aimed to provide scholars with a systematic evolution map to refine the characteristics of topology as a methodology applied in economics and finance, thereby aiding future potential interdisciplinary developments in these fields. By collecting abundant literature indexed in SCOPUS/WoS and other famous databases, with a qualitative analysis to classify and summarize it, we found that topological methods were introduced to modern economics when dealing with dynamic optimization, functional analysis, and convex programming problems, including famous applications such as uncovering equilibrium with fixed-point theorems in Walrasian economics. Topology can help uncover and refine the topological properties of these function space transformations, thus finding unchangeable features. Meanwhile, in contemporary economics, topology is being used for high-dimension reduction, complex network construction, and structural data mining, combined with techniques of machine learning, and applied to high-dimensional time series and structure analysis in financial markets. The most famous practical applications include the use of topological data analysis (TDA) and topological machine learning (TML) for different applied problems. Full article

Green agriculture represents the future of agricultural transformation in developing countries, such as China. Identifying an effective resource combination path is vital for enhancing the green quality of agriculture in these nations. This study draws on the resource spatial mismatch theory from New Economic Geography, using a “multisource heterogeneous” approach that combines qualitative comparative analysis (QCA) with the EBM-GML index measurement model. Using panel data from 2005 to 2021, the study investigated the effects and mechanisms of spatial resource combinations on improving green agricultural quality. The key findings are as follows: (1) While improving spatial resource misallocation helps boost green agricultural quality, the diversity of resource combination patterns has diminished, decreasing from five modes in 2005 to four in 2021. (2) In terms of mechanisms, reducing externalities, such as pollutant emissions, while strengthening material and human capital offers a potential pathway for improvement. (3) Negative externalities, including emissions from fertilizers and petroleum, significantly hinder improvements in green agricultural quality. (4) The absence of sufficient pesticide and fertilizer resources is a critical factor influencing the outcome. These findings provide practical insights for developing countries seeking to enhance regional resource allocation efficiency and improve agricultural green quality. Additionally, they contribute theoretical support to the enrichment of theories on resource allocation and sustainable agricultural development. Full article

Amidst global economic stagnation, China is undergoing a significant economic transformation by fostering new high-quality productive forces (NHPFs). In this transformative context, specialized and sophisticated small and medium-sized enterprises (SpecSof SMEs) play a critical role. This paper develops a framework from an evolutionary economic geography (EEG) perspective to analyze how these SMEs influence NHPFs. The study assesses the impact of specialized and sophisticated SMEs on NHPFs using OLS and addresses potential endogeneity issues through the application of instrumental variables. The results show that the Specialized and Sophistication Index (SSI) positively impacts NHPFs, with its effect strengthening from the company to the municipal level (by about 25%) but weakening at the provincial and national levels (to half of the municipal level), highlighting a clear marginal effect on regional NHPFs. Additionally, the geographically weighted regression (GWR) model was employed to investigate the complex and spatially varied relationships between key characteristics of specialized and sophisticated SMEs and NHPFs. Our findings suggest that while the relationship between SSI and NHPFs is generally positive, it is spatially heterogeneous, arising from variations in regional economic structures, market maturity, and industrial characteristics. This study provides a theoretical framework for understanding regional disparities in NHPF development through SpecSof SMEs and offers empirical evidence to inform region-specific policies and spatial planning strategies. Full article

Traditional villages are an important cultural heritage left by China’s agrarian civilization and serve as a testament to the historical development of the Chinese nation. The study of spatial and cultural differentiation in traditional villages is significant for their future preservation and development. Existing studies predominantly adopt a macro perspective, focusing on large-scale regions, and lack investigations from a micro perspective in medium- and small-scale areas. This study utilizes ArcGIS 10.8 for spatial analysis, multi-factor geographic detectors, and cultural geography spatial zoning methods to explore the spatial structure and cultural differentiation of 305 traditional villages in western Henan. The results indicate that the distribution of traditional villages in this region is significantly clustered and uneven, primarily concentrated in specific districts of Sanmenxia and Luoyang. Per capita GDP and the distance to roads are negatively correlated with the distribution of traditional settlements, reflecting the positive impact of lower economic levels and remote locations on village preservation. The spatial layout of traditional villages in western Henan exhibits clustering patterns, with cultural zoning characterized by distinct residential forms. This study, through the analysis of the spatial structure characteristics and influencing factors of traditional villages in the western Henan region, provides a new perspective on the formation and evolution of traditional villages, revealing the cultural differentiation characteristics of western Henan. The research results offer directional guidance for the conservation strategies of traditional villages in western Henan and provide a decision-making reference for cultural heritage conservation practices in similar regions. Full article

Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies. Full article

This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency (OIOE) to forecast bank failure. Using the bank failure list from the Federal Deposit Insurance Corporation (FDIC) database, I match the banks that failed in 2009 with a control sample based on geography, size, the ratio of total loans to total assets, and the age of banks. The model suggested by this paper could predict correctly up to 94.44% (97.15%) for the failure (and non-failure) of banks, with an overall 96.43% prediction accuracy, (p = 0.5). Specifically, the stepwise logistic regression suggests some proxies for capital adequacy, assets/loan risk, profit efficiency, earnings, and liquidity risk to be the predictors of bank failure. These results partially agree with previous studies regarding the importance of certain variables, while offering new findings that the three proposed proxies for LQ, IntMag, and OIOE statistically and economically significantly impact the probability of bank failure. Full article