Regional Development Research Articles

Impacts of business environment on regional development: Evidence from a natural experiment

Impacts of business environment on regional development: Evidence from a natural experiment

Emotional and behavioral problems are associated with hypothalamic development from childhood to adolescence: Findings from a longitudinal cohort study

BackgroundDespite the pivotal role of the hypothalamus in regulating various physiological processes, our understanding of its developmental trajectory and subregional organization during childhood and adolescence remains limited, as well as how emotional and behavioral problems can impact hypothalamic development, potentially leading to neurodevelopmental disorders. MethodsThis population-based longitudinal cohort study utilized data from a representative sample of 702 children, who were followed two to five times. Emotional and behavioral problems were assessed using the Strengths and Difficulties Questionnaire (SDQ). Linear mixed models were employed to delineate developmental trajectories and behavioral regulation. ResultsUsing an automated segmentation technique, we quantified the volumes and asymmetries of the hypothalamus and its subregions in a large longitudinal sample of 702 subjects aged 6–15 years with 1371 MRI scans, and mapped their developmental trajectories. Our findings indicate that while the anterior and posterior regions of the hypothalamus exhibit a tendency toward decline, the tubular region demonstrates a linear increase which is influenced by lateralization, sex, and intracranial volume. Furthermore, emotional and behavioral problems - particularly emotional symptoms and peer relationship problems - are related to faster development in superior tubular and anterior-superior regions. ConclusionsIn this study, we initially delineated the developmental trajectories of the hypothalamus and its subregions from childhood to adolescence based on a longitudinal cohort study. Our findings revealed that the development of hypothalamus followed the pattern of “lateral early to medial late, and dorsomedial early to ventromedial late”, and the emotional and behavioral problems are associated with faster hypothalamic development. This study provides preliminary evidence regarding the impact of emotional and behavioral problems on the dynamic development of the hypothalamus, offering a crucial foundation for future prevention and intervention strategies targeting cognitive and emotional behavioral problems.

Energy transition policy and rural revitalization of Chinese ethnic regions

This study addresses socio-economic disparities and environmental challenges in China's ethnic regions by examining the impact of energy transition policies on rural revitalization, with a particular focus on the mediating effects of population quality, economic growth, and industrial upgrading. Using data from 46 Chinese ethnic regions spanning 2003 to 2022, the analysis reveals that industrial upgrading significantly enhances the effectiveness of these policies, while educational attainment, as a measure of population quality, further amplifies their benefits. Although economic growth also contributes positively to rural revitalization, its mediating role is less pronounced. These findings highlight the need for a comprehensive strategy that integrates energy transition with socio-economic development to promote sustainable rural progress. The study underscores the critical role of incorporating energy transition into broader development policies, particularly through industrial and educational improvements, to achieve sustainable development in China's ethnic regions, and offers valuable policy recommendations.

Ecological analysis and multi-scenario simulation of Yellow River Delta wetland under clearing of Spartina alterniflora

In 2022, China released the Special Action Plan for the Control of Spartina alterniflora (2022-2025) with the goal of completely clearing S. alterniflora from China's coastal wetlands. It is crucial to conduct a thorough analysis of wetlands under artificial intervention and obtain reliable future wetland simulation results. The Yellow River Delta wetland is one of China's important wetland reserves, has faced severe impacts from S. alterniflora invasion over the past decade. This study focuses on the Yellow River Delta wetland, utilizing Landsat imagery and a combined object-oriented and pixel-based approach to achieve wetland mapping from 2013 to 2023. Through wetland analysis, we designed scenarios for natural development, ecological priority, farmland protection, and combined development. The patch generating land use simulation model was used to simulate wetland scenarios for 2030 and analyze factors influencing wetland use cover change and future conversion patterns. The results indicate that between 2013 and 2022, native vegetation was impacted by the invasion of S. alterniflora, with Suaeda glauca being the most affected. In 2023, the removal of S. alterniflora was effective, reducing its area by 42.37km2 compared to pre-removal conditions. In all three scenarios, the area of Phragmites australis increased and is expected to dominate the wetlands, while the live space of other native salt marsh vegetation has also been restored. However, the predictions indicate that S. alterniflora still poses a potential threat of expansion in the future, necessitating continuous monitoring of its dynamics. We recommend implementing zoned management of the wetland, balancing ecological restoration in coastal areas with agricultural development in the western regions.

A socio-hydrology model for Water-Urban-Land-Population-Production Nexus

The competition between urbanization patterns, agricultural practices, population dynamics, and economic production intensifies under limited water resources. To tackle this pressing issue, the Chinese government has issued a strategy of "Basing four aspects on water resources." The strategy aims to guide the scopes and scales of urban extension, land cultivation, population capacity, and economic production based on the constraints imposed by water availability, referred to as the Water-Urban-Land-Population-Production (WULPP) Nexus. While there is literature dealing with various water-related Nexus, few studies focus on WULPP Nexus. To understand the co-evolution and conflicts of WULPP Nexus between social development and water management, the study proposed a comprehensive framework and corresponding socio-hydrology model to address the WULPP Nexus. The model integrates the WULPP Nexus with the macro-economic Input-Output model and a multi-objective optimization (MO) solution to comprehensively explore the dynamic interactions between water and social development from the perspective of water, land, labor, products, and capital. The proposed model was fully applied in Yinchuan (YC), a pilot city for Ecological Protection and High-quality Development in the Yellow River Basin, to examine its effectiveness and sensitiveness as a case study. The results reveal a strong positive correlation between population growth and socio-economic development, with urban expansion offering both benefits and trade-offs, while land cultivation presents the most significant trade-off with other objectives. Water availability, especially under consumption constraints, is crucial to improve outcomes in wet years and intensify trade-offs in dry ones. These findings underscore the importance of a balanced approach to social development and water management, offering valuable insights for sustainable development in water-scarce regions globally.

Sustainable Rain Solutions And Microbial Safety Research For Indian Water Crisis

Environmental Impact Awareness has always been the forefront of many surveys and solutions while trying to find the perfect know-it-all answer to the bigger question- “What do we have and What is getting depleted and finished?”. Water scarcity remains a pressing issue in arid and semi-arid regions of India, making rainwater harvesting a critical solution. This research investigates rainwater harvesting as a means to enhance water security, focusing on its benefits for water supply, flood risk reduction, and climate change mitigation. The paper outlines the stages of rainwater collection, transportation, and storage, emphasizing the technology required for active implementation while addressing sustainability concerns. Additionally, it reviews regulations such as the National Water Policy and microbial safety standards from the Bureau of Indian Standards (BIS) to ensure safe water. Findings on microbial testing, filtration technology, and community engagement for water conservation demonstrate a dual approach to water protection and public health. This study argues that rainwater harvesting not only serves as a water source but also has implications for antimicrobial resistance (AMR) in both potable and agricultural water. Ultimately, this work supports rainwater harvesting as a vital strategy for climate resilience and sustainable development in water-scarce regions

Spatio-temporal Analysis of Agroforestry Systems in Hotan Using Multi-source Remote Sensing and Deep Learning

In the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially impacting their yield and quality. To address this issue and enhance the system's sustainability and productivity, precise delineation of the planting structure is critical. This study proposes a novel framework that leverages multi-source remote sensing data combined with advanced deep learning techniques to analyze the agroforestry planting structure. The methodological approach consists of three key phases. First, an instance segmentation model was employed to extract farmland parcels from high-resolution imagery, providing a basis for vegetation classification. Next, a time series model using irregular satellite image time series (irSITS) tracked the growth dynamics of the vegetation. Finally, the spatial planting structure of the walnut trees was quantified using the D-LinkNet model, integrated with a template filling algorithm.The results demonstrated a classification accuracy of 97.85% in extracting parcel-level planting structures, identifying 42,955 farmland parcels, including 21,153 intercropped parcels. The temporal and spatial characteristics of the agroforestry system were then analyzed, leading to a grading of canopy cover and walnut tree density within the intercropped areas. This comprehensive spatiotemporal planting structure offers a valuable foundation for informed local agricultural policy adjustments. In conclusion, this approach advances the understanding of complex agroforestry systems and provides a robust scientific basis for optimizing intercropping practices, contributing to sustainable agricultural development in arid regions.

Association of Maternal Toxoplasma Gondii Molecular and Serological Positivity With Child's Gross-Motor Development and Behavior in Tribal Regions of Gujarat, India: A Prospective Study.

Association of Maternal Toxoplasma Gondii Molecular and Serological Positivity With Child's Gross-Motor Development and Behavior in Tribal Regions of Gujarat, India: A Prospective Study.

Understanding how digital infrastructure construction could promote sustainable development

Progress made in environmental protection may negatively affect regional socioeconomic development, reducing or impairing the ability of local economic systems to defend against external shocks, culminating in weakened economic resilience. Implementing carbon abatement programs without risking economic resilience is therefore an urgent problem for the international community, especially for its emerging national economies. Digital infrastructure construction (DIC), as a driving force of technological progress and structural transformation, may offer a viable solution to that problem. Here, we used county-level data for China, the biggest emerging economy in the world, to investigate whether and how Smart City Pilot policy (SCP, a policy close to DIC) has lowered regional carbon emissions and influenced economic resilience. We find that the SCP could significantly promote carbon abatement goals, and this result is robust under several tests. Further, chain-based mediating effect analysis revealed that the SCP's beneficial impact could have arisen by first promoting innovation and then productivity, and public concern acting as a stressor that pressures officials to engage in environmental governance. Furthermore, our results show the SCP is capable of bolstering regional economic resilience, and could even offset or negate the adverse effects of strict environmental regulation on that resilience. However, the positive effects of the SCP on carbon abatement in China's developed counties, or ones located in a resources-based city, were not significant. Altogether, our empirical results highlight how DIC can serve as a way to help achieve sustainable development, but more studies on its contributing role are clearly needed.

Quenching thirst, easing debt: Improvement in water resource endowment alleviates local government debt risk

Water resources serve as a vital pillar sustaining both economic activities and daily life for residents. However, the uneven distribution of water resources across time and space has made water scarcity a bottleneck factor restricting the economic and social development of some regions. The implementation of the South-to-North Water Diversion project provides an excellent quasi-natural experiment for in-depth research on the effects and influence mechanism of cross-regional productive factor allocation on economic development. This study focuses on whether the water resource endowment improvement is reflected on the capital market. Based on the trading data of urban construction investment bonds from 2011 to 2016, we empirically examine how the South-to-North Water Diversion project affects the local government financing costs. The results indicate that the water diversion project significantly reduces the credit spread of urban construction investment bonds from the water-receiving areas. Further analysis reveals that the water diversion project improves the economic fundamentals and increases the land sales revenue of the water receiving cities, enhancing local governments' implicit guarantee ability for the financing platforms, thereby reducing the credit risks of urban construction investment bonds. This study not only enriches the research on the determinants of credit risk for urban construction investment bonds, but also provides empirical evidence from the capital market that the coordinated allocation of productive factors promotes regional balanced development. The conclusions imply that the government should actively advance the corresponding infrastructure construction and institutional arrangements to promote the flow and complementarity of economic elements between regions.

Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping.

Air cargo transport and economic development in emerging economies: A case study of Brazil with panel data analysis

Air cargo transportation accounts for less than 1% of the global trade volume, yet it represents approximately 35% of the total value of goods transported, highlighting its strategic importance in trade and economic development. This study investigates the relationship between domestic air cargo transport in Brazil and key macroeconomic variables, focusing on how regional economic dynamism, logistical infrastructure, and population density impact the country’s development. Using a panel data regression model covering the period from 2000 to 2020, the study analyzes the evolution of air cargo transportation and its role in redistributing economic growth across Brazil’s regions. The findings emphasize the key factors influencing the air cargo sector and demonstrate how these factors can be leveraged to optimize public policies and business strategies. This research provides valuable insights into the relevance of air cargo transportation for regional and national development, particularly in emerging economies like Brazil, offering guidance for the formulation of strategies that promote balanced economic growth across regions.

Mining interpretable fuzzy If-Then linguistic rules from energy and economic data to forecast CO[formula omitted] emissions of regions in China

Forecasting CO2 emission is the one of most important issues for the “30⋅60” CO2 emission target in China. Due to unbalanced socio-economic developments of regions in China, exactly forecasting CO2 emissions of provinces depend on their energy consumptions and economic developments. In the paper, a novel method based on K-means clustering method and computing with words is proposed to forecast CO2 emissions of 30 provinces, which is consisted by (1) K-means clustering method is used to respectively cluster energy consumption and economic datasets of provinces and the interpretable fuzzy If-Then linguistic rules of CO2 emissions are mined from the clusters; (2) computing with words method is utilized to transform fuzzy If-Then linguistic rules into fuzzy If-Then rules with membership functions on the universe of discourse; (3) a fuzzy inference method is adopted to forecast CO2 emissions of 30 provinces. To show the usefulness and effectiveness of the novel method, energy consumptions and economic datasets of 30 provinces from 1997 to 2021 are employed to forecast CO2 emissions, metrics of MAE, MAPE, RMSE and the out-of-sample Roos2 are utilized to evaluate CO2 emission forecasting of 30 provinces, means of them reach 13.304, 15.279, 0.081 and 0.965. By comparative analysis for forecasting CO2 emissions of 30 provinces, means of MAE, MAPE, RMSE and the out-of-sample Roos2 by the novel method are more than SVM, ANFIS and MLR methods. In addition, four kinds of mechanisms influencing CO2 are discovered and analyzed by the fuzzy If-Then linguistic rules of 30 provinces, which can help to improve energy consumption and sustainable development of 30 provinces in China.

Assessing National Governance Modernization: Leveraging the Ordered Weighted Geometric Composition Operator

The national governance modernization is an important part of Chinese-style modernization. It is of great importance to advance the modernization process by assessing the modernization level of national governance. In this paper, we propose a new method for assessing the national governance modernization using the ordered weighted geometric composition operator (OWGC). First, defining the OWGC by introducing the average operator. The application of the OWGC in measuring the national governance modernization is then demonstrated and the general measurement procedure is presented. Finally, empirical data are used to analyze the degree and regional differences in national governance modernization. The empirical results obtained by using the OWGC not only show uneven development between different provinces, but also reflect the development strengths and weaknesses of each region, which will help provide guidelines for improving the level of modernization and promoting coordinated regional development delivery.

Spatial-Temporal Evolution and Determinants of Green Economy Efficiency: An Integrated Analytical Approach

In response to global environmental challenges and the need for sustainable development, this study investigates the spatiotemporal evolution and determinants of green economy efficiency (GEE) across 30 Chinese provinces from 2005 to 2021. By employing a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, kernel density estimation, and spatial Markov chain analysis, this study provides a comprehensive assessment of GEE, incorporating both spatial and temporal dimensions. The analysis reveals a positive overall trend in GEE, with notable regional disparities, as eastern provinces exhibit significantly higher efficiency levels compared to central and western regions. Additionally, GEE improvements have been observed nationwide, although the central and western regions lag. Key factors influencing GEE include energy intensity, economic development, urbanization, industrialization, transport infrastructure, and government intervention, with distinct regional variations in their impact. The findings suggest that optimizing economic growth, reducing energy intensity, and fostering green urbanization and industrialization are essential strategies for enhancing GEE. This research contributes to the theoretical understanding of green economy efficiency and offers valuable policy recommendations for achieving balanced regional development and sustainable growth in China.

Evolution of Determinants of Regional Development in Selected European Union Countries

Evolution of Determinants of Regional Development in Selected European Union Countries

Lossy Compression of Integer Astronomical Images Preserving Photometric Properties* *This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) and by the European Regional Development Fund (FEDER), funded by MCIN/AEI/10.13039/501100011033/FEDER, UE, under grant PID2021-125258OB-I00; also by the Catalan Government under grant SGR2021-00643; and also by the “PhotSat” project under the Institute for Space Studies of

Observatories are producing astronomical image data at quickly increasing rates. As a result, the efficiency of the compression methods employed is critical to meet the storage and distribution requirements of both observatories and scientists. This paper presents a novel lossy compression technique that is able to preserve the results of photometry analysis with high fidelity while improving upon the state of the art in terms of compression performance. The proposed compression pipeline combines a flexible bi-region quantization scheme with the lossless, dictionary-based, LPAQ9M encoder. The quantization process allows compression performance and photometric fidelity to be precisely tailored to different scientific requirements. A representative data set of 16-bit integer astronomical images produced by telescopes from all around the world has been employed to empirically assess its compression-fidelity trade-offs, and compare them to those of the de facto standard Fpack compressor. In these experiments, the widespread SExtractor software is employed as the ground truth for photometric analysis. Results indicate that after lossy compression with our proposed method, the decompressed data allows consistent detection of over 99% of all astronomical objects for all tested telescopes, maintaining the highest photometric fidelity (as compared to state of the art lossy techniques). When compared to the best configuration of Fpack (Hcompress lossy using 1 quantization parameter) at similar compression rates, our proposed method provides better photometry precision: 7.15% more objects are detected with magnitude errors below 0.01, and 9.13% more objects with magnitudes below SExtractor’s estimated measurement error. Compared to the best lossless compression results, the proposed pipeline allows us to reduce the compressed data set volume by up to 38.75% and 27.94% while maintaining 90% and 95%, respectively, of the detected objects with magnitude differences lower than 0.01 mag; and up to 18.93% while maintaining 90% of the detected objects with magnitude differences lower than the photometric measure error.

Spatial externalities, R&D spillovers, and endogenous technological change

Forward-looking economic agents operating in a finite continuous geographic area choose how much to innovate at each point in time and space. Based on this assumption, the present study incorporates spatial interactions in endogenous growth models, addressing the criticism that such models are inconsistent with empirical evidence. More specifically, we introduce spatial production spillovers, knowledge diffusion across space, and the capability for spatial heterogeneity into a standard expanding variety growth model based on R&D. We study the properties of equilibrium and optimal allocations and argue that the characteristics are different from those of the non-spatial model, which alter the appropriate policy measures. Finally, we provide numerical examples demonstrating the importance of spatial dependent policy measures in achieving a balanced regional development.

POLARIZATION OF LOCAL COMMUNITY ORIENTATION TO THE DEVELOPMENT OF ECO-RURAL TOURISM IN KERINCI REGION, JAMBI PROVINCE, INDONESIA

Rural areas are increasingly the focus of exploration by various stakeholders. However, local communities, as landowners in these regions, often do not reap the full benefits. This issue stems from their limited involvement in regional development and a lack of awareness about ongoing projects. Local communities play a crucial role in the success of rural development, and understanding their attitudes is vital.This study aimed to explore local community attitudes toward eco-rural tourism development in Kerinci Regency, Jambi Province. A closed-ended questionnaire, designed with a "One Score One Indicator" system, assessed various factors including socio-cultural aspects, conservation, ethnic politics, economics, regional development, tourism, and landscape ecology across six villages. The analysis of local community orientation was conducted using quantitative descriptive methods and One-Way ANOVA statistical tests.Results indicated a positive polarization in community attitudes across all villages, showing strong support for eco-rural tourism. By focusing on the identified indicators, local communities are motivated to collaborate and enhance their rural areas through eco-tourism initiatives. The findings suggest that these communities possess significant potential to develop their regions in line with eco-rural tourism principles. Additionally, the insights gained can inform the creation of eco-rural tourism programs that emphasize community involvement and leverage local strengths. This approach aims to ensure that rural communities benefit from the exploration and development occurring in their areas.

Recent Regional Development Strategies in Turkey and Smart Cities: The Case of the Southern Aegean Region (TR32)

This article critically examines how the smart cities approach has been employed as a strategic tool in Turkey's recent regional development strategies. The paper explores the efficiency of development strategies prepared for Turkey's South Aegean Region (TR32) with a focus on smart city elements. These strategic elements include transportation, smart governance, energy efficiency, and digital transformation at the sub-regional level. The sub-regional plans prepared by the South Aegean Development Agency (GEKA) serve as a distinctive example of incorporating new technology-based urban and regional practices into regional development plans in Turkey. GEKA's smart city strategies, while addressing best practices, are criticized for a predominant focus on technological solutions, neglecting crucial human elements like sustainability and social inclusion. These strategies risk widening economic and social inequalities between urban and rural areas due to high costs and increased involvement of private sector companies, leading to an uneven distribution of projects. Critics argue that GEKA's plans lack transparency and public participation, prompting a call for a renewed debate on development agency strategies in Turkey. Furthermore, through a renewed emphasis on the integration of human-centric considerations and the cultivation of participatory governance mechanisms, GEKA's smart city strategies have the potential to facilitate sustainable and equitable development within the TR32 Region.