Search Results (107)

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

Spatial heterogeneity in economic benefits of water use provides crucial evidence for the evaluation of water diversion projects and the spatial equilibrium of water resource allocation. Using city-level data from 2017 on the sectoral water use and value added in 334 Chinese cities, we estimated the economic benefits of water use in the agricultural, industrial, and service sectors using the allocation coefficient method. We then revealed the spatial heterogeneity combining an exploratory spatial data analysis (ESDA) method. For the agricultural sector, the high economic benefit of water use regions are primarily concentrated on both sides of the “Hu Huanyong Line”; regions with high economic benefit of industrial water use are mainly found in the North China Plain, the middle and lower Huanghe River basin, the Yangtze River Delta, the Pearl River Delta, Chongqing and Chengdu, and the economic benefit of service water use is higher in the north than in the south. ESDA provides significant evidence for the analysis of spatial heterogeneity with regard to the economic benefits of water use in China. Based on the fundamental distribution of water resources and the spatial heterogeneity in the economic benefits of water use, potential water diversion areas can be preliminarily identified. The Haihe River Basin in the North China Plain and some areas in the southeast coastal region are potential receiving areas, and the eastern regions of Southwest China with abundant water resources and lower elevations, along with the middle and lower reaches of the Yangtze River are potential source areas. Further research about marginal benefits and water use costs, along with dynamic updates, is required for water resource allocation of China. Full article

Balancing tourism industry (TI) growth and ecological protection is critical for sustainable development in the Yellow River Basin (YRB), China’s vital ecological security barrier and economic belt. However, existing research lacks a spatial perspective on the coordinated development between TI and inclusive green growth (IGG), with limited understanding of cross-regional spillover mechanisms. Based on panel data from 75 cities in the YRB (2011–2023), this study constructs a comprehensive evaluation system encompassing the scale, structure, and potential dimensions of the TI and the economic, social, livelihood, and environmental dimensions of IGG. The study employs the coupling coordination degree (CCD) model, exploratory spatial data analysis (ESDA), and the Spatial Durbin Model (SDM) to examine spatiotemporal evolution and spillover effects. The results reveal an upward yet fluctuating coordination trend with pronounced spatial heterogeneity, characterized by a “downstream–midstream–upstream” gradient pattern, dual-core radiation centered on the Jinan–Qingdao and Xi’an–Zhengzhou agglomerations, and persistent High–High clusters in the Shandong Peninsula contrasted with Low–Low clusters in the upstream Qinghai–Gansu–Ningxia region. Critically, new-quality productive forces exert significant positive direct and spillover effects, while industrial structure and government intervention have inhibitory spatial effects on adjacent cities. Regional heterogeneity analysis confirms factor-endowment-driven differentiation across upstream, midstream, and downstream areas. These findings advance spatial spillover theory in river basin contexts and provide evidence-based pathways for balancing economic growth with ecological protection in ecologically sensitive regions worldwide, directly supporting multiple UN Sustainable Development Goals. Full article

In the context of global climate change and the transition to a low-carbon economy, utilizing crop straw as a resource is a key strategy for green transformation. Taking Guangdong province as a case, this study investigates the carbon reduction effects of integrated straw utilization and their spatiotemporal evolution, based on crop yield data from 2019 to 2023 across various municipalities. Different from one-way straw utilization for carbon reduction, this work analyzes the carbon reduction effects of five co-existing pathways to utilize straw as fertilizer, feed, energy, substrate, and raw material. The Theil index, slope value, and exploratory spatial data analysis (ESDA) method are employed to form an analytical framework for the spatiotemporal evolution of carbon reductions by straw utilization. Over this five-year period, the overall and off-field straw utilization steadily increased, and a 6.2% increase in straw utilization was achieved to realize a 19.8% rise in carbon reduction. In 2023, the carbon reduction from straw utilization was chiefly contributed by fertilization, subsequently followed by feed, energy, substrate, and raw material. Over 90% of the carbon reduction contributions came from four major crops, namely rice, peanuts, sugarcane, and potatoes. Carbon reduction across different areas in Guangdong showed positive spatial correlation, with high–high (HH) and low–low (LL) clusters being the primary local autocorrelation patterns. Model applications confirm that incentive policies and industrial development largely facilitate the integrated straw utilization in Guangdong. However, further increases in straw utilization will not necessarily ensure proportional carbon reduction. The regional heterogeneity and coordinated clustering development should be considered to strengthen carbon-reduction intensity. In particular, policies should be tailored to crop straw recovery and utilization, inter-regional straw allocation, and preferentially support straw utilization for energy and as a substrate. Full article

This study aims to extend the understanding of groundwater level dynamics in the Abu Dhabi Emirate by evaluating the performance of two interpolation models, local polynomial interpolation (LPI) and exponential ordinary kriging (EXP-OK), over a 20-year period. These models were selected for their demonstrated effectiveness in groundwater studies, with LPI offering strong local adaptability to spatial variability and EXP-OK providing robust geostatistical modeling for regional patterns. This study also aims to assess the performance of the two interpolation models in identifying missing groundwater level measurements to accurately estimate groundwater storage. The evaluation of the two models is conducted using ArcGIS and IBM-SPSS statistics, including cross-validation, descriptive statistics and exploratory spatial data analysis (ESDA). The findings revealed that both LPI and EXP-OK are effective in analyzing groundwater fluctuations in the study area, with LPI demonstrating a slight edge in predictive accuracy. The ability of the LPI to capture local data variations resulted in a smoother representation of groundwater level data. Owing to its superior performance, the LPI was selected for the estimation of groundwater storage. The study reports that the average change in groundwater storage over the study period could range from −0.066 to −2.112 cubic meters per square meter of aquifer area. These findings emphasize the importance of continuous monitoring and analysis for sustainable water resource management in the study area. Full article

The sustainable transformation of resource-based cities (RBCs) is a critical global challenge. The digital economy is emerging as a potential catalyst for this transition, but the precise mechanisms and spatial dynamics underlying its influence on urban resilience remain underexplored. This study addresses this gap by investigating how the digital economy impacts RBC’s resilience, with a focus on both internal mechanisms and cross-regional spatial effects. Fixed effects, mediation, exploratory spatial data analysis (ESDA) and spatial Durbin model (SDM) are used to examine the complex relationships observed in this context. The results reveal the following: (1) While the digital economy in Chinese RBCs demonstrated a stable upward trajectory, urban resilience, although it exhibited a general increase, remained fragile. (2) The digital economy significantly enhanced urban resilience (coefficient = 0.117, p < 0.05), in which context the most pronounced effects pertained to the social and economic resilience subsystems. (3) Green technological innovation (GTI) served as the core intermediary pathway (a × b = 0.017, p < 0.01). Industrial structure rationalization also served as a mediator. (4) The digital economy and urban resilience exhibited positive spatial autocorrelation (significant direct effects 0.032, p < 0.05), and the advancement of the digital economy in a focal city can enhance both the urban resilience of that city itself and that of neighboring cities indirectly. Full article

This study examines the relationship between technological innovation and economic development in the Yangtze River economic belt context. Specifically, the study employs the SBM-GML model to assess the efficiency of industrial green technology innovation across 110 prefecture-level cities between 2006 and 2022. The study also employs exploratory spatial data analysis (ESDA) and the Spatio-temporal transition method to analyze the spatial evolution pattern of the GML index of industrial green technology innovation. In addition, the study investigates the convergence mechanism using absolute and conditional β convergence models. The findings reveal that the GML index of industrial green technology innovation in the Yangtze River Economic Belt exhibits an upward trend, and technological progress is a key driver. Moreover, the spatial and temporal transition of the GML index of industrial green technology innovation shows substantial spatial dependence and solid spatial stability. The study also finds regional heterogeneity in the absolute and conditional β convergence characteristics and their influencing factors. Considering regional differences, the results suggest differentiated policy recommendations to promote the coordinated development of industrial green technological innovation efficiency in the Yangtze River Economic Belt. The study contributes to the literature on the relationship between technological innovation and economic development, highlighting the importance of spatial considerations and regional heterogeneity in promoting sustainable economic growth. Full article

With increasing discussions about energy security and sustainable electricity generation, the supply of biomass resources, such as sugarcane energy, has become increasingly important for regional development. In this study, the impact of spatial dependence and distribution of the supply of sugar-energy bioelectricity in Brazil was examined using a spatial econometric model. Data from ANEEL’s Generation Information System were utilized to represent the Brazilian territory. Exploratory Spatial Data Analysis (ESDA) was employed as a method, with both bivariate and univariate correlations evaluated. In the scenario analysis, the results indicated a 133% increase in the number of sugarcane bagasse-based power plants in Brazil over the past twenty years (from 189 to 442 power plants), along with a 229% increase in GW potential (from 4.11 to 13.55 GW) over the same period. The results demonstrated that the Brazilian sector is expanding rapidly. Regarding spatial dependence, the results indicated that in Brazil, there is no clear correlation between electricity consumption and sugarcane supply, but the univariate analysis revealed that power availability is spatially connected, with the presence of high-supply clusters in the country. The spatial agglomerations showed an I_{Moran_Global} of 0.543 for intermediate regions and 0.453 for immediate regions. Spatial agglomeration may have a positive effect on improving regional performance by reducing the challenges involved in site selection, licensing, and grid connection. Thus, this work contributes by analyzing the spatial distribution of supply, which can be useful for energy planning. Furthermore, spatial differences and disparities complicate the management and formulation of public policies aimed at regional energy development, requiring spatial methods that identify areas with similar characteristics, such as the one applied in this study. Full article

Clarifying the spatial relationships and impact mechanisms of forest carbon storage is essential for designing carbon sink policies and promoting coordinated regional and sustainable development. Using panel data from 21 cities in Guangdong Province between 2012 and 2021, this study employs the forest accumulation expansion method, exploratory spatial data analysis (ESDA), and spatial econometric models to investigate the distribution, spillover effects, and impact mechanisms of forest carbon storage. The results show the following: (1) During the study period, forest carbon storage in Guangdong Province exhibited a fluctuating upward trend and notable regional disparities, with the highest levels observed in the northern region. (2) Forest carbon storage exhibits spatial correlation characteristics and a positive spillover effect, with a value of 0.2394. (3) Temperature has a negative spillover effect on forest carbon storage, while gross regional product demonstrates a negative direct effect. In contrast, labor and afforestation are key factors that possess significant positive direct and spillover effects. Therefore, in developing forest carbon sinks, it is recommended that the government implement adaptation strategies and strengthen inter-city cooperation to promote sustainable development. Full article

Strengthening the exploration of synergistic promotion mechanisms between ecosystem services (ESs) and new urbanization is of great significance for watershed development. In this work, we revealed the evolution mechanism of coupling coordination development degree (CCD) between ESs and new urbanization and its driving factors in the Huaihe River Basin (HRB) from 1980 to 2020 using a combination of the CCD model, Exploratory Spatial Data Analysis (ESDA) method, and GeoDetector model. Additionally, we employed the PLUS model to investigate multi-scenario simulations. The results demonstrate that ESs showed a decline initially, followed by an increase, while the urbanization index showed consistent annual growth over the four decades. Furthermore, the CCD between the ESs and urbanization showed a yearly optimization trend. The CCD demonstrated notable spatial clustering characteristics, with factors such as precipitation, distance from water body, elevation, and per area GDP emerged as the primary drivers. Under scenarios of ecological protection, comprehensive development, and natural protection, the value of ESs from 2020 to 2050 maintained an upward trend; however, it fell with the decrease under the scenario of cropland protection. These research findings offer valuable decision-making support for the differentiated regulation of ecosystem functions and promotion of high-quality urbanization development in the HRB. Full article

Social and economic growth in developing countries has heightened the awareness of environmental challenges, with carbon emissions emerging as a particularly pressing concern. However, the impact of economic development on carbon emission intensity has rarely been considered from the perspective of economic agglomeration, and the relationships and mechanisms between the two remain poorly understood. We analyzed the impact of economic agglomeration on carbon emission intensity and its spatial spillover effect in Guangdong Province, the most economically advantaged province of China, based on a spatial weight matrix generated using geographic proximity, exploratory spatial data analysis (ESDA), and the spatial Durbin model. Between 2000 and 2019, economic agglomeration and carbon emission intensity in Guangdong Province exhibited persistent upward trajectories, whereas between 2016 and 2019, carbon emission intensity gradually approached zero. Further, 80% of the province’s economic output was concentrated in the Pearl River Delta region. Strong spatial autocorrelation was observed between economic agglomeration and carbon emission intensity in the cities, and the economic agglomeration of the province had a parabolic influence on carbon emission intensity. Carbon emission intensity peaked at an economic agglomeration level of 1.2416 × 10⁹ yuan/km² and then gradually decreased. The spatial spillover effect of the openness degree on carbon emission intensity was positive, while GDP per capita and industrial structure had negative effects. Further, the economic agglomeration effects of Guangdong Province increased the carbon emission intensity of major cities and smaller neighboring cities. The stacking effect of economic agglomeration between cities also affected the carbon emission intensity of neighboring cities in the region. During the period of rapid urban development, industrial development and population agglomeration increased resource and energy consumption, and positive externalities such as the scale effect and knowledge spillover were not well reflected, resulting in greater overall negative environmental externalities relative to positive environmental externalities. Full article

The imbalance in the development of the digital economy hinders the effective formation of economies of scale and synergies, thereby constraining the high-quality growth of China’s overall economy. This study employs panel data from 31 Chinese provinces spanning 2013 to 2021, using exploratory spatial data analysis (ESDA) and the Geodetector to investigate the spatial differentiation characteristics and driving factors of China’s digital economy. The findings reveal a gradient pattern of digital economy development, decreasing from east to central, west, and northeast China, with high-value clusters concentrated and spatially locked in the eastern region. Analysis of the gravity center and standard deviation ellipse indicates a spatial distribution dynamic of “longitudinal clustering and lateral expansion”, with a significant “westward migration” of the gravity center. Spatial disparities are driven by both inter-regional and intra-regional differences, with discrepancies between the eastern region and the other three regions being the primary source of spatial variation. The Geodetector analysis identifies human capital, foreign direct investment, and R&D expenditure as the main factors contributing to the spatial-temporal differentiation of China’s digital economy. Last, the study offers policy recommendations regarding infrastructure, resource allocation, and institutional mechanisms to promote balanced development of the digital economy in China. Full article

Recreational resources, fundamental to ecological experiences, are critical in balancing conservation with development. Effective ecotourism planning is especially vital for newly established protected areas such as the Hainan Tropical Rainforest National Park in China’s developing system of natural conservation areas. Targeting Hainan Tropical Rainforest National Park, this study applies nearest neighbor index, kernel density analysis, and exploratory spatial data analysis (ESDA) to study the spatial pattern of 274 recreational resource points. Results indicate a clustered spatial pattern with significant differences in resource density among municipalities. Specifically, 98% of these resources can be reached in 3 h, with an average travel time of 91 min, and cultural resources exhibit greater accessibility than natural resources. Natural resource availability and ethnic culture are major factors of resource distribution and accessibility. This research offers a theoretical basis and practical guidance for optimizing recreational resource allocation and promoting ecotourism in the park, contributing to the ongoing discussion of sustainable tourism development. Full article

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development. Full article

This study assesses the conservation effectiveness of 21 protected areas (PAs) in Northern Morocco, comprising 3 parks and 18 Sites of Ecological and Biological Interest (SBEIs), against five major landscape pressures (LSPs): deforestation, infrastructure extension, agricultural expansion, fires, and population growth. We propose a novel quantitative methodology using global remote sensing data and exploratory spatial data analysis (ESDA). Data were sourced from Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), and World Population datasets. The combined impact of the five LSPs was measured using a cumulative effect index (CEI), calculated with the Shannon–Wiener formula at a 1 km² scale. The CEI was analyzed alongside the distance to the PAs’ network using Moran’s index, identifying four spatial association types: high–high (HH), high–low (HL), low–low (LL), low–high (LH), and non-significant (NS) cells. This analysis defined four zones: inner zone (IZ), potential spillover effect zone (PSEZ), statistically non-significant zone (SNSZ), and non-potential effect zone (NPEZ). Conservation effectiveness was quantified using the conservation ratio (CR), which compared the prevalence of LL versus HL units within IZs and PSEZs. Four disturbance levels (very high, high, medium, and low) were assigned to CR values (0–25%, 25–50%, 50–75%, 75–100%), resulting in sixteen potential conservation effectiveness typologies. Initial findings indicated similar deforestation patterns between protected and unprotected zones, with wildfires causing over half of forest losses within PAs. Conservation effectiveness results categorized the 21 PAs into nine typologies, from high conservation to very high disturbance levels. A significant positive correlation (71%) between CRs in both zones underscored the uniform impact of LSPs, regardless of protection status. However, protected natural area zones in the parks category showed minimal disruption, attributed to their advanced protection status. Finally, we developed a methodological framework for potential application in other regions based on this case study. Full article