Spatiotemporal evolution and multi-scale coupling effects of land-use carbon emissions and ecological environmental quality

Advancing multiscale sustainable development in lake-dense regions: A dynamic management chain for ecosystem service supply-demand and ecological risks interactions

Dynamic management of lake-dense regions based on interactions between ecosystem service supply-demand (ESSD) and ecological risk (ER) is essential for sustainable development. This study integrates the ecosystem service value supply model, a land-population-economy-society demand model, and the landscape ER assessment to develop a comprehensive ESSD-ER framework for lake-dense regions. Bivariate spatial autocorrelation and geographically and temporally weighted regression (GTWR) models are applied to reveal the spatiotemporal interaction intensity between ESSD and ER. Subsequently, an integrated ESSD-ER sustainable management framework is constructed based on a six-quadrant model and dynamic change rate index. Applying this framework to the middle reaches of the Yangtze River urban agglomerations (MRYRUA) at multiscale, the results indicate: (1) A persistent mismatch existed in the study area, where ecosystem service supply remains lower than demand, with ER rising annually. (2) High ESSD-high ER clusters showed positive interactions within lake areas. Negative interactions intensify progressively from low-high clustered nearshore areas to high-low clustered inland forested areas. (3) MRYRUA comprised six management zones: optimal balance sustainable zones, potential balance sustainable zones, imbalanced improvement transitional zones, worsening imbalance transitional zones, risk alert transitional zones, and dual crisis unsustainable zones. At the macroscale, northern regions displayed higher unsustainable categories than southern regions, showing apparent spatial heterogeneity. Risk-alert transitional zones dominated (51.61 %), primarily distributed adjacent to water bodies. At the microscale, cropland-forest interlaced zones serve as optimal balance sustainable zones. Green industrial upgrading mechanisms are recommended in dual crisis unsustainable zones in northern regions. Southern regions should maintain their advantages in optimal and potential balance sustainable zones. These findings provide scientific guidance to achieve multiscale sustainable development in lake-dense regions.

Multiscale exploration of spatiotemporal dynamics in China's largest urban agglomeration: An interactive coupling perspective on human activity intensity and ecosystem health

Human economic construction increasingly impacts highly sensitive ecological zones, weakening ecosystem health in cross-regional urban agglomerations. Exploring the spatiotemporal dynamics of urban agglomerations from the interactive coupling perspective between human activity intensity (HAI) and ecosystem health index (EHI) is crucial for resolving human-land conflicts. This study developed a human-land coupling assessment framework integrating human footprint and ecosystem Maintain-Bearing-Service-Resilience models. Across multiple scales, from urban agglomerations and cities to grid cells, we initially employed exploratory spatiotemporal data analysis techniques to reveal HAI and EHI evolution patterns. Subsequently, we used the four-quadrant model, coupling coordination degree (CCD), and relative development model to explore their spatiotemporal interactions. Applied to China's largest urban agglomeration, the middle reaches of the Yangtze River urban agglomerations (MRYRUA), results revealed a significant spatiotemporal mismatch pattern between HAI and EHI. High HAI and low EHI areas were widely distributed in highly urbanized waterfront plains. At the urban agglomeration scale, HAI and EHI exhibited spatiotemporal differentiation patterns extending toward polarization along the Yangtze River Economic Belt, while their correlation intensity among cities indicated conflicting development patterns. At the grid scale, the spatiotemporal clustering pattern highlighted waterfront built-up areas as HAI hotspots and peripheral forest zones as EHI hotspots. The interactive relationship between HAI and EHI shifted increasingly towards Quadrant IV as HAI rose. The coupling levels between HAI and EHI will tend toward misalignment as urbanization advances, although current CCD shows positive trends. This study offers scientific guidance for achieving sustainable development in urban agglomerations across multiple scales.

Deciphering the point source carbon footprint puzzle: Land use dynamics and socio-economic drivers

Point source carbon emissions account for approximately 80 % of total emissions. Investigating the influence of land use and socio-economic indicators on these emissions is crucial for achieving sustainable development goals. Existing research faces challenges such as focusing on specific regions, mixing variables that may exhibit multicollinearity, and lacking sufficient land use information. This study takes China, the largest emitting country, as a case study, utilizing geospatial big data to subdivide land use into 11 categories based on emission sectors. The impacts of land use and socio-economic indicators on different emission sectors are discussed from the perspectives of bivariate and spatial statistical analysis, with spatial hotspots identified. Hierarchical regression is used to evaluate the explanatory power of the indicators and to establish models, and potential carbon reduction strategies are further explored. Key findings reveal: (1) Significant multicollinearity between land use and socio-economic indicators was demonstrated, with land use explaining 57.1 % of emissions compared to 37.4 % explained by socio-economic indicators. The spatial consistency between land use and emissions exceeds 80 %, and the spatiotemporal variability is relatively low, making land use a more advantageous factor in explaining point source carbon emissions. (2) Agricultural mechanization increases emission intensity, but this efficient farming method helps convert surplus plowland, the largest influencing factor (Coefficient = 0.717), into carbon sinks, thereby controlling agricultural emissions. (3) Land intensification helps control the area of industrial land, the main factor influencing industrial emissions (Coefficient = 0.392). It also contributes to the efficient use of carbon reduction technologies and industrial supporting land. (4) Mixed commercial and residential land has the greatest impact on commercial, service, and household emissions. However, its relationship with the economy (Correlation = 0.479) is stronger than its relationship with emissions (Correlation = 0.182), making it more applicable to cities that serve as economic growth hubs.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

Exploring dynamics relationship between carbon emissions and eco-environmental quality in Samarinda Metropolitan Area: A spatiotemporal approach

Carbon emissions have a negative impact on climate change. Environmental quality has faced significant challenges in the last decades. Eco-environmental quality helps assess the condition of the ecological environment to support humans' civilization and development. By using emissions raster dataset, remote sensing images, and LULC data, this study explores the status of carbon emissions (CE), eco-environmental quality (RSEICs), and the dynamic relationship between both variables in Samarinda Metropolitan Area, Indonesia. This study uses the spatiotemporal approach to deepen the understanding of CE-RSEICs during 2000-2021. The methods include the analysis of CE and the principal component of RSEICs. To understand the CE-RSEICs spatial features, the directional distribution ellipse method is used. Also, this study performs CE-RSEICs coupling analysis and identifies its LULC type composition. The findings show that CE status is still on an increasing trend, concentrating in the eastern region and keeping expanding during the period. The location of the low-emission ellipse is in the southwest, while the high-emission ellipse is in the east and intersects with the core cities. The mean RSEICs value is between 0.2878 to 0.4223, which indicates that the eco-environmental quality is categorized as fairly poor to inferior. Greenness, wetness, and Csink have a positive impact on RSEICs. The very poor-class ellipse is located in the inland region, and the very good-class ellipse is in the coastal area. The CE-RSEICs coupling status shows that the majority of the area has a weaker coupling degree. However, the higher coupling degree is concentrated in the population center and built-up region, which is the settlement area. The dominance composition of settlement area in higher coupling degree shows that settlement area has an impact on increasing CE-RSEICs coupling degree. So, sustainable low carbon development in coastal metropolitan area must continue to be carried out by considering CE-RSEICs and its spatial aspects.