Search Results (74)

As a carrier of civilization, cultural heritage reflects the dynamic relationship between humans and their environment within specific historical contexts. During the Ming and Qing Dynasties (1368–1912 CE), the Yangtze River Basin was one of the most prominent regions for economic and cultural activities in ancient China. The cultural heritage of this period was characterized by its dense distribution and continuous evolution. Considering the applicability bias of modern data in historical interpretation, this study selected four characteristic variables: population density, agricultural productivity, technological level, and temperature anomaly. A hierarchical Bayesian model was constructed and change points were detected to quantitatively analyze the driving mechanisms behind the spatiotemporal distribution of cultural heritage. The results show the following: (1) The distribution of cultural heritage exhibited a multipolar trend by the mid-period in both Dynasties, with high-density areas contracting in the later period. (2) Agricultural productivity consistently had a significant positive impact, while population density also had a significant positive impact, except during the mid-Ming period. (3) The cultural calibration terms, which account for observational differences resulting from the interaction between cultural systems and environmental variables, exhibited slight variations. (4) The change point for population density was 364.83 people/km², and for agricultural productivity it was 2.86 × 10⁹ kJ/km². This study confirms that the differentiation in the spatiotemporal distribution of cultural heritage is driven by the synergistic effects of population and resources. This provides a new perspective for researching human–land relations in a cross-cultural context. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

Urban morphology’s effects on carbon dioxide reduction and sustainable development have drawn more attention. The county scale is crucial in influencing urban development and is the central element of China’s recent urbanization. To achieve scientific urban planning and fully explore its potential in carbon emission reduction, local governments need to investigate the impact of urban morphology on carbon emissions (CE). However, previous studies have predominantly focused on provincial capitals and urban clusters. To address this gap, this study quantified four aspects of urban form, combined energy consumption, and nighttime light data to estimate CE in Chinese counties from 2000 to 2020 and analyzed the effects of these factors on CE using multiscale geographically weighted Regression(MGWR) models and geographic detectors. The following are the main findings: (1) Total CE at the county scale in China has consistently increased from 2000 to 2020. (2) The largest patch index (LPI) is the most influential urban morphological factor on CE, while the impact of Class Area (CA) has been increasing. (3) Bi-factor enhancement and nonlinear enhancement are the two primary interaction types of urban morphological factors; the most important interaction is between LSI and CA. (4) The urban morphological factors exhibit varying degrees of spatial heterogeneity, with the influencing factors ranked as CA > LPI > path density (PD) > edge density (ED) > patch cohesion index (COHESION), where LPI and CA consistently show a positive effect on CE. This study’s findings establish a scientific foundation for land spatial planning and tailored emission reduction methods at the county scale in China. Full article

Amid rapid urbanization, cities are becoming increasingly compact, leading to intensified land resource constraints and environmental pressures. As a result, urban parks and green spaces have emerged as critical areas for providing cultural ecosystem services (CESs). However, the spatial distribution of CES supply and demand within urban green spaces remains significantly unbalanced, necessitating precise identification and quantification of CES supply–demand patterns to enhance ecosystem service efficiency. This study uses Nanjing, China, as a case study to develop an indicator framework for urban green space CES supply and demand, leveraging multi-source data. By employing spatial autocorrelation analysis (Bivariate Moran’s I) and a coupling coordination model, this research systematically assesses the spatial patterns of CESs in urban parks and green spaces. The results indicate that the overall CES supply–demand coordination in Nanjing exhibits a “high in the city center, low at the edges, and mismatched in the suburbs” pattern. Specifically, while 9.71% of the areas demonstrate well-matched CES supply and demand, 4.14% of the areas experience insufficient CES demand, and 3.66% face CES supply shortages, primarily in the urban outskirts, leading to a mismatch in green space distribution. This study further reveals the spatial heterogeneity of CES supply–demand matching across different urban districts. Based on these findings, this research proposes optimization strategies to improve CES allocation, providing a scientific basis for urban green space ecosystem service management and promoting the sustainable development of cities. Full article

As an important ecological barrier and economic belt in China, the sustainable development of the Yellow River Basin (YRB) is of great significance to national ecological security and regional economic balance. Based on the coupled and coordinated development analysis of the water–soil–energy–carbon (W-L-E-C) system in the provinces of the Yellow River Basin from 2002 to 2022, this study systematically analyzed the interaction relationship among the various factors through WLECNI index assessment, factor identification, and driving factor exploration. Thus, it fully reveals the spatiotemporal evolution law of regional coordinated development and its internal driving mechanism. It is found that the coordinated development of the W-L-E-C system in different provinces of the Yellow River Basin presents significant spatiotemporal differentiation, and its evolution process is influenced by multiple factors. It is found that the coordination of the YRB presents a significant spatial difference, and Inner Mongolia and Shaanxi, as high coordination areas, have achieved significant improvement in coordination, through ecological restoration and clean energy replacement, arable land intensification, and industrial water-saving technology, respectively. Shandong, Henan, and Shanxi in the middle coordination zone have made some achievements in industrial greening and water-saving technology promotion, but they are still restricted by industrial carbon emissions and land resource pressure. The Ningxia and Gansu regions with low coordination are slow to improve their coordination due to water resource overload and inefficient energy utilization. Barrier factor analysis shows that the water resources utilization rate (W4), impervious area (L4), energy consumption per unit GDP (E1), and carbon emissions from energy consumption (C3) are the core factors restricting coordination. Among them, the water quality compliance rate (W5) of Shanxi and Henan is very low, and the impervious area (L4) of Shandong is a prominent problem. The interaction analysis of the driving factors showed that there were significant interactions between water resource use and ecological protection (W-E), land resource and energy use (L-E), and carbon emissions and ecosystem (C-E). Inner Mongolia, Shaanxi, and Shandong achieved coordinated improvement through “scenic energy + ecological restoration”, cultivated land protection, and industrial greening. Shanxi, Henan, and Ningxia are constrained by the “W-L-E-C” complex obstacles. In the future, the Yellow River Basin should implement the following zoning control strategy: for the areas with high coordination, it should focus on consolidating the synergistic advantages of ecological protection and energy development; water-saving technology and energy consumption reduction measures should be promoted in the middle coordination area. In the low coordination area, efforts should be made to solve the problem of resource overload, and the current situation of low resource utilization efficiency should be improved by improving the utilization rate of recycled water and applying photovoltaic sand control technology. This differentiated governance plan will effectively enhance the level of coordinated development across the basin. The research results provide a decision-making framework of “zoning regulation, system optimization and dynamic monitoring” for the sustainable development of the YRB, and provide a scientific basis for achieving high-quality development of the basin. Full article

As we are advancing deeper into the twenty-first century, new challenges as well as technical opportunities in agriculture are rising. One of these issues is the increasing need for food, which is crucial for supporting the population’s nutritional needs, promoting regional trade, and ensuring food security. Climate change is another ongoing challenge in the shape of changing rainfall patterns, increasing temperatures due to high CO₂ concentrations, and over urbanization which ultimately negatively impact the crop yield. Therefore, for increased food production and the sustainability of agricultural growth, an accurate and timely crop yield prediction could be beneficial. In this paper, artificial intelligence (AI)-based sustainable methods for the evaluation of wheat production (W_P) using multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN) techniques are presented. The historical data of around 60 years, comprising of wheat area (W_A), temperature (T), rainfall (RF), carbon dioxide emissions from liquid and gaseous fusion CE (CE_LF, CE_GF), arable land (AL), credit disbursement (CD), and fertilizer offtake (FO) were used as potential indicators/input parameters to forecast wheat production (W_P). To further support the performance efficiency of computed prediction models, a variety of statistical tests were used, such as R-square (R²), root means square error (RMSE), and mean absolute error (MAE). The results demonstrate that all acceptance standards relating to accuracy are satisfied by the proposed models. However, the SVM outperforms MLR and ANN approaches. Additionally, parametric and sensitivity tests were performed to assess the specific influence of the input parameters. Full article

Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance. Full article

Although both prefectural governmental green investment (GGI_city) and provincial governmental green investment (GGI_prov) have potentially diverse impacts on prefectural cities’ carbon emission reduction (CER), previous studies have rarely examined the effects of governmental green investment (GGI) on different indicators of CER such as total carbon dioxide emissions (CE), carbon emissions intensity (CEI) and per capita carbon emissions (PCE) in the context of prefectural cities nested in provinces in China. In our research, six hierarchical linear models are established to investigate the impact of GGI_city and GGI_prov, as well as their interaction, on CER. These models consider eight control factors, including fractional vegetation coverage, nighttime light index (NTL), the proportion of built-up land (P_built), and so on. Furthermore, heterogeneous impacts across different groups based on provincial area, terrain, and economic development level are considered. Our findings reveal the following: (1) The three indicators of CER and GGI exhibit significant spatial and temporal variations. The coefficient of variation for CEI and PCE shows a fluctuating upward characteristic. (2) Both lnGGI_city and lnGGI_prov have promoted CER, but the impact strength of lnGGI_prov on lnCE and lnPCE is more pronounced than that of lnGGI_city. GGI_prov can strengthen the effect of GGI_city significantly for lnCE. Diverse control variables have exerted significant impacts on the three indicators of CER, albeit with considerable variation in their effects. (3) The effect of GGI on CER is significantly heterogeneous upon conducting grouped analysis by provincial area size, terrain complexity, and economic development level. The interaction term lnGGI_city:lnGGI_prov is stronger in the small provincial area group and simple terrain group. Among the control variables, economic Development Level (GDPpc), the logarithm of gross fixed assets investment (lnFAI), NTL, and P_built exhibit particularly pronounced differences across different groups. This study provides a robust understanding of the heterogeneous and interactive effects of GGI on CER, aiding in the promotion of sustainable development. Full article

The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model’s ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies. Full article

Despite Guangxi’s unique ecological diversity and its important role in land-based ecological security and conservation, research on the assessment and prediction of its habitat quality under the influences of rapid urbanization and environmental pressures remains limited. This study systematically analyzes the spatial and temporal dynamics of land use and habitat quality in Guangxi from 2000 to 2020 using the PLUS-InVEST model and simulates future scenarios for 2030. These scenarios include the Natural Development (ND) scenario, Urban Development (UD) scenario, and Cropland and Ecological Protection (CE) scenario. The results indicate the following: (1) Over the past two decades, rapid urban and construction land expansions in Guangxi intensified their negative impact on habitat degradation. Additionally, the disproportionate change between rural settlement land and rural population warrants attention. (2) Although ecological restoration measures have played a positive role in mitigating habitat degradation, their effects have been insufficient to counterbalance the negative impacts of construction land expansion, highlighting the need for balanced land use planning and urbanization policies. (3) The expansion of rural residential areas had a greater impact on regional habitat quality degradation than urban and infrastructure expansion. Moderate urbanization may contribute to habitat quality improvement. (4) The CE scenario shows the most significant improvement in habitat quality (an increase of 0.13%), followed by the UD scenario, which alleviates habitat degradation by reducing pressure on rural land. In contrast, the ND scenario predicts further declines in habitat quality. Furthermore, land use planning, restoration measures, and sustainable development policies are key factors influencing habitat quality changes. These findings emphasize the importance of integrating land use strategies with ecological restoration measures to balance economic growth and biodiversity conservation, especially in rapidly urbanizing regions. Full article

Land-use change is an important cause of carbon emissions (CEs). In the context of achieving carbon peaking and carbon neutrality goals, understanding the coupling mechanisms between land-use change and CEs is of great significance for fostering regional low-carbon sustainable development. In this study, the land-use net carbon emissions (LCN) calculation and evaluation model was built based on the perspective of land-use change. The land-use variation matrix, standard deviation ellipse, and spatial autocorrelation analysis were used to analyze the spatio-temporal evolution of land-use and the LCN in the Chengyu urban agglomeration (CUA) from 2000 to 2020. Meanwhile, the economic contribution coefficient and ecological support coefficient were applied to evaluate the alignment among the CEs, socio-economic development, and the ecological environment. In addition, the modified Kaya and Logarithmic Mean Divisia Index (LMDI) models were used to quantitatively analyze the drivers and underlying influence mechanisms of the LCN. The results showed the following: (1) The area of built-up land and forest land expanded rapidly, mainly transforming grassland and farmland to built-up land and forest land in the CUA during the study period. The built-up land was the main source of the regional CEs. The land-use changes led to the migration of the LCN center and the variations in spatial clustering. (2) The growth rate of the LCN decreased after 2010, and the disparities in carbon productivity and the carbon compensation rate among the cities gradually narrowed from 2000 to 2020. The alignment among the regional CEs, socio-economic development, and ecological environmental governance was effectively improved. (3) The economic development level and energy consumption intensity were the primary facilitator and inhibitor of the LCN, respectively. The results could offer valuable references and insights for formulating regional carbon reduction strategies and policies. Full article

In this study, the landscape and ecological stability of the Dolný Spiš region are investigated, focusing on human-induced changes and land use patterns. The purpose is to assess the impact of industrial, agricultural, and social activities on the landscape structure, using current and historical data. Field mapping and data from the DATAcube (Database of the Slovak Statistical Office) and CORINE Land Cover databases (Landscape cover layer for the whole territory of Europe) were used to evaluate land use, with ecological stability measured through the coefficient of ecological stability (CES). Three methodologies—Míchal, Löw, and Miklós—were applied and adjusted for local conditions. The study area, predominantly covered by forests (over 80%), was classified as highly stable based on CES values, with forested areas contributing significantly to this classification. Additionally, the non-forested areas were analyzed to assess the full scope of anthropic influence, revealing low-intensity human activity, as indicated by the coefficient of anthropic influence (CAI), ranging from 0 to 0.45. The results demonstrate that the landscape’s ability to resist disruptive elements is strong, particularly in forested regions. Overall, in this study, the critical role of forests is highlighted in maintaining the ecological stability in the region and suggests that the landscape structure remains resilient despite ongoing changes in agricultural land use. Full article

This study focused on the land use (LU) structure and carbon emissions (CEs) in the Jiangsu, Zhejiang, Anhui, and Shanghai provinces of the Yangtze River Delta (YRD) in China from 2000 to 2020, using the STIRPAT model and scenario analysis (SA). We conducted an analysis of the influence exerted by relevant factors on land use carbon emissions (LUCEs) and made forecasts regarding the diverse development scenarios of CE trends, aiming to provide methodological guidance for validating the effectiveness of existing policies in reducing CEs and offer direction for achieving the peak CO₂ emissions target as soon as possible. It also constitutes a significant reference for the early realization of the peak CO₂ emissions target. The results indicated the following: (1) Between 2000 and 2020, CEs resulting from LU in the YRD rose from 2.70 × 10⁸ t to 9.10 × 10⁸ t, marking an increase of 243.77%. In 2020, the built-up area was the predominant contributor to CEs, representing 99.15% of the overall carbon sources, whereas forests served as the main carbon sink, comprising 92.37% of the total carbon sinks (CSs) for that year. (2) For each percent increase in the parameters considered in this study, the corresponding increases in LU CO₂ emissions were estimated to be: 1.932% (population), 0.241% (GDP per capita), −0.141% (energy intensity), 0.043% (consumption structure), 1.045% (industrial structure), and 0.975% (urbanization). (3) According to the existing policy framework and development plans, the YRD is expected to achieve peaking carbon dioxide emissions by 2030. If energy conservation and carbon reduction strategies are implemented, this peak could be achieved as early as 2025. However, if economic growth continues to depend primarily on fossil fuel consumption, the region may not hit its carbon peak until 2035. (4) The low-carbon scenario, which considers the needs of social progress alongside the intensity of carbon emission reductions, represents the most effective development strategy for reaching a carbon peak in LU within the YRD. Effectively managing population size and facilitating the upgrading of industrial structures are key strategies to hasten the achievement of peaking carbon dioxide emissions in the region. Full article

Human activities continuously alter the delivery of ecosystem services (ESs), which play a crucial role in human well-being. There is a pressing need for effective ecological management strategies that consider the spatial heterogeneity of ESs to support the transition from trade-offs to synergies. This study focuses on the Haba River Basin and examines characteristics of land-use change and the shift from trade-offs to synergies. The results indicate that from 1990 to 2000, the initial phase of land development, 10.65% of the land experienced change. Subsequently, during the intensive period of land development from 2000 to 2010, 30.29% of the land underwent significant transformation, with approximately 78% of grassland, sparse grassland, forested land, and desert converted into arable land. However, between 2010 and 2020, as the focus shifted towards the establishment of native vegetation. The intensity of land development decreased, and only a small percentage (3.65%) of the total area underwent changes. Based on an in-depth analysis of spatial heterogeneity from 1990 to 2020, it is believed there has been a shift from trade-offs to co-benefits between 2000–2010 and 2010–2020. The years 2010 and 2020 were pivotal time nodes for the transition from trade-offs to synergies and for reducing trade-offs, with NPP identified as a critical driving factor for comprehensive ES (CES) functions. By considering the trade-off–synergy relationship and hotspots of ecological service functions, combined with unified water resource management policies, comprehensive ecological management measures tailored to different regions are proposed. These measures have facilitated the implementation of robust ecological protection policies to shift ES development from trade-offs to synergies in arid areas, thereby enhancing overall ecosystem service functions in the Haba River Basin. The research findings offer crucial scientific support and guidance for ecosystem management in arid areas, particularly within Central Asia. Full article

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations. Full article