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Estimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and analyses the driving force of carbon emission growth using Henan Province as a case study. Based on the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82 percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and northern regions and low emissions in the southern. However, their carbon emissions have constantly decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of carbon emission reduction programs.

Background: High-speed urbanization has brought about a number of ecological and environmental problems, as well as the use of remote sensing to monitor the urban ecological environment and explore the main factors affecting its changes. It is important to promote the sustainable development of cities. Methods: In this study, we quantify the ecological quality of the study area from 2000 to 2020 based on the remote sensing ecological index (RSEI) and analyze its drivers through Geodetector and geographically weighted regression. Results: The RSEI of Fuzhou City from 2000 to 2020 showed an increasing followed by a decreasing trend, with obvious spatial autocorrelation. The main driving factors causing the spatial divergence of the RSEI were elevation (q = 0.48–0.63), slope (0.42–0.59), and GDP (0.3–0.42), and the driving effect and range of each factor changed with time. Conclusion: In this paper, we explore changes in the ecological environment in Fuzhou City over the past 20 years, as well as the scope and magnitude of the drivers, providing an important reference basis to improve the ecological environment quality of the city.

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures.

The investigation of the temporal-spatial characteristics and driving factors of vegetation ecosystem (VE) alterations held significant practical implications for the evaluation of the efficacy of rocky desertification management initiatives and safeguarding the ecological environment in the rocky desertification restoration region of Guizhou. We computed the comprehensive ecological quality index (Q) of vegetation based on the normalized difference vegetation index (NDVI) and net primary productivity (NPP). Combined with temperature, precipitation, sunshine duration, rocky desertification grade, land use, and the time series of various regions being included in national ecological functional zones, we analyzed the spatial–temporal distribution characteristics of VE changes and their response to climate change (CC) and ecological engineering (EE) by using partial derivative analysis method and scenario setting method in rocky desertification restoration areas in Guizhou. Results demonstrated that (1) the average values of NDVI, NPP, and Q all showed a fluctuating upward trend since 2000. Although the VE status of rocky desertification area was obviously worse than that of no rocky desertification area, it has a higher growth rate, especially the growth rates of NDVI, NPP, and Q in severe rocky desertification area were as high as 0.0050 year −1 , 9.0733 g C m −2 year −1 , and 0.7829 year −1 , and the area with high recovery degree accounted for 93.19%, followed by the middle rocky desertification area. (2) CC was the main driving factor for NDVI and Q recovery, and EE was the main driving factor for NPP recovery. The contribution of EE to NPP and Q recovery increased with the increase of rocky desertification, as high as 82.13% and 30.31% in severe rocky desertification area. (3) The more serious the rocky desertification was, the more dependent the vegetation restoration was on ecological engineering, and the more difficult the restoration was. It was urgent to solve the ecological environmental problems. (4) EE played a greater role in the restoration of VE in the early stage of implementation. Its role gradually decreased in the later stages of implementation, while the role of CC increased. We provide a scientific basis for the follow-up treatment of rocky desertification, ecological environment restoration, and ecological protection effectiveness evaluation in Guizhou.

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region.

As an ecological transition zone, the ecosystem of the Qin River Basin in the middle reaches of the Yellow River is of great significance to the regional ecological balance. With the rapid socio-economic development, land use changes are significant, and the spatial and temporal patterns of ecosystems are evolving. Exploring its dynamics and driving mechanisms is crucial to the ecological protection and sustainable development of watersheds. This research systematically examines the spatiotemporal dynamics and driving mechanisms of ecosystem patterns in the middle Yellow River’s Qin River Basin (1990–2020). Quantitative assessments integrating ecosystem transition metrics and redundancy analysis reveal three critical insights: (1) dominance of agricultural land and woodland (74.81% combined coverage), with grassland (18.58%) and other land types (6.61%) constituting secondary components; (2) dynamic interconversion between woodland and grassland accompanied by urban encroachment on agricultural land, manifesting as net reductions in woodland (−13.74%), farmland (−6.60%), and wetland (−38.64%) contrasting with grassland (+43.34%) and built-up area (+116.63%) expansion; (3) quantified anthropogenic drivers showing agricultural intensification (45.03%) and ecological protection measures (36.50%) as primary forces, while urbanization account for 18.47% of observed changes. The first two RDA ordination axes significantly (p < 0.01) explain 68.3% of the variance in ecosystem evolution, particularly linking land-use changes to socioeconomic indicators. Based on these findings, the study proposes integrated watershed management strategies emphasizing scientific land-use optimization, controlled urban expansion, and systematic ecological rehabilitation to enhance landscape stability in this ecologically sensitive region. The conclusions of this study have important reference value for other ecologically sensitive watersheds in land use planning, ecological protection policy making, and ecological restoration practice, which can provide a theoretical basis and practical guidance.

Moso bamboo forests, widely distributed in subtropical regions, are increasingly valued for their strong carbon sequestration capacity. However, the carbon flux variations and the driving mechanisms of Moso bamboo forest ecosystems of each phenology period have not been adequately explained. Hence, this study utilizes comprehensive observational data from a Moso bamboo forest eddy covariance observation for the full phenological cycle (2011-2015), fitting a light response equation to elucidate the evolving dynamics of carbon fluxes and photosynthetic characteristics throughout the entire phenological cycle, and employing correlation and path analysis to reveal the response mechanisms of carbon fluxes to both biotic and abiotic factors. The results showed that, First, the net ecosystem exchange (NEE) of Moso bamboo forest exhibits significant variations across six phenological periods, with LS demonstrating the highest NEE at -23.85 ± 12.61 gC·m ·5day , followed by LS at -19.04 ± 11.77 gC·m ·5day and FG at -17.30 ± 9.58 gC·m ·5day , while NF have the lowest value with 3.37 ± 8.24 gC·m ·5day . Second, the maximum net photosynthetic rate (P ) and apparent quantum efficiency (α) fluctuated from 0.42 ± 0.20 (FG ) to 0.75 ± 0.24 mg·m ·s (NF ) and from 2.3 ± 1.3 (NF ) to 3.3 ± 1.8 μg·μmol (LS ), respectively. Third, based on the path analysis, soil temperature was the most important driving factor of photosynthetic rate and NEE variation, with path coefficient 0.81 and 0.55, respectively, followed by leaf area index (LAI), air temperature, and vapor pressure difference, and precipitation. Finally, interannually, increased LAI demonstrated the potential to enhance the carbon sequestration capability of Moso bamboo forests, particularly in off-years, with the highest correlation coefficient with NEE (-0.59) among the six factors. The results provide a scientific basis for carbon sink assessment of Moso bamboo forests and provide a reference for developing Moso bamboo forest management strategies.

A systematic understanding of the driving mechanisms of ecosystem services (ESs) and the relationships among them is critical for successful ecosystem management. However, the impact of driving factors on the relationships between ESs and the formation of ecosystem service bundles (ESBs) remains unclear. To address this gap, we developed a modeling process that used random forest (RF) to model the ESs and ESBs of Zhejiang Province, China, in regression and classification mode, respectively, and the Shapley Additive Explanations (SHAP) method to interpret the underlying driving forces. We first mapped the spatial distribution of seven ESs in Zhejiang Province at a 1 × 1 km spatial resolution and then used the K-means clustering algorithm to obtain four ESBs. Combining the RF models with SHAP analysis, the results showed that each ES had key driving factors, and the relationships of synergy and trade-off between ESs were determined by the driving direction and intensity of the key factors. The driving factors affect the relationships of ESs and consequently affect the formation of ESBs. Thus, managing the dominant drivers is key to improving the supply capacity of ESs.

As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is −0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.

Urbanization and industrialization have led to obvious changes in the ecological environment and landscape pattern in the Beijing–Tianjin–Hebei region. Therefore, it is crucial to clarify the spatial–temporal changes in vegetation cover and its landscape pattern and conduct its analysis with the driving factors for ecological preservation in the Beijing–Tianjin–Hebei region. This study combined AVHRR GIMMS NDVI and MODIS NDVI data based on the ESTARFM model to obtain a high spatial–temporal resolution for vegetation cover; it then analyzed the vegetation cover changes at the type and landscape scales using a landscape index and explored the driving factors of the landscape pattern through principal component analysis. The results show that (1) the vegetation is mainly of medium and higher coverage and is distributed in the northeast, the western part of the Taihang Mountains and the central plains in the study area. From 1985 to 2022, there was no statistically significant difference in the overall change in its coverage. (2) From 1985 to 2022, at the landscape level, the vegetation cover landscape exhibited the following characteristics: increased fragmentation, an increase in the complexity of the landscape shape, a decrease in connectivity, a discrete landscape and a decrease in species diversity. At the type level, the medium vegetation demonstrated the most significant degree of fragmentation. The high-vegetation-cover areas exhibited a more concentrated distribution. Additionally, the low, lower and higher vegetation types displayed an increase in complexity, shape, discreteness and heterogeneity within the landscape. (3) Meanwhile, the principal component analysis showed that the changes in the landscape pattern of vegetation cover were mainly the result of the combined effects of climatic and anthropogenic factors in the Beijing–Tianjin–Hebei region. The human factor played the dominant role; this was followed by larger contributions from climatic factors. In addition to offering pertinent scientific insights for the maximization of the ecological environment and the fostering of regional ecological and sustainable development in the Beijing–Tianjin–Hebei region, the aforementioned analysis and research could serve as the foundation for the sustainable management and planning of vegetation cover.