Search Results (17)

Rural tourism plays a crucial role in promoting industrial revitalization in mountainous regions. Drawing inspiration from the site selection mechanisms of nature reserves, this study constructs a gap analysis framework tailored to rural tourism destinations, aiming to provide technical support for their spatial layout and systematic planning. By integrating a potential evaluation system based on tourism resources, market demand, and synergistic factors, the study identifies rural tourism priority zones and proposes a development typology and spatial optimization strategy across five provinces in Southwest China. The findings reveal: (1) First- and second-priority zones are primarily located in the core and periphery of provincial capitals and prefecture-level cities, while third-priority zones are concentrated in resource-rich areas of Yunnan and Guizhou and market-oriented areas of Sichuan, Chongqing, and Guangxi. (2) The Chengdu Plain emerges as the core region for rural tourism development, with hotspots clustered around Chengdu, northern and western Guizhou, central Chongqing, eastern Guangxi, and northwestern Yunnan, whereas cold spots are mainly situated in the western Sichuan Plateau and the Leshan–Liangshan–Zhaotong–Panzhihua–Chuxiong–Pu’er belt. (3) The alignment between tourism resources and rural tourism destinations is highest in Yunnan and Guizhou, while Chongqing exhibits the strongest match between destinations and tourism market potential and synergistic development conditions. Overall, 79.35% of rural tourism destinations in the region are situated within identified priority zones, with Chongqing, Guizhou, and Sichuan exhibiting the highest proportions. Based on the spatial mismatch between potential and existing destinations, the study delineates four development types—maintenance and enhancement, supplementation and upgrading, expansion, and reserve development—and offers regionally tailored planning recommendations. The proposed framework provides a replicable approach for spatial planning of rural tourism destinations in complex mountainous settings. Full article

Landslides are among the most destructive geological hazards, necessitating precise landslide susceptibility mapping (LSM) for effective risk management. This study focuses on the northeastern region of Leshan City and investigates the influence of various non-landslide sampling strategies and machine learning (ML) models on LSM performance. Ten landslide conditioning factors, selected by SHAP analysis, and six models were utilized: Convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), CNN-LSTM, CNN-LSTM with an attention mechanism (AM), Random Forest (RF), and eXtreme Gradient Boosting combined with Logistic Regression (XGBoost-LR). Three non-landslide sampling strategies were designed, with the certainty factor-based approach demonstrating superior performance by effectively capturing geological and physical characteristics, applying spatial constraints to exclude high-risk zones, and achieving improved mean squared error (MSE) and area under the curve (AUC) values. The results reveal that traditional ML models struggle with complex nonlinear relationships and imbalanced datasets, often leading to high false positive rates. In contrast, deep learning (DL) models—particularly CNN-LSTM-AM—achieved the best performance, with an AUC of 0.9044 and enhanced balance in accuracy, precision, recall, and Kappa. These improvements are attributed to the model’s ability to extract static spatial features (via CNNs), capture dynamic temporal patterns (via LSTM), and emphasize key features through the attention mechanism. This integrated architecture enhances the capacity to process heterogeneous data and extract landslide-relevant features. Overall, optimizing non-landslide sampling strategies, incorporating comprehensive geophysical information, enforcing spatial constraints, and enhancing feature extraction capabilities are essential for improving the accuracy and reliability of LSM. Full article

Contaminants of Emerging Concern (CECs), including per- and polyfluoroalkyl substances (PFASs) and organophosphate flame retardants (OPFRs), have raised global concerns due to their persistence, bioaccumulation potential, and toxicity. This study presents a comprehensive investigation of the occurrence, spatiotemporal distribution, potential sources, and the ecological and human health risks associated with 18 PFASs and 9 OPFRs in the surface waters of the upper Yangtze River, China. The water samples were collected from the main stream and five major tributaries (Min, Jinsha, Tuo, Jialing, and Wu Rivers) in 2022 and 2023. The total concentration of PFASs and OPFRs ranged from 16.07 to 927.19 ng/L, and 17.36 to 190.42 ng/L, respectively, with a consistently higher concentration observed in the main stream compared to the tributaries. Ultra-short-chain PFASs (e.g., TFMS) and halogenated OPFRs (e.g., TCPP) were the predominant compounds, likely originating from industrial discharges, wastewater effluents, and other anthropogenic sources. Ecological risk assessments indicated low-to-moderate risks at most sampling sites, with higher risks near wastewater discharge points. Human health risk evaluations suggested negligible non-carcinogenic risks but identified potential carcinogenic risks from OPFR exposure for adults at specific locations, particularly in Leshan city. This study highlights the importance of understanding the fate and impacts of PFASs and OPFRs in the upper Yangtze River, and provides valuable insights for developing targeted pollution control strategies and risk management measures. Full article

Clarifying the temporal and spatial characteristics of regional carbon emissions and low-carbon efficiency is of great significance for the realization of carbon peaking and carbon neutrality. This study calculated the carbon emissions in Sichuan Province from 2015 to 2022 based on four major units: energy activity, industrial production, forestry activity, and waste disposal, and its time evolution characteristics and key sources were investigated. Meanwhile, based on the Super-SBM-Undesirable model, the low-carbon efficiency of Sichuan Province and its 21 cities (states) was evaluated, and its spatial heterogeneity characteristics were investigated. The empirical results reveal the following: (1) energy activity was the main contributor to regional carbon emissions, with thermal power generation and industrial energy terminal consumption as the key sectors. Inter-regional power allocation could indirectly reduce the regional emission intensity. The carbon emissions of industrial production showed significant aggregation in cement and steel production. The forest carbon sink had a significant effect on alleviating the regional greenhouse effect. The carbon emissions of waste disposal were small. (2) From 2015 to 2022, the low-carbon efficiency of Sichuan Province showed an overall upward trend. Chengdu had a high level of economic development, a reasonable industrial organization, and a continuous increase in its urban greening rate. Heavy industrial cities such as Panzhihua and Deyang made great efforts to eliminate backward production capacity and low-carbon transformation of key industries. Therefore, they were the first mover advantage regions of low-carbon transformation. Zigong, Mianyang, Suining, and Leshan enjoyed favorable preferential policies and energy-saving space, and were developmental regions of low-carbon transformation. But they need to actively deal with the problem of industrial solidification. The low-carbon efficiency of plateau areas in western Sichuan was relatively low, but they have unique resource endowment advantages in clean energy such as hydropower, so the development potential is strong. Cities such as Ya’an and Bazhong faced a series of challenges such as weak geographical advantages and the risk of pollution haven. They were potential regions of low-carbon transformation. Full article

Sudden mountain landslides can pose substantial threats to human lives and property. On 4 June 2023, a landslide occurred in Jinkouhe District, Leshan City, Sichuan Province, resulting in 19 deaths and 5 injuries. This study, drawing on field investigations, geological data, and historical imagery, elucidates the characteristics and causes of the landslide and conducts a reverse analysis of the landslide movement process using Massflow V2.8 numerical simulation software. The results indicate that rainfall and human engineering activities are key factors that triggered this landslide. Numerical simulation shows that the landslide stopped after 60 s of sliding, with a movement distance of approximately 286 m, a maximum sliding speed of 17 m/s, and a maximum accumulation thickness of 7 m, eventually forming a loose landslide debris accumulation of approximately 5.25 × 10³ m³. The findings of this study provide significant reference value for research on landslide movement characteristics and disaster prevention and mitigation in mountainous areas. Full article

As the economic center and major grain-producing area in Southwest China, the calculation of the carbon budget and the protection of cultivated land in the Chengdu Plain are of vital significance for China to achieve a carbon peak strategy and ensure food security. For the purpose of clarifying the trend of land use focus and carbon emissions in the Chengdu Plain, the carbon peak level of land use in 33 counties in the Chengdu Plain was explored. Based on the gravity center model and IPCC carbon emission coefficient method, the changing trend of land use gravity center and carbon emission in Chengdu Plain from 2006 to 2022 was clarified. PLS regression model and LMDI model were used to explore the main influencing factors of the carbon emission of cropland and the carbon emission of building land. PLUS model was used to simulate future land use patterns and carbon emissions. (1) The center of gravity of cropland, building land, water, and other and unused land shifted to the northeast by 4.23 km, 5.46 km, 8.44 km, and 31.58 km, respectively, and that of forest and grass shifted to the southeast by 11.12 km and 3.41 km, respectively. For major food crops, the centers of gravity of rice and maize moved northeastward by 15.47 km and 7.52 km, respectively, while wheat moved southwestward by 17.77 km. (2) From 2006 to 2022, carbon emissions from land use in the 33 counties of the Chengdu Plain are all on the rise, with a total increase of 13.552 million tons, and carbon sinks in the 31 counties continue to decline, with a total decrease of 0.691 million tons. (3) Under the natural scenario, carbon sink scenario, and carbon reduction scenario, the carbon emissions from land use decrease by 0.5391 million tons, 3.4728 million tons, and 4.5265 million tons from 2022, respectively. Among the 33 counties in the Chengdu Plain, 11 counties did not achieve carbon peak under the natural scenario, 5 counties did not achieve carbon peak under the carbon sink scenario, and all the counties achieved carbon peak under the carbon sink scenario. During the study period, there was a serious loss of cropland in the Chengdu Plain, mainly to building land in the central part of the Chengdu Plain and to forests within the Longmen Mountain, Longquan Mountain, and Leshan City, and there is a need to strengthen cropland protection in this region in the future. Under the natural scenario, carbon sink scenario, and carbon reduction scenario, land use in the Chengdu Plain region can achieve carbon peak, and the carbon reduction model will be more helpful for the counties to achieve carbon peak. Full article

China is attaching increasing importance to the creation of regional integration, high-quality economic development and ecological civilization. An accurate grasp of the traction effect of the increased level of integration of urban agglomerations on the eco-efficiency (EE) of cities in the region will help to promote the steady improvement of urban economic development and the ecological environment. This paper constructs an index system to measure the level of integration of the Chengdu–Chongqing urban agglomeration (CCUA) and the EE of each city within it from 2011 to 2020 and explores the impact of regional integration on urban EE and its mechanism of action. The study presents the follow findings: (1) The level of integration of the CCUA increased nearly 10 times from 2011 to 2020, with the government playing a significant leading role. (2) The positive and negative effects of the level of integration of the CCUA on urban EE depend on factors such as the level of economic development, the stage of development and the location. There are several relationships between the level of intra-regional integration and urban EE: first, a nearly linear increase, as in Chongqing and Chengdu; second, an increase in fluctuation, as in Dazhou, Guang’an and Leshan; and third, a fluctuation, decrease, flat or even no real increase, as in Luzhou, Ya’an and Zigong. (3) Based on this, this paper considers the mechanism of the level of integration within the region on urban EE in terms of both economic and eco-environmental effects, with a view to exploring the future green development path of the CCUA. Full article

The evaluation of ecological sustainability is significant for high-quality urban development and scientific management and regulation. Taking the Chengdu urban agglomeration (CUA) as the research object, this paper combined the three-dimensional ecological footprint model (3DEF) and random forest to evaluate the ecological sustainability of the study area and identify the influencing factors. The study results indicate that: (1) From 2000 to 2019, the ecological sustainability of Chengdu urban agglomeration was divided into four types, and the overall ecological sustainability of this region showed a downward trend. The areas with higher ecological sustainability were mainly distributed in the northern part of the urban agglomeration (Mianyang City) and the southern part (Leshan City and Ya’an City), while the cities in the central region (Chengdu City, Meishan City, and Ziyang City) had lower ecological sustainability. (2) The main factors affecting the ecological sustainability of urban agglomerations are industrial wastewater discharge, industrial smoke (powder) dust discharge, and green coverage of built-up areas, followed by urbanization and population size. Through this study, we have two meaningful findings: (a) Our research method in this paper provides a new way to study the factors affecting the ecological sustainability of urban agglomerations. (b) The results of the identification of influencing factors might be the reference for urban environmental infrastructure construction and urban planning. Full article

In this study, four drought monitoring indices were selected to simulate drought monitoring in the study area and a correlation analysis was conducted using the self-calibrated Palmer Drought Index (sc-PDSI) to screen for the most suitable drought monitoring index for the study area. Then, the spatio-temporal variation characteristics of drought in the study area were discussed and analyzed. The results showed that the Crop Water Stress Index (CWSI) was most suitable for drought monitoring in the Sichuan Province. CWSI had the best monitoring in grasslands (r = 0.48), the worst monitoring in woodlands (r = 0.43) and the highest fitting degree of overall correlation (r = 0.47). The variation of drought time in the Sichuan Province showed an overall trend of wetting and the drought situation was greatly alleviated. In the past 20 years, the dry years in the Sichuan Province were from 2001 to 2007, in which the driest years were 2006 and 2007; 2012–2013 was the transition interval between drought and wet; any year from 2013 to 2020 was a wet year, showing a transition trend of “drought first and then wet”. The spatial distribution of drought was greater in the south than in the north and greater in the west than in the east. Panzhihua City and the southern part of the Liangshan Prefecture were the most arid areas, while the non-arid areas were the border zone between the western Sichuan Plateau and the Sichuan Basin. Looking at the spatial distribution of drought, “mild drought” accounted for the largest percentage of the total area (60%), mainly concentrated in the western Sichuan plateau. The second largest was “drought free” (33%), mostly concentrated in the transition area between the western Sichuan Plateau and the Sichuan Basin (western Aba Prefecture, Ya’an City, Leshan City and northern Liangshan Prefecture). The area of “moderate drought” accounted for a relatively small proportion (6%), mainly concentrated in Panzhihua City, the surrounding areas of Chengdu City and the southern area of the Liangshan Prefecture. The area of severe drought accounted for the least (1%), mostly distributed in Panzhihua City and a small part in the southern Liangshan Prefecture. The drought center ranged from 101.8° E to 103.6° E and 28.8° N to 29.8° N, with the movement trend of the drought center moving from the northeast to the southwest to the northeast. Full article

Previous studies lacked attention to the spatial heterogeneity of the impact of urbanization on carbon emissions. To fill this knowledge gap, this study analyzed the spatio-temporal variations of carbon emissions (TCE), the per capita carbon intensity (PCI), and the economic carbon intensity (ECI) in the Chengdu-Chongqing urban agglomeration (CUA) based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) from 2000–2018. Bivariate spatial autocorrelation, and spatial Durbin models were combined to quantify the spatial correlation and driving mechanisms between carbon emission intensity and multi-dimensional urbanization (population, economic, and land urbanization). The following are the main results: (1) The TCE in CUA increased by 3.918 million tons at an average annual growth of 6.86%; CUA ranked last among China’s national strategic urban agglomerations in terms of TCE, PCI, and ECI. (2) High carbon emission values were concentrated in the Chengdu and Chongqing metropolitan areas, presenting a spatial feature of “Core-Periphery” gradient decay. (3) Nearly 30% of the agglomeration had carbon emission growth at low rates, with the growth cores concentrated in the main urban areas of Chengdu and Chongqing. (4) The “Low-Low” positive correlation was the main correlation type between multi-dimensional urbanization and carbon emissions and was distributed mainly in mountainous areas (e.g., Leshan and Ya’an). (5) Among the urbanization dimensions, the impacts on carbon emissions in local and adjacent areas exhibited varying levels of spatial heterogeneity. Economic urbanization was found to have the strongest positive direct and spillover effects; land urbanization inhibited the growth of carbon emissions in local and adjacent areas; population urbanization promoted carbon emission reduction in adjacent areas. Our findings provide support for CUA to carry out cross-city joint governance strategies of carbon emissions, also proving that regional carbon emission reduction should be an integration of various efforts including low-carbon living of residents, green transformation of economy and optimal land management. Full article

Climate change poses a severe threat to biodiversity. Greenhouse gas emissions have accelerated climate warming and significantly impacted species distribution and population dynamics. Zanthoxylum armatum DC. is an ecologically, medicinally, and economically important plant; it is cultivated as an economic crop at large scales in China, and is a valuable medicinal plant in India, Nepal, etc. A precise prediction of the potential distribution areas of Z. armatum will contribute to its protection and determination of its planting areas. In this paper, based on 433 distribution points and 19 climate factors, the MaxEnt model was used to analyze the spatial distribution pattern of Z. armatum between 1970 and 2000, predict its spatial distribution pattern in 2040–2060 (the 2050s) and 2081–2100 (the 2090s), and comprehensively assess the critical climate factors limiting its geographical distribution. The findings are as follows: (1) in the 1970–2000 scenario, the potential suitable distribution areas of Z. armatum include the subtropical monsoon climate regions of Japan, the Korean Peninsula, the south of the Qinling–Huaihe Line of China, and the regions along the southern foot of the Himalayas (India, Bhutan, Nepal, etc.), with an area of 330.54 × 10⁴ km²; (2) the critical climate factors affecting the potential distribution of Z. armatum include temperature (mean diurnal temperature range, mean temperature of the coldest quarter, and temperature seasonality) and annual precipitation; (3) the distribution areas of Z. armatum will shift to higher latitudes and shrink under the three climate change scenarios in the 2050s and 2090s. In the 2090s–SSP585 scenario, the total area of suitable habitat will decrease most markedly, and the decrease rate of the highly suitable areas will reach up to 97.61%; only the region near Delong Town, Nanshan District, Chongqing City, will remain a highly suitable habitat, covering an area of merely 0.08 × 10⁴ km². These findings suggest that Z. armatum is susceptible to climate change. The border area between Guizhou Province and Chongqing City and the southwest district of Leshan City, Sichuan Province, will be a stable and moderately high potential suitable habitat for Z. armatum in the future. The above regions are recommended to be managed as key protected areas. Full article

This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-NN spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists’ interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists’ interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated; then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly. Full article

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method. Full article

The main purpose of the current study is to investigate if tourism affects economic growth of China. The data set has been acquired from the Beijing Municipal Bureau of Statistics, and the time span of the data set takes into account a 20-year time period, from 2000 to 2019. To determine the strength of the above-mentioned relationship previous models that have been used for this research are mainly VAR (vector auto-regression) and VECM (vector error correction) models. The VAR and VECM models have been conducted together with the Granger causality test. The internal revenue generated from tourism-related activities is taken as being the main indicator for the tourism industry, while economic growth is determined by GDP (gross domestic product). We support the above-mentioned notion, as we found that a strong relationship exists between the development of the tourism industry and economic growth. Moreover, our analysis also indicates that this industry has a major impact on long-term economic growth in the region as well. This study thus provides further support to the existing literature on the topic of tourism and the impact that tourism-related activities have upon economic development and growth. The existence and the impact of tourism-related activities upon long-term economic growth were confirmed by the results of the VAR models. At the same time, the unidirectional results of VECM models have confirmed the existence of economic growth in the short term. In our case, the cardinal relationship between the development of the tourism industry and the economic growth in the Beijing region of China have managed to provide strong empirical support to the earlier stated notions and to the literature alike. Full article

This paper focuses on the suitability of urban expansion in mountain areas against the background of accelerated urban development. Urbanization is accompanied by conflict and intense transformations of various landscapes, and is accompanied by social, economic, and ecological impacts. Evaluating the suitability of urban expansion (UE) and determining an appropriate scale is vital to solving urban environmental issues and realizing sustainable urban development. In mountain areas, the natural and social environments are different from those in the plains; the former is characterized by fragile ecology and proneness to geological disasters. Therefore, when evaluating the expansion of a mountain city, more factors need to be considered. Moreover, we need to follow the principle of harmony between nature and society according to the characteristics of mountain cities. Thus, when we evaluate the expansion of a mountain city, the key procedure is to establish a scientific evaluation system and explore the relationship between each evaluation factor and the urban expansion process. Taking Leshan (LS), China—a typical mountain city in the upper Yangtze River which has undergone rapid growth—as a case study, the logic minimum cumulative resistance (LMCR) model was applied to evaluate the suitability of UE and to simulate its direction and scale. The results revealed that: An evaluation system of resistance factors (ESRFs) was established according to the principle of natural and social harmony; the logic resistance surface (LRS) scientifically integrated multiple resistance factors based on the ESRF and a logic regression analysis. LRS objectively and effectively reflected the contribution and impact of each resistance factor to urban expansion. We found that landscape, geological hazards and GDP have had a great impact on urban expansion in LS. The expansion space of the mountain city is limited; the area of suitable expansion is only 23.5%, while the area which is unsuitable for expansion is 39.3%. In addition, it was found that setting up ecological barriers is an effective way to control unreasonable urban expansion in mountain cities. There is an obvious scale (grid size) effect in the evaluation of urban expansion in mountain cities; an evaluation of the suitable scale yielded the result of 90 m $\times$ 90 m. On this scale, taking the central district as the center, the urban expansion process will extend to the neighboring towns of Mianzhu, Suji, Juzi and Mouzi. Urban expansion should be controlled in terms of scale, especially in mountain cities. The most suitable urban size of LS is 132 km².This would allow for high connectivity of urban-rural areas with the occupation of relatively few green spaces. Full article