Amid global rural tourism growth and rural revitalization policies, traditional villages’ resource protection and tourism development have drawn international academic attention. To guide villages’ resource planning and management, this study constructed an evaluation index system of cultural, ecological, industrial, talent, and organizational amenities in traditional villages from the perspective of tourists’ perceptions using grounded theory and measured the spatial agglomeration differences, synergistic effects and their influencing factors of traditional village amenities by using location entropy, spatial autocorrelation, and gray correlation degree analysis. The results show that (1) the spatial distributions of cultural, ecological, industrial, and organizational amenities are more balanced, while talent amenities exhibit a more concentrated distribution. (2) The spatial concentration of amenities in traditional villages has a strong positive spatial correlation, the agglomeration level of the high-high type of concentration is distributed in clusters, the low–low type tends to be contiguous, and the low–high type is distributed sporadically around the high–high type; significant synergy between ecological and industrial amenities, and organizations play a supportive role in the spatial agglomeration of cultural, ecological, ecological and talent amenities. (3) Gross regional product, slope, and distance to 3A and above scenic spots significantly influence the spatial agglomeration of amenities. This study provides reference for the sustainable development of traditional villages from the perspectives of exerting agglomeration and radiation effects, synergistically promoting villages’ development, constructing the memory symbol system, and integrating the resource structural system based on the spatial agglomeration difference characteristics of traditional village amenities.

The urban built-up land green use efficiency (UBLGUE) of the Central Plains Urban Agglomeration (CPUA) is greatly affected by the Central China Rise policy. However, studies on how socioeconomic factors affect UBLGUE in underdeveloped urban agglomerations are lacking, and little empirical research has placed particular emphasis on the Central China Rise policy. Based on the statistical data of 2003–2020, this study explores the dynamic spatial–temporal characteristics and determines the influencing mechanism of UBLGUE in the CPUA via the super-SBM–DEA method and panel regression model. The empirical results indicate the following: The average UBLGUE in the prefecture cities of the CPUA presents a significant fluctuating trend from 2003 to 2020. The UBLGUE of the CPUA is characterized by spatial imbalance. Over the period of Central China Rise, the main factors influencing the spatial–temporal differentiation of the UBLGUE in the CPUA are the economic development, industrial structure, environmental regulation intensity, and energy consumption intensity. UBLGUE has strong economic attributes and is positively promoted by economic development. In contrast, the industrial structure, environmental regulation intensity, and energy consumption intensity significantly hinder the UBLGUE. Energy consumption intensity has the strongest negative effect on UBLGUE. Finally, corresponding policy recommendations are proposed to promote UBLGUE based on the conclusions obtained.

According to the tourism development in Jiaozuo, the paper discusses tourism information system based on GIS, including technical route, composition and structure. Through qualitative and quantitative researches on the study area, a tourism information system based on GIS of Jiaozuo is designed in this paper. The system can provide inquiry, statistics and analysis for the tourism resources of Jiaozuo, and it can provide relevant graphic information as well.

Arid inland river basins are highly sensitive to land-use change, yet the spatially heterogeneous predictors of ecosystem carbon storage remain insufficiently understood. In this study, a locally calibrated InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage in the Hei River Basin for 2010, 2015, 2020, and 2025. A CA–Markov (Cellular Automata-Markov chain Model) model was then used to project the 2030 land-use pattern and associated carbon storage under the assumption that the current policy framework remains unchanged. Random Forest models interpreted with GeoShapley were applied to quantify the nonlinear and spatially varying contributions of seven environmental and anthropogenic predictors. Total modeled carbon storage increased slightly from 5.29181 × 10⁸ t in 2010 to 5.33214 × 10⁸ t in 2020, before declining marginally to 5.32310 × 10⁸ t in 2025. The decline was mainly associated with reduced modeled grassland carbon storage. Carbon storage was generally higher in the southern and eastern parts of the basin, particularly in the Qilian Mountains, and lower in the northern and western desert regions. The whole-basin simulation projected a 4.34% increase by 2030, mainly associated with projected expansion of forestland and grassland. Predictor importance varied spatially: elevation ranked first in the upstream region, NDVI in the midstream oasis, and population density in the downstream desert. NDVI was the most important basin-wide predictor, although its importance partly reflected spatial covariance with land-cover classes. These findings demonstrate the value of integrating carbon-storage modeling, land-use simulation, and spatially explicit model interpretation for differentiated ecosystem management in arid inland basins.