Search Results (2,221)

Beautiful Leisure Tourism Villages (BLTVs) represent an effective pathway for advancing high-quality rural industrial development and promoting comprehensive rural revitalization. They are of great significance to enriching new rural business formats and new functions. The analysis is interpreted within an integrated location–distance attenuation framework. Based on the methods of spatial clustering analysis, geographical linkage rate and geographical weighted regression, the spatio-temporal evolution of 1982 BLTVs in China up to 2023 was examined to uncover the underlying driving mechanisms. Findings indicated that (1) a staged expansion in the number of villages across China, with the most pronounced growth occurring between 2014 and 2018, averaged 124 new villages per year; their stage characteristics showed an obvious “unipolar core-bipolar multi-core-bipolar network” development model; (2) the barycenters of villages were all located in Nanyang City of Henan Province; they migrated from east to west, and formed a push and pull migration trend from east to west and then east; (3) the spatial distribution of villages was highly aggregated and demonstrated marked regional heterogeneity, following a south–north and east–west gradient, with the highest concentration in Jiangzhe and the lowest in Ningxia Hui Autonomous Region; and (4) natural ecology, hydrological and climatic conditions, socioeconomic context, transportation accessibility, and resource endowment collectively shaped the spatial layout of villages, exhibiting pronounced spatial variation in the intensity of these driving factors. On the whole, topography, social economy, traffic condition and precipitation condition had greater influences on the spatial distribution of villages in the western than in the eastern part of China. In contrast, the effects of resource endowment and temperature on the spatial distribution of BLTVs were stronger in eastern China than in western China. These findings enhance the theoretical understanding of tourism-oriented rural development by integrating spatio-temporal evolution with a location–distance attenuation perspective and provide differentiated guidance for the sustainable development of BLTVs across regions. Full article

Promoting the clean energy transition in rural areas is essential for achieving China’s “dual carbon” goals and advancing rural revitalization, with important implications for ecological sustainability and household welfare. However, empirical evidence on rural household energy transitions remains limited, largely due to the scarcity of high-quality micro-level data. Using household survey data from Jiangxi Province, this study applies binary and ordered Probit models to examine the mechanisms underlying rural households’ clean energy adoption and usage intensity. The results indicate that modernity-related mechanisms, including education level and non-agricultural employment experience, as well as capability-based mechanisms such as participation in commercial and industrial activities, significantly increase both the probability of adopting clean energy and the intensity of its use. By contrast, identity-based mechanisms, including party membership and village cadre status, do not exhibit statistically significant effects on adoption decisions, suggesting a limited role. In addition, proximity-related factors reflecting transportation accessibility and infrastructure conditions exert the strongest influence on usage intensity. Marginal effects analysis supports these findings, while heterogeneity analysis reveals clear age-based differences: younger households respond more strongly to modernity and accessibility, whereas older households rely primarily on economic capacity and logistical convenience. This study underscores the importance of infrastructure conditions and household endowments in shaping rural clean energy transitions and offers policy-relevant insights for promoting inclusive and low-carbon household energy use in China and other developing economies. Full article

In the rapidly urbanizing Global South, megacities face a perplexing “paradox of idleness”: acute land scarcity in the urban core coexisting with inefficient rural homesteads in the hinterland. Using Shanghai as a representative case, this study integrates spatial autocorrelation analysis with Geographical Detector modeling to quantify the spatial differentiation patterns and driving mechanisms of this phenomenon. The results reveal a distinct core-periphery gradient, with vacancy density increasing from the inner suburbs to the remote hinterland. Four regional typologies were identified: dispersed-inefficient, high-density accumulation, sparse-stable, and intensive-efficient. Quantitative analysis identifies demographic aging and low agricultural efficiency as dominant drivers. Counter-intuitively, the study finds that top-down institutional pilots alone exert a negligible direct impact. Instead, interaction analysis confirms a significant policy-bundling effect, in which institutional tools promote revitalization only when coupled with economic and locational incentives. These findings expose a mechanism of “involuntary vacancy” trapped by institutional rigidity, distinct from the market-driven abandonment seen in shrinking or remote Western contexts. Consequently, a gradient-based governance framework is proposed to transition from “one-size-fits-all” regulation to targeted spatial restructuring pathways. Full article

This article analyzes the Solimene façade (Vietri sul Mare, Campania, Italy, 1952–1955). The survey, already acquired with active and passive sensors, was integrated with close-range photogrammetry of some sections of the main wall. The purpose of the new acquisitions was to generate data to inform a plug-in that, in the latest versions of the Revit software, correlates parametric and procedural environments. The focus of the study was the rationalization of the formal structure of the amphora, the heart of the main façade. Logic and geometric language guide the identification of a possible mathematical relationship aimed at parametrically modifying the model. The logical diagrams, converted into a Grasshopper preview, can be managed through graphical nodes. In the form of flowcharts (visual scripts), the finite sequence of procedural steps has the advantage of managing and modifying, in real time and in a user-friendly manner, the morphometric characteristics of the small “mummarella.” The results identify the morphometric characteristics common to a typological family composed of Vietri amphorae that, in the field of architectural design, uses the typical functions of system families. The goal is to approach sustainable and participatory design solutions by providing functions that can be graphically manipulated from within the software environment. Full article

For a long time, low efficiency in the transfer of rural collective land use rights and the ambiguous attribution of collective land property rights have not only restricted the mobility of rural labor factors but have also hindered the release of vitality in the rural collective economy. This has resulted in lagging growth in the income that rural residents obtain from collective economic factors, contributing to the persistent widening of the urban/rural income gap. As an important institutional innovation to address these issues, the effects of the reform of the rural collective property rights system urgently need to be clarified. The reform of the rural collective property rights system constitutes a major initiative in the transformation of the rural land system. Centered on asset verification and valuation, as well as the demarcation of membership rights and the restructuring towards a shareholding cooperative system, it aims to establish a collective property rights regime characterized by clearly defined ownership and fully functional entitlements. This study takes the national pilot reform of rural collective property rights launched in 2016 as a quasi-natural policy experiment, systematically examining the impact of this pilot policy on the internal income gap within households and its spillover effects on the urban–rural income gap. Based on microdata from the China Household Finance Survey (CHFS) and the China Longitudinal Night Light Data Set (PANDA-China), this study constructs a five-period balanced panel dataset covering 2304 rural households across 25 provinces. A relative exploitation index based on the Kawani index is constructed, and empirical analysis is conducted using a combination of multi-period difference-in-differences (Multi-period DID), discrete binary models, and propensity score matching-difference-in-differences (PSM-DID) models. The results show that: First, the pilot reform significantly reduced the level of income inequality within rural areas in the pilot regions, and its policy benefits further generated positive spillovers via market-driven factor allocation mechanisms, effectively bridging the urban–rural income gap. Second, institutional reforms activated the potential of rural non-agricultural economic factors, establishing new channels for a two-way flow of urban and rural factors, becoming an important path to achieve the goal of common prosperity. Third, the policy effects exhibited significant heterogeneity, specifically manifested in the attributes of major grain-producing regions, initial household income levels, and the human capital characteristics of household heads having significant moderating effects on reform outcomes. This study not only provides theoretical support and empirical evidence for deepening rural property rights reforms under the new rural revitalization strategy, but it also reveals the driving role of institutional innovation in factor mobility, thereby influencing the transmission mechanism of income distribution patterns. This finding offers a China-based solution for developing countries to address the imbalance in urban–rural development and the widening income gap. Full article

Unfavorable initial conditions and limited factor endowments often constrain economic development in border regions, whereas regional development policies can alter locational disadvantages and promote balanced regional development. Based on nighttime light data from 1999 to 2022, this paper employs a spatial regression discontinuity design (spatial RDD) to examine economic differences between border and non-border areas in Inner Mongolia, China, and to assess the effects of regional development policies in underdeveloped regions. The results show that, after controlling for initial endowments and economic characteristics, the Program of Border Areas Revitalization and Poverty Alleviation significantly enhances economic vitality in border regions and generates persistent growth effects; these findings remain robust after excluding potential confounding factors and conducting a series of robustness checks. Heterogeneity analysis indicates that the policy effects are more pronounced in eastern Inner Mongolia, in more densely populated areas, and in regions with higher market vitality. Mechanism analysis suggests that the Program of Border Areas Revitalization and Poverty Alleviation alters locational disadvantages by strengthening the provision of public goods, thereby enhancing economic linkages across regions; the development of characteristic and comparative advantage industries promotes industrial structure upgrading and drives economic growth in border areas. Further analysis finds that the Program of Border Areas Revitalization and Poverty Alleviation reduces, to some extent, intra-regional economic disparities within border areas and promotes sustainable economic development while improving the ecological environment, further indicating that there exists a compatible pathway between regional development and ecological sustainability. Overall, this study provides spatially explicit micro-level evidence on how regional policies can reshape geographical constraints and foster balanced development in underdeveloped border regions. Full article

Visual Impression in Architectural Space (VIAS) plays a central role in user response to environments, yet designer-controlled spatial variables often produce uncertain perceptual outcomes across cultural contexts. This study develops a multi-modal framework integrating VIAS theory, spatial documentation, and sentiment-aware NLP to evaluate temporary event spaces. Using a monthly market in Matsue, Japan as a case study, we introduce (1) systematic documentation of controlled spatial variables (layout, visibility, advertising strategy, (2) culturally balanced datasets comprising native Japanese and international participants across onsite, video, and virtual interviews, and (3) an adaptive sentiment-weighted keyword extraction algorithm suppressing interviewer bias and verbosity imbalance. Results demonstrate systematic modality effects: onsite participants exhibit festive atmosphere bias (+18% positive sentiment vs. video), while remote modalities elicit balanced critique of signage clarity and missing amenities. Cross-linguistic analysis reveals native participants emphasize holistic atmosphere, whereas international participants identify discrete focal points. The adaptive algorithm reduces verbosity-driven score inflation by 45%, enabling fair cross-participant comparison. By integrating spatial variable documentation with sentiment-weighted linguistic patterns, this framework provides a replicable methodology for validating architectural intent through computational analysis, offering evidence-based guidance for inclusive event space design. Full article

The coordinated development of the rural economy and the ecological environment remains a central challenge in China’s rural revitalization agenda. Against this backdrop, the rapid expansion of the digital economy (DE) has the potential to reshape traditional development pathways and ease the longstanding tension between economic growth and environmental sustainability. However, existing studies have predominantly examined the economic or environmental effects of digitalization in isolation, leaving its role in fostering their coordinated development largely unexplored. Using balanced panel data for 30 Chinese provinces from 2011 to 2021, this paper constructs an index of the coupling coordinated development of the rural economy–environment (CREE) and employs a two-way fixed-effects framework, complemented by mediation analysis, panel threshold regression, and a spatial Durbin model, to examine the impact of the DE on CREE and its transmission mechanisms. The results show that the DE significantly enhances CREE on average. This positive effect, however, is non-linear and conditional: it emerges only after rural educational attainment exceeds a critical threshold, and its marginal contribution diminishes as the level of digital development increases. Mechanism analyses indicate that the DE promotes CREE primarily by stimulating technological innovation and advancing urbanization, while improvements in the structure of human capital further strengthen this relationship. Spatial econometric evidence reveals pronounced spillover effects of the DE on CREE across regions, with spillovers based on economic distance outweighing those associated with geographic proximity. By adopting a coupling perspective that integrates economic and environmental dimensions, this paper clarifies the non-linear dynamics, transmission channels, and spatial diffusion processes through which the DE contributes to rural green development. The findings underscore the importance of strengthening rural education foundations, deepening the application of digital technologies, and enhancing regional coordination to fully harness the DE’s role in promoting coordinated economy–environment development. Full article

The building sector accounts for a large percentage of global greenhouse gas emissions, largely from the embodied carbon in common building materials like concrete and steel. Embodied carbon (EC) refers to the greenhouse gases released during the manufacturing, transportation, installation, maintenance, and disposal of building materials. Although growing in popularity, mass timber is still not nearly as common as other building materials. During the early building design stages, engineers often do not have the time or resources to holistically optimize material selection; consequently, concrete and steel remain the materials of choice. This research focused on the development of a fully automated parametric design tool, APDT, to showcase the viability of evaluating and optimizing mass timber in building construction. The APDT was developed using Autodesk’s Revit 2022 and the visual-based programming tool housed within Revit: Dynamo. The automated designer uses parametric inputs of a building, including size, number of stories, and loading, to create a model of a mass timber building with designed glulam columns and beams and cross-laminated timber floor panels. The designer calculates overall material quantities, which are then used to determine the building’s overall embodied carbon impact. Discussed herein is the development of a building design tool that highlights the benefits of optimized mass timber using existing software and databases. The tool allows the designer to expediently provide an estimate of the amount of material and embodied carbon values, thereby making it easier to consider mass timber when determining the structural system at the infancy stage of the project. The methodology outlined herein provides a replicable methodology for creating an APDT that bridges a critical gap in early-stage design, enabling rapid embodied carbon comparisons and fostering consideration of mass timber as a viable low-carbon alternative. Full article

Rural spatial commodification serves as a vital pathway toward comprehensive rural revitalization. Its development is closely intertwined with land use transition, with each process exerting reciprocal influence on the other. Research on the coupling between these two systems has emerged as a cutting-edge interdisciplinary field bridging rural geography and land system science. Based on a systematic review of research advances in rural spatial commodification and land use transition, this paper summarizes the existing gaps in the literature and attempts to construct a coupling framework integrating rural spatial commodification and land use transition. The findings indicate that, although the academic community has amassed a substantial body of research on rural spatial commodification, land use transition, and their coupled relationship with rural transformation, several gaps persist. These encompass the absence of systematic indicator frameworks and quantitative validation methods for rural spatial commodification, insufficient exploration into the coupling mechanisms between rural spatial commodification and land use transition, and a notable scarcity of empirical studies examining land use optimization driven by rural spatial commodification. Future research on the coupling between rural spatial commodification and land use transition should follow the logical framework of “elucidating theoretical connotations, characterizing coupling relationships, analyzing coupling mechanisms, simulating coupling processes, and regulating coupling states”. It is essential to strengthen the interdisciplinary integration of rural geography and land system science, thereby providing scientific guidance for the allocation of resources in rural areas and the implementation of rural revitalization practices. Full article

Conventional uses of building information modeling (BIM) in existing-building representation tend to prioritize geometric consistency and efficiency, but often at the expense of interpretive depth. This paper challenges BIM’s tendency to promote epistemic closure by proposing a method to foreground relational ambiguity, transforming view reconciliation from a default automated process into a generative act of critical inquiry. The method, implemented in Autodesk Revit, introduces a parametric reference frame within BIM sheets that foregrounds and manipulates reciprocal relationships between orthographic views (e.g., plans and sections) to promote interpretive ambiguity. Through a case study, the paper demonstrates how parameterized view relationships can resist oversimplification and encourage conflicting interpretations. By intentionally sacrificing efficiency for epistemic resilience, the method aims to expand BIM’s role beyond documentation, positioning it as a tool for architectural knowledge production. The paper concludes with implications for software development, pedagogy, and future research at the intersection of critical representation and computational tools. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

The essential prerequisite for the state to ensure the stable production and supply of grain and other key agricultural products is to enhance food security resilience and transform traditional agricultural production and management models. This study utilizes panel data from major grain-producing counties in China from 2012 to 2023. Adopting the 2020 “National Digital Rural Pilot Program” as a quasi-natural experiment, it applies a difference-in-differences (DID) model to assess the program’s impact on food security resilience and its underlying mechanisms. The results demonstrate that digital rural development has a significant driving effect on food security resilience, with more pronounced effects observed in Southern regions, areas endowed with abundant labor resources, and regions with lower economic development levels. Mechanism analyses indicate that digital rural development plays a role in enhancing food security resilience through scaled grain operations and agricultural technological progress. Furthermore, resource allocation efficiency and fiscal transparency exert a positive regulatory effect in impacting food security resilience through digital rural development. This study elucidates the mechanism through which digital rural development enhances food security resilience, offering valuable policy insights for the coordinated advancement of rural revitalization and agricultural digitization. Full article

The construction industry faces challenges in estimating costs because the processes are time-consuming and involve a high likelihood of making errors. For instance, quantity take-offs are often inaccurate, and there is not a simple way to integrate data from Building Information Modeling (BIM) platforms and cost databases. This study introduces a framework that utilizes the Model Context Protocol (MCP) to ensure seamless integration between large language models (LLMs) and BIM models through Autodesk Revit in order to enable fully automated cost estimation workflows. The developed system combines an AI-powered MCP server with cost databases that are standard in the industry, such as the 2025 Craftsman National Building Cost Manual and the ZIP code-based location modifiers. This system enables LLMs to automatically obtain quantities from BIM models, match components to cost items, make regional changes, and make professional cost estimates. A case study of estimating the cost of an electrical system shows that the framework can reduce estimation time from 2.5–3.5 h (manual baseline) to 42.3 ± 3.7 s (n = 5 runs, warm start), representing a 98.6% efficiency gain, while being more accurate with respect to industry standards. The system processed 187 BIM elements in three component groups (receptacles, conduits, and panels). It automatically matched them to the right cost database items, used location-specific modifiers for ZIP code 01003, and made a full cost estimate of USD 13,945.81 with detailed breakdowns and a percent difference of %5.1 of the manual estimation. This research enhances automation in construction by developing a methodology for AI-BIM integration using standardized protocols, shows the practical application of AI in construction workflows, and provides empirical evidence of the advantages of automation in cost estimation processes. The results indicate that MCP-based AI integration presents a novel approach for construction automation, delivering improvements while applying professional standards of accuracy and availability. Full article

This paper presents a hybrid pipeline based on zero-shot vision models for automatic node count estimation in Lisianthus (Eustoma grandiflorum) cultivation and a system for real-time growth information sharing. The multistage image analysis pipeline integrates Grounding DINO for zero-shot leaf-region detection, MiDaS for monocular depth estimation, and a YOLO-based classifier, using daily time-lapse images from low-cost fixed cameras in commercial greenhouses. The model parameters are derived from field measurements of 2024 seasonal crops (Trial 1) and then applied to different cropping seasons, growers, and cultivars (Trials 2 and 3) without any additional retraining. Trial 1 indicates high accuracy (R² = 0.930, mean absolute error (MAE) = 0.73). Generalization performance is confirmed in Trials 2 (MAE = 0.45) and 3 (MAE = 1.14); reproducibility across multiple growers and four cultivars yields MAEs of approximately ±1 node. The model effectively captures the growth progression despite variations in lighting, plant architecture, and grower practices, although errors increase during early growth stages and under unstable leaf detection. Furthermore, an automated Discord-based notification system enables real-time sharing of node trends and analytical images, facilitating communication. The feasibility of combining zero-shot vision models with cloud-based communication tools for sustainable and collaborative floricultural production is thus demonstrated. Full article