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This study conducts in-depth research on geographic information visualization and the sustainable development of low-carbon rural slow tourism under artificial intelligence (AI) to analyze and discuss the visualization of geographic information and the sustainable development of low-carbon slow tourism in rural areas. First, the development options related to low-carbon tourism in rural areas are discussed. Then, a low-carbon rural slow tourism recommendation method based on AI and a low-carbon rural tourism scene recognition method based on Cross-Media Retrieval (CMR) data are proposed. Finally, the proposed scheme is tested. The test results show that the carbon dioxide emissions of one-day tourism projects account for less than 10% of the total tourism industry. From the proportion, it is found that air transport accounts for the largest proportion, more than 40%. With the development of time, the number of rural slow tourists in Guizhou has increased the most, while the number of rural slow tourists in Yunnan has increased to a lesser extent. In the K-means clustering model, the accuracy of scenario classification based on the semantic features of scene attributes is 5.26% higher than that of attribute likelihood vectors. On the Support Vector Machine classifier, the scene classification accuracy based on the semantic features of scene attributes is 19.2% higher than that of the scene classification based on attribute likelihood vector features. CMR techniques have also played a satisfying role in identifying rural tourism scenarios. They enable passengers to quickly identify tourist attractions to save preparation time and provide more flexible time for the tour process. The research results have made certain contributions to the sustainable development of low-carbon rural slow tourism.

With the development of the civil aviation industry, the passenger throughput of airports has increased explosively, and they need to carry a large number of passengers every day and maintain operations for a long time. These factors cause the large space buildings in the airport to have higher energy consumption than ordinary buildings and have energy-saving potential. In practical engineering, there are problems such as low accuracy of prediction results due to inability to provide accurate building parameters and design parameters, some scholars oversimplify the large space building load forecasting model, and the prediction results have no reference significance. Therefore, establishing a load forecasting model that is closer to the actual operating characteristics and laws of large space buildings has become a research difficulty. This paper analyzes and compares the building and load characteristics of airport large space buildings, which are different from general large space buildings. The factors influencing large space architecture are divided into time characteristics and space characteristics, and the influencing reasons and characteristics of each factor are discussed. The Pearson analysis method is used to eliminate the influence parameters that have a very low connection with the cooling load, and then the historical data that affect the cooling load parameters are input. The MATLAB software is used to select a variety of neural network models for training and prediction. On this basis, the particle swarm optimization algorithm is used to optimize the prediction model. The results show that the prediction effect of the gated recurrent neural network based on particle swarm optimization algorithm is the best, the average absolute percentage error is only 0.7%, and the prediction accuracy is high.

In this work, we focus on investigating the relationship between urban morphology parameters and residential building space heating energy performance, comparing microclimate conditions of existing residential blocks with central heating supply. Firstly, a dataset composed of district morphological parameters that measured heat energy consumption was established. Then, effects of morphological indicators including cover ratio, average building height, and floor area ratio on building space heating energy efficiency were assessed specifically. Analysis results show that a larger floor area ratio induced a reduction in heating energy consumption density, the observed effect is notable at an initial increase of floor area ratio. Thirdly, the case study shows that the heating load of residential districts with a high built density is more sensitive to solar radiation. To further assess how and to what extent urban forms alter microclimates, on-site measurement investigated detailed changes in the thermal environment of selected residential districts before and after the operational stage of central heating supply. Analysis results demonstrate that heat energy delivered by a central heating supply could dampen the variations of local outdoor air temperatures, more notable for residential districts with a higher floor area ratio during the night period. Findings from this work would be useful for urban planners considering energy-efficient design practices.

Following the outbreak of the COVID-19 pandemic, it became significant to study how to improve the customer satisfaction for Japanese tourist accommodations for restart and recovery in the future, and in preparation for the 2021 Japan Olympics. Therefore, the current paper attempts to evaluate ryokans through descriptive statistics from a tourism accommodation survey and customer-satisfaction-related comprehensive assessment system for built environment efficiency (CASBEE) importance&ndash, performance analysis (IPA). Through three progressive studies, three findings were obtained: (1) ryokans are more flexible than hotels, have strong anti-risk capabilities, and have received more and more attention from tourists and support from the Japanese government, (2) improvement strategies for customer satisfaction after COVID-19 were provided from IPA, and (3) a dynamic evaluation model of green ryokans was discussed and may be employed in other countries and regions experiencing the same situation.

Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban&ndash, rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban&ndash, rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban&ndash, rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban&ndash, rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban&ndash, rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.

Against the background of the Rural Vitalization Strategy in the gully regions of the Loess Plateau, large number of residents living in gullies move to tableland. On the one hand, this causes the disappearance of slope villages and gully villages, on the other hand, it increases ecological pressure on tableland, which was originally densely populated. The villages&rsquo, locations, the developing conditions, and the ecological carrying capacity of the surrounding villages have influence on the development direction of these villages and regional system planning. Through comparative analysis of different analysis methods for determining ecological carrying capacity, this paper takes the limiting factor analysis method as the main method and improves it according to the local conditions. Through the analysis of maximum ecological carrying capacity, current carrying capacity, and current population, we can determine which villages have the potential to support more immigrants and which ones should be integrated with others to improve the human settlement environment. At the same time, villages are graded so that the present and future human settlements&rsquo, environment can be further analyzed and predicted, which can be also adopted as an important guide for the local villages&rsquo, relocation and combination. Finally, we select a representative region to prove the general applicability of our planning method in the Loess Plateau.

City shrinkage, as an ongoing worldwide phenomenon, is an issue for urban planning and regional development. City shrinkage is remarkable in Japan, with over 85% of municipalities experiencing population loss from 2005 to 2015. As Japan&rsquo, s society ages and with its low fertility rate, city shrinkage has had a tremendous negative effect on economic development and urban planning. Understanding the spatial dependence and spatial heterogeneity of city shrinkage and its determinants is essential for ensuring the sustainable development of a city or region. In this study, a semiparametric geographically weighted regression (SGWR) model was adopted to explore the spatiotemporal differences in determinants of city shrinkage. The results reveal that the SGWR model incorporating the global and local variables is more interpretive compared to ordinary least squares and geographically weighted regression models in exploring the correlates of city shrinkage. We found the spatial dependence and heterogeneity of shrinking cities resulted from demographic, economy, and social factors, and revealed low fertility, the ageing population, and enterprise change ratio influenced city shrinkage in different regions at different times in Japan, whereas foreign population ratio, industry structure, and social welfare had global impacts. The findings provide useful information for understanding city shrinkage at global and local scales.

This article uses a fractal observation to help delineate the constraints placed by multiple city walls on the growth of historical East Asian cities. By applying advanced technologies from economic geography and fractal indices, a staged scaling process within urban dimension coherence can be applied to both indices. In this study, a discovery is proposed based on the urban organism concept that is capable of indicating a proportional intra-urban structure from a fundamental wall-bounded urban element (local specificity) to other greater walled spatial properties (global variables). This local specificity potentially performs approximate scaling regularities, and spatially denotes an average historical threshold of urban growth for its overall size, with similar scaling law constraints. This finding involves territorial, urban planning, and ancient architectural perspectives, providing a historical and local response to the expansion of contemporary cities. By employing growing fractal estimation, data processing enables the logarithmic city size to be obtained by measuring each wall&rsquo, s specific features using the Ordinary Least Squares (OLS) method. On the basis of two-dimensional allometric scaling patches, a spatial unfolding mechanism is utilized to reproduce these dynamic changes with city walls as a result of the human trajectories in time geography.

Global greenhouse gas (GHG) emissions from the construction industry continue to increase at an annual rate of 1.5%. It is particularly important to understand the characteristics of the building life cycle to reduce its environmental impact. This paper aims to assess the environmental impact of prefabricated buildings and traditional cast-in-situ buildings over the building life cycle using a hybrid model. A case study of a building with a 40% assembly rate in Japan was employed for evaluation. It concluded that the total energy consumption, and carbon emissions of the prefabricated building was 7.54%, and 7.17%, respectively, less than that of the traditional cast-in-situ building throughout the whole life cycle. The carbon emissions reduction in the operation phase reached a peak of 4.05 kg CO2/year∙m2. The prefabricated building was found to cost less than the traditional cast-in-situ building, reducing the price per square meter by 10.62%. The prefabricated building has advantages in terms of reducing global warming, acid rain, and health damage by 15% reduction. With the addition of the assembly rate, the carbon emissions and cost dropped, bottoming out when the assembly rate was 60%. After that, an upward trend was shown with the assembly rate increasing. Additionally, this study outlined that the prefabricated pile foundations is not applicable due to its high construction cost and environmental impact.

Since the energy crisis in the 1960s, crucial research and activities were spurred to improve energy efficiency and decrease environmental pollution. To deal with the various problems the construction industry are facing, the concept of green buildings (GBs) has been gradually shaped and put forward all over the world, and green building rating systems (GBRSs) have been developed. The concept of GBs covers a wide range of elements, and its definition is constantly updated as the construction industry develops. This paper compares the development of backgrounds and statuses of green building development in various countries. It also presents an overview of the green building development situation within these countries, summarizing two influences for GB development: one external and the other internal. External factors include GB development policy support, economic benefits, and certification schemes. Internal factors are the development and application of GB technology, the level of building management, and how users interact with the GB technology. Currently, 49 worldwide green building standards and application have been sorted out, including 18 standard expert appraisal systems. Moreover, it discusses the research results and lessons learned from green building projects in different countries and summarizes their achievements and challenges. To correctly understand and use green building technology, it is essential to improve the policy and incentive system, improve the professional quality and technical ability of employees and accredited consultants, constantly develop and update the evaluation system, strengthen technological innovation, and integrate design and management. This paper aims to draw a clear roadmap for national standard development, policy formulation, and construction design companies, provide solutions to remove the obstacles, and suggest research direction for future studies.

The increasing penetration of renewable energy decreases grid flexibility, thus, decentralized energy management or demand response are emerging as the main approaches to resolve this limitation and to provide flexibility of resources. This research investigates the performance of high energy efficiency appliances and grid-integrated distributed generators based on real monitored data from a social demonstration project. The analysis not only explores the potential cost savings and environmental benefits of high energy efficiency systems in the private sector, but also evaluates public grid load leveling potential from a bottom-up approach. This research provides a better understanding of the behavior of high decentralized efficient energy and includes detailed scenarios of monitored power generation and consumption in a social demonstration project. The scheduled heat pump effectively lifts valley load via transforming electricity to thermal energy, its daily electricity consumption varies from 4 kWh to 10 kWh and is concentrated in the early morning over the period of a year. Aggregated vehicle to home (V2H) brings flexible resources to the grid, by discharging energy to cover the residential night peak load, with fuel cost savings attributed to 90% of profit. The potential for grid load leveling via integrating the power utility and consumer is examined using a bottom-up approach. Five hundred thousand contributions from scheduled electrical vehicles (EVs) and fuel cells provide 5.0% of reliable peak power capacity at 20:00 in winter. The outcome illustrates the energy cost saving and carbon emission reduction scenarios of each of the proposed technologies. Relevant subsidies for heat pump water heater systems and cogeneration are essential customers due to the high initial capital investment. Optimal mixes in structure and coordinated control of high efficiency technologies enable customers to participate in grid load leveling in terms of lowest cost, considering their different features and roles.