• Energy Research

In disaster management, evacuation modeling is considered a useful visual tool to disaster managers for reviewing current evacuation strategies, estimating the ability of shelters to accommodate all evacuees, and developing evacuation route planning. Though there are several existing studies on evacuation modeling in the case of an earthquake, research that integrates road blockades into evacuation simulations is quite limited. From that viewpoint, this research aims to develop evacuation modeling with consideration of road blockades to simulate how residents move to evacuation centers (hereafter, shelters) through urban areas following an earthquake occurrence. The research also determines difficulties that residents may encounter under earthquake conditions, compared with normal conditions, corresponding to considering or not considering road blockades, respectively, such as having no access to shelters, taking longer routes instead of shortest routes, and so on. Debris from damaged buildings in an urban area is assumed as the main source of debris that would cause a road blockade. The model is applied to a case study of the Daitoku school district in Kanazawa city. According to simulated results, due to road blockades, occupants of many damaged buildings did not have access to shelters, and a lot of evacuees needed to move to shelters with longer routes instead of taking the shortest routes. Furthermore, the research results show the possibility of considering road blockades for improving current evacuation modeling and making evacuation simulations more realistic.

By means of on-site and network investigation, we collected data relevant to residents of communities, point of interest (POI) data, and land-use data of Fuzhou. We set traffic walking time and leisure walking time as an independent variable, built environment as dependent variable, and gender, age, education level and income level as control variables. Six linear regression models were established using Statistical Product and Service Solutions (SPSS). The results showed that in the 5D (i.e., Density, Diversity, Design, Destination and Distance) elements of the built environment, the density was negatively correlated with the traffic walking time, whereas other elements were positively correlated with the walking time, but the degree of influence was different.

China has stepped into the accelerating phase of urbanization since the early 1980s, which has boosted the electricity consumption and CO2 emission of the urban area. Most existing studies discussed related issues on national or regional scale from the perspective of the energy sector, but little research has focused on the scale of urban level and topics in urban development. This paper intends to utilize the approach of spatial analysis and spatial statistics to identify the potential and feasibility of Chinese cities in developing distributed photovoltaic power system (D-PVPS) for meeting household electricity demand. The result shows that most Chinese cities are feasible to develop the D-PVPS which can totally cover the present household electricity consumption in residential area, based on the estimation carried out in this paper. Some of the rest cities are also suitable to develop the D-PVPS in the visible future when photoelectric ratio enhances. Finally, policy implications and suggestions are raised to promote the much broader D-PVPS application in Chinese cities under the background of future urbanization

Abstract Energy generation by photovoltaic (PV) installations on residential building envelopes in tropical area was evaluated by using a new practical method employing 3D analysis. Calculations of monthly global radiation were used to investigate solar potential in the study case area. In order to simulate optimal locations of PV panel installation, BIM Revit software was used to quantify the incident solar radiation and for color rendering of different levels experienced in different areas of the building envelope due to Sun path and the influence of the local environment. The amount of energy generated by hypothetical PV panels was compared to actual residential sector energy consumption which became a baseline for evaluating the prospective implementation of PV in a tropical area. The results indicated that the optimal location for installing PV panels was on the roof of a building where incident solar radiation levels are high and uniform throughout the year. In addition, the walls of the building oriented to the East and West have a great potential to harvest electrical energy as an optional location for photovoltaic panel installation. The potentially available photovoltaic energy generated was compared to the actual electrical energy consumption and demonstrated a favourable surplus for electrical energy supply. The energy surplus provided by three types of photovoltaic output varied from 172% to 1700% above the baseline consumption, thus installing photovoltaics on residential buildings could have a strong potential to secure both current energy consumption and future energy demand.

The COVID-19 pandemic has significantly changed urban life and increased attention has been paid to the pandemic in discussions of urban vulnerability. There is a lack of methods to incorporate dynamic indicators such as urban vitality into evaluations of urban pandemic vulnerability. In this research, we use machine learning to establish an urban Pandemic Vulnerability Index (PVI) that measures the city’s vulnerability to the pandemic and takes dynamic indicators as an important aspect of this. The proposed PVI is constructed using 140 statistic variables and 10 dynamic variables, using data from 47 prefectures of Japan. Factor Analysis is used to extract factors from variables that may affect city vulnerability, and the LambdaMART algorithm is used to aggregate factors and predict vulnerability. The results show that the proposed PVI can predict the relative seriousness of the COVID-19 pandemic in two weeks with a precision of more than 0.71, which is meaningful for taking controlling measures in advance and shaping the society’s response. Further analysis revealed the key factors affecting urban pandemic vulnerability, including city size, transit station vitality, and medical facilities, emphasizing precautions for public transport systems and new planning concepts such as the compact city. This research explores the application of machine learning techniques in the indicator establishment and incorporates dynamic factors into vulnerability assessments, which contribute to improvements in urban vulnerability assessments and the planning of sustainable cities while facing the challenges of the COVID-19 pandemic.

To promote the development of low-carbon industries of Pingtan, it is necessary to establish a scientific, comprehensive and reasonable index system to evaluate the energy-saving of low-carbon industrial development. This paper firstly introduced the development background of Pingtan. By analyzing the domestic and foreign industrial low carbon development evaluation theory, the energy-saving evaluation system of Pingtan industrial low carbon development is constructed by selecting three subsystems of energy consumption index, emission reduction index and technical index, including 12 specific indexes. Finally, the paper proposed a calculation model using Principal Component Analysis (PCA). The evaluation system has a crucial practical significance to optimize the industrial structure layout of Pingtan, promote the transformation and upgrading of traditional industries, accelerate the pace of low-carbon industries development, and improve resource utilization efficiency.

Many researchers have confirmed a correlation between the built environment and physical activity. However, most studies are based on the objective characteristics of the built environment, and seldom involve the residents’ subjective perception. The purpose of this study is to explore the relationship between the subjective and objective characteristics of the built environment and physical activity at the community scale. Data consists of that collected from a social survey, Points of Interest (POI), the road network, and land use in Fuzhou, China. The duration of moderate-to-vigorous physical activity (MVPA) within a week is used to represent the general physical activity of residents. Security perception is introduced as an intermediary variable. SPSS software is used for factor analysis and Amos software for statistical analysis. Structural equations are set up to analyse the relationship between these variables. The final results show that: (1) The objective characteristics of the built environment have no direct impact on the development of leisure MVPA, but it can indirectly affect leisure MVPA through residents’ subjective perception of the built environment; (2) The subjective perception of residents has a significant impact on the duration of MVPA, the subjective perception of humanized space has a direct impact on the duration of MVPA, and destination accessibility and urban environment maintenance has an indirect impact through community public security perception; and (3) The individuals’ attributes such as gender and self-evaluated socioeconomic status have negative effects on the duration of leisure MVPA, and an individual’s love of sports has a positive effect on MVPA.

Abstract Focusing on China, which has a special household registration system (called hukou ), this study examines the likely effects of the China’s National New-type Urbanization Plan (2014–2020) on rural migrant workers’ intentions to reside permanently in cities and their future energy consumption preference. Questionnaire surveys were conducted in 2014 and 2015 among rural migrant workers and residents with a hukou in three cities (985 migrants and 671 residents with a hukou). Analysis results show that 39% of migrant workers intended to reside permanently in cities under the aforementioned plan, and the current energy consumption of residents with a hukou is 1.2–4.0 times higher than that of rural migrant workers. Further analyses based on structural equation models confirm that migrants’ current less-satisfied life experience and lower level of viability in cities hinder them to become a permanent urban resident. Unexpectedly, migrants’ permanent residence intentions do not affect their future energy consumption preference, which is however mostly affected by their confidence and barriers to become a permanent resident, especially future concerns about health and lack of social security in their current daily life. Finally, it is argued that studies on rural migrants should pay more attention to capability-concerned and life-oriented aspects, which are useful to derive important insights into cross-sectoral energy policymaking.