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The purpose of this paper is to measure the influence factors of their preferences for urban forest, marginal values of various properties and relative values of different scheme portfolios, thus arriving indirectly at the city residents’ demand for urban forest improvement.

This paper, based on the data from the 2015–2017 field survey questionnaire of city residents over the radius of Beijing’s 5th Ring Road, uses the choice experiment method (CEM) to conduct a study of its residents’ demand for urban forest.

Beijing’s city residents are generally inclined to accept a relatively low payment of urban forest while hoping to access a relatively high urban afforestation coverage with the construction of relatively many city parks, especially focusing on the specialized park management; the marginal values of biodiversity and greenery coverage are far higher than those of greenbelts in quantity and the maximum marginal value of biodiversity remains as high as RMB29.42, indicating that the city residents do not favor much the number of greenbelts over other aspects but they generally hope to achieve a higher greenery coverage and a richer biodiversity.

Generally speaking, what Beijing City needs most is not continuing the increase in the number of greenbelts, but engaging in the rational retrofit of its existing greenbelts and optimizing its urban forest structure.

This paper may provide reference for determining the city residents’ payment criteria for urban forest and will be of equally great significance to developing cities and their urban forest.

The development of tourism around Wolong Nature Reserve changes the local communities' ways of life. This study discusses how ecotourism affects the households' use of their capitals, the livelihood strategies as well as illustrates the impact on the habitats in the reserve through Department for International Development’s (DFID) Sustainable Livelihood Framework (SLF) with data collected during fieldwork.

The study focuses on (1) Calculating Livelihood Capital Index. (2) The effects of livelihood capitals on livelihood strategy were calculated by multinomial logistic regression.

The study has yielded the following results: (1) In general, tourism promotes people's livelihood capitals. The growth in different types of households under tourism settings is ranked as full-time tourism operators > part-time tourism operators > traditional living households. (2) Tourism development mainly shifts livelihood strategies in two ways. Firstly, travel operating replaces some traditional practices that make livings; secondly, increased needs for potherbs and herbs from tourists let households enter into the hills to pick the plants more actively, which intensifies the destruction of giant panda's habitats. (3) Nine types of livelihood capitals indicators, namely farmland quality, distance between house and roads, number of laborers, average housing area, average income per person, whether family members being village cadres, and ever having received skills training shape livelihood strategies in different levels.

Three discussions are drawn from the study: (1) Enhancing the exploit for tourism resources to form a diversified competition. (2) Introducing herb growing to fulfill tourists' needs and improve people's livelihood in the meantime. (3) Optimizing the tourism surveillance and management system and improving the rules and regulations.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.