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Outdoor thermal comfort is known to be influenced by adaptations, which leads to different outdoor thermal environment preferences due to climate change by locations. This paper compared the reactions to outdoor thermal environment between local people and tourist in winter in Harbin, which locates in typical severely cold climate zone in China. It was found that, in winter, compared with local people, the thermal comfort vote (TCV) of tourist is more sensitive to thermal sensation vote (TSV), and the satisfaction vote (TSaV) of local people is more sensitive to thermal comfort vote (TCV), which means that, for tourists in Harbin in winter, the change of thermal sensation will strongly influence people's thermal comfort, but the satisfaction vote to the environment will stay in a high level range, and the local people react against that. For the local people, the satisfaction vote (TSaV) and thermal sensation vote (TSV) shows a liner positive relationship, and for the tourists, the satisfaction vote (TSaV) and thermal sensation vote (TSV) shows a quadric relationship, and when TSV equals to -2 (cool), the TSaV of tourists get a peak value, which should be due to people's travel expectations to Harbin climate. [ABSTRACT FROM AUTHOR]

Nobody on this planet is going to be untouched by the impacts of climate change. The ability to cope with climate change varies from region to region, resulting the difference among its effects on fragility. In this paper, we focus on figuring out how the climate change influences the state’s fragility. First, we build Multilevel Fuzzy Mathematic Appraisement Model to assess the grade of the fragility. Then we identify the climate change as an indirect indicator through studying and discussing. Furthermore, after conducting Multiple Linear Regression Models between quantified climate index and twelve factors, we select the indicators that is relevant to climate change to measure the effect of climate change. Then, apply our model to the countries we choose--Sudan and India. We figure out climate change will increase the fragility index by affecting the economy, brain drain and state legitimacy of Sudan, then we quantize the growth by the multiple liner regression equation. For India, we find that when the temperature raise 1.6°C or the fragility index reach 90, India will become fragile. Then we predict by Back-Propagation neural network, finding that it is hard for India to be fragile within 100 years. Additionally, we apply our model to China, finding that its fragility index decreased with its intervention by decreasing most indicator index. Besides, we measure the total cost of intervention by Markal―Macro Model, getting that the total cost takes up 4% of GDP. Finally, because the indicators affecting the fragility of the cities is different from these affecting states, we modified it by altering the indicators we considered to apply in a small “state”, so it could be effective for cities to reflect the climate change impact on fragility. [ABSTRACT FROM AUTHOR]