Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.
Decentralization development and changing urban form will increase the mobility and contribute to global CO₂ emissions, in particular for developing countries which are experiencing rapid economic growth and urban expansion. In this paper, an integrated analytical framework, which can quantify the impact of changing urban form on commuting CO₂ emissions, is presented. This framework simultaneously considers two emission dependent factors, commuting demand and modal share based on the concept of excess commuting and accessibility analysis, and ensures its applicability to other cities where the detailed individual travel data is not available. A case study of Beijing from 2000 to 2009 is used to illustrate this framework. The findings suggest that changing urban form in Beijing did have a significant impact on commuting CO₂ emission increase. Changing to a more decentralized urban form in Beijing had a larger impact on commuting distance and increased usage of cars, which resulted in a significant rise in CO₂ emissions. There is a larger space and an urgent need for commuting CO₂ emission reduction, in 2009 in Beijing, by planning and by strategic measures in order to promote sustainable transport.
Decomposition analysis of CO 2 emissions increase from the passenger transport sector in Shanghai, ChinaPublished: 01 August 2011 by Informa UK Limited in International Journal of Urban Sciences
This study identified the relationships between CO2 emissions from passenger transport and its driving factors by taking Shanghai as an example. The Logarithmic Mean Divisia Index (LMDI) technique was used to disassemble the total passenger transport CO2 growth into five driving factors: economic activity, population, modal share, passenger transport intensity and passenger transport CO2 emission factor. The study found that: (1) in 2009, the passenger transport CO2 emissions in Shanghai increased by 2.59 times against that of 2000; (2) the increased economic activity was the main factor driving passenger transport CO2 emissions growth from 2000 to 2009 that accounted for 75% of the total passenger transport CO2 emissions growth in Shanghai; (3) the effects of modal share change and population growth were relatively small but not trivial; and (4) The inhibitory effects of passenger transport CO2 emissions growth were 90% from the improvement of passenger transport intensity, and 10% from the changes of passenger transport CO2 emission factor. However, these effects were too small to offset the whole increase.
Estimation of CO2Mitigation Potential from Freight Transport Based on System Dynamics Model—A Case Study of Beijing, Chi...Published: 26 July 2010 by American Society of Civil Engineers (ASCE) in Seventh International Conference on Traffic and Transportation Studies (ICTTS) 2010
Freight transport has increased rapidly with the raid economic growth, leading to rising energy consumption and CO2 emission. In this paper, taking the urban freight transport of Beijing as a case, three scenarios are established to evaluate the potential of CO2 emission reduction based on system dynamics model. Scenario results show that freight turnover of Beijing will touch 80 billion t-km at the end of 2020, with an annual average growth rate around 5%, and the CO2 emission mitigation potential range from 13–30% by 2020.