This paper estimates the death underreporting rates in population censuses and sample surveys and their time trend since 1982 through Bayesian hierarchical regression model. While the trend of death underreporting rates at age 0 has distinct phases, the trend of underreporting rates at age 1 to 4 is inconspicuous; distinct phases and similarity also exist in the changes of death underreporting rates over time at all ages, 5-14, 15-59, 60-89, 90 and over. Due to the changing causes of underreporting and social environments at different periods, female death underreporting rates at age 1 to 4 are not necessarily lower than age 0. In censuses, death underreporting rates at different adult ages have large disparities, while those in sample surveys are relatively consistent. Lower mortality rates at age 90 and over in male of 2000 census and in both sexes of 2010 census are caused by serious underreporting. The effect of death underreporting on the calculation error of life expectancy varies with age, and the relationship between the two is weaker under age 5, while the relationships at other ages are significantly positively linearcorrelated.

Ｙｏｕｎｇ?ａｇｅ ａｎｄ ｏｌｄ?ａｇｅ ｄｅａｔｈ ｐｒｏｂａｂｉｌｉｔｉｅｓ ａｒｅ ｅｓｓｅｎｔｉａｌ ｆｏｒ ａｃｃｕｒａｔｅ ｅｓｔｉｍａｔｉｏｎ ｏｆ ｌｉｆｅ ｅｘ ｐｅｃｔａｎｃｙ ａｔ ａｇｅ ０ ａｎｄ ６０，ｗｈｉｃｈ ｈａｖｅ ｍａｊｏｒ ｉｍｐｌｉｃａｔｉｏｎｓ ｆｏｒ ｔｈｅ ｆｏｒｍｕｌａｔｉｏｎ ｏｆ ｅｎｄｏｗｍｅｎｔ ｉｎｓｕｒａｎｃｅ ａｎｄ ｏｔｈｅｒ ｐｏｌｉｃｉｅｓ．Ｐｒｅｖｉｏｕｓ ｓｔｕｄｉｅｓ ｈａｖｅ ｔｅｓｔｉｆｉｅｄ ｔｈａｔ ｄｅａｔｈ ｕｎｄｅｒｒｅｐｏｒｔｉｎｇ ｃｏｍｍｏｎｌｙ ｅｘｉｓｔｓ ｉｎ ｔｈｅ ｍｏｒｔａｌｉｔｙ ｄａｔａ ｏｆ ｙｏｕｎｇ?ａｇｅ ａｎｄ ｏｌｄ?ａｇｅ ｇｒｏｕｐ，ｓｕｃｈ ｔｈａｔ ｖａｒｉｏｕｓ ａｐｐｒｏａｃｈｅｓ ｏｆ ｍｏｄｉｆｉｃａｔｉｏｎ ｗｅｒｅ ｏｆｆｅｒｅｄ．Ｔｈｉｓ ｐａｐｅｒ ｒｅｃａｌｃｕｌａｔｅｄ ｓｅｘ?ｓｐｅｃｉｆｉｃ ｌｉｆｅ ｔａｂｌｅｓ ｏｆ Ｃｈｉｎａ ａｎｄ ｌｉｆｅ ｅｘｐｅｃｔａｎｃｙ ａｔ ａｇｅ ０ ａｎｄ ６０ ｆｏｒ ｔｈｅ ２０１０ ｐｏｐｕｌａｔｉｏｎ ｃｅｎｓｕｓ ｂａｓｅｄ ｏｎ ｔｈｅ ＤＣＭＤ ｍｏｄｅｌ ｌｉｆｅ ｔａｂｌｅｓ，ｕｓｉｎｇ ｃｈｉｌｄ ｍｏｒｔａｌｉｔｙ ｄａｔａ ｆｒｏｍ ＵＮＩＣＥＦ，ａｄｕｌｔ ｍｏｒｔａｌｉｔｙ ｉｎｆｏｒｍａｔｉｏｎ ｆｒｏｍ ＩＨＭＥ，ａｎｄ ｏｌｄ?ａｇｅ ｍｏｒｔａｌｉｔｙ ｒａｔｅｓ ｄｅｒｉｖｅｄ ｆｒｏｍ ｔｈｅ ｓｅｘ?ｓｐｅｃｉｆｉｃ ｐｏｐｕｌａｔｉｏｎ ａｇｅｄ ６０ ７５ ｉｎ ｔｈｅ ｃｅｎｓｕｓ．Ｔｈｅ ｕｎｄｅｒｒｅｐｏｒｔｉｎｇ ｒａｔｅｓ ｏｆ ｙｏｕｎｇ?ａｇｅ ａｎｄ ｏｌｄ?ａｇｅ ｍｏｒｔａｌｉｔｙ ａｒｅ ｅｓｔｉｍａｔｅｄ．Ｔｈｅ ｒｅｓｕｌｔ ｓｈｏｗｓ ｔｈａｔ ｉｎ ｔｈｅ ２０１０ ｐｏｐｕｌａｔｉｏｎ ｃｅｎｓｕｓ ａｄｊｕｓｔｅｄ ｉｎｆａｎｔ ｍｏｒｔａｌｉｔｙ ｒａｔｅｓ ａｒｅ １６．４１‰ ｆｏｒ ｍａｌｅ ａｎｄ １５．９４‰ ｆｏｒ ｆｅｍａｌｅ，ｉｍｐｌｙｉｎｇ ａｎ ｕｎｄｅｒｒｅｐｏｒｔｉｎｇ ｒａｔｅ ｏｆ ７７．３％ ａｎｄ ７５．５％ ｒｅｓｐｅｃｔｉｖｅｌｙ；ｔｈｅ ａｄｊｕｓｔｅｄ ｏｌｄ?ａｇｅ （ｆｒｏｍ ６０ ｔｏ ８９） ｄｅａｔｈ ｐｒｏｂａｂｉｌｉｔｉｅｓ ａｒｅ ０．７０３ ｆｏｒ ｍａｌｅ ａｎｄ ０．５９５ ｆｏｒ ｆｅｍａｌｅ ｗｉｔｈ ｔｈｅ ｕｎｄｅｒｒｅｐｏｒｔｉｎｇ ｒａｔｅ ｂｅｉｎｇ ２．３％ ａｎｄ ７．０％ ｒｅｓｐｅｃｔｉｖｅｌｙ．

Literature suggests that urbanization is one of the core drivers of energy consumption and carbon emissions，especially for the rapidly growth of residential electricity demand．Previous studies have only focused on the impacts of urbanization level，but ignored the multiple dimensions of urbanization and how they drive the residential electricity consumption at the local level．In this paper we decompose multiple dimensions of urbanization into level，pattern，structure and systemic position using 2000 and 2010 county-level data in Zhejiang Province，China，and test the local drivers of residential electricity consumption with fixed-effect models．Results reveal that higher per capital residential electricity usage is associated with higher urbanization level，more concentrated population distribution，higher systemic position，shrinking household size，declining labor participating rate，and higher average number of residential rooms．Policy implications are discussed．

There is a lack of official report by the Chinese government regarding the exact number of abnormal deaths during the Great Leap Forward．Using a modified Lee-Carter mortality prediction mod- el，this study conducts reverse prediction based on China’s official age-specific mortality data to recon- struct and calculate the single-year-age mortality rate and the normal deaths assuming that there was no great famine．Then we estimate the abnormal deaths during the Great Leap Forward occurring between 1958 and 1961．If there had been no famine，there would have been 43. 39 to 43. 85 million normal deaths．With famine，abnormal deaths are estimated to be between 16. 24 and 23. 37 million． Our results suggest that the medium estimation of the abnormal deaths during the Great Leap Forward stands at no more than 19. 8 million．

There is a lack of official report by the Chinese government regarding the exact number of abnormal deaths during the Great Leap Forward．Using a modified Lee-Carter mortality prediction model，this study conducts reverse prediction based on China’s official age-specific mortality data to recon- struct and calculate the single-year-age mortality rate and the normal deaths assuming that there was no great famine．Then we estimate the abnormal deaths during the Great Leap Forward occurring between 1958 and 1961．If there had been no famine，there would have been 43. 39 to 43. 85 million normal deaths．With famine，abnormal deaths are estimated to be between 16. 24 and 23. 37 million． Our results suggest that the medium estimation of the abnormal deaths during the Great Leap Forward stands at no more than 19. 8 million．

Electricity power is the most frequently used secondary energy and the most population / household-related energy in human societies. The age and gender structure of population in Chinese households have changed to be much more diversified，which would have a significant impact on the residential electricity consumption at the household level. In order to examine the age-gender pattern in household electricity consumption，we use pooled data from the CFPS baseline database which includes age and gender of all the household members，decomposing household-level electricity consumption into individual-level age-gender pattern by defining the age-gender index as a key variable in the regression model. The results indicate that the impact of household size on per capital electricity usage differs between age-gender population groups. The age pattern presents a"inverted U”shape，while the gender pattern is“higher for females than males". 25-34 year old female population，which has a positive effect on per capital electricity usage，is the most important subject in household electricity consumption. Making and adjusting policy of multistep electricity price and prediction and planning of residential electricity consumption should take age and gender structure of population in households into consideration.