China is undergoing large changes to tackle carbon dioxide emissions and air pollution. While the top-down governance allows for clear setting of emission reduction targets for industrial sectors and major cities, reducing emissions in residential sectors in smaller (the so-called low-tier) cities remain challenging and often unaddressed. This paper studies policy options to reduce emissions in residential sectors in low-tier Chinese cities. We conducted interviews and surveys in the city of Jingmen in the Hubei province and developed simulation models with feasible policy options and realistic consumption choice preferences. The simulation provided insights to the policies on reducing household coal consumption and ensuing emissions. Our research found that top-down restrictive policies such as coal ban and coal tax are effective in reducing emissions. They, however, restrict access to affordable energy for heating and cooking, especially within rural areas. They hence need to be combined with supportive policies such as electricity subsidy to yield long-term positive impact.

Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.