No abstract

The residential sector is one of the major sources of greenhouse gases, and as such it is key for achieving decarbonization targets. However, very diverse household characteristics affect energy use and emissions at the household level. Furthermore, demographic transitions characterized by ageing and shrinking in both total and household size can have enduring effects for some of these household characteristics at the societal level. Here, we explore how demographic transitions in Japan may affect the long-term emissions in the residential sector based on the scenarios to the year 2040. First, we draw from a large-scale survey conducted by the Japanese government (9,996 households) to develop a nuanced typology of households using an integrated clustering approach. This helps us identify high variability in emissions and adoption of emission mitigation technologies between households with different characteristics. We found that household size and age both play important role in terms of emissions. Finally, through different scenarios we identify that indeed the projected demographic transitions with the increasing prevalence of smaller and more elderly households in Japan will likely hinder emission mitigation in the residential sector, imposing significant constraints for achieving decarbonization in the long-term.

Urban livability has become a major policy and practice priority in many parts of the world, but its attainment remains challenging in many cities of developing and emerging economies. The lack of data with appropriate quality, coverage, and spatial/temporal resolution often complicates the assessment of livability in such cities, and the identification of priority areas for improvement. Here we develop an innovative framework to mobilize and synthesize open-source data to analyze spatially urban livability patterns in Shanghai. The framework brings together diverse open-source data such as housing prices, population distribution, transportation networks, and points of interest to identify city areas with low livability, and thus priority areas for improvement. Such findings can provide a comprehensive overview of the residential living environment in Shanghai, as well as provide useful information to urban planners and decision-makers. Furthermore, the developed method has the potential for application in other cities, subject to data availability.