Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019.

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques.

Matching end-of-life household vehicle generation and recycling capacity in Chinese cities: A spatio-temporal analysis for 2022-2050.

End-of-life vehicles (ELVs) present both opportunities and challenges for the environment and the economy, where effective recycling management plays a decisive role. Recently, the primary focus of recycling management has shifted from simply meeting demand to refining and optimizing processes at the city-scale. However, the mismatch in recycling capacity has become a significant obstacle to maximizing environmental and economic benefits.

A unified spatial multigraph analysis for public transport performance.

Public transport performance not only directly depicts the convenience of a city's public transport, but also indirectly reflects urban dwellers' life quality and urban attractiveness. Understanding why some regions are easier to get around by public transport helps to build a green transport friendly city. This paper initiates a new perspective and method to investigate how public transport network's morphology contributes significantly to its performance.