Harnessing big data for sustainable urban management: A novel approach to gridded urban GDP dataset development

Abstract

To effectively address the challenges posed by rapid global urbanization, cities must utilize sound scientific principles and methodologies. A key element is the development of robust socioeconomic indicators, such as Gross Domestic Product (GDP). This study introduces a novel framework for creating a spatially disaggregated urban GDP dataset, using a downscaling approach and integrating data from a vast range of sources, including 3.5 million industrial entities across 835 sub-sectors within 41 major sectors. The framework enhances accuracy by leveraging the benefits of Pollutant Discharge Permits (PDP) and National Enterprise Credit Information Publicity (NECIP) for pinpointing geographic locations, industry classifications, and reliable statistical data for urban GDP. Additionally, the dataset is regularly updated using publicly accessible data. Validation of the dataset was conducted using multi-level statistical data and the NOx hot grid index derived from TROPOMI NO2 column measurements. Results indicate that the dataset accurately reflects the spatial distribution of major industrial sectors. The gridded urban GDP dataset for China, with a high spatial resolution of 0.1° × 0.1°, reveals spatial heterogeneity among sectors, with the mining sector, concentrated in resource-rich areas, making the most significant contribution to this heterogeneity. Other sectors show a strong spatial correlation, with the gravity center located in central China. This study provides a valuable resource for sustainable urban resource management within Earth system boundaries and offers critical data, insights, and methodological guidance for tackling urbanization challenges.