The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

The authors wish to make the following corrections to this paper

This study aims to examine (1) the effect of the digital economy development on the imports of green products in terms of both volume and proportion, (2) the mechanism by which the digital economy affects the volume of green imports, (3) the heterogeneity of the effect on the import volume for different types of green products, and (4) whether or not a threshold of economic development level exists for the effect on the proportion of green imports and how the threshold works in the effect. In order to test these hypotheses, we conducted an empirical test using China’s province–commodity–year trade cubic dataset from 2012 to 2022. Our empirical results have yielded several findings. First, digital economy development expands the volume of green imports, and this impact is more pronounced for non-part products, for products for pollution treatment and purification, and for products for monitoring and forecasting. Second, the upgrading and rationalization of industrial structure act as mediators in this impact. Third, the impact on the proportion has a single threshold of regional economic development, with regard to which the impact is nonlinear. This study contributes to the literature by revealing a connection between the digital economy and green imports, and provides practical suggestions on how to boost high-quality green trade development by utilizing the digital economy.