• Energy Research
• 1. No poverty close

As absolute poverty in China, measured by the current standard, is being eliminated, the focus of future poverty reduction projects will necessarily shift to addressing relative poverty. Contiguous poverty areas have been identified in Hebei province around Beijing and Tianjin (HABT), and this is not conducive to the coordinated development of the Beijing-Tianjin-Hebei region. The dynamic identification of relative poverty at the county level within the region must be the basis for formulating scientific strategies for poverty reduction. Night light (NTL) data can reveal socio-economic information and reflect human activities, and has a wide range of other applications for evaluating and identifying poverty. For this reason, NPP/VIIRS (Visible Infrared Imaging Radiometer Suite equipped on the Suomi National Polar orbiting Partnership satellite) NTL data from 2012 to 2020 were corrected, and NTL data for HABT were obtained. A multidimensional relative poverty index (MRPI) that assesses being “free from worries over food and clothing and having access to compulsory education, basic medical services, and safe housing” using social statistical data was created with the analytic hierarchy process and entropy weight method. A panel regression model with fixed effects was established for MRPI and corrected NPP/VIIRS NTL data. The R2 of fitting was 0.6578 and confirmed a strong correlation between MRPI and corrected NPP/VIIRS NTL data. Based on this, the MRPI estimation model was constructed based on the MRPI and corrected NPP/VIIRS NTL data, and passed the accuracy test. Finally, using the national list of poverty counties, it was verified that, at the county scale, the corrected NPP/VIIRS NTL data could effectively identify areas of relative poverty. This study lays the foundation for the use of NPP/VIIRS NTL data in the identification of areas of relative poverty. It provides a feasible method and data reference for analyzing relative poverty at a smaller scale. The dynamic identification of areas of relative poverty can also provide a basis for formulating scientific poverty reduction strategies.

Abstract China's socioeconomic development, including urbanization, is now facing a key challenge of reducing carbon emissions. This study analyzes the driving factors of freight transport carbon emissions and the effects of urbanization on freight transport carbon emissions in China. The spatial durbin model (SDM)-stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and geographically weighted regression model (GWR)-STIRPAT model are constructed to analyze the common characteristics and regional disparity of the above effects in China. The results show that: (1) The total amount of freight carbon emissions in China has increased from 3.7352 Mt in 1988 to 96.4158 Mt in 2016. Road freight is the largest increasing sub-sector of carbon emissions in the freight transport sector. (2) Urbanization level has a positive impact on road and aviation transport carbon emissions and has a significant negative impact on railway and waterway transport carbon emissions in some provinces, but has a positive impact on their neighboring provinces. There is a significant regional disparity in multi-freight transport carbon emissions. (3) The carbon emissions of freight transport have a characteristic of “path dependence”. The population size and energy intensity have a significant impact on freight carbon emissions. Different from waterway freight, there is an inverted U-shape relationship between the carbon emissions of railway, road, aviation freight and per capita GDP. We provide policy implications based on the findings, which is expected to contribute to the carbon emissions reduction in China's transportation industry.