Regional economic inequality is a persistent problem for all nations. Meanwhile, satellite-derived night-time light (NTL) data have been extensively used as an efficient proxy measure for economic activity. This study firstly proposes a new method for correction of the NTL data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite and then applies the corrected NTL data to estimate gross domestic product (GDP) at a multi-scale level in China from 2014 to 2017. Secondly, incorporating the two-stage nested Theil decomposition method, multi-scale level regional inequalities are investigated. Finally, by using scatter plots, this paper identifies the relationship between the regional inequality and the level of economic development. The results indicate that: (1) after correction, the NPP-VIIRS NTL data show a statistically positive correlation with GDP, which proves that our correction method is scientifically effective; (2) from 2014 to 2017, overall inequality, within-province inequality, and between-region inequality all declined, However, between-province inequality increased slightly. As for the contributions to overall regional inequality, the within-province inequality was the highest, while the between-province inequality was the lowest; (3) further analysis of within-province inequality reveals that economic inequalities in coastal provinces in China are smaller than in inland provinces; (4) China’s economic development plays an important role in affecting regional inequality, and the extent of influence of economic development on regional inequality is varied across provinces.

The regional distribution and driving factors of total carbon emissions have been the focus of considerable research. However, carbon intensity rather than total carbon emissions has been selected as the emissions reduction index in China. The Chinese government has committed to reducing carbon intensity by 60-65% from 2005 levels. Currently, limited academic attention has been given to the regional distribution and driving factors of carbon intensity. To explore the means of achieving the carbon intensity target in China, Gini coefficients were employed in this paper to investigate regional differences in carbon intensity across 30 provinces from 1995 to 2014. Moreover, the FGLS (feasible generalized least squares) method was applied to identify the key influencing factors of carbon intensity at the national and three regional levels. The results indicate that: 1. Chinese inter-provincial Gini coefficients of carbon intensity have increased steadily in recent years, which indicates that the difference in carbon intensity between provinces in China has widened. 2. Economic growth, foreign direct investment, and trade openness were negatively correlated with carbon intensity. Conversely, coal consumption, industrial proportion, and urbanization were positively correlated with carbon intensity. Moreover, urbanization has proven to be the most important factor affecting China’s carbon intensity. 3. The dominant cause of carbon intensity varies by region. In particular, the dominant cause of carbon intensity in low- and medium-level regions is urbanization. However, the dominant cause of carbon intensity in high-level regions is coal consumption. 4. Based on these empirical findings, policy recommendations to reduce carbon intensity were proposed. In summary, the improvement of urbanization quality in both low- and medium-level regions is urgently needed. However, optimizing the energy structure is essential to carbon intensity reduction in high-level regions.

Inter-provincial migration causes dramatic changes in the population, as well as in the development of the social economy at both origin and destination, which is related to sustainable development in any country. Using inter-provincial migration data during the periods covering 1995–2000, 2000–2005, 2005–2010, and 2010–2015, we analyze the migration volume, intensity and flow, as well as its changes over time. We also examine the determinants associated with migration by applying Poisson pseudo-maximum-likelihood (PPML) estimation techniques. The results show that migrants move mainly from inland to coastal areas; however, since 2010, the number of migrants moving from coastal to inland areas has shown a continuous increase. This inter-provincial migration was driven largely by the influence of economic factors, such as high urban income per capita. A better model for the period of 2010–2015 is established by adopting an extended set of variables. New variables that represent regional disparities and industrial upgrades have a positive impact on inter-provincial migration, which shows that regional economic disparities and economic restructuring have played an important role in migration in recent years.