China has been undergoing a very rapid but unbalanced urbanization, characterized by under-urbanization of its population and faster urbanization of the land. In such a situation, the urbanization of the population and the land may produce different effects on the natural environment. In addition, due to substantial inter-regional differences, the influence of urbanization on the environment is likely to vary across regions at different stages of economic and social development. This article expands the basic STIRPAT–Stochastic Impacts by Regression on Population, Affluence and Technology, model by adding industrialization level, foreign trade degree, population urbanization level and land urbanization level. Based on panel data from 2006 to 2014 and using this extended STIRPAT model, the article analyses the effects of the driving forces, especially population urbanization and land urbanization, on the environmental impact for the whole of China as well as on its eastern, middle and western regions. The results indicate that for the whole of China, population urbanization produces a significant negative effect on the environmental impact, while land urbanization has a small, but not statistically significant, positive effect. The effects of population urbanization and land urbanization vary across the eastern, middle and western regions, which are at different stages of economic and social development. Population urbanization and land urbanization have no significant influences on environmental impact in the eastern and middle regions, while in the western region population urbanization has a significant negative influence on environmental impact. The main driving factors of environmental impact remain population, affluence and energy intensity. This study also quantitatively calculates the actual contribution rate of each driving force for the 2006–2014 period. It contributes to understanding the characteristics and key driving forces in each region, allowing for appropriate policy recommendations.

Purpose: The purpose of this paper is to identify the drivers of manufacturing transformation and upgrading and examine their influencing paths and effects. The study also dynamically evaluates and compares the achievements of the manufacturing transformation and upgrading of various economies. Design/methodology/approach: This paper establishes the mechanism model of manufacturing transformation and upgrading based on the partial least squares approach to structural equation modeling. With the help of a mechanism model, the study carries out empirical analysis using panel data from the G20 for 2005–2015. Findings: Innovation, measured by innovation input and innovation output, has the largest positive effect on manufacturing transformation and upgrading. Economic development has a negative direct effect, as well as positive indirect effects. Its total effect is positive but small. Economic openness has no significantly direct effect, but has significantly positive indirect effects. Its total effect is significantly positive, but is the smallest among drivers. Developed economies have relatively higher but decreasing achievements, whereas emerging economies have lower but increasing achievements and maintain the catch-up trend toward developed economies. Originality/value: Few macro empirical studies examine the drivers and achievements of manufacturing transformation and upgrading. A need has arisen for evidence-based research and policies to guide governments in their efforts to promote manufacturing transformation and upgrading. This may be the first macro-level study to empirically analyze the drivers of manufacturing transformation and upgrading, and their influencing paths as well as achievements.