The manufacturing sector, foundational for a nation and integral to national rejuvenation and strength, serves as a powerful engine for constructing the basics of an industrial modernization system, propelling employment, and achieving shared prosperity. In the current era of the new development paradigm, the manufacturing industry is in a crucial period of overcoming challenges, necessitating urgent and comprehensive implementation of green practices as a novel approach for new industrialization. However, the extensive growth model of high investment, high consumption, high pollution, and low efficiency in China's manufacturing industry has resulted in escalating resource and environmental constraints. The uneven allocation of factors has led to pronounced issues of regional disparities and insufficient green development, prompting the urgent need to unleash the industry's "green genes". Consequently, scrutinizing the green competitiveness of regional manufacturing becomes a pivotal breakthrough for China to achieve high-quality development. The focus of research on green competitiveness has shifted from enterprise-centric environmental governance to encompass green management models, production, supply chains, and ultimately achieving transformation, upgrading, and green development.
Despite existing empirical research studies predominantly concentrating on specific aspects of the spatial distribution of green competitiveness in manufacturing, there is a lack of a comprehensive, multi-dimensional, and multi-scale examination. To address this gap, this study integrates the five new development concepts and the connotation of the new industrialization road, selecting 41 evaluation indicators comprehensively from five dimensions: economic drive, innovation output, energy conservation, environmental regulation, and social security. A vertical and horizontal grading method is introduced to systematically quantify China's manufacturing industry's green competitiveness across 30 provinces, revealing regional differences and sources using the Dagum Gini coefficient and spatial kernel density estimation to grasp dynamic evolution trends. The study further carries out trend predictions through the Markov limit solution.
The results indicate that the overall green competitiveness of China's manufacturing industry shows a growing trend, which can be broadly divided into three characteristic periods on a temporal scale: a fluctuation adjustment period (2006-2010), a stable growth period (2011-2015), and a period of ups and downs (2016-2020). On a regional scale, the distribution pattern reveals a "leading in the east, middle-ranking in the central region, and lagging in the west". Additionally, at both national and regional levels (east, central, and west), there is a pronounced skewness in the distribution of green competitiveness in the manufacturing industry. The overall Gini coefficient of China's manufacturing industry's green competitiveness shows a decreasing trend, with inter-group differences being the primary source of overall variations in green competitiveness. Notably, the differences between the eastern and western regions exhibit a significant expansion and growth trend. On the basis of the diagnostic results of obstacle factors, it is found that the proportion of coal consumption in manufacturing to total energy consumption is a major hindrance. From a dynamic evolution perspective, under conditions without spatial considerations, China's manufacturing industry's green competitiveness displays strong path dependence. A higher level of green competitiveness in the current period may propel a continuous rise in green competitiveness in the later period. Under spatial conditions, while static and dynamic estimation results are similar, there are some differences. Under static conditions, manufacturing green competitiveness shows a certain positive spatial correlation, with the addition of the time factor not significantly affecting the distribution position and shape of China's manufacturing industry's green competitiveness probability subjects. However, under dynamic conditions, spatial spillover effects in the three major regions of the east, central, and west are not uniform. Traditional Markov chain analysis suggests that the development trend of green competitiveness in China's provinces' manufacturing industry is relatively stable, with a low probability of leapfrog transfer. Spatial Markov chain analysis indicates a "club convergence" in China's manufacturing industry's green competitiveness. Looking at the long-term evolutionary trend, the future development of China's manufacturing green competitiveness is expected to gradually improve over time with a trend of concentration towards high values. Different neighborhood status types have heterogeneous effects on the evolution of regional manufacturing green competitiveness, and thus it is essential to shore up weak links and make targeted measures that follow local characteristics to enhance green competitiveness in the manufacturing industry.