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In the context of green and sustainable development and rural revitalization, analysis of the relationship between economic development and the evolution of carbon metabolism is of great significance for China's future transformation of development models. This study analyzed the spatial characteristics and spatiotemporal evolution pattern of the decoupling status between carbon metabolism and economic development of Laiwu during two periods from 2001 to 2018 at the village and town unit scales by using the Tapio decoupling model. The results showed that the growth rate of carbon metabolism from 2001 to 2009 was significantly higher than that from 2009 to 2018. The spatial heterogeneity of the decoupling states between economic development and carbon metabolism from 2009 to 2018 was significantly stronger than that from 2001 to 2009 in two units. From 2001 to 2018, the development trend gradually trended towards spatial imbalance. The decoupling status between villages and towns had a high degree of consistency from 2001 to 2009 and inconsistency from 2009 to 2018. From 2001 to 2009, the decoupling status of about 78% of villages was consistent with that of towns. Moreover, from 2009 to 2018, the consistency reduced to 32.2%, and the decoupling status of about 48% of villages was weaker than that of towns. According to the reclassification results of different decoupling state change types, from 2001 to 2018, about 52.2% of the villages had a decoupling state evolution type of eco-deteriorated economic development, which is an unsatisfactory development trend in a short time. Moreover, about 12.1% of the villages had a decoupling state evolution type of eco-improved economic development, which is a satisfactory development trend., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Investigating the spatial distribution characteristics and influencing factors of various industry types is critical for promoting the high-quality transformation and development of China's industry. This study combined the Getis-Ord Gi* statistic method, the random forest-based importance assessment method, and the geographically weighted regression method to determine the spatial distribution characteristics of four industry types and their influencing factors. The results revealed that the raw material industry was primarily concentrated in the surrounding districts and counties of Linyi and Qingdao. The food and light textile industry was mainly concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in some counties of Linyi. The processing and manufacturing industry was also concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in the belt regions connecting Jinan, Zibo, and Weifang. The high-tech industry was mainly concentrated in the surrounding districts and counties of Jinan and Qingdao. The key spatial influencing factors of the four industry types were different. The number of employees in the secondary industry and road density were most important in determining the spatial distribution of the raw material industry. The financial environment and number of research institutions were most important to the spatial distribution of the food and light textile industry. The gross domestic product and number of medical facilities were most important to the spatial distribution of the processing and manufacturing industry. Urbanization rate, number of research institutions, and gross domestic product were most important to the spatial distribution of the high-tech industry. Geographically weighted regression analysis revealed that the impact intensity of these key factors on the industry exhibits significant spatial heterogeneity. Taken together, these results are useful for formulating the development strategy for each industrial type in different regions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Uncontrolled urban growth detracts from healthy urban development. Understanding urban development trends and predicting future urban spatial states is of great practical significance. In order to comprehensively analyze urbanization and its effect on vegetation cover, we extracted urban development trends from time series DMSP/OLS NTL and NDVI data from 2000 to 2015, using a linear model fitting method. Six urban development trend types were identified by clustering the linear model parameters. The identified trend types were found to accurately reflect the on-ground conditions and changes in the Jinan area. For example, a high-density, stable urban type was found in the city center while a stable dense vegetation type was found in the mountains to the south. The SLEUTH model was used for urban growth simulation under three scenarios built on the urban development analysis results. The simulation results project a gentle urban growth trend from 2015 to 2030, demonstrating the prospects for urban growth from the perspective of environmental protection and conservative urban development., Competing Interests: The authors have declared that no competing interests exist.