With the development of the social economy, exploring the temporal and spatial evolutions of the ecosystem service value (ESV) under land use change is greatly significant in optimizing the land use structure and regional sustainable development. Based on the land use data of Xinjiang, China from 1980 to 2020 and with the support of ArcGIS and GeoDa, this study uses the unit area equivalent factor method and spatial autocorrelation to analyze the temporal and spatial evolution characteristics of the ESV under land use change in Xinjiang. The results show that the main land use types in Xinjiang from 1980 to 2020 are grassland and unused land, which accounted for 91.00% of the total area of Xinjiang. The area of cultivated and construction lands increased by 58.89% and 166.79%, respectively. The water and forest land areas decreased the most by 29.95% and 26.62%, respectively. From 1980 to 2020, the ESV in Xinjiang generally showed a change trend of “first increasing, and then decreasing”, depicting a net decrease of 1114.51×10⁸ yuan or 6.68%. From the spatial distribution perspective, the high-and second high-value areas of the ESV in Xinjiang were mainly distributed in Altai, Kunlun, and Tianshan Mountains (i.e., Three Mountains) and the Ili River Valley. The medium- and second low-value areas were distributed in the oasis area. The low-value area was mainly distributed in the basin and desert areas. From 1980 to 2015, the ESV in Xinjiang only slightly changed. In contrast, from 2015 to 2020, that in the middle Tianshan Mountains significantly changed. During this period, the high-value area in the northern and middle Tianshan Mountains decreased by 75.29% and was replaced by the medium- and second low-value areas. The middle- and second low-value areas increased by 13.64% and 10.78%, respectively. The low-value areas move toward some medium-value ones. From the perspective of the spatial autocorrelation of the ESV, the local correlation and hot spot analysis showed the spatial distribution characteristic of high in the west and low in the east. The high-high-concentration and hot spot areas were distributed in the Three Mountains area, while the low-low-concentration and cold spot areas were distributed in the basin and desert areas. The water and forest land area decline is one of the primary reasons for the total ESV decline in Xinjiang from 2015 to 2020.

Shaanxi Province connects the eastern and western parts of China. Hence, its rational land-use planning is crucial to promote high-quality regional development. Guided by land functions, the study explored the characteristics and change trends of the production-living-ecology spaces in Shaanxi Province from 2000 to 2020 using various modeling methods (e.g., a land-use transfer matrix). Subsequently, the mechanisms of distribution changes at different scales were analyzed. The following are the conclusions. (1) The integrated land-use dynamic attitudes of the spaces fluctuated at 0.2%, with two instances of increase: the expansion of living spaces and the increase of ecological spaces. Among the single land-use dynamic attitudes, the rates of living spaces were the highest, mostly positive; the rates of production spaces were the second, mostly negative; and the rates of change of ecological spaces were mainly positive, stable at 0.1%. (2) From 2000 to 2020, the size of production space changed the most, decreasing by 2913 km², and other spaces increased, among which ecological spaces increased more. In the secondary classification, the size of production ecological spaces changed the most, decreasing by 4036 km², whereas the remaining types increased (except for the potential ecological space), and green ecological space increased the most (by 2025 km²). (3) The spatial distribution of spaces relates to topography. The ecology spaces that occupy the largest area were mainly located in the south, the production spaces were mainly located in the central area, and the living spaces that occupy the smallest area were mainly located in the Xi’an metropolitan zone and expanded outward yearly. (4) A factor detector indicates that the population factors dominated the spatial changes of production-living-ecology spaces in Shaanxi Province. Meanwhile, an interaction detector reveals that the results of multifactor could better explain the spatial distribution than those of single factors.

Accurate identification of specific focus points of industry carbon reduction is crucial to realize China’s goal of “carbon peak by 2030 and carbon neutral by 2060”. This study used the Logarithmic Mean Divisia Index (LMDI) method to decompose the influencing factors and their effects on the carbon emission of 13 subsectors (from 2010 to 2019) in Gansu Province. The Tapio decoupling model was used to analyze the relationship between carbon emission and economic growth. Accordingly, a decoupling effort model of influencing factors, excluding economic factors, was constructed to analyze the efforts made by other factors to decoupling. The following results are obtained. (1) From 2010 to 2019, carbon emissions for subsectors in Gansu Province increased by 3843.13×10⁴ t, mainly in petroleum, chemical, steel, and power industries. Specifically, the energy consumption structure of Gansu Province was characterized by high carbon emissions. Coal consumption made up 64.89% of the entire fossil energy consumption in 2019. Energy consumption intensity emerged a decreasing trend, whereas energy efficiency kept improving. (2) Economic growth and population scale exhibited an incremental effect caused by the economic growth effect. Energy intensity and structure demonstrated a reduction effect, and the reduction effect of energy intensity was more significant. However, the influence direction of the industrial structure effect fluctuated greatly in different time periods and industries. The industrial structure effect on chemical and construction industries had a relatively significant reduction, whereas that on steel and power industries was increased carbon emissions. (3) The decoupling effect of carbon emissions from the economic growth of 13 subsectors improved. From 2010 to 2013, all industries exhibited a weak decoupling effect, except for mining and light manufacturing that showed negative decoupling and expansion connection. From 2013 to 2016, some industries, such as agriculture, chemical, and steel manufacturing, underwent a strong decoupling effect. From 2016 to 2019, all sectors changed to strong or recessionary decoupling, except for the power sector, which remained weak. (4) The energy intensity effect played the most important role in decoupling. Particularly, the decoupling effect of energy and industrial structures was small but gradually increasing, whereas that of the population scale was not evident. Evidently, reducing energy consumption intensity and improving energy use efficiency are crucial points to accelerate the process of carbon emission reduction and effectively enhance the decoupling level in Gansu Province. On the basis of this finding, the following should be proposed. First, governments and enterprises should actively introduce low-carbon production technologies and high-efficiency energy-saving equipment, encourage innovation, and focus on the development and optimization of energy-saving and environmental protection technologies. Second, governments should comprehensively consider the characteristics of local industrial structures, carbon emission levels, and emission reduction potentials of subsectors and then formulate differentiated quota schemes for industrial carbon emissions for high- and low-energy industries.