No abstract

In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020–2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

Urbanization is a comprehensive process of mutual influence among the population, economy, society and living environment, and it depends on the synergy of a series of factors. This paper uses the statistical data of 76 counties in Xinjiang from 1996 to 2018 to construct a comprehensive urbanization evaluation system. Based on the entropy method, comprehensive evaluation model and coupling coordination model, from the scales of time and space, this paper discusses the current situation of the coordinated development of population, economy, society and living environment factors in counties in Xinjiang in the process of urbanization. Local spatial autocorrelation analysis is used to further study the spatial agglomeration effect of the coupling and coordination of urbanization development in the counties. The results show the following: (1) The comprehensive urbanization level of 76 counties in Xinjiang has the characteristics of "center-periphery" development, and high-level counties are clustered on the northern slopes of the Tian Mountains. (2) Most counties are in a serious state of imbalance; notably, the degree of population-economy-society-living environment coupling and coordination in the border counties and towns is in an unsatisfactory state. (3) The county-level cities in Northern Xinjiang belong to the diffusion and spillover areas, the county-level cities in southern Xinjiang belong to the polarization benefit areas, and most other counties are in the state of no spillover effect.

In order to timely understand the dynamic changes of the ecological environment quality and future development laws of the urban agglomeration on the northern slope of the Tianshan Mountains, combined with the actual situation of the urban agglomeration, 11 indicators are selected from the three aspects of natural ecology, social ecology, and economic ecology. Based on RS and GIS technology methods, Principal component analysis, coe cient of variation and analytic hierarchy process are used to reduce the dimensions of the indicators, and the ecological environmental quality (EQI) from 2000 to 2018 is dynamically evaluated, and the CA-Markov model is introduced to simulate the development status in 2026 predict.The main results are as follows: the overall ecological environment of the area shows a gradually improving distribution change from southwest to northeast; the proportion of ecological environment classi cation shows a gradually decreasing change pattern; the spatial differentiation of ecological environment quality shows a signi cant spatial positive correlation; from the in uencing factors It can be seen that natural ecological factors occupy a major position; the prediction accuracy veri cation shows that the CA-Markov model is suitable for the prediction of the ecological environment quality in the region and has high accuracy; the comprehensive regional ecological environment quality indexes are 5.7392, 6.1856 and 6.4366, respectively, and the forecast in 2026 The value is predicted to be 6.6285, indicating that the overall ecological environment quality of the region has been improved and developed well. The research reveals the law of dynamic changes and future development of the ecological environment quality in the region, which can be used as a theoretical reference for the formulation of ecological environmental protection measures in the region.

In order to timely understand the dynamic changes of the ecological environment quality and future development laws of the urban agglomeration on the northern slope of the Tianshan Mountains, combined with the actual situation of the urban agglomeration, 11 indicators are selected from the three aspects of natural ecology, social ecology, and economic ecology. Based on RS and GIS technology methods, Principal component analysis, coefficient of variation and analytic hierarchy process are used to reduce the dimensions of the indicators, and the ecological environmental quality (EQI) from 2000 to 2018 is dynamically evaluated, and the CA-Markov model is introduced to simulate the development status in 2026 predict.The main results are as follows: the overall ecological environment of the area shows a gradually improving distribution change from southwest to northeast; the proportion of ecological environment classification shows a gradually decreasing change pattern; the spatial differentiation of ecological environment quality shows a significant spatial positive correlation; from the influencing factors It can be seen that natural ecological factors occupy a major position; the prediction accuracy verification shows that the CA-Markov model is suitable for the prediction of the ecological environment quality in the region and has high accuracy; the comprehensive regional ecological environment quality indexes are 5.7392, 6.1856 and 6.4366, respectively, and the forecast in 2026 The value is predicted to be 6.6285, indicating that the overall ecological environment quality of the region has been improved and developed well. The research reveals the law of dynamic changes and future development of the ecological environment quality in the region, which can be used as a theoretical reference for the formulation of ecological environmental protection measures in the region.