Proportion Of Tertiary Industry Research Articles

Spatial-Temporal Patterns and Driving Factors of Logistics Carbon Emissions: Case Study of Yangtze River Delta in China

With the growing dependence of human beings on the logistics industry, the trend of logistics carbon emissions (LCEs) growth has become much more serious. To investigate the spatial-temporal pattern of LCEs as well as their driving factors, city-scale LCEs were calculated by combining them with nighttime light remote sensing data. In addition, a spatial panel data model was applied in the case of China’s Yangtze River Delta. The results showed that the overall LCEs performed as a rising trend during 2010–2019. The LCEs of the eastern cities and provincial capitals were significantly higher than those of other cities with obvious spatial agglomerations. For driving factors, the gross domestic product, population size, and proportion of tertiary industry all had significant positive influences on the LCEs. Overall, this study is of great practical significance to accurately obtain information on the spatial-temporal dynamics of LCEs at the city-level scale, so as to facilitate the differentiated implementation of carbon reduction measures.

Spatial response of urban land use intensity to ecological networks: a case study of Xi'an Metropolitan Region, China.

In the face of the persistent degradation of ecological environments and fragmentation of ecological networks brought about by rapid urbanization, this study focuses on examining the interaction between urban land use intensity and ecological networks in the Xi'an Metropolitan Region (XAMR), China, and their impact on ecological equilibrium and sustainable development. By comprehensively evaluating the changes in land use intensity in XAMR from 2010 to 2020, the aim is to underscore the pivotal role of ecological networks in maintaining urban ecological balance and promoting sustainable development. The findings indicate a transition in land use intensity in the XAMR from low to high concentration, reflecting an intensification in land resource utilization during urbanization. However, the establishment and management of ecological networks can significantly enhance urban ecological security and biodiversity. Notably, this research identified crucial ecological corridors and source areas, augmenting the connectivity of urban green infrastructure and providing vital support for urban biodiversity. Additionally, a significant finding of this study is the spatial spillover effects generated by socioeconomic factors such as the proportion of tertiary and secondary industries and per capita GDP through the ecological network, which have profound impacts on land use intensity in the surrounding areas. These insights offer a novel understanding of the complex interactions within urban ecosystems, emphasizing the importance of incorporating ecological network construction in urban planning. Overall, through a comprehensive analysis of the relationship between the ecological network and land use intensity in the XAMR, this study proposes new directions for urban ecosystem management and land use planning, highlighting the significance of scientific ecological network planning and management in achieving long-term sustainable development in urbanization processes.

Evolution characteristics, carbon emission effects and influencing factors of production-living-ecological space in Taihang Mountain poverty belt, China

The rapid advancement of urbanization and industrialization in China has gradually spread to the poor mountainous areas, which has not only brought about rapid economic development but has also caused the increasing competition for production-living-ecological spaces (PLES) and many ecological and environmental problems, carbon emissions have also increased. As an economically less developed and ecologically fragile area in China, whether the transition of the PLES in the mountain poverty belt has unique characteristics? How the PLES transition in mountainous areas affects carbon emissions and what are the important factors affecting carbon emissions? To explore these issues in depth, we studied the Taihang Mountain area in Shijiazhuang (TMS) using remote sensing image interpretation data from 2000, 2010, and 2020, and we analyzed the PLES evolution characteristics, carbon emission changes, carbon emission effects and its influencing factors of PLES. The results are as follows: 1) The TMS was dominated by ecological and production space. From 2000 to 2020, the production space decreased by 384.66 km2, the ecological space increased by 123.80 km2, and the living space increased by 260.86 km2. Agricultural production space was mainly converted to ecological and rural living space. Industrial and mining productive space was mainly converted to agricultural productive space and urban living space. 2) The study area was in a state of carbon deficit, the transition of ecological space and agricultural productive space to industrial and mining productive space and living space were the main transition types caused the carbon emissions increasing, and that of industrial and mining productive space to agricultural productive space was the main type caused the carbon emissions decreasing. 3) The proportion of construction land, urbanization rate and proportion of secondary industry are the main factors leading to the increase of carbon emissions. Per capita energy consumption, forest coverage and proportion of tertiary industry are the main factors leading to the decrease of carbon emissions. This can provide new ideas for research on carbon emissions from land-use changes and a theoretical basis for the optimization of territorial space in the mountainous areas of China.

Simulation and Forecasting Study on the Influential Factors of PM2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region

It is still necessary to regularly investigate the breakdown of socio-economic elements as a starting point for analyzing the effects of diverse human production activities on PM2.5 intensity from industrial and regional viewpoints. In this paper, the emission factor model was adopted to measure PM2.5 emissions in the Beijing–Tianjin–Hebei (BTH) region at the regional and industrial levels. The logarithmic mean Divisia index (LMDI) decomposition model was employed to analyze the factors affecting PM2.5 emissions related to energy consumption in the BTH region at the regional and sectoral levels. Building on this foundation, a system dynamics (SD) model was established to make a prediction regarding PM2.5 pollution in the BTH region in 2025. This study found that secondary industry was a major source of PM2.5 emissions in the BTH region. Coal remained the main form of energy consumption in the BTH region. Secondly, the effect size of the factors affecting PM2.5 intensity ranked in the order of energy intensity, energy structure, and industrial structure. Thirdly, in 2025, PM2.5 emissions in the BTH region will decline appreciably, but there is still a certain gap in terms of meeting the targets of “the 14th Five-Year Plan” between the three provinces and cities. These results indicate that the BTH region should achieve the effective management of PM2.5 pollution at the source through the following initiatives: it is necessary to carry out the continuous adjustment of energy structures to gradually increase the proportion of clean energy; we must steadily promote the decline in energy intensity reduction, and gradually strengthen scientific and technological innovation; and we must continue to promote the optimization of the industrial structure and increase the proportion of tertiary industry every year.

A Deep Neural Network-Based Intelligent Forecasting Approach for Multi-Dimensional Economic Indexes in Smart Cities

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from “Chengdu–Chongqing Economic Circle” in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data.

Evaluation of the Evolution of the Ecological Security of Oases in Arid Regions and Its Driving Forces: A Case Study of Ejina Oasis in China

Ecological security is an important guarantee of human security and survival, closely related to sustainable development. However, the ecological security evaluation and driving force analysis of oases in arid areas is still insufficient. Ejina Oasis’s ecological security has experienced significant shifts following the centralized management of the Heihe River’s water allocation. Understanding the shifts in ecological security in the Ejina region is paramount for the oasis’s long-term sustainability. This paper employed the Pressure–State–Response (P–S–R) model to select socioeconomic and ecological indicators, establish a comprehensive ecological security evaluation index system, and then analyze the evolving ecological security in the region. Additionally, this paper explored the relationship between changes in the water area, oasis area, and ecological security by using the gray correlation degree to quantify the influence of Land Use Changes (LUCCs) on the overall ecological security. From 2000 to 2012, the ecological security index of Ejina Oasis remained relatively stable at around 0.4. However, a noticeable upward trend in the ecological security index emerged from 2012 to 2020, indicating a shift toward improved ecological security in the region. Critical determinants of this change included the habitat degradation degree, total population, habitat quality, carbon stock, fractional vegetation cover (FVC), the proportion of tertiary industry, and the volume of water discharged from Wolf Heart Mountain. The degree of habitat degradation, fractional vegetation cover (FVC), and the proportion of tertiary industry had the greatest impact on the change in ecological security. The pressure index was dominant in influencing ecological security before 2012 but gradually transitioned to the state index. This study offers a valuable framework for assessing the intricate relationship between LUCCs and ecological security in water-scarce, arid-region oases.

Study on the Impact of RMB Exchange Rate on China's Industrial Structure Adjustment

This paper firstly summarizes the current situation of China's current industrial structure as well as the change of exchange rate system, systematically describes the research object of this paper, and outlines its main features. After that, this paper analyzes the impact of the real effective exchange rate of RMB on China's secondary and tertiary industries for the purpose of the study, takes into full consideration the main factors affecting the industrial structure, selects a large number of control variables, and completes the empirical analysis of China's overall as well as the eastern, middle and western regions by using the panel data of the country's 30 provinces in the 16-year period from 2005 to 2020. In addition, a variety of robustness tests were conducted to fully ensure the stability and interpretability of the model. It is finally concluded that the appreciation of RMB exchange rate will significantly benefit the development of China's tertiary industry, promote the industrial structure from the secondary industry to the tertiary industry, and complete the upgrade of industrial structure. Specifically, for every 1% increase in the real effective exchange rate of RMB, the proportion of tertiary industry in the GDP of the whole country, the east, the center and the west will increase by 0.7850%, 0.7171%, 1.0186% and 0.4873% respectively. Besides, this paper also verifies the indirect impact of the exchange rate on the industrial structure through the three paths of price effect, foreign trade openness and FDI, and verifies the existence of the intermediary effect. According to the empirical results, the price effect, FDI, and the increase of foreign trade openness will promote the advanced industrial structure of China. But both FDI and foreign trade openness are negatively correlated with the exchange rate, which is the core variable of this paper.

Spatial distribution and driving factors of Chinese dragon and lion dance athletes based on GIS

As the common wealth of all mankind, intangible cultural heritage carries the memory of history. The implementation of dragon and lion dance technical grade evaluation can be called pioneering work for the legacy inheritance. The purpose of this study is to analyze the distribution characteristics, trends, types, and driving factors of dragon and lion dance athletes. Athletes who were awarded a grade certificate by Chinese Dragon and Lion Dance Sports Association from 2018 to 2021 were taken as the analysis objects. In ArcGIS10.8, trend surface analysis and standard deviational ellipse are used to study the spatial distribution characteristics. Using the global and local Moran's I in GeoDa, this paper explores the concentration degree and types of dragon and lion dance athletes. The factors driving the distribution difference are analyzed with the help of geographical detectors. Continuous data such as total GDP, sports field areas, permanent populations, and altitude are taken as the software input after preprocessing. The results show that there are significant differences in the distribution of provinces and four geographical regions. For dragon dance athletes, there is a linear growth trend in the west-east direction and an inverted U-shape trend in the south-north direction. For lion dance athletes, there is a trend of log function in the west-east direction and an inverted U shape in the south-north direction. The global Moran's I is 0.2386, and there are obvious characteristics of H–H, L-L, and L-H aggregation types. Nomination of sports intangible cultural heritage, total GDP, permanent resident population, urbanization development level, the number of colleges and universities, and the proportion of tertiary industry are the leading factors, and the explanatory power of interactive factors is significantly enhanced. Therefore, it is suggested to strengthen the cultural heritage protection of dragon and lion dance, increase capital investment, enhance public participation, and raise government attention.

How do natural and socio-economic factors influence the sustainable development of the ecological environment in the World Natural Heritage Sites? Evidence from the Jiuzhaigou, China

With the rapid development of tourism in the World Natural Heritage Sites (WNHSs), it made ecological environment issues increasingly become prominent while boosting regional economic development. Exploring the influence and evolution of natural and socio-economic factors will help to maintain the authenticity and integrity of the WNHSs, and promote sustainable development. Here, to better understand the sustainable development situation of the WNHSs, we construct a nature-society-based comprehensive evaluation framework, and combining the remote sensing-based ecological index (RSEI) with the spatial autocorrelation model, and employed the obstacle factor diagnostic model, to reveal the spatial–temporal characteristics and the key driving factor of ecological environment in the Jiuzhaigou. Meanwhile, we assess the integrated ecological sensitivity of the Jiuzhaigou and determine the key conservation area. Through the analysis of results, we find that RSEI from 2013 to 2020 exhibits a fluctuation trend resembling an ”N-shape”, high RSEI points tend to cluster in the southern part of the study area, while low RSEI points cluster in the northern part, it has a significant spatial heterogeneity. We shed further light on that when the number of tourists exceeds the population carrying capacity of the heritage site, the negative impacts of tourism development on the natural environment will outweigh the positive impacts of tourism revenue growth on regional economic development and constrain the sustainable development of the site as a whole. Specifically, dissolved oxygen concentration in water, nitrogen dioxide concentration, and noise are the main drivers of nature. The proportion of tertiary industry, fixed asset investment, and total tourism consumption are the main drivers of society. Furthermore, the ecological sensitivity of Jiuzhaigou is moderately low. Therefore, we suggest that the government should improve and refine the existing disaster early warning mechanism, dynamically adjust and control the number of tourists received per day, harmonize the relationship between economic development and environmental protection, and strengthen the protection of authenticity and integrity in areas of high ecological sensitivity.

Impact of Economic and Environmental Factors on O3 Concentrations in the Yangtze River Delta Region of China

The concentration of atmospheric ozone (O3) pollution is showing a rapid growing tendency, and O3 pollution has become one of the bottleneck issues that restrict the continuous improvement of air quality in China. In this study, we first identified the primary factors based on the source apportionment of O3, then used factor analysis to divide these selected factors into economic and environmental categories. The geographical detector model was used to analyze the impact of factors and their interactions on O3 concentration in 41 cities in the Yangtze River Delta (YRD) region in 2020. The results showed that forest coverage ranked first among all the detected factors, suggesting a strong relationship between the regional O3 concentration and forest coverage. The driving factors of economic activity were ranked as follows: actual utilization of foreign capital (0.400) > gross domestic product (GDP) per capita (0.387) > proportion of tertiary industry (0.360) > urbanization rate (0.327) > per capita consumption expenditure (0.194) > research and development (R&D) of full-time equivalents of industrial enterprises above designated size (0.182) > number of industrial enterprises (0.126). The interaction between any two factors enhanced their influence on O3 concentration more than any single factor, indicating that the variability of regional O3 concentration was an outcome of a combination of multiple factors. This study could provide recommendations for the prevention and control of O3 pollution and the development of ecological integration in the YRD region.

Study on the Effect of Agricultural Supply Chain Finance Empowering Agricultural and Rural Modernization

Based on the data from 2011 to 2020, this paper constructs an index evaluation system of agricultural and rural modernization, and studies the effect and mechanism of agricultural supply chain financial development affecting agricultural and rural modernization. The following conclusions are drawn: First, the development of agricultural supply chain finance can significantly promote the modernization of agriculture and rural areas. Secondly, taking industrial structure adjustment as the threshold variable, there is a single threshold effect in the promotion of agricultural supply chain finance to agricultural and rural modernization: the higher the proportion of tertiary industry, the more significant the positive impact of agricultural supply chain finance on agricultural and rural modernization; Taking urban-rural integration as the threshold variable, its promotion effect has double threshold effect: the higher the level of urban-rural integration, the greater the promotion effect of agricultural supply chain finance on agricultural and rural modernization, but after the level of urban-rural integration rises to a certain extent, the promotion effect decreases slightly. At the same time, the promotion of agricultural supply chain finance to agricultural and rural modernization is more significant in middle and low-economic-level areas.

The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022

The Yellow River Basin is an important ecological barrier and economic development area in China, but it faces some problems such as the degradation of its ecological quality and a lagging economic level. Promoting the high-quality development of the Yellow River Basin is the only way for China’s economic construction to enter into high-quality development, and an objective evaluation of the development quality of the study area is the premise for effectively improving this development quality. Based on panel data during the period of 2010–2022, a framework of drivers, pressures, state, impact, and a response model was used to build an index system. The index of high-quality development for each province in the Yellow River Basin was calculated using the entropy TOPSIS model. Further, the descriptive statistics method and standard deviation ellipse were applied to analyze the spatio-temporal characteristics of high-quality development in the study area, and the geographical detector and spatio-temporal geographical weighted regression model were employed to reveal the driving factors for this high-quality development in the Yellow River Basin. The results showed that (1) the high-quality development index of the Yellow River Basin was steadily improved over the study period, with an average annual growth rate of 3.024%. (2) The high-value area of the high-quality development level in the study area was distributed from northwest to southeast, with the high values of each subsystem tending to be spatially stable, as well as the spatial differences of the subsystems increasing. (3) The proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population were the main factors affecting the spatio-temporal evolution of high-quality development level in the Yellow River Basin, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. (4) Except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the high-quality development level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.

Reshaping China's urban networks and their determinants: High-speed rail vs. air networks

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks.

The impact of double carbon goals on industrial structure in a region of China

As an extensive and profound systemic change, carbon emission reduction is driving the reform and transformation of China’s economic and industrial structure. Firstly, a variable fractional order model is used to predict carbon emissions under different carbon reduction policy scenarios. Based on the analysis of different scenarios, the non equal order adjacent accumulation model was established to analyze regional industrial structure. Empirical research finds that with the continuous increase of policy strength, the proportion of primary industry structure and tertiary industry structure in Hebei will increase, and the proportion of secondary industry structure will decrease; the proportion of primary industry and secondary industry in Tianjin will increase, and the proportion of tertiary industry will decrease; the industry structure in Beijing is relatively stable. Therefore, the implementation of double carbon target should be scientifically judged and advance gradually in regular order, and an implementation strategy of multi-dimensional balance and dynamic optimization should be sought. The research conclusions are conducive to providing scientific guidance for policy formulation and helping to achieve the double carbon goal.

Spatial-temporal evolution and driving factors of ecological security in China based on DPSIR-DEA model: A case study of the Three Gorges reservoir area

Ecological security (ES) is an important element in the construction of an ecological civilization and plays a vital role in ensuring the sustainable development of social economies. Further, ES is an important measure of regional sustainable development. Based on ecosystem theory, this study constructs a comprehensive evaluation framework using the driving force-pressure-state-impact-response (DPSIR) and slacks-based measure of data envelopment analysis (SBM-DEA) models. Taking the Three Gorges Reservoir Area as an example, the study develops an ES evaluation system and realizes a transformation from “efficiency” to “quality”.Gray correlation theory and spatial autocorrelation and are adopted to analyze the ecosystem's spatial–temporal evolution and driving factors. The results demonstrate that the overall level of ES in the Three Gorges Reservoir Area from 2010 to 2020 was good; however, the spatial distribution of ES revealed an unbalanced state. The influence of each subsystem on the result of the ES evaluation is state > response > impact > driving force > pressure. The spatial correlation analysis of ES in the Three Gorges Reservoir area exhibited a positive autocorrelation, and the agglomeration effect was significant; on this basis, the key factors affecting ES were identified. The ratio of environmental protection investment, sewage treatment rate, green coverage rate of built-up areas, proportion of primary industry in GDP, and proportion of tertiary industry in GDP had notable effects on the ES of the study area. The research contributes to the theory of ES and provides guidance for ecologically sustainable development.

Rural Resilience Evaluation and Influencing Factor Analysis Based on Geographical Detector Method and Multiscale Geographically Weighted Regression

Resilience evaluation is an important foundation for sustainable rural development. Taking the 57 counties in Guangdong province as examples, this study used the CRITIC method to construct a comprehensive evaluation index system for rural resilience and identified the main influencing factors and their spatial heterogeneity on the basis of the geographical detector method and multiscale geographically weighted regression. The results showed that: (1) Most of the counties in Guangdong province had medium or higher values of comprehensive resilience, and the high-value areas were mainly located in the Pearl River Delta region. (2) The comprehensive resilience and each dimensional resilience measure exhibited significant positive spatial correlations. (3) The geographic detector results showed that the per capita gross regional product and the number of industries above the scale were the main influencing factors for rural resilience, and each influencing factor had an enhanced effect after interaction. (4) The effect of each factor on rural resilience demonstrated spatial heterogeneity. Specifically, the proportion of secondary and tertiary industries showed negative effects in some counties in eastern and northern Guangdong and positive effects in other counties.

Change of urban and rural construction land and driving factors of arable land occupation.

Under the background of global urbanization, the continuous expansion and extensive utilization of urban and rural construction land has caused a large amount of arable land to be occupied, which seriously threatens national food security. This paper describes the spatio-temporal patterns of urban and rural construction land expansion and its occupation of arable land by using the urban and rural construction land expansion intensity, the urban and rural construction land expansion intensity difference index, and geo-detector model. It also explores the mechanisms through which the arable land was occupied. Results showed that construction land in both urban and rural areas expanded over the period 2009-2018 despite a large number of rural and urban migrants, and the major contributor to the rapid urbanization in China. This dual expansion could mainly be attributed to the tendency of these migrants to keep or even enlarge their rural construction land, which also resulted in a severer arable land loss than that caused by the expansion of urban construction land. Second, the rate of arable land occupied by urban and rural construction land in Henan province has been gradually slowing down, whereas the expansion of rural construction land is most dependent on arable land occupation. Third, according to the geo-detector model, the relationship between urbanization level and arable land occupied by urban and rural construction was the strongest, followed by the growth rate of fixed asset investment and the proportion of secondary and tertiary industries in GDP.

A study of light pollution risk level assessment based on multiple linear regression

Light pollution is affecting modern people’s lives, and it may damage people’s health and safety. To develop an applicable metric to identify the light pollution risk level of a location, this paper first analyzes the correlation of factors affecting light pollution, and obtains that the correlation coefficient with light pollution mainly includes population density, per capita income, GDP, green coverage, the proportion of the tertiary industry and employment rate. Select these explanatory variables which have high linear correlation with the degree of light pollution to establish a multiple linear regression model. Then, carry out heteroscedasticity and multicollinearity tests on the model, and next, after removing the three variables of population density, per capita income and the proportion of tertiary industry, the optimal linear regression model is determined through OSL. Finally, different levels of light pollution are classified by DN value, which represents the night light of a location, to achieve the assessment of light pollution risk level.

Does the energy efficiency of buildings bring price premiums? Evidence from urban micro-level energy data

Energy efficient buildings can facilitate the achievement of sustainable development goals. However, higher investment costs are not conducive to the development of energy efficient buildings. It is not clear whether the energy efficiency of buildings (EEBs) bring price premiums that could offset the incremental costs of adopting green practice. In this paper, a data envelopment analysis (DEA) model is constructed that takes into account the differences in regional and urban size to measure the EEBs in 54 cities of China during 2007–2018. We evaluate the influence of EEBs on price premiums by using fixed effects regression. We then adopt a moderated mediation model to explore the impact of industrial structure and the regional economic development level on this premium effect. The empirical results show that the cities’ EEBs is gradually improving, due to the fact that the level of technical progress in residential construction is rising. A 1% increase in house energy efficiency causes price premiums of 0.583%. Moreover, this premium effect is more obvious in cities with a high proportion of tertiary industries and economically developed areas. Considering energy consumption in household heating, the housing prices expectations were pulled down in heating cities, while the situation was reversed in non-heating cities. This study provides companies and governments with scientific evidence to promote EEBs.

Spatial and Temporal Heterogeneity of Rural Habitat Level Evolution and Its Influencing Factors—A Case Study of Rural Villages in Nature a Reserve of China

Taking China’s Qilian Mountains Nature Reserve as an example, entropy, hierarchical analysis and spatial autocorrelation analysis methods were combined with geographically and temporally weighted regression to construct an evaluation index system of a rural habitat environment. The spatiotemporal characteristics of the evolution of the rural habitat environment in the Qilian Mountains Nature Reserve from 2000 to 2020 were revealed, and the spatiotemporal heterogeneity of factors affecting the evolution of the rural habitat environment in the Qilian Mountains were analyzed. The results show that during the research period, the rural habitat environment of the Qilian Mountains Nature Reserve obviously improved. The advantaged areas, such as Liangzhou and Shandan, are located mainly on flat terrain, with perfect supporting resources and convenient transportation. The disadvantaged areas, such as Qilian and Menyuan, are concentrated in the mountainous areas with poor natural conditions and inconvenient transportation. The rural habitat in the Qilian Mountains Nature Reserve shows an obvious positive spatial correlation, and areas with similar habitat are adjacent to each other. From 2000 to 2015, the high–high agglomeration area was located in Liangzhou, and the low–low agglomeration area was located in Menyuan. The rural habitat environment in the Qilian Mountains Nature Reserve is influenced by the average temperature, investment in fixed assets, the proportion of secondary and tertiary industries, PM2.5 concentration and CO2 emissions. The influence of various factors on the rural habitat showed obvious spatial and temporal heterogeneity. In rural revitalization, it is necessary to allocate resources to local conditions to promote the continuous improvement of the rural habitat environment in nature reserves.