Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point

Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point

Research on Economic Bearing Capacity of Farmers to Agricultural Irrigation Water Prices in the Ebinur Lake Basin

The Ebinur Lake Basin is one of the few lakes in the arid area of northwest China where water shortages have always been an important factor hindering development. Therefore, this study takes as its objective an investigation of water prices as the main economic factor that restricts the sustainable utilization of water resources to explore the economic affordability of water prices for farmers. Furthermore, the study attempts to determine the appropriate range of prices for water price reform. In this study, a questionnaire was designed to survey 210 agricultural water users in Wenquan County, Bole City, and Jinghe County, areas where agricultural activities are relatively concentrated in the vicinity of the Ebinur Lake Basin. Based on the data from the field survey, the range of economic affordability for farmers in the Ebinur Lake Basin was categorized by the ELES model; according to the analysis method of water tolerability index, a suitable water price standard for farmers’ reform was obtained. The results show that (1) 97% of the farm households have the ability to pay irrigation water prices and only 3% do not have the ability to pay and (2) economically affordable water prices for farm households range from 79 to 143 €/hm2, the latitude for raising water prices is 64 €/hm2, and the adjustable range of unit water pricing is between 0.014 and 0.042 €/m3. It can be seen that increasing water prices is feasible

Spatial Distribution of Leisure Agriculture in Xinjiang and Its Influencing Factors Based on Geographically Weighted Regression

As a new industry in modern agriculture, leisure agriculture has a strong correlation with rural tourism, and provides rural areas with positive prospects for sustainable development. However, leisure agriculture tends to include a number of bottlenecks. In this study, we investigated the spatial distribution of leisure agriculture in Xinjiang, and the factors that affect it. Kernel density analysis, the nearest-neighbor index, and the geographic concentration index were used to analyze the distribution characteristics of leisure agriculture. Following the conclusion of the ordinary least squares tests, geographically weighted regression (GWR) was conducted to explore the factors affecting spatial distribution. The findings were as follows: (1) The spatial distribution of leisure agriculture in Xinjiang is uneven, and is concentrated in the northern and southern parts of the Tianshan Mountains in western Xinjiang. (2) In terms of the distribution density, there are four high-concentration centers (Bosten Lake, Hami, and the east and west sides of the Ili River Valley) and one subconcentration center (spreading outward from Urumqi). (3) Population, transportation, tourism resources, urban factors, and rainfall, all had significant effects on the distribution of leisure agriculture. These factors had positive and negative effects on the distribution of leisure agriculture, forming high- and low-value areas in space. (4) The leisure agricultural sector responded in varying degrees to the different factors, with large internal variability. Rainfall and population had greater differential effects on the spatial distribution of leisure agriculture compared to transportation, tourism resources, and urban factors, and there were significant two-way effects. Transportation, urban factors, and tourism resources all had consistent, predominantly positive, effects on the distribution of leisure agriculture

Spatial Distribution of Leisure Agriculture in Xinjiang and Its Influencing Factors Based on Geographically Weighted Regression

As a new industry in modern agriculture, leisure agriculture has a strong correlation with rural tourism, and provides rural areas with positive prospects for sustainable development. However, leisure agriculture tends to include a number of bottlenecks. In this study, we investigated the spatial distribution of leisure agriculture in Xinjiang, and the factors that affect it. Kernel density analysis, the nearest-neighbor index, and the geographic concentration index were used to analyze the distribution characteristics of leisure agriculture. Following the conclusion of the ordinary least squares tests, geographically weighted regression (GWR) was conducted to explore the factors affecting spatial distribution. The findings were as follows: (1) The spatial distribution of leisure agriculture in Xinjiang is uneven, and is concentrated in the northern and southern parts of the Tianshan Mountains in western Xinjiang. (2) In terms of the distribution density, there are four high-concentration centers (Bosten Lake, Hami, and the east and west sides of the Ili River Valley) and one subconcentration center (spreading outward from Urumqi). (3) Population, transportation, tourism resources, urban factors, and rainfall, all had significant effects on the distribution of leisure agriculture. These factors had positive and negative effects on the distribution of leisure agriculture, forming high- and low-value areas in space. (4) The leisure agricultural sector responded in varying degrees to the different factors, with large internal variability. Rainfall and population had greater differential effects on the spatial distribution of leisure agriculture compared to transportation, tourism resources, and urban factors, and there were significant two-way effects. Transportation, urban factors, and tourism resources all had consistent, predominantly positive, effects on the distribution of leisure agriculture

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins