Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target.

The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, - 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.

CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province.

The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022-2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030.

Analysis of Landscape Characteristics and Influencing Factors of Residential Areas on the Qinghai-Tibet Plateau: A Case Study of Tibet, China.

The Qinghai-Tibet Plateau is the largest ecological barrier and one of the most vulnerable areas of the ecological environmental system. However, the increasing frequency of human activities in the Qinghai-Tibet Plateau has led to strong interference. Residential areas are the main places in which human activities are carried out and, as such, can effectively reflect the intensity of activities. Based on this, this research takes the Tibet Autonomous Region as the study area and analyzes the distribution characteristics of Tibetan residential areas using Zipf's law and various landscape indices, as well as discussing the influences of altitude, hydrology, ecological environment, and location on residential area distribution. The obtained results indicate the following: (1) The residential areas in Tibet basically conform to the rank-size principle. The residential areas in central and northwest Tibet are concentrated in size distribution, and the relatively large residential areas are prominent, while the residential areas in the eastern Hengduan mountain region are relatively balanced in size distribution. (2) The landscape index results demonstrate that the counties with an unbalanced distribution of residential areas are mainly concentrated in the northwest of Tibet, while the residential areas in the counties and regions where the administrative stations of each prefecture-level city (or region) are located tend to present a polarization phenomenon, with large patches. The area distribution of residential areas showed a "medium-high-low" pattern from southeast to northwest. The residential areas in eastern Tibet have a high degree of fragmentation and a low degree of aggregation, while the residential areas in northwest Tibet have a low degree of fragmentation and a relatively high degree of aggregation. (3) The residential areas in Tibet are most concentrated in the altitude range of 3000-5000 m above sea level and their water affinity and road-affinity are strong, with the distribution of residential areas within 500 m of roads and water networks accounting for more than one-quarter. The vegetation coverage in the residential areas is low, inconsistent with the surface vegetation coverage rate over the whole of Tibet.

Spatio-Temporal Characteristics of Landscape Ecological Risks in the Ecological Functional Zone of the Upper Yellow River, China.

The Ecological Functional Zone of the Upper Yellow River (EFZUYR) is a critical water-catching area in the Yellow River Basin, the ecological security of which affects the sound development of the ecosystem in the entire basin. Recently, significant land use changes have aggravated regional ecological risks and seriously affected the sustainable development of EFZUYR. In this context, this paper provides an in-depth study of the ecological risks caused by land use landscape changes. With the help of land use data and dynamic degree analysis, the land use transfer matrix, and the landscape pattern index, this paper quantifies the distribution trends of land use landscape patterns in EFZUYR from 1990 to 2018. In addition, this research explores the temporal and spatial dynamic distribution characteristics of landscape ecological risks in this functional zone. The research results show the following: (1) The transfer of land use in EFZUYR from 1990 to 2018 mainly occurred among cultivated land, grassland, and woodland, with the transferred area accounting for 87.16% of the total changed area. (2) The fragmentation degree of built-up areas is 0.1097, 0.1053, 0.0811 and 0.0762 in 1990, 2000, 2010 and 2018, respectively, with a decreasing trend. The dominance degree of grassland has been maintained at the highest level for a long time, with all values above 0.59. The separation degree and the interference degree of built-up areas were the highest and the values of the four periods were above 1.2 and 0.44, respectively. The loss degree of water was the highest, with a value above 0.67, while the value of other land use was mostly below 0.4. (3) The landscape ecological risk of EFZUYR presented a fluctuating rising, falling, and then rising trend. The spatial distribution characteristic of EFZUYR presented "high in the north and south, low in the middle.", which has been maintained for a long time. The proportion of low-risk areas is as high as 70%, and the overall ecological risk of the region was low. However, the ecological risk of some areas, such as Linxia City and Magu County, increased. These findings can provide theoretical support for land use planning and achieving sustainable development of EFZUYR.

[Characteristics and driving forces of wetland landscape pattern evolution of the city belt along the Yellow River in Ningxia, China]. / å®å¤æ²¿é»„åŸŽå¸‚å¸¦æ¹¿åœ°æ™¯è§‚æ ¼å±€æ¼”å˜ç‰¹å¾åŠé©±åŠ¨åŠ›.

Wetlands are one of the most productive ecosystems in the world, with functions of water purification, climate regulation, and carbon sinks. Due to the stresses caused by human social development and changes of natural conditions, wetlands have been seriously damaged. We examined the evolutionary law of landscape pattern of wetland along the Yellow River, and acquainted the current situation of wetland resources and dynamic change. Based on satellite images of year 2000, 2009, and 2018 from Landsat, we used landscape indices and geographic detectors to quantitatively analyze the characteristics and driving forces of wetland landscape pattern evolution of the city belt along the Yellow River in Ningxia from 2000 to 2018. The results showed that the wetland area of the city belt along the Yellow River in Ningxia enlarged first and then decreased from 2000 to 2018. The wetland area increased by 52.2 km2 in the early stage of the study with an increasing rate of 8.2%, and decreased by 26.8 km2 with a reduction rate of 3.9% in the later stage. The wetland was mainly transformed to construction land and unused land, with transfer out area being 166.7 and 158.4 km2 respectively. New wetland was mainly transformed from unused land, forest, and grassland, with an area of 543.1 km2. The fragmentation degree of wetland landscape in city belt was increasing, the balanced distribution of all kinds of wetlands was gradually strengthened, the landscape diversity was increasing, and the dominant landscape types were gradually weakening. Natural factors and socio-economic factors jointly affected the evolution of wetland landscape pattern in city belt. Among all socio-economic factors population was the most important one. Among natural factors, precipitation and temperature were important. Other driving factors were relatively weak, but could not be ignored.

[Spatial differentiation characteristics and influencing factors of urban and rural settlements in Tibet, China]. / è¥¿è—åŸŽä¹¡å± æ°‘ç‚¹ç©ºé—´åˆ†å¼‚ç‰¹å¾ä¸Žå½±å“å› ç´ .

Located in the hinterland of the Qinghai-Tibet Plateau, Tibet has very limited space sui-table for human living. The spatial distribution of settlements in Tibet is restricted by multiple factors. To reveal the spatial distribution characteristics and explore the main influencing factors of settlements in Tibet, we extracted patch data from the high-resolution images of urban and rural settlements in Tibet based on visual interpretation. Methods such as the kernel density analysis, space hotspot detection, Geodetector and others were applied to analyze the spatial differentiation characteristics and influencing factors, with the aim to provide reference for future settlements selection and formulation of relevant policies on regional economic development in the plateau region. The results showed that urban and rural settlements in Tibet had a clustering pattern, with different overall density distribution. Lhasa was a high-density core, and Ngari Prefecture, Qamdo, and Nyingchi were the "core-edge" structures with low-density edges. The settlement density of Lhasa was as high as 220 ind·km-2, while that of Ngari Prefecture, Qamdo and Nyingchi was only 5.7 ind·km-2. The gap between the two regions was more than 43 times. The clustering of high and low value clusters in urban and rural settlements was remarkable. The number of high-value of large plaques was rare, and the number of low-values of small plaques was dominant. The area of high-value cluster accounted for only 3.7%, concentrated in Lhasa and Lhoka. The proportion of low value cluster area reached 67.2%, mainly distributed in Ngari Prefecture and Nagqu on the Qiangtang Plateau. There were six types of urban and rural settlements in Tibet, which formed two circle structures in the "One River and Two Streams" basin and the "Three Rivers" basin. From the inside to the outside, the large plaque-dominated type, medium-density and cluster-like type, high-density and point-scattered type, low-density and point-scattered type and high-altitude and uninhabited type was successively distributed. Lhasa was dominated by medium-density and cluster-like type, accounting for 31%. Lhoka was dominated by low-density and point-scattered type, accounting for 38%. Qamdo was mainly low-density and point-scattered type, accounting for 51%. The Ngari Prefecture, Nagqu and Shigatse were dominated by high-altitude and uninhabited type, and the proportion of the Ngari Prefecture was as high as 64%. The effects of different factors on the spatial distribution of urban and rural settlements in Tibet were distinctly different. The population and GDP were highly decisive for the distribution of urban and rural settlements. In addition, urban settlements showed strong road orientation, while rural settlements were more characterized by river orientation.