Analysis of Characteristics and Driving Factors of Land-Use Changes in the Tarim River Basin from 1990 to 2018

Wang, Yang; Xia, Tingting; Shataer, Remina; Shuai, Zhang; Li, Bingbing

doi:10.3390/su131810263

Cited by 19 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the expansion of cultivated land and construction land, the development and utilization of water resources have been strengthened; consequently, the water volume of the main stream of the Tarim River has been greatly reduced. Precipitation has been unable to meet water demand, resulting in a significant reduction in ecological land water areas (Wang et al, 2021), comprising a total reduction of 13,665.5622 km 2 during the study period. Moreover, industrialization, agricultural production, and other human activities require extensive water resources, which means that construction land is mostly distributed in areas close to roads and water bodies.…”

Section: Discussionmentioning

confidence: 99%

Land-use function evolution and eco-environmental effects in the tarim river basin from the perspective of production–living–ecological space

Wang

Xia

et al. 2022

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Land-use function evolution and eco-environmental effects in the tarim river basin from the perspective of production–living–ecological space

Wang

Xia

et al. 2022

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…NTL has already been used to extract [36] and identify tourist attractions [37]. Ecological quality has been closely linked to tourism development; therefore, NTL data are used to study ecological changes [38]. As the accessibility of tourism POI data gradually improved, it was found that the POI of tourism factors had a significant influence on the difference in the spatial distribution of NTL [39]; the spatial coupling between the two is good.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China

Chang

Pang

et al. 2022

Sustainability

View full text Add to dashboard Cite

Nighttime light (NTL) data have become increasingly practical and are now widely used in studies on urbanization, energy consumption, population estimation, socio-economic evaluation, etc. Based on NTL data and the basic tourism economy (TE) data from 31 provinces of China in 2019, this paper adopted a geographic concentration index, inconsistency index, spatial agglomeration coupling index, global and Local Moran’s index and geographical detector to explore the spatial relationship between NTL and TE. The results of the study were as follows. Firstly, there is a high spatial correlation between NTL and TE. Secondly, the concentration degree, as well as the concentrated distribution area of NTL and TE, are very similar, roughly showing a higher concentration in East and South-Central China. Thirdly, NTL and TE show a type of coordinated development in East and North China, and a TE surpassing NTL in Southwest and South-Central China. The spatial agglomeration coupling index is higher in North China, South-Central China and the coastal regions of East China, and relatively lower in Southwest and Northwest China. Furthermore, in the spatial agglomeration distribution of NTL and TE, there is an obvious high–high and low–low agglomeration. Finally, the geographical detector analysis showed that the driving factor of tourism economy level (TEL) also has a great influence on NTL. The spatial distribution of NTL and TE is integrated to reasonably allocate tourism resources for different areas and promote the sustainable development of NTL and TE among regions.

show abstract

“…Its application to geography began with calculating the population center of gravity, expressed through latitude and longitude coordinates. By using a migration model based on the center of gravity, we could simulate temporal changes in carbon emissions' center of gravity, providing a more intuitive representation of their spatial and temporal dynamics within the study area [46]. To investigate China's changing carbon emissions patterns, this model was used to calculate the carbon emissions' center of gravity between 2000 and 2019.…”

Section: Center Of Gravity Migration Modelmentioning

confidence: 99%

Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing

Zhang,

Fu,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.

show abstract

Analysis of Characteristics and Driving Factors of Land-Use Changes in the Tarim River Basin from 1990 to 2018

Cited by 19 publications

References 26 publications

Land-use function evolution and eco-environmental effects in the tarim river basin from the perspective of production–living–ecological space

Land-use function evolution and eco-environmental effects in the tarim river basin from the perspective of production–living–ecological space

Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China

Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing

Contact Info

Product

Resources

About