Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China

Yuan

Yang

et al. 2023

IJERPH

The Qinghai Lake Basin acts as a natural barrier, preventing the western desert from spreading eastward. This is an important link in preserving the ecological stability of the northeastern region of the Qinghai–Tibet Plateau (QTP). Therefore, quantitative research into the net primary productivity (NPP) of vegetation and its driving force in the Qinghai Lake Basin is required. The effects of land use/cover change (LUCC) and climate change on NPP in the Qinghai Lake Basin were studied using R-contribution ratio and partial correlation analysis methods using MOD17A3H products, Land Use/Land Cover (LULC) data, and meteorological data. (1) The LULC of the Qinghai Lake Basin showed a trend that "the area of grassland, cultivated land, and unused land continued to decrease, while the area of other LULC types increased" from 2000 to 2020, according to the study's findings. Grassland, water bodies, construction land, and unused land dominated the mutual transformation of LULC types. (2) The NPP of the basin showed a growing trend, with a growth rate of 3.93 gC·m–2·a–1 before 2010 and 0.88 gC·m–2·a–1 after 2010. Significant regional heterogeneity was found in NPP, with gradients decreasing from southeast to northwest. (3) The impact of LUCC on overall NPP changes had gradually increased. Climate change has been the primary driver of NPP changes in the Qinghai Lake Basin over the last 20 years.

Section: Introductionmentioning

confidence: 99%

Impacts of Climate Change and Land Use/Cover Change on the Net Primary Productivity of Vegetation in the Qinghai Lake Basin

Yuan

Yang

et al. 2023

IJERPH

“…The serious problems caused by industrial sprawl have exacerbated the unsustainability of urban development in the economy, society, and environment [ 52 , 53 ]. With the Sustainable Development Goals of the UN, it is necessary for us to establish a sustainability evaluation method for industrial sprawl.…”

Section: Discussionmentioning

confidence: 99%

The Industrial Sprawl in China from 2010 to 2019: A Multi-Level Spatial Analysis Based on Urban Scaling Law

Lin

Qiu

et al. 2022

IJERPH

Self Cite

Studying the spatial-temporal distribution industrial sprawl in China is important to solve industrial sprawl problems and promote urban sustainable development. This paper constructed a multi-level spatial analysis of the Chinese industrial sprawl during 2010–2019 by mainly using urban scaling law, supplemented by GIS methods. Results showed that: (1) China had obvious industrial sprawl with a growth rate of 31.79%, reaching 2762.37 km2 between 2010 and 2019. (2) There was a stronger industrial sprawl in large cities with a larger population according to urban scaling law, especially in the East. (3) The industrial sprawl was mainly concentrated in the cities in the Northeast, Beijing-Tianjin-Hebei region, Shandong Peninsula, Yangtze River Delta region, Pearl River Delta region, Middle Yangtze River region, Fujian Province, and some cities in the West. (4) The gravity center of industrial sprawl generally moved southwest and distributed in Hubei Province. This study provided references for improving the efficiency of industrial land use and promoting high-quality urban development.

“…It is committed to ensuring sustainable national development while promoting global climate cooperation and governance (Bokaie et al, 2016;He, 2019;Ren et al, 2022). Under the realistic scenario of severe carbon emission situation and local responsibility for emission reduction targets, local governments, as land suppliers and responsible parties for carbon emission reduction, have paid attention to intervene in carbon emissions through multi-dimensional land policies (Ou et al, 2013;Lai et al, 2016;Arneth et al, 2017;Zhang et al, 2022e). It is estimated that optimizing the land use structure can make a significant contribution of 27.6% to the carbon emission reduction of 40%-45% per unit of GDP by 2020 (Kalnay and Cai, 2003;Kotharkar and Bagade, 2018;Chen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…On the whole, these researches can be basically divided into two categories, one is mainly based on the urban ontology problem to propose the impact of urban land use on carbon emission and some land use optimization measures (Li et al, 2012;Chen Y. et al, 2016;Ahmad et al, 2021;Zhang et al, 2022e). The other category mainly reflects the analysis and explanation of carbon emission and land and land-related elements, and finally proposes a solution for the deterioration of carbon emission problem (Lai et al, 2016;Arneth et al, 2017;Tan et al, 2021b;Zhang et al, 2022f).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the coupling characteristics of land transfer and carbon emissions and its influencing factors: A case study of China

Tong

et al. 2023

Front. Environ. Sci.

Self Cite

The rapid and disorderly expansion of urban construction land has exacerbated the contradiction between land use and low-carbon development. In this paper, we use the spatial autocorrelation model and coupling model to analyze the spatial characteristics of the coupled coordination degree of land transfer and carbon emissions in 291 cities in China. The multi-scale geographically weighted regression (MGWR) model is used to explore the spatial heterogeneity of the influence of socioeconomic factors on their coupled coordination degree. The results show that: from 2005 to 2015, the scale of land transfer and carbon emissions has been increasing quantitatively and spatially showing a shift from the southeast coast to the central and western regions. In 2005, 2010, and 2015, the global Moran’s I of the coupled coordination degree are .3045, .3725, and .3388, respectively, indicating that the coupled coordination degree between land transfer and carbon emissions has a significant positive spatial autocorrelation. The MGWR model indicates that the influence of socioeconomic factors on the coupling coordination degree has significant spatial heterogeneity at different time nodes. In 2005 and 2015, the coefficients of the NGR on the coupling coordination of land transfer and carbon emissions have obvious stratification characteristics, with the coefficients decreasing from northeast to southwest. In 2010, the high coefficient (.924∼.989) of GPC is mainly distributed in the central region. The coefficient of the PD ranges from .464 to .918, but the difference of influence degree between the southeast coast and the northwest is obvious. This study may provide new clues for sustainable urban development and carbon reduction.