Spatiotemporal characteristics and prediction of carbon emissions/absorption from land use change in the urban agglomeration on the northern slope of the Tianshan Mountains

Wei, Bohao; Kasimu, Alimujiang; Reheman, Rukeya; Zhang, Xueling; Zhao, Yongyu; Aizizi, Yimuranzi; Liang, Hongwu

doi:10.1016/j.ecolind.2023.110329

Cited by 29 publications

(9 citation statements)

References 69 publications

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“…Thirdly, it was discussed from the point of the related influence factors on LUCES research. From the angle of view in land use carbon emission, previous studies have analyzed the impacts from the demographic, economic, social, technological, policy and natural factors [59,60]. For instance, on a time scale, based on the theoretical basis of IPAT (impact, population, affluence, technology) [61] or KAYA [62], scholars further extended it to STIRPAT (stochastic impacts by regression on population, affluence and technology) [63], LMDI (logarithmic mean index method) [58] and other models [64,65], as well as mainly discussing the anthropogenic impacts (economic, social and demographic factors) on land use carbon emission [66].…”

Section: Research Progressmentioning

confidence: 99%

“…The first one was the correlation and impact between ecosystem services and LUCES, and has become a research hotspot in multidisciplinary fields. For example, scholars used the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), PLUS (Patch-generating Land Use Simulation) model or others to quantify land use carbon stock of the past, present and future [60]. The second one was the relationship and mutual influence between extreme climate and LUCES.…”

Section: Future Perspectivesmentioning

confidence: 99%

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Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives

Liu,

Qu,

Gao

et al. 2024

Land

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The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WOS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality.

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Section: Research Progressmentioning

confidence: 99%

Section: Future Perspectivesmentioning

confidence: 99%

Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives

Liu,

Qu,

Gao

et al. 2024

Land

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“…A represents the area of each land type, and a represents the carbon emission coefficient of each land type. The carbon emission coefficients for arable land, forest land, grassland, water area, and unused land are as follows: 0 [10,13,22,35,36]. These values are based on previous research results and the specific conditions of the study area.…”

Section: Direct Carbon Emission Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In order to predict the spatiotemporal characteristics of land use changes and carbon emissions, Wei et al (2023) introduced a carbon emission model that included land use prediction [13]. The model utilized a gray backpropagation neural network and related carbon emission accounting models, along with four different 2030 scenario simulations [13]. When simulating construction land expansion, the commonly used methods include the Markov chain [14], system dynamics (SD) [15], and elemental dynamics cellular automata (CA) [16] (including CLUE-s (conversion of land use and its effects at small regional extent), SLUETH (slope, land use, excluded, urban, transportation, hillshade), and FLUS (future land use simulation model)), among others.…”

Section: Introductionmentioning

confidence: 99%

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Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Li,

Liu,

et al. 2023

Land

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With regard to the aims of achieving the “Dual Carbon” goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the “carbon peak” is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development.

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Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Han,

Zheng,

Han

et al. 2024

Land Degrad Dev

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In the context of climate change and rapid urbanization, there have been unparalleled changes in land use and land cover (LULC), resulting in substantial impacts on the surrounding habitat quality (HQ), particularly in ecologically vulnerable arid regions. However, previous studies on the influencing mechanisms of HQ in arid urban agglomerations and future multi‐scenario simulations remain limited. To fill this knowledge gap, this study aimed to reveal the influencing mechanisms in HQ changes and to develop a multi‐scenario HQ assessment framework within arid urban agglomerations. We assessed the spatiotemporal variations in HQ using the InVEST model and three periods of LULC data for the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM), and the partial least squares structural equation model was introduced to explore the interactions between natural and non‐natural factors and their impacts on HQ. Additionally, we coupled multi‐objective programming and PLUS models to predict the LULC under different optimization scenarios (natural development scenario (NDS), ecological protection scenario (EPS), ecological–economic coordinated scenario, and economic development scenario) for the UANSTM in 2030, and to assess HQ. Results show that (1) the HQ index of the UANSTM was 0.507, 0.520, and 0.495 in 2000, 2010, and 2020 respectively, with a spatial distribution pattern of high values in the west, low values in the east, and high in the central and low in the north and south; (2) geomorphic, climatic, and LULC factors have direct positive effects on HQ, while socio‐economic factors have a direct negative effect on HQ. In addition, geomorphic, socio‐economic, and climatic factors also influence HQ through potential indirect paths. Climatic and LULC factors enhance the positive effect of geomorphic on HQ while counteracting the direct negative effect of socio‐economic factors on HQ. Climatic factors have the largest negative effect on HQ through their influence on LULC; (3) according to the four scenarios in 2030, the highest HQ index (increased by 0.13%) was found under the EPS, which also aligns more closely with SDGs. Conversely, NDS showed the lowest HQ index (declined by 2.59%). The research results could provide a scientific basis for promoting sustainable land management and ecological conservation for the UANSTM.

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Spatiotemporal characteristics and prediction of carbon emissions/absorption from land use change in the urban agglomeration on the northern slope of the Tianshan Mountains

Cited by 29 publications

References 69 publications

Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives

Land Use Carbon Emissions or Sink: Research Characteristics, Hotspots and Future Perspectives

Urban Land Carbon Emission and Carbon Emission Intensity Prediction Based on Patch-Generating Land Use Simulation Model and Grid with Multiple Scenarios in Tianjin

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

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