Quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance: A case study in Beijing, China

Liu, Yang; Xia, Chuyu; Ou, Xiao‐Yang; Lv, Yingshuo; Ai, Xinping; Pan, Ruiqi; Zhang, Yaru; Shi, Mengyu; Zheng, Xiaojiao

doi:10.1016/j.ecolind.2023.110034

Cited by 24 publications

(7 citation statements)

References 82 publications

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“…Referring to the relevant research results [40,59], considering the situation of land use in the Chengdu Plain, 18 main driving factors were selected from the social economy and natural environment. Natural factors include elevation, slope, average annual precipitation, average annual temperature, and distance to water.…”

Section: Multi-scenario Land Use Pattern Simulationmentioning

confidence: 99%

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Wu,

Pu,

Dong

et al. 2024

Land

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As the economic center and major grain-producing area in Southwest China, the calculation of the carbon budget and the protection of cultivated land in the Chengdu Plain are of vital significance for China to achieve a carbon peak strategy and ensure food security. For the purpose of clarifying the trend of land use focus and carbon emissions in the Chengdu Plain, the carbon peak level of land use in 33 counties in the Chengdu Plain was explored. Based on the gravity center model and IPCC carbon emission coefficient method, the changing trend of land use gravity center and carbon emission in Chengdu Plain from 2006 to 2022 was clarified. PLS regression model and LMDI model were used to explore the main influencing factors of the carbon emission of cropland and the carbon emission of building land. PLUS model was used to simulate future land use patterns and carbon emissions. (1) The center of gravity of cropland, building land, water, and other and unused land shifted to the northeast by 4.23 km, 5.46 km, 8.44 km, and 31.58 km, respectively, and that of forest and grass shifted to the southeast by 11.12 km and 3.41 km, respectively. For major food crops, the centers of gravity of rice and maize moved northeastward by 15.47 km and 7.52 km, respectively, while wheat moved southwestward by 17.77 km. (2) From 2006 to 2022, carbon emissions from land use in the 33 counties of the Chengdu Plain are all on the rise, with a total increase of 13.552 million tons, and carbon sinks in the 31 counties continue to decline, with a total decrease of 0.691 million tons. (3) Under the natural scenario, carbon sink scenario, and carbon reduction scenario, the carbon emissions from land use decrease by 0.5391 million tons, 3.4728 million tons, and 4.5265 million tons from 2022, respectively. Among the 33 counties in the Chengdu Plain, 11 counties did not achieve carbon peak under the natural scenario, 5 counties did not achieve carbon peak under the carbon sink scenario, and all the counties achieved carbon peak under the carbon sink scenario. During the study period, there was a serious loss of cropland in the Chengdu Plain, mainly to building land in the central part of the Chengdu Plain and to forests within the Longmen Mountain, Longquan Mountain, and Leshan City, and there is a need to strengthen cropland protection in this region in the future. Under the natural scenario, carbon sink scenario, and carbon reduction scenario, land use in the Chengdu Plain region can achieve carbon peak, and the carbon reduction model will be more helpful for the counties to achieve carbon peak.

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Section: Multi-scenario Land Use Pattern Simulationmentioning

confidence: 99%

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Wu,

Pu,

Dong

et al. 2024

Land

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“…Specifically, soil heterotrophic respiration reflects the value of the net carbon exchange and carbon sink in terrestrial ecosystems [54], which can be quantitatively described as the carbon sink potential of the ecosystem [55]. Soil heterotrophic respiration was computed based on the methods of Liu et al [56], utilizing Equations ( 3)-( 5):…”

Section: Construction Of Biodiversity Potentiality Evaluation Modelmentioning

confidence: 99%

Evaluation of Biodiversity Maintenance Capacity in Forest Landscapes: A Case Study in Beijing, China

Liu

Jing

Zheng

et al. 2023

Land

Self Cite

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Understanding how landscape pattern changes impact forest biodiversity conservation and ecosystem management is crucial. This study evaluated the biodiversity maintenance capacity (BMC) of forest landscapes in Beijing, China from 2005 to 2020 based on habitat quality and carbon sink. For this, the moving window method was employed to compute landscape indices that depict variations in landscape patterns, including intermixing, connectivity, diversity, and compactness. Lastly, the relationship between landscape pattern changes and the BMC of forest landscapes was investigated using a combination of spatial correlation analysis and geographic weighted regression measurement models. The results showed the following. (1) The average BMC increased from 0.798 to 0.822. Spatially, 84.14% of the areas experienced an improvement, mainly in the mountainous region. In contrast, a decrease was observed in 4.03% of the areas, primarily concentrated in the transition zone between mountains and suburban plain. (2) The landscape pattern changed dramatically from 2005 to 2020. Landscape intermixing and compactness decreased slightly by 11.45% and 7.82%, while landscape connectivity and diversity increased significantly by 64.28% and 55.44%, respectively. (3) The BMC’s global Moran’s I values in 2005 and 2020 were 0.711 and 0.782, respectively, signifying a spatial bipolar agglomeration pattern. (4) Among the four selected landscape indices, the compactness was found to be the most critical factor. It attained a positive contribution to forests with high BMC, but had a negative impact on forests with low BMC. The results could provide a reference for planners coordinating forest management and biodiversity conservation.

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“…Using the PLS-STIRPAT model, some scholars have explored the impacts of population and economic, spatial, and ecological urbanization on carbon emissions and further analyzed the urban carbon balance state in the Middle Reaches of the Yangtze River urban agglomeration of China [27]. At the urban level, they have also explored quantitative structures and spatial pattern optimization in the Beijing green space from the perspective of carbon balance to improve the total carbon sink of the city [28]. In Ensenada, a coastal city in Mexico, the carbon flow and carbon balance of its urban system were estimated by measuring vertical and horizontal carbon fluxes [29].…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Patterns and Carbon Balance Zoning in Urban Territorial Spaces Based on Multisource Data: A Case Study of Suzhou City, China

Zhang,

Yu,

Hou

et al. 2023

IJGI

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The concept of green and low-carbon development is integrated into territorial spatial planning and district control research. It is one of the systematic policy tools for emission reduction and carbon sequestration, greatly contributing to achieving the double carbon goal. This paper presents a method for measuring the carbon emissions of urban territorial spaces using multisource big data, aiming to identify the spatial patterns and levels of carbon emissions at microspatial scales. The spatial patterns of carbon emissions were used to construct a carbon balance zoning method to evaluate the regional differences in the spatial distribution of carbon emissions, taking Suzhou as an example to achieve carbon balance zoning at the micro scale of the city. Based on our research, the following was determined: (1) Suzhou’s total carbon emissions in 2020 was approximately 240.3 million tons, with the industrial sector accounting for 81.32% of these emissions. The total carbon sink was about 0.025 million tons. (2) In Suzhou City, the high-value plots of carbon emissions are mainly located in industrial agglomeration areas. By contrast, low-value plots are primarily located in suburban areas and various carbon sink functional areas, exhibiting a scattered distribution. (3) The territorial space unit was divided into four functional areas of carbon balance, with 36 low-carbon economic zone units accounting for 37.11%, 29 carbon-source control zone units accounting for 29.90%, 14 carbon-sink functional zone units accounting for 14.43%, and 18 high-carbon optimization zone units accounting for 18.56%. As a result of this study, carbon balance zoning was achieved at the grassroots space level, which will assist the city in low-carbon and refined urban governance. Some ideas and references are also provided to formulate policies for low-carbon development at the micro scale of a city.

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Quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance: A case study in Beijing, China

Cited by 24 publications

References 82 publications

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Evaluation of Biodiversity Maintenance Capacity in Forest Landscapes: A Case Study in Beijing, China

Carbon Emission Patterns and Carbon Balance Zoning in Urban Territorial Spaces Based on Multisource Data: A Case Study of Suzhou City, China

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