Spatio-Temporal Differentiation and Driving Factors of Carbon Storage in Cultivated Land-Use Transition

Gai, Zhaoxue; Xu, Ying; Du, Guoming

doi:10.3390/su15053897

Cited by 9 publications

(4 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model allows for the addition of future spatial policy elements, which can more scientifically simulate future LUCC under different policy scenarios [10,11]. Previous research has explored the influence of LUCC on carbon storage, including national [12], provincial [13], urban agglomeration [14,15], arid area [16], watershed [17,18], coastal zone [19], city [20,21], and county [22] scales. In summary, the existing research has extensively discussed the influence of LUCC on carbon storage and predicted future changes in carbon storage.…”

Section: Introductionmentioning

confidence: 99%

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Chen²,

Li³

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

show abstract

Section: Introductionmentioning

confidence: 99%

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Chen²,

Li³

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In this paper, cultivated land is also considered a carbon source. Due to the high density of human activities on cultivated land, the carbon emission coefficient is not stable and needs to be determined by linear regression based on historical data [34]. The carbon emission factor of cultivated land in Guangzhou in 2035 is calculated as 181.735 t/km 2 .…”

Section: Relationship Between Land Use and Carbon Emissionsmentioning

confidence: 99%

“…With the expansion of construction land, the encroachment of ecological space and growth in energy consumption sharply increase the carbon emissions [38]. The carbon emissions from construction land can be accounted by a mature methodological system and have been proven to be in a constant state of change by existing studies [34,39,40]. Thus, the time series pattern of carbon emissions from construction land needs to be explored based on historical carbon emissions to determine future carbon emissions.…”

mentioning

confidence: 99%

Spatial Optimization of Land Use Pattern toward Carbon Mitigation Targets—A Study in Guangzhou

Ding,

Liu,

Chang

et al. 2023

Land

View full text Add to dashboard Cite

Global climate change is one of the major challenges facing the world, and the spatial optimization of land use patterns has been regarded as critical in realizing carbon mitigation. In this study, the linear programming model and the Markov Chain model are integrated in different scenarios to optimize land use structure for low-carbon development. The land use pattern is then simulated through the adjusted convolutional neural network and cellular automata model, taking Guangzhou City as the case study area. The results reveal that construction land with high economic efficiency will increase its area, and the reaming types will experience slight changes, in 2035 in the natural development scenario and the economic priority scenario. Ecological land such as forest land, grassland, and water is partly occupied by construction land in the urban–rural fringe areas. The total carbon emissions decrease by 2.32% and 1.57% in these two scenarios. In the low-carbon-oriented scenario, the expansion of construction land is restricted, and the forest land and grassland undergo great expansion. The total carbon emission decreases by 18.95%—a figure much larger than that in the natural development scenario and the economic priority scenario. Our paper embeds the needs and constraints in land spatial planning into the spatial optimization of the land use pattern, which provides valuable references for low-carbon city development in the future.

show abstract

“…The research shows that ecological construction and scientific and technological progress are important reasons for the improvement of EEQ, while population and economic development are the reasons for the deterioration of EEQ. Gai Zhaoxue 16 used geographical detectors to explore and find that the distance from water and road is an important factor affecting the spatial differentiation of EEQ in the Songhua River basin. Through the combination of Pearson correlation analysis and geographic detector 17 , it is concluded that the topographic factor is the main reason that affects the spatial differentiation of EEQ in the north-south transition zone of China.…”

Section: Introductionmentioning

confidence: 99%

Study on the Ecological Environment quality and its driving factors of the Spatial Transformation of Production-Living-Ecological Space in Baishan City

huang,

Zhang,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

In order to strengthen the overall planning and coordination of urban construction and ecological space in Baishan City, we should formulate a scientific land type planning of Production-Living-Ecological Space (PLES). In this paper, land use dynamic attitude model, land use transfer matrix, land use function center of gravity transfer model, eco-environmental quality index(EEQI) and geographic detector model are used to discuss the spatio-temporal evolution of ternary spatial types in Baishan City from 2000 to 2020. Spatio-temporal evolution of EEQI and its influencing factors. The results show that: (1) During the study period, Baishan City showed the characteristics that the production space first increased and then decreased, the ecological space decreased, and the living space continued to increase. Among them, the ecological space is the dominant space of Baishan City, covering an area of more than 80%. From the perspective of the transformation of PLES, from 2000 to 2020, Baishan City is mainly characterized by the transformation of ecological space into production space and living space. In the second type of space, forage ecological space, forestry ecological space and other ecological space have decreased, while other types of space have increased in varying degrees. (2) During the study period, the overall EEQI of Baishan City remained in good condition, and the ecological environment quality(EEQ) of the three periods were 0.6571, 0.6412 and 0.6562 respectively. The higher EEQI is distributed in Changbai County and Linjiang City, while the areas with lower EEQI are concentrated in the north-central part of Hunjiang City, the middle part of Jiangyuan District and the northwest of Fusong County. (3) According to the analysis of the influencing factors of EEQ, the influence of the factors of spatial differentiation of EEQI in Baishan City changed significantly from 2000 to 2020, and the average annual rainfall was the core factor affecting the spatial differentiation of EEQ in Baishan City, the second is the urbanization rate and the distance from the county government, and the interaction between the average annual rainfall and the distance from the county government has a strong influence on the spatial differentiation of EEQ in Baishan City. This study reveals the evolution of spatial types and EEQ of PLES in Baishan City, and provides a scientific reference for the effective management and utilization of land resources in Baishan City.

show abstract

Spatio-Temporal Differentiation and Driving Factors of Carbon Storage in Cultivated Land-Use Transition

Cited by 9 publications

References 57 publications

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Spatial Optimization of Land Use Pattern toward Carbon Mitigation Targets—A Study in Guangzhou

Study on the Ecological Environment quality and its driving factors of the Spatial Transformation of Production-Living-Ecological Space in Baishan City

Contact Info

Product

Resources

About