Spatial-Temporal Variations and Trade-Offs of Ecosystem Services in Anhui Province, China

Yang, Qi; Zhang, Pian; Qiu, Xiaocong; Xu, Guangtong; Chi, Jianyu

doi:10.3390/ijerph20010855

Cited by 6 publications

(4 citation statements)

References 75 publications

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“…However, in general, the increase in precipitation and NDVI has offset the negative impact of highway construction on ecological risk, so ecological risk in the study area is decreasing year by year. Spatially, previous studies (Wang, 2019) and this study found that higher ESV was mainly distributed across the northeast plain and Yellow River shore with lower risk levels. The areas of lower ESV were mainly concentrated in dunes in middle and other land areas in the west with higher risk.…”

Section: Influencing Factors and Strategies Based On Spatial Correlationsupporting

confidence: 57%

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

Liu

Wang

Sun

et al. 2023

Front. Environ. Sci.

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A clear understanding of the spatiotemporal evolution and driving factors of ecosystem service value (ESV) and the landscape ecological risk index (ERI) can effectively link human well-being and ecosystem security, which is essential for sustainable ecosystem management. Based on multitemporal land use data (1990, 2000, 2010, and 2018), the spatiotemporal evolution of ESV and the ERI in the Ulan Buh Desert was evaluated. The driving forces of ESV and the ERI were quantitatively evaluated by the Geodetector model. The results show that 1) from 1990 to 2018, total ESV in the Ulan Buh Desert increased from 7.00×108 yuan to 11.09×108 yuan. Low-ESV areas accounted for approximately 72.28% of the study area. High-ESV and moderate-high-ESV areas were mainly distributed along the Yellow River and the northeastern region. 2) During the study period, the ecological risk of the Ulan Buh Desert generally decreased; only the ecological risk of the northeastern region improved significantly, and high-risk areas were mainly distributed across sand dunes located in the middle of the desert. 3) There was a negative correlation between ESV and the ERI in the study area, and the main relationship was low value-high risk. 4) Driving force analysis results show that natural and human impact factors jointly affected the spatiotemporal differentiation of ESV and the ERI in the Ulan Buh Desert. Among the influencing factors, the interaction between the distance to a highway and annual precipitation had the strongest impact. The implementation of relevant policies in the study area should be guided by ESV and the ERI, and the protection and restoration of various ecosystems in the study area must be strengthened.

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Section: Influencing Factors and Strategies Based On Spatial Correlationsupporting

confidence: 57%

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

Liu

Wang

Sun

et al. 2023

Front. Environ. Sci.

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“…In fact, compared with other provincial research areas in China, the absolute value of carbon storage reduction in Liaoning Province from 2000 to 2020 was relatively small, at 0.51 Tg. During the same period, Anhui Province lost 33.84 Tg [68], Guangdong Province lost 6.53 Tg [69], and Shandong Province lost 6.57 Tg [70]. The reason for these differences may be the effective implementation of ecological protection policies in Liaoning Province.…”

Section: Influence Of Policy On Carbon Storage In Citiesmentioning

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

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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”.

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“…The runoff retention volume R f mi m 3 (P − Q p,i ) and runoff retention index R f mi (1 − Q p,i /P) represent the capacity of flood risk mitigation. With reference to the previous literature [39], this paper uses runoff retention volume as the supply of flood mitigation services. Specifically, the input parameter P s adopts the definition of a rain storm in China's meteorological standards, which is 50 mm rain within 24 h. (http://www.cma.gov.cn/2011xzt/kpbd/rainstorm/ 2018050901/201805/t20180509_468007.html, accessed on 9 February 2021).…”

Section: Methods Of Es Supply Quantification 221 Flood Mitigation Ser...mentioning

confidence: 99%

“…The capacity of the city's flood control and mitigation plays an important role in reducing urban waterlogging [38,39]. The latest version of the InVEST model added the Urban Flood Risk Mitigation model and the Urban Cooling Model, which are more suitable for finer assessment of urban ecosystem services on a smaller scale, and relevant to the ecological problems of the city's concern.…”

Section: Flood Mitigation Service Supply Quantificationmentioning

confidence: 99%

Identification of Priority Areas for Improving Urban Ecological Carrying Capacity: Based on Supply–Demand Matching of Ecosystem Services

Wang

Liu

et al. 2022

Land

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As the most concentrated area of human activities, cities consume many natural resources and discharge a large amount of waste into the natural environment, which has a huge environmental impact. Most of the ecological and environmental problems, such as environmental pollution, global climate change, and loss of biodiversity, are related to urban systems. How to coordinate urban development with the urban ecological carrying capacity is related to the destiny of the city itself, and also to whether its surrounding areas can successfully achieve the goal of high environmental quality and sustainable development. At present, the theory and methods of urban ecological carrying capacity research are relatively new, which has caused problems for policy makers in practical applications. This paper proposes a theoretical framework for urban ecological carrying capacity assessment based on the analysis of ecosystem services supply and demand. Combined with multi-source spatial data and spatial model methods, the supply and demand of ecosystem services were spatially quantified. The capital city of China, Beijing, was the case study area for this research. The spatial differentiation of the supply–demand relationship of ecosystem services is formed. The priority areas for ecological carrying capacity improvement at pixel scale and at the administrative level are obtained, respectively. The results show that the first priority area is concentrated in the center of the urban area, accounting for 31.11% of the total area of Beijing. According to the secondary zone and the specific ecosystem service type, the ecological carrying capacity improvement strategy of different zones is proposed. This study provides a new perspective for investigating urban ecological carrying capacity and for identifying the priority areas for ecological carrying capacity improvement, and helps the policy-makers to design tailored policy actions.

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Spatial-Temporal Variations and Trade-Offs of Ecosystem Services in Anhui Province, China

Cited by 6 publications

References 75 publications

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

Spatiotemporal evolution and driving forces of ecosystem service value and ecological risk in the Ulan Buh Desert

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

Identification of Priority Areas for Improving Urban Ecological Carrying Capacity: Based on Supply–Demand Matching of Ecosystem Services

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