Research on driving mechanism of ecological land loss based on Bayesian network

Zheng, Tao; Chen, Shuang; Zhang, Tong; Xu, Lu; Ma, Lei

doi:10.31497/zrzyxb.20201213

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The same land type is clustered and distributed in multiple contiguous areas in space, with strong spatial correlation, and its evolution direction is easily driven or restricted by adjacent land types (Liang et al, 2021). Additionally, the differentiated policy objectives of the area where the plot is located will also guide and intervene in eco‐land evolution (Zheng et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

A new framework for optimizing ecological conservation redline of China: A case from an environment‐development conflict area

Liang,

He,

Jin

et al. 2023

Sustainable Development

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As an important strategic measure in promoting sustainable development, China's current ecological conservation redline has mismatches between protected areas and redline boundaries, especially in economically developed areas with high environment‐development conflicts. This study constructs a new optimization framework that integrates in‐out balance and spatial replacement and takes the southern Jiangsu of China as an example to provide a reference for managing protected areas in similar regions. Results demonstrate that total eco‐land in the study area showed a downward trend from 2005 to 2015, with a 7.55% loss and 1.33% replenishment. Non‐eco land expansion inertia and human activities are the main driving forces of ecological land loss and replenishment, respectively. Effectively, the in‐out balance optimization model based on Bayesian Network makes a net increase of 705.97 km2 in redlines. The overlapping area with core agricultural production space has decreased by 9.11%, human activity has been reduced by 6.06%, and ecological stability has been significantly enhanced. An important implication is that more targeted schemes with boundary adjustment‐internal control‐external protection solutions can be put forward to identify ecological risks and policy conflicts, thereby improving conservation effectiveness.

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Section: Methodsmentioning

confidence: 99%

A new framework for optimizing ecological conservation redline of China: A case from an environment‐development conflict area

Liang,

He,

Jin

et al. 2023

Sustainable Development

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“…The conditional probability between each influential factor and each ecosystem service trade-off was calculated, and the state with the highest conditional probability of each variable was selected as its key state. A sensitivity analysis was conducted on Netica to explore the relative importance of impact factor nodes to ecosystem service trade-off nodes through variance reduction [47]. Variance reduction was used to quantify the magnitude of the changes.…”

Section: Modeling Ecosystem Services Based On the Bbnmentioning

confidence: 99%

Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

Li,

Cui,

Cai

et al. 2024

Sustainability

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Understanding the interactions between ecosystem services is the foundation for optimizing ecosystem management and improving human well-being. However, studies on the driving mechanism of ecosystem service relationship formation in arid and semiarid climates are scarce. The Beijing–Tianjin Sand Source Control Project (BTSSCP) has been underway for more than 20 years (2001–2022), and a comprehensive scientific assessment of the effects of its implementation is important for managing ecosystems more efficiently. Taking the BTSSCP region as a study area, four ecosystem services (water conservation (WC), soil conservation (SC), wind erosion control (WEC), and net primary productivity (NPP)) were quantified and mapped in 2000, 2010, and 2020. In this study, a Bayesian belief network (BBN) model was used to analyze ecological processes and determine the relationship between the potential influencing factors and ecosystem services. A sensitivity analysis identified the key factors affecting ecosystem service supply on the basis of a Bayesian belief network simulation. The results showed an increasing trend for four ecosystem services over the past 20 years. Regarding spatial distribution, WC, SC, and NPP exhibited an overall “high in the east and low in the west” pattern, while the spatial distribution of WEC was more dispersed. The intensity of the trade-offs among WC, SC, and NPP has increased, while that of the trade-offs between the rest of the variables has decreased in the BTSSCP over the past 20 years. The results of the Bayesian network modeling indicated that precipitation, NDVI, land use, and temperature were the major variables influencing the strength of ecosystem service trade-offs. The conditional probabilities of the key variables in different states showed that the Sunit Left Banner, Sunit Right Banner, and other areas of control of the desertification of arid grassland had a high probability of trade-offs in WC_SC and SC_NPP. However, the probability of a trade-off between WEC and NPP was higher in the southeastern part of the Yanshan Hills Mountain Water Source Reserve than in the other regions; thus, it should be prioritized as an area for ecological restoration in future planning. This paper provides a scientific reference for the effective protection of ecosystems and the formulation of sustainable policies.

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“…If a variable has no parent node, the conditional probability of this variable is the prior probability. The conditional probability table is the parameters of the Bayesian network, which reflects the strength of the causal relationship between nodes (Zheng and Chen, 2020). Bayesian networks rely on the Bayes rule formula (Eq.…”

Section: Business Operating Statusmentioning

confidence: 99%

Statistical analysis of small business survival under the shock of multiple COVID-19 waves: A case study from Wuhan, China

Rubinato

Zhou

et al. 2023

Front. Environ. Sci.

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The long-term viability of small businesses in the aftermath of multiple pandemics and consequent lockdowns has a crucial impact on the sustainable economic and social development of any region across the world. Thus, in order to investigate what has been the major impact of COVID-19 pandemic within local small businesses and to identify which main factors helped small businesses to survive none as well as multiple lockdowns, data were obtained from 382 small businesses in the main urban area of Wuhan, China, via two rounds of field investigations and surveys in July 2020 and July 2022. This paper presents the results of the field investigations and the surveys completed and describes the Bayesian methods applied to quantitatively explore the impact of different variables on the probability of each business to remain active and open even after experiencing none or multiple lockdowns. Results obtained show that the difference between survival rates associated with businesses hit by no pandemic outbreak with those hit by one or several waves is negligible. Furthermore, owners who had higher confidence in their abilities since the beginning or they implemented an accurate evaluation of their strategies to run their businesses since the start of the pandemic, demonstrated to have a higher probability to keep their business alive with none as well as additional waves of the pandemic. Reduction of employees, transition of operations and promotion activities online as well as rent subsidies and tax reduction were identified as crucial actions that enhanced the probability to maintain alive businesses that experienced at least one lockdown. Globally, there was no clear policy approach at the start of the pandemic, however this study clearly determines that in future governments should provide timely support to small businesses in regions experiencing more severe impacts of the pandemic, and this should consist of a mix of grants, loans, and temporary tax cuts since initial stages.

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Research on driving mechanism of ecological land loss based on Bayesian network

Cited by 4 publications

References 15 publications

A new framework for optimizing ecological conservation redline of China: A case from an environment‐development conflict area

A new framework for optimizing ecological conservation redline of China: A case from an environment‐development conflict area

Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

Statistical analysis of small business survival under the shock of multiple COVID-19 waves: A case study from Wuhan, China

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