Assessment of Ecological Carrying Capacity and Ecological Security in China’s Typical Eco-Engineering Areas

Zhang, Yaxian; Fan, Jiangwen; Wang, Suizi

doi:10.3390/su12093923

Cited by 18 publications

(10 citation statements)

References 48 publications

(42 reference statements)

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“…The ECC of the YRBS first decreased and then increased from 2000 to 2020, and it was affected by human activities and natural factors. This is consistent with other research findings [47,48]. From the composition of ECC, it can be seen that cultivated land, forest land, and grassland have the largest variation range and are the dominant types of ECC in the YRBS.…”

Section: Ecc Spatiotemporal Evolution and Its Determinantssupporting

confidence: 92%

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

Zhu

Mei

et al. 2022

Land

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Ecological carrying capacity (ECC), which requires simple scientific evaluation methods, is an important evaluation index for assessing the sustainability of ecosystems. We integrate an innovative research method. Geographic information systems (GIS) and remote sensing (RS) were used to evaluate the ECC of the Yellow River Basin in Shaanxi (YRBS) and to identify the underlying factors that influence it. A calculation method that combines RS and GIS data to estimate ECC based on net primary productivity (NPP) was established. The Carnegie–Ames–Stanford approach model was applied to estimate NPP. The NPP of each land type was used as an indicator to determine the yield factors. The ECC of the watershed was calculated with the carrying capacities of each land-use type. The geographical detector model was used to study the influencing factors of ECC, which provides a scientific basis for the formulation of ecological management policies in YRBS. The results show that from 2000 to 2010, it first decreased by 45.46%, and then increased by 37.06% in 2020, an overall decrease of 13.49 × 105 wha in 20 years. Precipitation is the dominant factor that affects ECC, while the impact of human activities on ECC was significantly enhanced during the study period. The developed method based on RS data serves as a reference for ecological evaluation in other similar regions.

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Section: Ecc Spatiotemporal Evolution and Its Determinantssupporting

confidence: 92%

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

Zhu

Mei

et al. 2022

Land

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“…In amphibians, phenotypic plasticity in size and growth may mitigate the proximal effects of temperature and hydroperiod stressors, but leads to energetic trade-offs that limit future stress responses and increase mortality (Amburgey et al 2016, Bredeweg et al 2019. Understanding and quantifying early-life drivers of cumulative mortality resulting from direct and carryover effects could help to prioritize management interventions (Sutherland et al 2004, Holmes 2007, Zhang et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Role of carryover effects in conservation of wild Pacific salmon migrating regulated rivers

et al. 2021

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Determining which factors are most effective for mitigative strategies in conservation management can be difficult for species with complex life cycles. Salmon (Oncorhynchus spp.) migrating through a hydroelectric power system experience conditions that can affect their survival directly within a life stage and indirectly in subsequent life stages through carryover effects. We quantified the association of covariates with survival across life stages of Chinook salmon (O. tshawytscha): juveniles migrating downstream, juveniles in the estuary, subadults in the ocean, and adults migrating upstream. We applied a hierarchical Bayesian mark-recapture model to~400,000 wild Snake River Chinook salmon (Pacific Northwest, USA). Modeled covariates of survival included migration timing, river temperature, flow, percent of water spilled over dams, snow water equivalent, juvenile fish transportation, juvenile fish length, dam powerhouse passage, sea surface temperature, and the North Pacific Gyre Oscillation. By analyzing these covariates in a unified model, we evaluated their relationships to survival within and across life stages in a common currency. Among direct effects, the negative relationship of sea surface temperature to ocean survival had the largest marginal effect size. Carryover effects also had substantial marginal effect sizes. Ocean survival was associated positively with juvenile length and snow water equivalent and negatively with river temperature. In comparison, direct effects on juvenile and adult survival generally showed smaller marginal effect sizes. Both juvenile and adult survival were negatively associated with river temperature. Juvenile survival increased with fish length, while adult survival was positively related to flow and negatively to percent water spilled at dams. The greatest variability in survival, however, was in the ocean stage. Thus, as the ocean continues to warm and as the human population exerts more pressure on the ecosystem, carryover effects will be increasingly important for recovering salmon and other migratory species.

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“…In terms of ecological environment, referring to the existing studies [ 17 , 18 ], in this study, we decompose the ecological environment into three first-level indicators: environmental pollution generation, environmental pollution control, and natural environment foundation. We build eleven secondary-level indicators, which include industrial solid waste generation (ten thousand tons), total wastewater discharge (ten thousand tons), total sulfur dioxide discharge (ten thousand tons), industrial solid waste investment (100 million yuan), industrial wastewater treatment investment (100 million yuan), wetland area (ten thousand hm 2 ), and forest coverage (%), etc.…”

Section: Research Methods and Data Sourcesmentioning

confidence: 99%

The Impact of Coordinated Development of Ecological Environment and Technological Innovation on Green Economy: Evidence from China

Li-ning

2022

IJERPH

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Promoting the coordinated development of ecological environment and technological innovation is significant to the development of a green economy. In this study, we construct an index system of ecological environment, technological innovation, and green economy based on the panel data of 30 provinces and cities in China from 2005 to 2016, using the entropy weight method, the coupling coordination model, and the panel vector autoregressive model (PVAR) to calculate the comprehensive development levels of ecological environment, technological innovation, and green economy and the coordination degree between ecological environment and technological innovation, and then further explore the impact of the coordinated development level of ecological environment and technological innovation on the development of a green economy. The research results include: First, from 2005 to 2016, the comprehensive development levels of ecological environment, technological innovation, and green economy in China’s 30 provinces and cities achieved different degrees of improvement as a whole. Among them, the comprehensive development level of green economy was the highest, followed by the development level of technological innovation, and the comprehensive development level of ecological environment was the lowest. Second, from 2005 to 2016, the coordination degree between ecological environment and technological innovation in China’s provinces and cities increased year by year, but on the whole, the coordination degree between ecological environment and technological innovation in various regions was in a state of imbalance. Third, there was a long-term equilibrium relationship among the coordinated development levels of ecological environment, technological innovation, and green economy. Fourth, through pulse analysis and Monte Carlo simulation, we found that the coordinated development level of ecological environment and technological innovation had a lagging positive impact on green economy. Finally, we provide a summary of the results of this study.

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Assessment of Ecological Carrying Capacity and Ecological Security in China’s Typical Eco-Engineering Areas

Cited by 18 publications

References 48 publications

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

Evaluation of Ecological Carrying Capacity and Identification of Its Influencing Factors Based on Remote Sensing and Geographic Information System: A Case Study of the Yellow River Basin in Shaanxi

Role of carryover effects in conservation of wild Pacific salmon migrating regulated rivers

The Impact of Coordinated Development of Ecological Environment and Technological Innovation on Green Economy: Evidence from China

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