Spatiotemporal variation of ecological carrying capacity in Dongliao River Basin, China

Zhang, Jiquan; Li, Xiaoyü; Tong, Zhijun; Du, Walian; Zhang, Jiquan; Liu, Xingpeng; Guo, Enliang; Liu, Mengshu

doi:10.1016/j.ecolind.2022.108548

Cited by 17 publications

(5 citation statements)

References 67 publications

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“…Our research shows that precipitation is the dominant factor that affects the ECC of the YRBS. In addition, human activities significantly influence the ECC and is responsible for its enhancement during the past 20 years, which is consistent with other research findings [54,55]. In the process of social development, the advancement of science and technology has led to the improvement of social productivity, which enables human beings to enhance the natural environment.…”

Section: Ecc Spatiotemporal Evolution and Its Determinantssupporting

confidence: 89%

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: 89%

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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“…Based on the DPSIRM model (Wang et al, 2022;Wang and Zhan, 2019) the government policy is measured by the percentage of environmental protection terms in the city government work report. After constructing the index system, we apply the entropy weight method to calculate the weight of the indicators, which is shown in Table 1.…”

Section: Dependent Variablementioning

confidence: 99%

Intellectual Property Protection and Synergizing the Reduction of Pollution and Carbon Emissions: Evidence from a Quasi-Natural Experiment

Xu,

Wang,

Yang

et al. 2023

Preprint

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The Chinese government is implementing a Synergizing the Reduction of Pollution and Carbon Emissions (SRPC) strategy to shift to green development. This study aims to assess whether a good intellectual property (IPR) environment is conducive to SRPC in Chinese cities. Based on data from 284 cities in China from 2004–2019, we employ the Driver-Pressure-State-Impact-Response-Management (DPSIRM) model to measure SRPC levels at the city level. We then use China's Intellectual Property Model City (IPMC) pilot policy as a quasi-natural experiment and apply a multi-period difference model to investigate the impact of establishment on urban SRPC. The study finds that the IPMC pilot policy promotes urban SRPC, and the findings hold after rigorous robustness tests. The mechanism is that the IPMC pilot policy promotes green technology innovation (GTI), draws government attention, and optimizes elements allocation. In addition, the heterogeneity test shows that the effect is greater in the Northeast cities as well as in small and medium-sized cities, while the effect is not significant in the West. Our findings provide theoretical and practical insights into how China can enhance IPR protection to advance SRPC.

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“…A specific framework and systematic evaluation should be established to assess the ECC of vulnerable eco-regions by coupling the interactions between ecosystems and socio-economic systems to maintain ecological balance. Furthermore, identifying the key factors driving ECC variation is important, as it is influenced by the efficiency of socio-economic development and resource, environmental, and ecological supplementation [18]. Ecosystem services proposed by the Millennium Ecosystem Assessment have been defined as the various benefits humans obtain from the ecosystem, including direct and indirect benefits [19].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the ecological carrying capacity of grasslands in Inner Mongolia

Guo,

Song,

Zhao

et al. 2023

PLoS ONE

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Quantifying the ecological carrying capacity has emerged as a crucial factor for maintaining ecosystem stability for sustainable development in vulnerable eco-regions. Here, we propose a new framework for ecological carrying capacity quantification suitable for vulnerable eco-regions. We applied this framework to calculate the ecological carrying capacity of Inner Mongolia from 1987–2015 and used a geographical detector to identify the driving factors behind spatial heterogeneity. Our results revealed the following. (1) The above-ground net primary production (ANPP) required to support the ecosystem service of soil conservation (ANPPSC) decreased from northeast to southwest, whereas the distribution pattern of ANPP required to support the ecosystem service of sand fixation (ANPPSF) exhibited a contrary trend. The average annual ANPP required to support the ecosystem service of natural regeneration (ANPPNR) in Inner Mongolia from 1987 to 2015 was 101.27 gCm-2year-1, revealing a similar spatial distribution with ANPP. (2) The total ecological carrying capacity of Inner Mongolian grassland was 78.52 million sheep unit hm-2. The regions with insufficient provisioning service capability accounted for 4.18% of the total area, primarily concentrated in the east and northwest. (3) The average optimal livestock number for grasslands in Inner Mongolia was 1.59 sheep unit hm-2 from 1987–2015, ranging from 0.77 to 1.69 sheep unit hm-2 across different zones. The average ecological carrying capacity of the cold temperate humid, medium-temperate arid, and warm temperate semi-humid regions was less than 1.08 sheep unit m-2, suggesting a need to prohibit grazing in these areas. (4) The primary influencing factors affecting ecological carrying capacity distribution were normalized difference vegetation index (NDVI), precipitation, and soil type. The framework developed herein can help identify sustainable development potential from the ecosystem service perspective and effectively contribute to decision-making in grassland ecosystem management.

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Spatiotemporal variation of ecological carrying capacity in Dongliao River Basin, China

Cited by 17 publications

References 67 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

Intellectual Property Protection and Synergizing the Reduction of Pollution and Carbon Emissions: Evidence from a Quasi-Natural Experiment

Quantifying the ecological carrying capacity of grasslands in Inner Mongolia

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