Identifying factors that affect environmental air quality using geographical detectors in the NKEFAs of China

Xu, Jie; Liu, Haijiang; Li, Baolin; Gao, Xinliang; Nie, Pingjing; Sun, Cong; Jin, Ziheng; Zhai, Dechao

doi:10.1007/s11707-021-0882-0

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Furthermore, contrary studies demonstrated that ecological destruction continues in NKEFZ, and many areas exhibit characteristics of governance and destruction (Xu et al, 2018; Xu et al, 2022), indicating that NKEFZ implementation has not had a positive impact on regional environmental sustainability. For example, Liu, Zhang, and Yang (2022) took Yanshan–Taihang Mountain area in Hebei Province of China as the research object, demonstrating that NKEFZ implementation significantly reduced the level of green development in the covered areas and had a continuous negative impact.…”

Section: Literature Review and Policy Contextmentioning

confidence: 97%

Exploring the impacts of spatial regulation on environmentally sustainable development: A new perspective of quasi–experimental evaluation based on the National Key Ecological Function Zones in China

Chen

Zheng

et al. 2023

Sustainable Development

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With increasing ecological damage caused by regional development, many countries seek to promote sustainable development through spatial regulation. This study treated China's development of National Key Ecological Functional Zones (NKEFZ) as a quasi–natural experiment, applying a time–varying difference–in–differences model to data from 148 water quality monitoring cross–section points of major river systems in China from 2004 to 2018. The results revealed that NKEFZ improved the mean of comprehensive water quality (CWQ) and individual water quality indicators of dissolved oxygen and ammonia nitrogen content index by restricting urbanization and increasing government investment. The mean value of CWQ was improved by about 0.22 grades, and the optimization effect of spatial regulation on average water quality was more prominent in coastal cities, cities with high–pollution levels, and large–sized cities. Nevertheless, the impact of NKEFZ on water quality exhibited a “Matthew effect”, in which NKEFZ were not effective in improving the bottom line standard of water quality in the pilot zones, as NKEFZ did not significantly improve the peak values of CWQ or individual pollutant indicator. In addition, an economic effect estimation indicated that NKEFZ significantly facilitated local economic growth and primary industry development and had a significantly negative impact on secondary industry growth and foreign investment. This study contributes new evidence for measuring the effect of the trans–regional spatial regulation, eliciting policy implications for improving ecological spatial regulation and incentive mechanisms, and promoting the sustainable development of the regional environment and economy.

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Section: Literature Review and Policy Contextmentioning

confidence: 97%

Exploring the impacts of spatial regulation on environmentally sustainable development: A new perspective of quasi–experimental evaluation based on the National Key Ecological Function Zones in China

Chen

Zheng

et al. 2023

Sustainable Development

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“…It assesses the strength of influence of each factor variable on the result variable, discerns the disparities in influence among different factor variables, and determines whether the impact of each factor variable on the result variable is independent or interactive (Xinge et al, 2016). Widely applied in diverse domains such as urbanization efficiency (Xiangyan et al, 2021;Kun et al, 2022), population aging (Xu et al, 2018), and medicine (Jiaojiao et al, 2017), this method has recently found application in ecological research, examining insect diversity (Liu et al, 2021), soil heavy metal detection (Qiao et al, 2017;Shiwei et al, 2021), and the intersection of air quality and social economy (Ju et al, 2016;Wang et al, 2017;Fan et al, 2021;Xu et al, 2021). For example, Liu et al (2021) quantified the interactive effects of 15 variables on beetle distribution using the geographical detector method, successfully analyzing the associated risk drivers.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Liu et al (2021) quantified the interactive effects of 15 variables on beetle distribution using the geographical detector method, successfully analyzing the associated risk drivers. Xu et al, 2021 used geographical detector to quantify the driving factors affecting air quality nd establish national key functional areas. However, there is a dearth of research on the detection of ecological risk factors based on remote sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes

Kang,

Yang,

Qing

et al. 2024

Preprint

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Examining the Saihanba Mechanical Forest Farm, this study utilized landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the vector machine method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units based on the degree of global landscape risk. Employing landscape pattern indices and a method around spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. Results indicate significant shifts in land use types since 1987, notably with a evident increase in forest landscape predominantly at the expense of grassland and sandy land. Over the 1987 to 2020 period, landscape risk demonstrated a gradual decline, with mid-high and high-risk areas clustering in specific locales, while the broader region predominantly featured low landscape risk. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. Since its establishment in 1962, the landscape changes caused by human factors such as afforestation projects and scientific forest management methods have reduced the ecological risk of the landscape. A comprehensive exploration using geographic detectors identified nine ecological driving factors, with soil type emerging as the principal risk determinant, and its synergistic interaction with precipitation and other factors surpassing the individual factor effects.

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“…It assesses the strength of influence of each factor variable on the result variable, discerns the disparities in influence among different factor variables, and determines whether the impact of each factor variable on the result variable is independent or interactive [32]. Widely applied in diverse domains such as urbanization efficiency [33,34], population aging [35], and medicine [36], this method has recently found application in ecological research, examining insect diversity [37], soil heavy metal detection [38,39], and the intersection of air quality and social economy [40][41][42][43]. For example, Liu et al [37] quantified the interactive effects of 15 variables on beetle distribution using the geographical detector method, successfully analyzing the associated risk drivers.…”

Section: Introductionmentioning

confidence: 99%

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

Kang,

Yang,

Qing

et al. 2024

Forests

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Examining the Saihanba Mechanical Forest Farm, this study utilized Landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the Support Vector Machine (SVM) method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units. Employing landscape pattern indices and a method of spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. The results indicate that since 1987, there have been significant changes in land use types, especially in forest landscapes, their proportion increasing from 23.19% to 74.55%. In 1987, the proportion of high-risk areas was 72.30%, but in 2020, high-risk areas had significantly decreased and clustered in specific locations. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. After comprehensive exploration using a geographic detector, we found that landscape type, temperature, and vegetation coverage are the main risk factors. Among them, landscape type has the greatest impact on the landscape and works together with slope, aspect, and precipitation. In forest farm management, only the adaptation and adjustment of single factors are often paid attention to, while the compound effects of multiple factors are ignored. The results of this study bring important reference value to the operation and development of forest farms.

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Identifying factors that affect environmental air quality using geographical detectors in the NKEFAs of China

Cited by 6 publications

References 19 publications

Exploring the impacts of spatial regulation on environmentally sustainable development: A new perspective of quasi–experimental evaluation based on the National Key Ecological Function Zones in China

Exploring the impacts of spatial regulation on environmentally sustainable development: A new perspective of quasi–experimental evaluation based on the National Key Ecological Function Zones in China

Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

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