Evaluation of Eco-Environmental Quality
and Analysis on Spatio-Temporal Variation
in Jinan, China

Cui, Jian; Zhu, Mingshui; Mi, Hongzhi; Liu, Yaohui

doi:10.15244/pjoes/140297

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…As shown in the figure below, the spatial trends of ecological quality are classified into three states: significantly reduced, no significant trend, and significantly increased (as shown in Figure 8) [36]. The result indicates that the overall improvement of the ecological environment quality in the study area is expected in the future [37], with only a small portion exhibiting stable trends, and the area showing worsened trends being the least. The specific proportions of these areas are detailed in Table 1.…”

Section: Trend Analysis Of Ecological Environment Quality In Luoyang ...mentioning

confidence: 89%

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Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City

Jing

Yan

et al. 2023

Sustainability

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The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals.

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Section: Trend Analysis Of Ecological Environment Quality In Luoyang ...mentioning

confidence: 89%

“…The specific proportions of these areas are detailed in Table 1. in the future [37], with only a small portion exhibiting stable trends, and the area showing worsened trends being the least. The specific proportions of these areas are detailed in Table 1.…”

Section: Trend Analysis Of Ecological Environment Quality In Luoyang ...mentioning

confidence: 98%

Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City

Jing

Yan

et al. 2023

Sustainability

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“…With the increase in the number of remote sensing satellites and the improvement of image resolution [3][4][5], advanced remote sensing technology and multi-spatial resolution remote sensing image data have gradually provided reliable technical means and data support for monitoring coastline change and coastal land use/land cover change (LUCC). The selection and application of the coastline extraction method is the key aspect in the process of monitoring coastline change using remote sensing technology [6].…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Changes in Land Use and Their Driving Forces in the Circum-Bohai Coastal Zone of China from 2000 to 2020

Cui

Wang³

et al. 2023

Remote Sensing

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Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources.

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“…Human-induced natural forest change is at the heart of the science of land change, aiming to understand how the relationship between people and the environment affects land and land cover [1,2]. The concept of land use transition links land cover change and common land-use patterns with increased human dominance in a given area [3,4].…”

Section: Introductionmentioning

confidence: 99%

Implications of Trade and Trade Adjustment on Forest Transition in Africa and Asia: Based on FMOLS and DOLS Approaches

Akhtar¹,

Li²,

Cheng³

et al. 2022

Pol. J. Environ. Stud.

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In Asian and African countries, the state of economic globalization has been distinct from developed countries since the 1980s. Existing literature focused mainly on trade in forest goods, but this study focused on trade and trade adjustment in Asian and African countries on forest cover change dynamics. To this end, we used Fully Modified Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) estimators to investigate the impact of trade and adjustment of trade structure on forest transition in Asian and African panels over the period 1990-2016. Our findings show that trade and trade adjustment indicators such as the percentage of non-primary goods in total exports (PNPEXP), total exports of manufacturing and service goods (TEXP), imports of forest goods (IMFG), and imports of agricultural goods (IMAG) have a positive and significant impact on forest cover in Asian and African countries. The causality test results also confirm that unidirectional causality runs from forest cover to PNPEXP, TEXP, IMAG, and IMFG. Bi-directional causality is found between IMFG and IMAG. We can conclude that Asian and African countries can improve their forest cover by adopting modern trade and trade adjustment strategies.

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Evaluation of Eco-Environmental Quality and Analysis on Spatio-Temporal Variation in Jinan, China

Cited by 6 publications

References 23 publications

Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City

Long-Term Ecological Environment Quality Evaluation and Its Driving Mechanism in Luoyang City

Spatial–Temporal Changes in Land Use and Their Driving Forces in the Circum-Bohai Coastal Zone of China from 2000 to 2020

Implications of Trade and Trade Adjustment on Forest Transition in Africa and Asia: Based on FMOLS and DOLS Approaches

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