Chao Ying, Yifan Li, Yuxin Chen, Jie Zhong, Shunyi Ai, Peng Tian, Qiyu Huang, Luodan Cao, Abdul M. Mouazen
{"title":"Evolution and prediction of rural ecological environment quality in eastern coastal area of China","authors":"Chao Ying, Yifan Li, Yuxin Chen, Jie Zhong, Shunyi Ai, Peng Tian, Qiyu Huang, Luodan Cao, Abdul M. Mouazen","doi":"10.3389/fenvs.2024.1403342","DOIUrl":null,"url":null,"abstract":"Introduction: Rural ecological environment construction, as a pivotal component of the rural revitalization strategy and ecological civilization construction strategy, plays an indispensable role in promoting sustainable agricultural development and safeguarding ecological security. An accurate assessment and prediction of Rural Ecological Environment Quality (REEQ) serves as the theoretical basis to achieving these goals, and provide scientific guidance for future rural ecological environment construction and planning. The field of regional ecology, proposed in the mid-20th century, represents an emerging interdisciplinary domain that integrates ecology, geography, and economics. It plays a pivotal role in addressing large-scale ecological challenges and fostering social sustainability. As global urbanization continues to advance, urban ecological environments undergo significant transformations under the pressures of intense human activities. Scholars have increasingly focused on the essence, evolutionary patterns, and causal mechanisms shaping urban ecological environment quality. Consequently, ecological environment assessments have evolved from singular pollution evaluations to comprehensive ecological appraisals. However, coastal rural area with complex geographical conditions and fragile ecological environments are often neglected and marginalized. Currently, there are few specialized evaluation systems for REEQ, making it difficult to accurately reveal the evolution pattern of rural ecological environment. This weakens its guidance on practical rural ecological environment governance and restoration.Methods: The Pressure-State-Response (PSR) model can simplify the identification process of driving factors for REEQ, reflect the feedback mechanism between indicators, and is conducive to scientific and accurate evaluation of REEQ. Therefore, we constructed an evaluation index system for REEQ based on the PSR. We measured REEQ in the eastern coastal area of China, analyzed its spatiotemporal characteristics and development trends, and used the obstacle degree model to identify obstacle factors. It is beneficial for rural areas to grasp the evolution laws of REEQ, provide theoretical basis for the formulation of sustainable development policies, and provide scientific policy recommendations.Results: Our findings indicate that: 1) From 2000 to 2020, REEQ in the eastern coastal area of China has continuously improved, with the index value increasing from 0.454 to 0.525, a total growth of 15.64%. The number of high-level REEQ areas increased from 0 to 29, showing a positive development trend. 2) High-density areas of REEQ in the eastern coastal area of China are concentrated in the northern parts of Guangdong and Zhejiang provinces. The center of REEQ has shifted from the southwest to the northeast. 3) The obstacle degrees of various criteria layers in REEQ are relatively stable, with the response subsystem being the highest, followed by the state and pressure subsystems. Forest coverage, per capita grain production, effective irrigation rate of farmland, afforestation area in the current year, per capita disposable income of rural residents, and per capita mechanical power of farmers are the main obstacle indicators. 4) From 2020 to 2035, REEQ in the eastern coastal area of China will continue to improve. The standard deviation ellipse will move towards the northwest, the center will shift from the southwest to the northeast, the rotation angle will slowly decrease, showing a northwestward trend.","PeriodicalId":509564,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Environmental Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fenvs.2024.1403342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Rural ecological environment construction, as a pivotal component of the rural revitalization strategy and ecological civilization construction strategy, plays an indispensable role in promoting sustainable agricultural development and safeguarding ecological security. An accurate assessment and prediction of Rural Ecological Environment Quality (REEQ) serves as the theoretical basis to achieving these goals, and provide scientific guidance for future rural ecological environment construction and planning. The field of regional ecology, proposed in the mid-20th century, represents an emerging interdisciplinary domain that integrates ecology, geography, and economics. It plays a pivotal role in addressing large-scale ecological challenges and fostering social sustainability. As global urbanization continues to advance, urban ecological environments undergo significant transformations under the pressures of intense human activities. Scholars have increasingly focused on the essence, evolutionary patterns, and causal mechanisms shaping urban ecological environment quality. Consequently, ecological environment assessments have evolved from singular pollution evaluations to comprehensive ecological appraisals. However, coastal rural area with complex geographical conditions and fragile ecological environments are often neglected and marginalized. Currently, there are few specialized evaluation systems for REEQ, making it difficult to accurately reveal the evolution pattern of rural ecological environment. This weakens its guidance on practical rural ecological environment governance and restoration.Methods: The Pressure-State-Response (PSR) model can simplify the identification process of driving factors for REEQ, reflect the feedback mechanism between indicators, and is conducive to scientific and accurate evaluation of REEQ. Therefore, we constructed an evaluation index system for REEQ based on the PSR. We measured REEQ in the eastern coastal area of China, analyzed its spatiotemporal characteristics and development trends, and used the obstacle degree model to identify obstacle factors. It is beneficial for rural areas to grasp the evolution laws of REEQ, provide theoretical basis for the formulation of sustainable development policies, and provide scientific policy recommendations.Results: Our findings indicate that: 1) From 2000 to 2020, REEQ in the eastern coastal area of China has continuously improved, with the index value increasing from 0.454 to 0.525, a total growth of 15.64%. The number of high-level REEQ areas increased from 0 to 29, showing a positive development trend. 2) High-density areas of REEQ in the eastern coastal area of China are concentrated in the northern parts of Guangdong and Zhejiang provinces. The center of REEQ has shifted from the southwest to the northeast. 3) The obstacle degrees of various criteria layers in REEQ are relatively stable, with the response subsystem being the highest, followed by the state and pressure subsystems. Forest coverage, per capita grain production, effective irrigation rate of farmland, afforestation area in the current year, per capita disposable income of rural residents, and per capita mechanical power of farmers are the main obstacle indicators. 4) From 2020 to 2035, REEQ in the eastern coastal area of China will continue to improve. The standard deviation ellipse will move towards the northwest, the center will shift from the southwest to the northeast, the rotation angle will slowly decrease, showing a northwestward trend.