Jiaxing Xin , Jun Yang , Huisheng Yu , Jiayi Ren , Wenbo Yu , Nan Cong , Xiangming Xiao , Jianhong (Cecilia) Xia , Xueming Li , Zhi Qiao
{"title":"走向生态文明:中国生态质量转型的时空异质性与驱动因素(2001-2020 年)","authors":"Jiaxing Xin , Jun Yang , Huisheng Yu , Jiayi Ren , Wenbo Yu , Nan Cong , Xiangming Xiao , Jianhong (Cecilia) Xia , Xueming Li , Zhi Qiao","doi":"10.1016/j.apgeog.2024.103439","DOIUrl":null,"url":null,"abstract":"<div><div>Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"173 ","pages":"Article 103439"},"PeriodicalIF":4.0000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)\",\"authors\":\"Jiaxing Xin , Jun Yang , Huisheng Yu , Jiayi Ren , Wenbo Yu , Nan Cong , Xiangming Xiao , Jianhong (Cecilia) Xia , Xueming Li , Zhi Qiao\",\"doi\":\"10.1016/j.apgeog.2024.103439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.</div></div>\",\"PeriodicalId\":48396,\"journal\":{\"name\":\"Applied Geography\",\"volume\":\"173 \",\"pages\":\"Article 103439\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143622824002443\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143622824002443","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Towards ecological civilization: Spatiotemporal heterogeneity and drivers of ecological quality transitions in China (2001–2020)
Global urbanization and climate change have a profound influence on the ecological quality (EQ) of China. In this study, utilizing the Google Earth Engine, we calculated the spatiotemporal heterogeneity of the China Remote Sensing Ecological Index (RSEI) for the period 2001–2020. We analyzed its drivers using land use, socioeconomic, and climate data. According to the results, the national average RSEI values for 2001, 2010, 2016, and 2020 were 0.39, 0.41, 0.46, and 0.45, respectively, and the proportions of the moderate and upper grades were 54 % in 2001, 64 % in 2010, 76 % in 2016, and 73 % in 2020. The RSEI value in the forest cover area was higher than that in the urban built-up and non-vegetation cover area by 0.1–0.2. The correlation coefficients between each variable and RSEI presented a ladder distribution (along the trend distribution of the Huanyong line). Moreover, maximum temperature (Tmmx) consistently contributed the most to RSEI (the contribution rate was between 35 % and 40 %), followed by precipitation accumulation (Pre, the contribution rate was between 18 % and 28 %), and then DEM, GDP, population (PPP), and wind speed (VS), all with relatively lower contributions around 10 %. Furthermore, temperature surpassing 24 °C, precipitation below 90 mm, population exceeding 50, or GDP above 10,000 showed a negative correlation with RSEI. This study analyzed the regional differences in RSEI drivers in different regions of China, providing a reference for local targeted improvement measures.
期刊介绍:
Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.