A temporospatial assessment of environmental quality in urbanizing Ethiopia

Spatiotemporal evolution and driving factors of the coupling coordination of the population‒land‒water‒industry system in the lower Yellow River

Exploring the interaction and coupling effects within the population‒land‒water‒industry (PLWI) system is conducive to promoting high-quality regional sustainable development. Taking the lower Yellow River during the period from 2000 to 2020 as a research sample, this study used the entropy weight TOPSIS method, the coupling coordination degree (CCD) model and kernel density estimation to synthetically evaluate the CCD of the PLWI system. The GeoDetector model was applied to explore the factors influencing the CCD of the PLWI system considering the nonlinear relationship. The major results can be summarized as follows: (1) From 2000 to 2020, the comprehensive development index (CDI) of the population, land, water and industry subsystems followed a gradual upward trend in the lower Yellow River, increasing by 0.293, 0.033, 0.111 and 0.369, respectively. However, the CDI of the land subsystem varied greatly between regions. Some cities, such as Jinan, Jining and Binzhou, experienced large declines in the CDI of the land subsystem, from 0.433, 0.534 and 0.572 to 0.358, 0.481 and 0.522, respectively. (2) The CCD of the PLWI system in the lower Yellow River showed an upward trend, increasing from 0.481 to 0.678, and became more concentrated during 2000-2020. Most of the region transitioned from near disorder to primary coordination. (3) Factors such as number of health technicians per 10,000 people, average salary, number of college students per 10,000 people, per capita GDP and per capita education expenditure were critical to the coordinated development of the PLWI system, the explanatory powers were 0.644, 0.639, 0.610, 0.498 and 0.455, respectively. Finally, this study proposed three policy recommendations to improve coupling coordination in the lower Yellow River Basin: Improving population quality, promoting green technology and rational land planning.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013-2020

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013-2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

Comprehensive comparison of two models evaluating eco-environmental quality in Fangshan

It is crucial to employ scientifically sound models for assessing the quality of the ecological environment and revealing the strengths and weaknesses of ecosystems. This process is vital for identifying regional ecological and environmental issues and devising relevant protective measures. Among the widely acknowledged models for evaluating ecological quality, the ecological index (EI) and remote sensing ecological index (RSEI) stand out; however, there is a notable gap in the literature discussing their differences, characteristics, and reasons for selecting either model. In this study, we focused on Fangshan District, Beijing, China, to examine the differences between the two models from 2017 to 2021. We summarized the variations in evaluation indices, importance, quantitative methods, and data acquisition times, proposing application scenarios for both models. The results indicate that the ecological environment quality in Fangshan District, Beijing, remained favorable from 2017 to 2021. There was a discernible trend of initially declining quality followed by subsequent improvement. The variation in the calculation results is evident in the overall correlation between the RSEI and EI. Particularly noteworthy is the significantly smaller correlation between EI and the RSEI in 2021 than in the other two years. This discrepancy is attributed to shifts in the contribution of the evaluation indices within the RSEI model. The use of diverse quantitative methods for evaluating indicators has resulted in several variations. Notably, the evaluation outcomes of the EI model exhibit a stronger correlation with land cover types. This correlation contributes to a more pronounced fluctuation in RSEI levels from 2017 to 2021, with the EI model's evaluation results in 2019 notably surpassing those of the RSEI model. Ultimately, the most prominent disparities lie in the calculation results for water areas and construction land. The substantial difference in water areas is attributed to the distinct importance assigned to evaluation indicators between the two models. Moreover, the notable difference in construction land arises from the use of different quantification methods for evaluation indicators. In general, the EI model has suggested to be more comprehensive and effectively captures the annual comprehensive status of the ecological environment and the multiyear change characteristics of the administrative region. On the other hand, RSEI models exhibit greater flexibility and ease of implementation, independent of spatial and temporal scales. These findings contribute to a clearer understanding of the models' advantages and limitations, offering guidance for decision makers and valuable insights for the improvement and development of ecological environmental quality evaluation models.

Gridded material stocks in China based on geographical and geometric configurations of the built-environment

Material stocks have created alternative perspectives in many environmental and climate studies. Their significance nonetheless may be under-explored, partially due to scarcity of more precise, timely and higher-resolution information. To address this limitation, our present study developed a gridded material stocks dataset for China in Year 2000 and 2020, by examining the geographical distribution and geometric configurations of the human-made stock-containing environment. The stocks of twelve materials embodied in five end-use sectors and 104 products and constructions were assessed at a resolution of 1 × 1 km grid. Material intensity in each product or construction component was carefully evaluated and tagged with its geometric conformation. The gridded stocks aggregately are consistent with the stock estimation across 337 prefectures and municipalities. The reliability of our assessment was also validated by previous studies from national, regional, to grid levels. This gridded mapping of material stocks may offer insights for urban-rural disparities, urban mining opportunity, and climate and natural disaster resilience.

Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 'problem areas' were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (-0.126) had a negative impact. Among the natural indicators, soil organic matter (-0.212) and temperature (-0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.