Sustainable development (SD) evaluations have attracted considerable attention from governments and scientific communities around the world. The objective and quantitative calculation of the importance of sustainable assessment indicators is a key problem in the accurate evaluation of SD. Traditional methods fail to quantify the coupling effects among indicators. This paper presents a weight determination approach based on the global sensitivity analysis algorithm known as the extended Fourier amplitude sensitivity test (EFAST). This method is efficient and robust and is not only able to quantify the sensitivity of the evaluation indictors to the target, but can also quantitatively describe the uncertainties among the indictors. In this paper, we analyze the sensitivity of 18 indicators in a multi-index comprehensive evaluation model and weigh the indicators in the system according to their importance. To verify the feasibility and advantages of this new method, we compare the evaluation result with the traditional entropy method. The comparison shows that the EFAST algorithm can provide greater detail in an SD evaluation. Additionally, the EFAST algorithm is more specific in terms of quantitative analysis and comprehensive aspects and can more effectively distinguish the importance of indicators.

The Sustainable Development Goals (SDGs) of the United Nations cover all living things on Earth. However, downscaling the SDGs to regional scales for implementation is challenging. In the paper, we convert the general SDGs into tangible and actionable goals, targets and indicators for use in integrated river basin management (IRBM). Further, we propose a decision support framework that can be used to support IRBM implementation based on the SDGs. The framework offers a context for open thinking in which IRBM decision makers envision socioeconomic and ecosystem goals and the development tracks of a river basin and explore the various paths that can be followed to reach the goals. In particular, indicators are proposed for use in IRBM, which consider five aspects of river basins, specifically water, ecosystems, socioeconomic development, ability and data. To enable decision-making that promotes progress toward the goals, five scenarios, 17 sub-scenarios and 29 key parameters are provided that form a diverse set of scenarios corresponding to specific decision schemes. Moreover, these scenarios, sub-scenarios and parameters consider future uncertainties and both engineering and non-engineering measures that can be taken to achieve the co-development of human and natural factors in a basin.

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.

The tea plant (Camellia sinensis), as a major, global cash crop providing beverages, is facing major challenges from droughts and water shortages due to climate change. The accurate estimation of the actual evapotranspiration (ET_a) of tea plants is essential for improving the water management and crop health of tea plantations. However, an accurate quantification of tea plantations’ ET_a is lacking due to the complex and non-linear process that is difficult to measure and estimate accurately. Ensemble learning (EL) is a promising potential algorithm for accurate evapotranspiration prediction, which solves this complexity through the new field of machine learning. In this study, we investigated the potential of three EL algorithms—random forest (RF), bagging, and adaptive boosting (Ad)—for predicting the daily ET_a of tea plants, which were then compared with the commonly used k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP) algorithms, and the experimental model. We used 36 estimation models with six scenarios from available meteorological and evapotranspiration data collected from tea plantations over a period of 12 years (2010–2021). The results show that the combination of R_n (net radiation), T_mean (mean air temperature), and RH (relative humidity) achieved reasonable precision in assessing the daily ET_a of tea plantations in the absence of climatic datasets. Compared with other advanced models, the RF model demonstrated superior performance (root mean square error (RMSE): 0.41–0.56 mm day⁻¹, mean absolute error (MAE): 0.32–0.42 mm day⁻¹, R²: 0.84–0.91) in predicting the daily ET_a of tea plantations, except in Scenario 6, followed by the bagging, SVM, KNN, Ad, and MLP algorithms. In addition, the RF and bagging models exhibited the highest steadiness with low RMSE values increasing (−15.3~+18.5%) in the validation phase over the testing phase. Considering the high prediction accuracy and stability of the studied models, the RF and bagging models can be recommended for estimating the daily ET_a estimation of tea plantations. The importance analysis from the studied models demonstrated that the R_n and T_mean are the most critical influential variables that affect the observed and predicted daily ET_a dynamics of tea plantations.