The 18th National Congress of the Communist Party of China put forward the optimization of territorial space development patterns as the primary measure of ecological civilization construction, and put forward the goal of “promoting intensive and efficient production space, livable and moderate living space, and beautiful (picturesque scenery) ecological space”. Through literature research and summing induction, this paper combs the research progress of the overall optimization of “Production–Living–Ecological” space (PLES) systematically. It is found that the existing work mainly focuses on the overall optimization of PLES from the perspectives of land-use quality, land-use suitability evaluation, resource and environmental carrying capacity, and comparative advantages. However, due to the lack of understanding of the scientific connotation of PLES, and the imperfect construction of quantitative identification methods and classification system, there are many problems in the technical approaches of the overall optimization of PLES, which remain to be clarified. In the future, the technological approach to the overall optimization of PLES should be guided by the vision of building a beautiful China, with the theory of a human–Earth coupling system as the core, and systematically build a theoretical system and technical framework to identify and optimize territorial space.

Mine water inrush disaster seriously threatens the production of coal mine. Rapid and accurate identification of mine water inrush sources is a key premise for mine water disaster prevention. The conventional research on the identification of water inrush source has focused on a single source, and the identification of mixed water samples from multi-source aquifers in deep coal mining environment is not yet fully explored. In this study, absorption spectrum technology was introduced into the identification of water inrush sources. The absorption spectra of the water samples with different mixing ratios were prepared using the ultraviolet and visible spectrophotometry (UV–Vis) spectrophotometer. In addition, spectral data preprocessing such as scattering correction, baseline correction, smoothing and denoising, and data enhancement were conducted to reduce the influence of experimental error, environment, radiation, molecular interaction, and other factors on the spectral data. Furthermore, a genetic algorithm (GA) was used to improve the seven parameters of the extreme gradient boosting (XGBoost) algorithm, such as learning rate, base model selection, tree parameters, regularization parameters, and iteration times. The deep-learning classifier of mine mixed water sources based on GA-XGBoost was established and used to identify 66 groups of mixed water sources in the Huangyuchuan Mine. The simulation results show that spectral preprocessing and normalization enhancement effectively improved the accuracy of the discriminant model. After 100 cross-validations, the average recognition accuracy of the GA-XGBoost model was 94%, and the results were accurate and reliable. This study provides a new direction and method for the identification of water inrush sources, particularly for mixed water inrush sources. It may also serve as a technical reference for decision-makers to formulate effective coal mine water inrush prevention and control programs and for mine water disaster prevention in similar coalfields in North China.

Energy crises, water shortages, and rising carbon emissions are constantly posing new demands and challenges to global economic development. Considering the problem of high emissions and high water consumption in the process of energy production and transformation in resource-based cities, this study established the LEAP-Jincheng model based on the low emissions analysis platform (LEAP) model. Taking 2020 as the base year, the baseline scenario (BS), policy scenario (PS), and intensified scenario (IS) were set to predict future energy and water consumption and carbon emissions of Jincheng from 2021 to 2050. The results show that both PS and IS can achieve energy conservation and emission reduction to some extent. The total energy consumption of PS will be 32.89 million metric tons of coal equivalent in 2050, 15.62% less than the BS. However, the carbon emissions in 2030 will reach 8221 metric tons CO₂ equivalent, which is significantly higher than that in other scenarios. In PS, carbon emissions after 2030 will not be significantly reduced, and the energy–water elasticity coefficient is −0.77, which fails to achieve effective emission reduction under energy–water synergy. The total energy consumption of the IS will be 22.57 million metric tons of coal equities in 2050, which has a total decrease of 31.38%, compared to BS. In the IS, the carbon emissions will reach a peak in 2030 (68.77 million metric tons CO₂ equivalent) and subsequently reduce to 50.72 million metric tons CO₂ equivalent in 2050, which has a total decrease of 50.64%, compared to BS. Furthermore, water consumption and energy–water synergy results show that the elastic coefficient is 1.37 in the IS. The IS is the best scenario for Jincheng to achieve coordinated development of energy and water resources from a low-carbon perspective. This study can provide a scientific basis for decision-making departments of Jincheng to formulate targeted sustainable development policies for energy and water and has an essential promoting significance for China to achieve the “double carbon” goals.