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The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents' commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers' commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples' jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents' travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]