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The jobs&ndash, housing balance concerns the spatial relationship between the number of jobs and housing units within a given geographical area. Due to the separation of jobs and housing, spatial dislocations have occurred in large cities, which have resulted in a significant increase in commuting distance and time. These changes have ultimately led to an increase in pressure on urban traffic, and the formation of tidal traffic. In this study we introduce a multi-agent approach to examine the jobs&ndash, housing relationship under the maximum location utility of agents. The jobs/housing ratio measures the balance of the of jobs&ndash, housing relationship, as well as comparing and analyzing jobs&ndash, housing separation in Beijing by district, county, and street scales. An agent-based model was proposed to simulate spatial location selection behavior of agents by considering environmental and economical influences on residential decisions of individuals. Results show that the jobs&ndash, housing relationship imbalance in Beijing has been mainly aggravated due to rapid population growth in the 6th Ring Road. An imbalance in the jobs&ndash, housing relationship has arisen due to a mismatch with the number of households available compared to the number of jobs, the surrounding urban areas cannot provide the required volume of housing to accommodate the increase in workers. Six sets of experiments were established to examine resident agents and enterprise agents. Differences in resident agents&rsquo, income level had a greater impact on residential location decision-making, and housing price was the primary factor affecting the decision of residents to choose their residential location. The spatial distribution of jobs and housing in Beijing under the maximization of micro-agent location utility was obtained in this study. Results indicated that the imbalance in the jobs&not, housing relationship in central Beijing has improved and, compared with the initial distributions, the number of jobs&ndash, housing balance areas in Beijing has increased.

Despite restructuring in institutions related to environmental protection, the multi-sectorial decentralized water management system of China continued to be widely criticized. To identify the problems in China&rsquo, s water management system and the direction of future reform, this article implemented social network analysis. From multiple perspectives (covering efficiency analysis, condensation analysis, and network centrality analysis), we quantitatively analyzed the structural change of the super-ministry reform of water environment management in China. We found that the 2018 super ministry reform of the system made the aggregation and central enhancement of China&rsquo, s water management network, hence the power of water environmental management is more concentrated in the core department. However, the function overlap still exists after the reform. Some key issues of water resources management are absent from the responsibility of the core management department. Therefore, the cohesion and management effectiveness of the overall management network need to be further improved. Finally, we summarized several practical implications for future water management system reform, and the kernel is to achieve integrated management of water resource and water environment.

The refined allocation of water resources and pollutant loads in a basin according to seasonal changes is an important measure for sustainable management. This study proposes a monthly water resource and pollutant load allocation model that is based on the water footprint and fallback bargaining. First, the water utilization and pollutant discharge demand and allocable resources are accounted for by taking their water footprints as indicators. Subsequently, various initial allocation schemes are designed based on several typical bankruptcy rules. Finally, with the goal of resource sustainability, the initial schemes are optimized by applying the fallback bargaining approach. The Huangshui River basin, which is located in Qinghai, China, is a typical seasonal basin with water use conflicts and it is considered for verifying the proposed methodology. The results show that the monthly allocation framework can effectively balance the water use and pollutant discharge demand of regions upstream and downstream in different seasons, improve the overall resource utilization efficiency in the basin, and ensure that the allocation each month reaches the Pareto optimum.

Carbon footprints have been widely employed as an indicator for total carbon dioxide released by human activities. In this paper, we implemented a multi-regional input-output framework to evaluate the carbon footprints and embodied carbon flows for the eight regions of China from consumption-based perspective. It is found that the construction, electricity/stream supply, and machine manufacturing rank as the top sectors with the largest total carbon emissions. The construction sector alone accounts for 20%–50% of the national emissions. Besides the sectoral carbon footprints, regional footprints and their differences in carbon emissions were also observed. The middle region had the largest total carbon footprints, 1188 million ton, while the capital region ranked the first for its per capita carbon footprint, 7.77 ton/person. In regard to the embodied carbon flows within China, the study detected that the embodied carbon flows take up about 41% of the total carbon footprints of the nation. The northwest region and the eastern coast region are found to be the largest net embodied carbon exporter and importer, respectively. Further investigation revealed significant differences between production-based and consumption-based carbon emissions, both at sectoral and total amounts. Results of this paper can provide specific information to policies on sectoral and regional carbon emission reduction.

"An inexact fuzzy multi-objective programming model (IFMOP) based on the environmental carrying capacity is provided for industrial structure optimization problems. In the IFMOP model, both fuzzy linear programming (FLP) and inexact linear programming (ILP) methods are introduced into a multi-objective programming framework. It allows uncertainties to be directly communicated into the problem solving processing, and it can effectively reflect the complexity and uncertainty of an industrial system without impractical simplification. The two objective functions utilized in the optimization study are the maximum total output value and population size, and the constraints include water environmental capacity, water resource supply, atmospheric environmental capacity and energy supply. The model is subsequently employed in a realistic case for industrial development in the Tongzhou district, Beijing, China. The results demonstrate that the model can help to analyze whether the environmental carrying capacity of Tongzhou can meet the needs of the social economic objectives in the new town plan in the two scenarios and can assist decision makers in generating stable and balanced industrial structure patterns with consideration of the resources, energy and environmental constraints to meet the maximum social economic efficiency."

A change in the usage of land is influenced by a variety of driving factors and policies on spatial constraints. On the basis of considering the conventional natural and socio-economic indicators, the landscape pattern indicators were considered as new driving forces in the conversion of land use and its effects at small regional extent (CLUE-S) model to simulate spatial and temporal changes of land-use in Beijing. Compared with traditional spatial restrictions characterized by small and isolated areas, such as forest parks and natural reserves, the ecological redline areas increase the spatial integrity and connectivity of ecological and environmental functions at a regional scale, which were used to analyze the distribution patterns and behaviors of land use conversion in the CLUE-S model. The observed results indicate that each simulation scenario has a Kappa coefficient of more than 0.76 beyond the threshold value of 0.6 and represents high agreements between the actual and simulated land use maps. The simulation scenarios including landscape pattern indicators are more accurate than those without consideration of these new driving forces. The simulation results from using ecological redline areas as space constraints have the highest precision compared with the unrestricted and traditionally restricted scenarios. Therefore, the CLUE-S model based on the restriction of ecological redline and the consideration of landscape pattern factors has shown better effectiveness in simulating the future land use change. The conversion of land use types mainly occurred between construction land and cropland during the period from 2010 to 2020. Meanwhile, a large number of grasslands are being changed to construction lands in the mountain towns of northwest Beijing and large quantities of water bodies have disappeared and been replaced by construction lands due to rapid urbanization in the eastern and southern plains. To improve the sustainable use of land resources, it is necessary to adopt the construction and development mode of satellite towns rather than encouraging a disorderly expansion of downtown areas.