Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

A study on sustainable development capacity of China's coastal areas using indices dimension reduction method

Sustainable Development Capacity (SDC) is a comprehensive concept. In order to obtain a relatively objective evaluation of it, many indices of various aspects are often used in assessing index systems. However, the overlapping information of indices is a frequent source deviating the result from the truth. In this paper, 48 indices are selected as original variables in assessing SDC of China's coastal areas. The mathematical method of dimension reducing treatment is used for eliminating the overlapping information in 48 variables. Five new comprehensive indices are extracted bearing efficient messages of original indices. On the base of new indices values, the sequencing of 12 coastal areas SDC is gained, and five patterns of sustainable development regions are sorted. Then, the leading factors and their relations of SDC in these patterns are analyzed. The gains of research are discussed in the end.

Measurement and assessment of water resources carrying capacity in Henan Province, China

As demands on limited water resources intensify, concerns are being raised about water resources carrying capacity（WRCC）, which is defined as the maximum sustainable socioeconomic scale that can be supported by available water resources and while maintaining defined environmental conditions. This paper proposes a distributed quantitative model for WRCC, based on the principles of optimization, and considering hydro-economic interaction, water supply, water quality, and socioeconomic development constraints. With the model, the WRCCs of 60 subregions in Henan Province were determined for different development periods. The results showed that the water resources carrying level of Henan Province was suitably loaded in 2010, but that the province would be mildly overloaded in 2030 with respect to the socioeconomic development planning goals. The restricting factors for WRCC included the available water resources, the increasing rate of GDP, the urbanization ratio, the irrigation water utilization coefficient, the industrial water recycling rate, and the wastewater reuse rate, of which the available water resources was the most crucial factor. Because these factors varied temporally and spatially, the trends in predicted WRCC were inconsistent across different subregions and periods.

The Change of Ecological Footprint and Its Effect on Sustainable Development in Beijing of China

Speeding up the environmental protection and construction is one of the major issues in ＂Greener Olympic Games＂ held in Beijing in 2008. Making a thorough study to find out the reliable measures to ecological resumption in Beijing has an important scientific and practical significance. It can provide scientific basis for making a better decision for ＂Greener Olympic Games＂ held in capital Beijing and development in harmony for society and economy, as well as management for ecological environment. By studying of change of ecological footprint from 1990 to 2003 in Beijing, the paper shows that ecological footprint per capita has a strong relativity with population and GDP, and both of the correlation coefficient between them is about 0.92, as well as the fossil energy land has a position in the lead in ecological footprint items. The study also shows that the ecological capacity per capita in Beijing occupied about 1.25% in global ecological capacity per capita. Began 1990, the ecological deficit in Beijing was about 0.72 hm2, which is at strong unsustainable development at present. Finally, the paper suggests social and economic structure should be adjusted as soon as possible, resources should be strengthen sustainable used and population increasing should be controlled strictly, as well as arable land also should be under control used for built-up areas. Simultaneity, the utilizing ratio of energy sources should be increased, and the consumption of energy sources and ecological deficit in Beijing should be reduced in order to speed up the society and economy development in harmony and sustainment.

A model to assess fundamental and realized carrying capacities of island ecosystem:A case study in the southern Miaodao Archipelago of China

Research on carrying capacity, aiming at maintaining the limited ability of the Earth＇s life supporting system to sustain human development, requires a comprehensive and ecosystem-based approach to monitor and assess the localized sustainability of coupled social and ecological systems. A definition termed the ecosystem-based carrying capacity of island（EBCCI） was developed in this paper of which the indices of fundamental and realized carrying capacities of island（FCCI and RCCI） were highlighted to emphasize the inter-dependencies between social systems and ecological systems. In order to avoid the difficulties and uncertainties of direct assessment, the related assessment model was established on the basis of synthetic evaluation of inherent and external factors affecting the EBCCI. The southern Miaodao Archipelago（SMA） located in the intersection of the Bohai Sea and the Yellow Sea, China, was taken as a typical example to acquire integrated recognition of the island ecosystem and its carrying capacity so as to discuss its sustainable development. The index scores of FCCI and RCCI in the SMA were 0.818 5 and 0.712 9 out of 1.0, respectively, with acceptable uncertainties. The results showed a relatively well capacity to sustain progress and relatively well realization of the carrying capacity of island ecosystem, owing to a well capacity of ecologically regulating, general performance of both ecologically supporting and resource provisioning, and a relatively high level of social supporting system. The study implied that it was critical to optimize the inter-dependencies and to sustain the relative balance between social systems and ecological systems so as to improve the RCCI and further facilitate the sustainability of SMA. The approach proposed in this paper provides a powerful tool which is well applicative to the regional level of an oceanic island or archipelago to study the sustainable development and can be further popularized to the coastal zone.