Does Urban Sprawl Inhibit Urban Eco-Efficiency? Empirical Studies of Super-Efficiency and Threshold Regression Models

Land Use Change, Urban Agglomeration, and Urban Sprawl: A Sustainable Development Perspective of Makassar City, Indonesia

Urbanization towards the expansion of the city area causes urban sprawl and changes in space use. Furthermore, urban agglomeration towards urban spatial integration causes a decrease in environmental quality. This study aims to analyze (1) land-use change and urban sprawl work as determinants of environmental quality degradation in suburban areas. (2) The effect of urban sprawl, urban agglomeration, land-use change, urban activity systems, and transportation systems on environmental quality degradation in suburban areas. A combination of quantitative and qualitative approaches is used sequentially in this study. Data obtained through observation, surveys, and documentation. The results showed that the expansion of the Makassar City area to the suburbs had an impact on spatial dynamics, spatial segregation, and environmental degradation. Furthermore, urban sprawl, land-use change, urban agglomeration, activity systems, and transportation systems have a positive correlation to environmental quality degradation with a determination coefficient of 85.9%. This study recommends the handling of urban sprawl, land-use change, and urban agglomeration to be considered in the formulation of development policies towards the sustainability of natural resources and the environment of Makassar City, Indonesia.

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km2. Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl.