Work

Research on Urban Sustainability Based on Neural Network Models and GIS Methods

Year: 2025

Type: article

Abstract: Ecologically sustainable urban design plays a pivotal role in mitigating climate change. This study develops an indicator group consisting of urban ecological emergy, land use change, population density, ecological services, habitat quality, enhanced vegetation index, carbon emissions, and carbon storage to assess urban sustainability. By leveraging a dataset from 2000 to 2020, we employ a neural network to predict emergy sustainability indicators over a time series, projecting the sustainable status of Xuzhou City from 2020 to 2050. The findings indicate that urbanization has led to significant changes in land use, population distribution, ecological service patterns, habitat quality degradation, vegetation fragmentation, and fluctuating carbon dynamics. Cropland constitutes the predominant land type (90.6%), followed by built-up land (8.49%). The neural network predictions suggest that Xuzhou City’s sustainable status is subject to volatility (15–20%), with stability expected only as the city matures into a developed urban area. This research introduces a novel approach to urban sustainability analysis and provides insights for policy development aimed at fostering sustainable urban growth.

Source: Sustainability

DOI: 10.3390/su17020397

Authors: Chunxia Zhang, Shuo Yu, Junxue Zhang

Institutions: Yangzhou University, Jiangsu University of Science and Technology

Language: English

Topic: Sustainability and Ecological Systems Analysis

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Life on land

Open Access status: gold

Estimated APC paid: $2,896