Work

A multi-source data-driven innovative evaluation approach for assessing low-carbon city performance

Year: 2025

Type: article

Abstract: Practicing Low-carbon city (LCC) is an important step for achieving urban sustainable development goal, and a thorough and accurate evaluation of LCC performance can provide reference for improvement. As big data technology developed, multi-source data (such as POI and remote sensing data) became widely used in evaluation studies. However, few studies have used multi-data sources to evaluate LCC performance from a holistic perspective. This study developed a Multi-Source Data-Driven (MSDD) model for evaluating LCC performance, using data from 38 districts in Chongqing during 2012 to 2022 as a case study. The results show that: 1) The MSDD evaluation model can dynamically and accurately assess LCC performance. 2) LCC performance in the 38 districts of Chongqing has gradually increased, presenting a spatial distribution of ‘high center - low border’. 3) The spatial association network in Chongqing increased apparent, and exchanges in different regions became more frequent. Theoretically, this study broadens the application of multi-source data and improves LCC evaluation model. Practically, the finding can provide city managers with the tools to identify LCC issues and develop effective strategies for carbon reduction.

Source: Sustainable Cities and Society

DOI: 10.1016/j.scs.2025.106416

Authors: Xiaoyun Du, Zhijie Li, Yingna Gao, Ying Che

Institutions: Zhengzhou University, Northwestern Polytechnical University

Language: English

Topic: Environmental Quality and Pollution

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: hybrid

Estimated APC paid: $4,870