As the world's second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran's I, while significant local Moran's I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.
energy carbon emissions; nighttime light (NTL) data; spatiotemporal evolution; Tapio decoupling analysis; Northeast China
Abstract: Rapid urbanization promotes socio-economic development but also poses challenges for urban management, particularly in achieving a balanced job-housing relationship. Such imbalances can aggravate traffic congestion, increase energy consumption, and reduce commuting efficiency. Addressing these urban issues requires accurate job-housing space identification (JHSI). The emergence of spatiotemporal big data in geography has popularized location-based service (LBS) data, especially mobile signaling data, for JHSI applications. However, employing mobile signaling data for JHSI presents challenges stemming from both dataset limitations and methodological complexities, including data accessibility constraints due to privacy concerns. This study develops an optimized JHSI approach using a novel LBS dataset and changing the identification lens into base stations. The newly adopted dynamic population data features simplified, privacy-sensitive fields. By establishing time thresholds for working and living hours based on local daily routines and applying straightforward statistical processing to these defined base station fields, we can derive estimated job-housing spaces. This approach not only achieves concise, high-precision identification with readily available data but also enables lightweight dataset applications with enhanced feasibility and broader applicability. We implemented this optimized approach in Haidian District, Beijing, using five days of 2023 data to evaluate method’s applicability and quantify job-housing imbalances at subdistrict and town scales. Results demonstrate the approach’s accuracy and multi-scale utility in assessing job-housing relationships. We contend that this optimized method advances JHSI-related perspectives in macro-level daily research, facilitates further LBS-driven urban applications, and contributes to improving human livability and quality of life in urban areas.
Urban areas; Population Dynamic Data; Job-Housing Space Identification (JHSI); Location-Based Service (LBS) Data; Haidian District of Beijing