Search Results (4)

As the urbanization and industrialization processes in developing countries continue to advance, environmental issues caused by carbon dioxide emissions (CDEs) have become a significant research topic in the field of sustainable development. However, existing research has primarily focused on macro and meso scales such as global, national, and urban levels, and due to limitations in data precision, in-depth exploration of spatial heterogeneity within cities remains insufficient. To address this, this study utilizes China high-resolution emission gridded data (CHRED) to establish a theoretical analytical framework for spatial zoning of urban carbon emissions. The main innovations of this study are as follows: first, a stepwise analysis method matching carbon emissions with spatial patterns was designed based on CHRED data; second, by establishing a “ring-layer and direction” model, the study systematically revealed the spatial differentiation characteristics of carbon emissions within cities. Empirical research using Shenzhen as a case study shows that the city’s CDE intensity (CDEI) is generally at a medium-to-low level, but exhibits significant spatial heterogeneity, with Nanshan District and Kuiyong District forming two major high-emission core areas. Further analysis reveals that during the processes of urbanization and industrialization, population density, nighttime light intensity index, and the proportion of construction land are the key drivers influencing the spatial pattern of carbon emissions. This study provides scientific basis and decision-making references for optimizing urban spatial layout to achieve the “dual carbon” goals. Full article

With ever-increasing urbanization and industrialization in developing countries, the challenge posed by carbon dioxide emissions (CDEs) has become a hot topic of concern in the realm of sustainable development from a socioeconomic perspective. However, previous studies have only been conducted at macro and meso scales, including at the global, country, and urban levels, and few researchers have delved into the territorial space of urban areas due to a lack of high-precision data. To address this deficiency, we established a theoretical framework to explore the spatial zoning of CDEs based on the newly emerging China high-resolution emission gridded data (CHRED). This study’s innovativeness lies in its provision of a step-by-step process for spatial matching of CDEs based on CHRED in the framework and the construction of square layers to reveal spatial heterogeneity of CDEs at the intra-city level. Taking Nanjing City as the case study area, our findings indicated that CDEs intensity (CDEI) shows an inverted “U-shaped” trend that first increased and then decreased, and finally stabilized from the center to the periphery of the city. With further urbanization and industrialization, the energy consumption sector was found to be the largest contributor to CDEs in Nanjing, and the expanding carbon source zonings will therefore shrink the existing carbon sink zonings. Collectively, these results can provide a scientific reference point to realize China’s “dual carbon” target from the perspective of spatial layout optimization. Full article

Anthropogenic carbon dioxide (CO₂) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO₂ with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO₂ emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO₂ (XCO₂). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO₂ anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO₂. The model is trained using long-term datasets of XCO₂ and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO₂ values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R² is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R² is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories. Full article

A series of carbon dioxide (CO₂) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of small administrational units (cities; counties). In this study, we investigate three open-source gridded CO₂ emission inventories (EDGAR; MEIC; PKU-CO₂) and two statistical data-based inventories (CHRED; CEADs) covering the period of 2000–2020 for 16 prefecture-level cities in Shandong province in order to quantify the cross-inventory uncertainty and to discuss potential reasons for it. Despite ±20% differences in aggregated provincial emissions, all inventories agree that the emissions from Shandong increased by ~10% per year before 2012 and that the increasing trend slowed down after 2012, with a quasi-stationary industrial emission proportion being observed during 2008–2014. The cross-inventory discrepancies increased remarkably when downscaled to the city level. The relative differences between two individual inventories for half of the cities exceeded 100%. Despite close estimations of aggregated provincial emissions, the MEIC provides relatively high estimates for cities with complex and dynamic industrial systems, while the CHRED tends to provide high estimates for heavily industrial cities. The CHRED and MEIC show reasonable agreement regarding the evolution of city-level emissions and the city-level industrial emission ratios over 2005–2020. The PKU-CO₂ and EDGAR failed to capture the emissions and their structural changes at the city level, which is related to their point-source database stopping updates after 2012. Our results suggest that cross-inventory differences for city-level emissions exist not only in their aggregated emissions but also in their changes over time. Full article