Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino-Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino-Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

The spatiotemporal evolution and influencing factors of urban green innovation in China

Green innovation (GI) is an important way to build an ecological civilization and an innovative country. The study on urban green innovation (UGI) is of great significance for enriching the research on GI and rationally formulating high-quality urban development policies. The green patent data obtained using a web crawler was used to represent the level of UGI. The spatiotemporal evolution and influencing factors of UGI in China were analyzed by standard deviation ellipses, spatial autocorrelation, and Geodetector. The research shows that: From 2005 to 2020, the level of UGI in China tended to rise rapidly. The center of gravity of UGI in China was located in the southeast of China's geometric center and tended to move to the south and west. The standard deviation ellipse was distributed in a "northeast-southwest" pattern, the area was gradually shrinking, and the length of the two semi-axes was shortening. UGI in China showed obvious global and local spatial autocorrelations. The degree of global spatial autocorrelation was gradually increasing. Among the types of local spatial autocorrelation, the largest number of low-low agglomeration cities was mainly located in the northwest and southwest part of China, while high-high agglomeration cities were distributed in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The government intervention expressed by the proportion of scientific and technological expenditure in fiscal expenditure and environmental regulation is the dominant factor affecting UGI.

Spatial-Temporal Characteristics of Multi-Hazard Resilience in Ecologically Fragile Areas of Southwest China: A Case Study in Aba

Aba's topography, weather, and climate make it prone to landslides, mudslides, and other natural disasters, which limit economic and social growth. Assessing and improving regional resilience is important to mitigate natural disasters and achieve sustainable development. In this paper, the entropy weight method is used to calculate the resilience of Aba under multi-hazard stress from 2010 to 2018 by combining the existing framework with the disaster resilience of the place (DROP) model. Then spatial-temporal characteristics are analyzed based on the coefficient of variation and exploratory spatial data analysis (ESDA). Finally, partial least squares (PLS) regression is used to identify the key influences on disaster resilience. The results show that (1) the disaster resilience in Aba increased from 2010 to 2018 but dropped in 2013 and 2017 due to large-scale disasters. (2) There are temporal and spatial differences in the level of development in each of the Aba counties. From 2010 to 2016, disaster resilience shows a significant positive spatial association and high-high (HH) aggregation in the east and low-low (LL) aggregation in the west. Then the spatial aggregation weakened after 2017. This paper proposes integrating regional development, strengthening the development level building, and emphasizing disaster management for Aba.

CO2 Emissions Inventory and Its Uncertainty Analysis of China's Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area

The Chinese government has pledged to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Industrial parks are the key to achieving the carbon peak and neutrality in industrial sectors. Establishing the CO₂ emissions inventory is the first step to achieve the carbon peak in industrial parks. In this study, a comprehensive CO₂ emissions inventory was established for industrial parks, including three parts: energy consumption, industrial process, and waste disposal. We considered scope 1, 2, and 3 emissions and established an uncertainty analysis framework. Accordingly, scope 1 covered the emissions within the park boundary, scope 2 emissions covered those resulting from electricity and heat usage inside the boundary, and scope 3 included those indirect emissions beyond the boundary. The Maanshan Economic and Technological Development Area (MDA), a typical booming national eco-industrial park of China, was chosen for this case study. The results showed that the MDA CO₂ emissions increased yearly, from 376,836.57 tons in 2016 to 772,170.93 tons in 2021. From the industrial structure perspective, heavy industry contributed the highest emissions. By dividing the emissions into scope 1, 2, and 3, scope 2 could be identified as the largest emissions source. In addition, we conducted inventory uncertainty analyses incorporated by activity levels, emissions factors, and unspecific factors. Overall, these results may promote the establishment of greenhouse gas accounting standards for Chinese industrial parks.