Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets

Under the leadership of the C40 Cities Climate Leadership Group (C40), approximately 1100 global cities have signed to reach net-zero emissions by 2050. Accurate greenhouse gas emission calculations at the city-scale have become critical. This study forms a bridge between the two emission calculation methods: (a) the city-scale accounting used by C40 cities-the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) and (b) the global-scale gridded datasets used by the research community-the Emission Database for Global Atmospheric Research (EDGAR) and Open-Source Data Inventory for Anthropogenic CO2 (ODIAC). For the emission magnitudes of 78 C40 cities, we find good correlations between the GPC and EDGAR (R 2 = 0.80) and the GPC and ODIAC (R 2 = 0.72). Regionally, African cities show the largest variability in the three emission estimates. For the emission trends, the standard deviation of the differences is ±4.7% yr-1 for EDGAR vs. GPC and is ±3.9% yr-1 for ODIAC vs. GPC: a factor of ∼2 larger than the trends that many C40 cities pledged (net-zero by 2050 from 2010, or -2.5% yr-1). To examine the source of discrepancies in the emission datasets, we assess the impact of spatial resolutions of EDGAR (0.1°) and ODIAC (1 km) on estimating varying-sized cities' emissions. Our analysis shows that the coarser resolution of EDGAR can artificially decrease emissions by 13% for cities smaller than 1000 km2. We find that data quality of emission factors (EFs) used in GPC inventories vary regionally: the highest quality for European and North American and the lowest for African and Latin American cities. Our study indicates that the following items should be prioritized to reduce the discrepancies between the two emission calculation methods: (a) implementing local-specific/up-to-date EFs in GPC inventories, (b) keeping the global power plant database current, and (c) incorporating satellite-derived CO2 datasets (i.e. NASA OCO-3).

Asymmetric Effects of Economic Development, Agroforestry Development, Energy Consumption, and Population Size on CO2 Emissions in China

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry; economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption; energy consumption is the cause of economic development and CO2 emissions; and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Spatiotemporal Variations and Driving Factors of Ecological Land during Urbanization—A Case Study in the Yangtze River’s Lower Reaches

Ecological land change is an important indicator of eco-environment quality when balancing urbanization and regional ecological safety. Nantong, located in the Yangtze River’s lower reaches, has experienced rapid urbanization since the reform and opening-up policy was implemented in China in 1978. To ensure the regional ecological conservation and restoration of the Yangtze River and the city’s sustainable development, we used remote sensing technology and statistical yearbook data as well as land use dynamic degree (LUDD) and Geodetector methods to determine the spatiotemporal dynamics of ecological land in the Nantong riverine area from 1980 to 2020 and further discussed the potential driving factors. We found that (1) from 1980 to 2020, the major types of ecological land changed from cropland (82.08%), water (17.19%), and grassland (0.69%) to cropland (70.11%), water (26.98%), and forestland (2.25%), and the ecological land area decreased by 4091.36 km2 during the same period with a significantly increased dynamic degree of land use. (2) Spatial heterogeneity existed in the distribution and variation of ecological land. Water was the dominant ecological land use in the Yangtze River levee’s inner area, with transitions to cropland and impervious surfaces as the primary conversion types; cropland was the primary land use in the levee’s external area, with transitions from cropland and water to impervious surface as the primary conversion types. In addition, in cities with an early start and a high level of urbanization, most of the ecological land had been converted to impervious surfaces by urban development, whereas cities without those characteristics had retained more of their ecological land. (3) Ecological land change was influenced by a combination of natural and socio-economic factors, and there were enhanced-bi and enhanced-nonlinear interactions between them. (4) The dominant factors influencing ecological land changes during the three stages of urbanization (1980–2000, 2000–2010, and 2010–2020) were the distance to the Yangtze River, the population, and the GDP (Gross Domestic Product) of secondary industry, respectively. The role of environmental policies has gradually increased in recent years, which has played a positive role in ecological land use restoration. The findings of this study can assist policymakers in optimizing land use and restoring ecological space to conserve biodiversity.

Temporal and Spatial Heterogeneity of Soil Erosion and a Quantitative Analysis of Its Determinants in the Three Gorges Reservoir Area, China

As the most typical ecologically fragile area in South China, the Three Gorges Reservoir Area (TGRA) suffers from water and soil loss, which has threatened the local ecological environment. Understanding the spatial heterogeneity of soil erosion and exploring its determinants are of great significance in preventing soil erosion and maintaining ecological sustainability in the TGRA. This study investigates the spatial heterogeneity of soil erosion and quantitatively identifies the determinants in the TGRA based on the Chinese Soil Loss Equation (CSLE) and geographical detector method. This study concluded that the soil erosion status generally improved from 1990 to 2015, showing an increasing trend from 1990 to 2000 and a decreasing trend from 2000 to 2010. Slope, land use, and vegetation coverage were the dominant individual factors affecting soil erosion in the TGRA. For the interaction factor, the combinations of land-use type and slope and vegetation coverage and slope were the key determinants, explaining 68.7% and 63.1% of the spatial heterogeneity of soil erosion in the TGRA from 1990 to 2015, respectively. Moderate and higher levels of soil erosion occurred in areas where the slope was greater than 25°. Among the land-use types, dry land and bare land were prone to soil erosion. These findings reveal that land-use type and vegetation coverage should be considered for the effective prevention of soil erosion, and cultivation on sloped farmland should be prohibited, especially on slopes higher than 25° in the TGRA.

Spatial Heterogeneity of Winter Wheat Yield and Its Determinants in the Yellow River Delta, China

Understanding spatial differences of crop yields and quantitatively exploring the relationship between crop yields and influencing factors are of great significance in increasing regional crop yields, promoting sustainable development of regional agriculture and ensuring regional food security. This study investigates spatial heterogeneity of winter wheat yield and its determinants in the Yellow River Delta (YRD) region. The spatial pattern of winter wheat in 2015 was mapped through time series similarity analysis. Winter wheat yield was estimated by integrating phenological information into yield model, and cross-validation was performed using actual yield data. The geographical detector method was used to analyze determinants influencing winter wheat yield. This study concluded that the overall classification accuracy for winter wheat is 88.09%. The estimated yield agreed with actual yield, with R2 value of 0.74 and root mean square error (RMSE) of 1.02 t ha−1. Cumulative temperature, soil salinity and their interactions were key determinants affecting winter wheat yield. Several measures are recommended to ensure sustainable crop production in the YRD region, including improving irrigation and drainage systems to reduce soil salinity, selecting salt-tolerant winter wheat varieties, and improving agronomy techniques to extend effective cumulative temperature.

Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China

In the context of economic globalization, the manufacturing production space in the global city-regions of developing countries have presented significant spatial characteristics, attracting attention to the problems of intensive and sustainable development of production space. Taking global city-region in the Pearl River Delta (PRD) as an example, manufacturing production space based on remote sensing (RS) technology and point of interest (POI) data extraction was more precise and continuous, which had more advantages for further analysis of spatial characteristics and influencing factors in multi-scale, and precise policy recommendations. The results show that: (1) under different scales, the distribution characteristics of manufacturing production space and the agglomeration characteristics of spatial form are different. It is not simply extensive agglomeration or diffusion that can accurately explain its diversified spatial characteristics. Meanwhile, for the local manufacturing production space optimization control, the local government should apply advanced experience according to local conditions instead of simply and roughly promotion or containment. (2) Influencing factors show a strong positive correlation with the urbanization rate, the number of foreign direct investment (FDI) enterprises and gross industrial production, and which shows a weak negative correlation with fixed asset investment and the employment population. In conclusion, the spatial characteristics of manufacturing production space in global city-regions in developing countries is significantly different from that in Western countries, and its influencing factors have similarities and differences. Therefore, when conducting multi-scale space optimization and sustainable regulation, the government should consider more about the actual multi-scale spatial characteristics of manufacturing production space and its influencing factors instead of copy the Western experience.