Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR

Soil total nitrogen content and pH value estimation method considering spatial heterogeneity: Based on GNNW-XGBoost model

Soil nitrogen content and pH value are two pivotal factors that critically determine soil fertility and plant growth. As key indicators of soil health, they each play distinct yet complementary roles in the soil ecosystem. Nitrogen is one of the essential nutrients for plant growth, while soil pH directly influences the activity of soil microorganisms. These microbes are essential for breaking down minerals and organic materials, which in turn affects the availability and conversion of key nutrients like nitrogen and phosphorus. A comprehensive understanding of the distribution of total nitrogen content and pH value is crucial for ensuring the sustainability of agricultural production and maintaining soil and ecosystem health. Existing models for estimating soil property based on near-infrared (NIR) spectral data often overlook the spatial non-stationarity of the relationship between soil spectra and composition content. Therefore, we proposed a new model for estimating soil total nitrogen content and pH value, which combined geographically neural network weighted regression (GNNWR) with extreme gradient boosting (XGBoost), utilizing neural networks to improve the accuracy of predicting total nitrogen content and pH value, efficiently captured the spatial heterogeneity between spectral reflectance and soil total nitrogen content and pH value in different regions. Using the soil nutrient and visible near-infrared spectral samples collected by Eurostat in 2009 for the land use and coverage area frame survey of the 23 members of the European Union, the Geographically Neural Network Weighted-eXtreme Gradient Boosting (GNNW-XGBoost) model was used to estimate total nitrogen content and pH value. The spatial correlation between reflectance of spectral characteristic bands and soil total nitrogen content, pH value was trained in the model to verify its robustness and superiority, and the experimental process was improved by 10-fold cross-validation. In terms of model evaluation, compared to the standalone XGBoost and GNNWR models, the GNNW-XGBoost model demonstrated superior predictive accuracy. It achieved a highest coefficient of determination (R2) of 0.84 for total nitrogen and 0.80 for pH. Additionally, it reduced the root mean square error (RMSE) by 7.64 %, 7.61 % for total nitrogen, and 8.96 %, 4.69 % for pH, respectively. This study not only provides a new method for accurate prediction of soil total nitrogen content and pH value, but also has significant reference value for other estimation issues involving geographic data, which can help to improve the accuracy of environmental monitoring, optimize resource management strategies, and promote the development of sustainable agriculture.

Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey model informed by two-stage enhanced multi-objective optimization

In the global quest for carbon neutrality, electricity is a critical sector for carbon reduction, for electricity consumption and carbon emissions are closely associated. Electricity consumption forecasts are divided into short-term and long-term, but previous studies have focused more on the former, while the latter is the foundation of power system planning and directly relates to urban development. To address the issue, this research proposed an innovative hybrid Hausdorff fractional grey model (HfGM) for electricity consumption prediction, weakening buffer operator (WBo) was incorporated to minimize interference of external shocks to original data, the optimal core parameters of HfGM were searched by a newly developed multi-objective enhanced version of slime mould algorithm in two stages, achieving Pareto optimal solutions theoretically. Experiments results demonstrated the proposed model outperformed comparative models, leading to its application in predicting electricity consumption trends in China during the 15th Five-Year Plan period and assessing the corresponding environmental impacts. Beyond advancing grey model theory, the research provides essential policy recommendations for integrating environmental management into electricity demand planning, assisting policymakers in addressing demand growth and supporting carbon reduction goals.

Analysis of dynamic evolution characteristics and driving factors of regional synergistic effect of reducing carbon and saving water: the case of Yangtze River Delta Urban Agglomerations, China

Carbon emissions and water consumption are both important factors affecting sustainable development. Therefore, it is necessary to put them in the same research framework and investigate the synergy. In this study, the dynamic evolution characteristics of the synergistic effect of reducing carbon and saving water (RCSW) were analyzed. Then, taking the Yangtze River Delta Urban Agglomerations (YRDUA) as the research object, the influencing factors and specific paths of the synergistic effect were clarified. The results showed that the low-carbon emission efficiency (LCEE) had a stable synergy with the intensive utilization efficiency of water resources (IUEWR) in the YRDUA. Government financial expenditure, actual use of foreign capital, and population density were the most significant driving forces for the synergistic effect of RCSW, with q values of 0.561, 0.363, and 0.240, respectively. In addition, most of the interactions of the driving factors were nonlinear enhancement and double-factor enhancement.

Modelling monthly-gridded carbon emissions based on nighttime light data

Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.

Coal fly ash and bottom ash low-cost feedstocks for CO2 reduction using the adsorption and catalysis processes

Combustion of fossil fuels, industry and agriculture sectors are considered as the largest emitters of carbon dioxide. In fact, the emission of CO2 greenhouse gas has been considerably intensified during the last two decades, resulting in global warming and inducing variety of adverse health effects on human and environment. Calling for effective and green feedstocks to remove CO2, low-cost materials such as coal ashes "wastes-to-materials", have been considered among the interesting candidates of CO2 capture technologies. On the other hand, several techniques employing coal ashes as inorganic supports (e.g., catalytic reduction, photocatalysis, gas conversion, ceramic filter, gas scrubbing, adsorption, etc.) have been widely applied to reduce CO2. These processes are among the most efficient solutions utilized by industrialists and scientists to produce clean energy from CO2 and limit its continuous emission into the atmosphere. Herein, we review the recent trends and advancements in the applications of coal ashes including coal fly ash and bottom ash as low-cost wastes to reduce CO2 concentration through adsorption and catalysis processes. The chemical routes of structural modification and characterization of coal ash-based feedstocks are discussed in details. The adsorption and catalytic performance of the coal ashes derivatives towards CO2 selective reduction to CH4 are also described. The main objective of this review is to highlight the excellent capacity of coal fly ash and bottom ash to capture and selective conversion of CO2 to methane, with the aim of minimizing coal ashes disposal and their storage costs. From a practical view of point, the needs of developing new advanced technologies and recycling strategies might be urgent in the near future to efficient make use of coal ashes as new cleaner materials for CO2 remediation purposes, which favourably affects the rate of global warming.