Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Spatio-temporal evolution mechanism and dynamic simulation of nitrogen and phosphorus pollution of the Yangtze River economic Belt in China

Excess nitrogen and phosphorus inputs are the main causes of aquatic environmental deterioration. Accurately quantifying and dynamically assessing the regional nitrogen and phosphorus pollution emission (NPPE) loads and influencing factors is crucial for local authorities to implement and formulate refined pollution reduction management strategies. In this study, we constructed a methodological framework for evaluating the spatio-temporal evolution mechanism and dynamic simulation of NPPE. We investigated the spatio-temporal evolution mechanism and influencing factors of NPPE in the Yangtze River Economic Belt (YREB) of China through the pollution load accounting model, spatial correlation analysis model, geographical detector model, back propagation neural network model, and trend analysis model. The results show that the NPPE inputs in the YREB exhibit a general trend of first rising and then falling, with uneven development among various cities in each province. Nonpoint sources are the largest source of land-based NPPE. Overall, positive spatial clustering of NPPE is observed in the cities of the YREB, and there is a certain enhancement in clustering. The GDP of the primary industry and cultivated area are important human activity factors affecting the spatial distribution of NPPE, with economic factors exerting the greatest influence on the NPPE. In the future, the change in NPPE in the YREB at the provincial level is slight, while the nitrogen pollution emissions at the municipal level will develop towards a polarization trend. Most cities in the middle and lower reaches of the YREB in 2035 will exhibit medium to high emissions. This study provides a scientific basis for the control of regional NPPE, and it is necessary to strengthen cooperation and coordination among cities in the future, jointly improve the nitrogen and phosphorus pollution tracing and control management system, and achieve regional sustainable development.

Carbon emission prediction models: A review

Amidst growing concerns over the greenhouse effect, especially its consequential impacts, establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and predict CO2 emission trends is imperative for climate change mitigation. A review of 147 Carbon Emission Prediction Model (CEPM) studies revealed three predominant functions-prediction, optimization, and prediction factor selection. Statistical models, comprising 75 instances, were the most prevalent among prediction models, followed by neural network models at 21.8 %. The consistent rise in neural network model usage, particularly feedforward architectures, was observed from 2019 to 2022. A majority of CEPMs incorporated optimized approaches, with 94.4 % utilizing metaheuristic models. Parameter optimization was the primary focus, followed by structure optimization. Prediction factor selection models, employing Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) for statistical and machine learning models, respectively, filtered factors effectively. Scrutinizing accuracy, pre-optimized CEPMs exhibited varied performance, Root Mean Square Error (RMSE) values spanned from 0.112 to 1635 Mt, while post-optimization led to a notable improvement, the minimum RMSE reached 0.0003 Mt, and the maximum was 95.14 Mt. Finally, we summarized the pros and cons of existing models, classified and counted the factors that influence carbon emissions, clarified the research objectives in CEPM and assessed the applied model evaluation methods and the spatial and temporal scales of existing research.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (-7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

Exploring the relationship between land use/land cover and apparent temperature in China (1996-2020): implications for urban planning

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), [Formula: see text]. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Synergistic effect of pollution reduction and carbon emission mitigation in the digital economy

Synergetic control of environmental pollution and carbon emissions (SCEPCE) is essential to green development. The emergence of the digital economy has become a significant component in regional economic growth. Investigating the digital driving mode for SCEPCE in developing countries is crucial. This paper empirically analyzes the effect of establishing big data comprehensive experimental areas (BDCEAs) on air pollutants and carbon emissions using panel data of prefecture-level cities from 2009 to 2020 and the time-varying difference-in-differences method. The research found that (1) BDCEA inhibits pollution and carbon emissions, and the policy effect is sustainable. (2) The synergistic effect is significant, particularly in small and medium-sized cities and old industrial-base cities. The benefit of reducing pollution is only significant in the east. The effect of reducing CO₂ emissions is only significant in the west. (3) The pollution reduction effect of digital economic development has the characteristics of an increasing marginal effect, and the marginal effect of its carbon reduction effect is not apparent. (4) The technological innovation and energy efficiency improvement effects are effective mechanisms. This paper enriches the studies on the factors influencing SCEPCE, which will help to realize SCEPCE and the harmonious coexistence of humans and nature in developing countries. However, policy incentives and green development strategies must be fine-tuned to achieve global SCEPCE.

Exploring the coupling coordination relationship between eco-environment and renewable energy development in rural areas: A case of China

China's rural need an energy transition to achieve the goal of "carbon neutrality". However, renewable energy development will bring about great changes in rural supply and demand. Therefore, the spatial-temporal coupling coordination relationship between rural renewable energy and the eco-environment needs to be re-examined. Firstly, the study analyzed the coupling mechanism based on the rural renewable energy system. Secondly, the evaluation indicator system of rural renewable energy development and eco-environment was constructed respectively. Finally, a coupling coordination degree (CCD) model was established based on 2-tuple linguistic gray correlation multi-criteria decision-making, prospect theory and coupling theory. The results show that the coupling coordination presented an evolutionary trend from low to high levels from 2005 to 2019. Under the influence of energy policies, it was predicted that the average CCD in China will increase from 0.52 to 0.55 by 2025. In addition, the CCD and external influencing factors of provinces varied widely under different times and spaces. Each province should promote the coordinated development of eco-environment and rural renewable energy with their advantages of resources and economy.

Prediction of CO2 emissions in China by generalized regression neural network optimized with fruit fly optimization algorithm

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.

Research Progress of Tungsten Oxide-Based Catalysts in Photocatalytic Reactions

Photocatalysis technology is a potential solution to solve the problem of environmental pollution and energy shortage, but its wide application is limited by the low efficiency of solar energy conversion. As a non-toxic and inexpensive n-type semiconductor, WO3 can absorb approximately 12% of sunlight which is considered one of the most attractive photocatalytic candidates. However, the narrow light absorption range and the high recombination rate of photogenerated electrons and holes restrict the further development of WO3-based catalysts. Herein, the studies on preparation and modification methods such as doping element, regulating defects and constructing heterojunctions to enlarge the range of excitation light to the visible region and slow down the recombination of carriers on WO3-based catalysts so as to improve their photocatalytic performance are reviewed. The mechanism and application of WO3-based catalysts in the dissociation of water, the degradation of organic pollutants, as well as the hydrogen reduction of N2 and CO2 are emphatically investigated and discussed. It is clear that WO3-based catalysts will play a positive role in the field of future photocatalysis. This paper could also provide guidance for the rational design of other metallic oxide (MOx) catalysts for the increasing conversion efficiency of solar energy.