Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province

The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022-2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030.

Study on the spatial and temporal correlation and allometric growth mechanism between population aging and carbon emissions in China

Population aging and carbon emissions are critical issues for China's development. As an enormous complex system, the population and the carbon emission development process have non-negligible differences in time, space, and speed. Therefore, this paper first demonstrates the spatial and temporal correlation between population aging and carbon emissions from 1995 to 2020, then uses the allometric growth analysis model to make a cross-sectional temporal comparison and a vertical spatial comparison of the relationship and development rate of the two, and finally uses the ridge regression model to determine the forces and interaction mechanisms of the factors influencing the relationship between population aging and carbon emissions at allometric rates. The results show that (1) China has a long-term positive temporal correlation effect relationship between population aging and carbon emissions from 1995 to 2020, and the overall correlation is high. The spatial correlation intensity between population aging and carbon emissions varies significantly across Chinese provinces, with a general spatial distribution trend of high in the south, low in the north, and prominent in the center. (2) China's population aging and carbon emissions mainly show a negative allometric growth type of relationship, i.e., a strong trend of population aging expansion and a strengthening trend of carbon emission system shrinking. The number of provinces with negative allometric growth is gradually increasing, mainly in North, East, Central, and Southwest China. (3) From 1995-2010 period to the 2011-2020 period, the influence of the factors of the population, production, and economic dimensions on the population aging index and the carbon emission allometric scalar index gradually weakened, and the influence of the consumption and technology dimensions increased significantly. The factors on the population and consumption side of the dimension mainly contribute to the expansion of carbon emissions and drive positive allometric growth. The production side, the economic structure, and technology dimension factors drive negative allometric growth. The paper fully explores the bidirectional correlation, differential development trend, and interaction mechanism between the two systems of population and carbon emissions and effectively compensates for the lack of research content in terms of elemental correlation, spatial and temporal connection, and speed synergy.

Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach

The escalating global greenhouse gas emission crisis necessitates a robust scientific carbon accounting framework and innovative development approaches. Achieving emission peaks remains the primary goal for emission reduction. Guangdong Province, a pivotal region in China, faces pressure to reduce carbon emissions. In this study, data was leveraged from the China Carbon Accounting Database (CEADS) and panel data from the "Guangdong Statistical Yearbook" spanning 1997 to 2022. Factors impacting carbon emissions were selected based on Guangdong Province's carbon reduction goals, macroeconomic development strategies, and economic-population dynamics. To address multicollinearity, lasso regression identified key factors, including population size, economic development level, energy intensity, and technology factors. A novel STIRPAT extended model, combined with the BP neural network optimized using the TPE algorithm, enhanced carbon emission predictions for Guangdong Province. Employing scenario analysis, five scenarios were generated in alignment with the planning policies of Guangdong Province, to forecast carbon emissions from 2020 to 2050. The results suggest that to achieve a win-win situation for both economic development and environmental protection, Guangdong Province should prioritize the energy-saving scenario (S2), which aligns with the "13th Five-Year Plan's" ecological and green development directives, to reach a projected carbon peak of 637.05Mt by 2030. In conclusion, recommendations for carbon reduction are proposed in the areas of low-carbon transformation for the population, sustainable economic development, and the development of low-carbon technologies.

A novel grey Verhulst model with four parameters and its application to forecast the carbon dioxide emissions in China

In the context of dual carbon targets, a reliable prediction of China's carbon dioxide emissions is of great significance to the design and formulation of emission reduction policies by Chinese government. To this end, a novel grey Verhulst model with four parameters is proposed in this paper according to the evolution law and the data characteristics of China's carbon dioxide emissions. The new model solves the defect of poor structural adaptability of the traditional grey Verhulst model by introducing a nonlinear correction term. Besides, the range of values for the order of the grey generation operator of the new model is expanded from a positive real number to any real number (r ∈ R+ → r ∈ R) by expanding the value range of the Gamma function. The new model is used to simulate China's carbon dioxide emissions, and its comprehensive mean relative percentage error is only 0.65 %, which is better than that of the other three grey models (2.39 %, 2.34 %, 2.35 % respectively). It shows that the proposed new model has better modeling ability. Finally, the new model is applied to predict China's carbon dioxide emissions, and the results show that it will still increase year by year, reaching 13,687 million tons by 2028 (only 11,420 million tons in 2021). Therefore, some countermeasures and suggestions are proposed to control China's carbon dioxide emissions in this paper.

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

Addressing global warming is one of the most pressing environmental challenges and a crucial agenda for humanity. In this literature study, we employed bibliometrics to reproduce nearly two decades of research on carbon emission reduction in China, the largest carbon emitter worldwide. The scientometrics analysis was conducted on 1570 academic works published between 2001 and 2021 concerning China's carbon emission reduction to characterize the knowledge landscape. Using CiteSpace and VOSviewer, the basic characteristics, research forces, knowledge base, research topic evolution, and research hotspots were identified and revealed. The analysis results show that the attention to and research on China's carbon emissions have increased in recent years, giving rise to leading institutions and relatively stable core journal groups in this field. The research disciplines are relatively concentrated, but the research collaboration needs strengthening. The research hotspots are mainly carbon emission causes, impacts, and countermeasures in China, and the research frontiers have been constantly advanced and expanded. In the future, research on countermeasures needs more effort, and research cooperation needs to strengthen. The changing landscape of hotspot clusters reveals China's transition towards a low-carbon economy. Through comprehensive analysis of the potential and obstacles to China's transition to low-carbon development, we identified three promising areas of action (low-carbon cities, low-carbon technologies and industries, and transforming China's energy system) and proposed research directions to address remaining gaps systematically.