Carbon Emission Peak Research Articles

Study on carbon emissions of a small hydropower plant in Southwest China

Hydropower plants with a small installed capacity, which are widely distributed in mountainous areas with abundant rainfall and steep rivers, play an important role in resolving energy problems in remote rural areas. These plants are a crucial source of clean electricity generated from water power. Harnessing local water resources not only helps alleviate energy shortages, but also reduces reliance on fossil fuels, contributing significantly to China’s national goals of achieving peak carbon emissions and carbon neutrality. This study investigates the carbon footprint of the Huangshadong Reservoir Project in Chongqing, China. The entire life cycle of the hydropower plant is assessed, including the preparation, construction, operation and maintenance, and demolition phases. The uncertainty was evaluated using the error propagation method. Following analysis, suggestions for carbon footprint reduction measures were proposed. Results showed that the total carbon footprint and the carbon intensity of the Huangshadong Reservoir Project over its entire life cycle are 33,148.29 t CO2e and 417.75 g CO2e/kWh, respectively. Of the total carbon footprint, the preparation phase, construction phase, operation and maintenance phase, and demolition phase account for 0.04%, 67.06%, 26.2%, and 6.7%, respectively. It means that the requirement for cement during the construction phase represents an important contribution to the entire life cycle carbon footprint of a small hydropower plant. As an integrated water conservancy project, the carbon intensity of the Huangshadong Reservoir Project is higher than that of medium-sized and large hydropower plants. However, its carbon intensity is lower than the emission factor of fossil power plants. The research results provide reference for both planning and construction of small hydropower plants and low-carbon development of rural hydraulic engineering.

Prediction and Scenario Simulation of Carbon Emissions Peak of Resource-Based Urban Agglomeration with Industrial Clusters—Case of Hubaoe Urban Agglomeration Inner Mongolia Autonomous Region, China

China has implemented a “dual-carbon” policy in response to the Paris Agreement’s global climate change objectives. Hohhot, Baotou, and Ordos (HBO-UA) is a resource-based urban agglomeration that is noteworthy for having significant heavy industry in China. Based on the extended STRIPAT model, which broadens the study indicators into six aspects—population, economics, technology, urbanization, industrial energy, and industrial structure—this paper develops a research framework of “Driving–Predicting–Simulating” for carbon emissions. According to the “one formula for one city” principle, driver models were constructed for Hohhot, Baotou, and Ordos, respectively. The following conclusions were drawn: (1) Population and urbanization are the dominant factors of carbon emissions in HBO-UA, following the economy and industrial energy. (2) Carbon emissions are multifactor-driven in Hohhot, double-factor-driven in Baotou, and single-factor-driven in Ordos. (3) Hohhot can achieve its carbon emissions peak under more efficient and lower policy costs, while Ordo is under great pressure to reduce carbon emissions. (4) We suggest multiple strategies to accomplish the “dual-carbon” goals for resource-based urban agglomeration with industrial clusters. These strategies include fostering diversified consumption by continuously enhancing urban functions, directing the transformation of the industrial structure, and fostering the growth of emerging industries.

Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities—From the Perspective of Urban Planning Content and Process

In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning’s content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms.

Assessing the bioenergy potential of abandoned cropland in China: Toward an optimal distribution of bioenergy crops

Bioenergy has gained wide attention due to its potential to reduce fossil fuel consumption and greenhouse gas emissions. Abandoned cropland is a promising option for cultivating bioenergy crops, as it does not compete with food production. The vast amount of abandoned cropland in China provides extensive opportunities for the development of bioenergy. However, the bioenergy potential of China's abandoned cropland remains unclear. In this study, we identified abandoned cropland in China for the period of 2000–2020. Based on this, we estimated the bioenergy potential from conventional food crops (maize and wheat), and perennial bioenergy crops (miscanthus and switchgrass), on the abandoned cropland in China. We optimized the planting of conventional food and two perennial bioenergy crops by maximizing crop yield and accounting for water limits. The results show that 29.49 Mha of abandoned cropland was found in the past twenty years. Spatially, it was mainly located in eastern China with relatively high soil quality, in contrast to that in the U.S. and Europe. By optimizing the crop distribution on abandoned cropland, the bioenergy potential primarily shows a spatial distribution of higher potential in the south and lower potential in the north, with a total yield reaching 9.52 EJ. This is approximately 1.43 times higher than that of solely cultivating miscanthus and 8.28 times that of wheat. This potential accounts for 6 % of China's total primary energy consumption in 2022 and 7.66 % of the carbon emission peak target for 2030. It holds significant importance for the national emission reduction strategy. These findings highlight the immense bioenergy potential of abandoned cropland, providing support for the development of bioenergy.

Pathways to innovation: How city pilots leverage digital technology to reduce carbon emissions

Technological innovation through various pathways is crucial for achieving carbon neutrality and peak carbon emissions. The Innovative City Pilot (ICP) initiative, a key effort to promote national innovation, has garnered increasing attention. However, most studies have not examined the effects of ICP on carbon emissions through diverse digital technologies. Using city-level data from 2000 to 2022, we constructed a staggered difference-in-differences (DID) model to analyze changes in carbon emissions before and after the ICP period in both ICP and non-ICP areas. The carbon emission reduction pathway of the ICP influences various forms of digital technology, which then empower local green product innovation, market-oriented innovation, and industrial innovation. The impact of ICP emission reduction through digital technology is more significant in western, first- and second-tier, sub-provincial, populous, and coastal cities. In conclusion, policymakers should tailor policies according to the action pathways of digital technology and specific city characteristics. We outline the specific pathways of green product innovation, market-oriented innovation, and industrial innovation enabled by digital technologies and provide policy recommendations for the sustainable development of China.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

Impact of Carbon Tax on Renewable Energy Development and Environmental–Economic Synergies

Global warming caused by greenhouse gas emissions has become a worldwide environmental problem, posing a great threat to human survival. As the world’s largest emitter of carbon dioxide, China has pledged to reach peak carbon emissions by no later than 2030 and carbon neutrality by 2060. It is found that a carbon tax is a powerful incentive to reduce carbon emissions and promote an energy revolution, but it may have negative socio-economic impacts. Therefore, based on China’s 2020 input–output table, this paper systematically investigates the impacts of a carbon tax on China’s economy, carbon emissions, and energy by applying a computable general equilibrium model to determine the ideal equilibrium between socio-economic and environmental objectives. Based on energy use characteristics, we subdivided the energy sector into five major sectors: coal, oil, natural gas, thermal power generation, and clean power. The results show that when the carbon emission reduction target is less than 15%, that is, when the equilibrium carbon tax price is less than 54 yuan/ton, the implementation of a carbon tax policy can significantly reduce carbon emission and fossil fuel energy consumption, while only slightly reducing economic growth rate, and can achieve the double dividend of environment and economy. Moreover, because the reduction of coal consumption has the greatest impact on reducing carbon emissions, the ad valorem tax rate on coal after the carbon tax is imposed is the highest because coal has the highest carbon emission coefficient among fossil fuels. In addition, as an emerging clean energy source, hydrogen energy is the ideal energy storage medium for achieving clean power generation in power systems. If hydrogen energy can be vigorously developed, it is expected to greatly accelerate the deep decarbonization of power, industry, transportation, construction, and other fields.

Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model

Systematically predicting carbon emissions in the building sector is crucial for formulating effective policies and plans. However, the timing and potential peak emissions from urban buildings remain unclear. This research integrates socio-economic, urban planning, building technology, and energy consumption factors to develop a LEAP-SD model using Shenzhen as a case study. The model considers the interrelationship between socio-economic development and energy consumption, providing more realistic scenario simulations to predict changes in carbon emissions within the urban building sector. The study investigates potential emission peaks and peak times of buildings under different population and building area development scenarios. The results indicate that achieving carbon peaking by 2030 is challenging under a business as usual (BAU) scenario. However, a 10% greater reduction in energy intensity compared to BAU could result in peaking around 2030. The simulation analysis highlights the significant impact of factors such as population growth rate, per capita residential building area, and energy consumption per unit building area and the need for a comprehensive analysis. It provides more realistic scenario simulations that not only enhance theories and models for predicting carbon emissions but also offer valuable insights for policymakers in establishing effective reduction targets and strategies.

CFD Investigation on Air-Staged Combustion Characteristics of Deep Peak Shaving Wall-Fired Burner Boilers

ABSTRACT In alignment with China’s ambitious goals of reaching peak carbon emissions and achieving carbon neutrality, stringent load peak regulations are being enforced for coal-fired power generation. This underlines the significance of deploying deep load peaking strategies in coal-fired units. The current research focus is on wall-fired boilers operating under ultra-low loads of 600 MWe. It delves into the flow dynamics, combustion characteristics, component concentration field, and NOx emission characteristics under varying air coefficients in the primary combustion zones (α). The findings suggest the formation of a consistently high-temperature region in the primary combustion zone, where temperatures surpass 1500 K. The middle burner exhibits superior combustion stability compared to the bottom burner. As α diminishes, several changes occur. The flow field intensity in the primary combustion zone lessens, the ignition distance contracts, and the O2 concentration in this area drops. This engenders a reducing atmosphere characterized by low O2 and high CO levels, which hampers the formation of NOx. At α = 0.95, the NO concentration peaks, with the primary generation mechanism being temperature-driven. At this juncture, the NO concentration at the furnace flue gas outlet is 287.36 mg/m3.

Can combined wind and solar power meet the increased electricity load on heatwave days in China after the carbon emission peak? A case study in southern Hebei

Wind and photovoltaic (PV) power are the fastest-growing renewable energy sources; however, they are vulnerable to weather extremes. In the context of global warming, the frequency of extreme weather events, particularly heatwaves (HW), is anticipated to increase. Compared to normal summer days, HW days not only cause a rise in electricity demand but also exhibit a complementary growth in wind/PV generation. Therefore, determining whether the increase in wind/PV generation on HW days can meet the corresponding surge in electricity load is pivotal for replacing fossil fuel-based electricity. To address this question, this study conducts a case study for southern Hebei Province, which has the highest combined wind/PV installations in China, for the period 2031–2040 under the scenario that China will achieve its carbon peak (2030) and carbon neutrality goals as planned. We use recently developed load and wind power models and calibrate a boosting ensemble learning model to simulate PV generation. The results reveal that the total increase in wind/PV generation on HW days can fully meet the total increase in electricity consumption starting in 2039. However, due to the uneven distribution of wind/PV generation and load changes throughout the HW days, 9 GWh of energy storage capacity is required to ensure electricity supply in the early morning hours. Under China's climate change adaptation strategy, Hebei Province can harness wind/PV energy to address peak load demands during HW conditions and initiate climate-resilient urban development pilot projects.

Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks

Mitigating the rate of global warming is imperative to preserve the natural environment upon which humanity relies for survival; greenhouse gas emissions serve as the principal driver of climate change, rendering the promotion of urban carbon peaking and carbon neutrality a crucial initiative for effectively addressing climate change and attaining sustainable development. This study addresses the inherent uncertainties and complexities associated with carbon dioxide emission accounting by undertaking a scenario prediction analysis of peak carbon emissions in Dalian, utilizing the STIRPAT model in conjunction with a GA-BP neural network model optimized through a genetic algorithm. An analysis of the mechanisms underlying the influencing factors of carbon emissions, along with the identification of the carbon emission peak, is conducted based on carbon emission accounting derived from nighttime lighting data. The GA-BP prediction model exhibits significant advantages in addressing the nonlinear and non-stationary characteristics of carbon emissions, attributable to its robust mapping capabilities and probabilistic analysis proficiency. The findings reveal that energy intensity, tertiary industry value, resident population, and GDP are positively correlated with carbon emissions in Dalian, ranked in order of importance. In contrast, population density significantly reduces emissions. The GA-BP model predicts carbon emissions with 99.33% accuracy, confirming its excellent predictive capability. The recommended strategy for Dalian to achieve its carbon peak at the earliest is to adopt a low-carbon scenario, with a forecasted peak of 191.79 million tons by 2033.

Research on the prediction and realization path of urban carbon peak along the Yellow River Basin

China and the international community attach great importance to sustainable development goals such as peaking carbon emissions. As an important energy base in China, the Yellow River Basin has significant capacity for reducing carbon emissions and increasing carbon sinks. To accelerate the achievement of carbon peak targets, social and natural development data as well as carbon emission data of 56 cities along the Yellow River Basin from 2000 to 2021 were analyzed. Based on the evaluation indicators of the model, the PSO-XGBoost-RF model was selected from the comparative models to predict the carbon peak of cities in the Yellow River Basin under different paths. Under the current economic and social development, cities along the Yellow River Basin in China are facing huge pressure to reduce emissions. It is expected to achieve a carbon peak in 2033, with a peak of 2,051,320,000 tons, so the target of reaching the peak of carbon emissions before 2030 will not be accomplished. But through comprehensive optimization of the industrial structure and reduction of energy consumption, cities along the Yellow River Basin as a whole may achieve carbon peak by 2026, with a peak of 1,917,132,000 tons.

CNN-GRU-Attention Neural Networks for Carbon Emission Prediction of Transportation in Jiangsu Province

With the increasing energy use and carbon emissions in the transportation industry, its impact on the greenhouse effect is gradually being recognized. Therefore, this study aims to explore the achievement of carbon emission peak and carbon neutrality in transportation through prediction. The research employs a deep learning model, the CNN-GRU-Attention model, to predict carbon emissions in the transportation industry in Jiangsu, China. We select influencing factors through an extended STIRPAT model coupled with Lasso regression, and construct the CNN-GRU-Attention traffic carbon emission prediction model according to data indicators from 1995 to 2021. The model predicts carbon emissions from the transportation industry in Jiangsu Province between 2022 and 2035 under six distinct scenarios and proposes corresponding emission reduction strategies. The results show that the model in this study has higher prediction accuracy compared with other models, with a mean absolute error (MAE) of 0.061582, root mean square error (RMSE) of 0.085025, and R2 of 0.91609 on the test set. Scenario-based predictions reveal that emission peak in the transportation industry in Jiangsu Province can be achieved under the clean development and comprehensive low-carbon scenarios, with technological innovation being the primary driver of low-carbon emission reductions. This study provides a novel approach for forecasting carbon emissions from the transportation industry and explores the implementation path of emission peak through this method.

How can Transportation Sector Achieve High-quality Carbon Peaking in China?

Promoting the peak of carbon emissions in the transportation sector as soon as possible is the key to advancing carbon peaking process in China. This article combines the KAYA model, peak quality standards based on Tapio decoupling model, and international experience to predict the future trend of carbon emissions of transportation sector, and to analyze the path for the transportation sector to achieve high-quality peak in China. Research has found that: (1) Based on China's goals (by 2030, the proportion of oil and gas will decrease to 75%), it has been found that China's transportation sector may achieve carbon peak by 2032, with lower peak quality than developed countries such as Japan. (2) Based on international experience, stable GDP growth and technological progress are key to ensuring the quality of carbon peak in the transportation sector. (3) On the basis of reducing the share of oil and gas to 75% by 2030, if China further decreases the energy intensity of its transportation sector by 27% compared to 2018 levels (a 7% greater decrease than the target scenario), it is projected that China's transportation sector may achieve a high-quality carbon peak by 2030 and potentially match the peak quality level of France. This paper can provide theoretical support for promoting carbon emissions reduction and facilitating the peak in China's transportation sector.

How megacities can achieve carbon peak through structural adjustments: an input–output perspective

Abstract There is still a huge gap between the emissions pathways of megacities and the pathways to meeting the targets set by the Paris agreement. Compared with technological emission reductions, structural emission reduction can provide cities with more stable and sustainable carbon-peaking solutions. This study constructs a scenario-based input–output optimization model, adopting a novel carbon emission accounting method for purchased electricity that considers shared responsibility, and systematically evaluates the decarbonization paths of megacities and their impacts on economic growth, energy consumption, and carbon emissions. The results show that (a) through industry substitution and manufacturing restructuring, Shenzhen is projected to peak at 57.68 MtCO2 emissions in 2026, with a 10.57% energy and a 19.55% carbon reduction by 2030. (b) Shenzhen can achieve its carbon emission peak target through the energy transition while accepting a loss of 0.97%–3.23% of GDP, requiring the maximum economic concession of 16.45% from the transportation sector (S10) in the early stage of transformation, while 12.24% from the extractive industry (S2) in the later stage. (c) The comprehensive structure adjustment proved to be more effective than other mitigation approaches, capable of achieving high-quality economic growth of 6.4% during the study period while reaching a peak target of 53.55 million tons of CO2 by 2026. (d) The emission reduction effect of the power sector was the most significant among all the scenarios, with emission reduction rates between 6.26% and 35.63%, and the cumulative emission reduction potential reached 38.1–110.6 MtCO2. The priority for emission reduction in the power sector is the coal phase-out plan, which is essential for achieving these significant reductions. This study provides an important reference for megacities facing similar challenges, especially those in developing countries, to achieve a stable and sustainable carbon peak pathway through structural adjustment.

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

Identification of Urban Carbon Emission Peaks through Tree-Ring 14C.

Independent identification of carbon emission peaks determined from fuel inventories is a challenging goal. Because of the complete depletion of radiocarbon (14C) in fossil fuel sources, the measurement of atmospheric 14CO2 has proven to offer a means of achieving this goal. Here, we present a study identifying peak carbon emissions from two Chinese cities using urban tree-ring Δ14C time series during 2000-2019. After subtracting background atmospheric Δ14C from urban tree-ring Δ14C to isolate local Δ14C (Δ14Clocal), we find a minimum in 2010 (-51.1 ± 4.5‰) in Beijing and in 2013 in Xi'an (-52.5 ± 0.5‰). These levels correspond to an urban carbon emission peak in 2010 and in 2013 in the two respective cities. The urban carbon emission peaks are further identified by the declines of the mean absolute interannual rate of decrease of tree-ring Δ14C during a period, with the respective values of 3.6 and 6.4 ‰/yr after and before a turning point in Beijing and 3.0 and 6.0 ‰/yr after and before a turning point in Xi'an. This study provides an observation method to identify carbon emission peaks in basin cities.

The Pressure of Carbon Loss in Industrial Upgrading: Pathways and Challenges of Low-Carbon Transition in China’s Manufacturing Sector

China’s manufacturing sector, the largest globally, is at the forefront of the country’s efforts to transition to a low-carbon economy amidst growing domestic and international pressures. As China commits to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the sector faces significant challenges in balancing economic growth with substantial carbon reductions. This paper explores the pathways and challenges of achieving a sustainable low-carbon transition in China’s manufacturing sector. It examines the pressures of carbon loss on industrial upgrading, the potential of technological innovation and digital transformation, the role of green finance, and the importance of policy and regulatory measures. The analysis highlights the key barriers, such as economic costs, technological constraints, regulatory inconsistencies, and social impacts, while providing strategic recommendations to foster a balanced transition. By integrating policy support, technological advancement, financial incentives, and international cooperation, China can navigate its journey toward a low-carbon manufacturing sector, ensuring long-term economic sustainability and environmental goals.

Carbon peaking trajectory links to development of a coupled urbanization-industrialization-energy system

On the way towards carbon emission peaking, the influence of the interactions within urbanization, industrialization and energy consumption on the peaking process from the perspective of system evolution remains exclusive. Herein, targeting 30 Chinese provincial regions, we design an integrated framework consisting of 21 indices to represent a coupled urbanization-industrialization-energy (UIE) system. The coupling and coordination within the UIE system are measured and linked to regional carbon emission trajectory during 2000–2021 to explore the influence mechanism on its peaking process. The findings reveal levels of the coupling and coordination ranging from 0.30 to 0.99, with an inverted U-shaped pattern for the coupling and a progression from rapid to gradual growth for the coordination. The comprehensive coupling-coordination degree for regions has not yet reached the state beneficial for achieving the emission peak. The development quality of urbanization subsystem and industrialization subsystem is progressively relieving reliance on the energy subsystem, while a complete decoupling from carbon emissions has not been achieved. The influences of driving forces of the coordination exhibit spatiotemporal variations. The region-specific insights into steering the UIE system towards peaking carbon emissions with emphasis on the key driving forces are of practical significance.

Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China

With the acceleration of urbanization and the improvement of people's living standards, the carbon emissions of residential buildings (BCE) in China have been increasing, hindering the achievement of carbon peaking and carbon neutrality goals. It has become increasingly urgent and important to analyze and grasp the dynamic trends of BCE. Given the notable regional variations in carbon emissions, it is necessary to delve into the distribution characteristics of BCE and the underlying factors influencing them. First, based on the emission factor method, the energy consumption and carbon emissions calculation models are established. Next, the main influencing factors are obtained by improving Kaya's constant equation. Meanwhile, the GA-PPC-Kmeans model is established to divide BCE into different regions. Then, the STIRPAT-Ridge model is built to predict the peak carbon emissions. Finally, a combination of static and dynamic methods is used to forecast peaks under three scenarios, which are low-carbon (LC) scenario, baseline (BA) scenario, and high-carbon (HC) scenario. The results show that five pivotal factors significantly impact BCE in China, which are energy carbon emission intensity (ECEI), energy intensity (EI), economic density (ED), residential housing level (RHL) and population size(P). Furthermore, BCE is categorized into five distinct regions: low-carbon demonstration area, energy efficiency improvement area, carbon concentration and innovation area, high-carbon optimization area, and carbon poverty development area. Under BA scenario, the peaks for BCE in these regions converge primarily around 2040 and 2045. This study provides a regional research perspective for China's residential buildings to reach the peak of carbon emissions, and serves as a reference for formulating carbon emission reduction plans in different regions.