Influencing Factors Of Carbon Emissions Research Articles

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident.

Environmental Kuznets curve of carbon emissions from China’s forest products industry and decomposition of factors influencing carbon emissions

Carbon emissions from China’s forest products industry were considered based on the data of 2001-2020. Then a carbon emissions Kuznets curve was constructed to judge the relationship between the economic development level and carbon emissions. The logarithmic mean Divisia index (LMDI) was used to analyze the influencing factors of carbon emissions. The carbon emissions from China’s forest products industry showed a trend of rapid growth in the early stage and slow decline in the later stage, increasing from 19.46 million tonnes in 2001 to 54.18 million tonnes in 2020. Consumption of raw coal was the main reason for the increase in carbon emissions. There was an inverted-U relationship between the economic development level and carbon emissions, and the industry output value of CNY 3306.56 billion was the theoretical inflection point. The current economic development level of the industry was in the left-half part of the inverted “U” shape, indicating that carbon emissions from this industry will continue to increase with the increase of industrial output. Economic development was the key factor driving the increase of carbon emissions in the forest products industry, while the energy intensity was the key factor inhibiting the growth of carbon emissions.

Analysis of influencing factors of carbon emissions from China’s marine fishery energy consumption under different development scenarios

China’s rapid economic development has consumed a large amount of energy, causing serious environmental pollution problems and contributing to global warming. This paper calculates the carbon emissions of the fishery sector and uses Random Forest (RF) for the first time to analyze the influencing factors of future carbon emissions. The results of the study show that increasing carbon sinks dominate the reduction of carbon emissions in the fisheries sector. Carbon sinks will continue to dominate emission reductions in the fisheries sector if positive mitigation measures are taken. Continuing the current pattern of fisheries development, the fishery population has a significant impact on future carbon emissions. Per capita incomes under a crude economic model will inhibit carbon emission reductions. The research results can provide guidance for the development of fishery low carbon economy and the formulation of emission reduction policies.

The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model

To promote the low-carbon, high-quality development of China’s industrial sector and achieve the national carbon peak goal as soon as possible, this study explores the influencing factors of carbon emissions among industrial sectors. Based on the panel data of 36 industrial sectors in China from 2009 to 2021, the spatial effects and characteristics of industrial sectors are examined by the spatial Durbin model (SDM) based on analyzing the spatial correlation among industrial sectors. The results show the following: (1) Moran’s I statistical results show that China’s industrial carbon emissions have a strong positive spatial correlation, and with time, the spatial correlation between industrial sectors gradually increases. (2) The empirical results of the whole industrial sector show that the property rights structure, capital intensity, and energy structure are the main driving forces promoting carbon emission reduction; the grouping analysis results show that the impact of FDI and property rights structure on the carbon emissions of the industrial sector in different sample groups is different. Among them, the energy structure and research and development play a role in reducing carbon emissions in each sample group. (3) Therefore, in the future, to reduce carbon emissions in the industrial sector, it is necessary to inhibit growth factors and promote the role of reduction factors; optimizing the energy structure and improving the rationality of the property rights structure are effective ways to achieve energy conservation and emission reduction.

Research on the Impact of China's Import and Export Trade on Carbon Emission under the Perspective of Carbon Emission Reduction ——Empirical Evidence Based on Provincial Panel Data

With the development of economic globalization, China's import and export trade with other countries around the world has become increasingly frequent. However, the huge energy gap has led to serious environmental pollution problems in China, and how to realize sustainable development has become an important issue. Therefore, based on the research of scholars at home and abroad, this paper utilizes the panel data of 30 provinces and cities from 2012 to 2021 as well as the method of multiple stepwise regression to study the influencing factors of carbon emissions in China. The results show that China has achieved some success in improving the level of regional economic development, transitioning from traditional finance to green finance, and improving the level of scientific and technological development, which can effectively curb the growth of carbon emissions; however, the total amount of import and export trade, the level of advanced industrial structure, and the energy structure are still the key factors hindering the cause of carbon emission reduction.

A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN.

As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R2 of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.

Drivers and decoupling analysis of carbon emissions in the non-ferrous metal industry-evidence from 28 provinces in China.

Emissions from the non-ferrous metal industry are a major source of carbon emissions in China. Understanding the decoupling of carbon emissions from the non-ferrous metal industry and its influencing factors is crucial for China to achieve its "double carbon" goal. Here, we applied the Tapio decoupling model to measure the decoupling status and developmental trends of carbon output and emissions of the non-ferrous metal industry in China. The panel interaction fixed effects model is used to empirically analyze the influencing factors of carbon emissions in China's non-ferrous metal industry. The results show that carbon emissions from China's non-ferrous metal industry have experienced three main states: strong decoupling, growth connection, and negative growth decoupling. The carbon emissions of the non-ferrous metal industry in some eastern and central provinces from 2000 to 2004 were in a negative decoupling state. Most provinces in the western and central regions were either in a strong or weak decoupling state based on the developmental trend of the decoupling state of carbon emissions. However, from 2015 to 2019, the decoupling status of carbon emissions in most provinces in western and central China had a significantly negative, weakly negative, or a negative growth decoupling status. Energy structure, energy intensity, cost, and non-ferrous metal production all have a positive driving effect on carbon emissions in the non-ferrous metal industry. Production had a mitigating effect on carbon emissions in the non-ferrous metal industry between 2010-2014 in the eastern region of China. From the results of our study, we propose policy recommendations to promote a strong decoupling of carbon emissions from the non-ferrous metal industry by improving energy structure, reducing energy intensity, and optimizing production capacity.

Prediction of CO2 Emissions Related to Energy Consumption for Rural Governance

In the context of rural revitalization, many industries have begun to shift towards rural areas. Industrial agglomeration not only brings economic growth to rural areas, but also increases local carbon emissions. This is particularly evident in some industrialized rural areas with high energy consumption. To accurately implement rural environmental governance, this study selected population, energy consumption, coal proportion, urbanization rate, and other factors as the influencing factors of carbon emissions. The grey correlation analysis method was used to obtain the correlation coefficient of the influencing factors. Then, the relationship between carbon emissions and economic growth, energy consumption, and other influencing factors was analyzed from multiple perspectives. In addition, this study constructed an energy consumption carbon emission prediction model based on deep learning networks, aiming to provide reference data for rural greenhouse gas emission reduction. These results confirmed that the correlation coefficients of the influencing factors of carbon emissions were all higher than 0.6, indicating that their carbon emissions were highly correlated. These test results on the dataset confirm that the RMSE values of the proposed model are all around 0.89, indicating its good prediction accuracy. Therefore, the proposed carbon emission prediction model can provide scientific and reasonable reference data for rural air governance.

Analysis of Influencing Factors on the Value of Carbon Assets in China Under the Background of Double Carbon — Take the Pilot Carbon Emission Trading Program in Guangdong Province as an Example

In recent years, with the rapid development of the global industrial economy, carbon dioxide emissions, the greenhouse effect, and other environmental problems have become increasingly obvious, the development of a low-carbon economy has become the goal pursued by all countries in the world, carbon market trading has also emerged and developed. With the continuous development of the carbon market, carbon assets are playing an increasingly important role for enterprises, and the assessment of carbon assets has greater potential for development prospects. However, at this stage, there are few studies on the value of carbon assets in the field of assessment, and the studies on the influencing factors of the value of carbon assets are more focused on the analysis of the price of carbon emission rights. This paper takes carbon asset trading as the background and carbon emission trading pilot in Guangdong Province as an example and selects five indicators from different fields to establish a linear regression model to analyze the influencing factors of carbon emission rights price.

Study on the Decoupling Effect of Energy and Carbon Emissions in the Yangtze River Economic Belt under the Dual Carbon Target

To achieve the goal of "double carbon" is the only way for our country to achieve emission reduction strategy. The Yangtze River Economic Belt is one of the strategic support belts for emission reduction, so it is of great significance to study the decoupling effect of energy and carbon emissions. Based on the carbon emission data of the Yangtze River Economic Belt from 1995 to 2021, this paper firstly analyzes the basic characteristics and spatial pattern of carbon emission in the Yangtze River Economic Belt. Secondly, a spatial econometric model is constructed to decompose the main influencing factors of carbon emissions in the Yangtze River Economic Belt, and the main influencing factors of economic and energy decoupling in the Yangtze River Economic Belt are explored by using the canonical correlation analysis method. Finally, BP neural network is used to forecast and analyze the carbon emission of the Yangtze River Economic Belt from 2022 to 2030. The results show that: (1) The center of gravity of carbon emissions in the Yangtze River Economic Belt is gradually shifting to the southeast, and there is a spatial correlation between regional carbon emissions. (2) The strong influencing factors of carbon emission in the Yangtze River Economic Belt are industrial composition and development differences; The influencing factors are city size and resource consumption intensity. The weak factor is the level of energy #consumption. The level of energy consumption and industrial composition are the main influencing factors of economic and energy decoupling in the Yangtze River Economic Belt. (3) Carbon emissions in the Yangtze River Economic Belt will continue to grow from 2022 to 2030, and will grow fastest from 2029 to 2030.

CO2 emissions change in Tianjin: The driving factors and the role of CCS

The environmental damage caused by the greenhouse effect is intensified by the continuous rise of carbon dioxide emissions. As a major industrial base in northern China, Tianjin has been emitting more carbon and thus faces a greater challenge to reduce its emissions. To meet the dual carbon goals of Tianjin, it is critical to identify influencing factors of carbon emissions and to find suitable technologies to effectively reduce emissions. In this study, the energy consumption data of Tianjin from 2005 to 2020 were analyzed via the Logarithmic Mean Divisia Index (LMDI). The carbon emission driving factors were decomposed and the contributions of four primary factors, including energy structure, energy intensity, gross domestic product (GDP) per capita, and population to CO2 emissions of this region, were evaluated. The ideal characteristics of the oil reservoirs with low permeability and the suitable carbon dioxide sinks make carbon capture and storage(CCS) technology implementation feasible in Tianjin. Despite this, the effect of CCS technology on CO2 emissions in the future is still uncertain. Thus, the Bass model was firstly introduced to assess the future development trend of CCS technology and its capacity of CO2 emission reduction. The results revealed that energy intensity was the primary contribution to reducing carbon emissions, while GDP per capita was the main contributor to the overall increase of CO2 emissions in 2005–2020. The CCS technology diffusion process is fast, with rapid growth starting in 2030 and reaching growth saturation in 2040. According to the scenario analysis, CCS could make a substantial contribution of 2674.78× 104 tons per year after 2040 to the carbon emission reduction of Tianjin. Therefore, the implementation of energy conservation and emission reduction policies as well as the expansion of CCS projects are essential to ensure that Tianjin achieves its carbon neutrality goal by 2060. This study offers helpful guidance for other cities to forecast their carbon emission reduction trajectories and establish emission reduction strategies.

Measurement of carbon effect in land consolidation projects and evaluation of low-carbon promotion paths: a case study of Wudi County, Shandong Province, China.

Against the backdrop of China's "double carbon" objective, the exploration of low-carbon land consolidation has become a prominent area of focus for enhancing the development of ecological civilization. In this study, three typical projects at different time points (2016, 2019, and 2022) in Wudi County were selected to measure the carbon effect of land consolidation from four perspectives: artificial and industrial materials, mechanical shift consumption, land use structure, and farmland ecosystem. Based on the calculation of carbon effect of land consolidation by using carbon emission coefficient method, the changes of land use structure and carbon storage of farmland ecosystem before and after land consolidation were corrected by using GIS tools and net ecosystem productivity (NEP) model based on remote sensing technology, and the carbon emission intensity of each land consolidation project was finally obtained. The study summarized the influencing factors of carbon emissions through the above analysis and uses the fuzzy interpretation structure (FISM) model to provide the hierarchy of influencing factors of carbon emissions, thus proposing a low-carbon promotion path for land consolidation. The findings of this study can serve as a useful reference for low-carbon land consolidation efforts. The results showed that (1) the first, second, and third projects emitted 6140.06 t, 1243.78 t, and 17,604.62 t of carbon, respectively. Among them, the largest contributors to these emissions were labor and industrial materials, followed by mechanical shift; land use structure and farmland ecosystem were the main sources of carbon sinks and have a positive impact on the carbon cycle. (2) The carbon emission intensity of project one, project two, and project three after standardization is 0.26, 0.49, and 0.25, respectively, which are all at a high level. (3) According to the FISM model categorized 15 low-carbon upgrading paths, it was recommended that the government take a leading role in Wudi County by developing a scientific and rational construction plan. Additionally, efforts were made to actively protect farmland and forest land from destruction, reduce energy and material consumption, increase carbon storage in the farmland ecosystem, and promote low carbonization of land consolidation to the fullest extent possible.

A multi-factor forecasting model for carbon emissions based on decomposition and swarm intelligence optimization

With the growth of national economy, carbon emissions are increasing day by day, which has caused a series of adverse effects on people’s production, life and environment. Therefore, it is very important to establish an accurate carbon emissions forecasting system for developing a low-carbon economy. Aiming at the problems of many influencing factors and randomness of carbon emissions, a multi-factor forecasting model for carbon emissions with generalized phase permutation entropy (GPPE), singular spectrum decomposition (SSD), bidirectional long short-term memory (BiLSTM) optimized by artificial tasmanian devil optimizer (TDO), named GPPE-SSD-TDO-BiLSTM, is proposed. Firstly, Pearson correlation coefficient method is used to analyze the main influencing factors of carbon emissions. Secondly, carbon emissions is decomposed by GPPE-SSD to obtain different singular spectrum components (SSCs). Finally, each component and influencing factors are brought into the TDO-BiLSTM forecasting model, and each forecasting result is reconstructed to obtain the final forecasting result. To verify the validity of the proposed model, carbon emissions of China and India in recent five years are used as experimental data, compares eight different forecasting models, and evaluates the forecasting models with six evaluation indexes. Taking China’s carbon emissions as an example, the MAE, MAPE, RMSE, R2 and STD of the proposed model can reach 0.1593, 0.5226, 0.2131, 0.9973 and 3.1056 respectively, which shows that its forecasting effect is better than that of other comparative models.

Sustainable Low-Carbon Layout of Land around Rail Transit Stations Based on Multi-Modal Spatial Data

With the ever-increasing demand for transport in modern cities, emissions from urban transport are rising. The proportion of carbon emissions in exhaust gas accounts for a large share of society’s total carbon emissions and is increasing. Therefore, urban transport has a sustainable responsibility to reduce carbon emissions. Investigating the factors that influence carbon emissions from transport has become an important practical issue that needs to be addressed. This paper adopts a “bottom-up” theoretical calculation method of transport carbon emissions and establishes the basic distribution model of inter-modal land use around rail transit stations. It clarifies the connection mode of rail transit stations and establishes the distribution model of carbon emission of stations under different building distribution modes, suggesting the planning of building distribution patterns around rail transit stations. This paper proposes a new method to analyze the influencing factors of carbon emissions at rail transit stations based on multi-modal spatial data in order to make full use of the dense characteristics of rail transit stations and reduce carbon emissions.

Study on the influencing factors of carbon emission of hydrogen fuel vehicles

Study on the influencing factors of carbon emission of hydrogen fuel vehicles

Asset Structure, Asset Utilization Efficiency, and Carbon Emission Performance: Evidence from Panel Data of China’s Low-Carbon Industry

With the development of a low-carbon economy, corporate carbon emission performance has become an important premise for green financing. Compared with high-carbon industries, companies in low-carbon industries have their own carbon advantages and receive less attention. In order to highlight the value of carbon emission performance in low-carbon industries, further investigation on the basis of low-carbon industries is still needed. In terms of fixed assets, which are indicators for an important source of carbon emissions in intensive carbon industries, this study explores the relationship between asset structure, asset utilization efficiency, and carbon emission performance in low-carbon industries. This study selects Chinese listed companies from low-carbon industries that have disclosed their carbon emission performance from 2010 to 2021 as samples. The panel model is used for regression analysis, and then the Arellano-Bover/Blundell-Bond panel dynamic data model is used to solve the problem of endogeneity. The results show that the higher the fixed asset ratio, the worse the carbon emission performance. Asset utilization efficiency weakens the inhibitory effect of the fixed asset ratio on carbon emission performance. This study verifies the significant impact of fixed assets on the carbon emission performance of low-carbon industries as well as the promotion effect of asset utilization efficiency on carbon emission performance. Further investigation verified the promoting effect of corporate growth capabilities on carbon emission performance with two mechanisms, namely the relationship between fixed assets (independent variable)-asset utilization efficiency and (mediator)-corporate growth capabilities (dependent variable) or the relationship between asset utilization efficiency (independent variable)-corporate growth capabilities and (mediator)-fixed assets (dependent variable) from perspectives of enterprise value and expansion. This study expands the influencing factors of carbon emissions in low-carbon industries and is a theoretical supplement to a large number of high-carbon studies. At the same time, it also has certain implications for the carbon emission management practices of enterprises in low-carbon industries. It also reveals the urgency for the government and research institutions to clarify the carbon emission capacity of different fixed assets. Thus, it is convenient for low-carbon industries and high-carbon industries to carry out more refined carbon management and give full play to their carbon advantages.

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

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province's carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry's cleaner production and sustainable development of society.

Research on carbon emissions of production-consumption sectors under the background of "Double Carbon" Take Beijing as an example

With the development of the Green and Low-carbon, Beijing has set a model for other provinces and cities in carbon emissions reduction, and it will help achieve the goal of carbon peaking rapidly. This paper studies various influencing factors of carbon emissions from both production and consumption by improving the Kaya identity. Through analyzing productive and consumptive CO2 emissions of Beijing with LMDI technology, this paper finds that: (1) For the production sectors in Beijing, the total carbon emissions exhibited a downward trend, and the energy intensity factor was the foremost factor to restrain the increase of carbon emissions, while the restraining effects of energy structure factors and industrial structure factors were limited, and the total production promoted emissions to increase significantly. (2) For the living sectors in Beijing, the total carbon emissions first increased and then decreased, and the urban and rural structural factor played a weak inhibitory role in the increase of CO2. The population and energy intensity were significantly promoted the increase of it. Therefore, this paper proposes to continue to adjust the structure of industry and urban, develop clear energy and make full use of energy, so as to realize the "dual-carbon" goal earlier.

Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model.

In order to cope with global warming, China has put forward the "30 · 60" plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the "carbon peak" goal by 2030years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of "carbon peaking" and "carbon neutralization" as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.