Influencing Factors Of Carbon Emissions Research Articles

Investigating carbon peak prediction scenarios and implementation strategies in higher education institutions: A case study of Shandong Jianzhu university

The potential for energy conservation and emission reduction in Chinese universities is significant. The exploration of scenarios for predicting carbon peaking and identifying implementation pathways can offer valuable insights and demonstrations for achieving dual carbon goals. This paper develops a research methodology for carbon peaking in universities, with a focus on four main aspects: carbon emission accounting, the analysis of influencing factors, carbon emission prediction, and implementation pathways for carbon peaking. By utilizing the campus of Shandong Jianzhu University as a case study, the paper practically demonstrates the prediction of carbon peaking scenarios and offers technical solutions for universities. The results demonstrate that: (1) Using the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the influencing factors of carbon emissions in universities, we confirm that the energy structure effect, economic effect, and population effect have a promoting impact on the increase of carbon emissions at Shandong Jianzhu University, while the energy intensity effect exhibits a significant inhibitory effect. (2) By employing Partial Least Squares (PLS) regression analysis of annual carbon emission statistics from universities, we construct a STIRPAT model with high predictive accuracy for carbon emission regression. The carbon emission prediction model for Shandong Jianzhu University is: lnC^t=-25.899+3.571lnPt+0.966lnARt+0.716lnTMt-0.279lnTEt (3) Both the low-carbon scenario and the enhanced scenario for Shandong Jianzhu University are capable of achieving carbon peaking by 2027. From the perspectives of energy-saving renovation of existing building envelopes, improving the efficiency of lighting systems, utilizing solar photovoltaic buildings, and electrifying cooking energy, implementation pathways for carbon peaking under low-carbon and enhanced scenarios are delineated

Accounting for carbon emissions in social water cycle system in nine provinces along the yellow river and analysis of influencing factors.

Water resources is an essential factor to ensure the sustainable development of the society, but along with the utilization and treatment of water resources, a large amount of carbon emissions will be generated. The study of carbon emissions in social water cycle system is of great significance in promoting the achievement of carbon peaking and carbon neutrality. This study calculated the carbon emissions generated in social water cycle system in nine provinces along the Yellow River, used the Tapio decoupling model to analyze the decoupling relationship between water and carbon emissions, and constructed the STIRPAT expanded model to analyze the main influencing factors of carbon emissions. (1) The total carbon emissions of the nine provinces showed an increasing trend over time, with a growth rate of 25.13%. (2) The carbon emission intensity of water use (1.60kg/m3) and drainage (1.45kg/m3) system is higher, the carbon emission intensity of water supply (0.30kg/m3) and water withdrawal (0.56kg/m3) system is lower. (3) The relationship between water resources utilization and carbon emissions along the Yellow River is generally in a state of negative decoupling and coupling. (4) Energy structure and population growth are the main factors affecting carbon emissions in social water cycle system, while water supply quantity and water use system are secondary factors. Water use system is the main body of carbon emissions in social water cycle system, and as the water consumption increases, the carbon emissions will continue to increase. In order to reduce carbon emissions and mitigate climate change, carbon emission factors should be incorporated into water resources management.

Research on the Influencing Factors of Carbon Emissions in Low-carbon Communities based on Complex Network Theory

Achieving carbon peak before 2030 and carbon neutrality before 2060 is a solemn commitment made by China to address global climate change, and is also one of the main goals for economic and social development in the 14th Five Year Plan and the 2035 vision period. The changes in carbon emissions are directly related to the progress of China's "carbon peak" and "carbon neutrality" goals. Therefore, in-depth research on the influencing factors of carbon emissions has become a key link in promoting the achievement of this goal. In the existing research on carbon emission influencing factors, countries mainly focus on macro scale low-carbon urban carbon emission influencing factors and micro scale low-carbon building full life cycle carbon emission influencing factors. However, there is relatively little research on the influencing factors of carbon emissions in low-carbon communities of urban micro units, and there is still considerable research space. This study conducted an in-depth analysis of the influencing factors of carbon emissions in low-carbon communities using complex network methods. By constructing a complex network model of factors affecting carbon emissions, we identified key nodes and pathways, and explored their interrelationships. The results indicate that factors such as energy structure, resident behavior, building design, and policy implementation play an important role in carbon emissions in low-carbon communities.

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations.

Key influence factors of carbon emissions in Malaysian construction operations

The urgency to mitigate carbon emissions has gained significant societal attention as the consequences of climate change continue to unfold. There is a notable scarcity of studies conducted on carbon emissions for construction operations. Hence, the aim is to identify the key influence factor of carbon emissions to develop a carbon reduction framework for construction operations. A survey was conducted with 297 Malaysian construction stakeholders to analyze 18 factors. Descriptive and Rasch analysis were used. Results revealed the reliability and validity of the model, which could help to explore ways to reduce carbon emissions during the construction phase. The findings indicate that a gradual transformation process is necessary to expedite the transition towards a low-carbon construction. The research enhances the existing literature by emphasising the key factors for stakeholders who aim to efficiently manage carbon emissions during a construction project. There are different ideas on how to estimate and manage carbon emissions in construction projects, but there are no standardized ways. This study looks into the main factors that contribute to the generation of carbon emissions in construction, to identify ways to reduce them evaluating these factors from the perspective of different stakeholders.

Carbon emission intensity measurement and spatial effect of high energy consuming industries: evidence from China

<p style="text-indent:20.0pt"><span style="font-size:10.5pt"><span style="font-family:"Times New Roman",serif"><span style="color:black"><span class="fontstyle01" style="font-family:仿宋"><span style="color:black"><span lang="EN-US" style="font-size:10.0pt"> High energy consumption industry is an important source of carbon dioxide emissions, and reducing pollution and carbon is an important measure for China to achieve the goal of "2030 carbon peak 2060 carbon neutral". Based on the improvement of the traditional calculation method of IPCC carbon emission intensity, this paper measures the carbon emission intensity, selects the data of high energy consuming industries in 30 provinces in China from 1997 to 2022 as samples, uses the stipat model and Moran index to analyze the correlation of the influencing factors of carbon emission, and uses the spatial measurement model to study the spatial effect of carbon emission intensity. The results show that: first, the overall carbon emission intensity of high energy consuming industries shows a downward trend, with typical spatial heterogeneity. During the sample period, the carbon emission intensity of high energy consuming industries in 30 provinces in China was calculated based on the IPPC method, with the overall decline. Second, the carbon emission intensity of high energy consuming industries has significant spatial autocorrelation characteristics. According to the global Moran index, the center of gravity moves from east to West as a whole. Third, the carbon emission intensity of high energy consuming industries is affected by multiple environmental factors. Industrial structure (INS), regional gross domestic product (GDP) and regional economic development (ECO) have a significant impact. Fourth, the carbon emission intensity of high energy consuming industries has a significant spatial spillover effect. According to the regression results of spatial Dobbin model with double fixed effects, the direct and indirect effects of carbon emission intensity of high energy consuming industries are significant.</span></span></span></span></span></span></p>

Evolutionary characteristics and influencing factors of carbon emissions in China: An examination of a 43-year urban scale

Since 1978, China's rapid urbanization and industrialization have significantly increased carbon emissions. This study employs spatial autocorrelation, kernel density estimation, and spatiotemporal geographically weighted regression (GTWR) methods to analyze the spatiotemporal evolution characteristics of carbon emissions across 336 Chinese cities from 1978 to 2020. It also explores the dominant influencing factors for different cities at various stages of development. The findings reveal that carbon emissions in Chinese cities exhibit a stepwise growth pattern: “slow growth (1978–1995) - low-level stability (1996–2000) - rapid growth (2001–2012) - high-level stability (2013–2020)." The gap between cities has widened rapidly, and spatially, the distribution follows a “core-periphery” pattern. The increase in carbon emissions in core cities has transformed the urban hierarchy from a “generally low-carbon” structure to a “pyramid” structure. Compared to 1995, the influence of population size on carbon emissions decreased in 2020 (0.54–0.38), while the impact of infrastructure development and technological advances increased (0.02–0.25, 0.09 to 0.19). Due to the varying stages of urban development across regions, the influencing factors of carbon emissions exhibit spatial heterogeneity. Specifically, population size has a stronger positive impact on carbon emissions in the Southeast, technological advances in East and North China, and industrial structure in the Yangtze River Basin region. Infrastructure construction and investment levels show a dampening effect on carbon emissions in the Yangtze River Basin. Finally, the study proposes policy recommendations focusing on implementing regional “gradient” carbon reduction and promoting regional collaborative carbon reduction driven by core cities.

Prediction of Carbon Emissions Level in China’s Logistics Industry Based on the PSO-SVR Model

Adjusting the energy structure of various industries is crucial for achieving China’s carbon peak and carbon neutrality goals. Given the significant proportion of carbon emissions from the logistics industry in the tertiary sector, the research on predicting the carbon emissions of the logistics industry is of great significance for China to achieve its “Dual carbon” target. In this paper, the gray relational analysis (GRA) methodology is adopted to screen the influencing factors of carbon emissions in the logistics industry firstly. Then, the particle swarm optimization (PSO) algorithm was used to optimize the penalty coefficientand kernel function range parameter of the support vector regression (SVR) model (i.e. PSO- SVR model). The data from 2000 to 2021 regarding carbon emissions and related influencing factors in China’s logistics industry are analyzed, and the mean absolute percentage error (MAPE) of the PSO-SVR model is 0.82%, which shows that the proposed PSO-SVR model in this paper is effective. Finally, instructive suggestions are provided for China to achieve the “Dual Carbon” goal and upgrading of the logistics industry.

Research on Coupling Coordination of Agricultural Carbon Emission Efficiency and Food Security in Hebei Province, China

The delineation and measurement of carbon emissions in agricultural production systems constitute a complex issue involving multiple factors. Previous research in this area has been limited in terms of comprehensive carbon emission assessment throughout the agricultural production process and systematic measurement. This study focuses on both dynamic and static aspects, systematically analyzing the agricultural carbon emissions and emission efficiency in Hebei Province from 2000 to 2020. It comprehensively explores the influencing factors of carbon emissions and delves into the relationship between agricultural carbon emission efficiency and food security. The experimental results revealed the following: (1) From 2000 to 2020, the agricultural carbon emissions in Hebei Province exhibited a fluctuating downward trend, with a spatial distribution pattern where they were high in the south and low in the north. And the carbon emissions caused by chemical fertilizers and plowed land accounted for 42.6% of the total. (2) The efficiency of agricultural carbon emissions in the static dimension fluctuated at a rate of 0.0265, whereas the ML index fluctuated less in the dynamic dimension, and the agricultural industrial structure had the most significant impact. (3) The coupling coordination degree of food security and agricultural carbon emission efficiency increases with time, and “coordination” gradually dominates in spatial change. The conclusions of this study are of great significance in stabilizing grain production and achieving low-carbon production in the Hebei Province.

Carbon emissions path of public buildings based on LEAP model in Changsha city (China)

China is currently the world's top carbon emitter due to the acceleration of urbanization, the rapid growth of energy consumption and the rise in carbon emissions, with the construction industry leading the three energy sectors in terms of emission reduction potential. Among the various sectors, the construction industry holds immense potential for emission reduction, with public buildings playing a crucial role. In China, the expansion of public building spaces is closely linked to the pace of urbanization and contributes to a rising share of total building area. Public buildings, characterized by high energy usage, offer a great opportunity for energy conservation and emission reduction. Changsha is experiencing rapid growth and change, and the city is becoming more industrialized and urbanized, which leads to a prominent contradiction between energy supply and demand. There is also great potential for energy conservation and emission reduction in public buildings. Additionally, Changsha is an important node city in the Yangtze River urban agglomeration and the middle reaches of the Yangtze River Economic Belt. It is a typical area that has hot summer and cold winter, thus Changsha is selected as the research case to examine the carbon emission path of public buildings. The primary influencing factors of carbon emissions of public buildings in Changsha City are determined by using the LMDI factor decomposition method addition model. The scenario analysis method is adopted to set the two energy consumption forecast scenarios, which are the baseline scenario and the green scenario. Based on LEAP model and GREAT framework structure, the LEAP prediction model is constructed to predict the carbon emissions of public buildings in Changsha City from 2021 to 2035. The Baseline scenario indicates that while carbon emissions from public buildings in Changsha continue to rise, they are expected to decline starting in 2030. The peak carbon emission of 34.279 million tons of CO2 is predicted for 2032. In contrast, the Green scenario reveals a significantly slower growth rate in carbon emissions from public buildings, with the peak carbon emission projected to reach 27.054 million tons in 2030. This aligns with China's national goal. The findings of this study support the accuracy of the long-term prediction of the model, and offer recommendations for energy conservation and emission reduction in Changsha. This study also provides an example and certain guidance for other provinces and cities that have similar development models and climate conditions. Pertinent development strategies serve as a point of reference for other typical hot summer and cold winter regions.

Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region

The greenhouse effect has a negative impact on social and economic development. Analyzing the factors influencing industrial carbon emissions and accurately predicting the peak of industrial carbon emissions to achieve carbon peak and carbon neutrality is therefore vital. The annual data from 2000 to 2022 were used to study the influencing factors of carbon emission and the path of carbon emission reduction. In this study, the gray correlation method and stepwise regression method were used to explore the effective factors that met the significance test and the STIRPAT expansion model was constructed to analyze the characteristics and influencing factors of industrial carbon emissions in the Sichuan-Chongqing region. Finally, the changing trend of regional industrial carbon emissions is predicted by scenario analysis and four development scenarios are set up, which show that (1) from 2000 to 2022, the change in total industrial carbon emissions in Sichuan Province and Chongqing Municipality presents an inverted U-shaped trend, reaching a phased peak in 2013 and 2014, respectively, then declining and then rising again after 2018. (2) Industrial scale foreign trade dependence and population are the effective factors of industrial carbon emission in Sichuan, and all have positive effects. Energy structure and per capita income are the effective factors in Chongqing, showing negative and positive effects, respectively. (3) Analysis of four scenarios shows that the time range of the industrial carbon peak in the Sichuan-Chongqing region is 2030–2035 and that its peak height ranges from 81.98 million tons to 87.64 million tons. Among them, the green development scenario is the most consistent path to achieve the carbon peak as soon as possible; in this case, industrial carbon emissions will peak in 2030, in line with the national target time, and the lowest peak level of 81.98 million tons. The suggestions in this paper are continuously optimizing the energy structure, adjusting the industrial scale, and accelerating scientific and technological progress to achieve sustainable development.

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.

Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry

Abstract This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

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.