Energy Consumption Carbon Emissions Research Articles

Study on carbon emission characteristics and its influencing factors of energy consumption in Sichuan Province, China

Clarifying the key sectors and influencing factors of carbon emissions from energy consumption is an important prerequisite for achieving the “carbon peaking and carbon neutrality” goals. This study calculated the carbon emissions of fuel combustion in 7 major departments and regional electricity trading of Sichuan Province from 2000 to 2021, and empirically analyzed the impact of energy structure effect, energy intensity effect, industrial structure effect, economic development level effect, and population size effect on the carbon emissions of energy consumption based on the LMDI model. The main research conclusions are as follows: (1) LMDI model has the advantages of no residual and high interpretation. By refining the multi-departments of energy consumption and different types of fuel, it is helpful to improve the accuracy of empirical analysis results. (2) The carbon emissions of energy consumption mainly come from the fuel combustion process. Specifically, the industry sector composed of steel, building materials, chemicals and machinery is the key emission sector, and transportation and residential life are also vital. Regional electricity trading can indirectly reduce the intensity of carbon emissions while ensuring the safety of energy supply. (3) From 2000 to 2021, the energy intensity effect and the economic development level effect were key factors in slowing down and promoting the carbon emission growth of energy consumption in Sichuan Province, respectively. The population scale effect mainly played a positive role in driving carbon emissions, but the impact is small and almost negligible. Before 2012, the energy structure effect and the industrial structure effect were mainly positive driving effects, and after 2012, they all turned into negative inhibitory effects. This was mainly due to the low-carbon transformation of energy structure and the optimization of industrial structure.

Exploring the effective distance of 3D urban compact form correlated with building energy consumption and optimization strategies

ABSTRACT With the acceleration of urbanization, building energy consumption carbon emissions (BECCE) were increasing, weightily influencing global warming and the social-economic developments in cities. The detailed exploration of the most effective distance of urban three-dimensional (3D) compact form on BECCE guides urban space optimization. In this study, 135 People’s Banks of China (PBC) were taken as samples. Based on the banks building features, socioeconomic conditions, macroclimate, and urban 3D compact form at different buffers, the most effective distance on the BECCE was determined. According to Partial least-squares regression (PLSR), the results show that the most effective distance of the urban compact form on the BECCE was 150 m. The study divided the normalized 3D compactness index (NVCI) values from 135 buildings for the distance of 150 m into five categories and used the geographic detector to recognize statistically obvious differences between the subregions. The natural breakpoint method was used to categorize the study area into five classes, specifically: low, medium-low, medium, medium-high, and high compact form. Based on the geographic detector method result, the compact form of medium, medium high, and high compact form was optimized to low, medium low. The optimization can effectively reduce the BECCE due to changing the compact form of the building by 65.2% or 65.7%. The study combined mathematical statistics with spatial analysis approaches to identify the most effective distance for reducing energy consumption. Our results will contribute toward considerable reductions in the BECCE.

How far need to go from peak to neutrality? Perspective from energy consumption emission offset analysis in Beijing-Tianjin-Hebei Agglomeration

Carbon emissions elimination in the rapid urbanization areas contributes to regional sustainable development and global climate change mitigation. With the target of carbon neutrality, the global top-class Beijing-Tianjin-Hebei Urban Agglomeration which confronts with the dual pressure of excess carbon emissions and shrink ecological space has increased its carbon reduction coordination cooperation, among which energy consumption carbon emission control and natural resource sink maintenance have been recognized as the key strategy. However, the study on carbon balance prediction is lack and the amount of energy consumption carbon emissions in the future can be offset by the ecological carbon sink is still unknown. To estimate the gap to carbon neutrality, this study proposes a framework for carbon offset analysis and prediction which takes various carbon emission scenarios and the potential of natural resource on carbon reduction into consideration. After the framework conduction in the agglomeration, it is revealed that the regional least carbon emissions of 29.51 Mt mainly derive from the population and economic growth brought by urbanization process, while the carbon sink predicted to be 2552.31 Kt is still far to cover the emissions. With the regional carbon offset rate decline from 0.68% to 0.63%, the ecological carbon sink in Beijing will offset 1.21% of its carbon emission, while the least ecological space keep city Tianjin is predicted to suffer the lowest offset rate of 0.09%. The research results contribute to recognize the key factors on carbon emission control and key areas for carbon maintenance in following years, as well as narrowing the gap to neutrality with both consideration of carbon emission reduction and sink maintenance.

Rapid carbon emission measurement during the building operation phase based on PSO–SVM: electric big data perspective

With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE (“electricity-carbon” model) based on particle swarm optimization algorithm–support vector machine (PSO–SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R2 evaluation indicators, the constructed “electricity-carbon” model is compared and evaluated. Finally, the generalization ability of the “electricity-carbon” model is verified. The research results show that (1) the “electricity-carbon” model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO–SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R2 are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the “electricity-carbon” model constructed in this paper. The “electricity-carbon” model constructed in this paper can provide a scientific reference for building emission reduction.

Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm

Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.

Pulse fractional grey model application in forecasting global carbon emission

Global climate problem has attracted attention from all over the world. How to correctly reflect greenhouse gas emission trend is not only an environmental problem, but also concerns human society's sustainable development. Energy consumption is the primary source of greenhouse gas emission. It is necessary to accurately forecasting energy consumption greenhouse gas emission future trend. COVID-19 epidemic has brought respite from global climate change problem by reducing energy consumption through home office, work stoppage, and global travel ban. However, in the post-epidemic period, energy consumption greenhouse gas emission intensity trend has become the center research field. This study takes global energy consumption carbon emission problem as the main research line and focuses on forecasting global energy consumption carbon dioxide emission trend in major regions based on COVID-19 epidemic shock background. The study considering epidemic shock volatility characteristic. Firstly, fractional order grey mode (FGM(1,1))is used as the baseline model to balance time series data weight. Secondly, Median absolute deviation data preprocessing is introduced to reduce data fluctuation. Finally, a novel delaying pulse shock function optimized background grey forecasting model is also proposed to reflect epidemic shock-response characteristic. The proposed model is compared with existing models. It is found that data preprocessing and novel proposed model not only improves historical data's fitting quality by reflecting COVID-19 epidemic's shock characteristic, but also showed excellent forecasting performance for future trend. The novel grey model largely solves existing model underfitting/overfitting problem. In the end, based on forecasted results, we summarize research conclusion and implication.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China.

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Analysis and Prediction of Factors Influencing Carbon Emissions of Energy Consumption Under Climate Change

Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.

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.

Relationship Between Carbon Emissions and Economic Development of Industrial Parks Based on Decoupling Index

Industrial parks are the main carriers of industrial activities and are also key areas for carbon emissions. To deeply explore the decoupling state of carbon emissions and economic development of industrial parks and the driving forces, Zhengzhou Economic Development Zone were taken as example, based on the energy consumption data of industrial enterprises above a designated size from 2011 to 2020, the IPCC carbon emission accounting method, Tapio decoupling model, and logarithmic mean Divisia index decomposition method were used to analyze the characteristics of carbon emissions from energy consumption in the park, the relationship between carbon emissions and economic development, and the driving factors of decoupling. The results showed that:① in terms of carbon emission characteristics, the carbon emissions of energy consumption in Zhengzhou Economic Development Zone were mainly indirect carbon emissions, and the total carbon emissions showed a trend of rapid growth in the early stage, slowing down in the medium term, and negative growth in the later stage. The carbon emission intensity was decreasing annually. ② From the perspective of decoupling, the decoupling index between total carbon emissions and economic development in Zhengzhou Economic Development Zone from 2011 to 2016 was 1.021, which was in a state of growth linkage, and the decoupling index decreased to 0.089 from 2016 to 2020, turning into a weak decoupling state. ③ From the analysis of driving factors, from 2011 to 2016, four factors, namely carbon emission coefficient, energy efficiency, industrial structure, and economic level, all had a restraining effect on the decoupling of carbon emissions in Zhengzhou Economic Development Zone, and from 2016 to 2020, they all turned into promotion except for the economic level. This study showed that among the factors for the decoupling of carbon emissions in the Zhengzhou Economic Development Zone, the economic level played a major inhibitory role, and energy efficiency played a major role in promoting it. The results of this study can provide a reference for the industry-city integrated industrial park represented by Zhengzhou Economic Development Zone to formulate corresponding carbon emission reduction policies and achieve the carbon peaking and carbon neutrality goals.

State monitoring and fault detection for convolutional neural network integrated energy systems

Abstract The upgrading of the energy industry structure will certainly affect the development of the environment, and the study of the impact of export restrictions is an inevitable trend. This paper firstly establishes time series and panel series models based on Kuznets curve. The concept and mathematical derivation of environmental Kuznets curve are analyzed. The green Solow model is selected to extend the exogenous technology of Solow model to the field of pollution reduction and establish the relationship between energy industry upgrading and environmental development. Secondly, the relationship between economic development and resource consumption and environmental pollution is analyzed based on the concept of decoupled development. Finally, the relationship between total energy consumption and total carbon emission and economic growth is investigated. The carbon emission coefficient is 0.712, the oil emission coefficient is 0.576, and the natural gas emission coefficient is 0.437. The carbon emission of total energy consumption is also in the left half of the inverted U-shaped curve, i.e., the carbon emission is still rising as the economy grows.

The impact of urban–rural integration on carbon emissions of rural household energy consumption: evidence from China

The impact of urban–rural integration on carbon emissions of rural household energy consumption: evidence from China

Impact of urbanization size and deforestation environmental degradation on economic growth in Nigeria

The study examined the impact of urbanization size and deforestation environmental degradation on economic growth in Nigeria. Specifically, the study sought to: determine the impact of urban population size environmental degradation on the economic growth in Nigeria and ascertain the impact of deforestation environmental degradation on the economic growth in Nigeria. This study made use of ex post-facto research design. The method of data analysis was Autoregressive distributive Lag model. The data of the study were real gross domestic product (RGDP), total deforestation (FOREST), urban population size (UPS), non-renewable energy consumption carbon emission (NREC), industrial sector carbon emission (ISC), electricity generation carbon emission (EGC) and were sourced from on-line World Bank Data indicators over the period of 1991 to 2022. The empirical results show that: urban population size environmental degradation has 22% negative and insignificant impact on economic growth in short run (Probability value of 0.7660 > 0.05) and deforestation environmental degradation has 49 % positive and insignificant impact on economic growth in short run (Probability value of 0.7660 > 0.05). The study government should provide farmers with farm inputs and extension services to educate rural peasant farmers on latest local techniques for maximum output. Farmers should engage in bush fallowing to allow unfertile land to regenerate after some reasonable number of years before putting the land into use.

Can forest carbon sequestration offset industrial CO2 emissions? A case study of Hubei Province, China

Carbon peaking and carbon neutrality goals are proposed by the Chinese government, which aims to reach the highest level of CO2 emissions by 2030 and achieve carbon neutrality by 2060. It is of great significance to accelerate green production and lead high-quality development. Energy carbon emissions account for 88% of total CO2 emissions, while forest carbon sequestration account for 80% of China's terrestrial ecosystems. In this paper, we analyzed the spatial and temporal evolutions of industrial energy consumption carbon emissions and forest carbon sequestration of Hubei Province, then demonstrated the offsetting effect of forest carbon sequestration on the industrial CO2 emissions. Our results documented energy carbon emissions and forest carbon sequestration in Hubei Province both increased from 2000 to 2020, with the growth rates of 4.9423 Mt/yr and 0.28015 Mt/yr. Forest carbon sequestration could offset energy carbon emissions before 2005, while the offsetting effect was weak due to the continuous increase in industrial energy consumption after 2005. Significant spatial heterogeneity was observed in both energy carbon emissions and forest carbon sequestration. Wuhan had the biggest carbon emission with annual average carbon emission of about 20.046 Mt, while Enshi had the biggest carbon sequestration with annual average forest carbon sequestration of about 14.411 Mt. Spatial autocorrelation between energy carbon emissions and forest carbon sequestration was significant in Hubei Province in the past two decades. Our findings provided evidence that local-scale forest carbon sequestration could offset carbon emissions caused by industrial energy consumption at a certain extent, helping to draw up the scientific energy-saving and emissions-reducing measures.

The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data

The spatiotemporal evolution patterns of carbon emissions and their influence mechanisms are important topics for regional climate change monitoring and research on sustainable development goals. At present, due to the limitation of statistical data collection scale, it is difficult to analyze the spatiotemporal variation of carbon emission and its influence mechanism at a finer scale in China. With the development of new remote sensing platforms and technologies, multisource remote sensing data such as nighttime light remote sensing data and XCO2 concentration data have become important information resources for carbon emission monitoring. Therefore, this study monitors the spatiotemporal evolution of carbon emissions in China based on multisource remote sensing data and conducts impact mechanism research. The main conclusions of this study include: (1) The partial least squares carbon emission estimation model and the downscaled inversion model estimate carbon emissions with high accuracy. The estimated carbon emissions of both have high correlation with statistical carbon emissions, with R2 of 0.86 and 0.87, respectively, and no significant overestimation or underestimation. (2) The overall spatial pattern of energy consumption carbon emissions in China from 2010 to 2018 is high in the east and low in the west and high in the north and low in the south, but this spatial distribution pattern is gradually weakening. China's energy consumption carbon emissions varied considerably from 2010 to 2018, with an overall slow positive growth trend. (3) The mechanisms of population growth, economic development, urbanization and industrialization on carbon emissions are more complex, and most of their influencing factors promote carbon emission generation, while carbon emission impacts have spatial spillover. This study designs and studies a regional energy consumption carbon emission estimation model in China based on multisource remote sensing data, and explores the characteristics of regional multiscale carbon emission spatiotemporal variation and its influence mechanism, so as to provide scientific references for China's carbon emission reduction targets.

A Study of Carbon Emission Driving Factors of a Metal Chemical Enterprise in China Based on the LMDI Model

The chemical industry is a typical high-carbon emitting industry, and achieving the goal of net zero emissions by 2050 is challenging. Therefore, metal chemical enterprises have to explore a special path of low-carbon development. This article conducted a case study on a Chinese metal chemical production enterprise with a processing scale of 28,000 t/year. Starting from the analysis of energy consumption carbon emissions, this article used available statistical data at the enterprise level to build a carbon emission estimation model for the enterprise combining different emission categories. Moreover, we also calculated the carbon emissions and carbon emission intensity of the enterprise from 2014 to 2022. Further quantitative analyses on the impact of production scale, energy efficiency, energy structure, and emission coefficient on carbon increment were also conducted using a logarithmic mean divisia index (LMDI) model. The results showed that the reduction in carbon emissions of the enterprise during the research period was due to the improvement of energy efficiency, while the production scale and energy structure served as important driving factors. Based on the results, this article proposes some policy suggestions on the future direction and focus of the enterprise’s carbon reduction work.

Carbon Emission Influencing Factors and Scenario Prediction for Construction Industry in Beijing–Tianjin–Hebei

In this paper, the factors causing the change in carbon emissions from direct energy consumption in the construction industry in Beijing–Tianjin–Hebei are decomposed using the logarithmic mean divisia index (LMDI) method to analyze the effect values and contribution rates of each macrofactor. Based on the decomposition results and given relevant national policies, five scenarios are set up for each influencing factor, and a regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis is applied to each scenario combination for scenario prediction, forming a scientific and reasonable theoretical system to predict the future time of carbon peaking and carbon neutrality in the construction industry of Beijing–Tianjin–Hebei. The results show that (1) energy intensity and energy structure have a suppressive effect on direct energy consumption carbon emissions in the construction industry in Beijing–Tianjin–Hebei, and the industrial structure, economy, and population will promote an increase in carbon emissions. Energy intensity and the economy have a more significant effect on carbon emissions in the construction industry. (2) The peak year of carbon emissions varies with different scenarios, and the energy efficiency scenario achieves peak carbon in 2028, the earliest peak time, and the lowest peak, as it is the optimal emission reduction projection scenario.

Optimization Model of Substation Building Envelope–Renewable Energy Utilization Based on Life-Cycle Minimum Carbon Emissions

Maximizing the carbon reduction in substations with minimum cost investments can be achieved by taking advantage of the potential of substations in terms of the envelope and renewable energy, which is significant in promoting carbon reduction in substations. Therefore, firstly, the relationship between building cost–energy consumption–carbon emissions is explored, and then the global optimal calculation model of substation envelope–renewable energy is established, with the lowest life-cycle carbon emission of the substation as the optimization goal. Finally, the validity of the model is verified based on a case study of a typical 110 kV outdoor substation. The model calculation results show that, without considering the cost constraint, Harbin has the highest maximum carbon reduction of 180,350 kg, which is 25.15% and 13.74% higher than the maximum carbon reduction in Shanghai and Haikou, respectively. Furthermore, based on the comparison of the cost and benefits of each carbon reduction technology, a prioritization of various carbon reduction technologies is given for each climate zone. The model established in this paper can provide the optimal configuration of substation carbon reduction technologies with different incremental cost constraints, and provide a reference for the low-carbon design of substations.

Impact of intelligence on the carbon emissions of energy consumption in the mining industry based on the expanded STIRPAT model

The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China’s mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China’s mining industry is proposed.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China.

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.