Monitoring Carbon Emission from Key Industries Based on VF-LSTM Model.

China's energy chemical industry accounts for about 12.01% of the national carbon emissions, while the heterogeneous carbon emission characteristics exhibited by the subsectors have not been reliably investigated. Based on the energy consumption data of the energy chemical industry subsectors in 30 Chinese provinces from 2006 to 2019, this study systematically identified the carbon emission contributions of high-emission subsectors, examined the evolutionary changes and correlation characteristics of carbon emissions from different perspectives, and further explored the carbon emission drivers. According to the survey, coal mining and washing (CMW) and petroleum processing, coking, and nuclear fuel processing (PCN) were high-emission sectors of the energy chemical industry, with annual emissions of more than 150 million tons, accounting for about 72.98% of the energy chemical industry. In addition, the number of high-emission areas in China's energy chemical industries has gradually increased, and the spatial disequilibrium of carbon emissions in industrial sectors has gradually deepened. The development of upstream industries had a strong correlation with carbon emissions, and the upstream industry sector still has not achieved carbon decoupling. The decomposition of the driving effects of carbon emissions showed that the economic output effect is the largest contributor to the growth of carbon emissions in the energy chemical industry, while energy restructuring and energy intensity reduction help reduce carbon emissions, but there is heterogeneity in the driving effects of subsectors.

Cross-industry synergistic emission reduction has become a new strategy for achieving a carbon emissions peak and carbon neutrality. To explore the typical spatial distribution and cross-industry synergy effect of carbon emissions in key industries, this paper analyzes the carbon emissions of coal and power industries in Jiangsu Province from 2006 to 2020 using the empirical orthogonal function (EOF) and a panel vector autoregressive (PVAR) model. The results show that: (1) The distribution of coal resources determines the distribution of carbon emissions in the coal industry. Carbon emissions in the power industry have two typical distributions: consistent changes in cities and a “south-north” inverse phase, with a cumulative variance contribution rate of 86.74%. (2) The impulse response of carbon emissions from the coal industry to the power industry is >0 in the first period. There is a synergistic relationship of carbon emissions from the energy consumption side to the energy production side. (3) The shock effect of carbon emissions on economic development is >0. In resource-based cities, economic development explains about 2% of carbon emission fluctuations in the coal industry and 9.9% in the power industry, which is only 2% in non-resource-based cities. Carbon emissions would promote economic development. However, the impact of economic development on them varies significantly by industry and region. These findings can provide scientific support for developing differentiated measures to carbon emissions reduction and serve as an important reference role for other regions to promote collaborative carbon emission reduction in key industries.

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China’s urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China’s urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China’s urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China’s urban industrial carbon emissions was “low carbon-horizontal expansion” dominated by positive growth, and in 2016, it was “low carbon-vertical expansion” dominated by scale growth; (4) China’s urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.

Carbon emissions are the main culprit of global warming. Accurate carbon emission forecasting helps government departments formulate effective carbon emission reduction policies and helps the carbon emission market develop orderly. Implementing an accurate and effective carbon emission monitoring model requires the collaboration of many parties because carbon emission-related data involves many sectors and industries. However, for the relevant characteristics of carbon emission monitoring, due to the different collection and storage standards of various departments, poor maintenance environment, lack of data, data loss, and abnormal severe, resulting in high frequency and high precision carbon emission monitoring. As privacy protection and data security issues are gradually taken seriously by government departments and related enterprises, the inability or unwillingness to share carbon emission-related data among enterprises or even among various departments within enterprises has created an increasingly severe data silo phenomenon. In addition, how effectively breaking the data barriers between various sectors is an urgent problem in grasping carbon emission change changes accurately. Therefore, this paper proposes a carbon emission monitoring model for key urban sectors based on vertical federated deep learning and multi-source heterogeneous data fusion and sharing. The experimental results show that the model accurately predicts carbon emission change trends in various application scenarios under the data availability and invisibility of each participant.

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis. The results show that the emission intensity effect and production structure effect contributed greatly to the reduction of CCEs across various regions, while the final demand effect had contrary impacts. The local nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) generally contributed significantly to the emission intensity effect, production structure effect, and final demand effect across most regions. The consumption of local c13, c14, and c24 by the construction industry (c27), namely “local c13→c27”, “local c14→c27”, and “local c24→c27” were generally the important structural paths of the CCEs changes across various regions. Nonlocal industries such as Hebei c14 and nonlocal structural paths such as “Hebei c14→c27” contributed substantially to the CCEs changes in many regions such as Beijing. The emission intensity effect, first-order production structure effect, and final demand effect typically dominated the effects of the critical structural paths of the CCEs changes across various regions. This study can help policymakers better understand the changes in China's provincial CCEs to formulate region-specific emission reduction measures and provide a comprehensive reference for related research.

Monitoring carbon emissions, as well as evaluating and controlling the level of carbon emissions, are important prerequisites for combating climate change and promoting sustainable development. In this paper, real-time estimation of carbon emissions based on non-intrusive electricity load monitoring is investigated, existing machine learning approaches and related issues of load identification and marginal carbon emission factors are elucidated, and existing carbon emission monitoring approaches are analyzed and summarized. The methods of real-time carbon emission measurement are summarized, and their development is anticipated. Most of the existing methods for measuring and analyzing carbon emissions in power systems are unable to meet the development needs of real-time carbon emission calculation and have the disadvantages of higher installation and maintenance costs and poorer economy of monitoring equipment. Therefore, in this paper, a real-time carbon emission monitoring scheme based on deep learning is used to estimate carbon emissions by dividing them into direct carbon emissions and indirect carbon emissions.

Similar to the problems surrounding carbon transfers that exist in international trade, there are severe carbon emission headaches in regional industrial systems within countries. It is essential for emission reduction control and regional industrial restructuring to clarify the relationship of carbon emissions flows between industrial sectors and identify key carbon-emitting industrial sectors. Supported by the input-output model (I-O model) and social network analysis (SNA), this research adopts input-output tables (2017), energy balance sheets (2021) and the energy statistics yearbooks (2021) of the three Chinese provinces of Hei-Ji-Liao to construct an Embodied carbon emission transfer network (ECETN) and determine key carbon-emitting industrial sectors with a series of complex network measurement indicators and analysis methods. The key abatement control pathways are obtained based on the flow relationships between the chains in the industrial system. The results demonstrate that the ECETNs in all three provinces of Hei-Ji-Liao are small-world in nature with scale-free characteristics (varying according to the power function). The key carbon emission industry sectors in the three provinces are identified through centrality, influence, aggregation and diffusion, comprising coal mining, the chemical industry, metal products industry, machinery manufacturing and transportation in Liaoning Province; coal mining, non-metal mining, non-metal products, metal processing and the electricity industry in Jilin Province; and agriculture, metal processing and machinery manufacturing in Heilongjiang. Additionally, key emission reduction control pathways in the three provinces are also identified based on embodied carbon emission flow relationships between industry sectors. Following the above findings, corresponding policy recommendations are proposed to tackle the responsibility of carbon reduction among industrial sectors in the province. Moreover, these findings provide some theoretical support and policy considerations for policymakers.

The textile industry is one of the pillar industries in the Yangtze River Delta Region and its green and low-carbon transformation is important for supporting the high-quality development of the Yangtze River Delta. Considering the demonstration zone of green and integrated ecological development of the Yangtze River Delta as an example, this integrated study was conducted on the carbon emission inventory of the textile industry, the driving factors of carbon emissions in the industry, and decoupling effects. Based on the emission factor method, the carbon emissions of Scope 1 and 2 of the textile industry in the demonstration zone were estimated. The carbon emission efficiency of the industry was analyzed using the super efficiency slack-based measure （SBM） model with unexpected outputs. Combining the LMDI factor decomposition method and Tapio decoupling analysis, the driving factors of carbon emissions in the textile industry in the demonstration zone and the decoupling situation between emissions and economic development were identified. The results indicated： ① Between 2014 and 2021, the carbon emissions of the textile industry in the demonstration zone showed a fluctuating trend, reaching the highest value in 2019 at 9.19 million tons of CO2 equivalent. Wujiang District was the primary emission area, with electricity, heat, and coal consumption emissions being the top three emission sources. ② The overall carbon emission efficiency of the textile industry in the demonstration zone showed an upward trend； however, significant differences were present in carbon emission efficiency between regions, with Jiashan County having considerable room for improvement in carbon emission efficiency. ③ Between 2014 and 2021, the driving factors of carbon emissions in the textile industry in various regions of the demonstration zone showed significant changes, with the level of economic development being a positive driving factor affecting carbon emissions. ④ In terms of the decoupling status between carbon emissions and economic development, the overall textile industry in the demonstration zone showed a transition from negative decoupling to decoupling status between 2014 and 2016. The research results provide a scientific basis for the future balanced development of the green and low-carbon transformation of the textile industry and the high-quality development of the economy in the demonstration zone.

Excessive carbon emissions will lead to catastrophic consequences such as global warming and rising oceans and will also have a serious negative impact on the human food supply and living environment. The steel industry is characterized by high pollution, and about 18% of China’s carbon emissions come from the steel industry. The ‘double carbon’ strategy has brought important tasks and severe challenges to China’s steel industry. With a view to evaluating the achievements of carbon emission control, carbon emission monitoring systems at home and abroad have been continuously established and improved. For the steel industry, accurate and efficient carbon monitoring technology has a guiding role in guiding energy conservation and carbon reduction. Traditional carbon emission accounting methods have some problems, such as long cycles and poor data quality, which restrict the improvement of the lean level of carbon emission monitoring management. Firstly, this paper investigates and analyzes the productive process and carbon emission process of the steel industry and constructs an entropy weight-grey correlation -TOPSIS analysis method for the correlation between carbon emissions and influencing factors. Based on the above content, a carbon emission monitoring method based on multiple influencing factors is put forward, and the high monitoring accuracy of the model is proved by taking the Tianjin steel industry as an example. The results show that information mining of relevant data can strikingly increase the accuracy of carbon emission monitoring in the steel industry.

This paper describes an industrial energy combustion use and industrial process emissions accounting method. By utilizing three set of widely used energy combustion carbon emission factors, Chinas industrial energy consumption carbon emissions are calculated. By using the methods provided by the IPCC, the industrial process carbon emissions for extractive industries, chemical industries and metal industries are calculated. The results show that in 2010 China's industrial energy consumption carbon emissions reached approximately 6.91×108 t C (2.53×109 t CO2), 85% from coal burning. The industrial process emitted approximately 9.47×108 t C (3.48×109 t CO2). About 5.55×108 t C (2.04×109 t CO2) is emitted by providing heat and power to industrial processes. In addition, this paper also proposed an improved model coupling industrial carbon emissions data and input-output analysis. It will help to quantify and evaluate the trans-sector carbon emissions shift.

To achieve the goals of “carbon peaking and carbon neutrality”, this paper puts forward the connotation and measurement method for the carbon emission intensity of urban industrial land and conducts an empirical study with the Yangtze River Economic Belt (YREB) as an example. We defined the carbon intensity of urban industrial land as the industrial carbon emissions per unit area of land, which is a spatial mapping of urban industrial economic development and carbon spillover and a key indicator for urban and territorial spatial planning oriented towards the “dual carbon” goal. Findings: The carbon emission density of industrial land in the YREB varied greatly between cities and exhibited significant positive spatial autocorrelation. In addition, the geographical pattern and spatio-temporal evolution model of the urban industrial land carbon emission density had a very complex driving mechanism, and different factors had significant synergistic effects. Therefore, it is suggested that while striving towards the goal of “dual carbon”, the government should incorporate the carbon emission density indicator of urban industrial land into the urban and territorial spatial planning system, and based on the threshold of the medium suitable density, they should design differentiated management policies according to concrete urban policies and encourage cooperation among cities to jointly promote carbon emission management of urban industrial land. In policy design, emphasis should also be placed on highlighting the interactive effects of foreign direct investment, fiscal expenditure, and the number of patent authorizations as well as constructing a combination of policies centered around them to better leverage the impacts of globalization, government intervention, and innovation.

The objective of this study is to identify the spatiotemporal change law and the leading factors of industrial carbon emission decoupling. Based on the industrial carbon emission level of the Yangtze River Delta urban agglomeration (YRDUA) from 2006 to 2020, the spatiotemporal heterogeneity was explored with the help of the spatial Markov chain, the Tapio decoupling model was used to analyze its decoupling state from the industrial economy, and its driving factors were decomposed using the Kaya identity and logarithmic mean Divisia index (LMDI) model. The results show that (1) in 51.9% of the YRDUA's cities, the industrial carbon emission situation was stable, the emission reduction observation area (medium carbon) occupied a dominant position, and the emission reduction key area (relatively high carbon) gradually decreased. (2) Industrial carbon emissions had spatial overflow and path dependency characteristics, and the probability of carbon emission type transfer maintaining the original state reached 80.0%. From 2006 to 2011, the average probability of the downward migration of high-carbon cities was 5.0%. From 2011 to 2020, the average probability of the upward transfer of low-carbon cities was 9.4%. (3) The negative decoupling rate of carbon emissions in the YRDUA experienced a transition from 3.7% to 44.4% and then back to 7.4%, showing spatial imbalance. Unsatisfactory decoupling cities were concentrated along the Yangtze River and in coastal areas. (4) The promoting efficiency of energy intensity, carbon emission coefficient, and employment structure was gradually strengthened, and the carbon-increasing effect of labor input was gradually weakened. (5) The decoupling mode of heavy difficult cities is dominated by the three-factor balanced type, which is jointly affected by industrial production, labor input, and carbon emission coefficient. The findings in this study can provide inspiration for industrial carbon emission reduction in megalopolises.

Regional tourism needs to respond positively to the “carbon peak and neutrality” target, and the key and most difficult aspect is the prediction of carbon emissions. In this paper, the total carbon emissions of the tourism industry in Jiangxi Province from 2000 to 2019 are calculated by using terminal consumption and the tourism development coefficient. The factors influencing the carbon emissions of the tourism industry are studied by means of logarithmic mean weight Divisia index decomposition (LMDI), and the timing of the tourism industry carbon peak is predicted by combining the extensible random environmental impact assessment model (STIRPAT) and scenario analysis method. The results show three key aspects of this system. (1) In the historical period, the carbon emissions of the tourism industry in Jiangxi Province increased from 71.365×104 t in 2000 to 2342.456×104 t in 2019, with an average annual change rate of 21.09%. The scale of tourism investment was the most important factor affecting the carbon emissions of tourism industry in this period. (2) The main factor that will affect the change of tourism carbon emissions in Jiangxi Province in the future is the carbon emission intensity, and its influence coefficient reaches 0.810. The degrees of influence of tourism income, tourism number and tourism investment follow in sequence. (3) The peak time of carbon emissions from tourism in Jiangxi Province varies under different scenarios. In the baseline scenario, it is estimated to be around 2035, and the average annual change rate will be –0.88%. In the medium- and low-carbon scenarios, the peak carbon emissions will be realized around 2030 and 2025, with the average annual change rates being –1.11% and –1.58%, respectively, indicating that the government's low-carbon policy will have an impact on the carbon emission intensity of tourism and promote the tourism industry in Jiangxi Province to advance by 5 to 10 years. This study provides a theoretical basis for allowing regional tourism to achieve its carbon peak in advance, which supports the prediction of the whole country's “carbon peak and neutrality”, and also provides a measurement basis for the realization of carbon neutralization in tourism.

The production of construction projects is carbon-intensive and interrelated to multiple other industries that provide related materials and services. Thus, the calculations of carbon emissions are relatively complex, and the consideration of other factors becomes necessary, especially in China, which has a massive land area and regions with greatly uneven development. To improve the accuracy of the calculations and illustrate the impacts of the various factors at the provincial level in the construction industry, this study separated carbon emissions into two categories, the direct category and the indirect category. The features of carbon emissions in this industry across 30 provinces in China were analysed, and the logarithmic mean Divisia index (LMDI) model was employed to decompose the major factors, including direct energy proportion, unit value energy consumption, value creation effect, indirect carbon intensity, and scale effect of output. It was concluded that carbon emissions increased, whereas carbon intensity decreased dramatically, and indirect emissions accounted for 90% to 95% of the total emissions from the majority of the provinces between 2005 and 2014. The carbon intensities were high in the underdeveloped western and central regions, especially in Shanxi, Inner-Mongolia and Qinghai, whereas they were low in the well-developed eastern and southern regions, represented by Beijing, Shanghai, Zhejiang and Guangdong. The value creation effect and indirect carbon intensity had significant negative effects on carbon emissions, whereas the scale effect of output was the primary factor creating emissions. The factors of direct energy proportion and unit value energy consumption had relatively limited, albeit varying, effects. Accordingly, this study reveals that the evolving trends of these factors vary in different provinces; therefore, overall, our research results and insights support government policy and decision maker’s decisions to minimize the carbon emissions in the construction industry.