Analysis and Prediction of the Influencing Factors of China’s Secondary Industry Carbon Emission under the New Normal

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.

The carbon emission of industrial energy consumption has an important impact on a region's carbon emission reduction and optimization of carbon emission path. Based on the STIRPAT model and the TAPIO decoupling model, this study analyzes the driving factors and decoupling relationship between carbon emissions from industrial energy consumption and economic growth in Sichuan Province. The research shows that: (1) The industrial carbon emissions in Sichuan Province are increasing first and then decreasing, with an increase of 18.78% from 2005 to 2022, and the carbon emission intensity has decreased from 1.94 tons/10,000 yuan to 0.29 tons/10,000 yuan; (2) The decoupling relationship between industrial carbon emissions and economic growth in Sichuan Province presents three development periods, mainly strong decoupling and weak decoupling. According to the decomposition factors, the decoupling relationship between industrial carbon emissions and economic development is mainly affected by the decoupling coefficient of value creation and emission reduction; (3) The proportion of secondary industry, the number of industrial enterprises, and the total industrial assets are the main carbon promoting factors of industrial carbon emission in Sichuan Province, with elastic coefficients of 0.097, 0.057 and 0.040 respectively; population size is a carbon reduction factor with an elastic coefficient of -0.087. Finally, the study puts forward some suggestions to realize industrial carbon emission reduction.

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.

Urban energy consumption is mostly concentrated in industrial regions, and carbon emissions from industrial land use have significantly increased as a result of fast urbanization and industrialization. In the battle against climate change, the affluent regions of developing countries are increasingly being used as models for reducing carbon emissions. Therefore, in order to accomplish global sustainable development, it is crucial to understand how industrial land use and carbon emissions are decoupled in wealthy areas of rising nations. This study investigates the decoupling effects and the factors influencing them in six East Chinese provinces and one city between 2005 and 2020 using the Tapio decoupling model and the LMDI decomposition approach. At the same time, the industrial carbon emissions from 2021 to 2035 were predicted using a BP neural network model combined with scenario analysis. The findings indicate that: (1) From 29.921 million tons in 2005 to 40.2843 million tons in 2020, the carbon emissions from industrial land in the East China area have nearly doubled. Of these, Shandong and Jiangsu emit more than half of the region’s total emissions around East China. (2) The decoupling effect analysis shows the East China region’s decoupling trajectory’s phased characteristics, with the degree of decoupling gradually increasing from weak decoupling (2006–2012) to strong decoupling (2013–2018) and finally to negative decoupling (2019–2020). (3) The primary causes of the rise in carbon emissions in the East China region are the scale of per capita economic output and industrial land use. (4) The overall industrial carbon peak time in East China is roughly distributed between 2028 and 2032. It is expected that Shanghai, Shandong, Jiangsu, and Zhejiang will be among the first to achieve carbon emission peak.

The reduction of the carbon emissions of construction industry is urgent. Therefore, it is essential to accurately predict the carbon emissions of the provincial construction industry, which can support differentiation emission reduction policies in China. This paper proposes a carbon emission prediction model that optimizes the backpropagation (BP) neural network by genetic algorithm (GA) to predict carbon emission of construction industry, or "GA-BP". To begin with, the carbon emissions of construction industry in Sichuan Province from 2000 to 2020 are calculated by the emission factor method. Further, the electricity correction factor is introduced to eliminate the regional difference in electricity carbon emission coefficient. Finally, four factors are selected by the grey correlation analysis method to predict the carbon emission of construction industry in Sichuan Province from 2021 to 2025. The results show that the carbon emissions of construction industry in Sichuan Province have been trending up in the past two decades, with an average increase rate of 10.51%. The GA-BP model is a high-precision prediction model to predict carbon emissions of construction industry. The mean absolute percentage error (MAPE) of the model is only 6.303%, and its coefficient of determination is 0.853. Moreover, the carbon emissions of construction industry in Sichuan Province will reach 8891.97 million tons of CO2 in 2025. The GA-BP model can effectively predict the future carbon emissions of construction industry in Sichuan Province, which provides a new idea for the green and sustainable development of construction industry in Sichuan Province.

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.

This article uses data on eight common forms of energy consumption used in industrial production to measure industrial carbon emissions from 2002 to 2020 in Zhengzhou City, China’s industrial sector. The study analyzes the factors influencing industrial carbon emissions using the ridge regression approach and the STIRPAT model. The findings show that between 90.01% and 97.49% of all industrial carbon emissions are attributable to the use of raw coal. According to the data, industrial carbon emissions are on the rise, peaking in 2013 at 70.11 million tons, and subsequently declining. Additionally, the study identifies that industrial carbon emissions rise with increases in the number of industrial employees, labor productivity, industrialization rate, energy intensity, and carbon emission intensity. In order to encourage sustainable industrial practices and lower carbon emissions, the study suggests improving the industrial structure and boosting funding for technological research and development.

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

In the twenty-first century, global warming and other environmental issues have become the focus of international attention. The total generation of carbon emissions for the railway transportation industry in the BRIC countries (Brazil, Russia, Indian and China) accounted for 25.73% of the global carbon emissions in this industry during 2017. Therefore, it is necessary to identify the influencing factors of carbon emission in the railway transportation industry for the BRIC, in order to better control and reduce carbon emissions and to achieve the global goal of "net-zero emission." The logarithmic mean divisia index (LMDI) decomposition method was used to examine the factors that influenced carbon emissions from the railway transportation industry in the BRIC from 1997 to 2017. According to the findings, the total carbon emissions of the railway transportation industry in BRIC were 60.92 million tons in 2017, increased by 98.62% compared to 1997. The factor of economic output effect has contributed positively to the increase in carbon emissions in all identified countries. However, the effect of population size effect, energy structure, and transportation intensity effect for carbon emission demonstrated heterogeneity in BRIC. In addition, policy suggestions are put forward for the reduction of carbon emissions from the railway transportation industry in BRIC.

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.

To respond to national policy requirements, reduce carbon emissions in the construction industry, and achieve the goal of "carbon peaking and carbon neutrality" as soon as possible. In this paper, a system dynamics model is proposed to predict and analyze carbon emissions in the construction industry in Liaoning Province. First, the influencing factors of carbon emissions in the construction industry in Liaoning Province are sorted out by searching relevant literature. Secondly, combined with the relevant data of the construction industry in Liaoning Province from 2009 to 2019 and the "14th Five-Year Plan" policy, set different simulation scenarios and use Vensim software to reasonably predict the carbon emissions of the construction industry from 2020 to 2030, and find out the impact of carbon emissions. The internal logical relationship of the main factors provides a theoretical basis and emission reduction path for reducing the energy consumption of the construction industry, reducing the total carbon emissions, and realizing the green and sustainable development of buildings.

As a key region in the western region and an important source of China' s industrial foundation, analyzing the driving factors of industrial carbon emissions and predicting carbon peak in Xi' an is crucial for China' s sustainable development. . This study employed the Logarithmic Mean Divisia Index (LMDI) method to decompose the influencing factors of industrial carbon emissions in Xi’an. Then, the ridge regression method and the Stochastic Impacts by Regression on STIRPAT model was used to analyze the quantitative impact of four driving factors on industrial carbon emissions. Finally, scenario analysis was adopted to predict the carbon emissions and carbon peaking time of Xi' an' s industrial sector under three different development scenarios in the next 15 years. The study found that: (1) From 2013 to 2022, the total industrial carbon emissions in Xi' an exhibited aa negative growth. energy intensity and energy structure exhibited significant negative effects, with contribution rates of 376. 92% and 210. 95%, respectively. In contrast, economic development and population size factors played a positive effects on carbon emission increments, with contribution rates of-288. 64% and-199. 24% respectively. (2) From 2023 to 2040, the predicted total industrial carbon emissions in Xi' an primarily showed a trend of first rising and then declining, which primarily influenced by population size, with an elasticity factor of 1. 735. (3) Both low-carbon and benchmark development scenarios could help Xi' an' s industrial sector achieve its carbon peaking target earlier, but a high-carbon model would struggle to reach carbon peaking even by 2040. The baseline scenario represented the optimal development model for Xi' an, with carbon peaking projected to occur in 2028, reaching a peak of 49. 4189 million tons. This study provided a theoretical basis for developing a reasonable carbon peaking pathway for Xi' an' s industrial sector and assisted the government in formulating corresponding high-quality development paths.

With the continuous growth in the volume of global air transportation, the carbon emissions of the civil aviation industry have received increasing attention. Carbon emission reduction in civil aviation is an inevitable requirement for achieving sustainable social development. This article aims to use system dynamics (SD) methods to establish a carbon emission model for the civil aviation industry that includes economic, demographic, technological, policy, and behavioral factors; analyze the key factors that affect carbon emissions; and explore effective emission reduction strategies. Researchers have found that SD-based carbon emission prediction has a high accuracy and is suitable for predicting carbon emissions in civil aviation. Through different scenario simulations, it has been found that any single emission reduction measure will struggle to effectively contribute to the expected carbon reductions in China’s civil aviation. Simultaneously adopting measures such as improving fuel efficiency, adopting clean energy, and using new-power aircraft is an effective way to reduce carbon emissions from civil aviation. In addition, policy intervention and technological innovation are equally crucial for achieving long-term emission reduction goals. The research results not only provide a scientific basis for the sustainable development of the aviation industry but also provide a reference for policymakers to formulate comprehensive emission reduction strategies.

As the second largest CO2 emission department, transportation industry's carbon peak and carbon reduction are very important for China to smoothly achieve carbon peak by 2030 and carbon neutrality by 2060. This paper analyzes the influencing factors from the perspectives of population, economy, technology and transportation equipment structure, subdivides 20 scenarios to predict the carbon emissions of the transportation industry of the whole China and various regions based on scenario analysis method, explores the carbon peak path, and puts forward corresponding policy recommendations. The study found that (1) from the overall trend of carbon emissions, the total carbon emissions of China's transportation industry showed an overall upward trend from 2010 to 2019 while the growth rate of carbon emissions showed a downward trend. (2) From the perspective of influencing factors, population size, urbanization rate, economic scale, traffic development, traffic carbon intensity, and highway mileage have positive effect on the growth rate of China's transportation CO2 emissions. The increase in the proportion of energy structure and railway cargo turnover has the negative effect on carbon emissions in the transportation industry. (3) From the prediction results at the national level, technological breakthroughs have a limited effect on carbon emission reduction in China's transportation industry, while structural equipment optimization has the most significant effect on its emission reduction. When technological breakthroughs and equipment structure optimization are carried out simultaneously, the carbon emission reduction effect is the best. The carbon peak of China's transportation industry would achieve as early as 2030, with a peak range of 70,355.54-84,136.17 million tons. (4) From the perspective of prediction results at the regional level, the provinces with rapid population growth and per capita GDP growth, the provinces with rapid population growth and per capita GDP growth, and the provinces with low population growth and per capita GDP growth should control their average annual growth rate of carbon emissions of the transportation industry to 1.13%, 0.72% and 0.58% respectively in 2019-2030, in order to ensure the achievements of the carbon peak target.