Grey Correlation Analysis between Industrial Structures and Carbon Emission in Guangxi Beibu Gulf Economic Zone

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.

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.

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.

The relationship between industrial structure and carbon emissions has been widely identified as a critical research topic by international organizations and academics. Using bibliometrics analysis, this study aimed at dissecting the global characteristics and trends of research on industrial structure and carbon emissions. Based on the 806 documents from 2004 to 2019 in Web of Science, this work was implemented from four aspects, including basic characteristics analysis, country/territory and institution analysis, category and journal analysis, and reference and keyword analysis. The results of this study showed rapid growth trends of research on industrial structure and carbon emissions from 2015 to 2019. The collaborations among countries and institutions were extensive worldwide with China, the USA, and the UK as the main participants. Furthermore, the corresponding research topics, research priorities, and research paths were summarized according to the references co-citation analysis and keywords cluster analysis, which from the perspective of the correlation between different types of industry with carbon emissions. Finally, the timezone view of the top 100 keywords indicated that the emerging trends in the research on industrial structure and carbon emissions were regional analysis, industrialization, and environmental efficiency, and prediction of carbon emissions peak and the spatial distribution in different types of industries were the hotspots in recent years. The findings provide a better understanding of global characteristics and trends that have emerged in this field, which can also offer reference for future research.

Under the background of ecological civilization, the energy saving and emission reduction, low carbon eco-city construction is booming. At present, China industrial carbon emissions account for more than half of total carbon emissions in the whole society. The analysis of industrial carbon emissions and its spatial distribution is quite important. Consider the Park of Shenzhen international low-carbon city as the research object; based on the enterprise data from the 2011 industrial census, it combines both the top-down and bottom-up methods to evaluate its current industrial carbon emission levels and then discusses the relationship between the industrial structure and carbon emissions. Comprehensive utilization of the spatial distribution function of GIS, the spatial distribution characteristics of industrial carbon emissions and carbon intensity is evaluated, so as to provide the references for the related policy-making of low-carbon ecological city.

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.

In the context of tax sharing reform and land reform during the 1990s, local governments in China relied heavily on land finance. Local governments have fierce competition in attracting investment, omitting the development of green economy. Based on the data of industrial land sales and carbon dioxide emissions, this study constructed the panel data of 196 cities in China from 2007 to 2017 and analyzed the spatial and temporal evolution characteristics of urban industrial land price distortion and carbon emission intensity. Furthermore, a multiple linear regression model was constructed from the aspects of scale effect to empirically analyze the overall impact and differential impact of urban industrial land price distortion on carbon emission intensity. With the help of the mediating effect model, the mechanism of urban industrial land price distortion on carbon emission intensity was investigated from the perspective of industrial structure upgrading. The results showed that (1) the higher the price distortion of industrial land is, the more detrimental it is to the development of green and low-carbon economy. (2) The regional heterogeneity test showed that the impact of industrial land price distortion on carbon emission intensity is most significant in the central region, medium cities, and cities with low fiscal self-sufficiency rates, respectively. (3) The higher the distortion degree of industrial land price, the greater the restriction on the upgrading of industrial structure, further increasing the carbon emission intensity. This paper provides policy implications for the market-oriented reform of land factors and the realization of the “double carbon” goal.

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.

Under the “dual carbon” goal, the new quality productivity in the industrial energy sector will become an important force in promoting the green and high-quality development of Hubei Province’s economy and society. The article comprehensively uses the Tapio decoupling model and LMDI decomposition method, and empirically analyzes the decoupling effect and driving factors of industrial carbon emissions in Hubei Province from 2006 to 2022 using panel data of industrial industries. Research has found that the growth of Hubei’s industrial economy and carbon emissions have undergone a fluctuating process of "strong decoupling → weak decoupling → expansion negative decoupling". For a considerable period of time in the future, the industrial economic growth and carbon emissions in Hubei will still be in a weak or expanding negative decoupling state. From the decomposition results, it can be seen that the trend of changes in the energy intensity index and carbon intensity index shows a high degree of consistency, and the energy intensity index has become the main factor driving the decrease in Hubei’s industrial carbon intensity index. However, the impact of energy structure effects and industrial structure effects on industrial carbon emissions is relatively weak, and the dividends brought by structural effects are still not significant. On this basis, the article proposes relevant policy recommendations, providing theoretical basis and policy basis for empowering high-quality industrial development in Hubei Province with new quality productivity in the post epidemic era.

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.

Global warming caused by greenhouse gas emissions seriously threatens a region’s sustainable environmental and socioeconomic development. Promoting industrial restructuring and strengthening technological innovation have become an important path to achieving pollution and carbon reduction as well as the green transformation of economic structure. This paper explored the mechanism of the mediating effect of technological innovation on industrial restructuring and carbon reduction while accounting for the direct effect of industrial restructuring on carbon emissions. Then, based on China’s provincial panel data from 2001 to 2019, we estimated the carbon emission intensity using the Intergovernmental Panel on Climate Change (IPCC)’s methods and analyzed its spatiotemporal evolution characteristics. Finally, we constructed a fixed-effect model and a mediating effect model to empirically analyze how industrial restructuring and technological innovation affect carbon emission intensity. The results are as follows: (1) From 2001 to 2019, China’s carbon emission intensity showed a continuous downward trend, with a pronounced convergence trend; there were obvious differences in carbon emission intensity between eastern, central, and western regions (western region > central region > eastern region) due to the unbalanced industrial structure. (2) In terms of direct effects, industrial restructuring can significantly reduce carbon emission intensity. The intensity of the effect is inversely proportional to the level of industrial restructuring, and the results of sub-regional tests are similar. Nevertheless, there is an obvious regional difference in the size of the carbon emission reduction effect of industrial restructuring in the east, central, and western regions. (3) In terms of indirect effects, industrial restructuring can reduce carbon emission intensity by enhancing technological innovation, and it acts as a mediating variable in the process of industrial restructuring to reduce carbon emission. Finally, we put forward recommendations for promoting industrial restructuring, strengthening green technological innovation, and properly formulating carbon reduction measures to provide a reference for countries and regions to achieve the goals of carbon neutrality, carbon peaking, and high-quality economic development.

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.

Tianjin is in a stage of rapid industrialization, and its demand for energy has been growing steadily. The industrial energy consumption accounts for over 60% of total energy consumption in Tianjin. A retrospective analysis is carried out on Tianjin industrial energy consumption. In 1995-2008, Tianjin's industrial energy intensity declined 7.52% annually, higher than the national average rate of 4.27%. Construct the formula of Tianjin's industrial economy and energy consumption by using the history data in 1999-2009. On this basis, forecast total industrial energy consumption in 2015, and the results show that Tianjin's energy intensity will achieve a decrease of 18% target. Using the calculation method of IPCC, account key industries' carbon emission intensity, and analyze the energy consumption structure to identify the potential energy saving industries. At last, establish industrial carbon emission benchmarks to provide a basis for the implementation of Tianjin "12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Five-year Plan" and the industrial carbon reduction decision-making process.

Low-carbon economy is becoming a new approach to optimize economic development, ensuring energy security and coping with climate change. As one of the important emission sources of greenhouse gases (GHG), the industrial sector should be prioritized in the development of low-carbon economy. In this study, the carbon emission from industrial energy use of Chongqing is accounted. On basis of industrial carbon emission (ICE) accounting, main factors responsible for industrial CO2 emission are identified and quantitatively analyzed using the Log-Mean Divisia Index method. The factors influencing ICE include energy mix, energy intensity, industrial structure and industrial output. It is found that the industrial output is the main driving force of ICE. The energy structure performs as a negative factor in carbon emission growth. By means of decomposing the influencing factors, several policy proposals were suggested for policy makers to build a low carbon city.

Decoupling and attribution analysis of industrial carbon emissions in Taiwan