How can industrial structure affect carbon emissions: Facilitating or inhibiting? Research based on spatial panel econometric models: Direct effects and spatial spillover

The impact of industrial structure adjustment on the spatial industrial linkage of carbon emission: From the perspective of climate change mitigation

The paper explores the spatial spillover of carbon emissions and influencing factors based on spatial econometric model using panel series data over the period 2000-2012 in case of China. The results confirm that carbon emissions have spatial dependence, spatial spillover effects of provincial carbon emissions and influencing factors are obvious; Economic growth, industrial structure and export dependence coefficients are positive, technology progress on reducing carbon emissions growth effects is remarkable, energy prices effects failed to pass the test of significance. The spatial spillover effects of carbon emissions and related influencing factors among neighbor provinces must be considered when government departments formulate policies and development planning. The paper opens up new insights for China decrease China's carbon emissions from different dimension as a whole.

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.

The paper explores the spatial spillover of carbon emissions and financial development based on spatial econometric model using panel series data in China. The results show that carbon emissions have spatial dependence, spatial spillover effects of provincial carbon emissions and financial development are obvious; local correlation exhibits the characteristics of spatial agglomeration, spatial spillover effect is significant; the elasticity modulus of financial development on carbon emissions is significantly positive; Chinese government promotes the development of low-carbon finance, carbon finance role in the development of monetary policy taking into account the mechanism of action space on carbon emissions, the paper opens up new insights for China decrease China's carbon emissions from financial development visual threshold.

The upgrading of industrial structure is the core means of coordinating economic development and environment protection. Its spatial agglomeration can also reduce environmental pollution partly. The upgrading of China’s industrial structure has become an important issue concerned by the whole society. To better understand this issue, based on the provincial data of China (1997–2017), this paper strives to explore the spatial effects of foreign trade and foreign direct investment (FDI) on the upgrading of China’s regional industrial structure by constructing the weight matrix of economic distance, and by introducing the spatial autocorrelation analysis method and spatial panel econometric model. The results show that: 1. The Moran’s I index of China’s import, export, FDI, and industrial structure upgrading has passed the 5% significance level test, displaying remarkable spatial agglomeration characteristics. 2. Foreign trade and FDI are important driving factors to upgrade China’s industrial structure. 3. Foreign trade has a significant spatial spillover effect. Imports and exports can not only promote the upgrading of local industrial structure, but also radiate to other regions, promote or inhibit the development of its industry, and further affect the national data. 4. The spatial spillover effect of FDI is not significant. Finally, some policy suggestions are put forward.

In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

Carbon emissions are the most direct and effective indicators to measure the level of low-carbon economy development. The influencing factors of carbon emissions can be decomposed into carbon emissions intensity effects, industrial structure effects, economic development effects, and population growth effects. This study based on the LMDI model, the factors which affected the carbon emissions changes of Henan province, was decomposed from 1990 to 2010. The results show that it is still at a relatively high carbon development stage in Henan Province, the pressure to realize low-carbon development is comparatively large. The direction and strength of the carbon emissions influencing factors were relatively largely different and in continuous dynamic changes. On the whole, the economic development is the main factor to promote the growth of carbon emissions and has contributed the most to the carbon emissions growth; Population growth also played a certain role in promoting the growth of carbon emissions, but is relatively limited. Carbon emission intensity changes are the main factors that inhibit the growth of carbon emissions and play the most significant role in reducing carbon emissions. The adjustment of industrial structure has limited effects on carbon emissions. It is the most effective way to realize low-carbon development, such as changing the energy structure, improving the industrial structure, and thereby reducing the carbon emissions.

Excessive carbon emissions from energy consumption seriously restrict China's sustainable development and eco-environmental protection. Although the carbon emissions from the construction industry are less than that of the power, transportation, and manufacturing sectors, the carbon emissions released by the construction industry cannot be ignored due to its extensive development trend of high energy consumption and low efficiency. Based on this, this paper studies energy-related carbon emissions and emissions reduction of China's construction industry from 2007 to 2017 by adopting the input-output analysis method, energy consumption method, and structural decomposition model. The results show that within the sample range: (1) The optimization of the construction industry energy consumption structure has a significant reduction effect on the growth of energy carbon emissions from the construction industry in China, and the reduction effect has shown an increasing trend over time. However, it should be noted that in this sample range, the optimization of energy consumption structure in the construction industry is mainly reflected in the decrease of the proportion of high-carbon energy consumption such as raw coal, while low-carbon energy such as natural gas has not played a significant role. Therefore, the future energy optimization space of China's construction industry is still huge. (2) Energy intensity effect and input structure effect have a positive inhibitory effect on carbon emission growth of the construction industry, and the inhibitory effect of energy intensity effect is stronger than that of input structure effect. It shows that in the sample range, the generalized technological progress and energy efficiency of the construction industry have been better optimized and improved. (3) Except for 2015-2017, the final demand effect in other intervals has a positive effect on the growth of carbon emissions in the construction industry, and the secondary and tertiary industries play a major role in the final demand effect. It shows that the total demand for the construction industry in various industries still maintains a growth trend. This paper provides a theoretical analysis basis and practical guidance for China's construction industry to carry out more accurate and efficient emission reduction from the supply-side energy varieties and demand-side industry level, and further enriches the existing research on carbon emissions of the construction industry from the perspective of input-output analysis.

Hunan province energy consumption carbon emissions based on the industrial structure was analyzed with carbon emissions factor method in 2000-2012. Results show that Hunan province’s carbon emissions have a rapid growth in 2000-2012. Since 2007 the growth of carbon intensity is slowly, and there is an emergence of signs of decline. Recently the correlation between the growth of GDP and carbon emissions in Hunan Province becomes weakening, but carbon intensity is still higher. Industry occupies a dominant position in the energy consumption carbon emissions. Since 2007 the proportion of industrial carbon emissions is decreased form 79.41% to 72.30% in 2012, there is an obvious decline. Recently, the growth rate of industrial carbon emissions is relative lower. The growth of carbon emissions from the construction industry and the tertiary industry is the most obvious. Relevant policies should be formulated as soon as possible, to promote the level of construction technology, control energy consumption and carbon emissions per unit of output.

How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China

As climate change has become a common challenge to global sustainable development, China has also proposed carbon peaking and carbon neutrality goals to cope with it. To achieve the dual-carbon goal, it has released a series of specific measures, like controlling both the amount and intensity of carbon emissions. It has also put in place a “1+N” policy framework for carbon peak and carbon neutrality, among which the industrial structure adjustment and technological progress are the most direct and effective ways to achieve climate-friendly sustainable development. So, it is of great benefit to examine the industrial structure adjustment and corresponding carbon emissions effect for the formulation of reasonable industrial adjustment policies. Based on the provincial panel data of China from 2005 to 2019, this paper adopts the panel threshold model to investigate the influence of industrial structure adjustment on carbon emissions at different levels of green innovation. Its findings show that there exists a nonlinear relationship between the industrial structure adjustment and carbon emissions and the influence of the former on the latter has the threshold effect of green innovation. Specifically, when green innovation capacity falls below a certain threshold value, the industry structure adjustment has no significant correlation with carbon emissions; when the threshold value is exceeded, changing industrial structure can dramatically reduce carbon emissions. According to the findings, it is suggested that in the process of attaining the dual-carbon goal, the government should highly promote industrial restructuring and technological advancement, especially supporting low-carbon and green technological innovation and ensuring the continuity and consistency of green innovation policy to enhance the carbon emission reduction effect of industrial optimization.

China’s petrochemical industries are playing an important role in China’s economic development. However, the industries consume large amounts of energy and have become primary sources of carbon emission. In this paper, the change in carbon emissions from China’s petrochemical industries between 2000 and 2010 was quantitatively analyzed with the Log-Mean Divisia Index method, which was decomposed into economic output effect, industrial structural effect and technical effect. The results show that economic output effect is the most important factor driving carbon emission growth in China’s petrochemical industries; industrial structural effect has certain decrement effect on carbon emissions; adjustment of industrial structure by developing low-carbon emission industrial sectors may be a better choice for reducing carbon emissions; and the impact of technical effect varies considerably without showing any clear decrement effect trend over the period of year 2000–2010. The biggest challenge is how to make use of these factors to balance the relationship between economic development and carbon emissions. This study will promote a more comprehensive understanding of the inter-relationships of economic development, industrial structural shift, technical effect and carbon emissions in China’s petrochemical industries and is helpful for exploration of relevant strategies to reduce carbon emissions.

Energy consumption and industrial activities are the primary sources of carbon emissions. As the “world’s factory” and the largest carbon emitter, China has been emphasizing the core role of technological innovation in promoting industrial structure upgrades (ISU) and energy efficiency (EE) to reduce carbon emissions from industrial production and energy consumption. This study investigated the mechanism (through ISU and EE) and spillover effect of technological innovation on carbon emission reduction using the panel dataset of 30 Chinese provinces from 2008 to 2019 and spatial econometrics models. The study concluded that (1) technological innovation had a negative direct effect on provincial carbon emissions, while it also showed a spatial spillover effect on neighboring provinces; (2) technological innovation had an indirect effect on provincial carbon emissions reduction through the mediation of energy efficiency improvement, while the mediation effect of industrial structure upgrading is not yet significant; and (3) the effect of technological innovation on carbon emission reduction showed heterogeneity in the eastern, central, and western regions of China. This study provided empirical and theoretical references to decision-makers in China and other developing countries in promoting technological and carbon control policies. More specifically, direct technology investment and indirect investment in industrial structure upgrades and energy efficiency could help with regional carbon emissions reduction.

Examining industrial structure changes and corresponding carbon emission reduction effect by combining input-output analysis and social network analysis: A comparison study of China and Japan