Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI method

Analysis on the influencing factors of carbon emission in China's logistics industry based on LMDI method

China is confronting great pressure to reduce carbon emissions. This study focuses on the driving factors of carbon emissions in China using the Logarithmic Mean Divisia Index (LMDI) method. Seven economic factors, including gross domestic product (GDP), investment intensity, research and development (R&D) intensity, energy intensity, research and development (R&D) efficiency, energy structure and province structure are selected and the decomposition model of influencing factors of carbon emissions in China is constructed from a sectoral perspective. The influence of various economic factors on carbon emissions is analyzed quantitatively. Results show that the R&D intensity and energy intensity are the main factors inhibiting the growth of carbon emissions. GDP and investment intensity are the major factors promoting the growth of carbon emissions. The contribution of R&D efficiency to carbon emissions is decreasing. The impacts of energy structure and province structure on carbon emissions are ambiguous through time. Finally, some policy suggestions for strengthening the management of carbon emissions and carbon emission reduction are proposed.

Achieving carbon peak before 2030 and carbon neutrality before 2060 is a solemn commitment made by China to address global climate change, and is also one of the main goals for economic and social development in the 14th Five Year Plan and the 2035 vision period. The changes in carbon emissions are directly related to the progress of China's "carbon peak" and "carbon neutrality" goals. Therefore, in-depth research on the influencing factors of carbon emissions has become a key link in promoting the achievement of this goal. In the existing research on carbon emission influencing factors, countries mainly focus on macro scale low-carbon urban carbon emission influencing factors and micro scale low-carbon building full life cycle carbon emission influencing factors. However, there is relatively little research on the influencing factors of carbon emissions in low-carbon communities of urban micro units, and there is still considerable research space. This study conducted an in-depth analysis of the influencing factors of carbon emissions in low-carbon communities using complex network methods. By constructing a complex network model of factors affecting carbon emissions, we identified key nodes and pathways, and explored their interrelationships. The results indicate that factors such as energy structure, resident behavior, building design, and policy implementation play an important role in carbon emissions in low-carbon communities.

Carbon Emissions and Economic Development in Transport Industry - An Empirical Research Based on Decoupling Theory and Structure Decomposition Model

Analyzing the driving mechanisms behind provincial carbon emissions is crucial to formulating appropriate carbon reduction policies， which is vital for achieving China's "carbon peaking and carbon neutrality" goals. This study employed the LMDI method to examine the influences of six key factors （population size， economic development， industrial structure， energy intensity， energy structure， and carbon emission coefficient） on carbon emissions across 30 regions in China from 2010 to 2021. By using the contribution rate of each driving factor to changes in carbon emissions as the clustering variable， the K-means clustering method was used to categorize the 30 regions into five groups. This facilitated identifying the similarities and differences in carbon emission driving mechanisms across various regions. The results of the study follow： ① For most regions， economic development and population growth are the primary drivers of carbon emission increases， while energy intensity and industrial structure are important factors in carbon emission reductions. ②The driving factors of carbon emissions vary significantly between the Twelfth and Thirteenth Five-Year Plan periods， with the growth in both the amount and rate of carbon emissions being notably lower in the former period. ③ Importantly， the driving mechanisms of carbon emissions differ greatly across the five region types identified. The first and fifth types of regions face greater challenges in achieving carbon emission peak goals， whereas the second and third types are better positioned to attain these objectives. Based on the characteristics of the different region types and representative provinces and cities， targeted carbon reduction policies are proposed.

Emissions from the non-ferrous metal industry are a major source of carbon emissions in China. Understanding the decoupling of carbon emissions from the non-ferrous metal industry and its influencing factors is crucial for China to achieve its "double carbon" goal. Here, we applied the Tapio decoupling model to measure the decoupling status and developmental trends of carbon output and emissions of the non-ferrous metal industry in China. The panel interaction fixed effects model is used to empirically analyze the influencing factors of carbon emissions in China's non-ferrous metal industry. The results show that carbon emissions from China's non-ferrous metal industry have experienced three main states: strong decoupling, growth connection, and negative growth decoupling. The carbon emissions of the non-ferrous metal industry in some eastern and central provinces from 2000 to 2004 were in a negative decoupling state. Most provinces in the western and central regions were either in a strong or weak decoupling state based on the developmental trend of the decoupling state of carbon emissions. However, from 2015 to 2019, the decoupling status of carbon emissions in most provinces in western and central China had a significantly negative, weakly negative, or a negative growth decoupling status. Energy structure, energy intensity, cost, and non-ferrous metal production all have a positive driving effect on carbon emissions in the non-ferrous metal industry. Production had a mitigating effect on carbon emissions in the non-ferrous metal industry between 2010-2014 in the eastern region of China. From the results of our study, we propose policy recommendations to promote a strong decoupling of carbon emissions from the non-ferrous metal industry by improving energy structure, reducing energy intensity, and optimizing production capacity.

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

With the proposal of China’s “double carbon goal,” as a high energy‐consuming industry, it is urgent for the mining industry to adopt a low‐carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low‐carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China′s mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

基于LMDI分解的厦门市碳排放强度影响因素分析

The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.

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.

Tourism has had some negative effects while generating positive results. The carbon emissions produced by tourism, which is not a “smokeless industry” in traditional cognition, account for a certain proportion of the global greenhouse gas emissions. Tourism transportation, tourist accommodation, and other tourism activities all contribute to the carbon emission of tourism, and various tourism activities not only stimulate the economy but also increase air pollution. As a big industry, tourism’s growth and development have continuously increased energy consumption, and the pressure on energy conservation and emission reduction has also been greatly aggravated. In this study, the tourism carbon emissions in each province of China were estimated using a “top-down” calculation model, the tourism energy consumption factors were decomposed using a logarithmic mean Divisia index model, and the driving factors of tourism carbon emissions were analyzed through a panel data model. Results show that the tourism carbon emissions in China rapidly increased from 360.74 million tons in 2006 to 853.28 million tons in 2021. The driving factors of tourism energy consumption in China are economic development, energy efficiency, and population, while the inhibiting factors are tourism intensity and energy structure. The per capita GDP, the proportion of the tertiary industry, the turnover of tourists, and the level of urbanization all significantly promote the growth of tourism carbon emissions in China at 1%. The research results are of great significance to the proposal of measures for tourism carbon emission reduction in combination with the situation of various provinces and cities, promoting regional economic development and boosting the development of tourism in China under the background of a low-carbon economy.

The greenhouse effect has a negative impact on social and economic development. Analyzing the factors influencing industrial carbon emissions and accurately predicting the peak of industrial carbon emissions to achieve carbon peak and carbon neutrality is therefore vital. The annual data from 2000 to 2022 were used to study the influencing factors of carbon emission and the path of carbon emission reduction. In this study, the gray correlation method and stepwise regression method were used to explore the effective factors that met the significance test and the STIRPAT expansion model was constructed to analyze the characteristics and influencing factors of industrial carbon emissions in the Sichuan-Chongqing region. Finally, the changing trend of regional industrial carbon emissions is predicted by scenario analysis and four development scenarios are set up, which show that (1) from 2000 to 2022, the change in total industrial carbon emissions in Sichuan Province and Chongqing Municipality presents an inverted U-shaped trend, reaching a phased peak in 2013 and 2014, respectively, then declining and then rising again after 2018. (2) Industrial scale foreign trade dependence and population are the effective factors of industrial carbon emission in Sichuan, and all have positive effects. Energy structure and per capita income are the effective factors in Chongqing, showing negative and positive effects, respectively. (3) Analysis of four scenarios shows that the time range of the industrial carbon peak in the Sichuan-Chongqing region is 2030–2035 and that its peak height ranges from 81.98 million tons to 87.64 million tons. Among them, the green development scenario is the most consistent path to achieve the carbon peak as soon as possible; in this case, industrial carbon emissions will peak in 2030, in line with the national target time, and the lowest peak level of 81.98 million tons. The suggestions in this paper are continuously optimizing the energy structure, adjusting the industrial scale, and accelerating scientific and technological progress to achieve sustainable development.

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

Factor analysis of energy-related carbon emissions: a case study of Beijing