Analysis of Carbon Emissions from China's Petrochemical Industries Using the LMDI Method

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.

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

Characteristics of Carbon Emission in China and Analysis on Its Cause

The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.

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.

Carbon emissions from energy consumption in China: Its measurement and driving factors

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.

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.

This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents’ consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003–2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003–2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (−2.54%) and energy intensity effect (−18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The “Eastern aggregation” phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (−11.10 million tons) was significantly smaller than in the central (−21.61 million tons) and western regions (−13.29 million tons), and the carbon emission factor effect in the central area (−3.33 million tons) was significantly higher than that in the eastern (−2.00 million tons) and western regions (−1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

The urban agglomerations in the middle reaches of the Yangtze River (MYR-UA) are facing a severe challenge in reducing carbon emissions while maintaining stable economic growth and prioritizing ecological protection. The energy consumption related to land urbanization makes an important contribution to the increase in carbon emissions. In this study, an IPAT/Kaya identity model is used to understand how land urbanization affected carbon emissions in Wuhan, Changsha, and Nanchang, the three major cities in the middle reaches of the Yangtze River, from 2000 to 2017. Following the core idea of the Kaya identity model, sources of carbon emissions are decomposed into eight factors: urban expansion, economic level, industrialization, population structure, land use, population density, energy intensity, and carbon emission intensity. Furthermore, using the Logarithmic Mean Divisia Index (LMDI), we analyze how the different time periods and time series driving forces, especially land urbanization, affect regional carbon emissions. The results indicate that the total area of construction land and the total carbon emissions increased from 2000 to 2017, whereas the growth in carbon emissions decreased later in the period. Energy intensity is the biggest factor in restraining carbon emissions, followed by population density. Urban expansion is more significant than economic growth in promoting carbon emissions, especially in Nanchang. In contrast, the carbon emission intensity has little influence on carbon emissions. Changes in population structure, industrial level, and land use vary regionally and temporally over the different time period.

Economic development has largely contributed to the increment of CO2 emission. This study uses spatial econometric models to investigate the relationship between economic growth and carbon emission in China with data of 30 provinces of China during the period of 2000 to 2012. Results show that the relationship between carbon emission and economic growth in China during the recent decade has the development tendency toward an inverse U-shaped curve, approximately confirming the carbon emission’s Kuznets curve hypothesis in China. There exists a significant spatial correlation between carbon emission and economic growth, implying that carbon emission in a province may be influenced by economic growth in adjacent provinces. When economic growth reaches 279.91 million Yuan/km2 GDP (at a comparable price in 2000), the contradiction between economic growth and carbon emission begins to be gradually alleviated. These findings provide new insights and valuable information for reducing carbon emissions in China.

Decoupling effect and influencing factors of carbon emissions in China: Based on production, consumption, and income responsibilities

With the rapid development of the global economy, the amount of China’s carbon emission has been increasing consistently in a high speed, causing huge environment problems. The construction industry, as the leading pillar of the national economic and social development, accounts for a large proportion of the total carbon emissions in China. Several calculation methods have been used to calculate carbon emissions. However, the main influencing factors need to be found to reduce carbon emissions. In this paper, the Logarithmic Mean Divisia Index (LMDI) technique is used to decompose the energy-induced carbon emissions of the construction industry into four factors: construction areas, construction investment efficiency (output value per unit), energy intensity, and carbon intensity. Based on IPCC carbon emission factors and data from Chinese Energy Statistical Yearbooks and Chinese Construction Industry Statistical Yearbooks, the factors of energy-induced carbon emissions in China were decomposed with LMDI method and Kaya equation. Proper countermeasures are proposed to reduce the energy-induced carbon emissions of the construction industry in China.

Refined assessment and decomposition analysis of carbon emissions in high-energy intensive industrial sectors in China

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.