Search Results (11)

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO₂ emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development. Full article

Electricity consumption is closely linked to economic growth, social development, and carbon emissions. In order to fill the gap of previous studies on the decomposition of electricity consumption drivers that have not adequately considered carbon emission constraint, this study constructs the Kaya extended model of electricity consumption and analyzes the effects of drivers in industrial and residential sectors using the Logarithmic Mean Divisia Index (LMDI) method, and empirically explores the temporal and spatial differences in electricity consumption. Results show that: (1) During 2005–2021, the total final electricity consumption growth in Guangdong was much higher than that in Yunnan, but the average annual growth rate in Guangdong was lower, and the largest growth in both provinces was in the industrial sector. (2) The labor productivity level effect is the primary driver that increases total final electricity consumption (Guangdong: 78.5%, Yunnan: 87.1%), and the industrial carbon emission intensity effect is the primary driver that decreases total final electricity consumption (Guangdong: −75.3%, Yunnan: −72.3%). (3) The year-to-year effect of each driver by subsector is overall positively correlated with the year-to-year change in the corresponding driver, and declining carbon emission intensity is a major factor in reducing electricity consumption. (4) The difference in each effect between Guangdong and Yunnan is mainly determined by a change in the corresponding driver and subsectoral electricity consumption. Policy implications are put forward to promote energy conservation and the realization of the carbon neutrality goal. Full article

To deal with global warming and fulfil China’s commitment to carbon neutrality by 2060, reducing carbon emissions has become a necessary requirement. As one of China’s three major economic circles, the Beijing–Tianjin–Hebei region (B–T–H) has a great responsibility. This paper measures energy-related carbon emissions of B–T–H from 2005 to 2019 and uses the extended Logarithmic Mean Division Index (LMDI) to decompose the carbon emission effect factors. Then, a Tapio index model was constructed to analyse the contribution of each effect factor. The results showed that: (1) the total carbon emissions of B–T–H increased by 1.5 times, with Hebei having the highest proportion, followed by Tianjin and Beijing. Coal was the biggest emitter in all three regions. Natural gas emissions in Tianjin and Beijing were growing rapidly. (2) Consistent with most studies, economic development promoted carbon emissions, while energy intensity and energy structure inhibited them. It was found that innovative factors also have significant impacts: research and development efficiency was the primary emission inhibition factor in Hebei and the secondary inhibition factor in Tianjin and Beijing. The effects of investment intensity and research and development intensity differed between regions. (3) Beijing took the lead in achieving strong decoupling, followed by Tianjin. Hebei maintained weak decoupling. Innovative factors also played an important role in decoupling, which cannot be ignored in achieving emission reduction targets. Full article

Rational water use is the basis for sustainable development. The issue of how to use limited water resources to satisfy the high rate of economic development has attracted a great deal of attention from society. This paper presents a quantitative analysis of the intrinsic relationship between economic growth and industrial water use changes in Hubei Province based on panel data from 2004 to 2019. With the help of the Tapio decoupling model, the problem of decoupling the economic growth of Hubei Province and the water use of the three industries in 15 years was discussed. On the basis of Kaya’s extended identity, the Logarithmic Mean Divisia Index (LMDI) index decomposition method is used to evaluate the driving factors and steady state changes in the three industries’ water use. The results show that, with regard to the decoupling state, there are three decoupling states between economic growth and industrial water use in Hubei province: negative decoupling, strong decoupling, and weak decoupling, which showed a phase characteristic. From the decomposition of the factors, the industrial structure effect and the water intensity effect are the key factors that determine the decoupling of economic growth and industrial water use in Hubei Province, as well as the core driving force to promote the decoupling state. According to the development trend, Hubei Province needs to take into account the efficiency and affordability of water resources in the process of promoting social and economic development. Therefore, in line with the research outcomes, this study provides effective and feasible recommendations for promoting sustainable economic and social development in Hubei Province. Full article

Analyzing the evolution law of carbon emissions is particularly important for the designation of policies on energy conservation and emission reduction. Based on the regional division of China, this paper uses a spatial panel model to find the causes of the differences in carbon emission, and the non-parametric model, logarithmic mean Divisia index (LMDI) model and the extended STIRPAT model to analyze the relevant influencing factors in detail. From the studies in this paper, there come the following conclusions: (1) The environmental Kuznets curve (EKC) in the eastern region resembles the national EKC, demonstrating the same “N” pattern. However, the “upside-down U” pattern in the middle and western regions not only confirms the assumption of EKC in some Chinese regions but also demonstrates the effective restraint in high energy consumption and high emission levels when narrowing down the gaps between the central and western regions and the eastern regions. (2) In addition, good education can effectively suppress the increase in carbon emission, and every 1% increase in the proportion of educated people (college and above) results in emission reduction, respectively, by 0.22%, 0.51% and 0.44% in the eastern, central and western regions of China. (3) Significantly, the effect of tertiary industry structure on carbon emissions is positive, reflecting the trend of “deterioration” of China’s industrial structure over long time scales. This study functions positively in understanding the evolutionary pattern of regional carbon emissions and proposing differentiated policies on emission reduction. Full article

Greenhouse gas (GHG) emissions are a significant cause of climate change, and municipal solid waste (MSW) is an important source of GHG emissions. In this study, GHG emissions from MSW treatment in Beijing during 2006–2019 were accounted, basing on the Intergovernmental Panel on Climate Change (IPCC) inventory model; the influencing factors affecting GHG emissions were analyzed by the logarithmic mean Divisia index (LMDI) model combined with the extended Kaya identity, and the GHG mitigation potential were explored based on different MSW management policy contexts. The results showed that the GHG emissions from MSW treatment in Beijing increased from 3.62 Mt CO₂e in 2006 to 6.57 Mt CO₂e in 2019, with an average annual growth rate (AAGR) of 4.68%, of which 89.34–99.36% was CH₄. Moreover, the driving factors of GHG emissions from MSW treatment were, in descending order: economic output (EO), GHG emission intensity (EI), population size (P), and urbanization rate (U). The inhibiting factors were, in descending order: MSW treatment pattern (TP) and MSW treatment intensity (TI). Furthermore, compared with the BAU (business–as–usual) scenario, the GHG mitigation potential of the MSW classification and the population control scenario were 35.79% and 0.51%, respectively, by 2030. Full article

As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO₂ emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008–2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity. Full article

China is the world’s largest emitter of CO₂. As the largest sector of China’s fossil energy consumption and carbon emissions, manufacturing plays an important role in achieving emission reduction targets in China. Using the extended logarithmic mean division index (LMDI) decomposition model, this paper decomposed the factors that affect the CO₂ emissions of China’s manufacturing industry into eight effects. The results show the following: (1) China’s manufacturing CO₂ emissions increased from 1.91 billion tons in 1995 to 6.25 billion tons in 2015, with an average annual growth rate of 6%. Ferrous metal smelting and rolling were the largest sources of carbon dioxide emissions, followed by chemical raw materials and products and then non-metallic minerals. (2) During the research period, the industrial activity effects were the most important factor leading to increased CO₂ emissions in manufacturing and energy intensity was the most important factor in promoting the reduction of CO₂ emissions from manufacturing. The investment intensity was the second most influential factor leading to the increase in China’s manufacturing CO₂ emissions after the industrial scale and this even exceeded the industrial activity effect in some time periods (2000–2005). R&D efficiency and R&D intensity were shown to have significant roles in reducing CO₂ emissions in China’s manufacturing industry. The input of R&D innovation factors is an effective way to achieve emission reductions in China’s manufacturing industry. (3) There were differences in the driving factors of CO₂ emissions in the manufacturing industry in different periods that were closely related to the international and domestic economic development environment and the relevant policies of the Chinese government regarding energy conservation and emission reduction. (4) Sub-sector research found that the factors that affect the reduction of CO₂ emissions in various industries appear to be differentiated. This paper has important policy significance to allow the Chinese government to implement effective energy-saving and emission reduction measures and to reduce CO₂ emissions from the manufacturing industry. Full article

Against the backgrounds of emission reduction targets promised by China, it is crucial to explore drivers of CO₂ emissions comprehensively for policy making. In this study, Shandong Province in China is taken as an example to investigate drivers in carbon density by using an extended Kaya identity and a logarithmic mean Divisia index model (LMDI) with two layers. It is concluded that there are eight positive driving factors of carbon density during 2000–2015, including traffic congestion, land urbanization, etc., and seven negative driving factors comprising energy intensity, economic structure, etc. Among these factors, economic growth and energy intensity are the main positive and negative driving factor, respectively. The contribution rate of traffic congestion and land urbanization is gradually increasing. Meanwhile, 15 driving factors are divided into five categories. Economic effect and urbanization effect are the primary positive drivers. Contrarily, energy intensity effect, structural effect, and scale effect contribute negative effects to the changes in carbon density. In the four stages, the contribution of urbanization to carbon density is inverted U. Overall, the results and suggestions can give support to decision maker to draw up relevant government policy. Full article

The logistics industry is one of the major fossil energy consumers and CO₂ emitters in China, which plays an important role in achieving sustainable development as well as China’s emission reduction targets. To identify the key influencing factors regarding the logistics of CO₂ reductions and ensure that the development of China’s logistics industry becomes less dependent on CO₂ emissions, this paper built an extended log-mean Divisia index model (LMDI) to decompose the logistics of CO₂ changes between 1985 and 2015. Then, we introduced a decoupling model that combined the decomposition results to analyze the decoupling state and identify the main factors that influenced the decoupling relationship. The results show the following. (1) The urbanization effect was the decisive factor in CO₂ emissions increases, followed by structural adjustment effects, while technological progress effects played a major role in inhibiting CO₂ emissions. Particularly, the energy structure showed great potential for CO₂ emissions reduction in China. (2) Highways appeared to have dominant promoting roles in increasing CO₂ emissions regarding transportation structure effects; highways and aviation proved to have the largest impact on CO₂ emission reduction. (3) There has been an increase in the number of expansive negative decoupling states between 2005 and 2015, which implies that the development of the logistics industry has become more dependent on CO₂ emissions. Finally, this paper puts forward some policy implications for CO₂ emission reductions in China’s logistics industry. Full article

With China’s rapid economic growth, energy-related CO₂ emissions have experienced a dramatic increase. Quantification of energy-related CO₂ emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended “Kaya identity” and “IPAT model” with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO₂ emissions in China during 1995–2009. Empirical results show that the main obstacle that hinders China’s transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO₂ emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China. Full article