Emissions reduction in China׳s chemical industry – Based on LMDI

Carbon emissions reduction in China's food industry

The energy-related carbon emissions of China’s manufacturing increased rapidly, from 36988.97 × 104 tC in 1996 to 74923.45 × 104 tC in 2012. To explore the factors to the change of the energy-related carbon emissions from manufacturing sector and the decoupling relationship between energy-related carbon emissions and economic growth, the empirical research was carried out based on the LMDI method and Tapio decoupling model. We found that the production scale contributed the most to the increase of the total carbon emissions, while the energy intensity was the most inhibiting factor. And the effects of the intrastructure and fuel mix on the change of carbon emissions were relatively weak. At a disaggregative level within manufacturing sector, EI subsector had a greater impact on the change of the total carbon emissions, with much more potentiality of energy conservation and emission reduction. Weak decoupling of manufacturing sector carbon emissions from GDP could be observed in the manufacturing sector and EI subsector, while strong decoupling state appeared in NEI subsector. Several advices were put forward, such as adjusting the fuel structure and optimizing the intrastructure and continuing to improve the energy intensity to realize the manufacturing sustainable development in low carbon pattern.

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption in 1997–2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China’s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP (11.72%). Secondly, the trend of Moran’s I indicated that China’s carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China’s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.

Economic growth, industrial structure and nitrogen oxide emissions reduction and prediction in China

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.

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.

The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990–2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030, 2030, 2040, and 2040, respectively. At last, under the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.

Public buildings, with the increasing level of energy consumption, are a key area of energy conservation and emission reduction in China’s construction industry. In order to reduce carbon emissions of public buildings, realize the targets of energy conservation and emission reduction, the LMDI method is used to construct China’s public building macrocarbon factor decomposition model, identify the main driving factors affecting the change of carbon emissions from public buildings, and build a system dynamics model based on the decomposition results to predict China’s public building carbon emissions peak. The results show that reducing carbon emission coefficient and reducing energy intensity are the main driving factors to restrain the growth of carbon emission from public buildings. Under the base scenario, carbon emissions from public buildings will peak at 1.242 billion tons in 2041. Under the comprehensive regulation of energy structure, economic growth rate, investment level of scientific research and education, and carbon sink capacity, the carbon emissions of public buildings will reach the peak in 2030, and the adjustment of energy structure has a significant impact on the peak and peak time of carbon emissions of public buildings.

Energy conservation and emissions reduction in China—Progress and prospective

Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach

China can be regarded as a group of disparate economies, so the responsibilities of reduction have to be decided by considering different development stages over the provinces as well as reaching fairness of allocation. This study analyzed factors that influenced carbon dioxide emission changes due to energy-related consumption of 30 mainland provinces in China from 2005 to 2011, which was to promote carbon emission reduction and allocate carbon emission allowance. First, the Logarithmic Mean Divisia Index (LMDI) technique was adopted to decompose the changes in carbon emissions at the provincial level into five effects that were carbon coefficient, energy structure, energy intensity, economic output and population-scale effect. Next, according to the LMDI decomposition results, the overall contributions of various decomposition factors were calculated and applied to distribute carbon emission allowance over 30 provinces in China in 2020. The total effects of economic output, population-scale effect and energy structure on carbon emissions were positive, whereas the overall effect of energy intensity was negative. The allocation of carbon emission allowance can facilitate decision makers to reconsider the emission reduction targets and some related policies.

As the proportion of carbon emissions from urban areas rises, cities like Chengdu have become critical frontiers for emission reduction in China. Consequently, constructing low-carbon cities has emerged as the primary strategy for mitigating carbon emissions. According to the LMDI additive decomposition analysis, Chengdu’s carbon emissions increased by 4,397,700 tons during the 13th Five-Year Plan, primarily due to economic expansion, which alone accounted for an increase of 8,078,200 tons. Meanwhile, shifts in industrial structure and reductions in energy intensity contributed to declines of 657,700 tons and 3,016,500 tons, respectively, while changes in energy structure resulted in a marginal decrease of 640 tons. The LMDI multiplicative decomposition indicates a 10.3% growth in carbon emissions, with economic size amplifying emissions by 1.197 times, while enhanced energy intensity mitigated growth, reducing emissions to 0.935 times 2016 levels. Adjustments in industrial and energy structures exerted minimal impact on emission reductions. By developing a decomposition model for carbon emission influencing factors across various industries, this study identifies challenges to economic growth within the context of carbon reduction constraints and proposes pathways for low-carbon city development, including industrial restructuring, urban optimization, green building initiatives, and comprehensive transportation systems, thereby offering valuable insights for national low-carbon city initiatives.

As a typical resource-based region, Xinjiang is under increasing pressure to reduce carbon emissions. The ability to identify the drivers of carbon emissions in the industry and mapping out key emission reduction paths are core components of achieving carbon emission reduction in Xinjiang. This research incorporates energy and environmental factors, constructs a hybrid input–output model of “energy-environment-economy,” and uses the environmental input–output structural decomposition (EIO-SDA) and structural path decomposition (SPD) methods to analyze the drivers of carbon emissions from energy consumption and key emission reduction paths in Xinjiang. First, it shows that the economic-scale effect and the energy-intensity effect are the biggest facilitators of carbon emissions and most significant barriers to carbon emission reduction in Xinjiang. Second, capital formation and domestic trade are the primary sources of demand driving changes in carbon emissions in Xinjiang. Third, sectors involving the production and supply of electricity and heat, such as heavy manufacturing and energy industries, including petroleum processing, coking, nuclear, fuel processing, and chemical industries, are the key sectors responsible for carbon emission reduction in Xinjiang. Last, in terms of industry chains, “metal smelting and rolling processing industry/non-metallic mineral products industry—construction industry—fixed capital formation” and “oil and gas extraction industry (S20)—(intermediate sector)—final demand” are the most important paths driving the growth and decline of carbon dioxide (CO2) emissions from Xinjiang industries, respectively. To promote Xinjiang's carbon emission reduction targets, the Xinjiang government should actively improve its energy structure, promote a change in demand growth patterns, and formulate comprehensive management policies for high-carbon-transfer industries according to an industry chain perspective.