Energy-related CO2 Emissions In China Research Articles

Analysis of the Spatial and Temporal Evolution of Energy-Related CO2 Emissions in China’s Coastal Areas and the Drivers of Industrial Enterprises above Designated Size—The Case of 82 Cities

The energy consumption by industrial enterprises above designated size in China’s coastal region is the main source of CO2 emissions. This study analyzes the spatial and temporal evolution patterns and driving factors of CO2 emissions due to the energy consumption by industrial enterprises above designated size. Enterprises in 82 cities in China’s coastal regions were studied from 2005 to 2020 based on their CO2 emissions and socio-economic data. The Exploring Spatial Data Analysis (ESDA) methodology and Logarithmic mean Divisia Index decomposition (LMDI model) were used. The results show that, during the study period, energy-related CO2 emissions from industrial enterprises above designated size in China’s coastal areas generally show a fluctuating upward trend. However, a few cities showed a trend from steady growth to a peak and then a slow decline, which may realize the “double carbon” target in advance. The spatial correlation of CO2 emission intensity showed a decreasing and then increasing trend, and there were spatial aggregation characteristics in some cities. Among the driving factors, the pull effect is higher than the inhibition effect; the output scale contributes the most to the pull effect, and labor productivity contributes the most to the inhibition effect. The results of this study have a certain reference value for the realization of the “double carbon” target in China’s coastal regions.

Spatial evolution and decomposition of energy-related CO2 emissions in China's mining industry: from the perspective of regional heterogeneity.

To ensure China's energy security, the mining industry faces increasing emissions reduction and energy conservation pressures. This study combined index and production-theoretical decomposition analyses to decompose the energy-related CO2 emissions in mining industry (ERCEMI) influencing factors into seven major effects and adopted a gravity model to dynamically visualize the transfer path and gravity distribution from 2000 to 2015. As investment effects were introduced into the decomposition analysis, the results fully considered the regional heterogeneity and spatiotemporal dynamics. The main findings were as follows: (i) a typical heavy emissions trend along the Heihe-Tengchong line, with a concentration of large ERCEMI values; (ii) the gravity center of ERCEMI had shifted to the southwest, and the migration trends were divided into three stages; (iii) the ERCEMI had strong regional heterogeneity, with a diffusion trend from north to south and shrinking from east to west; (iv) the potential energy intensity and investment efficiency effects had significantly inhibited the ERCEMI, while the investment scale had boosted it. Implications for regional layouts, energy intensity reductions, and investment optimization are discussed. This research provides a comprehensive regional analysis for ERCEMI reductions and the sustainable development of the mining industry and provides a reference for local industrial development planning.

The effects of urban land use on energy-related CO2 emissions in China

Land use change caused by urbanization is widely believed to be the primary way human activities affect energy use and, thus, CO2 emissions (CEs) in China. However, there is a limited understanding of the role of land use with detailed categories in energy-related CEs is still absent. This paper aims to narrow the knowledge gap using multi-dimension metrics, including land use scale, mixture, and intensity. These metrics were derived from three years of sequential POI data. A GWR analysis was carried out to examine the associations between land use change and energy-related CEs. Our results show that (1) the scale of most land use types exerted a bidirectional effect on CEs, demonstrating apparent spatiotemporal heterogeneity; (2) land use mixture of mature city agglomerations had a significant suppressive effect on CEs, suggesting mixed land use be advocated in the urbanization process; (3) Land use intensity had a bi-directional association with CEs in most cities, but its adverse effect gradually spread from the west to the northeast. Therefore, systematically regulating land transaction to control land scale, appropriately interplanting biofuel plants, and utilizing renewable energy are encouraged to reduce energy footprints and mitigate CEs in China. The findings and conclusions of this paper enhance our knowledge on the relationship between land use and CEs and present the scientific basis for policy-making in building low-carbon cities in the context of rapidly urbanizing China.

Determinants of net energy-related CO2 emissions in China: A source-to-sink decomposition analysis

• A decomposition method incorporating carbon sources and carbon sinks is proposed. • Drivers of net energy-related CO 2 emissions during 2010–2018 in China are identified. • Economic growth, energy intensity, and vegetation productivity are dominant factors. • For increasing carbon sinks, improving climate adaptability may be more important than adjusting land structure.

Creating a decarbonized economy: Decoupling effects and driving factors of CO2 emission of 28 industries in China

Identifying the carbon emission characteristics, driving factors, and decoupling status of the industrial subsectors is important for developing effective policy measures. This allows for implementing industrial emission reduction that, eventually, decouple carbon emission and economic growth. Such an analysis is especially important for the case of China on its way towards sustainable development and increasing global interrelationships. However, the literature still lacks comprehensive analysis, especially, at the industry level. This study uses the Logarithmic Mean Divisia Index and decoupling indicator to analyze how different factors contribute to CO2 emissions in 28 industries in China during 2002–2017. The results reveal that the growth of industrial CO2 emissions has been positive but decreasing. The highest CO2 emission change is observed for production and supply of electric and heat power, processing of petroleum, coking, and nuclear fuel, and smelting and pressing of metals. These sectors also show high carbon intensity levels. The economic output (scale) effect and population effect comprise the two major factors promoting the CO2 emission. The energy intensity effect is the key inhibiting factor of the industrial energy-related CO2 emission in China. The suppressive effects of energy and industrial structure have been continuously increasing. The economic growth and CO2 emission has been gradually decoupling in the case of the 28 sectors analyzed. Manufacture of cloths, leather, fur, feather, and related products as well as production and supply of gas exhibit a relatively stable strong decoupling. Based on the decoupling analysis, this study shows that energy intensity has induced the decoupling, whereas the opposite effect has occurred due to economic growth, and the other factors showed little effect on CO2 emission decoupling.

Factors Decomposition and Scenario Prediction of Energy-Related CO2 Emissions in China

Factors Decomposition and Scenario Prediction of Energy-Related CO2 Emissions in China

Spatiotemporal dynamics of energy-related CO2 emissions in China based on nighttime imagery and land use data

It is difficult to accurately estimate the spatial distribution of carbon dioxide (CO2) at the grid scale because of the lack of city-level statistical data. In this paper, CO2 emission regions were devided into urban area, industrial area and rural area in order to accurately calculate CO2 emissions. In addition, a more accurate calculation model for energy-related CO2 emissions was proposed through integrating nighttime light datasets and land use data. The maps of estimated CO2 emissions in different regions were compared and examined through using spatial dependence and grid overlay methods to understand the spatial distribution characteristics and spatiotemporal dynamics of CO2 emissions in China. The results showed that the model proposed in this study was appropriate and reliable for CO2 emissions not only in urban and rural areas but also in industrial areas. CO2 emissions in China were mainly concentrated in the coastal areas and the northern area, and the amount of carbon emissions increased rapidly from 2000 to 2018 in the Middle Yellow River. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at gride scale, and provide a scientific reference for the formulation of energy conservation and emission reduction policies by the local government.

What drives the decoupling between economic growth and energy-related CO2 emissions in China's agricultural sector?

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO2 emission growth. To better explain the mechanism behind the decoupling between economic growth and CO2 emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China's agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.

Discerning drivers and future reduction paths of energy-related CO2 emissions in China: combining EKC with three-layer LMDI.

Recognizing the process and identifying the drivers of energy-related CO2 emissions can provide suggestions for designing carbon emission reduction paths, and then promote the further reduction of carbon emission. In this paper, the carbon emissions in China and its subordinate provinces during 2005-2017 are firstly divided into four stages, named S1, S2, S3, and S4. The results show that China has just entered the S3, and it is impossible to reach the peak of energy-related CO2 emissions with steady economic growth before 2030. Then, three-layer LMDI is utilized to explore the drivers of CO2 emissions, and the impact of urbanization which is separated from the population is considered innovatively. The economic development increases CO2 emissions, while the other drivers have diverse effects, which may be positive or negative, on carbon emissions in different regions. Therefore, four emission reduction paths with provincial characteristics should be followed in the future: (i) three provinces, namely, Ningxia, Shaanxi, and Xinjiang, should optimize multiple basic objectives in parallel; (ii) four provinces, such as Inner Mongolia and Hainan, should optimize the energy structure; (iii) six provinces, such as Jiangxi and Hunan, should optimize the industry structure; and (iv) the other provinces should develop new clean energy according to regional conditions.

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis.

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China’s energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future.

Energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture: An input–output perspective

The greenhouse effect caused by excessive carbon emissions seriously threatens the sustainability of the global economy. Although the secondary and tertiary industries are the principal contributors to CO2 emissions’ growth, the continuous and stable development of agriculture also intensifies carbon emissions to a certain extent. Based on this, this paper uses the input–output method, an energy consumption model, and structural decomposition analysis to study energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture from 2007 to 2017. The research results show that (1) In 2007–2017, the input structure effect and the energy intensity effect inhibited the growth of energy-related CO2 emissions in China’s agriculture, with the reduction effect of energy intensity effect being the more prominent, whereas the final demand effect and the energy structure effect contributed to promoting China’s agricultural energy-related CO2 emissions’ growth, with the final demand effect being the greater promoting factor. (2) The energy structure effect failed to reduce energy-related CO2 emissions in China’s agriculture because the optimization of consumption structure is still not obvious on China’s agricultural supply-side. The share of energy consumption from high-carbon fuels such as raw coal, coke, and other washed coal has increased, rather than decreased, and the optimization of low-carbon energy sources such as natural gas is still slow, thus limiting the emissions’ reduction effect in the energy structure. (3) The reason why the final demand effect has promoted the growth of energy-related CO2 emissions in China’s agriculture is the expanding demand for agriculture in the entire industrial system. Specific to each industry, the secondary industry has played a major role in the final demand effect, accounting for more than 50% of the final demand effect, followed by the primary and the tertiary industries. Finally, this paper provides a theoretical and practical basis for accurately and efficiently implementing emissions’ reduction from the perspectives of the industrial system on the demand-side and the energy system on the supply-side and also in China’s agriculture.

Residential Energy-Related CO2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi

The residential sector is the second-largest consumer of energy in China. However, little attention has been paid to reducing the residential CO2 emissions of China’s less developed or undeveloped regions. Taking Jiangxi as a case study, this paper thus aims at fully analyzing the difference of the residential energy-related CO2 emissions between urban and rural regions based on the Log-Mean Divisia Index (LMDI) and Tapio decoupling model. The main results are showed as follows: (1) Since 2008, residential energy-related CO2 emissions have increased rapidly in both urban and rural Jiangxi. From 2000 to 2017, the residential energy-related CO2 emissions per capita in rural regions rapidly increased and exceeded that in urban regions after 2015. Furthermore, the residential energy structures had become multiple in both urban and rural regions, but rural regions still had room to optimize its energy structure. (2) Over the study period, consumption expenditure per capita played the dominant role in increasing the residential energy-related CO2 emissions in both urban and rural regions, followed by energy demand and energy structure. Energy price had the most important effect on decreasing the urban and rural residential energy-related CO2 emissions, followed by the carbon emission coefficient. However, urbanization increased the urban residential energy-related CO2 emissions but decreased the CO2 emissions in rural regions. Population made marginal and the most stable contribution to increase the residential energy-related CO2 emissions both in urban and rural regions. (3) Overall, the decoupling status showed the weak decoupling (0.1) and expansive negative decoupling (1.21) in urban and rural regions, respectively.

Drivers of change in China’s energy-related CO2 emissions

CO2 emissions are of global concern because of climate change. China has become the largest CO2 emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2 emissions in China from 1978 when the reform and opening-up policy was launched. We find that 1) there has been a 6-fold increase in energy-related CO2 emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (−79%) and carbon intensity (−13%) slowed the growth of carbon emissions over most of this period; 2) energy-related CO2 emissions are positively related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and 3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future reductions in China’s CO2 emissions will require transformation toward low-carbon energy systems.

Analysis of energy-related CO2 emissions in China's pharmaceutical industry and its driving forces

China's pharmaceutical industry has experienced a rapid growth and is now facing a great challenge of reducing its CO2 emission. However, a detailed study concerning its energy consumption and corresponding CO2 emission characteristics, including emission trajectory, spatial distribution and driving forces at the whole sectoral scale is still lacking. In order to fill these research gaps, this study first uncovers the CO2 emission trajectory of China's pharmaceutical industry and then identifies the key driving forces of its emission growth by utilizing an extended Logarithmic Mean Divisia Index (LMDI) method. The results show that the total emission increased from 23.03 Mt in 2000 to 55.34 Mt in 2016. The consumptions of electricity, raw coal and heat were three major CO2 emission sources. From the provincial perspective, the top 5 provinces emitted 51.77% of the total CO2 emission in 2016, of which Shandong province was the biggest emitter, followed by Jiangsu, Jilin, Inner Mongolia and Ningxia. The analysis of relevant driving forces reveals that production scale effect was the most prominent factor of CO2 emission growth and investment intensity also contributed to CO2 emission growth. Conversely, energy intensity and R&D efficiency were the main contributors of CO2 emission reduction. Mitigation measures are finally proposed by considering the Chinese realities, including improving pharmaceutical industrial structure, promoting cleaner production strategy, encouraging rational R&D and investment activities, optimizing energy structure and initiating capacity-building efforts.

Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model

Cities produce over 70% of the global CO2 emissions that result from energy use, and thus play a key role in climate mitigation and adaptation. While the factors influencing CO2 emissions have been subject to extensive study, via research that has explored the path of developing a low-carbon economy, little work has been undertaken at the city level as a result of a deficiency in data availability. Addressing this gap, this study firstly estimated CO2 emissions of cities in China using Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light imagery. We then analyzed spatial variations in the estimated CO2 emissions at the city level, using a spatial analytical model, finding significant spatial autocorrelation in CO2 emissions. Subsequently, we compared the effects of different socioeconomic factors on CO2 emissions, using both global and local regression models. The results from the global regression model revealed that private car ownership, economic growth, and energy consumption were the major factors promoting CO2 emissions in China’s cities, while population density had an effect in reducing CO2 emissions. The use of a Geographically Weighted Regression (GWR) model provided more detailed results, revealing significant spatial heterogeneity in the impacts of different factors. Economic growth, private car ownership, and energy consumption all posed positive effects on CO2 emissions while the remainder of the factors studied were found to pose a bidirectional impact on CO2 emissions in different areas of China. Economic growth and private car ownership were to found to exert the strongest positive effects in the cities of western and central China, and energy consumption was shown to significantly and positively influence CO2 emissions in the southernmost part of China. Urban expansion and road density were identified as key promoting factors in CO2 emissions in the northeast of China; and the industrial structure demonstrated significantly positive effects in relation to CO2 levels in cities located in the Beijing-Tianjin-Hebei region. The role of foreign direct investment (FDI) was not found to be significant in most cities expect Guangdong, where a significant positive relationship appeared.

The driving forces and potential mitigation of energy-related CO2 emissions in China's metal industry

Utilizing the logarithmic mean Divisia index (LMDI) method, this paper decomposes the CO2 emissions of China's metal industry into five factors, namely, industrial output, industrial structure, energy intensity, energy consumption structure, and carbon dioxide emissions coefficient. Then, a scenario analysis is used to predict future CO2 emissions and estimate the emission mitigation potential from 2016 to 2020. The results indicate that (1) from 2000 to 2015, China's metal industry witnessed substantial growth in CO2 emissions, showing an increase of 1015.56 million tons (Mt). (2) Of the five driving factors, the industrial output effect was mainly responsible for CO2 emissions growth, with a contribution ratio of 245.51% over the total CO2 emission change, with other driving factors held constant. In contrast, the energy intensity decline was decisive for reducing CO2 emission, followed by the industrial structure. (3) The energy consumption structure had the weakest effect on CO2 emission changes and presented an overall positive effect with some volatility. (4) The CO2 emission mitigation potential under the moderate and aggressive emission mitigation scenarios is predicted to be 219.30 and 1445.26 Mt in 2020, respectively. These research findings propose that the Chinese government should take full advantage of the mitigating factors (i.e., decreasing energy intensity and industrial restructuring), paying particular attention to the subsector of the smelting and pressing of ferrous metals, and strive for breakthroughs.

Exploring the driving forces of energy-related CO2 emissions in China's construction industry by utilizing production-theoretical decomposition analysis

The construction industry is one of the largest energy consumers and CO2 emitters in China. This paper primarily aimed to explore the driving forces of energy-related CO2 emissions (ECE) from the construction industry. Adopting a comprehensive decomposition approach, this study decomposes the changes in ECE into eight factors. The main results provide the following findings. (1) Industrial activity was the largest factor pushing the growth of CO2 emissions, driving up CO2 emissions in all years and contributing to a 174.65 Mt CO2 emissions increase in total. (2) In contrast, advances in industrial output technology represented the dominant factor inhibiting CO2 emissions, cumulatively reducing CO2 emissions by 99 Mt. (3) The effects of potential energy intensity changes and industrial output technical efficiency fluctuated in different years but exerted positive effects on CO2 emissions over the entire period. (4) Additionally, changes in spatial structure, energy-saving technology, and energy consumption structure as well as energy usage efficiency all contributed to emissions reductions to varying degrees. The CO2 emissions of the construction industry increased in all provinces, while emissions changes and their factor effects varied distinctly across provinces.

Decomposition of energy-related CO2 emissions in China's iron and steel industry: A comprehensive decomposition framework

The rapid growth of CO2 emissions is not only due to increased energy consumption but also to multiple driving factors. Based on data from 2000 to 2014, this study proposes a comprehensive decomposition framework that combines production-theoretical decomposition analysis with index decomposition analysis to identify the driving factors of CO2 emissions from China's iron and steel industry. Furthermore, the different characteristics and drivers of CO2 emissions have been analyzed at the national, regional and provincial levels. The results indicate the following: (1) During 2000–2014, the average annual growth of CO2 emissions from China's iron and steel industry is 11.23%, and economic activity is the main reason for the dramatic increase. (2) Desirable output technology change has the greatest potential to mitigate CO2 emissions, followed by energy usage efficiency and energy saving technology change. Furthermore, the desirable output technology change and energy saving technology change are the constraint factors for CO2 emissions in all provinces. (3) Energy mix change, potential energy intensity change and desirable output technological efficiency generate important effects on the growth of CO2 emissions in most provinces. (4) The effects of energy usage efficiency to mitigate CO2 emissions in eastern region are not as great compared with those in central and western regions, and have mixed result across provinces. Therefore eliminating obsolete production capacities, improving R&D investment, accelerating the application of advanced technologies, reforming the ownership of enterprises and encouraging private and foreign investment represent the primary measures for mitigating the CO2 emissions from China's iron and steel industry.

Investigating the drivers of energy-related CO2 emissions in China’s industrial sector: From regional and provincial perspectives

This paper utilizes the Logarithmic Mean Divisia Index (LMDI) to quantify the contributions of various factors on industrial CO2 emissions (ICE) at both the regional and provincial levels, and then analyzes the decoupling index and its components. The main results include: (1) Industrial activity was the leading promoting factor in ICE, while energy intensity was the decisive factor for controlling ICE in most provinces. (2) Energy structure change only had a marginal impact on ICE and varied considerably across provinces. (3) The effects of various factors are distinctly diverse in different provinces; generally, the inhibiting effects only partly offset the promoting effects. (4) Most provinces exhibited weak decoupling effect, energy intensity decline was the crucial factor in their decoupling progress, while energy structure switch only played a negligible role in decoupling progress, even exerted an opposite effect in emission mitigation in some provinces, and therefore dragged down their decoupling progress.

Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Presently, China is the largest CO2 emitting country in the world, which accounts for 28% of the CO2 emissions globally. China’s CO2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO2 emissions in China accurately, considering population, the CO2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO2 emissions. Finally, taking the forecasting value of the CO2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO2 emissions in China from 2018 to 2025.