Carbon Dioxide Emissions In China Research Articles

Influencing factors of the carbon dioxide emissions in China's commercial department: A non-parametric additive regression model

The commercial department plays a significant role in China's energy conservation and carbon dioxide emission mitigation. The study employs a panel data set during 2001–2015 and adopts a non-parametric additive regression model to analyse the drivers of commercial department's carbon dioxide emissions in China. Non-parametric additive regression model based on the provincial panel data is first applied to investigate the carbon dioxide emissions in China's commercial department. The consequences demonstrate that the nonlinear impact of economic growth upon carbon dioxide emissions is in accordance with the Environmental Kuznets Curve (EKC) hypothesis. Energy intensity is currently in the left half of the “overturned U-shaped” relationship. In the long run, the overturned “U-shaped” impact of energy intensity may be due to the discrepant level of technological development in different phases. Nevertheless, the urbanization demonstrates an erected “U-shaped” impact on carbon dioxide emissions owing to the further urban commercial consumption and fixed asset investment in recent times. Aggregate population presents a fast-to-slow growth relationship, which is the result that residents require massive commercial goods with the augment in population and income. Energy structure also reveals an erected “U-shape” relationship on account of slight electricity consumption in former times and the large amount of thermal power consumption in later phases. As a result, we should take into full consideration the disparate fluctuant impacts of these influencing indicators when it comes to carbon dioxide emissions in commercial department.

Does modernization affect carbon dioxide emissions? A panel data analysis

Modernization refers to the general trend of developmental progress that occurs within human societies. We now know that global warming, a result of carbon dioxide emissions, severely threatens the sustainability of human society. It is therefore of significant theoretical and practical implications that the scientific community more thoroughly investigate the impacts of modernization on CO2 emissions. Surprisingly, only a limited number of studies have addressed this topic previously. As the world's largest developing economy and carbon emitter, China faces the dual challenge of peaking carbon emissions by 2030 while realizing basic modernization by 2035. With the purpose of identifying the implications of China's 2035 modernization goal for its 2030 emission peak goal, this study explored the effects of modernization on carbon dioxide emissions in China. Using a comprehensive indicator system, five modernization indexes—addressing industrialization, agricultural modernization, informatization, urbanization, and ecological modernization—were estimated, along with carbon dioxide emissions, for the period 1997–2016, for 30 Chinese provinces. Panel data modeling was then used to examine the impacts of the five modernization indexes on CO2 emissions in China. The results demonstrate that industrialization, agricultural modernization, informatization, and urbanization exerted positive effects on CO2 emissions during the study period, suggesting these aspects of modernization led to increased carbon dioxide emissions. A negative correlation between ecological modernization and carbon dioxide emission was identified, indicating that ecological modernization helped to abate CO2 emissions. The findings emerging from this study hold significant implications for China's policy makers in promoting decarbonization, suggesting the utility of pursuing new-type industrialization, developing organic agriculture and eco-agriculture, popularizing electronic equipment with low power dissipation, building low-carbon cities, and promoting the ecology-oriented transformation of the modernization model.

The impact of agriculture on CO2 emissions in China

This study empirically analyses the long-term relationship between agricultural production and carbon dioxide emissions in China by using annual data covering 1971-2010. In estimating the relationship between agriculture and CO2 emissions, the study also includes real income and energy consumption as variables in the model, in line with the EKC hypothesis. To identify the existence of a long-term relationship between CO2 emissions and agriculture, the bounds test approach for cointegration and autoregressive distributed lag (ARDL) methods are used. To determine the robustness of the results, other single-equation cointegration methods such as FMOLS, DOLS, and CCR are also estimated. The results confirm cointegration among variables and the presence of an inverse U-shaped agriculture-induced EKC curve for China. Agriculture increases a country?s long-term CO2 emissions. The government, policymakers, and agricultural producers should set strategies covering energy-intensive economic activities, including agriculture, to solve environmental problems.

Dynamic evolution of characteristics and decomposition of factors influencing industrial carbon dioxide emissions in China: 1991–2015

In China, industry produces the largest amount of CO2 emissions and, thus, is the area with the greatest potential for emissions reductions. In this paper, we calculate the CO2 emissions of 18 different energy sources for China's industry and subsectors from 1991 to 2015 (including indirect emissions from production processes). The LMDI (Logarithmic Mean Divisia Index) method and the Tapio decoupling model are combined to study the dynamic evolution of the characteristics and factors influencing the CO2 emissions. The changes in CO2 emissions were further decomposed into emissions coefficients, energy structure, energy intensity, CO2 emissions intensity in production processes, industrial structure, per capita output, and population size. The high energy-consumption industrial sectors were analyzed in particular. The results show that: 1) in most years, the relationship between industrial economic growth and CO2 emissions shows a weak decoupling. Only from 1996 to 1999 and 2014 to 2015 was a strong decoupling shown. 2) For the industry as a whole, the impact of per capita output on CO2 emissions was positive from 1991 to 2015. The second factor is population size, and its effect was more obvious for 2006–2015. The CO2 emissions coefficient is the most significant factor in suppressing CO2 emissions, followed by the energy intensity and the CO2 emissions intensity in production processes. In terms of CO2 emissions reduction, the effect of industrial structure and energy structure are not as obvious. 3) The proportion of high energy-consumption sectors’ emissions to total industrial emissions shows a steadily increasing trend, from 71.60% in 1991 to 90.59% in 2015; the total emissions for the Manufacture of Non-Metallic Mineral Products subsector was the highest, followed by the Smelting and Pressing of Ferrous Metals subsector.

Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: Evidence of a threshold effect from China

Natural resource endowment has a significant influence on manufacturing structure which in turn influences CO2 emissions. This paper investigates the effect of the rationalization and upgrading of manufacturing structure on carbon dioxide emissions in China, based on the perspective of natural resource dependence. The results of the STIRPAT model on provincial panel data of the manufacturing sector from 2003 to 2014 show that manufacturing structure rationalization and upgrading will help curb CO2 emissions, while such effects are restricted by a region's reliance on natural resources. The panel threshold model estimations further indicate that the greater the ratio of industrial output to GDP, the weaker the restricting effect of resource dependence on the emissions reduction of manufacturing structure. These findings reveal that the development strategies of manufacturing structural rationalization and upgrading that aim to reduce emissions depend on the level of a region's resource dependence and industrialization.

Determinants analysis of carbon dioxide emissions in passenger and freight transportation sectors in China

To identify the similarities and differences of the role that driving factors playing in carbon dioxide emissions between passenger and freight transportation sectors, this work uses an LMDI (Log-Mean Divisia Index) method to decompose factors that influence carbon dioxide emissions in China’s passenger and freight transportation sectors between 1990 and 2015. The results indicate that: (1) Carbon dioxide emissions of passenger and freight transportation underwent growth from 1990 to 2015; (2) Per capita GDP, and population promoted the increase of carbon dioxide emissions for both passenger and freight transportation, and transportation structure promoted the increase of carbon dioxide emissions for freight. Among them the economic factor made the biggest contribution; (3) Activity intensity restrained the increase of carbon dioxide emissions and transportation structure restrained the increase of carbon dioxide emissions for passenger.

Forecasting of CO2 Emission in China, Using Least Squares Support Vector Machine Based on Improved Particle Swarm Optimization

In recent years, a growing problem of haze issue has threatened human's survival and normal social economic sustainable development. In order to be able to find out the influence factors of China's carbon dioxide emissions more accurately, so as to adopt corresponding policies on energy conservation and emission reduction, an improved the least squares support vector machine optimized by improved particle swarm optimization algorithm (PSO-LSSVM) model for predicting carbon dioxide emissions in China was proposed in this paper, which can overcame the shortcomings of dependency in LSSVM model, which indicates that the IPSOLSSVM model established in this paper is more applicable to the current prediction of carbon emissions.

Economic Growth, Energy Intensity, and Carbon Dioxide Emissions in China

Economic Growth, Energy Intensity, and Carbon Dioxide Emissions in China

Analysis of carbon emissions reduction of China's metallurgical industry

The metallurgy industry plays a very important role in China's economic growth. Due to its energy concentration, the energy consumption of the industry accounts for a large proportion of China's total energy consumption, and thus contributes to carbon emissions. Whether the metallurgy industry can successfully reduce emissions is related to the future of China's carbon emissions targets. In this paper, the logarithmic mean Divisia index model is used to analyze the change in China's carbon dioxide emissions from 1991 to 2014. The empirical results show that there are three main factors affecting the carbon dioxide emissions of China's metallurgy industry. The three factors are labor productivity, energy intensity and industry size. In addition, we further analyzed the deep motivation of carbon dioxide emissions in the industry, and calculated the future emission reduction potentials. Lastly, according to the empirical results, the paper proposes policy recommendations for the future emission reduction of China's metallurgy industry.

The role of energy efficiency improvement and energy substitution in achieving China’s carbon intensity target

Energy efficiency improvement and energy substitution are not only main ways to reduce energy consumption and carbon dioxide emissions in China, but also play important roles in achieving China’s international commitments and coping with the challenges of global climate change. From the beginning of the 11th Five-Year Plan (FYP), China has adopted a series of policies and measures in ways of improving energy efficiency, optimizing energy structure, developing low-carbon city pilots and so forth. This study takes use of the China-in-Global Energy Model to analyze the future carbon dioxide emissions and energy consumption under different scenarios, and then calculates the contributions of energy efficiency improvement and energy substitution to realizing the mitigation target of China’s National Determined Contributions. The results show that energy efficiency improvement plays more important role in reducing carbon intensity than energy substitution. But as time goes on, the contribution of energy substitution will increase while the effect of energy efficiency improvement will diminish gradually. In addition, energy substitution can play a greater role in achieving China’s climate pledge when increasing the strength of carbon emissions reduction.

The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.

CO2 emissions of China, U.S. fell in 2016

Carbon dioxide emissions in China and the U.S., the world’s two largest emitters and energy users, fell in 2016 while these countries experienced economic gains, according to the International Energy Agency. Worldwide, CO2 emissions were flat for the third straight year despite an overall economic growth rate of 3.1%, IEA says, signaling a decoupling of emissions from economic activity. A report from IEA attributes the decline to renewable power generation, switches from coal to less-polluting natural gas, technology improvements, and energy efficiency. The U.S. economy grew by 1.6% last year, yet the nation showed a CO2 decline of 3%. U.S. carbon emissions last year were the lowest since 1992, despite economic growth of 80% between 1992 and 2016. China’s emissions fell by 1% last year while its economy expanded by 6.7%. IEA attributes the greenhouse gas reduction in China to less coal use and more renewables, nuclear, and natural gas.

Driving factors of carbon dioxide emissions in China: an empirical study using 2006-2010 provincial data

The rapid urbanization of China has increased pressure on its environmental and ecological well being. In this study, the temporal and spatial profiles of China’s carbon dioxide emissions are analyzed by taking heterogeneities into account based on an integration of the extended stochastic impacts using a geographically and temporally weighted regression model on population, affluence, and technology. Population size, urbanization rate, GDP per capita, energy intensity, industrial structure, energy consumption pattern, energy prices, and economy openness are identified as the key driving factors of regional carbon dioxide emissions and examined through the empirical data for 30 provinces during 2006‒2010. The results show the driving factors and their spillover effects have distinct spatial and temporal heterogeneities. Most of the estimated time and space coefficients are consistent with expectation. According to the results of this study, the heterogeneous spatial and temporal effects should be taken into account when designing policies to achieve the goals of carbon dioxide emissions reduction in different regions.

How Do Regional Interactions in Space Affect China's Mitigation Targets and Economic Development?

China is faced with the big challenge of maintaining a remarkable economic growth in an environmental friendly manner; that is why forecasting the turning point is of necessity. Traditional econometric approaches do not consider the spatial dependence that inevitably exists in the economic units, which probably risks misspecification and generating a biased estimation result. This paper firstly constructs Theil index to measure the intra- and inter-regional inequality of CO2 emissions, we find that difference in emissions between regions is narrowed but gap within the Western China is sharply expanding. Then the Spatial Durbin model is employed to shape the relationship between mitigation and economic growth using the panel data of 29 provinces ranging from 1995 to 2011. Results show that the peak of per capita carbon dioxide emissions in China would be seen when GDP per capita reaches between $USD 21594 to 24737 (at 2000 constant price), much smaller when compared with the estimations of models which ignore the spatial dependence. This implies that territorial policy and industry transfer, on one hand would favor those underdeveloped regions with investment, technology and labors transfer; on the other hand enables developed regions more potential to mitigation, thus, chances are that China achieves the emissions peak of carbon dioxide earlier than conventional wisdom.

Technology advance and the carbon dioxide emission in China – Empirical research based on the rebound effect

At present, technology advance is the greatest contributor to the carbon dioxide mitigation. However, the real effect of technology advance on mitigation is worth further studying due to the existence of rebound effect (RE). A key issue is how to quantify the relationship between technology advance and carbon dioxide emission accurately. This paper figures out a comprehensive and modified framework involving around the RE of carbon emission from the macroeconomic perspective. Using this framework, this paper quantitatively evaluates the relationship between technical change and carbon emission based on the data of 30 provinces in China. It is founded that: (1) the carbon RE is about 10–60% in Chinese provinces; (2) the RE of carbon emission differs among the regions in China; (3) carbon reduction and environment issues should be solved step by step regionally in China. (4) According to our results, a reasonable control on total energy consumption and fossil-energy pricing adjustment, should be taken as the supplementary policy in China; at the same time, carbon financing, carbon trading and other aspects of institutional innovation should be taken into account at the appropriate time.

Carbon Balance and Contribution of Harvested Wood Products in China Based on the Production Approach of the Intergovernmental Panel on Climate Change.

The carbon sequestration of harvested wood products (HWP) plays an important role in climate mitigation. Accounting the carbon contribution of national HWP carbon pools has been listed as one of the key topics for negotiation in the United Nations Framework Convention on Climate Change. On the basis of the revised Production Approach of the Intergovernmental Panel on Climate Change (2013) (IPCC), this study assessed the accounting of carbon stock and emissions from the HWP pool in China and then analyzed its balance and contribution to carbon mitigation from 1960 to 2014. Research results showed that the accumulated carbon stock in China’s HWP carbon pool increased from 130 Teragrams Carbon (TgC) in 1960 to 705.6 TgC in 2014. The annual increment in the carbon stock rose from 3.2 TgC in 1960 to 45.2 TgC in 2014. The category of solid wood products accounted for approximately 95% of the annual amount. The reduction in carbon emissions was approximately twelve times that of the emissions from the HWP producing and processing stage during the last decade. Furthermore, the amount of carbon stock and emission reduction increased from 23 TgC in 1960 to 76.1 TgC in 2014. The annual contribution of HWP could compensate for approximately 2.9% of the national carbon dioxide emissions in China.

Decoupling economic growth from carbon dioxide emissions in China: A sectoral factor decomposition analysis

China is facing the dilemma of how to coordinate economic development and carbon dioxide (CO2) emissions reduction. This paper investigates the decoupling effect of economic growth from CO2 emissions in China during the period 1992–2012. Using the Logarithmic Mean Divisia Index (LMDI) method, we decompose the driving forces of the decoupling of economic growth from CO2 emissions in China's five major economic sectors into several factors, and measure the contribution of each factor to the decoupling of China's overall economy from CO2 emissions. The results show that China performed the weak decoupling of economic growth from CO2 emissions during 1992–2012, and so did all its five major sectors. Such a state did not occur smoothly and presented five different stages. Energy intensity and economic activity level were the most important factors affecting the decoupling in China. Energy intensity decreased in all sectors, indicating a large acceleration for the sectoral decoupling states. This finding is applicable to process carbon intensity in industrial sector and energy emission factor in agricultural, industrial, construction, and service sectors. In terms of sectoral effects on the decoupling in China, industrial sector played a dominant role, while construction sector had the most marginal influence. The economic activity level and economic share of industrial sector exerted the most negative effects on the decoupling. The energy intensity of industrial sector played a key role in promoting the national decoupling state, while the energy emission factor of agricultural sector contributed the least.

Decomposition analysis of carbon dioxide emissions in China's regional thermal electricity generation, 2000–2020

A major share of China's total carbon dioxide (CO2) emissions is from the electric power sector. In 2010, almost 40% of all CO2 emissions in China were from this sector. This is because the country predominantly depends on thermal electricity generation to meet its power requirement. This study analyses the CO2 emissions from thermal electricity generation in China between 2000 and 2012 at the regional grid level. Logarithmic Mean Divisia Index methodology is employed to identify the factors that influence changes in CO2 emissions over time. This study also predicts China's energy consumption and CO2 emissions patterns between 2013 and 2020, forecasting rates of increase in energy consumption across six regional grids from 0.9% to 9.7%. CO2 emissions related to thermal electricity generation increased from 981.33 million tons (Mt) in 2000 to 3342.79 Mt in 2012, which is an annual growth rate of 10.75%. These increases are not aligned with China's commitments on reducing emissions to the Asia-Pacific Economic Cooperation forum. China's CO2 emissions are forecasted to increase to 5596 Mt by 2020 if the current increasing trends is not effectively curbed after 2012.

Evolution of carbon dioxide emissions in Chinese cities: trends and transitional dynamics

ABSTRACTThe aim of this paper is to examine the transitional dynamics of carbon dioxide emission in China by using a prefecture-level database. Convergence analysis is conducted and mobility probability plots (MPPs) are employed to examine the distribution dynamics of 286 cities from 2002 to 2011. The empirical investigation is conducted in three steps. In the first step, the research is carried out at the national level, and it provides an overall view of the evolution of carbon dioxide emission in China. In step two, the database is divided into smaller spatial groupings so as to investigate the relationship between carbon dioxide emission and geographical location. In the third step, the evolution of carbon dioxide emission in the key cities and in other non-key cities is examined, along with its implications for environmental protection. This study offers valuable information on convergence of carbon dioxide emission in China.

Interregional differences of coal carbon dioxide emissions in China

Coal is one of the main fuel sources in China. This paper sheds light on the evolution of China's interregional differences in CO2 emissions from coal by constructing a Gini coefficient and decoupling elasticity index for emissions from 1997 to 2012 and explains why emission differences deviate from economic growth differences. The study decomposed the Gini coefficient of CO2 emissions from coal by source, incremental source, and region. It also divided the decoupling elasticity of carbon emissions into two components: effects of environmental expenditure and effects of emission reduction policy. The findings of the study are as follows: First, interregional differences in China's overall CO2 emissions from coal are characterized by periodic fluctuation. Second, the differences in emissions from raw coal, the concentration effect of emissions, and the emission differences within regions are the three main factors in the overall difference changes in coal's carbon emissions in China. Last but not least, the decoupling between provincial CO2 emissions from coal and economic growth is on the whole weak. Based on the above findings, the author offers four suggestions for emission reduction.