Decomposition of energy-related CO2 emission over 1991–2006 in China

doi:10.1016/j.ecolecon.2009.02.005

Ecological Economics

Volume 68, Issue 7, 15 May 2009, Pages 2122-2128

https://doi.org/10.1016/j.ecolecon.2009.02.005 Get rights and content

Abstract

This paper presents a decomposition analysis of energy-related CO₂ emission in China for the period 1991–2006 divided into three equal time intervals. The complete decomposition method developed by Sun is used to analyze the nature of the four factors: CO₂ intensity, energy intensity, structural changes and economic activity. The results show that economic activity has the largest positive effect in CO₂ emission changes in all the major economic sectors and China has achieved a considerable decrease in CO₂ emission mainly due to the improved energy intensity. However, the impact of CO₂ intensity and structural changes is relatively small. Structural changes only exhibit positive effect to the CO₂ mitigation in agricultural sector, and CO₂ intensity also contributes to the decrease of CO₂ emission in transportation sector. Moreover, a formula about CO₂ mitigation is presented in this paper, which shows that China has made a significant contribution to reducing global CO₂ emission.

Introduction

The global warming has become a serious issue in the world since the late 1980s. Among six kinds of GHG, the largest contribution to the greenhouse effect is carbon dioxide (CO₂), and its share of greenhouse effect is about 56% (IPCC, 1995). Anthropogenic activities, primarily the combustion of fossil fuels and the resultant carbon emission cause a significant warming of the global climate. The reduction of emitted GHG and atmospheric pollutants constitutes a foremost objective of contemporary energy and environmental policy in the world. In particular, the findings of the scientific community with respect to the rising of energy-related CO₂ emission raised the international awareness.

Next to the United States, China is the second source of GHG in the world. As a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), the Chinese government announced its approval of the Kyoto Protocol in August 2002. As a non-Annex party, China would not be bound in the initial commitment period (2008–2012) to any quantitative restrictions on its GHG emission. Consequently, it would obligate to monitor and report to the Conference of Parties on the status of GHG emission sources and sinks, and identify measures to dampen growth of net emission in the future (Liu et al., 2007). Moreover, many scientists and environmental groups are attempting to identify targets for CO₂ reductions so as to supply the base information for making the international policies to address global climate change. In future agreements to reduce GHG, the Chinese commitment will be essential. Whether developing the report on GHG emission or formulating future commitment, it is necessary to know fully changes in China's CO₂ emission. Now that, many factors influence CO₂ emission, such as economic and demographic developments, technological change, institutional frameworks, lifestyle, and international trade. Thus it is very necessary for China's energy and environmental policy makers to investigate the driving forces governing CO₂ emission levels and their evolution.

China's CO₂ emission and CO₂ emission intensity have been investigated by a number of decomposition studies (Wang et al., 2005, Wu et al., 2005, Liu et al., 2007, Fan et al., 2007, Guan et al., 2008). However, with respect to the total CO₂ emission in China, those studies do not take the importance of sectoral dimension into account. Sun (1998) proposed a complete decomposition analysis where the residual term is distributed among the considered effects. Zhang and Ang (2001) refer to this as the refined Laspeyres method, which has been widely adopted due to ease of both calculation and understanding. In this study, we attempt to use the complete decomposition technique to identify the factors influencing the sectoral changes in CO₂ emission, i.e. to determine the contribution of the factors which influence energy-related CO₂ emission by sector. This analysis is also based on a timescale and factors different from those considered by Nag and Parikh (2000). To better investigate changing trends of the factors' relative contribution with time, the time period of statistical data from 1991 to 2006 used in this paper is divided into three equal time intervals (sub-periods), namely 1991–1996, 1996–2001, and 2001–2006.

This paper is organized as follows. Section 2 briefly reviews the literature. In Section 3, we describe the IPCC method to calculate the CO₂ emission, and use the proposed complete decomposition approach to decompose the change of aggregate CO₂ emission, and then give a formula about CO₂ mitigation. Section 4 discusses the disaggregating method of sectoral data. The present analysis on energy consumption and CO₂ emission are carried out in Section 5. And in Section 6, the main results are reported. Finally, we conclude this study.

Section snippets

Literature review

In the literature two well-known decomposition techniques, namely the structural decomposition analysis (SDA) and the index decomposition analysis (IDA), have been widely applied to analyze the driving forces. SDA is based on the input–output model in quantitative economics. Rose and Casler (1996) provided a review on its theoretical foundation and major features. Casler and Rose (1998), Chang and Lin (1998), and Chang et al. (2008) use SDA to analyze CO₂ emission. IDA uses index number concept

Methodology

The symbol definitions are as follows.

CE^t

total CO₂ emission in year t (in tons, t);

CE_i^t

total CO₂ emission of the ith sector in year t;

CE_ij^t

total CO₂ emission of the ith sector based on fuel type j in year t;

E_i^t

total energy consumption of the ith sector in year t (TJ);

E_ij^t

total energy consumption of the ith sector based on fuel type j in year t (TJ);

EF_j

carbon emission factor of the jth fuel (t-C/TJ);

CS_j^t

the fraction of the jth fuel is not oxidized as raw materials in year t;

O_j

the fraction of

Data management

The GDP and energy consumption used in this study is statistical data from 1991 to 2006 from CSY (1992–2007) and CESY (1991–1996, 1997–1999, 2000–2002, 2003, 2004. 2005, 2006, 2007) respectively. GDP is 1978 price. CO₂ emission is estimated based on energy consumption and CO₂ emission factor by fuel.

To prepare the data for undertaking the complete decomposition analysis by sector, the economy of China has been divided into four distinct sectors: the primary agricultural sector; the secondary

Economic growth

Fig. 1 shows the development of GDP in the period 1991–2006, which gives an insight into China economy. GDP has increased from 1121 billion yuan (BY) in 1991 to 4863 BY in 2006 in 1978 prices, representing an overall annual growth of 10.28%. Fig. 2 shows the shares of four sectors' GDP. The share of transportation sector (from 6.52% in 1991 to 5.71% in 2006) decreases slightly in the period 1991–2006. There is a substitution between the increasing shares of the industrial sector (from 41.79% in

Results and discussion

In this section, the contribution of each factor to the energy-related CO₂ emission change for per time interval (1991–1996, 1996–2001, 2001–2006) and the entire period from 1991 to 2006 are discussed based on the proposed model.

Conclusions

In this paper, the complete decomposition method is used to analyze the nature of the factors that influence the changes in energy-related CO₂ emission during the period 1991–2006. To better investigate likely changes with time in the relative contribution of the examined factors, this period is divided into three equal time intervals. The factors, including CO₂ intensity effect, energy intensity effect, structural changes effect and economic activity effect, lead to changes of CO₂ emission.

Acknowledgments

The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (NSFC) under the grant 70873013 and the Doctoral Fund of Ministry of Education of China (RFDP) under the contract No. 200801410024. We would also like to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper, on which we have improved the content.

References (23)

AngB.W. et al.
Inter-regional comparisons of energy-related CO₂ emission using the decomposition technique
Energy
(1999)
AngB.W. et al.
A survey of index decomposition analysis in energy and environmental analysis
Energy
(2000)
ChangY.F. et al.
Structural decomposition of industrial CO₂ emission in Taiwan: an input–output approach
Energy Policy
(1998)
ChangY.F. et al.
Comprehensive evaluation of industrial CO₂ emission (1989–2004) in Taiwan by input–output structural decomposition
Energy Policy
(2008)
DiakoulakiD. et al.
Decomposition analysis for assessing the progress in decoupling industrial growth from CO₂ emission in the EU manufacturing sector
Energy Economics
(2007)
FanY. et al.
Changes in carbon intensity in China: empirical findings from 1980–2003
Ecological Economics
(2007)
GuanD.B. et al.
The drivers of Chinese CO₂ emission from1980 to 2030
Global Environmental Change
(2008)
HoekstraR. et al.
Comparing structural and index decomposition analysis
Energy Economics
(2003)
LeeK. et al.
Analysis of CO₂ emission in APEC countries: a time-series and a cross sectional decomposition using the log mean Divisia method
Energy Policy
(2006)
LiuL.C. et al.
Using LMDI method to analyze the change of China's industrial CO₂ emission from final fuel use: an empirical analysis
Energy Policy
(2007)

MaC.B. et al.

China's changing energy intensity trend: a decomposition analysis

Energy Economics

(2008)

Cited by (296)

Modelling monthly-gridded carbon emissions based on nighttime light data
2024, Journal of Environmental Management
Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.
Quantifying the heterogeneous impacts of the urban built environment on traffic carbon emissions: New insights from machine learning techniques
2024, Urban Climate
The configuration of the urban built environment is critical for promoting sustainability and achieving carbon neutrality. However, existing studies mostly use linear and spatial econometric models to investigate the relationship between urban built environments and traffic carbon dioxide (CO₂) emissions, in-depth studies exploring the heterogeneous impacts of related features on traffic CO₂ emission by interpretive machine learning models are scarce. Hence, we extract four dimensionless features to depict the size, compactness, irregularity, and isolation of built-up areas, and road network-related features (i.e., average cluster coefficient, road topological density, and road geometric density), respectively. Subsequently, we develop an interpretive machine learning framework based on the extracted features related to the urban built-up areas and road networks. The interpretive results of the proposed framework uncover that urban morphological features, especially population density (POP), GDP per capita (GDPpc), and urban physical compactness (UPC), have a heterogeneous impact on the per capita traffic emission (PCCE) across different cities. GDPpc is more like a linear relationship with PCCE, and UPC has a significant influence on PCCE when its value is between 62% and 78%. Our results also reveal the nonlinear relationships and interactive effects between these features, providing the implications of urban morphological planning and carbon emission reduction.
Evolutionary characteristics of global offshore carbon emissions network and responsibility allocation of emissions reduction
2023, Patterns
Offshore carbon emissions from the international shipping trade are significant contributors to climate change. Based on the complex shipping trade networks, offshore carbon emissions are correlated rather than independent, and allocating responsibility for reducing emissions does not depend solely on the amount but on linkages. We use the global container shipping data covering more than 98% of routes from 2015 to 2020 to calculate the offshore carbon emissions from shipping. Subsequently, we construct an offshore carbon emissions network based on the shipping routes and emissions to identify the evolutionary tendency of network and clarify emissions reduction responsibilities by considering equity and efficiency. We discover that global offshore carbon emissions present a complicated network structure dominated by developed countries and large economies. Countries on the same continent or within the same economic organizations have closer and more frequent carbon correlations. Greater responsibilities should be allocated to countries who are at the center of the network.
Is fiscal deficit ‘curse’ or ‘haven’ for environmental quality in India? Empirical investigation employing battery of distinct ARDL approaches
2023, Heliyon
Undoubtedly, throughout the past half-century, environmental quality has emerged as a significant obstacle to both economic and social endeavors. Recent local and international policy debates have focused on environmental deterioration and global warming, but how governments balance economic growth and environmental sustainability is still enigmatic. For this reason, we have examined the determinants of environmental quality in India from 1972 to 2021. More specifically, we have investigated whether the fiscal deficit is ‘curse’ or ‘haven’ for environmental quality (CO2) in India. Moreover, this study deliberated four other predictors, comprising technological development (TIN), fossil fuel consumption (FFC), urbanization (Ub), and human capital index (HCI). In order to attain this objective, a range of econometric estimation techniques are employed to ensure the validity and reliability of the outcomes. For instance, we have employed a battery of ARDL approaches, such as standard ARDL, nonlinear ARDL, and multiple threshold NARDL approaches. In light of our research findings, we will be focusing directly on the examination of the NARDL and MTNARDL outcomes. This is due to the empirical evidence indicating the existence of asymmetric effects resulting from FD on CO2 emissions in India. The NARDL approach reveals that the consequence of fiscal deterioration is more pronounced, and the influence of fiscal progress is mild in terms of CO2 emission growth. Further, the outcomes of the MTNARDL approach revealed that the size of the extremely low changes in FD is much higher than the extremely high changes in FD in both models. This implies that as the FD rises, CO2 ascends more significantly, and when the FD lowers, CO2 declines progressively. In a nutshell, FD has a long-run positive and asymmetric impact on CO2 in India; thus, we may conclude that FD is considered the ‘curse’ for CO2 in India. Furthermore, TIN, HCI, and Ub have detrimental effects on CO2, whereas FFC stimulates CO2 in India. This research work provides some important policy implications for environmentalists, economists and macroeconomic policymakers to promote a green and healthy environment.
Can listed companies’ poverty reduction investments improve carbon emissions efficiency in poor regions? Evidence from China
2023, Journal of Cleaner Production
Developing countries usually face the twofold challenges of poverty eradication and carbon emissions reduction. How to reduce carbon emissions while achieving poverty eradication has become a headache for governments. In 2020, China announced its victory in poverty eradication. In the battle against poverty, Chinese listed companies have made meaningful contributions by actively investing in poverty reduction projects. However, the impact of their poverty reduction investments on carbon emissions in poor regions is not clear. This study empirically investigates this question using a difference-in-difference model and manually obtains data on poverty reduction investments in 592 counties in China from 2014 to 2017. Results find that Chinese listed companies' poverty reduction investments do help poor counties improve their carbon emissions efficiency, reflecting the possibility of synergy between poverty eradication and emissions reduction in developing countries. According to the mediation test, listed companies' poverty reduction investments improve the carbon emissions efficiency in China's poor counties through increasing green total factor productivity. In addition, results show that the improvement is more pronounced by strengthening government supervision, establishing economic development industries, and investing in counties with high ecological protection requirements and low per capita income.
The coordinated relationship among industrialization, environmental carrying capacity and green infrastructure: A comparative research of Beijing-Tianjin-Hebei region, China
2022, Environmental Development
Industrialization has a great impact on environmental carrying capacity when it promotes economic development. Similarly, the level of environmental carrying capacity also determines the quality of industrialization. As one of important means to deal with environmental problems, the construction of green infrastructure not only affects environmental carrying capacity and quality of industrialization, but also is impacted by their levels. In the context of increasing prominence of environmental problems and promoting sustainable development, realizing the coordinated development of industrialization, environmental carrying capacity and green infrastructure is key to economic high-quality development and environmental protection. Based on sustainable development theory and system coordination theory, this paper takes Beijing-Tianjin-Hebei region in China as an example to analyze their coordinated relationship. Coupling coordination degree model is used to evaluate and compare the coordinated development levels among industrialization, environmental carrying capacity and green infrastructure of three regions based on their combination evaluations. The results show that the levels of coordinated development of industrialization, environmental carrying capacity and green infrastructure have difference among Beijing, Tianjin and Hebei, but their coordinated development levels were relatively low and have large room for improvement in all of these three regions. The related policy and planning needs to be enriched and strengthened on promoting their coordinated development in Beijing-Tianjin-Hebei region. And the supervision and management institutions also should adjusted and improved on their coordinated development.

View all citing articles on Scopus

View full text

AnalysisDecomposition of energy-related CO2 emission over 1991–2006 in China

Abstract

Introduction

Section snippets

Literature review

Methodology

Data management

Economic growth

Results and discussion

Conclusions

Acknowledgments

Energy

Energy

Energy Policy

Energy Policy

Energy Economics

Ecological Economics

Global Environmental Change

Energy Economics

Energy Policy

Energy Policy

Energy Economics

Analysis
Decomposition of energy-related CO₂ emission over 1991–2006 in China