Estimation of energy-related carbon emissions in Beijing and factor decomposition analysis

An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing

This paper calculated the energy-related carbon emissions from production, household and energy transformation sectors in Shanghai and decomposed the effects of their changes in carbon emissions resulting from 11 causal factors of reflecting the changes in socioeconomic activity, intensity of energy and the structure by logarithmic mean divisia index. The results show that the changes of economic activity (EA), population size (PS), total energy consumption in transformation and energy consumption per capita (ECPC) increase CO2 emissions obviously. The changes of energy intensity (EI), urban and rural population distribution structure, energy mix of household and mix of energy in transformation drive the decrease of CO2 emissions. The changes of economic structure (ES), energy mix of production, and energy transformation structure (ETS) can’t increase or decreased CO2 emissions continuously in 3 periods respectively. Therefore, adjusting ES, ETS, energy mix of transformation and decreasing the EI of each production sector will be the main routes to reduce CO2 emissions. Developing clean energy to substitute fossil energy and enforcement of carbon capture will be necessary in the future.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于LMDI分解的厦门市碳排放强度影响因素分析 DOI: 10.5846/stxb201304020585 作者: 作者单位: 中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,水利部珠江水利委员会,中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,中国科学院研究生院;中国科学院城市环境与健康重点实验室,中国科学院城市环境研究所,赤峰学院，资源与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（71003090和71273252）；福建省自然科学基金资助项目（2012J01306） Factor decomposition of carbon intensity in Xiamen City based on LMDI method Author: Affiliation: Institute of Urban Environment, Chinese Academy of Sciences,,Institute of Urban Environment, Chinese Academy of Sciences,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:研究碳排放强度的变化趋势及其影响因素对于指导低碳城市建设具有重要意义。应用对数平均权重分解法（LMDI），基于厦门市2005-2010年各部门终端消费数据对碳排放强度指标进行因素分解，并将传统分析仅注重产业部门的能源碳排放，拓展到全面考虑产业部门和家庭消费的能源活动和非能源活动影响。研究结果表明：2005-2010年厦门市碳排放强度下降17.29%，其中产业部门能源强度对总碳排放强度变化影响最大（贡献63.07%），家庭消费能源强度是碳排放强度下降的主要抑制因素（-45.46%）。从影响效应角度看，经济效率对碳排放强度下降贡献最大，碳排系数减排贡献最小；从部门减排贡献角度看，第二产业贡献最大，家庭消费贡献最小。总体而言，厦门市未来碳减排重点部门在第二产业，优化产业结构和能源结构有较大减排潜力。 Abstract:It is of great significance for guiding the low-carbon city development to explore the trends and influencing factors of carbon intensity. Most traditional decomposition studies only focused on the energy carbon emissions from industrial sectors. This paper extended the application of the Logarithmic Mean weight Divisia Index (LMDI) method to a full consideration of the industrial and household sectors, as well as their energy and non-energy activities. Taking Xiamen City as a study case, the carbon emissions was calculated by IPCC's methods based on the end-use consumption data of the industrial and household sectors from 2005 to 2010. Then the aggregated carbon intensity was decomposed by LMDI method into ten driving factors, which covering energy and non-energy related emissions from industrial and household sectors. The ten driving factors were further categorized into four groups: carbon emission efficiency effect (including efficiency factors of energy related industrial carbon emissions, energy related household carbon emission, non-energy related industrial carbon intensity, and non-energy related household carbon intensity), energy intensity effect (including industrial energy intensity factor and that of household), industry structure effect (energy related industrial structure factor and non-energy one) and economic efficiency effect (energy related economic efficiency factor and non-energy one). Results showed that carbon intensity of Xiamen City decreased by 17.29% from 2005 to 2010. From perspective of driving factors, the energy intensity of industrial sector had the greatest effect on carbon intensity reduction (a contribution rate of 63.07%), and the energy intensity of household sector was the largest hinder of carbon intensity reduction (-45.46%). So energy intensity had significant impact on carbon intensity reduction for Xiamen City. Except for reducing the energy intensity of industrial sectors, it is also very important to control the growth of household's energy intensity at the same time. From the effect perspective, the economic efficiency effect became the dominant driver of carbon intensity reduction, followed by energy intensity effect and industry structure effect, and carbon emission efficiency effect contributed the less. The economic efficiency contributed 50.85% of total carbon intensity reduction, which greatly promoted household's carbon intensity reduction. Although industrial structure adjustment had relatively small effects at the study periods, the industry structure in which secondary industry has large proportion is anticipated to have large reduction potentials in the future. The carbon emission efficiency effect was chiefly determined by energy structure, and the current carbon-intensive energy structure also has large reduction potentials. From the sector perspective, the contribution of the secondary industry was the largest (contributing 67.04%), sequentially followed by the primary industry, the tertiary industry, and the household sector. The carbon intensity reduction by secondary and tertiary industries mainly lied in energy related carbon emissions; whereas the carbon intensity reduction by the primary industry and household sectors mainly relied on non-energy emissions. Thus the non-energy related carbon emissions were an non-negligible part while analyzing carbon intensity reduction. Even though energy efficiency of household sector was the biggest disincentive to reduce carbon intensity, household sector had the less contribution on carbon intensity reduction due to other factors' offset effect. Furthermore, the key sector for future carbon reduction lies on the secondary industry. However, the primary Industry and household sector has limited reduction potential. Overall, optimizing industry structure and energy structure have large reduction potential, and secondary industry has largest reduction potentials. 参考文献 相似文献 引证文献

PDF HTML阅读 XML下载 导出引用 引用提醒 新疆能源消费碳排放过程及其影响因素——基于扩展的Kaya恒等式 DOI: 10.5846/stxb201410152033 作者: 作者单位: 广州地理研究所,中国科学院新疆生态与地理研究所,广州地理研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 广东省科学院青年科学研究基金(qnjj201501); 广州地理研究所优秀青年创新人才基金(030) The process of energy-related carbon emissions and influencing mechanism research in Xinjiang Author: Affiliation: Guangzhou Institute of Geography,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:新疆,中国西部的欠发达区域,如何在保持社会经济持续快速发展的同时实现碳排放的减速增长是现阶段的重要发展命题,对于实现国家的减排目标有着至关重要的作用。通过对经典的Kaya恒等式进行扩展,并采用基于LMDI的完全分解模型,解析了1952年-2010年新疆的一次能源消费的碳排放的主要驱动因素。依据1952年以来新疆社会经济发展状况和碳排放总量演变特征,并结合一定的历史背景等,将新疆的一次能源消费的碳排放划分为6个演变阶段,定量分析了人口规模效应、经济产出效应、能源强度效应、能源结构效应和能源替代效应在不同发展阶段的贡献作用,主要的研究结论如下:(1)经济产出效应和人口规模效应是新疆碳排放增长的最主要贡献因子。(2)能源强度效应在1978年之前对碳排放的增长表现为正效应,主要原因是极低的能源利用效率和落后的生产工艺。改革开放之后,能源强度效应成为遏制碳排放增长的重要贡献因子。(3)能源结构效应和能源替代效应也是遏制新疆碳排放增长的主要贡献因子,但是其贡献作用还比较小,主要是因为可再生能源在能源消费总量中的比重还比较低和以煤为主的能源消费结构还没有发生根本性的改变。 Abstract:Reduction of greenhouse gases (GHG) has become a primary concern for policy makers and government managers globally. China has become the world's largest primary energy consumer and carbon emitter after decades of rapid economic growth. Research on regional carbon emissions is crucial for China to achieve its reduction targets. Presently, the biggest challenge faced by the local government is to reduce carbon emissions, and ensure that it does not hinder social-economic development. This case study in Xinjiang, a less developed area in western China, aimed to determine the most important carbon emission contributors and analyze energy-related carbon emissions. Our estimates were based on the provincial and national energy statistics. Data resources available for the present study included statistics on populations, gross domestic product (GDP), and total energy consumption from 1952 to 2010. Carbon emissions due to energy consumption were calculated according to the method of the IPCC Guidelines for National Greenhouse Gas Inventories. It was observed that the total energy consumption in Xinjiang increased from 0.393 Mtce in 1952 to 82.902 Mtce in 2010, representing a 210.95-fold increase over the period of 59 years. Energy-related carbon emissions in the area increased from 0.285 Mt in 1952 to 53.662 Mt in 2010, representing a 188.23-fold increase over the study period. We analyzed the changes in the total carbon emissions and carbon emissions structure from 1952 to 2010. Coal consumption was found to be the biggest contributor to total carbon emission in Xinjiang. The share of carbon emissions from coal consumption decreased until 2004, but increased afterward. The share of carbon emissions from natural gas increased steadily from 0.12% in 1954 to 8.66% in 2010. The Logarithmic Mean Divisia Index (LMDI) technique based on an extended Kaya identity was used to determine the five main energy-related carbon emissions in Xinjiang. We first used the LMDI method to decompose carbon dioxide emissions on a yearly basis. To understand of the factors influencing long-term carbon emissions, we divided the carbon emissions process into six stages based on the changing trends of socio-economic development and carbon emissions, historically. This method included measurements of the effects of population, affluence, energy intensity, renewable energy penetration, and emission coefficient for the different stages of the process. Decomposition results showed that affluence and population effects are the two most important contributors to increased carbon emissions, but their contributions are different in the special development period. Energy intensity was positive in curbing carbon emissions during the pre-reform period, but became relatively dominant after 1978. Renewable energy penetration and emission coefficients played important negative but relatively minor effects on carbon emissions. The insignificant effect of renewable energy penetration is largely attributed to the small shares of renewable energy, amounting to less than 6% of the total energy consumption. The emission coefficient effect plays a minor role in curbing carbon emissions, because the coal-dominated energy consumption structure has not fundamentally changed. An effective solution to these problems will help Xinjiang to reduce carbon emissions and environmental damage with economic growth. 参考文献 相似文献 引证文献

What are the main factors that influence China’s energy intensity?—Based on aggregate and firm-level data

Iran economy has high energy intensity and CO2 emission compared to other peer countries. This study adopts the Logarithmic Mean Divisia Index (LMDI-I) method to decompose the total energy intensity changes Δ(E/GDP) of the Iranian economy by considering inter-fuel substitution impacts of the economic sectors over the period of 2004–2017. Results show that the total energy intensity of Iran's economy increased by 43.59 barrels of oil equivalent to IRR 1000 (Iranian Rial at a constant price in 2011). Furthermore, results indicate that while inter-sector structural change, per capita GDP impact, and sectoral energy intensity impact lead to an increase in total energy intensity, household energy intensity impact leads to a decrease in total energy intensity during the research period. Extended findings from decomposition analysis demonstrate that household, services, and agriculture sectors have decreasing impact, while industry, power plant and transportation sectors have increasing impact on Δ(E⁄GDP). In addition, removing the consumption of kerosene in household sector and reducing its share in most economic sectors has caused decline, whereas high consumption of fuel oil, gasoline and gasoil compared to other energy carriers, has led to an increase in total energy intensity. As such, it is concluded that substituting various types of fuels in economic sectors couldn't reduce energy intensity. Besides, serious revisions are needed in the energy policies and energy efficiency programs. It is recommended that non-price policies along with price-based energy policies be implemented to reduce energy intensity and CO2 emission.

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.

Driving forces for carbon emissions changes in Beijing and the role of green power

This research analyzes the energy consumption of transport service sectors in Vietnam and its changing trend in the past twenty-five years using Input-Output (IO) tables and Logarithmic-mean Divisia index (LMDI) method. IO table of 28 economic sectors in 1996, 2000, 2007, 2012 and 2018 is used to determine energy consumption, in which the transport service sector was always the third or second largest energy consumer, accounting for between 9% and 16% of total energy consumption. LMDI method is used to define influencing factors including transport activity, transport structure, transport intensity, and energy intensity. In these four impacts, the change of transport activity contributes the largest effect (occupied 74.3%), followed by the change of energy intensity (occupied 17.7%) of total increased share for energy consumption. Among the transport service sectors, it is found that Freight transport service by road played the mainstream role in the increasing trends of energy consumption in the period of 2007-2018. In order to improve the energy efficiency of the sector, investments in green transport technologies and modernization of trucks to be more efficient and eco-friendlier will be the key contributors.

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.

Guangdong is China’s largest province in terms of energy consumption. The energy-related carbon emissions in Guangdong province are calculated, and two extended and improved decomposition models for energy-related carbon emissions are established with the Logarithmic Mean Divisia Index method based on the basic principle of Kaya identity. Main results are as follows: (1) the energy-related carbon emissions from the three strata of industry, except the primary industry, and household energy consumption in Guangdong province show increasing trend from 1995 to 2009; (2) the main driving and inhibiting factors which influence energy-related carbon emissions are economic output and energy intensity, respectively, while the contributions of energy mix, industrial structures, population size and living standards are not significant during the period of interest. It is concluded that optimizing the energy mix by exploiting new energy sources and cutting down energy intensity by developing low-carbon technologies are the two most effective approaches to reduce carbon emissions for Guangdong province in the future. The results and proposals in this paper provided reference for relevant administrative departments in the Government of Guangdong province to develop policies for energy conservation and emission reduction as well as to promote development of low-carbon economy.

There are large gaps in energy consumption data and consequently in the estimates of CO2 emissions from fuel combustion in Kazakhstan. This study provides the first comprehensive review of energy consumption trends in Kazakhstan, discusses several important discrepancies in energy statistics and presents an improved versions of Energy Balances, developed using additional data. The results indicate that Kazakhstan’s energy intensity of gross domestic product (GDP) declined by 30% from 1.14 to 0.8 toe/thousand 2005USD between 2000 and 2014. To understand factors influencing this decline, the change in energy intensity of GDP was decomposed using the Logarithmic Mean Divisia Index I method. The upstream sector (mainly oil and gas) played the most important role in the observed GDP energy intensity change. Although the share of this sector in total GDP increased, causing an increase in energy intensity due to inter-sectoral structural effects, the consequences were counteracted by a twofold decline in the sector’s energy intensity, resulting in a net decrease. On the contrary, the power and heat, transport and household sectors saw an increase in energy intensity between 2000 and 2014. The results clearly demonstrate that there is an urgent need for policies and measures to be put in place in the power and heat, household and transport sectors, to support renewable energy development, increase buildings’ energy efficiencies, replace inefficient stoves and improve heating systems and encourage changes in public transportation systems. Furthermore, improving energy statistics and setting appropriate sectoral energy intensity reduction targets are crucial for achieving real efficiency improvements in the economy.

Energy is vital for the proper functioning of the various sectors of the economy and social life. During the pandemic, there have been some changes in these aspects that need to be investigated. The main objective of this article is to identify the direction of change caused by the COVID-19 pandemic in energy consumption and energy intensity in sectors and economic areas in EU countries. The specific objectives are to identify the importance of energy consumption in sectors and areas of the economy in individual EU countries; to determine the dynamics of change and variability during the pandemic in energy consumption in individual sectors and areas of the economy in EU countries, especially during the COVID-19 pandemic; to determine the changes in energy intensity of individual economic sectors and the differences in energy intensity between individual EU countries, including during the COVID-19 pandemic. Using a purposive selection method, all 27 EU Member States were selected for the study on 31 December 2020. The analysed period covered the years 2005–2020. The sources of material were literature and data from Eurostat. Descriptive, tabular and graphical methods, dynamic indicators with a fixed base and variable base, Gini coefficient, coefficient of variation, Pearson’s linear correlation coefficient, and multi-criteria analysis were used for analysis and presentation. It was found that the structure of energy consumption had remained unchanged for several years, with transport, industry and households dominating. There were no significant differences between countries. The COVID-19 pandemic reduced energy consumption in all sectors of the economy, the largest in transport and services and the smaller in industry. At the same time, household energy consumption increased. As a result of the pandemic, there was an increase in energy intensity in all sectors of the economy, the largest in industry. Western European countries had a lower energy intensity of the economy than Central and Eastern European countries. There was little change over several years. Countries generally maintained their ranking. The pandemic did not change anything in this respect, meaning that it had a similar impact on individual EU countries.

Central Asia is a major emerging energy player but is also affected by global climate change. To both maintain its economic growth and cope with climate change, Central Asia is in urgent need of environmental and sustainable energy strategies, as well as effective carbon emissions mitigation. To this end, we investigated the characteristics of country-level total carbon emissions in Central Asia (i.e., Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Tajikistan). Then, the logarithmic mean Divisia index method was applied to identify and quantify the driving forces behind the changes in carbon emissions. In addition, country-level long-run relationships between economic growth and carbon emissions were tested by means of the environmental Kuznets curve hypothesis. The results are as follows. (1) There were pronounced differences in per capita gross domestic product, energy intensity, and carbon emissions structures across this region, mainly owing to the oil and gas endowment and economic development stage. (2) Impacts and influences of various drivers of carbon emissions varied across countries over the different stages. (3) During the economic recession period, carbon emissions decreases were largely driven by the decreasing economic growth effect associated with political instability. (4) During the economic transition periods, economic growth effect played a dominant positive role in accelerating carbon emissions in the five countries, followed by the population scale effect. Energy intensity effect was the most important factor in curbing carbon emissions in the five countries. Emissions increases during these periods were partly or largely compensated by the improving energy intensity in the different countries. Carbon intensity effect mostly had a negative but relatively minor effect on carbon emissions. (5) There was only an inverted U-shaped curve existing in the lower-middle-income country (Uzbekistan). Considering these differences and disparities in emissions characteristics and determinants can provide important insights for the energy sustainability and carbon mitigation in Central Asia.

Substantive decomposition research focuses on the energy-related carbon emissions from industrial sectors rather than from the household sector or non-energy-related activities. We extended the application of the Logarithmic Mean Divisia Index (LMDI) method to a comprehensive analysis of GHG emission intensity [GHG/unit of gross domestic product (GDP)] related to the industrial and household sectors, and to their energy-related and non-energy-related activities. Chinese carbon intensity was decomposed and analyzed by the LMDI method for the latest three 5-year plans (9th FYP, 10th FYP and 11th FYP), from 1996 to 2010. Results show that Chinese GHG emission intensity has experienced an unconscious reduction stage, an unconscious increasing stage and a conscious reduction stage, respectively, during the three FYPs. Industrial energy intensity had the dominant effect on GHG emission intensity reduction among all coefficients in the three periods. However, the non-energy-related activities cannot be ignored; they had an average 12% effect on GHG emission intensity reduction during the three periods. The household sector averaged about a 10% reduction effect. Looking forward to the 12th FYP, there are still huge challenges to achieving the energy-saving and carbon-reduction goals, due to the opposing effects of national urbanization and eco-civilization construction strategies.