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

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

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.

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 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

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

Economic policy and energy policy are two major factors of energy consumption and carbon emissions. The economic factor is external and energy supply structure and efficiency are intrinsic factors. Based on a carbon emissions completely decomposed analysis model, the logarithmic mean Divisia Index (LMDI) system analyzes the impact of carbon emission changes and the contribution rate in China from 1995 to 2010. The decomposition factors include four parts: economies of scale, structure effect, energy intensity effect and carbon intensity effects. Model results show that the contribution rate of the four effects is different and from 1995 to 2010 the greatest factors impacting increases in carbon emissions were economic development (contribution rate of 155%) and industrial structure change (contribution rate of 10.6%). The reduction in carbon emissions was mainly the result of a decline in energy intensity (contribution rate of -63.7%). The increase in carbon emissions in recent years is the result of changes in major economies of scale with 168.2% contribution rate, changes in carbon intensity (contribution rate of 4%) and industrial restructuring (contribution rate of 1.3%) have also contributed to increasing carbon emissions. Energy intensity declined only played a role in reducing carbon emissions (contribution rate -73.5%). These results suggest that China needs to rethink industrial policy and energy development measures, strengthen future energy saving and emission mitigation policies and strengthen investment in low—carbon energy technologies and policy support.

Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

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

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents’ consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003–2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003–2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (−2.54%) and energy intensity effect (−18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The “Eastern aggregation” phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (−11.10 million tons) was significantly smaller than in the central (−21.61 million tons) and western regions (−13.29 million tons), and the carbon emission factor effect in the central area (−3.33 million tons) was significantly higher than that in the eastern (−2.00 million tons) and western regions (−1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

By using the logarithmic mean Divisia index (LMDI) method, this paper decomposed the factors that affect carbon emissions both at a national and multi-regional level, and comparatively analysed the difference of driving factors between 2002-2008 and 2008-2014. It is found that economic growth and the energy intensity are two major factors that drive up carbon emissions in the two periods. The economic structure effect and the energy structure effect had little influence on national carbon emissions, and the inhibitory impact was more obvious in 2008-2014 than the first period in most regions. Both the change of economic structure and energy structure can result in the change of national carbon emissions. Also, the variety of different regions can be attributed to the effect of economic growth and the energy intensity.

In this paper, the factors causing the change in carbon emissions from direct energy consumption in the construction industry in Beijing–Tianjin–Hebei are decomposed using the logarithmic mean divisia index (LMDI) method to analyze the effect values and contribution rates of each macrofactor. Based on the decomposition results and given relevant national policies, five scenarios are set up for each influencing factor, and a regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis is applied to each scenario combination for scenario prediction, forming a scientific and reasonable theoretical system to predict the future time of carbon peaking and carbon neutrality in the construction industry of Beijing–Tianjin–Hebei. The results show that (1) energy intensity and energy structure have a suppressive effect on direct energy consumption carbon emissions in the construction industry in Beijing–Tianjin–Hebei, and the industrial structure, economy, and population will promote an increase in carbon emissions. Energy intensity and the economy have a more significant effect on carbon emissions in the construction industry. (2) The peak year of carbon emissions varies with different scenarios, and the energy efficiency scenario achieves peak carbon in 2028, the earliest peak time, and the lowest peak, as it is the optimal emission reduction projection scenario.

Operationalizing marketable blue carbon

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