Energy-related Carbon Emission in Tianjin and Strategy Implications

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

Following the IPCC guidelines (2006), the total carbon emissions (2005-2009) from energy consumption and the carbon emissions in three sectors (primary industry, secondary industry and tertiary industry) in Beijing were estimated. Results show that (1) the carbon intensity decreased year by year, and (2) the total carbon emissions and carbon emissions per capita tended to fall down when they reached to the highpoints in 2007. In order to find the path of carbon emissions reduction in the future, present paper introduced and estimated the factors (industrial structure, energy structure and energy efficiency) which had impact on the carbon emissions of Beijing.

Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper estimates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 using ARIMA model and BP neural network model. ArcGIS and standard elliptic difference are used to visually analyze the spatio-temporal evolution characteristics, and LMDI model is further used to decompose the driving factors affecting carbon emissions. The results show that: (1) China's total carbon emissions increased year by year from 2000 to 2035, but the growth rate of carbon emissions decreased gradually; The carbon emission structure is "secondary industry > residents' livelihood > tertiary industry > primary industry". the growth rate of carbon in secondary industry and residents' livelihood is relatively fast, while the change trend of primary industry and tertiary industry is relatively small. (2) the spatial distribution of carbon emissions in China's provinces presents a typical "eastern > central > western" and "northern > southern" distribution pattern, with the carbon emission center moving to the northwest; (3) The regions with higher development level of digital economy, industrial structure and new quality productivity have relatively less carbon emissions, with significant group difference effect; (4) Energy consumption intensity effect is the main factor to drive the continuous growth of carbon emissions, per capital GDP and energy consumption structure effect are the main factors to curb carbon emissions, and the impact of industrial structure and population size effect is relatively small. Based on the research conclusions, policy suggestions are put forward from the aspects of energy structure, industrial structure, new quality productivity and digital economy.

An in-depth study of the energy related carbon emissions has important practical significance for carbon emissions reduction and structural adjustment in Shandong Province and throughout China. Based on the perspective of industrial structure, the expanded KAYA equation to measure the energy related carbon emissions of the primary industries (Resources and Agriculture) and secondary industries (Manufacturing and Construction) and tertiary industries (Retail and Service) was utilized in Shandong Province from 2011 to 2017. The carbon emissions among industries in Shandong Province were empirically analyzed using the Logarithmic Mean Divisia Index decomposition approach. The results were follows: (1) Under the three industrial dimensions, the energy structure effect and the energy intensity effect have a restraining influence on the carbon emissions of the three industries. (2) The development level effect and the employment scale effect play a pulling role in carbon emissions. (3) From the perspective of the employment structure effect of the primary industry, there is a restraining effect on carbon emissions, while the employment structure effects of the secondary and tertiary industries play a pulling role in carbon emissions, and the employment structure effect of the tertiary industry has a greater pulling effect on carbon emissions than the secondary industry.

With the support of the total output of primary industry, secondary industry and tertiary industry and industry value of cultivating industry, forestry industry, animal husbandry industry and fishery industry of 19 country-level units in Yellow River Delta in 2007, the paper calculated the variation index of economic development based on country level, the MDS analysis was explored for primary industry, secondary industry and tertiary industry and compared with spatial difference characteristics of 19 country-level units in Yellow River Delta. The study carried out the analysis of combination analysis of primary, secondary and tertiary industry, and optimized the cultivating industry, forestry, animal husbandry and fishery industry. Autocorrelation characteristics was clarified by Moran‘s I calculation, the new development mode of agricultural industrial structure was systemically concluded that the different mode presented in the different country units. The results showed that the industry evolution stage of primary, secondary and tertiary industry structure of 19 country units is in primary and tertiary industries stage and secondary, primary and tertiary industries stage according to industry structure stages theory, the obvious characteristics is that the cultivating industry value has spatial variability based on classical statistics combinig with universal Kriging. The emphasis of industry adjustment should be the focus on the secondary industry driving transformation into the primary, secondary and tertiary industries jointly driving economic growth, straight-pushing mode and tilting mode were put forward to new ideas about promoting industrial optimization and coordination development in Yellow River Delta. The study drew a conclusion that the measures were took to optimize industrial structure of cultivating industry, forestry industry, animal husbandry and fishery industry by combing straight-pushing mode with tilting mode, during the coordination development, focused on association effects of land and marine industry, extended agriculture industrialization chain and formed the diversification mode.

System dynamic modeling of urban carbon emissions based on the regional National Economy and Social Development Plan: A case study of Shanghai city

PDF HTML阅读 XML下载 导出引用 引用提醒 我国城市发展与能源碳排放关系的面板数据分析 DOI: 10.5846/stxb201911292591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学重点基金项目（71533005）；国家重点研发项目（2017YFF0207303） The impact of urbanization on carbon emissions: Analysis of panel data from 158 cities in China Author: Affiliation: 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:城市化与城市能耗及其碳排放密切相关，城市发展过程中的人口城市化进程和产业总量与结构调整都是能源碳排放变化的主要驱动因素。以2006-2015年全国158个地级城市的面板数据为基础，从总量变化趋势和空间变化趋势两个角度分析了研究期内的我国城市发展特征及能源碳排放特征；并利用面板计量分析方法研究了城市发展因素对城市总能耗、总能耗碳排放、单位能耗碳排放量的驱动特征。结果表明：城市化每提升0.095%，总能耗上升1%。虽然城市总能耗及能耗碳排放在降低，但是单位能耗碳排放在增加；第二产业和第三产业发展对总能耗及能耗碳排放的驱动作用大；城市第三产业的发展有利于能源结构优化调整等；并基于研究发现给出一些政策建议。 Abstract:Urbanization is closely related to urban energy consumption and associated carbon emissions. The process of population urbanization and industrial structure adjustment in urban development are the main drivers of changes in carbon emissions. Based on the panel data of 158 prefecture-level cities in China from 2006 to 2015, this study analyzes the urban development characteristics and energy carbon emission characteristics of China from total volume and spatial variation. The study uses panel measurement to analyze the driving characteristics of urban development factors on total urban energy consumption, total carbon emissions, and carbon emissions per unit energy consumption. The results show that for every 0.095% increase in urbanization, the total energy consumption increases by 1%. Although the total urban energy consumption and carbon emissions are decreasing, the carbon emissions per unit energy consumption are increasing. The total energy consumption of secondary and tertiary industries is also increasing. The development of tertiary industries in the city is beneficial for the optimization and adjustment of the energy structure. Based on the findings, some policy suggestions are proposed. 参考文献 相似文献 引证文献

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province.

The production scheduling problem employing non-identical parallel machines with due dates considering carbon emissions and multiple types of energy sources

With the rapid development of Chinese economy and increasing improved living standards, the amount of carbon emissions in China has been increasing consistently in a high speed, which consists of the largest percentage of the world’s total carbon emissions in recent years. The construction industry, playing an important role in the Chinese economy, accounts for a large proportion of the total carbon emissions in China. In this paper, the carbon emissions from construction industry in China in 2009 are analyzed by adopting Multi Regional Input-output (MRIO) Model and the World Input-Output Database (WIOD). Results show that, according to the data in 2009, the construction industry is the largest carbon emitter among all industries in China, responsible for the emissions of 2,121,649.31 kt CO2, accounting for 66.54 % of Chinese total carbon emissions. This emission value is contributed by other economic sectors and activities, and it has been found that the industrial sector “Electricity, Gas and Water Supply” is the largest contributor to the carbon emissions of Chinese construction industry, with an amount of 984,830.85 kt CO2, accounting for 46.42 % of the total carbon emissions of Chinese construction industry. Furthermore the carbon emissions in the construction industry comprise 71,418.19 kt CO2 (3.37 %) of direct carbon emissions and 2,050,231.12 kt CO2 (96.63 %) of indirect carbon emissions. The carbon emissions of domestic goods, exports and imports within construction industry are 2,129,974.07, 8663.33 and 338.58 kt CO2, respectively, consisting of 100.39, 0.41 and 0.02 % of the total carbon emissions of Chinese construction industry. The results can help identify critical areas where policymakers can formulate effective policy measures for carbon emissions reduction in Chinese construction industry.

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

Studies on carbon dioxide (CO2) emissions at provincial level can provide a scientific basis for the optimal use of energy and the formulation of CO2 reduction policies. We studied the variation of CO2 emissions of primary energy consumption and its influencing factors based on data in Xinjiang Uygur autonomous region from 1952 to 2008, which were calculated according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Xinjiang’ CO2 emission process from 1952 to 2008 could be divided into five stages according to the growth rates of total amount of CO2 emissions and CO2 emission intensity. The impact factors were quantitatively analyzed using Logarithmic Mean Divisia Index (LMDI) method in each stage. Various factors, including government policies and technological progress related to the role of CO2 emissions, were comprehensively analyzed, and the internal relationships among various factors were clarified. The results show that the contribution rates of various impact factors are different in each stage. Overall, economic growth and energy consumption intensity were the main driving factors for CO2 emissions. Since the implementation of the birth control policy, the driving force of population growth on the increase in CO2 emissions has slowly weakened. The energy consumption intensity was further affected by the industrial structure and energy consumption intensity of primary, secondary and tertiary industries, with the energy consumption intensity of the secondary industries and the proportion of secondary industries being the most important factors affecting the energy consumption intensity. Governmental policies and technological progress were also important factors that affected CO2 emissions.

China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions. The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. Based on the research conclusion, this paper puts forward some policy suggestions from energy structure, industrial structure, new quality productivity and digital economy.

With the rapid economic development of Xinjiang Uygur Autonomous Region (Xinjiang), energy consumption became the primary source of carbon emissions. The growth trend in energy consumption and coal-dominated energy structure are unlikely to change significantly in the short term, meaning that carbon emissions are expected to continue rising. To clarify the changes in energy-related carbon emissions in Xinjiang over the past 15 years, this paper integrates DMSP/OLS and NPP/VIIRS data to generate long-term nighttime light remote sensing data from 2005 to 2020. The data is used to analyze the distribution characteristics of carbon emissions, spatial autocorrelation, frequency of changes, and the standard deviation ellipse. The results show that: (1) From 2005 to 2020, the total carbon emissions in Xinjiang continued to grow, with noticeable urban additions although the growth rate fluctuated. In spatial distribution, non-carbon emission areas were mainly located in the northwest; low-carbon emission areas mostly small and medium-sized towns; and high-carbon emission areas were concentrated around the provincial capital and urban agglomerations. (2) There were significant regional differences in carbon emissions, with clear spatial clustering of energy consumption. The clustering stabilized, showing distinct "high-high" and "low-low" patterns. (3) Carbon emissions in central urban areas remained stable, while higher frequencies of change were seen in the peripheral areas of provincial capitals and key cities. The center of carbon emissions shifted towards southeast but later showed a trend of moving northwest. (4) Temporal and spatial variations in carbon emissions were closely linked to energy consumption intensity, population size, and economic growth. These findings provided a basis for formulating differentiated carbon emission targets and strategies, optimizing energy structures, and promoting industrial transformation to achieve low-carbon economic development in Xinjiang.