Analysis of the Driving Factors and Contributions to Carbon Emissions of Energy Consumption from the Perspective of the Peak Volume and Time Based on LEAP

With the proposal of China’s “double carbon goal,” as a high energy‐consuming industry, it is urgent for the mining industry to adopt a low‐carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low‐carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China′s mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

According to IPCC carbon emission calculation instruction, the amount of industrial carbon emission of downtown of Nanning from 2003-2012 is evaluated. With LMDI element decomposition method, the carbon emission of industrial energy consumption in Nanning downtown is decomposed into effect of five aspects such as energy structure, energy intensity, industrial structure, economic scale and population size. It turns out that: the energy structure change can promote the increase of carbon emission. The energy consumption structure should be optimized and the proportion of high-carbon energy consumption should be reduced; The energy intensity is the leading driving factor of carbon emission. The energy efficiency should be further improved to control the increase of carbon emission to some degree; The industrial structure restrains the increase of carbon emission in a great degree. Industrial restructuring should be strengthened and low-carbon industry should be developed; The scale of economy is the main driving factor of the increase of carbon emission. The extensive way of economic growth which depends on the large input of production factors should be changed; The population has a promoting function the increase of carbon emission, while the driving effect is weak, and the growth rate of the population should be strictly controlled.

Carbon peaking is of great significance for China to achieve the "dual carbon" target goal and promote the green transformation of the economy and society. Based on the improved STIRPAT model, to analyze the main factors affecting carbon emissions in Fujian Province, we set up three scenarios and predicted the carbon emissions in Fujian Province from 2022 to 2035 using the hybrid CNN-LSTM neural network model. The results showed that ① Population, GDP per capita, and industrial structure positively drove carbon emissions in Fujian Province, while energy intensity, energy structure, and foreign trade degree negatively drove them. ② The baseline scenario achieved peak carbon in 2033 with a peak value of 361.107 9 Mt； the low-carbon scenario and the optimization scenario could reach the peak one year earlier, and the peak value decreased to a different extent, with 333.028 4 Mt and 301.748 3 Mt, respectively. ③ Comparing the optimization scenario and the low carbon scenario, adjusting the industrial and energy structure could control the reduction in the peak carbon value in Fujian Province by 10.37%, and accelerating the promotion of the optimization and transformation of the energy and industrial structure is the key to unlocking the constraints between carbon emissions and economic development. Finally, combined with the current policy planning and development status of Fujian Province, we put forward suggestions for low-carbon development from the perspectives of energy emission reduction, industrial structure, and institutional system.

In order to cope with global warming, China has put forward the “30 · 60” plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the “carbon peak” goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of “carbon peaking” and “carbon neutralization” as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

The transient climate response to cumulative carbon emissions (TCRE) is a highly policy-relevant quantity in climate science. The TCRE suggests that peak warming is linearly proportional to cumulative carbon emissions and nearly independent of the emissions scenario. Here, we use simulations of the Earth System Model (ESM) from the Geophysical Fluid Dynamics Laboratory (GFDL) to show that global mean surface temperature may increase by 0.5 °C after carbon emissions are stopped at 2 °C global warming, implying an increase in the coefficient relating global warming to cumulative carbon emissions on multi-centennial timescales. The simulations also suggest a 20% lower quota on cumulative carbon emissions allowed to achieve a policy-driven limit on global warming. ESM estimates from the Coupled Model Intercomparison Project Phase 5 (CMIP5–ESMs) qualitatively agree on this result, whereas Earth System Models of Intermediate Complexity (EMICs) simulations, used in the IPCC 5th assessment report to assess the robustness of TCRE on multi-centennial timescales, suggest a post-emissions decrease in temperature. The reason for this discrepancy lies in the smaller simulated realized warming fraction in CMIP5–ESMs, including GFDL ESM2M, than in EMICs when carbon emissions increase. The temperature response to cumulative carbon emissions can be characterized by three different phases and the linear TCRE framework is only valid during the first phase when carbon emissions increase. For longer timescales, when emissions tape off, two new metrics are introduced that better characterize the time-dependent temperature response to cumulative carbon emissions: the equilibrium climate response to cumulative carbon emissions and the multi-millennial climate response to cumulative carbon emissions.

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.

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

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

Abstract. Recent studies have identified an approximately proportional relationship between global warming and cumulative carbon emissions, yet the robustness of this relationship has not been tested over a broad range of cumulative emissions and emission rates. This study explores the path dependence of the climate and carbon cycle response using an Earth system model of intermediate complexity forced with 24 idealized emissions scenarios across five cumulative emission groups (1275–5275 Gt C) with varying rates of emission. We find the century-scale climate and carbon cycle response after cessation of emissions to be approximately independent of emission pathway for all cumulative emission levels considered. The ratio of global mean temperature change to cumulative emissions – referred to as the transient climate response to cumulative carbon emissions (TCRE) – is found to be constant for cumulative emissions lower than ∼1500 Gt C but to decline with higher cumulative emissions. The TCRE is also found to decrease with increasing emission rate. The response of Arctic sea ice is found to be approximately proportional to cumulative emissions, while the response of the Atlantic Meridional Overturning Circulation does not scale linearly with cumulative emissions, as its peak response is strongly dependent on emission rate. Ocean carbon uptake weakens with increasing cumulative emissions, while land carbon uptake displays non-monotonic behavior, increasing up to a cumulative emission threshold of ∼2000 Gt C and then declining.

High-energy consumption and high-emission industries contribute a lot to economic development, but their carbon emissions are also huge. In order to achieve the dual-carbon target as early as possible, it is necessary to reduce the carbon emissions of high-energy consumption and high-emission industries. This paper selected five representative factors (population, per capita gross domestic product (GDP), energy intensity, energy structure and carbon emission coefficient) and adopted the logarithmic mean divisia index (LMDI) method to decompose the driving factors of carbon emissions. Therefore, this paper uses Tapio decoupling model to analyze the decoupling relationship between the two factors with the greatest impact on carbon emissions and carbon emissions. The results show that: (i) There is a good decoupling between high-energy consumption and high-emission industries and per capita GDP, and the impact of per capita GDP on carbon emissions will gradually decrease in the future; (ii) The decoupling relationship between carbon emissions and energy intensity is poor. For some industries, the reduction of energy intensity can help reduce carbon emissions. Finally, this paper puts forward some suggestions to promote carbon emission reduction. This paper provides theoretical support for studying how to reduce carbon emissions and formulate relevant emission reduction policies in the high-energy consumption and high-emission industries.

Achievability of the Paris Agreement targets in the EU: demand-side reduction potentials in a carbon budget perspective

Climate change is increasingly serious, and analysing the determinants of carbon emissions and studying carbon emission reduction is significant. The research used additive decomposition method of LDMI to build a factor decomposition model of carbon emissions from the energy consumption and conducted an empirical analysis of above scale industrial industries from five aspects including energy intensity, industrial structure, energy structure, economic output and the employed population scale. The results revealed that energy intensity and industrial structure had significant negative effects on carbon emissions; while economic output and the employed population scale had strong positive effects; energy structure had no significant negative effect on carbon emissions. Of the industrial industries in Anhui, electricity and heat production and supply industry, coal mining and dressing industry, petroleum processing, coking and nuclear fuel processing industry, ferrous metal smelting and rolling processing industry, non-metallic mineral products industry were the main industries affecting industrial carbon emissions. To reduce carbon emissions, policy suggestions on, reducing energy intensity, adjusting industrial structure and reducing the proportion of five industries were proposed. ［Ch, 4 fig. 5 tab. 14 ref.］

A spatial-temporal decomposition analysis of China's carbon intensity from the economic perspective

Analyzing the driving mechanisms behind provincial carbon emissions is crucial to formulating appropriate carbon reduction policies， which is vital for achieving China's "carbon peaking and carbon neutrality" goals. This study employed the LMDI method to examine the influences of six key factors （population size， economic development， industrial structure， energy intensity， energy structure， and carbon emission coefficient） on carbon emissions across 30 regions in China from 2010 to 2021. By using the contribution rate of each driving factor to changes in carbon emissions as the clustering variable， the K-means clustering method was used to categorize the 30 regions into five groups. This facilitated identifying the similarities and differences in carbon emission driving mechanisms across various regions. The results of the study follow： ① For most regions， economic development and population growth are the primary drivers of carbon emission increases， while energy intensity and industrial structure are important factors in carbon emission reductions. ②The driving factors of carbon emissions vary significantly between the Twelfth and Thirteenth Five-Year Plan periods， with the growth in both the amount and rate of carbon emissions being notably lower in the former period. ③ Importantly， the driving mechanisms of carbon emissions differ greatly across the five region types identified. The first and fifth types of regions face greater challenges in achieving carbon emission peak goals， whereas the second and third types are better positioned to attain these objectives. Based on the characteristics of the different region types and representative provinces and cities， targeted carbon reduction policies are proposed.

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.