LMDI Model Research Articles

Empirical Analysis of Environmental Constraints and Influencing Factors in Beijing-Tianjin-Hebei Region

This paper focuses on carbon emissions problem and adopts the “bottom-up” method proposed by IPCC to calculate the energy consumption carbon emission of leading industries in the Beijing-Tianjin-Hebei region. And constructed the Tapio decoupling index model to analyze the “decoupling relationship” between the development of leading industries and energy consumption and carbon emissions in various regions, so as to quantify the environmental constraint levels of different industrial development. Finally, this paper uses LMDI model to decompose the carbon emission factors, and explores the environmental impact of industrial leading industries in Beijing-Tianjin-Hebei region from the four dimensions of carbon emission intensity, energy intensity, economic development and population size, in order to effectively promote the optimization of industrial structure in Beijing-Tianjin-Hebei region and to achieve pareto optimization.

The optimization of regional industrial structure under the water-energy constraint: A case study on Hebei Province in China

Because of the rapid development of the economy, the shortages of water and energy in northern China have reached crisis levels. Solving the contradiction between economic development and resource constraint is a significant problem in China. Based on the 2000–2017 panel data of Hebei Province, a significant arid and semi-arid region in the north of China, this paper tries to create a valid path to resolve water-energy problems by calculating the growth driving factors of the industrial water-energy with LMDI Model, then plans the internal structure of the industrial sector through the linear programming model. The results show: (1) The seeking of the growth of gross domestic product (GDP) is the essential factor that promotes the increasing of water-energy crises; while, industrial sectors' structural adjustments will boost efficiency and performance of water and energy. (2) The primary industry in Hebei province plays the main key to energy conservation; meanwhile, reducing the proportion of crops with high water consumption is an excellent way to alleviate the shortage of water resources. (3) Manufacturing sectors’ structural adjustments, especially reducing the proportion of heavy industry sectors, will provide an essential means to foster water-energy efficiency; at the same time, increasing the proportion of tertiary industry can well balance the relationship between economic growth and water energy utilization. Finally, this paper provides a possible path for the optimization and adjustment of industrial-economic restructuring through emphasizing sustainable solutions of industrial-economic interactions under the situation of water-energy constraint in Hebei province.

Research on influencing factors and countermeasures of industrial carbon emission in Hebei province based on Kaya model

In view of the world carbon emission problem, the current status of low carbon economy research is reviewed. Based on the historical data of industrial carbon emissions in Hebei Province from 2008 to 2017, based on the historical data of industrial carbon emissions in Hebei Province, the Kaya decomposition model was used to find the industry affecting Hebei Province. The driving factors of carbon emission levels and the trends of various factors, and then combined with the LMDI model, the absolute contribution of each driving factor to the economic development of Hebei Province, and the specific analysis of carbon emission levels, and finally put forward rationalized emission reduction measures and suggest.

Analysis of factors affecting carbon emissions in Tianjin

According to the IPCC carbon emission calculation method, Tianjin's carbon emissions were calculated from 2000 to 2017, and the main factors affecting carbon emissions were analyzed and studied using the LMDI model system. The results show that Tianjin’s energy consumption carbon emissions continued to increase from 2000 to 2017, and have declined significantly in recent years. Among the carbon emissions of energy consumption, coal produced the most carbon emissions, followed by crude oil and coke; Among the factors that affect Tianjin’s carbon emissions from 1997 to 2017, economic development has the largest positive effect, and energy consumption intensity has the largest negative effect; Tianjin’s energy consumption is unreasonable, and it mainly depends on coal, crude oil and other energy sources.

Estimation and decomposition analysis of carbon emissions from the entire production cycle for Chinese household consumption

To guide households on implementing low-carbon consumption patterns, it is necessary to comprehensively measure carbon emissions of household consumption. This study expands the input-output relationship into the production-consumption relationship. It uses optimized data of the relationship between household consumption and production industry to calculate the entire production-side carbon emissions, including from capital formation, of Chinese household consumption, and uses LMDI model to analyze the factors affecting the growth of carbon emissions from Chinese household consumption. The results show that the carbon emissions of Chinese household consumption grew steadily from 2005 to 2015 almost 50% of carbon emissions were accounted for by high growth rates in residence consumption. Carbon emissions and growth rate of urban households’ consumption are significantly higher than the same figures for rural households. The carbon emissions intensity of all types of household consumption except residence and education has shown a downward trend. Household consumption structure and income level are the two main factors that promote the growth of household carbon emissions. Urbanization level and population size are secondary factors while household consumption carbon intensity is an important factor for curbing the growth of household consumption emissions. The study also proposes policy recommendations on how to improve the consumption structure of households, reduce the carbon intensity of household consumption, and curb the growth of carbon emissions from urban households.

Research on Characteristics and Effecting Factors of Crop Farming Carbon Emission in Henan Province, China

The greenhouse gas emissions from crops were carculated, and the LMDI model were used to analyze the influencing factors of carbon. The results indicate: The total carbon emissions of crops in Henan Province from 1991 to 2015 showed an overall upward trend, from 9.7651 million tons of CO2-eq in 1991 to 17.235 million tons of CO2-eq in 2015. The dominant position in greenhouse gas production in Henan Province is N2O for agricultural land, followed by rice field CH4. Compared with 1991, in 2015, the carbon emission intensity of unit GDP in Henan Province decreased by 59.52%, and the carbon emission intensity per unit of grain output decreased by 12.34%. LMDI analysis shows that the most important factors affecting the increase of carbon emissions in crop production in Henan Province are the agricultural economic level and industrial structure.

Features and drivers for energy-related carbon emissions in mega city: The case of Guangzhou, China based on an extended LMDI model.

Based on the apparent energy consumption data, a systematic and comprehensive city-level total carbon accounting approach was established and applied in Guangzhou, China. A newly extended LMDI method based on the Kaya identity was adopted to examine the main drivers for the carbon emissions increments both at the industrial sector and the residential sector. Research results are listed as follow: (1) Carbon emissions embodied in the imported electricity played a significant important role in emissions mitigation in Guangzhou. (2) The influences and impacts of various driving factors on industrial and residential carbon emissions are different in the three different development periods, namely, the 10th five-year plan period (2003–2005), the 11th five-year plan period (2005–2010), and the 12th five-year plan period (2010–2013). The main reasons underlying these influencing mechanisms were different policy measures announced by the central and local government during the different five-year plan periods. (3) The affluence effect (g-effect) was the dominant positive effect in driving emissions increase, while the energy intensity effect of production (e-effect-Production), the economic structure effect (s-effect) and the carbon intensity effect of production (f-effect-Production) were the main contributing factors suppressing emissions growth at the industrial sector. (4) The affluence effect of urban (g-effect-AUI) was the most dominant positive driving factor on emissions increment, while the energy intensity effect of urban (e-effect-Urban) played the most important role in curbing emissions growth at the residential sector.

我国主要海域海水养殖碳汇能力评估及其影响效应——基于我国9个沿海省份面板数据

PDF HTML阅读 XML下载 导出引用 引用提醒 我国主要海域海水养殖碳汇能力评估及其影响效应——基于我国9个沿海省份面板数据 DOI: 10.5846/stxb201801080049 作者: 作者单位: 中国海洋大学 经济学院 青岛,中国海洋大学 经济学院 青岛,中国海洋大学 经济学院 青岛 作者简介: 通讯作者: 中图分类号: 基金项目: 山东省自科基金（ZR2016GQ05）；国家社科基金（11BJY064）；山东省社科基金（18CSJJ01） Evaluation of carbon dioxide capacity and the effects of decomposition and spatio-temporal differentiation of seawater in China's main sea area based on panel data from 9 coastal provinces in China Author: Affiliation: School of Economics, Ocean University of China, Qingdao,School of Economics, Ocean University of China, Qingdao,School of Economics, Ocean University of China, Qingdao 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:随着海水养殖业的碳汇功能逐渐被认识和肯定，海水养殖不再单是一项经济活动，而是对环境具有正向影响的碳汇生态活动。以我国沿海9个省份为例，选取海水养殖业碳汇主要贡献的贝类和藻类海产品，并按照各自的碳汇方式对我国沿海地区2008-2015年海水养殖碳汇能力测算，进一步将9个沿海省份按照主要海域划分为渤海、黄海、东海、南海，利用LMDI模型从海水养殖的结构效应和规模效应角度分析碳汇能力的区域差异和主要影响因素。研究结果显示，黄海沿岸海水养殖碳汇能力最强，南海沿岸海水养殖的碳汇转化比例最高，规模效应与我国沿海地区海水养殖碳汇能力始终呈正相关，结构效应的作用显著但不稳定。基于上述结论，我国沿海地区碳汇养殖业应首先提升碳汇养殖技术、稳定海水养殖产量，其次注重优化养殖结构，对碳汇潜力巨大的贝类多加关注。 Abstract:The function of carbon sinks in marine aquaculture have been gradually recognized and affirmed; therefore, marine aquaculture is no longer merely an economic activity, but a positive carbon ecological activity. This research focused on fishery carbon sinks as a main process for transferring carbon from aquatic products during harvest. According to the data of marine aquaculture production in the coastal areas of the China Fishery Yearbook, we studied 9 coastal provinces of China and selected the major species of shellfish and algae for this study. Dry and wet weight carbon coefficients of the selected marine products were obtained from reported values in the literature; then we combined those values with the yield data to obtain the mariculture carbon sinks of different biological species in the coastal provinces of China. Since there is a relationship among carbon sinks, a carbon sink coefficient and the yield of different species, we measured the annual carbon sequestration capacity of aquaculture seawater in the coastal areas of China from 2008 to 2015. Furthermore, we divided the coastal provinces into different sea areas to compare the carbon sink capacity and carbon sink conversion ratio of the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea. Finally, from the perspective of the structural effects and scale effects of marine aquaculture carbon sinks, we identified the main factors that affect the carbon sink potential of coastal areas in China using an LMDI model. The potential for carbon sinks in the different sea areas were comprehensively evaluated. The results showed that since 2008 the amount of carbon in China's marine aquaculture industry has exceeded 1.05 million tons. The carbon sink conversion ratio and carbon sink capacity of coastal provinces has increased over the years, indicating that the carbon sink capacity of China's marine aquaculture industry cannot be neglected. Shellfish can significantly increase the carbon sink conversion ratio; therefore, the carbon sink conversion ratio of the South China Sea was the highest and had the best culture structure of the 4 sea areas, and that of the East China Sea was the lowest. The conversion ratio of carbon sinks along the Bohai Sea and the Yellow Sea coast were similar. From the time dimension, the carbon sink conversion ratio in the South China Sea was stable, while the East China Sea had a significant downward trend of carbon sink conversion ratio due to its aquaculture structure, which is biased towards algae. The carbon sink capacity of each sea area has increased over time, with the highest in the Yellow Sea and the lowest in the Bohai Sea before 2012 and the lowest in the South China Sea after 2012. The gap among the different sea areas has increased each year. An LMDI method compared the effects of aquaculture structure and the scale effect on the carbon sink capacity of China's marine aquaculture industry. It was found that the scale effects of each sea area were always positive, while the structural effect were sometimes significantly negative. In some years, the changes in aquaculture structure inhibited the carbon sinks, indicating that the structural effects were more significant but unstable. Based on the above conclusions, China's coastal carbon sequestration industry should improve carbon sink technology and stabilize marine aquaculture production, followed by optimizing the aquaculture structure and paying more attention to shellfish with large carbon sink potential. 参考文献 相似文献 引证文献

Temporal-spatial decomposition computing of regional water intensity for Yangtze River Economic Zone in China based on LMDI model

The shortage of water resources has become a burning issue constraining China’s sustained development. Quantifying the driving effect of temporal and spatial differences of water intensity in Yangtze River Economic Zone is very important to Three Red Lines and the dual implementation action of water resources and intensity. In this study, an Index Decomposition Analysis-Logarithmic Mean Divisia Index (IDA-LMDI) model is developed to decompose the temporal-spatial differences of water intensity into intensity effect and structure effect from 2000 and 2015 in Yangtze River Economic Zone. Our findings are as follows. (1)Industrial water intensity and industrial structure are primary and secondary factors that promote the reduction of water intensity, the water intensity of the three industries has generally declined. (2)The primary industry and secondary industry contribute more to the decline of water intensity than the tertiary industry and the primary industry transfers to the secondary industry and the tertiary industry to drive the decline of water intensity. (3)The gap between the water intensity of each province and the average level of the Yangtze River Economic Zone gradually narrowed, and there are significant differences in the evolution trend of intensity effect and structural effect in each province. (4)Only water intensity of the three industries in Zhejiang is always lower than average level, nevertheless, Jiangxi, Hunan are always higher than average level, the proportion of primary industry in GDP in the Yangtze River Delta (Shanghai, Jiangsu and Zhejiang) is less than the average level. Therefore, the provinces should give consideration to the improvement of industrial water efficiency and industrial structure adjustment and other water resources policies to promote the decline of water intensity, greatly improve the effective utilization coefficient of irrigation water and reuse of industrial water. Government should increase support for the management, capital and technology of less developed provinces in the Yangtze River Economic Zone. Provinces should focus on policies that reduce the water intensity, Zhejiang and Chongqing can be a model for other provinces to learn from advanced experience, especially in Zhejiang.

An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030

The ferrous metal industry is one of the biggest source of industrial energy consumption and CO2 emissions in China. This paper first employs the LMDI model to explore what influenced CO2 emissions from China's ferrous metal industry from 1991 to 2016. The CO2 emission variation is decomposed into industry activity effect (ΔCG), energy intensity effect (ΔCEI), industry scale effect (ΔCL), energy structure effect (ΔCES), industry structure effect (ΔCIS) and emission coefficient effect (ΔCCOE). The grey prediction method and scenario analysis are then adopted to investigate the CO2 emission mitigation potential in China's ferrous metal industry during 2017–2030. The results reveal that: 1) CO2 emissions from China's ferrous metal industry grew dramatically —by 508.35%— from 213.48 Mt in 1991 to 1298.71 Mt in 2016; 2) overall, ΔCG was the leading aggravating factor and ΔCEI was key mitigating factor of emissions from the ferrous metal industry, while ΔCL and structural factors played minor roles; 3) if the policies for the 13th five-year plan period are enforced vigorously, CO2 emissions from China's ferrous metal industry could be reduced by 724.07 Mt in 2020. Policy suggestions are proposed for emission abatement in China's ferrous metal industry.

Spatial and Temporal Disparities in Agricultural Carbon Emissions in Xinjiang and Decomposition of Impact Factors

This paper describes the measurement method for agricultural carbon emissions in the Xinjiang Uygur Autonomous Region over the past 16 years. Here is analysis on spatial and temporal disparities in the carbon emissions with diverse characteristics. LMDI model decomposes some key factors that affect agricultural carbon emissions. The results reveal that the gross carbon emissions from agriculture in Xinjiang show an upward trend during the past 16 years, while the carbon intensity gets moderate; the carbon emissions in various prefectures present different intensities and patterns. Agricultural economy development level and the size of agricultural labor force have a positive impact on carbon emissions, whereas agricultural production efficiency and industrial structure go the other way around.

Driving effects of spatial differences of water consumption based on LMDI model construction and data description

Quantifying the driving effect of the spatial difference of the provincial water consumption is conducive to the development and implementation of the differentiated water resources policy and is of great significance to the implementation of the dual-control action on total water consumption and intensity. In this paper, the LMDI model is used to decompose the driving effect of the spatial difference of provincial water consumption from 2000 to 2015 into intensity effect, structure effect, income effect and population effect. According to the trend of the time-varying driving effect of provincial water consumption, the water consumption in more than half of the provinces is less than the national average. The eastern provinces gradually increase over time, while the central provinces decrease. In addition to Xinjiang, Guangxi and Sichuan in the western regions, the water consumption in other provinces is always less than the national average; except for Jiangsu and Shanghai, the water efficiency in eastern provinces is generally higher than the national average, which is the driving force for water consumption decline. The provinces with water efficiency lower than the national average are mainly concentrated in the Midwest regions; the provinces whose industrial structure adjustment is more conducive to the decrease in water consumption are mainly concentrated in the eastern region with gradually increasing provinces, while the industrial structure in most of the central and western provinces needs to be further optimized and upgraded; in addition to Hebei and Hainan, the economic development levels of other provinces in the eastern region are generally high, which has effectively promoted the increase of water consumption. The economic development in most of the Midwest provinces lags behind and the growth of water consumption is suppressed. The population is mainly concentrated in the eastern and central regions, while the population in the western region is relatively small, which limits the increase of water consumption. From the differences among the three industries in terms of the intensity effect and the structural effect, the provinces with generally lower water intensity in the three industries are mainly concentrated in the eastern region. The declining share of the primary industry in the eastern provinces has contributed to the drop in water consumption, while the Midwest provinces need to further adjust and optimize the industrial structure in order to promote the reduction of water consumption. Therefore, when implementing the state policy, provinces need to take targeted and differentiated water saving measures to reduce water consumption. Specific consideration can be given to technological progress, industrial restructuring and population control. Instead economic growth is the driving force for social progress, which will increase water demand.

Energy savings evaluation in public building sector during the 10th–12th FYP periods of China: an extended LMDI model approach

Energy savings can be treated as an indicator to reveal the effectiveness of energy efficiency task (EET) in the building sector, especially in the public buildings. However, evaluating the values of energy savings in public buildings (ESPB) was challenged by the missing data sources and inadequate tools in China. To overcome these problems, this study applied an extended Logarithmic Mean Divisia Index model to examine the contributions of different impact factors affecting the public building energy consumption (PBEC) and further evaluated the ESPB values during the 10th–12th Five-Year Plan (FYP) periods in China. Results included three aspects: (1) Absolute values of the contribution of the adjusted PBEC intensity to PBEC denoted the ESPB values in China. (2) Total values of ESPB were 99.9 Mtce during the 10th–12th FYP periods of China. Concretely, the ESPB values during the three FYP periods were as follows: 71.091 Mtce (the 12th FYP period), 19.075 Mtce (the 11th FYP period), and 9.734 Mtce (the 10th FYP period). (3) Effective EET of public buildings was a strong support for the rapidly growing ESPB during the three FYP periods. Furthermore, this study suggested that China should issue the official data on energy consumption in the building sector as quickly as possible, and this action would deeply help the government design targeted plans and policies for the future EET in the building sector.

Factors analysis of PM2.5 emission reduction in Chinese thermal power industry based on LMDI model

The Laspeyres index and its complete decomposition method are introduced to construct a complete decomposition model of PM2.5 emission reduction in power industry. Based on the scenario analysis, the main driving factors of the emission reduction in the thermal power industry from 2007 to 2030 were studied. The results show that energy-saving technologies such as the elimination of backward production capacity and the implementation of emission reduction technologies have the greatest contribution to the management of haze in power industry, followed by the structural adjustment effect of large pressure and small structure. With the change of thermal power from the main electricity supply to the main capacity supply, the scale reduction effect will gradually appear after 2020.

Analyzing and Predicting CO2 Emissions in China Based on the LMDI and GA-SVM Model

Analyzing and Predicting CO2 Emissions in China Based on the LMDI and GA-SVM Model

Accounting carbon emission changes under regional industrial transfer in an urban agglomeration in China's Pearl River Delta

Industrial sectors account for a large part of urban carbon emissions. To address this, regional industrial transfer has recently been proposed in China, especially for areas consisting of urban agglomerations. In this manuscript, by applying LMDI method to the industrial transfer theory, driving forces of carbon emission changes from manufacturing industries were evaluated in the Pearl River Delta (PRD) of China. Firstly, the theories of industrial transfer were thoroughly analyzed to systematically study the pattern and trend. Then, a multi-region LMDI model was made to simulate the effect of the driving forces of regional transfer on carbon emissions, considering the production element flows of capital, labor, and technology under the background of industrial transfer through the cities. Finally, using the integrated model with spatial analysis, the temporal and spatial characteristics of carbon emission changes were quantified. The results revealed that the economic growth triggered the high speed of carbon increases, while industrial structure effect especially for labor-intensive industries and technology-intensive industries, help to prevent the carbon emissions from ever increasing. The developed cities effectively reduce high energy consuming industries, mainly the capital-intensive industries, by transferring to the developing places, causing the inequality distribution of carbon emissions of manufacturing industries in the PRD. The results may implied the adjustments of coordinated development to reduce the carbon disparities.

Study on influencing factors of carbon emissions for industrial energy consumption in Dalian based on LMDI model

After calculating and analysing the carbon emissions for industrial energy consumption in Dalian during 2007 to 2014, the LMDI model is built to decompose the influencing factors on carbon emissions from the perspectives of energy structure, energy intensity, industrial structure and output scale. As many as 17 types of energy are involved in the calculation of emissions, and based on the specific industrial structure of Dalian, the analysis for the influence on carbon emissions from industrial structure is made from the perspective of light and heavy industry. The result shows, output scale effect is a major contributor for the growth of the carbon emissions for industrial energy consumption in Dalian, while energy intensity effect is the principal inhibitor. Energy structure and industrial structure influence relatively weakly on the energy-consumption emissions, which indicates that the emission reduction potentials from these two perspectives are still not achieved.

Atmospheric pollution reduction effect and regional predicament: An empirical analysis based on the Chinese provincial NOx emissions

Atmospheric pollution emissions have become a matter of public concern in recent years. However, most of the existing researches on NOx pollution are from the natural science and technology perspective, few studies have been conducted from an economic point, and regional differences have not been given adequate attention. This paper adopts provincial panel data from 2006 to 2013 and the LMDI model to analyze the key driving factors and regional dilemmas of NOx emissions. The results show that significant regional disparities still exit on NOx emissions and its reduction effect 27 provinces didn't accomplish their corresponding reduction targets. Economic development factor is the dominating driving factor of NOx emissions during the study period, while energy efficiency and technology improvement factors offset total NOx emissions in the majority of provinces. In addition, the industrial structure factor plays a more significant role in reducing the NOx emissions after 2011. Therefore, the government should consider all these factors as well as regional heterogeneity in developing appropriate pollution mitigating policies. It's necessary to change NOx emissions control attitude from original key areas control to divided-zone control, not only attaches great importance to the reduction of the original key areas, but also emphasizes the new potential hotspots with high NOx emissions.

Chinese innovation-driving factors: regional structure, innovation effect, and economic development—empirical research based on panel data

The implementation of innovation-driven strategy is of great significance. Analysis of the driving factors of innovation performance from quantitative and systematic perspectives is needed for policy making. By drawing upon the LMDI model, this study identifies the driving factors and their corresponding contributions in the innovation performance of 30 Chinese provincial-level regions during the period 2000–2012. The innovation performance is decomposed into the regional economic structure effect, R&D intensity effect, innovation efficiency effect, and economic development effect according to the driving mechanism. The results indicate that the third effect in this list, innovation efficiency, contributes the most to innovation performance at 54.28%, followed by the regional R&D intensity at 27.49%, and China’s economic development at 19.92%. The effect of the regional economic structure is negative, at −1.69%. This study further analyzes four major economic areas of China and identifies the channels through which each area conducts their innovation activities. The empirical findings provide information for policy measures to implement innovation-driven strategies.

Factors decomposition of carbon emissions embodied in China's agricultural export

An input-output model is established to calculate the volume of carbon emissions embodied in China's agricultural export and its distribution in China's agricultural sectors during the period of 2001-2013. The contributing factors in growth of carbon emissions embodied in China's agricultural export is then decomposed into scale effect, structure effect and technical effect, with which overall and sectional analysis are made through LMDI model. The empirical results show that scale effect is the main reason for the rise of carbon emissions embodied in China's agricultural export. Technical effect is the most important factor to inhibit the growth of embodied carbon emissions from agricultural export products. But it does not necessarily lead to a drop of embodied carbon emissions of agricultural products due to the increases of the rigid demand for energy and intensity of energy consumption in China's agricultural sectors. The influence of structure effect on changes of carbon emissions embodied in China's agricultural export remains less significant so far.