Log Mean Divisia Index Method Research Articles

Decomposition of Carbon Dioxide (CO2) Emissions in Hungary

Elevated levels of global greenhouse gases (GHGs), primarily produced through fossil fuel combustion, present environmental risks such as prolonged droughts, global warming, and catastrophic floods. Despite raising awareness and international efforts to combat climate change, fossil fuel and GHGs demand persists. At this juncture, this study investigates Hungary's transportation sector, a notable CO2 emitter. Using the Kaya Identity and Log Mean Divisia Index method, this study decomposes the primary components of CO2 emissions in Hungary's transport sectors from 2001-2021. This analysis is done in two decomposition levels, annual and periodic decomposition analysis for each of the five years starting from 2001. The analysis reveals that the economic activity effect ∆CGPE is the predominant driver of CO2 emissions, while the energy intensity effect ∆CEIE improvements have substantially mitigated these increases. Population changes affect ∆CPOPE, and the carbon emission intensity effect ∆CCEIE has had minimal impacts. The study provides insights and recommendations for policy and strategic interventions to reduce emissions and promote sustainable transportation practices in Hungary.

Influencing factors and prediction of net carbon sink in the primary sector of the coastal city in China.

To achieve the goal of urban carbon dioxide emission reduction, how to increase carbon sequestration has become a top priority. The biological sink is mainly divided into green carbon sink and blue carbon sink. Coastal cities have two kinds of carbon sinks. There, the study of carbon sinks in coastal cities is the primary choice to cope with climate change. Therefore, this study chooses coastal cities with primary industries including agriculture, fishery, and forestry as the study subjects. The LMDI (Log-Mean Divisia Index) method and multiple regression prediction models were used to explore the low-carbon countermeasures which increase urban net carbon sink from the perspective of influencing factors and future potential. The study found that the average output value of employees in the primary industry is the main driving factor, and the change in the purchasing power of unit carbon sinks and the change in the proportion of employees in the primary industry have inhibited the increase in net carbon sinks. Projections based on the primary industry's output and afforestation area as independent variables show an overall upward trend in net carbon sinks, reaching 15.70 million tons of net carbon sinks in 2060, offsetting 10-20% of total carbon emissions in the same year. Based on the calculation results, this paper puts forward some corresponding countermeasures to increase carbon sinks. This paper provides a theoretical reference for the low-carbon development of coastal cities in China, and the strategies can be also expanded to other cities with similar resources around the world.

Classifying Regional and Industrial Characteristics of GHG Emissions in South Korea

South Korea officially committed to reducing 40% of its total carbon emissions by 2030, but the country has a carbon-dependent economic structure based on the manufacturing industry. Additionally, the industrial structure of each region in South Korea is heterogeneous. In this regard, policymakers should analyze the carbon emission condition at a regional level because abatement aspects are heterogeneous by urban spatial production. However, although various studies have developed a methodology to evaluate the GHG emission condition, these studies failed to consider the fundamental aspect of regional heterogeneity. In this regard, this study suggests a quantitative method to assess the potential of the carbon neutrality of regions and industries by using both shift-share analysis and the Log Mean Divisia Index method. Shift share analysis is used to quantify the relation between the industry and regional characteristics, and the Log Mean Divisia Index method can decompose each effect for economic growth and technological progress. By combining these two methods, this study suggests four classifications to evaluate regional and industrial characteristics of GHG emissions and analyze each region’s emission status in terms of the mining and manufacturing industry in South Korea.

Impacts of Emission Reduction Technological Changes on China’s City-Level PM2.5 Concentration Based on Sustainable Development

As an important field for human activities, cities play a critical role in PM2.5 reductions. Among the determinants for PM2.5 concentration, technological progress is considered to exhibit significant inhibitory effects. Although most extant research has focused on energy technologies or total factor productivity, due to limitations in data and methods, few scholars have focused on emission reduction technological changes at a city-level scale. Therefore, based on the combination of k-means clustering and the log-mean Divisia index method, this study estimates and explores the impact of PM2.5 emission reduction technology (PME) on the temporal changes and spatial differences of 262 Chinese cities’ PM2.5 concentration during 2003–2017. The findings show the following: (1) although the results based on econometric methods indicate that emission reduction technological changes decreased China’s city-level PM2.5 concentration, there were turning points in the yearly impacts, indicating that the improvements to emission reduction efficiency were not stable; (2) compared with PME, energy intensity played a more stable role in PM2.5 emissions reductions, implying that the improvement of energy efficiency was still very important in controlling PM2.5 concentrations; (3) based on the classified groups after clustering, most cities’ PME contributed to negative differences, but the PME of a small number of cities was very weak to largely lower the average level of their group; and (4) distributions of the spatial decomposition of the three classified groups were stable in the period of 2003–2017, implying that the catch-up and transcendence effects of PME within the group were limited. Thus, policymakers should focus on the impact of different policies on PME differences between cities.

Measuring embodied carbon dioxide of the logistics industry in China: based on industry stripping method and input-output model.

Due to the lack of carbon dioxide (CO2) emission data on China's logistics industry, this study uses the industry stripping method to calculate the embodied energy consumption CO2 emissions based on input-output tables for China's logistics industry from 1997 to 2017. And then the Log-Mean Divisia Index method is used to decompose the influencing factors of carbon emission from five aspects. The empirical study mainly focused on the application of carbon emission tools and approaches in China, and this resulted in four key findings. First, the use of transportation, warehousing, and postal industries as a proxy for the logistics is simply not in line with reality and could result in greatly underestimating the CO2 emissions of logistics. Second, the annual direct CO2 accounts for roughly 40% in the embodied CO2 emissions in the logistics industry. Third, construction industry makes a greater contribution to the embodied CO2 of the logistics industry, followed by manufacturing. Fourth, economic output, population size, industrial structure, and energy structure are factors that contributed to the increase of logistics CO2 emissions, while the restraining factors included energy intensity. There is immense scope for adjustment in the energy and industrial structures.

Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis.

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector

China is now facing the challenge of modernizing its agricultural sector while reducing agricultural energy intensity to slow down the growth of agricultural energy consumption. This paper aims to investigate the determinants of changes in agricultural energy intensity (CAEI) in China. Specifically, this paper first uses the Log-mean Divisia Index method to decompose CAEI into changes in efficiency component (CEC) and changes in structure component (CSC) based on national time-series data during 1981–2017 and then employs panel data models with fixed effects to discern the determinants of CAEI, CEC, and CSC based on provincial data during 1991–2015. The results are as follows: (1) CEC dominates CAEI with a correlation coefficient of 0.9927, while the impact of CSC is limited; (2) an inverted U-shaped relationship exists between CEC and income. Capital and labor significantly improve CEC, while the impact of energy price is significantly negative; (3) a U-shaped relationship is observed between CSC and income. Capital and labor significantly decrease CSC; (4) a U-shaped relationship exists between CAEI and capital. Energy price and income significantly reduce CAEI, while labor has the opposite effect. Finally, it is suggested to improve farmers' environmental awareness, deepen agricultural capital accumulation, and deregulate rural energy prices.

Research on carbon productivity of the construction industry in Beijing-Tianjin-Hebei region using system dynamics model

In this paper, the carbon productivity of construction industry in Beijing-Tianjin-Hebei is researched to guide regional low-carbon development. First, the log mean divisia index method is adopted ...

Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition.

To figure out which factor contributes more on carbon emissions caused by energy consumption, this research took multisector analysis based on the Log-Mean Divisia Index Method (LMDI) and decoupling theory to assess the driving factors of carbon dioxide (CO2) emissions in China's six sectors from 2003 to 2016. Our empirical results reveal that China's economy can be divided as three decoupling stages and exhibited a distinct tendency toward strong decoupling with a turning point in 2008. Thus, we discuss the impact of 2008 economic crisis on carbon emissions based on decomposition results. The empirical results of our study show the following five conclusions. (1) Most sectors in China are in weak decoupling state due to the inhibition of energy intensity on carbon emissions. (2) Different factors contribute differently to reducing emissions in different sectors, economic output has the most prominent effect, followed by energy intensity and population scale. (3) China's current carbon emission reduction measures benefit more on energy efficiency. (4) The economic crisis has greatly reduced energy efficiency and has no significant impact on other factors. (5) If all industries adjust their energy mix, carbon emissions in China can be reduced by almost 17% every year.

Tourism and electricity consumption in 9 European countries: a decomposition analysis approach

ABSTRACT Tourism is a major economic activity constituting one of the main sectors in economic terms. European countries traditionally play a significant role in the overall international tourism flow. However, while tourism has a noticeable positive impact on economic development, it also contributes to environmental degradation by increasing energy consumption and therefore emissions. This paper analyses the relationships between Hotel and Restaurant electricity consumption and tourism growth in 9 European countries during 2004–2012, for which there is a sufficient amount of data available. A decomposition analysis based on log-mean Divisia index method (LMDI I) is conducted to examine electricity consumption on this sector and their components. Five decomposition factors are considered: energy intensity (EI), physical capital intensity (KI), physical and human capital relationship (KL), human capital intensity (LI) and the tourism factor (T). Results show the evolution of energy consumption related to this sector, identifying the driving forces that have influenced it.

A comparison of decomposition the decoupling carbon emissions from economic growth in transport sector of selected provinces in eastern, central and western China

The transport sector is steadily becoming a new major source of carbon emissions in China. A better understanding of decoupling economic outputs from carbon emissions in the Chinese transport sector could facilitate the country's move to low-carbon transport. This study combines the Tapio decoupling model and Log Mean Divisia Index method to develop a decoupling effort model, in order to quantify the state of decoupling between economic outputs and carbon emissions, investigate the effects of carbon emissions, and evaluate these decoupling efforts in the transport sector of six provinces in eastern, central and western China. The results show that: (i) the eastern region had achieved stable decoupling; the development of the central and western regions was imbalanced, with frequent flux in their states of decoupling; (ii) the pattern of driving factor of transport carbon emissions in three eastern, central and western was similar. The economic scale effect was the primary contributor to carbon emissions, while the energy intensity effect was the main inhibitor. The influence of the carbon intensity effect and the population effect was found to be quite weak; (iii) the three economic regions had all undertaken efforts to achieve decoupling, with the eastern region ranking first in the extent of these efforts, and the western region ranking last; (iv) the energy intensity was a weak effect in decoupling effort, indicating that their energy utilization efficiency is not optimal, and that there still exists considerable room for improvement. Finally, some policy recommendations are proposed.

Climate co-benefits of alternate strategies for tourist transportation: The case of Murree Hills in Pakistan.

This study explores the climate impacts of on-road tourist transportation with alternate mitigation strategies. To this end, greenhouse gas (GHG) emissions for 2016 and emissions under four "what-if" scenarios were estimated for a popular tourist site in Pakistan, i.e., Murree Hills, using the international vehicle emissions model. Alternate scenarios included occupancy optimization, bus transit system, and Euro II and Euro IV implementation. The emissions were further decomposed using the log mean Divisia index method to study the drivers of global warming potential (GWP) mitigation. As per the results, the total 20-year GWP for 2016 was equal to 51,262 tons CO2 equivalent, and maximum reduction was achieved under the bus transit system scenario having a 20-year GWP of 25,736 tons CO2 equivalent, i.e., 49.8% reduction. Relative to the base year, GWP reductions were also quite significant for Euro IV (46.8%), Euro II (45.8%), and occupancy optimization (32.3%) scenarios. For the base year, CO2 held a share of 87.3% in total emissions; however, its share in the 20-year GWP was 39.7% indicating its reduced impact on total GWP as compared to N2O, CO, NOx, VOC, and CH4. Based on the decomposition results for alternate scenarios, GWP mitigation was mainly driven by CO, CH4, NOx, VOCs, and partially by CO2, while N2O negatively affected GWP mitigation. These results provide several policy-level instruments for developing countries to design a transition to an eco-friendly tourist transport management system. The policy implications from this study can be used to promote an eco-tourism industry.

Determinants analysis of carbon dioxide emissions in passenger and freight transportation sectors in China

To identify the similarities and differences of the role that driving factors playing in carbon dioxide emissions between passenger and freight transportation sectors, this work uses an LMDI (Log-Mean Divisia Index) method to decompose factors that influence carbon dioxide emissions in China’s passenger and freight transportation sectors between 1990 and 2015. The results indicate that: (1) Carbon dioxide emissions of passenger and freight transportation underwent growth from 1990 to 2015; (2) Per capita GDP, and population promoted the increase of carbon dioxide emissions for both passenger and freight transportation, and transportation structure promoted the increase of carbon dioxide emissions for freight. Among them the economic factor made the biggest contribution; (3) Activity intensity restrained the increase of carbon dioxide emissions and transportation structure restrained the increase of carbon dioxide emissions for passenger.

Exploring the influencing factors and decoupling state of residential energy consumption in Shandong

Abstract This paper aims at analyzing the current situation of residential energy consumption in Shandong, identifying the influence factors governing energy consumption based on the LMDI (Log Mean Divisia Index) method, and describing the decoupling relationship between residential energy consumption and residential income based on the Tapio decoupling method. The main results are as follows: (1) The residential energy consumption structures of urban and rural have become multilevel. Furthermore, the gap in per capital residential energy consumption between urban and rural regions narrowed. (2) The energy intensity had an obvious inhibitory effect on decreasing urban residential energy consumption. Per capital income and population structure increased the urban residential energy consumption over the study period. However, per capital income and energy intensity played a more significant role in promoting rural residential energy consumption. And the population structure decreased rural residential energy consumption. During the study period, the contributions from both energy consumption structure and total population effect were negligible. (3) Since 2000, the decoupling index for both urban and rural resident gradually declined, which also shows that residential energy consumption in urban and rural regions was gradually less depend on residential income.

How can Chile move away from a high carbon economy?

This paper quantitatively evaluates the performance of Chile's CO2 emissions between 1991 and 2013 using a ‘complete decomposition’ technique to examine emissions and their components. A decomposition analysis based on log-mean divisia index method (LMDI I) was conducted. Six decomposition factors were considered: Carbon Intensity effect (CI), RES penetration effect (RES), Energy Intensity effect (EI), Economy Structure effect (ES), Income effect (Yp) and Population effect (P). To know how these factors could influence each other in the future, the Innovative Accounting Approach (IAA) was used, including forecast error variance decomposition and Impulse Response Functions (IRFs).These two methodologies allow us to identify the drivers of CO2 emission changes in the past (1991–2013), test policy measures and learn how these drivers could influence each other in the future, to evaluate whether the current measures meet the Paris commitments. The LMDI analysis results show that the Energy Intensity Factor is the main compensating factor of Chile's CO2 emissions and the only effect with a clear trend to aid the economy's decoupling. IAA and IFRs results react similarly and confirm that carbon intensity reacts to shocks more significantly in the short term. The reaction to RES has the same and opposite behavior to shocks in ES and Yp, to disappear in the long term.

The log mean divisia index based carbon productivity in the Australian construction industry

Environmental protection and economic growth are two indicators of sustainable global development. This study aims to investigate the performance of environmental protection and economic growth by measuring carbon productivity in the construction field. Carbon productivity is the amount of gross domestic product generated by the unit of carbon emissions. The log mean Divisia index method is used to investigate influential factors including carbon intensity, energy intensity and regional adjustment that impact on changes of carbon productivity. The study utilises a range of data from the Australian construction industry during 1995-2004 including energy consumption, industry value added and carbon dioxide equivalent consumption. The research indicates carbon productivity in the Australian construction industry has clearly increased. Energy intensity plays a significant positive role in promoting carbon productivity, whereas carbon intensity and regional adjustment have limited influence. Introducing advanced construction machinery and equipment is a feasible pathway to enhance carbon productivity. The research method is generic and can be used to measure other performance indicators and decomposing them into influential factors.

Using a new decoupling indicator (ZM decoupling indicator) to study the relationship between the economic growth and energy consumption in China

The decoupling analysis has become an important tool to explore whether an economy is becoming less dependent on energy resources. Based on the LMDI (Log-Mean Divisia Index) method, this paper defines a new decoupling indicator (ZM decoupling indicator), which depicts the relationship between energy saving influence factors and energy driving influence factors. Then, the ZM decoupling indicator is utilized to explore the state of decoupling between economic growth and energy consumption in China. The main results are as follows: (1) The gap of economic structure between the secondary industry and tertiary industry gradually narrowed during the study period 1991–2012. (2) The economic growth effect (\(\Delta E_{\text{g}}^{t}\)) was the critical factor in the growth of the final energy consumption in China. However, the energy intensity effect (\(\Delta E_{\text{ei}}^{t}\)) played an important role in decreasing the final energy consumption. (3) Based on the definition of ZM decoupling indicator, only four decoupling statuses occurred in China over the study period: weak decoupling, expansive coupling, strong decoupling, and expansive negative decoupling.

Regional disparity analysis of Chinese freight transport CO2 emissions from 1990 to 2007: Driving forces and policy challenges

Freight transportation and logistics act as the artery of the national economy. With a booming economy, China's freight transport sector has experienced dramatic growth in recent decades and has become a key driving force of China's CO2 emissions. Therefore, effective and efficient mitigation policies in the freight transport sector are critical for China to promote CO2 emission mitigation strategies. In contrast to other countries, China's challenge stems not only from technical issues but also from regional socioeconomic disparities, which in turn require the implementation of locally oriented policies. For this reason, an analysis based on regional disparity is of vital importance for future policy making. However, to date, there have been few pertinent studies on the freight transportation sector. To fill this gap, this paper aims to conduct an in-depth comparative study of CO2 emission characteristics and the driving forces in the freight transport sector in China's three regions (covering 31 provinces) from 1990 to 2007. The log mean Divisia index method (LMDI) is employed to analyse the driving forces, and the Gini coefficient is used to investigate regional inequity. Additionally, regional disparity is explored in-depth based on the analytical results and practical concerns. The results highlight that economic structure is a key driving force for emissions change and reveal significant regional disparity and inequity in freight transport emissions. The results are critical for future policy-making to address regional concerns.

How to Move China toward a Green-Energy Economy: From a Sector Perspective

With China’s rapid economic growth, energy-related CO2 emissions have experienced a dramatic increase. Quantification of energy-related CO2 emissions that occur in China is of serious concern for the policy makers to make efficient environmental policies without damaging the economic growth. Examining 33 productive sectors in China, this paper combined the extended “Kaya identity” and “IPAT model” with the Log-Mean Divisia Index Method (LMDI) to analyze the contribution of various factors driving of energy-related CO2 emissions in China during 1995–2009. Empirical results show that the main obstacle that hinders China’s transition to a green energy economy is the economic structure characterized by high carbon emissions. In contrast, the increased proportion of renewable energy sources (RES) and the improvement of energy efficiency play a more important role in reducing carbon emissions. Moreover, the power sector has a pivotal position in CO2 emissions reduction, primarily because of the expansion of electricity consumption. These findings suggest that policies and measures should be considered for various industrial sectors to maximize the energy efficiency potential. In addition, optimizing the industrial structure is more urgent than adjusting the energy structure for China.

Decomposition analysis of energy-related CO2 emissions in South Africa

South Africa has become one of the most developing countries in the world, and its economic growth has occurred along with rising energy-related CO2 emission levels. A deeper understanding of the driving forces governing energy-related CO2 emissions is very important in formulating future policies. The LMDI (Log Mean Divisia Index) method is used to analyse the contribution of the factors which influence energy-related CO2 emissions in South Africa over the period 1993-2011. The main conclusions drawn from the present study may be summarized as follows: the energy intensity effect plays the dominant role in decreasing of CO2 emission, followed by fossil energy structure effect and renewable energy structure effect; the economic activity is a critical factor in the growth of energy-related CO2 emission in South Africa.