Logarithmic Mean Divisia Index Analysis Research Articles

Energy consumption decomposition analysis using European official statistics: Methodology and input data

Deciphering energy efficiency is a critical component for the sustainability and energy policies of the European Union (EU) and its Member States. Decomposition analysis is a key method that helps distinguish real energy efficiency gains from other external factors. This article presents a decomposition analysis method using official EU statistics, its results, and associated limitations. Although separating structural changes and activity levels from energy efficiency poses a challenge, an adapted Logarithmic Mean Divisia Index (LMDI) method was utilised to isolate and highlight energy efficiency. This article outlines this method and how the European official statistics were exploited. Firstly, it examines the factors affecting energy consumption in various sectors within EU-27. Secondly, it examines the factors affecting energy consumption in various sectors. In doing so, it also addresses potential obstacles in data collection, and presents improvements to the LMDI analysis. The findings in this study make a substantial contribution to the fields of national statistics, methodological applications, and energy data analysis in the context of the EU’s energy policies.

Decomposition analysis of electricity generation on carbon dioxide emissions in Ghana

This study analyses the factors driving CO2 emissions from electricity generation in Ghana from 1990 to 2020. Employing Logarithmic Mean Divisia Index (LMDI) and Autoregressive Distributed Lag (ARDL) techniques, the research decomposes electricity generation into different factors and assesses their impact on CO2 emissions, considering both short and long-run effects. The LMDI analysis reveals that the total CO2 emissions from electricity generation amount to 3.33%, with all factors contributing positively in each subperiod. Notably, fossil fuel intensity, production, and transformation factors exhibit substantial contributions of about 1.16%, 0.49%, and 0.48%, respectively. Contrastingly, the ARDL results highlight that only electricity intensity and production factors significantly increase CO2 emissions by about 0.20% and 0.09% (0.38% and 0.10%) in the short-run (long-run), while other factors contribute to a reduction in electricity generation emissions. Overall, we conclude that electricity intensity and production factors are the primary drivers of CO2 emissions from electricity generation in Ghana. Nevertheless, effective measures to address all decomposition factors is crucial for effective mitigation of electricity generation CO2 emissions.

Exploring the spatiotemporal pattern evolution of carbon emissions and air pollution in Chinese cities

Based on data from 335 cities in China, this study employs the standard deviation ellipse method to portray unbalanced and differential spatiotemporal evolution patterns of environmental emissions and socioeconomic elements. A logarithmic mean Divisia index analysis and in-depth discussion are carried out to disclose the main driving factors and underlying reasons for the differences. Decoupling trends exist among carbon emissions, gross domestic product (GDP) and population in terms of their gravity center migrations. The standard deviation ellipse direction of carbon emissions gradually changed from ‘northeast‒southwest’ to ‘northwest‒southeast’, and the standard deviation ellipse areas of carbon emissions and air pollution continuously expanded over time; at the same time, that of GDP contracted. Economic growth has always been the main driver of carbon emissions and air pollution nationally, but its role has weakened. Moreover, decreases in the energy intensity and carbon and pollution intensities are the main factors contributing to emissions reductions. Differentiated spatiotemporal economic structure evolution, regional heterogeneities in the energy intensity and efficiency, and cross-region power energy transmissions are identified as the underlying reasons for the unbalanced spatiotemporal patterns of the environmental emissions and socioeconomic elements. Based on these findings, policy suggestions can be made to address the imbalances and promote carbon mitigation, air quality improvement and high-quality social-economic development at the city level.

Can China achieve ecological sustainability? An LMDI analysis of ecological footprint and economic development decoupling

Nowadays, economic development and ecological environment protection are difficult to synergize, which poses a challenge to global sustainable development. This paper uses the Tapio decoupling model to evaluate the decoupling between ecological footprint and economic growth, and uses the Logarithmic Mean Divisia Index (LMDI) decomposition methods to identify the driving factors of decoupling at national and provincial levels. There are four driving factors: industrial structural, technological innovation, economic development, and population size. It is found that: (1) The ecological footprint of 28 provinces show an upward trend, with an average increase of 40.26%. Among them, the ecological footprint of three provinces in the northwest region increased by more than 100%, and there is an urgent need to reduce natural resource consumption and ensure resource sustainability; (2) The number of provinces with good decoupling show a change of first declining and then rising. Moreover, the spatial distribution characteristics of the decoupling state are obvious, with the northeastern region in negative decoupling; and (3) Technological innovation plays a positive and leading role in decoupling, with a contribution rate of 0.5402. However, economic development inhibits decoupling, with a contribution rate of 0.3829. According to the results, this paper suggests countermeasures and approaches to promote the decoupling of ecological footprint and economic growth, and establishes three key regional concerns. It provides a basis for further analysis of environmental governance in China. Not only that, the regulatory measures and methods proposed in this study will also help promote the decoupling process of other developing countries in the world.

Factors driving aggregate service sector energy intensities in Asia and Eastern Europe: A LMDI analysis

The economywide energy intensities in the service sectors are declining in many countries worldwide. We identify the drivers of the declining trends by employing the Logarithmic Mean Divisia Index (LMDI) on annual data from 16 countries in the Asia and Eastern Europe for the 2000–2014 period. We find that the change in fuel mix has little contribution to driving the economywide energy intensity of the service sector down during the study horizon. Instead, the change in energy intensity contributes to a decrease in economywide energy intensity of service sectors in most countries except the Czech Republic, Estonia, Latvia, and Turkey. Moreover, since energy intensity is inseparable from economic development, changes in economic structure are an essential determinant of the economywide energy intensity of service sectors. This work also analyzes the sectoral attribution and energy feedstocks attribution of economywide energy intensity of the service sectors.

Multi-Scale Analysis of the Evolution of Jiangsu’s Ecological Footprint Depth and Its Factor Decomposition

The ecological footprint (EF), as a set of land-based ecological indicators, plays an important role in land ecology and evaluations of ecological pressure. Multi-scale levels of Jiangsu’s three-dimensional EF were analyzed, and 3D maps were presented to demonstrate the geographical distribution of the ecological footprint depth (EFD) of Jiangsu’s counties in 1995–2015 at the geographic scales of prefecture-level cities and counties. The results show that the overall EFD of Jiangsu gradually increased during the study period. The county-scale results show that the distribution of EFDs was high in the south and low in the north, and EFDs were mainly concentrated in urban areas of prefecture-level cities. The logarithmic mean Divisia index (LMDI) was used to decompose the factors in explaining the change in EFD. The LMDI analysis shows that the changes in factors every year differ among geographical units on different scales. Affluence is the main factor that promotes EFD, and the change in the ratio between EFD and scientific and technological level is the main factor that suppresses EFD. Countermeasures and suggestions for balancing ecological pressure in specific regions and reducing the depth of the EF from various factors with multi-scale heterogeneity are suggested.

Investigating the residential electricity consumption-income nexus in Morocco: a stochastic impacts by regression on population, affluence, and technology analysis

In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given.

Comparison of end-of-life vehicles management in 31 European countries: A LMDI analysis.

In 2018, a total of 6,083,000 end-of-life vehicles (EVLs) with a total weight of 6,732,000 tonnes (including waste parts) were generated in the European Union. These vehicles are a subject of particular attention because of their bulky mass, the hazardous components they contain, and the valuable materials present. This article analyses the quantities of ELVs and parts of ELVs that have been recycled, energy recovered, disposed, and reused to assess the impact of the changes in these management options on the total quantities managed. The analysis covered the statistical data on EVLs in 31 European countries for the period 2006-2018, using a new extended version of the logarithmic mean divisia index (LMDI) analysis model, which allows analysts to compare and analyse different waste management options simultaneously. The results show that the changes in the waste intensities and household final consumption expenditure were the greatest drivers of changes in the total quantities managed, while changes in the quantities recycled, energy recovered, disposed of, and reused had the least impact. All countries met or were close to meeting the targets during the period analysed. Changes in the total quantities of waste managed depended on the economic activity, which influenced the quantities of ELVs generated and their subsequent management.

Drivers of changes in natural resources consumption of Central African countries

<abstract> <p>Consumption of nine different natural resources has kept an increasing trend in Central African countries from 1970 to 2018. This study therefore, investigates the changes and major determinants that have driven the patterns of resource use in six Central African countries over almost fifty years. We used the logarithmic mean Divisia index (LMDI) method to quantitatively analyze different effects of technology, affluence and population associated with domestic material consumption (DMC) of Cameroon, Chad, Central African Republic, Equatorial Guinea, Democratic Republic of the Congo and Gabon from 1970 to 2018. We further subdivided the affluence effect into energy productivity (GDP/energy) and per capita energy use (energy/cap) and conducted a four-factor LMDI analysis of Cameroon as a case study. The results highlight that decreased affluence during certain periods has slowed down DMC growth in four of six Central African countries except for Cameroon and Equatorial Guinea, while significant technology offset in Equatorial Guinea reduces DMC growth by 28%. Population remains the main positive driving factor of DMC growth, with the highest share in the Democratic Republic of the Congo. The case of Cameroon shows that technological intensity and energy intensity play different roles in changing DMC. This study confirms that the rising population and economic growth, combined with a gradual improvement in technology in the region are insufficient to reduce natural resource use. A stringent management plan of natural resources for Central African countries should focus on technological improvement while remaining balanced with the future demand for socioeconomic development in the coming decades.</p> </abstract>

Carbon emissions and electricity generation modeling in Saudi Arabia.

Fossil fuel electricity generation in Saudi Arabia increased greatly from 1980 to 2017. This paper aims to quantify the electricity generation effect on the environmental quality of Saudi Arabia and explore the role of energy-efficient technological innovation. A structural time series model (STSM) to estimate long-run elasticities and logarithmic mean Divisia index (LMDI) is employed. The results showed that variables (GDP, electricity generation, and population) have a significant effect on carbon dioxide (CO2) emissions. Also, the underlying energy demand trend (UEDT) showed an upward slope for the entire period, which suggests that over the study time there is no improvement in energy efficiency. In decomposing the factors for carbon emissions growth in Saudi Arabia, the findings of applying additive LMDI analysis showed a 1377.56 million tonne (MT) increase in CO2 emissions from the three factors between 1980 and 2017 in the country. The results of additive decomposition showed that the primary factor that drives the carbon emissions growth in Saudi Arabia was the structure effect. Saudi Arabian policymakers could make more informed decisions regarding electricity generation by focusing on increasing energy efficiency and demanding strict environmental regulations to contribute to sustainable economic growth.

How UK farewell to coal – Insight from multi-regional input-output and logarithmic mean divisia index analysis

Exploring the drivers of the decline in coal consumption in the United Kingdom (UK) may provide useful lessons for other countries, which want to control coal consumption and reduce carbon. This study investigated the key drivers of the decline in UK's production- and consumption-based coal consumptions from a global perspective, by combining the multi-regional input-output analysis and Logarithmic Mean Divisia Index methods. From a global view, both consumption- and production-based coal consumptions of UK declined during 1995–2009, except consumption-based coal consumption increased from 1995 to 2007. As a net importer, UK's coal consumption flows have been mainly embodied in exports for developed countries and embodied in imports from developing countries. On a national level, the offset effects of UK's coal consumption were from the energy transformations that reduced the proportions of coal consumption and general developments in the energy efficiency. It is found that its decline of coal consumption was only the superficial success, as the decrease actually was partly due to the transfers to other countries. But there are also lessons to be learned from that, because UK had also ultimately achieved the goal of using less energy consumption to generate higher gross domestic production, by technological improvements to promote an improvement in energy intensity. Therefore, coal consumption declined in the UK was the result of the combined effects of energy transition and outsourcing.

Understanding Technology, Fuel, Market and Policy Drivers for New York State's Power Sector Transformation.

A thorough understanding of the drivers that affect the emission levels from electricity generation, support sound design and the implementation of further emission reduction goals are presented here. For instance, New York State has already committed a transition to 100% clean energy by 2040. This paper identifies the relationships among driving factors and the changes in emissions levels between 1990 and 2050 using the logarithmic mean divisia index analysis. The analysis relies on historical data and outputs from techno-economic-energy system modeling to elude future power sector pathways. Three scenarios, including a business-as-usual scenario and two policy scenarios, explore the changes in utility structure, efficiency, fuel type, generation, and emission factors, considering the non-fossil-based technology options and air regulations. We present retrospective and prospective analysis of carbon dioxide, sulfur dioxide, nitrogen oxide emissions for the New York State’s power sector. Based on our findings, although the intensity varies by period and emission type, in aggregate, fossil fuel mix change can be defined as the main contributor to reduce emissions. Electricity generation level variations and technical efficiency have relatively smaller impacts. We also observe that increased emissions due to nuclear phase-out will be avoided by the onshore and offshore wind with a lower fraction met by solar until 2050.

A LMDI decomposition analysis of carbon dioxide emissions from the electric power sector in Northwest China

AbstractTaking advantage of the electrification strategy, Northwest China has made full use of its natural resources endowment, to develop renewable energy as the substitution of thermal power. To evaluate carbon dioxide (CO2) emissions from electric power sector, an extended Kaya identity equation and the Logarithmic mean Divisia index decomposition method are applied to Northwest China from 1998 to 2017. Six explaining factors are analyzed, including carbon intensity, energy mixes, generating efficiency, electrification, economy and population. The results show that driving forces of CO2 emissions from electricity system varied greatly among provinces. Generally, economic growth has mainly contributed to increase CO2 emission, while the improvement in the power‐generating efficiency has crucially decreased CO2 emission. In 2017, Promoting electrification directly increased CO2 emissions from electric system, but indirectly reduced CO2 emissions from the whole region by 5.10% through the estimation of a clean development mechanism method. Therefore, local governments are suggested continuing to promote electrification to guide future emission reduction, while enterprises and individuals need to make their own contributions to low‐carbon development.Recommendations for Resource Managers: Variations of carbon dioxide (CO2) emissions of all five provinces in Northwest China are analyzed. Logarithmic mean Divisia index analysis is used to study the main drivers of CO2 emission change. Improvements in the generating efficiency significantly reduced CO2 emissions. Due to electrification effects, CO2 emissions from electric power increased, but CO2 emissions from the region decreased. Economy effects were still the biggest drivers affecting CO2 emission.

Will China's fertilizer use continue to decline? Evidence from LMDI analysis based on crops, regions and fertilizer types.

China implemented the Action Plan for the Zero Increase of Fertilizer Use in 2015, which led to a decrease in fertilizer use. However, Will fertilizer use continue to reduce? With data obtained from 2006 to 2017, the paper used the logarithmic mean Divisia index (LMDI) method to analyze the scale effect, intensity effect and structural effect of fertilizer use change in China from three aspects: crops, regions and fertilizer types. Our finding suggests that (1) The intensity effect was the most critical factor affecting the decline in fertilizer use in China. (2) The sowing scale and fertilization intensity of grain, vegetables and fruits had the most significant driving effect on fertilizer reduction. (3) The three effects of each region were different in space, and the eastern region contributed most to the fertilizer decrement. (4) Nitrogen fertilizer and compound fertilizer had the most considerable influence on fertilizer reduction, especially in the sowing scale and fertilization intensity since 2009. The government should establish a fertilizer reduction management system, which includes scale control, intensity reduction, structural adjustment and other measures.

Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016.

As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008–2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity.

Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

What accounts for the China-US difference in solar PV electricity output? An LMDI analysis

China's solar PV installation has seen dramatic growth since 2008. Although in 2015 China's cumulative installed capacity of solar PV was much higher than that of the US, the actual quantities of solar PV electricity output in the two countries were very close. Using Logarithmic Mean Divisia Index (LMDI) analysis approach, this study examined the factors leading to the unsatisfactory performance of Chinese solar unit PV electricity output compared to that of the US. It is found that the differences between China and the US solar PV electricity output in 2015 were mostly caused by solar system efficiency, followed by curtailment issues and unfavorable solar resources. The poor quality of solar panels, and of their operation and maintenance, combined with heavy pollution and smog have lowered the solar system efficiency in China. China's curtailment arises from the low proportion of distributed solar PV (DSPV) and the existing power sector regime that is heavily regulated by administrative planning. Policy implications are to improve solar PV system efficiency, increase the proportion of DSPV installation, enhance the support for energy storage development, and remove institutional constraints on renewable energy integration.

Comparative analysis on the socioeconomic drivers of industrial air-pollutant emissions between Japan and China: Insights for the further-abatement period based on the LMDI method

Abstract Japan has achieved the decoupling of economic growth from air-pollutant emissions since the rapid decrease in air-pollutant emissions in the 1970s and the further-abatement period. Air-pollutant emissions also decreased in China recently. We analyzed the factors driving the changes in air-pollutant emission, which enabled us to identify each factor's contribution and compare the further-abatement period in Japan with the primary reduction stage in China. This study performed the Logarithmic Mean Divisia Index analysis to decompose the industrial-emission changes (SO2 and NOx) in Japan and China into the socioeconomic factors that drive these changes. Results showed that changes in these factors, especially energy intensity and economic level, contributed differently to the emission reduction in Japan and China because the two countries differed in emission-reduction periods. The decline in emission coefficient, measured by emissions per unit of energy consumption, was the most important contributor to emission reduction in Japan. The other factors did not exert considerable influence. However, energy intensity decrease in China significantly contributed to emission reduction besides emission coefficient, and economic growth had a substantial negative impact on emission reduction. Differences also appeared in contributions from the industrial and economic structure between the two countries. Some factors may have reached their limitation after the rapid reduction period and thus contributed less significantly in the further-abatement period. A tendency toward a similar condition to Japan was also observed in some factorial contributions in China. From a sectoral perspective, emission change was distributed evenly among sectors in Japan than in China. Based on the comparison between the two countries and the “new normal” that China is experiencing, we provided insights for China for further abating industrial air-pollutant emissions in succeeding years.

How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes

A decoupling elasticity analysis and a two-level decomposition analysis of energy consumption in Colombia from 2000 to 2015 are developed. Firstly, the decoupling elasticity approach is used to analyse the importance of energy consumption changes in relation to the GDP changes. Then, a Logarithmic Mean Divisia Index analysis is carried out, decomposing the changes in energy consumption into four effects: Population, Activity, Structural and Intensity. Secondly, a decoupling index determines the main drivers of the inhibiting effect on energy consumption. The results show that the Population and Activity effects contribute to the country's increase of energy consumption, while the Intensity effect and, to a lesser extent, the Structure effect help to decrease it. From a sectoral perspective, variations in the energy consumption are mainly caused by the Transport and Industrial sectors. In the light of the results obtained, current decoupling-oriented measures are steps in the right direction, but more efforts should be made because until now they have not been effective. New policy recommendations are provided.

Spatio-temporal patterns of energy consumption-related GHG emissions in China's crop production systems

This paper aims to reveal the spatio-temporal patterns of energy consumption-related greenhouse gas (ECR-GHG) emissions in China's crop production systems (CPSs). The relevant crop production data from 31 provinces during 1997–2014 are utilized. In order to fully reflect the energy consumption and ECR-GHG emissions in CPSs, energy balance techniques are adopted from a consumption perspective. The driving factors behind ECR-GHG emissions are identified by means of a Logarithmic Mean Divisia Index analysis at both national and provincial levels. The results are as follows: (1) The yield of China's CPS is not positively correlated with energy consumption, and China's CPS has the relatively high potential to conserve energy and reduce ECR-GHG emissions; (2) Most of China's provinces have experienced enormous growth in ECR-GHG emissions; however there are relatively significant regional disparities; (3) ECR-GHG emissions from CPSs were mostly derived directly from the consumption of chemical fertilizers and diesel oil; (4) Areal productivity is the determining factor in the growth of ECR-GHG emissions, whereas the emission coefficient and energy mix are the main inhibiting factors; (5) Energy intensity has not achieved its full potential to decrease ECR-GHG emissions. This study provides insights into the potential for sustainable crop production in China.