Logarithmic Mean Divisia Index Technique Research Articles

Predicting energy consumption through the LEAP model based on LMDI technique along with economic analysis: A case study

This research reports the implementation of logarithmic mean Divisia index (LMDI) and categorizes the growth of total energy usage in three different industrial sectors for the years of 2010 to 2021. Furthermore, it classifies and evaluates the factors influencing on energy consumption in Punjab province thru a sustainable way. The growth consumption is classified into scale influence, structure influence, and efficiency influence. Likewise, the long-term energy alternatives planning-Punjab model is executed with the energy consumption, scale impact, structure impact, and efficiency impact. Besides, comprehensive adjustment scenarios are also introduced to examine the impact of three different factors on overall energy usage. The results from the qualitative decomposition of LMDI indicate that the high scale can lead to high-energy consumption in Punjab Province. However, it can be reduced by high-efficiency reinforcement. The total energy consumption in 2024, 2036, and 2044 under reference scenario is 304.12, 460.01, and 590.04 million tons compared to structure influence analysis for slow terminology (SIAS) and comprehensive scenario. For that reason, it can predict and provide earlier energy management planning for the province. Conversely, the structure factor does not display obvious effect on the energy use. Equally, the quantitative results of the long-term energy alternative planning (LEAP) model are relatively consistent with those of LMDI model, whose advantageous impact on the structure influence is reasonably extrapolated. This phenomenon indicates that the structure influence and efficiency influence will maintain the disruptive impact on increasing overall energy use for the future perspectives. Consequently, the LEAP model predicts the energy consumption of Punjab Province among the years of 2020 to 2040 under medium-term development framework.

Decoupling and mitigation potential analysis of CO2 emissions from Pakistan's transport sector

The transport sector has become one of the major economic, huge fossil fuel energy consumption, and carbon dioxide (CO2) emitting sector of Pakistan. This study applies the logarithmic mean Divisia index (LMDI) and Tapio's decoupling approach to estimate decoupling state and mitigation potential of CO2 emissions from the transport sector during 1984–2018. LMDI technique is applied to detect the influencing variables (i.e. carbon coefficient, fuel consumption, total energy consumption, and turn over economy), which oversee CO2 emissions. The outcomes show that CO2 coefficient effect is the factor which is decreasing CO2 emissions while economic growth (EG) effect is the factor which is growing CO2 emissions. The decoupling index is also applied to influencing factors which reflect the EG factors on CO2 emissions from the transport sector. The consequences confirm that during 1984–2018, the CO2 emissions show an expensive coupling with EG. Weak decoupling occurred only in the sub-periods 1999–2003, 2004–2008, and 2009–2013. Similarly, the CO2 emissions occurred from only three decoupling grades. Furthermore, a mitigation model based on the above impacting variables estimates the mitigation rate of CO2 emissions and showed that the CO2 mitigation seemed in 1999–2003, 2004–2008, and 2009–2013. Finally, forecasting outcomes of Tapio decoupling index show a weak decoupling during 2018–2030. Therefore, based on the empirical outcomes, this study puts forward a few policy suggestions to efficiently enhance the decoupling between Pakistan's transport CO2 emissions and EG.

Analysis of the EU Residential Energy Consumption: Trends and Determinants

This article analyses the status and trends of the European Union (EU) residential energy consumption in light of the energy consumption targets set by the EU 2020 and 2030 energy and climate strategies. It assesses the energy efficiency progress from 2000 to 2016, using the official Eurostat data. In 2016, the residential energy consumption amounted to 25.71% of the EU’s final energy consumption, representing the second largest consuming sector after transport. Consumption-related data are discussed together with data on some main energy efficiency policies and energy consumption determinants, such as economic and population growth, weather conditions, and household and building characteristics. Indicators are identified to show the impact of specific determinants on energy consumption and a new indicator is proposed, drawing a closer link between energy trends and policy and technological changes in the sector. The analysis of these determinants highlights the complex dynamics behind the demand of energy in the residential sector. Decomposition analysis is carried out using the Logarithmic Mean Divisia Index technique to provide a more complete picture of the impact of various determinants (population, wealth, intensity, and weather) on the latest EU-28 residential energy consumption trends. The article provides a better understanding of the EU residential energy consumption, its drivers, the impact of current policies, and recommendations on future policies.

Analysis of energy related CO2 emissions in Pakistan

CO2 (carbon dioxide) emissions from fossil fuels have turned out one of the most perilous and complex issues driving the climate change debate. The issue of increasing demand for electricity at the same time reducing CO2 emissions has become important worldwide. This study evaluates the CO2 emissions from the power sector of Pakistan during 1978–2017 using Logarithmic Mean Divisia Index technique and scenario analysis. We estimate LMDI to check the change under prearranged elements. According to results, the shift in population, activity effects, and gross domestic product are the essential factors in charge of the increase in CO2 emissions while carbon intensity and energy intensity effects participate in cutting emissions. Finally, scenario analysis found overall activity increases due to population and intensity of CO2 emissions, but carbon intensity could be reduced by using cleaner energy technologies. The findings suggest that CO2 emissions will possibly arrive at 277.9 Mt in 2035 in Pakistan. In relation with the main findings, some targeted policies suggestions are provided.

Factor analysis of projected carbon dioxide emissions according to the IPCC based sustainable emission scenario in Turkey

The Greenhouse gas emissions one of the hot topics all around the world which are causing climate change. In the face of anthropogenic climate change, increasing the carbon dioxide emissions as well as the development of Turkey seems to be a serious challenge for the success of global carbon dioxide emission reduction efforts. A decomposition analysis of historical and projected carbon dioxide emissions from fossil fuel combustion of Turkey has been investigated by using the Logarithmic Mean Divisia Index technique by considering in particular carbon intensity, energy mix, energy intensity, affluence and population effects. The projected data have been obtained from International Panel on Climate Change based sustainable emission scenario. The results show that the all effects on carbon dioxide emissions were positive for historical evaluation. On the other hand, carbon intensity, energy mix and energy intensity effects on carbon dioxide emissions were negative while affluence and population effects were still positive for projected evaluations. Therefore, decision makers should be reconsider the carbon dioxide emissions reduction targets and some related policies.

Driving forces underlying sub-national carbon dioxide emissions within the household sector and implications for the Paris Agreement targets in Japan

This study investigated insights into reducing energy-related CO2 emissions in households by examining individual socio-economic drivers at a sub-national level. Specifically, the logarithmic mean Divisia index technique was used to decompose CO2 emission trends into six drivers in all 47 prefectures of Japan during the period from 1990 to 2015. Drivers included the change in the number of households (household effect), distribution of households (distribution effect), household size (size effect), per-capita household energy consumption (consumption effect), household energy choice (choice effect), and sectoral CO2 emission intensity (intensity effect). The results showed that, in contrast to size and the distribution effects, the number of households had a positive, significant effect on CO2 emissions, indicating that recent demographic trends are responsible for the increase in CO2 emissions observed in most of the prefectures during the study period. With regard to effects related to consumption and choice, CO2 emissions due to changes in lifestyle dropped in only seven prefectures and reductions due to changes in sectoral energy choice were seen in only two prefectures in 2015. The intensity effect boosted the emissions of these prefectures the most in 2015 because of the shutdown of nuclear power plants due to the Great East Japan Earthquake. Further, we identified those prefectures that needed to reduce their per-capita energy consumption level in order to attain the reduction targets for household CO2 emissions in 2030 from 2015, given projected changes in demographic trends and recent and projected emission intensities. In order to achieve reductions in total CO2 emissions in line with the Paris Agreement, it is important to prioritize national and local policy interventions for the transfer of new household energy technologies, upgrade household appliances, and encourage people to limit energy consumption in light of the differences in these key drivers in each prefecture.

Socio-Demographic Drivers of Residential Co2 Emissions in the 47 Prefectures of Japan

This study examined which socio-economic drivers contributed to energy-related CO2 emissions in Japanese households in all 47 prefectures since 1990. We adopted the logarithmic mean Divisia index technique to decompose the prefectural trends in CO2 emissions during the period 1990-2013 into six drivers: change in the number of households (household effect), distribution of households (distribution effect), household size (size effect), per-capita household energy consumption (consumption effect), household energy choice (choice effect), and sectoral CO2 emission intensity (technology effect). The results showed that, in contrast to the size effect, the household effect and the distribution effect both had positive and significant effects on CO2 emissions, indicating that these recent demographic trends were responsible for the increase in CO2 emissions observed in most of the prefectures during the period. Regarding the consumption effect and the choice effect, i.e., changes in CO2 emissions due to changes in lifestyle and technology, only ten and seven prefectures reduced their CO2 emissions in 2013, respectively. Thus, in order to further reduce CO2 emissions through these key drivers among prefectures, it is considered necessary to prioritize the transfer of new household energy technologies, upgrade household appliances, and encourage people to limit energy consumption.

Investigating Low-Carbon Agriculture: Case Study of China’s Henan Province

Developing low-carbon agriculture requires investigating the trajectory, decoupling statuses, and driving forces of agricultural carbon emissions. This study explored the evolution of agricultural carbon emissions based on 18 kinds of major carbon emission sources in Henan Province of China, which produces approximately one-tenth of China’s total grain output. We then analyzed the relationship between carbon emissions and economic growth using the decoupling elasticity model, and identified the factors driving the decoupling status. This analysis was done with a decoupling elasticity model, using the Logarithmic Mean Divisia Index technique. There were three key results: (1) Agricultural carbon emissions totaled 16.61 million tons in 1999, and increased by 7.99% to 17.93 million tons in 2014, with an average growth rate of approximately 0.65%; (2) The decoupling relationship between agricultural carbon emissions and economic output was dominated by weak decoupling during the study period; (3) Agricultural labor productivity was the leading contributor to changes in agricultural carbon emissions, followed by farming-animal husbandry carbon intensity, labor, and agricultural structure.

Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method

Uncovering the driving factors of CO2 emission intensity declining is important for China. This paper improves the logarithmic mean Divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995–2012. The main results are: (1) energy consumption intensity plays a more important role than carbon emission density (CD) for a rapid decrease in CI during the research period, so a much room is left for a significant CD reduction through carbon emission reduction technology, energy structural reduction, and energy consumption proportional reduction. (2) The decrease in energy consumption technology and energy structure in secondary industries contributes the most reduction in energy consumption intensity. (3)The energy consumption proportions of secondary and tertiary industries are the two most important drivers to decrease CD. (4) During the research period, the energy consumption proportions of secondary industries result in the most decrease in CD, whereas the energy consumption proportions of tertiary industries cause the most increase in CD.

Energy consumption and CO2 emissions in Eastern and Central China: A temporal and a cross-regional decomposition analysis

With heavy industrial structure moving westward, to make out the difference of low-carbon development between eastern and central China seems important. For this purpose, this paper compares several representative indicators about energy-related CO2 emissions among nine typical regions from 1990 to 2010. Then two forms of carbon intensity, namely, CO2 emissions per unit of GDP and CO2 emissions per capita are decomposed with a temporal and a cross-regional Logarithmic Mean Divisia Index technique. Thus the roles of driving forces for the changes in carbon intensity are analyzed by region during five study periods, and the main causes for the variation in carbon intensity between eastern and central China in 2010 are also compared. The analysis results show that central China has a challenge to keep the relative lower levels of these two forms of carbon intensity compared with that of eastern China, economic growth in central China needs to choose a low-carbon development road. In terms of eastern China, growth mode of Beijing and its surrounding areas should be integrally planned from the viewpoint of regional coordination. Finally, high-efficient utilization of coal should be promoted in heavy industrial sector for the mitigation of CO2 emissions in central and eastern China.

Index decomposition analysis of residential energy consumption in China: 2002–2010

Residential energy consumption in China increased dramatically over the period of 2002–2010. In this paper, we undertake a decomposition analysis of changes in energy use by Chinese households for five energy-using activities: space heating/cooling, cooking, lighting and electric appliances. We investigate to what extent changes in energy use are due to changes from appliances and to change in floor space, population and energy mix. Our decomposition analysis is based on the logarithmic mean Divisia index technique using data from the China statistical yearbook and China energy statistical yearbook in the period of 2002–2010. According to our results, the increase in energy-using appliances is the biggest contributor to the increase of residential energy consumption during 2002–2010 but the effect declines over time, due to energy efficiency improvements in those appliances. The second most important contributor is floor space per capita, which increased with 28%. Of the four factors, population is the most stable factor and energy mix is the least important factor. We predicted electricity use, with the help of regression-based predictions for ownership of appliances and the energy efficiency of appliances. We found that electricity use will continue to rise despite a gradual saturation of demand.

Socioeconomic Driving Factors of Nitrogen Load from Food Consumption and Preventive Measures

To diagnose environmental nitrogen (N) load from food consumption and to suggest preventive measures, this study identified relationships between nitrogen load from food consumption and driving factors by examining six representative countries and regions for the period 1970-2009 as an example. The logarithmic mean Divisia index technique was used to disassemble nitrogen load growth into four driving factors: population, economic activity, food intensity of the economy, and nitrogen content of food. In all study areas, increased economic activity was the main factor driving nitrogen load increase. The positive effect of population growth was relatively small but not negligible and changes in food intensity had a decreasing effect on nitrogen load. Changes in nitrogen content of food varied between areas. Broad strategies to reduce and mitigate nitrogen loading and decouple nitrogen load from economic growth in both developed and developing countries are suggested.

Energy CO2 Emissions for 1990–2020: A Decomposition Analysis for EU-25 and Greece

This article deals with the decomposition analysis of energy-related CO2 emissions in Greece and in the EU-25 from 1990 to 2020, by means of the Logarithmic Mean Divisia Index technique and a baseline scenario approach based on the PRIMES simulation modeling system for energy demand and supply. The Logarithmic Mean Divisia Index technique is used and changes in CO2 emissions are decomposed into four factors: income effect, energy intensity effect, fuel share effect, and population effect. For comparison reasons, we have included in our analysis results for three other countries (i.e., Ireland, Portugal, and Belgium) of similar size with Greece and varying energy mix and economy structure. Finally, a sectoral decomposition analysis of CO2 emissions for the Greek economy is applied.