Log Mean Divisia Index Decomposition Research Articles

A multiplicative log mean Divisia index decomposition analysis of energy‐related carbon emissions in Pakistan: From the perspective of economic development phases

AbstractTo successfully implement mitigation policies, it is first needed to identify the factors that contribute to carbon emissions. Based on the extended Kaya identity approach, this study investigates the driving factors of energy‐related carbon emissions in Pakistan from 1990 to 2019 using the multiplicative LMDI‐I decomposition technique with no residual term. The decomposition results reveal that the population and affluence contributed significantly to accelerating carbon emissions from energy consumption across all the sub‐periods. The cumulative effect of population size increased to 2.286 times in 2019, far greater than the impact of other factors. The cumulative effect of economic activity per person reached 1.566 in 2019. The impact of fuel quality and energy intensity is relatively minor (as the index value is less than one) most of the time. The cumulative effect of renewable energy penetration has increased from 0.998 in 1990 to 1.034 in 2019 due to the country's less mature renewable energy technologies. Fossil fuel switching is a factor that reduces carbon emissions, as the index value decreased from 0.945 in 1990 to 0.918 in 2019. The results of the study contain helpful information for lowering carbon emissions.

Environmental impact of economic activities: Decoupling perspective of Singapore using log mean Divisia index decomposition technique

The chase for economic growth results in global environmental degradation, threatening the socioeconomic aspects of human lives. Singapore is a global economic player, transforming its rural setup into an urban structure to achieve higher economic growth (EG). However, the drive for EG drastically affected its environmental quality. In this respect, the present study analyzes the relationship between Singapore's economic activities and environmental quality. This study uses the Tapio decoupling indicator, Kaya Identity, and the Log Mean Divisia Index (LMDI) decomposition techniques to assess the relationships between these paramount factors from 1990 to 2016. The LMDI analysis reveals that EG and population are the main contributors to carbon emissions (CE), whereas carbon intensity reduces the environmental impact. However, energy intensity and energy structure have depicted mixed effects on CE. Further, Tapio analysis reveals that Singapore has experienced strong decoupling (SD) in most study years. Additionally, expensive negative decoupling (END), weak decoupling (WD), and strong negative decoupling (SND) were also observed during the study period. An expanded decomposition analysis reveals that population and EG deteriorate environmental quality in Singapore. While carbon intensity is the critical driver that strengthens the decoupling progress, energy intensity and structure depict a mixed effect on the decoupling process.

Characteristics and driving factors of carbon emissions in China

Many regions or countries seek to transition to a low-carbon economy, but there is ambiguity about how carbon emissions change with economic growth. This paper captures characteristics of carbon emissions in China using the Tapio Method. To detect driving factors of carbon emissions in economic growth, we adopt the Log-Mean Divisia Index decomposition method. The results of the paper are as follows: (1) Economic growth in most regions of China has outpaced the growth rate of carbon emissions, showing the characteristics of decoupling. (2) Metropolitan cities have a greater impact on the level of decoupling in neighboring provinces and municipalities. (3) The decoupling of carbon emissions from economic growth is caused by declining energy intensity in China. (4) The energy mix and industrial structure have little impact on the growth rate of carbon emissions. In addition, some policy implications are summarized according to the findings of this paper.

Estimation of CO2 Emissions Embodied in Domestic Trade and Their Influencing Factors in Japan

CO2 emissions embodied in domestic trade between Japanese prefectures are gradually increasing and becoming an important growth point in the country’s CO2 emissions. The primary objective of this study is to evaluate the CO2 emissions embodied in Japan’s domestic imports and exports to visualize the carbon transfer paths between prefectures according to the attributes of production and consumption: also to identify the influencing factors of the carbon flow. This study estimated the CO2 emissions embodied in domestic imports and exports by prefectures using input–output analysis, followed by the log-mean Divisia index decomposition approach, which is used to quantify the influencing factor of net export CO2 emissions across prefectures. The results show substantial regional differences in the CO2 emissions embodied in domestic imports and exports across prefectures. Manufacturing prefectures satisfy most of Japan’s domestic demand for industrial products and are the main net exporters of CO2 emissions. Carbon flow is more obvious in economically advanced regions (such as the Kanto and Kansai regions) and covers more prefectures through carbon transfer. Consumer prefectures import the most CO2 emissions and export large amounts of CO2 emissions to other prefectures. Among the three factors influencing net export CO2 emissions, the technology effect has the most significant impact through the carbon intensity of domestic trade flows. These findings highlight the substantial differences in CO2 emissions embodied in domestic trade and the influencing factors across prefectures in Japan. The responsibility for emission reduction is attributable to both manufacturing and consumer prefectures.

Key Factors Influencing the Achievement of Climate Neutrality Targets in the Manufacturing Industry: LMDI Decomposition Analysis

The manufacturing industry is often caught in the sustainability dilemma between economic growth targets and climate action plans. In this study, a Log-Mean Divisia Index (LMDI) decomposition analysis is applied to investigate how the amount of industrial energy-related CO2 emissions in Latvia has changed in the period from 1995 to 2019. The change in aggregate energy-related CO2 emissions in manufacturing industries is measured by five different factors: the industrial activity effect, structural change effect, energy intensity effect, fuel mix effect, and emission intensity effect. The decomposition analysis results showed that while there has been significant improvement in energy efficiency and decarbonization measures in industry, in recent years, the impact of the improvements has been largely offset by increased industrial activity in energy-intensive sectors such as wood processing and non-metallic mineral production. The results show that energy efficiency measures in industry contribute most to reducing carbon emissions. In the future, additional policies are needed to accelerate the deployment of clean energy and energy efficiency technologies.

Decoupling and decomposition analysis of environmental impact from economic growth: a comparative analysis of Pakistan, India, and China

The dispute between economic growth and greenhouse gas emissions is one of the major challenges of the twenty-first century. The central issue of the emerging economies revolves around the decoupling of economic growth and the rising carbon dioxide (CO2) emissions. This study examines the decoupling the CO2 emissions from the economic growth through the employment of the Tapio decoupling index and decomposition of CO2 emissions into its pre-determined factors through the Log Mean Divisia Index (LMDI) decomposition technique for Pakistan, India, and China (PIC) for a time span of 1990–2014. The findings of the Tapio elasticity analysis depict that in a few years, environmental impact has been seen to be decoupled from the economic growth in the respective PIC countries. However, relatively Pakistan experienced expensive negative decoupling; India mostly experienced weak decoupling and expensive coupling, while China exhibited weak decoupling in multiple years. In addition, the analysis of Tapio decoupling elasticity showed that energy intensity is the key factor supporting the decoupling in PIC countries, while population, affluence (GDP per capita) and energy structure have weakened the progress of decoupling. Furthermore, the analysis of the LMDI decomposition suggested that population, energy structure and affluence in PIC countries increase the CO2 emissions, while energy intensity reduces CO2 emissions, while mixed effects are reflected by carbon intensity.

Log Mean Divisia Index Decomposition Analysis of the Demand for Building Materials: Application to Concrete, Dwellings, and the U.K.

Dwellings are material intensive products. To date, material use in dwellings has been investigated mainly using economic (exogenous) or dwelling (endogenous) drivers, with few studies comprehensively combining both. For the first time, we identify a comprehensive set of such drivers of demand for building materials and analyze them using the logarithmic mean divisia index (LMDI) method. We combine the LMDI method, the concept of dynamic material flow analysis, and physical and monetary flows to decompose the demand for building materials into the following six effects: material intensity, floor area shape, dwelling type, dwelling intensity, economic output, and population. We analyze these six effects on demand for concrete in new dwellings in the U.K. from 1951 to 2014, classified into six dwelling types and four subregions. Of these six effects, the material intensity effect is the most important, overall contributing to increasing concrete demand by +79 Mt from 1950 to 2014, while the dwelling intensity effect plays an opposite role, overall reducing concrete demand from 1950 to 2014 by −56 Mt. The economic output effect is also significant (+38 Mt from 1950 to 2014). A comparative analysis of the six effects in the four U.K. nations reveals that most of the effects arise from England, while the other nations have minor effects due to their smaller populations. Our results show that changes to the demand for concrete in the U.K. fluctuate and have mainly remained between ±30 Mt year–2 from 1950 to 2014, and thus the inflows of concrete into the in-use stock of dwellings have experienced neither entirely increasing or decreasing trends during this period. This study contributes to understanding changes in resource demand due to social, economic, and technological factors and thus improves the capability to reliably and quantitatively model the use of materials in the built environment.

Indirect carbon dioxide emissions from interregional trade in Japan under the target of carbon neutrality

With Japan’s emphasis on domestic demand and the gradual expansion of interregional trade, indirect carbon dioxide (CO2) emissions have become an important factor limiting Japan’s realization of carbon neutrality in 2050. In this study, using regional trade data, input-output analysis was applied to study indirect CO2 emissions from the domestic exports/imports of 30 prefectures in Japan in 2010, and log-mean Divisia index decomposition analysis was conducted to analyze the factors influencing the net indirect CO2 emissions of each prefecture. The main results are as follows: (1) some manufacturing prefectures with obvious characteristics of an export-oriented economy supply the market demand of most areas in Japan with their industrial products and have become the main net CO2 exporters undertaking the vast majority of CO2 emission reduction tasks; (2) consumer prefectures not only guide the output of manufacturing prefectures to a certain extent but also export a substantial amount of indirect CO2 emissions to other prefectures through the service industry, which is widely seen as a low-carbon emitter and is as such neglected by regulations aimed at reducing emissions. Therefore, the implementation of carbon neutrality needs to balance the relationship between production and consumption. While reforming the technology of the manufacturing prefecture from the production side to reduce CO2 emissions, the market demand of the consumer prefecture should also be adjusted from the consumption side to enhance the concept of sustainable consumption.

Importance of Energy Efficiency in Manufacturing Industries for Climate and Competitiveness

Abstract The manufacturing industry in Europe is currently enfacing one of its greatest challenges due to the emission reductions needed to reach carbon neutrality by the middle of this century. The European Union’s Energy Efficiency Directive and Green Deal will force manufacturing industries to significantly reduce their present energy consumption, but at the same time sustain their competitiveness globally. Here we use the Latvian manufacturing industry as a case to analyse how different macro-level factors have affected its energy use and how the industrial energy efficiency has progressed during the last decade. We apply the Log-Mean Divisia index decomposition method to decompose the energy use in the manufacturing subsectors over the period of the past ten years from 2010 to 2019. The findings unravel the key driving factors of industrial energy consumption, which could serve as a valuable basis for effective energy efficiency policymaking in the future. The results show that energy consumption trends differed across industrial subsectors and the effect of industrial energy efficiency improvements was more pronounced in the period following the entry into force of Energy Efficiency Law in Latvia. Significant increases in energy consumption are observed in the two largest Latvian manufacturing subsectors, such as the non-metallic minerals production sector and the wood processing sector, where the current pace of energy efficiency improvements cannot compensate for the effect of increasing industrial activity, which increases overall industrial energy consumption. The results suggest that the Latvian manufacturing industry is at the crossroads of the sustainability dilemma between economic gains and energy saving targets.

Drivers of global and national CO2 emissions changes 2000–2017

ABSTRACT Understanding the drivers of CO2 emissions changes is useful in supporting future mitigation. This study applies a log-mean divisia index decomposition to assess four drivers of CO2 emissions changes – population, income, energy intensity and carbon intensity – in 138 countries worldwide over the period 2000–2017. At the global level, income and population are the main drivers of increased emissions over time, with contributions of 116% and 60% to global CO2 emissions changes, respectively. Energy intensity is the key mitigation driver, with a contribution of −80%. Although carbon intensity increased CO2 emissions overall over the period 2000–2017 with a contribution of 4%, it has started to reduce emissions in recent years. China, the United States of America, the European Union, India and Russia are the five regions responsible for most changes in global emissions. The five regions together contribute −73% of the energy intensity effect, and China’s income contribution is 83% in relation to the total of 116%. At the national level, in 2017, CO2 emissions returned to below 2000 levels in 62% of Annex I (developed) countries but increased in 88% of non-Annex I (mostly developing) countries. Among the 35 countries realizing CO2 emissions reductions, 24 were driven primarily by energy intensity, six by carbon intensity, three by economic recession, and one by population decrease. Among the 103 countries with increasing CO2 emissions, 63 were driven primarily by income, 26 by population, nine by carbon intensity increase, and five by energy intensity increase. Our analysis emphasizes the necessity of considering differences in national development stages when formulating climate change mitigation policies. Key policy insights Over the period 2000–2017, at the global and national levels, CO2 emissions increases were driven mainly by economic development and population growth, and mitigation was driven mainly by energy intensity improvement. Improving energy intensity and carbon intensity is the key to mitigating CO2 emissions. Carbon intensity is expected to play an increasing role in the future. In over one-third of Annex I countries, CO2 emissions increased from 2000 to 2017. To meet the Paris Agreement goals, Annex I countries will need to enhance mitigation ambition by further tapping the mitigation potentials of energy and carbon intensity. In accordance with national circumstances, development needs and international support, non-Annex I countries should achieve low-carbon economic and energy transitions and peak CO2 emissions as early as possible.

The Decline of the US Labor Share Across Sectors

We provide novel insights on the functional distribution of income in the postwar US economy, based on a Log Mean Divisia Index decomposition of the labor share by 14 sectors. We identify contributions from four components: real compensation, labor productivity, employment shares, and relative prices. The results are presented for the entire period as well as golden age (1948–1979) and neoliberal era (1979–2017), painting a detailed picture of structural changes. We find that (1) real compensation and labor productivity dominate; (2) manufacturing plays an important role in the recent decline of the labor share; (3) employment shifts toward service sectors with higher labor shares have buffered the decline; and (4) relative prices of services are increasing. We discuss these results in the context of Baumol’s and Lewis’s seminal contributions. Both theories build on the notion of coexistence of progressive and stagnant activities, which is documented in our sectoral decomposition.

Analytical tool for unpacking the driving forces of environmental impact: an IPAT analysis of Pakistan

The continuous increase in carbon dioxide (CO2) emission leads to an adverse climate change, that has a disastrous impact on the socio-economic lives of the human being. Therefore, to reduce the environmental impact on human life, it is necessary to provide mitigation measures for CO2 emissions and to investigate the influence of pre-determined factors such as population, affluence, and technology (IPAT) on the environment. Hence, the current study is undertaken to ascertain the impact of the IPAT factors on environmental quality by using the Compound Annual Growth Rate (CAGR) and Log Mean Divisia Index (LMDI) decomposition techniques for Pakistan over the time period 1990–2014. The CAGR result exhibits that the 142.53 folds in CO2 emissions are driven by the population growth which is accompanied by the income growth, while the technological effect significantly decreased the growth of CO2 emissions by − 82.72 folds during 1990–2014, the energy induced CO2 emissions shows a negative growth rate during the study period that is attributed to the energy crises during most of the study years. Besides, the LMDI decomposition technique shows that the impact of population and affluence leads to an increase in CO2 emissions. Furthermore, the technological effect helps to reduce CO2 emissions in all phases except phase 2 (1995–1999) where it has been identified as a significant contributor to CO2 emissions.

Sectoral Decomposition of Korea’s Energy Consumption by Global Value Chain Dimensions

This paper analyzed the annual trends in energy consumption of 14 industries in Korea from 2000 to 2014 using an extended log mean Divisia index (LMDI) method that embedded global value chain (GVC) divisions in the standard LMDI decomposition. Using a world input–output table, we calculated foreign value-added share in the GVC activities for each industry. Based on a Cobb–Douglas production technology, we embedded GVC divisions in the ordinary LMDI factor decomposition. The key findings indicate that the production effect mainly drives energy consumption, while energy consumption has decreased by both the foreign-structure effects and the foreign-intensity effects. Together with a decline in the domestic energy intensity effects, both of the GVC effects have improved energy efficiency. Energy-intensive industries have consumed more energy than other industries, while they have more incentive to save energy costs because these costs are a large proportion of total import costs. The opposite pattern occurred in other industry groups. Industries that do not naturally depend on energy tend to consume more energy and became more energy-intensive.

Energy efficiency in Indonesia\u2019s manufacturing industry: a perspective from Log Mean Divisia Index decomposition analysis

This research discusses energy intensity in Indonesia’s manufacturing sector from 1980 to 2015. The manufacturing sector is the second-largest energy-consumer in Indonesia (after the transportation sector) and one of the largest contributors to Indonesia’s output. Thus, it is important to know the energy usage performance of this sector. This study discusses the factors affecting changes in energy consumption in various subsectors of Indonesia’s industry and investigates the energy intensity across manufacturing subsectors. This paper analyses the specific characteristics of energy intensity in the manufacturing sector in Indonesia from 1980 to 2015. This has not been investigated a great deal in the past, particularly when employing the Log Mean Divisia Index II method.The overall energy intensity of Indonesia’s manufacturing sectors has seen a strong and continuous decline, with a reduction of 65% over the 35 yr, reinforced by some limited changes in industry structure towards lower intensity. Over the entire period, this reduction was dominated by increases in energy efficiency within industries, as indicated by a 62% fall in the within-industry intensive index. By contrast, the effect of moving to a less intensive industry structure was much less important (a 9% fall in the structural index). The greatest rise in energy efficiency within the industry happened before the financial crisis (from 1980 to 97). the shock of the financial crisis saw an unexpected reaction when value-added fell by 13% but energy use remained largely unchanged, implying a rise in energy intensity. From 2000 to 2015 the earlier trends resumed, but at a more subdued pace, where over this period aggregate intensity fell by 23%.

The measurement and influencing factors of carbon transfers embodied in inter-provincial trade in China

Based on a measurement model for carbon transfers in bilateral trade between regions and an improved log-mean Divisia index decomposition method, this study estimates carbon transfers caused by inter-provincial demand and inter-provincial exports for 2007, 2010, and 2012 and investigates the main factors that affect net carbon transfers between provinces. The results show the following. First, in 2007–2012, the average carbon emissions caused by inter-provincial demand and inter-provincial exports account for about 35.4% and 8% of total production-based carbon emissions, respectively, which indicates that carbon transfers between provinces are widespread. Second, total carbon transfers caused by inter-provincial demand keep increasing, and in 2012, 19 provinces show net carbon outflows, and provinces located in the northern and central regions receive most net carbon inflows. By contrast, net carbon outflows induced by inter-provincial export keep decreasing and are concentrated in eight provinces in the Beijing–Tianjin, eastern, and southern coastal areas. Third, the scale and structural effects increase net carbon transfers between provinces in 2007–2010 and 2010–2012, respectively, whereas the technological effect reduces net carbon transfers between provinces in 2007–2012. Our empirical results suggest that carbon emissions and inter-provincial carbon transfers can be reduced effectively through inter-provincial cooperation and narrowing provincial differences in emissions reduction technology.

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.

An index decomposition analysis of tourism demand change

•Tourism demand change is broken down into components.•The log mean Divisia index decomposition is used.•Contributions of key variables in tourism demand change are separated.

Industrial Distribution and LMDI Decomposition of Trade‐Embodied CO2 in China

This paper investigates trade‐embodied CO2 in China, with a special focus on the production processes and materials of each industrial sector from 1990 to 2013. It uses an input–output model, which analyzes the specific causes of periodic waves. Our findings suggest that the machine‐manufacturing sector produced the largest amount of emissions, whereas the metal and nonmetal sector had the highest intensity of emissions. Moreover, the total emission quantities in trade increased from 612 to 3,331 million tons from 1990 to 2013. We employ the log‐mean Divisia index (LMDI) model to decompose the carbon emissions changes into export structure, export volume, energy structure, and energy intensity and estimate the influence of these factors at a number of time points. We find that energy intensity curbed emissions increase and that export volume expansion was the strongest driving force.

The potential water-food-health nexus in urban China: A comparative study on dietary changes at home and away from home

In China, urbanization strengthens the water-food-health nexus by driving dietary changes both at home and away from home (AFH). However, few studies have compared the effects of dining location on water footprint generation and/or linked such habits to the prevalence of non-communicable diseases (NCDs), such as hypertension, diabetes, asthma, myocardial infarction, stroke and cancer. Here, household survey data were used to develop a multiple-component urbanization sequence, on which the diet-related water footprint was quantified and the mechanisms under the water-food-health nexus were explored. Significant dietary changes due to urbanization have occurred at home (instead of AHF), and increases or decreases in various food groups are stratified across dining locations. Log mean Divisia index decomposition shows that the diet-structure effect outweighing the intake effect dominates the water footprint changes during China's urbanization. Animal products contribute 92(94)% of the diet-structure effect on net water footprint growth at home(AFH); in contrast, vegetal foods dominate the intake effect, contributing 67(49)%. The at-home water footprint ratio of animal products to vegetal foods is highly related to the prevalence of diabetes, hypertension, cancer, and asthma, and reaching statistical significance. Two urbanization components, namely housing situation and community diversity, serve as the key drivers of water-food-health nexus enhancement in urbanized China. Due to the complexity of water-food-health links, nexus thinking is needed to benefit human health and diet-related water consumption; besides, it may be reasonable to expand current dimension of food-energy-water nexus topic to include health issues.

Discerning the factors explaining the change in energy efficiency

Due to the high rates of energy consumption and its impact on environment over the last decades, policy decision-makers are increasingly recognising the need to take actions that allow to address problems associated with the deployment of non-renewable resources and climate changes. One field of action has been the promotion of measures that contribute to improve energy efficiency of countries. The purpose of this study is to identify the main factors explaining changes in energy efficiency applying the multiplicative Log Mean Divisia Index decomposition method for a set of countries (Portugal, UK, Brazil and China) with different socio-economic background and energy mix. The results show that overall energy efficiency trends display different patterns between countries and the same happens within each country from a sectoral perspective. Major drivers of improvements of overall energy efficiency were the intensity effect and the affluence effect, whereas the driver that hampered those improvements was the energy consumption per capita. Some policy implications derived from the results achieved are: policy decision-makers should support measures that promote the adoption of energy-saving technologies resulting from new technological developments; policy measures should be directed to raise awareness of end-users regarding energy efficiency and energy conservation efforts; policy measures promoting economic growth through the development (or expansion) of sectors of activity that consume less energy can also be implemented; finally, policy instruments may also be used to reduce the costs of implementing energy efficiency and energy-saving measures to households and firms.