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Abstract
Since it was first used by researchers to analyze industrial electricity consumption in the early 1980s, index decomposition analysis (IDA) has been widely adopted in energy and emission studies. Lately its use as the analytical component of accounting frameworks for tracking economy-wide energy efficiency trends has attracted considerable attention and interest among policy makers. The last comprehensive literature review of IDA was reported in 2000 which is some years back. After giving an update and presenting the key trends in the last 15 years, this study focuses on the implementation issues of the logarithmic mean Divisia index (LMDI) decomposition methods in view of their dominance in IDA in recent years. Eight LMDI models are presented and their origin, decomposition formulae, and strengths and weaknesses are summarized. Guidelines on the choice among these models are provided to assist users in implementation.
... Therefore, studies of emission and energy widely use the index decomposition analysis (IDA) as an analytical component. This use has attracted policymakers to follow energy efficiency trends (Ang, 2015). This study uses the additive logarithmic mean Divisia index (LMDI) decomposition approach to assess the change in carbon emissions between a reference year, denoted by the superscript 0, and an end year, denoted by the superscript T, into additive components that call factors. ...
... This study uses the additive logarithmic mean Divisia index (LMDI) decomposition approach to assess the change in carbon emissions between a reference year, denoted by the superscript 0, and an end year, denoted by the superscript T, into additive components that call factors. In additive decomposition analysis, the aggregate change (the arithmetic or difference change) and decomposition results are given in a physical unit (Ang 2015). However, by implementing the additive LMDI decomposition approach suggested by Ang et al. (1998) for one sector, which is the electricity sector, the carbon emissions can be formulated as follows: ...
... The LMDI becomes the preferred method for decomposition analysis because its results give a perfect decomposition and do not produce any unexplained residuals. Also, it simplifies the result interpretation, as noted by Ang (2005Ang ( , 2015. ...
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.
... Among many measures, economic restructuring is an effective way to achieve economic development and emission reduction (Zhou et al., 2013). From a structural perspective, an economy can reduce pollution by reducing the scale of production, reducing the emission intensity of each industry, and lessening the share of highly polluting sectors (Ang, 2015). Among these factors, the scale of production-as the main part of the natural development process of an economy-cannot be reduced over time. ...
... Among these factors, the scale of production-as the main part of the natural development process of an economy-cannot be reduced over time. Emission can, therefore, be limited by reducing the proportion of high emission sectors and reducing the emission intensity in each sector (Ang, 2015). These changes have both negative and positive effects on economic growth. ...
... The literature has documented different aspects of environment and economic variables. For example, studies have primarily focused on the relationship between economic growth and environmental pollution (Grossman & Krueger, 1995), the relationship between economic restructure and environmental pollution (Ang, 2015;Zhou et al., 2013), the social costs of pollution (Clarkson & Deyes, 2002;OECD, 2015OECD, , 2016Tol, 2009;Watkiss & Downing, 2008), the optimal combination between economic, energy, and environment targets (Oliveira & Antunes, 2004), the underlying driving forces of economic growth and environmental pollution (Dhakal, 2009;Guan et al., 2009;Lyu et al., 2016;Ma et al., 2016), the impacts on the economy and environment caused by changes in sectors or some exogenous variables (Leontief, 1951(Leontief, , 1970, or trying to find out which sector should be expanded or narrowed (Vaninsky, 2006(Vaninsky, , 2014(Vaninsky, , 2018. However, an adequate tool for specifying a quantified optimal solution of economic structure to minimizing aggregate emission intensity of the economy without hindering economic growth is left unexplored. ...
This paper proposes the projected gradient algorithm to find a solution that allows economic restructuring to minimize carbon emission intensity and not hinder targeted economic growth. It develops a general formula to calculate how much each sector should change its share in gross domestic products and its emission intensity such that the economy can have a lower aggregate emission intensity without hindering economic growth. Used data for 30 nations during 1997–2009, the study indicates that if a country follows this proposed optimal restructuring solution, it experiences a lower carbon emission intensity than its initial scenario. This solution is helpful to (1) find the optimal direction and quantitative solutions of a low emission economic structure; (2) continuously check and quantify the differences between the optimal and actual directions of economic structuring for adjusting economic and environmental policies.
... Based on the emissions model (Equation (3)), this study investigates the impact of each factor on carbon emission changes and further seeks the source of past emissions Buildings 2021, 11, 510 6 of 17 abatement of the commercial building operations [33]. Thereby, Log-Mean Divisia Index (LMDI) decomposition was used to achieve this goal [34]. LMDI is a classical method used to quantify the impact of every factor that affects emissions changes [35,36]. ...
... Moreover, there are double-control energy demand targets in the future operations, set through dynamic simulation under the BAU scenario (energy peak: 445.7 Mtce, peak time: 2045). In the positive energy benchmark scheme, the implementation probability is 43.01%, which is suggested to control the energy peak at 299. 34 Mtce in 2034. The negative scheme would achieve the energy peak in 2049 with the peak value at 491. 15 Mtce under an 83.86% implementation probability. ...
Carbon neutrality has positive impacts on people, nature and the economy, and buildings represent the “last mile” sector in the transition to carbon neutrality. Carbon neutrality is characterized by the decarbonization of operations and maintenance, in addition to zero emissions in electricity and other industry sectors. Taking China’s commercial buildings as an example, this study is the first to perform an extensive data analysis for a step-wise carbon neutral roadmap of building operations via the analysis of a dynamic emission scenario. The results reveal that the carbon emissions abatement of commercial building operations from 2001 to 2018 was 1460.85 (±574.61) mega-tons of carbon dioxide (Mt CO2). The carbon emissions of commercial building operations will peak in the year 2039 (±5) at 1364.31 (±258.70) Mt, with emission factors and energy intensity being the main factors influencing the carbon peak. To move toward carbon neutral status, an additional 169.73 Mt CO2 needs to be cut by 2060, and the low emission path toward carbon neutrality will lead to the realization of the carbon peak of commercial buildings in 2024, with total emissions of 921.71 Mt. It is believed that cutting emissions from the operation of buildings in China will require a multi-sectoral synergistic strategy. It is suggested that government, residents, enterprises, and other stakeholders must better appreciate the challenges to achieve a substantial carbon reduction and the need for urgent action in the building sector in order to achieve carbon neutrality.
... Since each factor in the RUSLE model is a spatially distributed raster image dataset, the contribution values of each factor to soil erosion modulus for each pixel can be calculated using the LMDI approach. Additive decomposition and multiplicative decomposition were equally valid and had the equivalent interpretive power (Ang, 2005(Ang, , 2015. Compared with the multiplicative case, the decomposition results of the additive case are given as physical units instead of indices so that the addictive decomposition is easier to be interpreted and utilized . ...
... Second, there is no requirement for the order of factor decomposition in the calculation process. Third, the LMDI method can quantify the impacts of the influencing factors (Ang, 2015) and quantitatively calculate the contribution values of the influencing factors to the variation of soil erosion pixel by pixel, thus producing spatial distribution maps of the contribution values; in contrast, the regression relationships established by the traditional methods are mostly used to describe the influences of vegetation and precipitation on soil erosion in the whole region, which cannot quantitatively identify the specific contribution value from the pixel scale. Because the influences of vegetation and precipitation on soil erosion vary in different regions, the spatial heterogeneity would be neglected by directly establishing regression relationships in traditional methods. ...
... To implement the proposed multiplicative SDA framework, the LMDI decomposition method is adopted in this study. The LMDI method is constant in decomposition forms and is simpler in application and computation with no residual (Su and Ang 2012;Ang 2015). Individual effects in Eq. ...
... When using other indicators to capture this question, the additive form could produce some new insights. Besides, although Ang (2015) summarized the little difference of weight choices in calculating the exact effects of each driver, it is still an interesting work to compare the different results to verify and promote the development of the method. Second, in addition to the nationallevel analyses, the proposed temporal and spatial SDA models could be applied to analyze carbon emissions or intensityrelated issues for other countries or specific sectors based on the WIOTs, etc. ...
This study aims to explore the driving determinants on the export-related carbon intensity (ECI) of China, to better understand the impact of international trade on climate change governance and facilitate China’s carbon intensity mitigation goals. First, China’s ECI evolution and its gaps with the USA and India are measured during 2002–2014. Then, the main drivers of China’s ECIvert study further discusses the influencing factors of ECI in the manufacturing industry using the environmental-extended STIRPAT model and GMM method. The results show that (1) China’s overall ECI increases from 1.50 Kg/US$ in 2002 to 1.92 Kg/US$ in 2005 and then decreases to 1.27 Kg/US$ in 2014. The ECI of the manufacturing industry is significantly higher than that of the agriculture and service industry. China’s ECI gap with the USA is greater than that with India, and both show a downward trend. (2) Carbon emission coefficient is the domain factor to reduce China’s ECI during 2002–2014; the effects of the value-added coefficient, input–output structure, and final demand are limited. The input structure dominantly expands China’s ECI gaps both with the USA and India, followed by the value-added coefficient. The carbon emission coefficient enlarges the ECI gap with the USA while reduces that with India. (3) Industrial productivity and value-added rate are negatively correlated with ECI in the manufacturing industry, while per capita capital stock plays the opposite role. The positive correlation between energy intensity and CIE becomes significant after distinguishing technology heterogeneity. In contrast to the non-tech-intensive manufacturing industry, the increase of backward GVCs participation of tech-intensive ones will reduce the ECI. The threshold effect of backward GVCs participation exists in the whole manufacturing industry. Targeted ECI reduction policy implications are suggested.
... Examples of SDA studies adopting LMDI-I include Pothen (2017) and Su et al. (2019). A guide for implementation of LMDI-I is given in Ang (2015). To handle negative and zero values in the decomposition, the analytical limit strategy proposed by Ang and Liu (2007) is adopted. ...
... Regional contributions to EEX changes, 2005-2015 ...
A R T I C L E I N F O Keywords: Belt and Road Initiative CO 2 emissions Prospective analysis Multi-region structural decomposition analysis A B S T R A C T The Belt and Road Initiative (BRI) has promoted economic growth of participating countries while giving rise to profound environmental consequences. To steer the BRI towards a low-carbon and green development, it is necessary to analyze past trajectories and future trends of BRI's CO 2 emissions. To this end, we assess the patterns and determinants of emission flows along the BRI during 2005-2030 using the multi-region structural decomposition analysis technique. For the period 2005-2015, we show that intermediates export of the BRI embodied more CO 2 emissions than final goods export. The significant technological improvement only partly offset the emission growth stemming from the deteriorated cross-border production structure and the surging final demand of the BRI in the past. For the period 2015-2030, our prospective analysis indicates that emissions embodied in exports of participating countries increase by over 20% in the reference scenario where historical development patterns of the BRI continue. The rise might be even higher if the initiative ends. On the contrary, enhancing diffusion and adoption of low-carbon technologies and promoting green trade within the BRI show substantial emission mitigation potential. Our empirical results reveal directions and priorities for policymaking in the pursuit of a green BRI.
... IDA methods are more widely used compared to SDA, being easily applicable and requiring fewer input data (Wang et al., 2017). Among the IDA methods, the Logarithmic Mean Division Index (LMDI) method has the advantages of complete decomposition with no residual, sound theoretical foundation, adaptability, ease of use, and ease of result interpretation (Ang, 2015). It is often recommended for quantitatively identifying the relative impacts of different factors on the corresponding changes (Ang and Liu, 2007;Wood and Lenzen, 2009;Zou et al., 2018). ...
Water sustainability is imperative for socio-economic development. The grey water footprint (GWF) is a useful indicator for quantifying how human activities influence freshwater systems. This study identified the change in GWF for Yinchuan City, an agricultural region in the Yellow River Basin, by the validation of acquired water-quality data. The driving forces of the GWF were analyzed using the logarithmic mean division index (LMDI) decomposition method. The results indicated that the total GWF showed a declining trend during the study period between 2002 and 2017 within the range of 12.6–21.5 × 10⁸ m³/yr. The agricultural GWF had a relatively stable contribution (30–40%), and the focus should be on the increased proportion of domestic sector in this irrigated region. Economic development and technology effects were the dominant negative and positive factors, respectively, behind the decline in GWF. Although the water pollution levels (0.06–0.12) indicating that the waste assimilation capacity was sufficient to take up the pollutant load, the effect of pollution load on groundwater was cause for concern based on the actual water-quality data. A significant increase in NO3–N in the phreatic water was detected between 2003 and 2013. The dominance of NH4–N for the water supply wells in confined aquifers hinted at the deteriorating water quality, which was attributed to the aquifer leakage under pumping conditions. This study better reflected the current water pollution situation by combining GWF with understanding of the hydrological cycle and the actual water-quality data. Findings in this study will be valuable for addressing water quality threats and to develop sustainability strategies for local authorities.
... denotes the ratio of the value added of various sectors in total GDP, named industrial structure; AVY i ¼ Y i P i denotes per capita disposable income, named income level. Adopting the additive LMDI method (Ang 2005, Ang 2015, the aggregate changes of energy use for an economy between the base year 0 and target year t can be decomposed into five driving forces: energy intensity effect (ΔE_EI), industrial structure effect (ΔE_S), GDP value-added effect (ΔE_G), income improvement effect (ΔE_Y), and population-scale effect (ΔE_P), as following Eq. (2). ...
With the rapid growth of global demand for water and energy, the two increasingly restrict economic and social development. The total energy consumption and water use are positively correlated. Identifying the key drivers influencing the energy-water development can realize national resource management and sustainable supplement. In this context, this study aims to capture the key driving forces that affect the sustainable energy-water development characteristics in Chinese change processes throughout 2000–2017. Five driving forces, the EW intensity effect, industrial structure effect, GDP value-added effect, income improvement effect, and population-scale effect, were further decomposed by the logarithmic mean Divisia index (LMDI) model to explore the energy consumption and water use. Our findings indicated that the largest and lowest energy consumers were the manufacturing and construction sectors, while agriculture accounted for the largest share in water use. During the three time intervals, the cumulative effects increased the EW use, but the contributions were declining. Further, these effects had a more prominent influence on water use than energy consumption; GDP value-added effect, income improvement effect, and population-scale effect increased the EW use, while intensity effect played a vital role in decreasing EW use during the study period. Notably, the industrial structure effect had a seesaw role during 2000–2006, which led to a tradeoff between various driving factors. In future sustainable issues, policymakers should pay more attention to energy-saving than water-saving to achieve the national energy and water conservation targets.
... The former had huge residual in some special cases, and the latter not only solved the problem of residual items, but also solved the problem of negative and zero values in data sets; in addition, LMDI has the perfect decomposition attribute on aggregation consistency and sub-category level. Therefore, they recommended using LMDI method to solve energy data with different level properties (Ang 2015). The Divisia index decomposition analysis method which is verified by Ang et al. is applied to make the carbon emission distinction among different countries or regions . ...
The Logarithmic Mean Divisia Index (LMDI) model is applied to study Chinese national and regional power sector carbon emission changes through consumption side from 2003 to 2017, and regional power sector carbon emissions are estimated through the production and consumption accounting principle. The two-factor ANOVA and one-factor ANOVA are used to compare the differences of regional power sector carbon emissions through the two principles. In addition, the Tapio decoupling analysis model is used to investigate the decoupling state between carbon emissions of power sector and the corresponding driving forces through the consumption side. There are several results: (1) Through the two different principles, regional power sector carbon emissions are statistically significant, yet national power sector carbon emissions are not statistically significant; (2) the main factors contributing to the power sector carbon emission growth are economic scale effect and income level effect, and the main restraining factors are electricity consumption carbon intensity effect and production sector electricity intensity effect; (3) the highest contribution effect to the decoupling indexes between various influencing factors and power sector carbon emissions was scale effect, and technical effect had the second largest contribution value; (4) in 2003-2017, economic scale effect was the first significant factor causing the difference of regional power sector carbon emissions, followed by production sector electricity intensity effect and electricity consumption carbon intensity through the regional decomposition analysis. Finally, this paper gives some targeted suggestions for the low-carbon development of the power sector through national and regional perspectives.
... Multiplicative decomposition does not significantly differ from additive decomposition [46]. While the change between the base and target years is expressed by delta (∆ ) in additive decomposition, multiplicative decomposition uses the growth rate ( / . ...
For this study, we conducted a decomposition analysis of industrial electricity consumption based on the logarithmic mean Divisia index approach. An empirical dataset consisting of 11 industrial sectors in Korea from 2000 to 2018 was used. The three-factor decomposition equation was extended to include four factors by decomposing the energy intensity effect into electrification and electricity consumption efficiency effects. The empirical results are summarized as follows: The increase in electricity consumption in the Korean industrial sector from 2000 to 2018 is mostly caused by the production effect. While the structure effect decreases electricity consumption, the intensity effect increases it. The key findings indicate that the hidden electrification effect can be confusing to researchers with regard to the intensity effect. The empirical evidence suggests that the intensity effect has a positive effect on electricity consumption induced by the electrification effect, although the efficiency effect continuously decreased electricity consumption. The decomposition results of some sectors show that electrification, rather than the production effect, contributed the most to the increase in electricity consumption. This implies that while replacing fuel with electricity has been successfully achieved in several sectors, there are still challenges regarding increasing energy efficiency and expanding clean electricity generation.
... Wang et al. [44] and Ang [45] report the latest progress of IDA, SDA, and LMDI decomposition models in studying the factors of energy consumption and changes in energy emissions. These are widely used in energy and climate policy assessment and development and have become widely accepted analytical tools for policy formulation on national energy and environmental issues. ...
Achieving Net zero emissions is a common challenge facing all mankind. Low-carbon electricity has always been the main research field of global GHG emission control. The current article aims to use the bibliometric analysis to describe the characteristics and trends of low-carbon electricity publications from 1983 to 2021. We found that: (1) the number of publications in this area has shown an overall increase in the past 33 years. (2) the United States is the most powerful country in this field of research. Moreover, with the exception of major developed countries, more and more emerging economies have also joined the research on low-carbon power systems. (3) co-citation analysis and literature clustering characteristics show that the knowledge base in this field is focused on the decomposition of driving factors for carbon dioxide emissions and the optimization of the operation of renewable energy (RE) in low-carbon power systems. (4) the utilization of RE is a hot topic in low-carbon power research. Through this research, global scholars can be provided with the latest overview of valuable low-carbon energy research trends.
... Index decomposition method (IDA) is a common method to explore the influencing factors of carbon emissions changes. Logarithmic Mean Divisia Index (LMDI) is a more reasonable method in IDA, which can not only decompose the remainder completely but also deal with non-positive values properly [3][4]. Therefore, considering the advantages of the LMDI model, we use LMDI to decompose the ...
... The study used generalized Divisia index method (GDIM) proposed by Vaninsky (2014), to decompose the changes in CNI in Chinese cities. GDIM is more advanced than the popularly used logarithmic mean Divisia index (LMDI) approach (Ang, 2015). The approach overcomes the disadvantages of LMDI by 1) only quantitatively describing indicators (e.g., economic and population scale), while their intensity indicators (e.g., per capita gross domestic product (GDP) and per capita CO 2 emission) are scarcely analyzed together in a single decomposition framework, 2) using different factorial decompositions due to different factor models. ...
While China pledged to achieve carbon neutrality (CN) by 2060, the regional heterogeneity of CN received a limited attention, causing biased Pareto optimality when facilitating regional CN policy. This study focused on regional heterogeneity of CN based on vegetation carbon sequestration from perspectives of convergence, driver, and inequality across nearly all China's cities and counties during 2000–2017. Eleven city clubs and eighteen county clubs of CN index (CNI) were identified to be convergent in the long run. Decomposition results indicated that economic scale was the largest driver contributing to the increase in CNI, and the driver pattern changed in the post-Kyoto era, where technological level was an increasingly important contributor to improve CN for cities. Inequality analysis showed that Gini coefficients of CNI were declining for cities and counties, where individual between-group and within-group subcomponents nexus diversified, such as inverted U-shaped and linear relationships. The results from this study can facilitate CN-related policies, especially those associated with regional heterogeneity.
... Although synergy and other interactions between driving forces cannot be detected, over time, the relative contributions of various driving factors behind various parameter changes can be quantified to design future policies and measures (Ang, 2005(Ang, , 2015Guan et al., 2008;Zheng et al., 2019). ...
Avoiding climate change from exceeding its critical threshold is a serious challenge facing humanity at present and in the future. As the mode of global cooperative action is stranded, multi-center and multi-level efforts are needed to deal with global warming in the future. In order to provide information for the formulation of low-carbon development policies, it is essential to assess the maintain or cross of climate change threshold on different scales. In this study, the carbon footprint calculated based on the process coefficient approach is systematically integrated with the climate change indicator of the planetary boundaries framework improved with the goals of the Paris Agreement to identify the climate change risks of Tibet and its prefecture-level cities from 2000 to 2017. Moreover, the main driving factors behind carbon footprint were analyzed. The findings showed that: (1) Since 2000, Tibet's CO2 emissions have demonstrated steady and rapid increase. The sector composition is dominated by cement production-related and transportation sector-related emissions. The type composition is dominated by diesel-related, process-related, and coal-related emissions. There are significant differences in CO2 emissions among all prefecture-level cities, with Lhasa having the largest contribution. (2) Except for Lhasa and Shannan's CO2 emissions that have crossed their critical threshold of climate change and are in an unsafe state, Tibet and other prefecture-level cities have not yet crossed their critical threshold. (3) Except for Ngari, per capita GDP, energy intensity, population size, and carbon intensity positively affect the increase of CO2 emissions in Tibet and its prefecture-level cities. Our study helps actors at less aggregated scales to determine appropriate policy strengths based on globally agreed goals and ambitions in the process of responding to global warming in a bottom-up manner.
... We explore how the socio-economic transition restructures dietary GHG emissions in the United States with the LMDI method. Developed by Ang 48 , this method enables quantification of the contribution of each driving force in proportion without residuals 49 . To conduct the decomposition analysis, we rewrite the equation for calculating the environmental impacts as: ...
Environmental implications of food choice are the focus of increasingly extensive research, but less is known about the impacts of dietary patterns of different socio-economic groups of a country, and the trade-offs between nutritional quality and environmental impacts of diet within those groups. We evaluate the impacts of US household dietary patterns on greenhouse gas emissions, blue water footprint, land use and energy consumption across supply chains using an environmentally extended input–output analysis. We compare the nutritional quality of these dietary patterns using healthy eating index scores across individuals’ income and other socio-economic characteristics. Individuals with higher income or education levels are more likely to adopt healthier diets but are also responsible for larger environmental impacts of diet primarily due to a higher consumption of dairy and livestock products, seafood and items with lower energy density but higher nutrient density. Our optimization shows that a healthy diet with lower environmental impacts is achievable within current food budgets for almost 95% of people, and results in average decreases of 2% in food-related greenhouse gas emissions, 24% in land use and 4% in energy consumption, but a 28% increase in blue water consumption. However, such dietary patterns are unaffordable for 38% of Black and Hispanic individuals in the lowest income and education groups. Policies that affect income and food prices making nutritious food more affordable would be needed to achieve better nutrition and improved environmental outcomes simultaneously, particularly for more vulnerable socio-economic groups.
... By comparison, LMDI has the advantage of leaving no residuals, easing interpretation of factor effects, and having consistent decomposition formulae for different numbers of factors [23]. Therefore, in practical application, the LMDI method has more extensive application [24]. For example, Liao et al. (2019) [25] adopted the LMDI method to analyze the driving factors of provincial-level CO 2 emissions from the power sector in China and found the change characteristics of each driving factor over time. ...
Climate change has become a global concern, and the development of a green economy has attracted wide attention. Understanding the driving factors and growth potential of provincial-level carbon productivity is crucial for China’s green economic development in the new normal phase. In this study, the logarithmic mean Divisia index (LMDI) is adopted to systematically investigate the driving factors of provincial carbon productivity and explore the growth potential of provinces’ carbon productivity based on the clustering analysis. The results show that: (1) China’s provincial carbon productivity presents an increasing trend in 2001–2017, but the differences in carbon productivity among provinces are widening. (2) Economic activity and industrial structure are key to push up regional carbon productivity in China, while energy intensity is the main factor pulling it down. (3) The potential for carbon productivity improvement varies greatly among provinces in the four groups. Specifically, in groups 1 and 2, the developed provinces have little potential for improving carbon productivity, while the developing provinces in group 4 are just the opposite. These findings can enlighten policymakers that the development of a green economy should focus on optimizing and upgrading industrial structure and reducing energy intensity, and provincial heterogeneity must be considered when formulating green economic development policies.
... According to the index theoretical basis of the decomposition method, the existing index decomposition methods can be divided into two types: Laspeyres IDA and Divisia IDA. Because the log-mean Divisia index (LMDI) has the strongest ability to deal with zero values and has the advantages of time reversibility, factor reversibility, and aggregation, it is considered to be the optimal decomposition method by Ang 2005Ang , 2015. Therefore, we use the LMDI decomposition method for analysis in the first stage of the IDA model. ...
In order to effectively analyze and explore the socio-economic impact of haze pollution, the article constructs a comprehensive two-stage decomposition model to verify that technological progress plays a key role in controlling haze pollution. And for the first time, a macro-level research framework for the rebound effect of haze pollution has been constructed to compare and analyze the heterogeneity of the rebound effect of technological progress in different industries in different regions. The study found that (1) during the period 2000–2017, haze pollution situation deteriorated. Economic effects were the main reasons for haze pollution. Among these effects, technological progress was the main driving force for haze control, followed by the emission intensity during 2000–2011 and the reduction of industrial structure since 2014. (2) The significant drive of emission reduction is in the secondary industry, showing a trend of first increasing and then decreasing. Besides, there was a difference in spatial distribution, which shows an increased trend from east to west. (3) The rebound effect of haze pollution at the macro level in China presented high-level fluctuations, and there were certain spatial distribution differences. However, due to the convergence of technological development stages, regional differences have a gradual convergence trend. In the future, in the process of haze control, it is necessary to increase support for technological innovation, implement energy total control and price reform, promote technological progress, and implement differentiated haze reduction policies to solve problems according to local conditions.
... Second, the LMDI approach introduces no uncertainties mathematically as the decomposition results do not contain residual terms 15 . Although the use of different decomposition methods might generate different decomposition results, the LMDI is regarded as the most preferred index decomposition analysis method due to its theoretical foundation, adaptability and ease of use and result interpretation 63 . Third, PM 2.5 concentrations simulated by the WRF/ CMAQ model are also subject to uncertainties due to the model's imperfect representation of chemical and physical processes 60 . ...
Between 2002 and 2017, China’s gross domestic product grew by 284%, but this surge was accompanied by a similarly prodigious growth in energy consumption, air pollution and air pollution-related deaths. Here we use a combination of index decomposition analysis and chemical transport modelling to quantify the relative influence of eight different factors on PM2.5-related deaths in China over the 15-year period from 2002 to 2017. We show that, over this period, PM2.5-related deaths increased by 0.39 million (23%) in China. Emission control technologies mandated by end-of-pipe control policies avoided 0.87 million deaths, which is nearly three-quarters (71%) of the deaths that would have otherwise occurred due to the country’s increased economic activity. In addition, energy-climate policies and changes in economic structure have also became evident recently and together avoided 0.39 million deaths from 2012 to 2017, leading to a decline in total deaths after 2012, despite the increasing vulnerability of China’s ageing population. As advanced end-of-pipe control measures have been widely implemented, such policies may face challenges in avoiding air pollution deaths in the future. Our findings thus suggest that further improvements in air quality must not only depend on stringent end-of-pipe control policies but also be reinforced by energy-climate policies and continuing changes in China’s economic structure. Emission controls avoided some 870,000 deaths in China between 2002 and 2017 but further air quality improvements need energy-climate policies and changed economic structure, according to index decomposition analysis and chemical transport models.
... 62,63 The LMDI is one of the index decomposition analysis (IDA) approaches, which quantify the contribution of factors to changes in indicators such as energy consumption and CO 2 emissions. 64 The LMDI is recommended among IDA approaches because of its theoretical foundation, adaptability, ease of use and result interpretation, and perfect decomposition. 65 It has been adopted to analyze not only CO 2 emissions 66-68 but also material use-related concerns. ...
The projected GHG emissions cannot reach the climate goal under any SSP. Further efforts on lowering per capita in-use metal stocks and GHG emission intensity of metal production and promoting recycling are the key to achieve the climate goal.
... Firstly, in order to simplify the discussion the Logarithmic Mean Divisia Index (LMDI) was calculated [57]. An additive decomposition scheme was applied as its more suitable to quantitative analysis [58]. Figure 16. ...
Transformation of road transport sector through replacing of internal combustion vehicles with zero-emission technologies is among key challenges to achievement of climate neutrality by 2050. In a constantly developing economy, the demand for transport services increases to ensure continuity in the supply chain and passenger mobility. Deployment of electric technologies in the road transport sector involves both businesses and households, its pace depends on the technological development of zero-emission vehicles, presence of necessary infrastructure and regulations on emission standards for new vehicles entering the market. Thus, this study attempts to estimate how long combustion vehicles will be in use and what the state of the fleet will be in 2050. For obtainment of results the TR3E partial equilibrium model was used. The study simulates the future fleet structure in passenger and freight transport. The results obtained for Poland for the climate neutrality (NEU) scenario show that in 2050 the share of vehicles using fossil fuels will be ca. 30% in both road passenger and freight transport. The consequence of shifts in the structure of the fleet is the reduction of CO2 emissions ca. 80% by 2050 and increase of the transport demand for electricity and hydrogen.
... Suppose that the global SEI varies from time t − 1 to t (i.e., V t−1,t tot = SEI t − SEI t−1 ). Such a change can be expressed in the following additive form [49] as Equation (15), which indicates that global SEI change is related to three factors: energy structure, energy intensity and industrial structure. ...
International trade links countries consuming goods and services to those where products and related SO2 pollution are produced, thereby affecting national mitigation responsibilities. This study combined accounting and decomposition techniques to investigate the patterns and drivers of SO2 emissions embodied in international trade from 1995 to 2015 and quantified the contribution of each country or region on the production and consumption sides. The global embodied emissions increased at an accelerated rate before the global financial crisis and peaked at 51.3 Mt in 2008, followed by a fluctuating decline from 2008 to 2015. Spatially, the transfers of SO2 emissions tended to flow from developed countries to less developed ones, but the trend has weakened after the financial crisis. Our decomposition analysis suggests that the energy and production system transitions and the slowdown in international trade jointly accounted for the peak and decline in emissions. Our contribution analysis indicates that developing economies have contributed to decreased emissions due to their recent efforts in production technology upgrading, energy efficiency improvement and energy structure optimization. The influence of developed economies on emissions decreased due to their reduced dependency on imports. Targeted policy methods are provided from the production and consumption perspectives for developing and developed economies, respectively.
This study investigates the factors influencing the carbon emission of Chinese residents in urban and rural areas. Drawing on the Logarithmic Mean Divisia Index (LMDI), the results of this study identify the increasing demand for energy consumption from residential buildings and the strengthening of the volume effect as the primary causes for the surging carbon emission by residents. Rural areas feature a larger cumulative contribution to carbon emission due to energy demand from housing and transportation and a higher contribution to the volume effect of energy consumption. In contrast, urbanization, a key driver for residents’ energy consumption, shows a year-by-year decline.
The study offers thorough analysis of the impact of the European Union (EU) energy policy on decoupling greenhouse gas (GHG) emissions from economic growth between 1996 and 2017. Two types of GHG measurements, production-based accounting (PBA) and consumption-based accounting (CBA), are used to account for the possible outsourcing with the Tapio framework. Five factors taken from the Log-Mean Divisia Index are considered in order to determine the main contributors of emission mitigation.
The results demonstrate decoupling GHG emissions from economic growth in almost all countries, regardless of the type of GHG measurements. However, in the case of production-based emissions, the decoupling occurs faster. After 2005, when the EU launches its energy policy, the decoupling speeds up. LMDI results indicate that the improvements in energy intensity and the energy structure are the most significant drivers of GHG emissions reduction. There are differences between the reduction of the two types of GHG emissions and its determinants in old and new EU countries.
In almost all EU countries the decoupling is linked to the EU energy policy but the reduction of GHG emissions partly comes at the expense of outsourcing, i.e. the relocation of emission-intensive industries to countries outside the EU.
Over the past three decades, Saudi Arabia's greenhouse gas (GHG) emissions have increased sharply. This study exposes the factors that affect GHG emissions in nine sectors via the logarithmic mean Divisia index (LMDI) method for 1990 to 2016. The analysis demonstrates that the energy effect was a leading factor increasing greenhouse gas emissions, at 386.76 million tonnes of carbon dioxide-equivalent (MTCO2e). Activity and population effects also contributed to the increase in emissions at 339.56 (MTCO2e) and 267.38 (MTCO2e), respectively, but the energy effect was greater than the other effects. Results reveal that activity, energy and population effects are greater in the electricity sector than the transport sector. The electricity sector increased greenhouse gas emissions by 4298.05 (MTCO2e) and transport, 2243.63 (MTCO2e). Therefore, policymakers need to consider climate change when they are developing economic growth plans to achieve sustainable development. This may be done through adopting a new policy to transfer from traditional sources to renewable energy sources or focusing on raising energy efficiency and changing energy structure to impact the growth of greenhouse gas emissions.
Energy quota trading (EQT) is a market-driven method to reduce distortions in energy allocation. In 2016, an EQT policy was proposed in China. However, the economic and environmental effects of this new policy remain unclear. To study these problems, this paper conducts an empirical analysis using data envelopment analysis (DEA) and index decomposition analysis (IDA). Following a simulation of the optimal energy input and maximum output in three sectors of the 30 Chinese provinces that have adopted China's EQT policy, the main conclusions are as follows: (1) The energy consumption of the industrial sectors is excessive, yet the primary and service sectors need further energy input, while there is vast economic potential in China's western provinces and the service sectors. (2) The optimal energy intensity effect generated by the trading policy is the largest impetus of carbon intensity reduction in China, but the obstacles to carbon intensity reduction are different in each region. (3) EQT has the potential to drive a significant decline in carbon intensity in China, resulting in a simulated average annual decline of approximately 4.09% from 2005 to 2016.
Domestic intercity air travel accounts for nearly 80% of all air travel in the U.S. As low-cost airlines and economic growth have increased the affordability of intercity air travel, the benefits to travelers and cities are touted by most. Yet, an accounting of the energy consumption and emissions from domestic intercity air travel is rarely discussed. This paper fills that gap and provides an analysis of the trends in fuel consumption and emissions by intercity air travel as well as an analysis of the contributing factors to these emissions. Using flight tracking and carrier data, emissions per passenger are found to have declined by 40% over the past two decades while total emissions have increased by 10% over the past 8 years. A decomposition analysis of the market factors highlights changes in route frequency, fleet deployment, and fleet efficiency as the leading factors contributing to the decline of intercity emissions.
This paper analyses population effects of increase in world energy use and CO2 emissions between 1990–2019 following a decomposition framework with interaction effects. The analysis has also been carried out for the 44 countries which accounted for most of the increase in world energy use and CO2 emissions during 1990–2019. Population growth was found to have a significant effect on both the increase in energy use and CO2 emissions at the global level, although the contribution of population growth to these increases has varied widely across countries. There is a need for integrating population factors in the sustainable development processes, particularly efforts directed towards environmental sustainability.
Cropland ammonia (NH3) emission is a critical driver triggering haze pollution. Many agricultural policies were enforced in past four decades to improve nitrogen (N) use efficiency while maintaining crop yield. Inadvertant reductions of NH3 emissions, which may be induced by such policies, are not well evaluated. Here, we quantify the China’s cropland-NH3 emission change from 1980 to 2050 and its response to policy interventions, using a data-driven model and a survey-based dataset of the fertilization scheme. Cropland-NH3 emission in China doubled from 1.93 to 4.02 Tg NH3-N in period 1980-1996, and then decreased to 3.50 Tg NH3-N in 2017. The prevalence of four agricultural policies may avoid ~3.0 Tg NH3-N in 2017, mainly located in highly-fertilized areas. Optimization of fertilizer management and food consumption could mitigate three quarters of NH3 emission in 2050 and lower NH3 emission intensity (emission divided by crop production) close to the European Union and the United States. Our findings provide an evidence on the decoupling of cropland-NH3 from crop production in China, and suggest the need to achieve cropland-NH3 mitigation while sustaining crop yields in other developing economies.
Net primary productivity, which reflects vegetation's carbon sequestration capacity, should not be ignored in the process of low-carbon development. To explore the paths of achieving low-carbon development goals from the perspective of carbon sequestration, this study takes net primary productivity as the study object and uses the logarithmic mean Divisia index decomposition model to analyze the influences of urbanization and land utilization change on net primary productivity in China. The results show that urbanization is the driving factor of the net primary productivity increase, and land utilization change is the main determinant of its decrease. This study argues that making reasonable land utilization planning is essential to reduce the net primary productivity loss caused by land utilization changes in the process of urbanization.
In this study, the LMDI method is used to decompose the production power consumption changes of the Yangtze River Economic Zone into an advanced technology effect, structural effect, income effect, and population effect. The regional and industry differences in the driving effect of electricity consumption change are also investigated. From the perspective of time differences, the progress of power-saving technology is the main factor restraining the growth of electricity consumption; whereas, economic growth, industry structure adjustment, and population size adjustment promote the growth of electricity consumption; and the other three influencing factors, in turn, decrease. From the perspective of regional differences, regional electricity consumption varies significantly, and various regions have great room for improvement in industry structure. Regional population affects electricity consumption from two aspects: absolute population quantity change and population mobility. From the perspective of industry differences, electricity consumption varies significantly among industries and is mainly reflected in the effect of technological progress and structural effects. As a result, control measures on electricity consumption in the Yangtze River Economic Zone should focus on the promotion of power-saving technology and industry structure adjustment.
The fundamental goal of sustainable urban development is to maximize human well-being with minimum ecological consumption. The ecological intensity of urban well-being (EIWB) achieves an effective linkage among economic, social, and ecological systems, and it is an effective indicator for evaluating urban sustainable development. This study analyzed the spatio-temporal evolution characteristics and driving effects of the ecological intensity of urban well-being over 2000–2019 in the Yangtze River Delta. It was found that as the ecological consumption per unit well-being output decreased gradually, the improvement in well-being level and the increase in ecological consumption were increasingly delinked, and regional EIWB and its sub-dimensions tended to fluctuate. Urban EIWB was dominated by low and lower levels, urban economic well-being (ECWB) was increasingly dominated by the lower type, and urban social well-being (SOWB) and environmental well-being (ENWB) were dominated by the low level. The resource consumption, technology, and well-being effects distinctively inhibited the decrease in regional EIWB and the economic effect exerted an obvious boosting function, whereas environmental consumption effect, scale effect, and efficiency effect had no obvious impact. The variation in urban EIWB was mainly driven by two-factor dominance, featuring economic and technological effects.
The reduction of carbon intensity of the whole process of agricultural products logistics is of great significance to the comprehensive control of China's carbon intensity. LMDI decomposition method, STIRPAT model and quantitative decoupling analysis model are used to study the influencing factors, future development scenarios and decoupling effect with economic development of the whole process carbon intensity of agricultural products logistics in China from 2000 to 2017. The countermeasures and suggestions to reduce the carbon intensity of the whole process of agricultural products logistics in China are put forward based on the research results of influencing factors, scenario prediction and decoupling effect.
Water-energy-food (WEF) nexus system enables to examine the implications for sustainability. Inevitably, shocks from socioeconomic subsystems would impact the stability and reliability of the WEF nexus system. This paper aims to identify the reasons for changes in water withdrawal, energy consumption, and crop consumption, and uncover differentiated effects from both the national and provincial perspectives, and finally offer the main strategies in China that balance socioeconomic development and resource-saving from the consumption perspective. Empirical results in China from 2011 to 2017 show that (1) the reduction of per capita energy consumption leads to a decrease in water withdrawal, while increased population and water withdrawal intensity in energy sectors have adverse effects on diminishing water withdrawal. (2) The decrease in energy consumption mainly results from improved energy use efficiency for the whole period. (3) Reduction in national crop consumption is mainly caused by the decline in energy consumption per person over the years, which implies a positive effect of food-energy nexus effect on the decrease in crop consumption. (4) Spatially-temporarily contribution rates of drivers offer implications to the implementation of resource-saving and sustainability in the future. Directions and magnitudes of driving forces are different from the sub-nation perspective, hence, finally takes Beijing-Tianjin-Hebei (BTH) region as an example, we analyze clearly the relative planning and reports and propose several pathways to accelerate the sustainable development in the BTH region.
China’s carbon peak greatly impacts global climate targets. Limited studies have comprehensively analyzed the influence of the COVID-19 pandemic, changing emission network, and recent carbon intensity (CI) reduction on the carbon peak and the corresponding mitigation implications. Using a unique dataset at different levels, we project China’s CO2 emission by 2035 and analyze the time, volume, driver patterns, complex emission network, and policy implications of China’s carbon peak in the post- pandemic era. We develop an ensemble time-series model with machine learning approaches as the projection benchmark, and show that China’s carbon peak will be achieved by 2021–2026 with > 80% probability. Most Chinese cities and counties have not achieved carbon peaks response to the priority-peak policy and the current implementation of CI reduction should thus be strengthened. While there is a "trade off" between the application of carbon emission reduction technology and economic recovery in the post-pandemic era, a close cooperation of interprovincial CO2 emission is also warranted.
Quantifying the drivers of energy intensity change could provide valuable information for policy analysis and making. This study applied production-theoretical decomposition analysis to investigate the driving forces behind the energy intensity changes in Chinese cities between 2006 and 2017. The empirical results show that technological change and capital-energy substitutions were responsible for 6% and 4% of the fall in energy intensity on average, respectively, while technical efficiency contributed to about 2% of the increase in energy intensity, with significantly different magnitude across various cities. We further classified the sample cities based on the simultaneous effect of decomposition results, identifying the effective combinations of drivers on energy intensity reduction for different types of cities. The classification reveals that double-drivers and triple-drivers of energy intensity change prevail, which explains why energy intensity significantly fell in eastern and central regions but increased in the western region. Furthermore, the cities were categorized into four groups based on the differences in changes in energy intensity and energy efficiency. It reveals the key measures used to reduce energy intensity, and identifies that over a half of the cities with similar changing trends of energy intensity might be misjudged by policymakers with respect to energy efficiency performance.
Singapore is a small island-state and meticulously planned city. The lack of energy resources has underpinned its unwavering focus on energy efficiency. Yet to date it is unclear to what extent energy efficiency, or the so-called “hidden fuel” or “invisible fuel”, has contributed to reductions in its energy consumption. Using a comprehensive and unique set of energy and sectoral activity data, this study applies the energy intensity concept and index decomposition analysis technique to study the role of energy efficiency in reducing Singapore's energy consumption from 2005 to 2018. The results obtained show variations across energy consuming sectors, but overall energy efficiency has led to significant energy savings. A composite energy efficiency index is also constructed to quantify the extent of energy efficiency improvement economy-wide over time. The performance as captured by this index shows a slower rate of improvement as compared to that given by the reduction in Singapore's aggregate energy-to-GDP ratio. This development is similar to that observed in most OECD countries. The framework developed can be applied to the study of other major cities especially those in Southeast Asia. It should be noted that, while the approach used has been widely accepted in the energy efficiency literature, the energy efficiency trends derived are dependent on the activity indicators chosen to represent the useful work performed in the respective sectors. The results should therefore be interpreted in this context.
Limited empirical evidence exists on the role of electricity intensity of residential income and disposable income in China's electricity consumption. Furthermore, little attention has been paid to the variation in the growth rate of electricity consumption. We develop a Logarithmic Mean Divisia Index model to examine factors that affect electricity consumption and whether there are structural changes impacting electricity consumption. The drivers of slowing electricity consumption in China between 2007 and 2019 are quantified. We find that China's electricity consumption is closely associated with economic growth during the current New Normal period 2013–2019. Decelerated electricity consumption rate is linked to adjustments in industrial structure and a decline in energy intensity. Decreasing the electricity intensity of residential income also contributed to the decelerated rate. In 2014 there was a clear structural break in the growth rate of China's electricity consumption. We conclude that the decline in the electricity consumption growth rate is structural, and the low average annual growth rate will be sustained if nascent energy efficiency improvements and industry transition continue. Therefore, both electricity consumption and its influencing factors should be monitored continuously. Policies should be aimed at promoting energy efficiency and optimizing industrial structure.
The spatial layout of the steel industry has an impact on the regional atmospheric environment. In this study, the steel industry evolution model and the driving force analysis model were combined to analyze the evolution of spatial layout of the steel industry in China and the driving factors of this evolution. In addition, the WRF-SMOKE-CMAQ model was used to analyze the spatial dynamics of SO2 emissions from the steel industry. Our analysis presents the evolution of the steel industry in China in four stages: policy-determining, resource-oriented, economic promotion and market-oriented stage. The change in the spatial layout of the Chinese steel industry resulted in a continuously decreasing trend of pollutants in temporal characteristics and a decreasing share of emissions in North China and a continuous growth in East China in spatial characteristics. Our simulation shows that, by 2025, the pollutant SO2 emission concentration will migrate to the southeast, subject to market-oriented factors.
While marine capture fishery contributes to ever-increasing economic output, it also produces ever-increasing carbon emissions. Increasing the economic output of marine capture fishery while controlling its carbon emissions requires a better understanding of the relationship between the economic output of marine capture fishery and its carbon emissions. To this end, this work investigated the relationship between carbon emission and economic output, the key influencing factors of carbon emission, and the decoupling efforts in China’s marine capture fishery using decoupling index, decomposition technique, and decoupling efforts model. The results indicate: (i) Decoupling state of all coastal regions got improved, especially Guangdong province which presented the best decoupling state, the stably strong decoupling. (ii) Carbon intensity is not only major contributor to carbon reduction, but also major effort maker to decoupling progress. On the contrary, industry structure was the primary inhibitor in almost coastal regions. (iii) All coastal regions made decoupling efforts in studying period, and over half of them made strong decoupling efforts. Last but not least, some policy implications are proposed.
Xinjiang has experienced a relatively rapid economic growth over the last few decades due to China's silk road economic belt initiative. This study aims to develop a coordinated development index for exploring the relationship between urbanization, resources and environment from 2004 to 2018. The inflection points of resources environment pressure caused by urbanization rate were measured using the Kaya identity, Logarithmic Mean Divisia Index model and regression analysis. The results show that: (1) Urbanization has growth rapidly, with the comprehensive urbanization level of 0.851 in 2018, which is about eight times higher than that in 2004. In contrast, resources and environment have decreased at an average annual declining rate of 0.77% and 5.29%, respectively; (2) From 2004 to 2018, the coordination degrees of the resources-environment, urbanization-environment, and urbanization-resources had a fluctuating growth trend, with the coordination values of 0.505–0.987, 0.171–0.847 and 0.469–0.923, respectively. Overall, the coordinated development index of urbanization, resources and environment has achieved an average annual growth rate of 1.89%; and (3) The urbanization rate of Xinjiang has reached 50.91% in 2018, enters middle rising channel of inverted N-shaped curve implied that the resources environment pressure tended to deteriorate first and then gradually improve. Therefore, it is necessary continue to follow the path of resources-conserving urbanization and environment-friendly urbanization to achieve the sustainable development in Xinjiang.
China's extensive and growing carbon dioxide (CO2) emissions are linked to rapid economic development and advancing urbanization, posing serious concerns in the context of climate change. Decomposition analysis has been widely performed to identify the drivers of China's CO2 emissions. However, to date, no researchers have examined the drivers of the change in CO2 emissions under the progress of urbanization across all of its provinces. Using provincial statistical data and six key factors influencing CO2 emissions (carbon intensity, energy intensity, resident consumption, consumption inhibition, population urbanization, and population size), we applied the logarithmic mean Divisia index decomposition method to examine how urbanization affect CO2 emission changes across 30 provinces during 1990–2016. We elucidated that while urbanization's effects on CO2 emissions increased in China as a whole during this period, they were regionally differentiated. The energy intensity effect was the main driver of reduced CO2 emissions, with carbon intensity exerting weaker effects in the 30 provinces, differentiated by their energy structures. The resident consumption effect, strongly linked to advancing urbanization, was the primary driver of increased CO2 emissions in all the provinces. While the consumption inhibition and population urbanization effects were positive at the national level, they were negative in highly urbanized provinces and in highly industrial provinces. These findings highlight the need to promote environmentally friendly consumption and to design regionally differentiated policies and optimized energy structures tailored to particular urbanization contexts. Moreover, they can provide valuable inputs for other developing countries undergoing continuous urbanization, contributing to efforts to balance economic development and environmental sustainability.
Eradicating poverty and mitigating greenhouse gas (GHG) emissions are core issues of global sustainable development goals (SDGs), and China is struggling in realizing these targets. The poverty reduction that leads to popualtion structure and lifestyle changes would have an impact on GHG emission changes. However, few studies have assessed the historical and future impacts of the poverty allevation on China's emissions. Here by linking Chinese Multi-Regional Input Output (MRIO) database to the global MRIO database EXIOBASE, and using provincial household consumption data, we identified the distribution of Chinese household greenhouse gas footprints (HGFs) by income groups in 2015 at the national and provinical levels. Moreover, we focused on the historical impact of poverty alleviation on HGFs during 2010–2015, and developed four scenarios to project future HGFs changes due to poverty alleviation by 2030. We find that eradicating extreme poverty in the secanrio S2, i.e., bringing people to an income above $1.9 daily, does not cause a large emission impact with current technological level. However, lifting people from a higher poverty line of $5.5 per day in the sceanrio S4 results in a 1.6% increase in emissions compared with the scenario S1 without any poverty reduction goals. Furthermore, realizing a higher poverty reduction target will result in an increase of emissions contribution from internatioanl supply chains due to the differences in consumption patterns among different income groups. Our study highlights the conflict between the high poverty alleviaition goal and emission reduciton in China, and reminds us of the need to make more technological efforts for avoiding the large emissions embodied in international supply chains.
Household greenhouse-gas footprints (HGFs) are an important source of global emissions but can vary widely between urban and rural areas. These differences are important during the ongoing rapid, global, urbanization process. We provide a global overview of HGFs considering this urban-rural divide. We include 16 global regions, representing 80% of HGFs and analyze the drivers of urban and rural HGFs between 2005 and 2015. We do this by linking multi-regional input-output (MRIO) tables with household consumption surveys (HCSs) from 43 regions. Urban HGFs from high-income regions continue to dominate, at 75% of total HGFs over 2010–2015. However, we find a significant increase of rural HGFs (at 1% yr⁻¹), reflecting a convergent trend between urban and rural HGFs. High-income regions were responsible for the majority of urban HGFs (USA: 27.8% and EU: 18.7% in 2015), primarily from transport and services, while rural HGFs were predominately driven in emerging regions (China: 24% and India: 21.8% in 2015) mainly driven by food and housing. We find that improving emission intensities do not offset the increase in HGFs from increasing consumption and population during the period. A broad transition of expenditure from food to housing in rural areas and to transport in urban areas highlights the importance of reducing the emission intensities of food, housing, and transportation. Counterintuitively, urbanization increased HGFs in emerging regions, resulting in a >1% increase in China, Indonesia, India and Mexico over the period, due to large migrations of people moving from rural to urban areas.
China has been placing a substantial focus on biogas for reducing energy consumption and carbon dioxide (CO2) emissions. The operation mode of biogas systems may make the CO2 reduction target over-optimistic. There is limited research to investigate the influential factors that may be causing the gap between the actual and theoretical CO2 reduction costs of biogas systems in China. In this research, by using field survey data of 209 biogas users and 489 non-biogas users from 19 villages in 2015, the gap between actual and theoretical unit CO2 reduction cost is quantified at approximately 156 USD/t CO2. By employing the Logarithmic Mean Divisia Index I (LMDI) model, it is found that both the cost effect (48%) and the reduction effect (52%) contribute to the unit CO2 reduction cost gap. Four influential factors–household labor, accessibility to the energy resource, acceptance of biogas technology, and subsidy–significantly narrow the gap between actual and theoretical CO2 reduction costs, while the levelized subsidy contributes to widening the gap. On average, biogas systems should be operated for at least four years and the substitution rate should be more than 67% in order to keep the gap between actual and theoretical CO2 reduction costs under 50%.
Decomposition studies typically present structural change effect – such as the impact of changes in industry output shares on energy demand – as a single, aggregate contribution. However, if the purpose is to identify the role of individual sectors, an alternative approach is required. The generalized Divisia index method (GDIM) proposed by Vaninsky (2014) allows to decompose the structural change effect by attributing its parts to changes in individual shares. At the same time, GDIM takes into account interdependence of individual shares, stemming from the unit-sum constraint. In this paper we propose an arguably simpler and intuitive formulation of such a decomposition, building on the Harrison, Horridge, and Pearson (2000) decomposition method. This formulation also provides a rationale for the interpretation of individual share effects. We demonstrate that unravelling the pattern of structural change in a formal way can lead to identifying a few expanding or contracting activities as key contributors to change in energy use. This point is supported by an illustrative application, in which we decompose changes in electricity and heat demand in the European Union in the years 2000–2014.
Different technological and policy solutions have been developed to decarbonise energy systems and improve energy efficiency. Combined heat and power (CHP) is one solution that can bring about emission reductions by improving fuel use efficiency through the simultaneous generation of electricity and heat. Index decomposition analysis (IDA) is a tool that has been widely used to study emission trends and quantify the contribution of different measures to emission reductions. Techniques to estimate the emission reductions from CHP using IDA, however, have yet to be developed. This study develops new decomposition frameworks for two methods that have been used to estimate the carbon intensities of CHP and compares their performance theoretically and empirically using the data of Canada, Denmark and Finland. Based on the analysis, the substitution method is recommended as it can be more universally applied to different energy systems. Through the substitution method, the impact of different climate mitigation measures such as power generation efficiency and CHP can be compared within a single IDA framework.
Carbon emissions from the electricity industry (CEEI) account for more than 40% of China's total emissions. This paper examines the influential factors of China's CEEI at both national and provincial level and explores targeted provincial strategies, which are critical for China to control its CEEI effectively and to achieve its carbon peaking aim. First, this study quantifies the contributions of nine factors influencing China's CEEI increase using Logistic Mean Divided Index (LMDI) decomposition. The results show that economic growth is the dominant driver, while power consumption intensity, energy intensity of thermal power generation (TPG) and power mix are the main inhibitors. After stepping into the new era in 2012, in general, the evolutions of all the 4 main factors aided CEEI control. Second, according to the recent status of the main factors, we classify 30 provinces into 4 groups with K-means clustering. And then, based on the characteristics of each group, the paper puts forward provincial targeted recommendations to address the rebound of CEEI since 2017 and to promote the low-carbon transformation of China's electricity industry. This study confirms that it is a promising direction for LMDI model to combine with cluster analysis and proposes a basic flow for this combination: LMDI → main influencing factors → clustering variables → cluster analysis → targeted strategies, which will conduce to deepen LMDI applications.
Energy plays a key role in economic development. Improving energy efficiency through the use of financing instruments requires a thorough understanding of energy efficiency dynamics. This study investigates the driving factors of energy efficiency that are necessary for decision makers to focus on, and characterizes the long-run tendency of energy efficiency. This is based on a dataset covering 59 major economies in the world from 2000 to 2017. First, this chapter adopts an index decomposition approach to quantify the driving factors. The results show an overall improvement in energy efficiency between 2000 and 2017, which is driven by a technology-led efficiency effect as well as an economic structure effect, despite the heterogeneity across economies. Second, the transition matrix approach based on the Markov chain is employed to explore the steady state distribution of energy efficiency, in which around 21.23% of sample economies would stay at levels lower than the world average. The results suggest the persistent gap in energy efficiency across economies and highlight the importance of energy efficiency financing for those economies in which energy efficiency is low ranking or deteriorates, which are mostly emerging Asian economies.
As the world's largest primary aluminum producer, China's primary aluminum industry (PAI) faces a huge challenge in reducing greenhouse gas (GHG) emissions. However, detailed research on presenting the historical trajectory of GHG emissions from China's PAI and identifying the main driving factors affecting its changes has not been completed thus far. This study quantifies the GHG emission trajectory of China's PAI from 1990 to 2018 and identifies the key driving factors affecting its changes. The results show that the total GHG emissions from China's PAI from 1990 to 2018 increased by approximately 18 times, reaching 481 Tg CO2-eq in 2018, of which 69 %, 17 %, and 14 % were electricity-related, fuel-related, and process-related, respectively. Additionally, the production activity effect is the main factor driving the increase in GHG emissions; however, the energy intensity and energy emission factor effects can effectively reduce GHG emissions. Based on this, scenario analysis is used to evaluate the GHG emission mitigation potential of China's PAI by 2030. According to our analysis, policy suggestions for mitigating the GHG emissions in China's PAI are proposed, including reducing the energy intensity, promoting clean energy use, controlling the production capacity, and decarbonizing electricity.
This paper analyzes the energy, environmental and economic influences of three electricity scenarios in Korea by 2050 using the “Long-range Energy Alternatives Planning system” (LEAP) model. The reference year was 2008. Scenarios include the baseline (BL), new governmental policy (GP) and sustainable society (SS) scenarios. The growth rate of electricity demand in the GP scenario was higher than that of the BL scenario while the growth rate in the SS scenario was lower than that of the BL scenario.Greenhouse gas emissions from electricity generation in 2050 in the BL and GP scenarios were similar with current emissions. However, emissions in 2050 in the SS scenario were about 80% lower than emissions in 2008, because of the expansion of renewable electricity in spite of the phase-out of nuclear energy.While nuclear and coal-fired power plants accounted for most of the electricity generated in the BL and GP scenarios in 2050, the SS scenario projected that renewable energy would generate the most electricity in 2050. It was found that the discounted cumulative costs from 2009 to 2050 in the SS scenario would be 20 and 10% higher than that of the BL and GP scenarios, respectively.
This study analyzes toxic chemical substance management in three U.S. manufacturing sectors from 1991 to 2008. Decomposition analysis applying the logarithmic mean Divisia index is used to analyze changes in toxic chemical substance emissions by the following five factors: cleaner production, end-of-pipe treatment, transfer for further management, mixing of intermediate materials, and production scale. Based on our results, the chemical manufacturing sector reduced toxic chemical substance emissions mainly via end-of-pipe treatment. In the meantime, transfer for further management contributed to the reduction of toxic chemical substance emissions in the fabricated metal industry. This occurred because the environmental business market expanded in the 1990s, and the infrastructure for the recycling of metal and other wastes became more efficient. Cleaner production is the main contributor to toxic chemical reduction in the electrical product industry. This implies that the electrical product industry is successful in developing a more environmentally friendly product design and production process.
The amount of materials used worldwide in production and consumption increased by 56% from 1995 to 2008. Using an index decomposition analysis based on the logarithmic mean Divisia index, we investigate the drivers of material use, both on a global and a country scale. We exploit a panel dataset of 40 countries, accounting for 75% of worldwide material extraction and 88% of GDP, from 1995 to 2008. The results show that economic growth and structural change towards material-intensive countries explain most of the growth in global material use. Slight gains in material efficiency and falling importance of material-intensive sectors have decelerating effects. The country-level analysis reveals substantial heterogeneity. Some nations exhibit stable or falling material use, while it increases notably in most countries. Improving material efficiency is able to dampen growth of material use in important industrializing nations like China or India.
This paper presents future scenarios of Irish energy-related CO2 emissions to 2020, using a combination of
multi-sectoral decomposition analysis with scenario analysis. Alternative development paths, driving forces
and sectoral contributions in different scenarios have been explored. The scenarios are quantified by using
decomposition analysis as a Divisia Index SCenario GENerator (DISCGEN). The driving forces of population,
economic and social development, energy resources and technology and governance and policies are
discussed. A set of four integrated or ‘hybrid’ qualitative and quantitative baseline emission scenarios are developed.
It is found that sectoral contributions and emissions in each scenario vary significantly. The inclusion
of governance, social and cultural driving forces are important in determining alternative development paths
and sustainability is crucial. Our empirical results show that decomposition analysis is a useful technique to
generate the alternative scenarios.
China's water scarcity problems have become more severe because of the unprecedented economic development and population explosion. Considering agriculture's large share of water consumption, obtaining a clear understanding of Chinese agricultural consumptive water use plays a key role in addressing China's water resource stress and providing appropriate water mitigation policies. We account for the Chinese agricultural water footprint from 1990 to 2009 based on bottom up approach. Then, the underlying driving forces are decomposed into diet structure effect, efficiency effect, economic activity effect, and population effect, and analyzed by applying a log-mean Divisia index (LMDI) model. The results reveal that the Chinese agricultural water footprint has risen from the 94.1 Gm3 in 1990 to 141 Gm3 in 2009. The economic activity effect is the largest positive contributor to promoting the water footprint growth, followed by the population effect and diet structure effect. Although water efficiency improvement as a significant negative effect has reduced overall water footprint, the water footprint decline from water efficiency improvement cannot compensate for the huge increase from the three positive driving factors. The combination of water efficiency improvement and dietary structure adjustment is the most effective approach for controlling the Chinese agricultural water footprint's further growth.
The iron and steel industry accounted for approximately 27% of China's primary energy use for the manufacturing industry in 2010. This study aims to analyze influential factors that affected the energy use of steel industry in the past in order to quantify the likely effect of those factors in the future. This study analyzes the energy use trends of China's key medium- and large-sized steel enterprises during 2000–2030. In addition, the study uses a refined Logarithmic Mean Divisia Index decomposition analysis to quantify the effects of various factors in shaping energy consumption trends in the past and in the future. The result of our forecast shows the final energy use of the key steel enterprises peaks in year 2020 under scenario 1 and 2 (low and medium scrap usage) and in 2015 under scenario 3 (high scrap usage). The three scenarios produced for the forward-looking decomposition analysis for 2010–2030 show that contrary to the experience during 2000–2010, the structural (activity share of each process route) effect and the pig iron ratio (the ratio of pig iron used as feedstock in each process route) effect plays an important role in reducing final energy use during 2010–2030.
Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices.
We envision this capability being driven by similarity-based queries posed to a cloud search front-end, which in turn dynamically retrieves media objects from mobile devices that best match the respective queries within a given time limit.
Building upon a crowd-sensing framework, we have designed and implemented a system called MScope that provides this capability.
MScope is an extensible framework that supports nearest-neighbor and other geometric queries on the feature space (e.g., clusters, spanners), and contains novel retrieval algorithms that attempt to maximize the retrieval of relevant information.
From experiments on a prototype, MScope is shown to achieve near-optimal query completeness and low to moderate overhead on mobile devices.
This paper proposes an approach for explaining and forecasting global industrial energy demand, at the country level, that could be seen as a trade-off between the two main options in use: ‘top-down’ and ‘bottom-up’ models. It relies on a two-term decomposition of industrial energy intensity, one evaluating the contribution of changes in the industrial structure, the other one reflecting the contribution of changes in sectoral efficiency. The former can be projected ‘bottom-up’ using microeconomic forecasts. The latter is modelled ‘top-down’ as a function of real per capita gross domestic product and electricity's market share. Hence, this approach enables both to project future demand and to disentangle the effects of structural mutations and efficiency gains for explaining past or future changes. The latter driver might further be decomposed into two factors: efficiency gains resulting from fuel substitutions and ‘other’ sectoral efficiency gains.
In this paper we extend the methodology of index decomposition analysis (IDA) in energy studies by quantifying the contribution of individual attributes to the percent change of factors such as the real energy intensity index and structural change index. We apply the proposed method to the real energy intensity index in the multiplicative Logarithmic Mean Divisia Index (M-LMDI) approach, a major IDA technique. Since the M-LMDI is based on geometric mean type indices and chain computation, we need some appropriate method to cope with the difficulties that arise. We present a numerical illustration of the proposed method using the energy consumption and real value added data of the US manufacturing industry, and compare the results obtained by the Fisher real energy intensity index.Research highlights► We extend the methodology of index decomposition analysis (IDA) in energy studies by quantifying the contribution of individual attributes to the percent change of factors such as the real energy index. ► We apply the proposed method to the real energy intensity index in the multiplicative Logarithmic Mean Divisia (M-LMDI) approach. ► We present a numerical illustration using the energy consumption and real value added data of the US manufacturing industry.
The use of corn for ethanol production in the United States quintupled between 2001 and 2009, generating concerns that this could lead to the conversion of forests and grasslands around the globe, known as indirect land-use change (iLUC). Estimates of iLUC and related ‘food versus fuel’ concerns rest on the assumption that the corn used for ethanol production in the United States would come primarily from displacing corn exports and land previously used for other crops. A number of modeling efforts based on these assumptions have projected significant iLUC from the increases in the use of corn for ethanol production. The current study tests the veracity of these assumptions through a systematic decomposition analysis of the empirical data from 2001 to 2009. The logarithmic mean divisia index decomposition method (Type I) was used to estimate contributions of different factors to meeting the corn demand for ethanol production. Results show that about 79% of the change in corn used for ethanol production can be attributed to changes in the distribution of domestic corn consumption among different uses. Increases in the domestic consumption share of corn supply contributed only about 5%. The remaining contributions were 19% from added corn production, and –2% from stock changes. Yield change accounted for about two-thirds of the contributions from production changes. Thus, the results of this study provide little support for large land-use changes or diversion of corn exports because of ethanol production in the United States during the past decade. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
Various policies have been implemented in the last decade to tackle rising greenhouse gas emissions. In this context it remains an open question of how to find a cost-efficient approach to climate change mitigation. Marginal abatement cost (MAC) curves are a useful tool to communicate findings on the technological structure and the economics of CO 2 reduction to decision makers. Existing ways of generating MAC curves fail to combine technological detail in the graphical representation with the incorporation of system-wide interactions and a framework for uncertainty analysis. This paper suggests a new approach to overcome the present shortcomings by using a bottom-up energy system model in combination with index decomposition analysis. For illustration purposes, this technique is applied to the transport sector of the United Kingdom in scenarios with varied fossil fuel production cost assumptions for the year 2030. The resulting MAC curves are found to be relatively robust to different fuel costs. The findings indicate that CO 2 reduction comes first from fuel decarbonisation, i.e. electricity, hydrogen and diesel, and at higher CO 2 prices from structural shifts. A minor contribution to emission savings comes from demand reduction, while efficiency improvements do not contribute to emission savings.
Several methods for decomposing energy consumption or energy-induced gas emissions in industry have been proposed by various analysts. Two commonly encountered problems in the application of these methods are the existence of a residual after decomposition and the handling of the value zero In the data set. To overcome these two problems, we modify the often used Divisia index decomposition method by replacing the arithmetic mean weight function by a logarithmic one. This refined Divisia index method can be shown to give perfect decomposition with no residual. It also gives converging decomposition results when the zero values in the data set are replaced by a sufficiently small number. The properties of the method are highlighted using the data of the Korean industry.
Index decomposition methodology was a technique first used in the late 1970s to study the impact of changes in product mix on industrial energy demand. A survey in 1995 listed a total of 51 studies. Since then, many new studies and several new decomposition methods have been reported and the methodology has been increasingly used in energy-related environmental analysis. We trace these new developments, discuss method formulation using an index number framework, and classify more than one hundred studies based on application area, aggregate indicator, and decomposition scheme. Application issues useful to researchers undertaking new studies and possible areas for future research are presented.
We introduce a decomposition method for factorizing changes in energy demand or gas emissions over time. This method has the advantage of giving perfect decomposition. It can also handle cases with zero values in the data set. We compare this new method with three existing methods and summarize the respective decomposition formulae for various applications. Three application studies using data for Singapore, China, and Korea are presented. In each case, the change of a different energy or environmental indicator is decomposed using the four methods and the results obtained are compared. Our new method is superior to any of the three existing methods and may be generally applied in energy and environmental decomposition studies.
We study the properties and linkages of some popular index decomposition analysis (IDA) methods in energy and carbon emission analyses. Specifically, we introduce a simple relationship between the arithmetic mean Divisia index (AMDI) method and the logarithmic mean Divisia index method I (LMDI I), and show that such a relationship can be extended to cover most IDA methods linked to the Divisia index. We also formalize the relationship between the Laspeyres index method and the Shapley value in the IDA context. Similarly, such a relationship can be extended to cover other IDA methods linked to the Laspeyres index through defining the characteristic function in the Shapley value. It is found that these properties and linkages apply to decomposition of changes conducted additively. Similar properties and linkages cannot be established in the multiplicative case. The implications of the findings on IDA studies are discussed.
Understanding the mechanisms of change of energy consumption in industry has attracted much attention since the 1973 world oil crisis. A popular line of research has been to decompose changes in the aggregate energy intensity of industry to give the relative impacts arising from energy intensity change and product-mix change using a decomposition technique. Many empirical studies covering a large number of countries have been reported. The main objective of this paper is to put together the empirical results reported in these studies in a coherent framework and identify possible systematic features. Of particular interest are the basic trends in this line of research, the relative importance of energy intensity change and product-mix change, possible variations over time and between country groups in the energy impacts of these changes, and inconsistencies in findings among studies. An overview of the decomposition technique, including its strengths, weaknesses, and role in industrial energy consumption studies, is also presented.
To analyze and understand historical changes in economic, environmental, employment or other socio-economic indicators, it is useful to assess the driving forces or determinants that underlie these changes. Two techniques for decomposing indicator changes at the sector level are structural decomposition analysis (SDA) and index decomposition analysis (IDA). For example, SDA and IDA have been used to analyze changes in indicators such as energy use, CO2-emissions, labor demand and value added. The changes in these variables are decomposed into determinants such as technological, demand, and structural effects. SDA uses information from input–output tables while IDA uses aggregate data at the sector-level. The two methods have developed quite independently, which has resulted in each method being characterized by specific, unique techniques and approaches. This paper has three aims. First, the similarities and differences between the two approaches are summarized. Second, the possibility of transferring specific techniques and indices is explored. Finally, a numerical example is used to illustrate differences between the two approaches.
Many differences can be found among the existing accounting systems for tracking economy-wide energy efficiency trends. There is a need for greater uniformity in the design and application of such systems but a formal study does not exist. This paper seeks to fill some of the gaps. It begins by introducing the basic concepts, indicators and terminology in this study area. This is followed by a review of the existing economy-wide energy efficiency accounting systems with a focus on the analytical framework. The merit of having a precise and meaningful relationship between two basic energy indicators, the energy efficiency index and the energy savings due to efficiency improvement, is elaborated. An accounting framework based on the LMDI decomposition technique which possesses a number of desirable properties is proposed. Numerical examples are presented to highlight these properties and show the differences among the various accounting frameworks. Several methodological and application issues are discussed, and the study concludes with key findings and recommendations.
Although a large number of energy decomposition analysis studies have been reported in the last 25 years, there is still a lack of consensus among researchers and analysts as to which is the “best” decomposition method. As the usefulness of decomposition analysis has now been firmly established in energy studies and its scope for policymaking has expanded greatly, there is a need to have a common understanding among practitioners and consistency on the choice of decomposition methods in empirical studies. After an overview of the application and methodology development of decomposition analysis, the paper attempts to address the above-mentioned issues and provide recommendations.
The desirability of separating the effects of underlying shifts in the composition of the economy from changes in energy use patterns has been recognized by many researchers. The similarity between this decomposition of aggregate measures of energy intensity into its component parts and decomposition of aggregate output (cost) data into price and quantity indices is less well known. This paper makes some comparisons between energy intensity decomposition and the formulation of economic indices. We illustrate the useful properties of one particular index, the Divisia index in performing energy intensity decomposition.
In a recent article, Albrecht et al. (Energy Policy 30 (2002) 727) presented a new decomposition technique based on the Shapley value and used it to study CO2 emissions in four OECD countries. This technique makes it possible to present decomposition without residuals, a very desirable property in decomposition analysis. We show that their proposed technique and the method by Sun (Energy Economics 20 (1998) 85) are exactly the same. As there has been a great deal of interest in decomposition analysis in energy policy studies, we extend the work by Albrecht et al. (Energy Policy 30 (2002) 727) by giving a more complete and up-to-date overview of perfect decomposition techniques and their role in energy demand and related analysis.
In a recent study, Ang (Energy Policy 32 (2004)) compared various index decomposition analysis methods and concluded that the logarithmic mean Divisia index method is the preferred method. Since the literature on the method tends to be either too technical or specific for most potential users, this paper provides a practical guide that includes the general formulation process, summary tables for easy reference and examples.
A central pillar of the Canadian government's recent greenhouse gas plan is to decrease the greenhouse gas intensity of production. We consider the proposal in light of historical trends between 1990 and 2002 by decomposing the change in emission intensities into composition and technique effects using a divisia index approach. Our results demonstrate that the proposed policy would push businesses into reductions in emission intensities that they have not previously accomplished. It would not be business as usual. Our analysis also suggests that achieving these targets by technological improvements alone may be quite difficult