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Regional comparisons and decomposition analyses of CO2 emissions in Japan
... They studied the differences in CO2 emissions between Germany and UK, and Chung [6] evaluated the differences for China, Japan and South Korea. De Nooij et al. [7] extended the scope to more countries and evaluated the differences on energy consumption among eight OECD countries while Hasegawa [8] did the comparison among regions in Japan. ...
... (For example, in = ⋅ , it could be specified Δ = ( )Δ + ( )Δ , where t1 and t2 refer to: two periods (initial vs. final); to different geographic areas or two economies (regions vs. average or national)). Obviously, SDA supports different variations depending on the assumptions made about the base period or regions used (see [6] and [8]). ...
In this paper, we evaluate the generating capacity of Greenhouse Gases (GHG) emissions that all productive sectors have in the EU-27 of 2010. The analysis is performed using the social accounting matrices (SAMs) of each Member State (MS) and evaluating the interactions among industries, productive factors, and households with respect to the aggregated SAM for the EU-27. The main advantages and contributions of this study with respect to the existing literature are two. First, the availability of the whole income distribution detailed in the SAMs and second, their comparability across countries. The aim of this research is to better understand how productive sectors may damage the environment depending on their productive structure and final demand, particularly in a period of economic recession, which is very relevant in the context of COVID-19 and the near future. The results show that intersectoral connections are very diverse by MS and consequently, there are more differences in the generation capacity of GHG emission by country than by sector. Our results reinforce the idea of involving regional and national governments in the design and implementation of EU abatement strategies, taking into account the peculiarities of each region.
... They also performed a second-stage decomposition of the structural effect into industrial sectors. Hasegawa (2006) investigated CO 2 emissions at the prefectural level in Japan and found that the effects influencing changes in emissions differed among regions. ...
This study investigates Japan’s energy transitions in 2005–2015, which involved massive economic disruptions due to the 2011 Great East Japan earthquake and the Great Recession. A hybrid input-output (IO) table that conforms to the energy conservation condition was newly compiled by integrating the Japanese energy-balance and linked-IO tables. This was employed to conduct a structural decomposition analysis (SDA), which attributes changes in energy consumption and CO 2 emissions to the effects of intensity, structure, domestic final demand, and export. These effects were successfully segregated into a profile of energy sources. The results revealed that the structural effect became the dominant driver for decisively reducing energy consumption and emissions of manufacturing and service sectors in the latter period 2011–2015. This suggested that it took time to materialize energy-saving innovations in response to the sudden economic disruptions. Over the entire period, the structural effect was the largest driver contributing to the overall reductions, in part because the other effects tended to cancel out either between energy sources or periods. Therefore, a sensible way to transform Japan to a less energy-intensive, carbon-free society in the future is to improve the non-energy input structures of the manufacturing and service sectors.
... It is also possible to apply the decomposition in spatial fashion, though it is done relatively scarcely -examples includeAng and Zhang (1999), de Nooij et al. (2003),Alcántara and Duarte (2004),Hasegawa (2006),Hajko (2012), andXu and Ang (2014). ...
Analyzing the factors that affect the spatial differences in production water consumption in China is necessary to implement its most stringent water resource management system effectively. Based on the input-output (IO) tables of 31 provinces and the water-consumption data of provinces by sectors in 2017, the water consumption-economy input-output tables of 31 provinces are constructed. The spatial structural decomposition analysis method is used to analyze the impact of technology level, economic scale, and regional characteristics on spatial differences in production water consumption. The final demand effect is then decomposed into final demand sectoral structural effect, final demand distribution structure effect, population-scale effect, and consumption-level effect. The results show that production water consumption depends on the economic scale and regional characteristics. Xinjiang, Jiangsu, Guangdong, Heilongjiang, and most provinces in the central region use more production water than the average level, while those in the Beijing-Tianjin region and most in the North-west region use less than average. The decomposition results show that the technical and the final demand effects are the main factors for the spatial differences. The impact of population-scale and consumption-level contribute the most to the final demand effect.
As China plays an important role in global climate change, investigating its low-carbon development (LCD) will shed light on the global low-carbon economy. This study aims to explore the drivers of LCD inequality and evolution based on an extended spatial decomposition model. We find that potential energy intensity is the most important factor responsible for both national LCD inequality and its evolution. Further analysis suggests that the central region made some progress in LCD due to the decline of potential energy intensity. Nonetheless, the west must pay attention to the high potential energy intensity and deteriorating industry structure for the persistent poor LCD. And the provincial results indicate that the poorer the economy, the worse the LCD. The LCD formation and evolution mechanism of the various provinces are highly heterogeneous. Overall, we underscore the comprehensive investigation of inequality issues of LCD and provide insights for other regions desiring to develop the low-carbon economy.
City, which consumes the majority of the total energy consumptions, has great influences on the national energy issues. As the widespread spatial differences, a quantitative energy analysis among multi-city allows exploring the targeted and distinguishing energy policy implications at the city level in China. Considering the significant role of final demands, the input-output model and spatial structural decomposition analysis were adopted to evaluate the energy performances and quantify the spatial differences. In 2012, the spatial differences in the energy consumptions were distinct, with the greatest gap between Shanghai and Chongqing, which were mainly driven by the domestic Leontief structure effect and the total final demand effect. In particular, the spatial differences in the embodied energy by net domestic outflow were remarkable. For the relative energy efficiency, Beijing was discovered the best, followed by Tianjin, Shanghai and Chongqing. The spatial differences in their energy intensity were primarily influenced by the energy intensity effect and the domestic Leontief structure effect. In addition, the spatial differences in the aggregate embodied energy intensity by domestic investment were prominent. Inspired by the results, the structure adjustments with respect to the production and final demands and regional cooperation are strongly advised at the city level.
The Copenhagen and Paris Agreements, in which developed countries committed to mobilise USD 100 billion a year by 2020, indicate that climate finance will continue to grow. Even though economic development is not the aim of climate finance, climate-related disbursements will generate an economic impact on recipient countries’ economies. This impact will also reach other countries (including climate finance donors) through induced international trade. In this paper, we apply a structural decomposition analysis to study why the economic impact of climate finance varies between countries. We focus on specific climate actions and quantify the contribution of four drivers: value-added intensity, domestic multiplier, foreign multiplier and trade structure. The paper helps identifying the factors with the greatest potential to enhance the economic gains of climate finance in each country. This information can be useful for policy-makers trying to design national strategies that exploit the synergies between climate action and economic development.
Index decomposition analysis (IDA) and structural decomposition analysis (SDA) are analytical techniques that have been extensively used by researchers to study drivers of changes in energy consumption and energy-related emissions for energy and climate policy assessment and development. We compare the two techniques from the methodological and application viewpoints and with specific reference to economy-wide analysis where the overlap between the two is the greatest. Our study brings up to date several previous studies and provides a detailed assessment of the post-2010 developments. In addition, a framework for additive and multiplicative decomposition methods is presented, specific application in policy analysis is discussed with representative examples given, and the selection between the two techniques is described. Despite the differences between the two techniques in terms of origin, there has been some convergence in their application in some specific areas. However, even if the same dataset is used, application of the two techniques will lead to different numerical results due to underlying differences in some core concepts and the meanings of the drivers of change defined. A good understanding of these similarities and differences will help researchers in making sound judgment in their adoption and implementation in policy studies.
Structural decomposition analysis (SDA) has been widely used by researchers to study changes in carbon emissions or aggregate emission intensity over time in a country. These studies may be called temporal-SDA analysis. Similarly, SDA analysis can be conducted by studying variations in carbon emissions or aggregate emission intensity between countries or between regions in a country, i.e. a decomposition analysis conducted spatially. In spatial-SDA analysis, the objective is often to understand the contributions of factors such as emission intensity, Leontief structure, and final demand in explaining the difference in total carbon emissions or aggregate emission intensities between two countries or regions. We review the literature of spatial-SDA analysis and propose a spatial-SDA framework for multi-region comparisons. Both the additive and multiplicative SDA forms are presented in the framework. Using the framework, 30 geographical regions in China are compared and ranked based on their emission performance. This proposed framework can also be used to evaluate other performance indicators in multi-region comparisons.
This study aims to investigate Korea's final demand structure and its impacts on CO2 emissions in order to reduce CO2 emissions and develop environmental policy directions. Based on the environmentally extended input–output model, this study adopts a two-step approach: (1) to estimate the embodied emissions and their intensities for 393 sectors induced by final demand; and (2) to calculate the driving factors of emission growth between 2003 and 2011 and then evaluate the result by using Structural Decomposition Analysis (SDA). The findings of this study demonstrate that the impact of composition change in export with less embodied emission intensities tends to offset the increase in CO2 emission by the export scale growth. The relatively low residential electricity price has resulted in the rapid growth of household electricity consumption and significantly contributed to emissions growth. The result of SDA indicates that Korea's final demand behavior yielded high carbonization over the same period. The findings suggest that Korean government should promote exports in industries with less embedded CO2 in order to protect environments. In addition, emission information of each product and service should be provided for consumers to change their purchase patterns towards contributing to low carbon emissions as active players.
The only comprehensive study comparing structural decomposition analysis (SDA) and index decomposition analysis (IDA) was conducted around 2000. There have since been new developments in both techniques in energy and emission studies. These developments have been studied systematically for IDA but similar studies for SDA are lacking. In this paper, we fill the gap by examining the new methodological developments in SDA. A new development is a shift towards using decomposition methods that are ideal. We compare four such SDA methods analytically and empirically through decomposing changes in China's CO2 emissions. We then provide guidelines on method selection. Finally, we discuss the similarities and differences between SDA and IDA based on the latest available information.
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