A strategy for reducing CO2 emissions from buildings with the Kaya identity – A Swiss energy system analysis and a case study

Within the general context of Greenhouse Gas (GHG) emissions reduction, decomposition analysis allows the quantification of the contribution of different factors to changes in emissions as well as the assessment of the effectiveness of policy and technology measures. The Kaya identity has been widely used for that purpose in order to disaggregate carbon emissions into various driving forces. In this paper, it is applied for the analysis of emissions resulting from energy use at three different scales. First, a decomposition analysis of the carbon emissions for the complete Swiss energy system is presented using the future projections from the Swiss Energy Strategy 2050. The Kaya identity is then applied to the Swiss building sector after it is adapted with factors that are more relatable to building parameters, such as floor area instead of Gross Domestic Product (GDP). Finally, the last level of analysis is a small scale community energy system for a unique Swiss village that aims to significantly reduce its emissions. An energy strategy is developed and its effectiveness is assessed with the adapted Kaya identity and benchmarked against the Swiss average values. The presented method demonstrates how the performance of buildings under various retrofitting scenarios can be benchmarked against future emission targets.     

安徽省能源消费碳消费排放影响因素分解分析

为探寻安徽省碳排放总量变动的影响因素以实现节能减排的目标,基于安徽省1995～2009年能源碳排放量,利用Kaya恒等式和Laspeyres指数分解方法,分析了碳排放强度、能源强度、人均GDP和常住人口规模对碳排放变动的贡献。结果表明,人均产出效应是促进碳排放增加的主要拉动因素,累积贡献率为149.03%;能源强度效应是唯一抑制碳排放增加的因素,累积贡献率为-100.12%;其他两因素对碳排放增加的促进作用不明显,并从改善能源结构、提高能源效率、重点关注高耗能高排放行业三方面提出了政策建议。     

Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis

Research on the driving factors behind carbon dioxide emission changes in China can inform better carbon emission reduction policies and help develop a low-carbon economy. As one of important methods, production-theoretical decomposition analysis (PDA) has been widely used to understand these driving factors. To avoid the infeasibility issue in solving the linear programming, this study proposed a modified PDA approach to decompose carbon dioxide emission changes into seven drivers. Using 2005–2010 data, the study found that economic development was the largest factor of increasing carbon dioxide emissions. The second factor was energy structure (reflecting potential carbon), and the third factor was low energy efficiency. Technological advances, energy intensity reductions, and carbon dioxide emission efficiency improvements were the negative driving factors reducing carbon dioxide emission growth rates. Carbon dioxide emissions and driving factors varied significantly across east, central and west China.     

The implications of the historical decline in US energy intensity for long-run CO2 emission projections

This paper analyzes the influence of the long-run decline in US energy intensity on projections of energy use and carbon emissions to the year 2050. We build on our own recent work which decomposes changes in the aggregate US energy–GDP ratio into shifts in sectoral composition (structural change) and adjustments in the energy demand of individual industries (intensity change), and identifies the impact on the latter of price-induced substitution of variable inputs, shifts in the composition of capital and embodied and disembodied technical progress. We employ a recursive-dynamic computable general equilibrium (CGE) model of the US economy to analyze the implications of these findings for future energy use and carbon emissions. Comparison of the simulation results against projections of historical trends in GDP, energy use and emissions reveals that the range of values for the rate of autonomous energy efficiency improvement (AEEI) conventionally used in CGE models is consistent with the effects of structural changes at the sub-sector level, rather than disembodied technological change. Even so, our results suggest that US emissions may well grow faster in the future than in the recent past.     

Features,trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing,Tianjin, Shanghai and Chongqing

With China’s rapid economic development and urbanization process, cities are facing great challenges for tackling anthropogenic climate change. In this paper we present features, trajectories and driving forces for energy-related greenhouse gas (GHG) emissions from four Chinese mega-cities (Beijing, Tianjin, Shanghai and Chongqing) during 1995–2009. First, top-down GHG inventories of these four cities, including direct emissions (scope 1) and emissions from imported electricity (scope 2) are presented. Then, the driving forces for the GHG emission changes are uncovered by adopting a time serial LMDI decomposition analysis. Results indicate that annual GHG emission in these four cities exceeds more than 500 million tons and such an amount is still rapidly growing. GHG emissions are mainly generated from energy use in industrial sector and coal-burning thermal power plants. The growth of GHG emissions in four mega-cities during 1995–2009 is mainly due to economic activity effect, partially offset by improvements in carbon intensity. Besides, the proportion of indirect GHG emission from imported energy use (scope 2) keeps growing, implying that big cities are further dependent on energy/material supplies from neighboring regions. Therefore, a comprehensive consideration on various perspectives is needed so that different stakeholders can better understand their responsibilities on reducing total GHG emissions.     

Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models

This study explores the driving forces of the changes of national and regional CO₂ emissions using temporal decomposition analysis model, and investigates the driving forces of the differences of CO₂ emissions between China's 30 regions and the national average using spatial decomposition analysis model. The changes or the differences in national and regional CO₂ emissions during 2000–2014 are decomposed into nine underlying determinants. Temporal decomposition results show that economic scale effect is the dominant driving force leading to the increases in both national and regional CO₂ emissions, while energy intensity effect is the main contributor to the reduction of CO₂ emissions. Contribution of various variables to CO₂ emissions between eastern region and central region are roughly same. Spatial decomposition results demonstrate that the differences of CO₂ emissions among China's 30 regions are expanding increasingly. Economic scale effect is main driving force responsible for the difference in CO₂ emissions among regions, and energy intensity effect, energy structure effect and industrial structure effect are also important factors which result in the increasing differences in regional CO₂ emissions. In addition, resource-based and less developed regions have greater potential in the reduction of CO₂ emissions. Understanding CO₂ emissions and the driving forces of various regions is critical for developing regional mitigation strategies in China.     

Biomass and China's carbon emissions: A missing piece of carbon decomposition

A number of previous studies on China's carbon emissions have mainly focused on two facts: (1) the continuous growth in emissions up till the middle of the 1990s; (2) the recent stability of emissions from 1996 to 2001. Decomposition analysis has been widely used to explore the driving forces behind these phenomena. However, since 2002, China's carbon emissions have resumed their growth at an even greater rate. This paper investigates China's carbon emissions during 1971–2003, with particular focus on the role of biomass, and the fall and resurgence in emissions since the mid-1990s. We use an extended Kaya identity and the well-established logarithmic mean Divisia index (LMDI I) method. Carbon emissions are decomposed into effects of various driving forces. We find that (1) a shift from biomass to commercial energy increases carbon emissions by a magnitude comparable to that of the increase in emissions due to population growth, (2) the technological effect and scale effect due to per-capita gross domestic products (GDP) growth are different in the pre-reform period versus the post-reform period, (3) the positive effect of population growth has been decreasing over the entire period, and (4) the fall in emissions in the late 1990s and resurgence in the early 2000s may be overstated due to inaccurate statistics.     

Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities

Aggregate intensity indicators, such as the ratio of a country's energy and emissions to its GDP, are often used by researchers and policymakers to study energy and environmental performance. This paper analyzes the relationship between energy (or emissions) and value added (or GDP) from a different viewpoint, namely from the demand rather than the production perspective, using the input–output (I–O) framework. The aggregate embodied intensity (AEI), defined as the ratio of embodied energy (or emissions) to embodied value added, can be defined at the aggregate, final demand category and sectoral levels. The total aggregate intensity can be presented as a weighted sum of the AEIs at the final demand category or sectoral level. Changes of the AEI at different levels can be decomposed to identify the driving forces using multiplicative SDA. A study using the latest 2007 and 2012 datasets of China indicates that (a) its aggregate intensity of CO₂ emissions was mainly determined by the AEI in investment and (b) the emission intensity effect generally contributed the most to the AEI ratio changes at different levels. The proposed framework can be applied to other aggregate intensity indicators and extended to multi-country/region analysis.     

Transport sector CO2 emissions growth in Asia: Underlying factors and policy options

This study analyze the potential factors influencing the growth of transport sector carbon dioxide (CO₂) emissions in selected Asian countries during the 1980–2005 period by decomposing annual emissions growth into components representing changes in fuel mix, modal shift, per capita gross domestic product (GDP) and population, as well as changes in emission coefficients and transportation energy intensity. We find that changes in per capita GDP, population growth and transportation energy intensity are the main factors driving transport sector CO₂ emission growth in the countries considered. While growth in per capita income and population are responsible for the increasing trend of transport sector CO₂ emissions in China, India, Indonesia, Republic of Korea, Malaysia, Pakistan, Sri Lanka and Thailand; the decline of transportation energy intensity is driving CO₂ emissions down in Mongolia. Per capita GDP, population and transportation energy intensity effects are all found responsible for transport sector CO₂ emissions growth in Bangladesh, the Philippines and Vietnam. The study also reviews existing government policies to limit CO₂ emissions growth, such as fiscal instruments, fuel economy standards and policies to encourage switching to less emission intensive fuels and transportation modes.     

Scenario analysis of China’s emissions pathways in the 21st century for low carbon transition

China’s growing demand for energy – and its dependence on coal – has seen its carbon emissions increase more than 50% since 2000. Within the debate about mitigating global climate change, there is mounting pressure for emerging economies like China to take more responsibility for reducing their carbon emissions within a post-2012 international climate change policy framework. For China, this leads to fundamental questions about how feasible it is for the country to shift away from its recent carbon intensive pattern of growth. This paper presents some general results of scenarios that have been developed to investigate how China might continue to develop within a cumulative carbon emissions budget. The results show how changes in the key sectors of the Chinese economy could enable China to follow four different low carbon development pathways, each of which complies with a cumulative emissions constraint. Each scenario reflects different priorities for governmental decision making, infrastructure investments and social preferences. Having compared the key features of each scenario, the paper concludes with some implications for Chinese government policy.     

CO2 emissions embodied in China's exports from 2002 to 2008: A structural decomposition analysis

This study examines the annual CO₂ emissions embodied in China's exports from 2002 to 2008 using environmental input–output analysis. Four driving forces, including emission intensity, economic production structure, export composition, and total export volume, are compared for their contributions to the increase of embodied CO₂ emissions using a structural decomposition analysis (SDA) technique. Although offset by the decrease in emission intensity, the increase of embodied CO₂ emissions was driven by changes of the other three factors. In particular, the change of the export composition was the largest driver, primarily due to the increasing fraction of metal products in China's total export. Relevant policy implications and future research directions are discussed at the end of the paper.     

Structural path and decomposition analysis of aggregate embodied energy and emission intensities

Aggregate energy and emission intensities have respectively been widely used to measure the overall performance of energy consumption and environmental pollution from the production perspective. Recently, Su and Ang (2017) propose the aggregate embodied intensity (AEI) indicator, defined as the ratio of embodied energy (or emissions) to embodied value added, to analyze the relationship between energy (or emissions) and value added or GDP from the demand perspective using the input-output (I-O) framework. Besides I-O analysis, structural path analysis (SPA) can be used to split the I-O analysis results into different layers to extract the important paths in terms of energy consumption and the resulting emissions. This paper incorporates the SPA technique with the AEI indicators and structural decomposition analysis (SDA) technique in the context of energy and emission studies. An empirical study using China's 2007 and 2012 datasets is presented to illustrate the AEI at the detailed transmission layers, show their relationships with the AEI indicators at different levels, and further investigate the driving forces to the changes of these AEI indicators. The proposed multi-level AEI framework can also be applied to other indicators and extended to multi-country/region analysis.     

China’s pre-2020 CO2 emission reduction potential and its influence

China achieved the reduction of CO₂ intensity of GDP by 45% compared with 2005 at the end of 2017, realizing the commitment at 2009 Copenhagen Conference on emissions reduction 3 years ahead of time. In future implementation of the “13th Five-Year Plan (FYP),” with the decline of economic growth rate, decrease of energy consumption elasticity and optimization of energy structure, the CO₂ intensity of GDP will still have the potential for decreasing before 2020. By applying KAYA Formula decomposition, this paper makes the historical statistics of the GDP energy intensity decrease and CO₂ intensity of energy consumption since 2005, and simulates the decrease of CO₂ intensity of GDP in 2020 and its influences on achieving National Determined Contribution (NDC) target in 2030 with scenario analysis. The results show that China’s CO₂ intensity of GDP in 2020 is expected to fall by 52.9%–54.4% than the 2005 level, and will be 22.9%–25.4% lower than 2015. Therefore, it is likely to overfulfill the decrease of CO₂ intensity of GDP by 18% proposed in the 13th FYP period. Furthermore, the emission reduction potentiality before 2020 will be conducive to the earlier realization of NDC objectives in 2030. China’s CO₂ intensity of GDP in 2030 will fall by over 70% than that in 2005, and CO₂ emissions peak will appear before 2030 as early as possible. To accelerate the transition to a low-carbon economy, China needs to make better use of the carbon market, and guide the whole society with carbon price to reduce emissions effectively. At the same time, China should also study the synergy of policy package so as to achieve the target of emission reduction.     

Drivers of regional decarbonization through 2100: A multi-model decomposition analysis

This study explores short and long-term drivers of alternative decarbonization pathways in four major economies (China, India, Europe and USA), using a multi-model decomposition analysis. The paper focuses on determining the energy system transformations that drive the changes in carbon emissions and identifying the model characteristics that lead to differences in the decarbonization strategies. First, we compare the decomposition over time of near-past carbon emissions and near-future model projections as a methodology to validate baseline scenarios. We show that a no-policy baseline scenario is in line with historical trends for all regions except China, where all models project higher improvements in energy and carbon intensity than the near-past historical development. Second, we compare regional decarbonization drivers across models in a scenario with moderate policy targets that represent the current fragmented international climate policy landscape. The results from the different models show that energy efficiency improvements represent the main strategy in achieving the moderate climate targets. Finally, we develop an LMDI decomposition analysis to determine the additional energy system changes needed to achieve a global GHG concentration target of 450 ppm compared to the moderate policy case. In all models, reducing regional carbon intensity of energy is the major decarbonization strategy after 2030. In the long-term (after 2050), most of the models find that negative carbon emissions are critical in such scenario, emphasizing the key role of biomass with CCS. However, the level of contribution of the decarbonization factor varies significantly across models, due to the large uncertainty in the availability of renewables and the development of CCS technologies. Overall, we find that the main differences in the decomposition results across models are due to assumptions concerning availability of natural resources and variety of backstop technologies.     

广东省主体功能区碳排放特征及驱动因素研究

基于不同类型主体功能区的发展定位与碳排放驱动要素分解,提出有针对性的区域差异化低碳发展路径是推进主体功能区可持续发展的重要内容。基于调研资料,分析了广东省各主体功能区自2010年以来的碳排放演变特征,从人口效应、经济效应、能源强度效应、产业结构效应以及碳排放因子效应五个因素对造成不同主体功能区碳排放差异的原因进行了分析。要素分解发现,经济规模和人口数量增长对优化开发区碳排放量增长的贡献率最大;产业结构的优化从2012年开始成为使优化开发区碳排放量降低的影响因素,对重点开发区和生态发展区碳排放量降低的作用仍不明显;产业能源强度变动均使三类功能区碳排放量降低,但是贡献率呈现明显差异。建议：（1）加快发展优化开发区服务业,积极推动实施居民碳排放管理;（2）重点开发区应以提高能效和推进低碳技术为主实施低碳转型;（3）生态发展区要大力推广清洁能源,促使农业低碳化发展。     

Spatial spillover effects in determining China's regional CO2 emissions growth: 2007–2010

This study proposes an alternative input–output based spatial structural decomposition analysis to elucidate the importance of domestic regional heterogeneity and inter-regional spillover effects in determining China's regional CO₂ emissions growth. Our empirical results, based on the 2007 and 2010 Chinese inter-regional input–output tables, show that changes in most regions' final demand scale, final expenditure structure, and export scale have positive spatial spillover effects on other regions' CO₂ emissions growth; changes in most regions' consumption and export preference help reduce other regions' CO₂ emissions; changes in production technology and investment preferences may exert positive or negative effects on other region's CO₂ emissions growth through domestic supply chains. For some regions, the aggregate spillover effect from other regions may be larger than the intra-regional effect in determining regional emissions growth. All these facts can significantly help provide a better, deeper understanding of the driving forces behind the growth of regional CO₂ emissions and can thus enrich the policy implications concerning a narrow definition of “carbon leakage” through domestic inter-regional “trade” as well as a relevant political consensus about responsibility sharing between developed and developing regions inside China.     

How ambitious are China and India’s emissions intensity targets?

Several developing economies have announced carbon emissions targets for 2020 as part of the negotiating process for a post-Kyoto climate policy regime. China and India’s commitments are framed as reductions in the emissions intensity of the economy by 40–45% and 20–25%, respectively, between 2005 and 2020. How feasible are the proposed reductions in emissions intensity for China and India, and how do they compare with the targeted reductions in the US and the EU? In this paper, we use a stochastic frontier model of energy intensity to decompose energy intensity into the effects of input and output mix, climate, and a residual technology variable. We use the model to produce emissions projections for China and India under a number of scenarios regarding the pace of technological change and changes in the share of non-fossil energy. We find that China is likely to need to adopt ambitious carbon mitigation policies in order to achieve its stated target, and that its targeted reductions in emissions intensity are on par with those implicit in the US and EU targets. India’s target is less ambitious and might be met with only limited or even no dedicated mitigation policies.     

Driving forces behind the stagnancy of China’s energy-related CO2 emissions from 1996 to 1999: the relative importance of structural change,intensity change and scale change

It is noteworthy that income elasticity of energy consumption in China shifted from positive to negative after 1996, accompanied by an unprecedented decline in energy-related CO₂ emissions. This paper therefore investigate the evolution of energy-related CO₂ emissions in China from 1985 to 1999 and the underlying driving forces, using the newly proposed three-level “perfect decomposition” method and provincially aggregated data. The province-based estimates and analyses reveal a “sudden stagnancy” of energy consumption, supply and energy-related CO₂ emissions in China from 1996 to 1999. The speed of a decrease in energy intensity and a slowdown in the growth of average labor productivity of industrial enterprises may have been the dominant contributors to this “stagnancy.” The findings of this paper point to the highest rate of deterioration of state-owned enterprises in early 1996, the industrial restructuring caused by changes in ownership, the shutdown of small-scale power plants, and the introduction of policies to improve energy efficiency as probable factors. Taking into account the characteristics of those key driving forces, we characterize China's decline of energy-related CO₂ emissions as a short-term fluctuation and incline to the likelihood that China will resume an increasing trend from a lower starting point in the near future.     

Index decomposition analysis for comparing emission scenarios: Applications and challenges

As global action on climate change gathers momentum, an area of interest is what the world's greenhouse gas emission trajectory will be in the future and whether it can meet the emission targets set forth. To address these questions, emission scenarios which range from business-as-usual to deep decarbonization scenarios have been developed by researchers using different assessment models. An understanding of the sources of variations across the range of these emission trajectories is of interest for discussions, debates and policy planning. However, since these models often have different structures and input variables, comparisons of the results are not straightforward. In this regard, index decomposition analysis (IDA) has emerged as a tool to facilitate comparisons in a harmonized way. It allows differences in emissions over time or between models to be broken down based on a set of accepted driving factors. This paper reviews the literature and summarizes the features and challenges. A multi-level scenario decomposition framework is proposed to address the challenges which include how to quantify differences arising from the energy transformation sector, the adoption of renewable energy, carbon capture and storage, and bio-energy carbon capture and storage technologies. A case study and guidelines for implementing the framework are presented.     

Towards a low carbon growth in Mexico: Is a double dividend possible? A dynamic general equilibrium assessment

This paper simulates the medium- and long-term impact of proposed and expected energy policy on the environment and on the Mexican economy. The analysis has been conducted with a Multi-sector Macroeconomic Model for the Evaluation of Environmental and Energy policy (Three-ME). This model is well suited for policy assessment purposes in the context of developing economies as it indicates the transitional effects of policy intervention. Three-ME estimates the carbon tax required to meet emissions reduction targets within the Mexican “Climate Change Law”, and assesses alternative policy scenarios, each reflecting a different strategy for the recycling of tax revenues. With no compensation, the taxation policy would reduce CO₂ emissions by more than 75% by 2050 with respect to Business as Usual (BAU), but at high economic costs. Under full redistribution of carbon tax revenues, a double dividend arises: the policy appears beneficial both in terms of GDP and CO₂ emissions reduction.