• Energy Research

Abstract The building sector contributes a major proportion of the global energy consumptions and carbon emissions. The energy performance or efficiency of buildings can be improved through a wide range of retrofitting measures which can have very different costs. Under budget, time and other resource constraints, it is not practical to apply all energy saving measures to a given retrofitting project. As such, there is a need to rank and select the most cost-effective measures to meet efficiency improvement goals. Traditionally, energy efficiency improvement measures and their costs are evaluated separately which makes prioritising among the measures difficult. In response, an integrated approach by soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method is proposed to rank and select the most cost-effective retrofitting measures. The metamodel-based method is used to compute building energy consumptions before and after retrofit; and the cost-assessment method is used to evaluate the life cycle cost of implementing each measure. A selection of nine retrofitting measures are ranked according to life cycle energy savings, life cycle cost, and cost-effectiveness (measured by cost per unit energy saved). Findings from the Singapore case study suggest that retrofitting building envelop is the third least cost-effective measure although it can lead to highest energy savings. Lighting replacement has the least life cycle energy savings, but it is the most cost-effective measure. Electricity price has little influence on the cost-effectiveness ranking of all nine measures but discount rates (tested for 4%, 7% and 12%) can influence the ranking of home appliances. Based on the findings from the case study, the proposed integrated approach can help identify an optimum retrofit strategy and the cost of achieving energy efficiency targets for existing buildings.

Abstract During the 12th Five-Year Plan Period (2011–2015), the Chinese government has released a series of energy conservation plans to launch and deepen the transition in the energy use. Particularly, the dual control of the energy consumption and energy intensity was firstly emphasized. This paper intends to explore the targeted and effective energy policy implications through investigating the drivers of changes in energy consumption/intensity of China during the 12th Five-Year Plan Period. Under the input-output framework, the embodied energy consumptions/intensities by final demand category were estimated at aggregate/sector level and the total energy consumption/intensity was decomposed using the additive/multiplicative structural decomposition analysis. Among the final demand categories, China's energy consumption/intensity was mainly contributed by the investment, followed by the household consumption. According to the SDA results, China's total energy consumption increased by 28.8%, of which the energy intensity effect and the investment effect were the most retardant and accelerator, respectively. At the same time, China's total aggregate energy intensity decreased by 16.0%, of which the energy intensity effect and the household consumption effect were the most retardant and accelerator, respectively. In detail, the key sectors with respect to the critical effects were picked out. Above all, policy implications are discussed for achieving the energy goals during the 13th Five-Year Plan Period and launching the energy revolution during the 14th Five-Year Plan Period.

Abstract Energy-related CO2 emissions embodied in international trade have been widely studied by researchers using the environmental input–output analysis framework. It is well known that both sector aggregation and spatial aggregation affect the results obtained in such studies. With regard to the latter, past studies are often conducted at the national level irrespective of country or economy size. For a large economy with the needed data, studies may be conducted at different levels of spatial aggregation. We examine this problem analytically by extending the work of Su et al. ([Su, B., Huang, H.C., Ang, B.W., Zhou, P., 2010. Input-output analysis of CO2 emissions embodied in trade: The effects of sector aggregation. Energy Economics 32 (1), 166–175.]) on sector aggregation. We present a numerical example using the data of China and by dividing the country into eight regions. It is found that the results are highly dependent on spatial aggregation. Our study shows that for a large country like China it is meaningful to look into the effect of spatial aggregation.

Abstract Energy/emission intensity indicators measure the relationship between economic development and climate change. These intensity indicators are preferentially used in assessment criteria for mitigation goals in places such as China. This paper investigates the driving factors of changes in the national CO2 emission intensity and their contributions to reductions at the sector level in China from 2002 to 2012 using multiplicative structural decomposition analysis (SDA) and attribution analysis. Both Leontief and Ghosh input-output models are used in the study. The empirical results indicate: 1) the energy intensity effect is the main driver that decreases the aggregate emission intensity from both the demand and supply sides; 2) structural effects, such as the energy structure effect and domestic Ghosh structure effect, promote the increase in aggregate emission intensity principally; and 3) to a large extent, sector “smelting and pressing of metals”, “manufacture of non-metallic mineral products” and “chemical industry” are the top three sectors that contribute to the negative energy intensity effect. Ultimately, several discussions and conclusions associated with the empirical results, result stability and research extensibility are presented.

Abstract Subsidizing energy has been widely used but is economically unfavorable. The Malaysian government has shown strong intention to reduce energy subsidies recently, but face challenges to prepare policy instruments to manage the impact. This study develops a Computable General Equilibrium (CGE) model with breakdown of households by income level to evaluate the potential impacts of removing energy subsidies on the Malaysian economy. It is shown that removing petroleum and gas subsidy would improve economic efficiency and increase GDP up to 0.65%. Budget deficit would be largely reduced after removing the petroleum subsidies, especially when the saved subsidy cost is not budgeted for other expenditure. Households would be worse off in most scenarios due to higher price level, but some compensation policy could make the lowest income group no worse than baseline, without harm the economy. The reduction in carbon emissions ranges 1.84–6.63% in different scenarios. The simulation results suggest Malaysia to completely remove all fuel subsidies and use the saved funding to cut budget deficit or spend on education, health and other service sector. It is also necessary to set a compensation scheme to minimize public resistance and make sure such scheme is affordable.

Abstract Emissions have been a critical concern for the shipping sector, calling for climate mitigation measures to be implemented in this field urgently. It is therefore useful to understand the factors that influence the relevant empirical trends so that policies can be better tailored to address the climate challenges. Given the high reliance on fossil fuels, this paper has focused on the energy consumption from international shipping, by examining 6 factors across product types and shipping routes. This is based on comprehensive microdata of almost all vessels in the world over 2014–2017. The study finds that improvements in energy intensity is consistent across product types and shipping routes, reinforcing the importance of energy efficiency as a key climate mitigation measure. However, shifts in freight transport activity across different regions have offset gains in energy efficiency and was the most dominant factor contributing to an increase in energy consumption. Monitoring of the transport structure effect is critical for progress tracking and the potential of this effect to alter energy consumption should also be factored into emission projections.

Effectively treating industrial SO2 emissions depends on the synergy of different factors from the industrial SO2 generation source to the end of treatment. Applying a whole process treatment perspective, this paper decomposes industrial SO2 emissions into six specific driving factors in three whole process treatment dimensions (i.e. source prevention, process control, and end-of-pipe treatment), and economic scale. A temporal index decomposition analysis (Temporal-IDA), attribution analysis (AA), and spatial index decomposition analysis (Spatial-IDA) methods are then applied to quantify each dimension's treatment effect and its spatial differences. The empirical study across 30 regions in China using data from 2005 to 2015 shows that: (1) The end-of-pipe treatment is the dominant dimension for decreasing industrial SO2 emissions, followed by process control. The contribution of source prevention to reduce industrial SO2 emissions has begun to appear, however, there remains room for further improvement; (2) End-of-pipe treatment strength and energy intensity are key factors in reducing industrial SO2 emissions; Inner Mongolia, Henan, and Shandong are the main contributors; (3) The treatment emphasis are different among regions; as such, there are different treatment effects across the three dimensions of the whole process treatment. Regions can be classified into four categories: the Leading type, Process-dependent type, End-dependent type, and Lagging type. Based on the empirical results, this paper identifies the policy implications of promoting whole process treatment on China's industrial SO2 emissions.