Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions

Dai, Hancheng; Mischke, Peggy; Xie, Xu; Xie, Yang; Masui, Toshihiko

doi:10.1016/j.apenergy.2015.06.069

Cited by 80 publications

(33 citation statements)

References 35 publications

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“…• Harmonizing all the exogenous parameters common to both models, i.e., typically demography, but also possibly international energy prices, if these are exogenous to the sustaining any modeling output of IMACLIM-S with reduced forms is not easily pinpointed. 23 The approach is similar to that of Dai et al (2015) but with data exchange extended to prices; or to that of Fortes et al (2014) or Labriet et al (2015), although energy prices, rather than exogenously taken from the bottom-up source, remain largely endogenous variables of IMACLIM-S (cf. below).…”

Section: Coupling Through Iterative Convergence Of Linking Variables mentioning

confidence: 99%

Hybrid Bottom-up/Top-down Energy and Economy Outlooks: A Review of IMACLIM-S Experiments

Ghersi

2015

Front. Environ. Sci.

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Section: Coupling Through Iterative Convergence Of Linking Variables mentioning

confidence: 99%

Hybrid Bottom-up/Top-down Energy and Economy Outlooks: A Review of IMACLIM-S Experiments

Ghersi

2015

Front. Environ. Sci.

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“…Table 4. The auxiliary system of energy consumption ratio κ can be calculated by Formula (9), and it fluctuated between 2.18% and 7.62% from 2007 to 2014, as seen from Table 4, and the average value of 4.38% is used as the calculation parameter for the comprehensive EI calculation model.…”

Section: Calculation Parametermentioning

confidence: 99%

“…However, due to their structure, BU models are unable to capture the full macro impacts of energy policies. By contrast, TD models are able to calculate macro-economic costs and the impacts of energy policies, however at the expense of specific sectoral or technological details [9].…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Potential of China’s Steel Industry According to Its Development Plan

Wang

Hongliang³

et al. 2018

Energies

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The energy consumption of China's steel industry accounted for 53% of the global steel industry energy consumption in 2014. This paper aims to analyze the energy saving potential of China's steel industry, according to its development plan of the next decade, and find the key of energy conservation. A multivariate energy intensity (MEI) model is developed for energy saving potential analysis based on the research on China's energy statistics indexes and methods, which is able to capture the impacts of production routes, technology progress, industrial concentration, energy structure, and electricity (proportion and generation efficiency). Different scenarios have been set to describe future policy measures in relation to the development of the iron and steel industry. Results show that an increasing scrap ratio (SR) has the greatest energy saving effect of 16.8% when compared with 2014, and the maximum energy saving potential reaches 23.7% after counting other factors. When considering coal consumption of power generation, the energy saving effect of increasing SR drops to 7.9%, due to the increase on the proportion of electricity in total energy consumption, and the maximum energy saving potential is 15.5%, and they can increase to 10.1% and 17.5%, respectively, with improving China's power generation technology level.Energies 2018, 11, 948 2 of 16The advantages of BU models are the disadvantages of TD models and vice versa. Due to disciplinary and structural differences, these two types of models cannot demonstrate wide ranges of future projection of energy trajectories at both the macroscopic and technical levels. Ref.[10] tried to complement each type model's advantages and disadvantages by exchanging information between a detailed BU model with simplified macroeconomic module; Proença and Aubyn [11] integrated TD and BU models by representing detailed energy technologies within the TD framework; Fujimori etc. Ref.[12] developed an Integrated Assessment Models (IAM)/computable general equilibrium (CGE) model that was integrating detailed energy end-use technologies.These studies focused on coupling existing models without exploring the calculation approach or EI impact factors of the steel industry besides existing ones. According to the development plan issued by the Chinese government in recent years, the production situation of China's steel industry will change significantly by in next decade, including both macro and technical impact factors, such as raw materials, production routes, technology progress, industrial concentration, energy structure, and electricity (proportion in energy consumption and generation efficiency). However, using existing models in early studies cannot comprehensively analyze the future energy consumption trends of China's steel industry, as no existing models are able to capture the influence of all these factors. Furthermore, the energy statistics system are the basis of energy related analysis and research, and data calculated according to their definition and statistical met...

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“…Studies estimating transport CO 2 emissions have used either the top-down or the bottom-up approach [31][32][33][34]. In the top-down approach, emissions are calculated on the basis of transportation fuel sales and CO 2 emission factors for each fuel type.…”

Section: Co 2 Emissions Modelmentioning

confidence: 99%

Consumer Travel Behaviors and Transport Carbon Emissions: A Comparative Study of Commercial Centers in Shenyang, China

Zhang

et al. 2016

Energies

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Current literature highlights the role of commercial centers in cities in generating shopping trips and transport carbon emissions. However, the influence of the characteristics of commercial centers on consumer travel behavior and transport carbon emissions is not well understood. This study addresses this knowledge gap by examining shopping trips to eight commercial centers in Shenyang, China, and the CO 2 emissions of these trips. We found that the locations and types of commercial centers strongly influence CO 2 emissions. CO 2 emissions per trip to commercial centers in the suburbs of Shenyang were on average 6.94% and 26.92% higher than those to commercial centers in the urban core and the inner city, respectively. CO 2 emissions induced by wholesale centers were nearly three times higher than the lowest CO 2 emissions of commercial centers in the inner city. These empirical results enhance our understanding of shopping-related transport carbon emissions and highlight the importance of optimizing urban space structure, in particular, the layout of commercial centers.

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Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions

Cited by 80 publications

References 35 publications

Hybrid Bottom-up/Top-down Energy and Economy Outlooks: A Review of IMACLIM-S Experiments

Hybrid Bottom-up/Top-down Energy and Economy Outlooks: A Review of IMACLIM-S Experiments

Energy-Saving Potential of China’s Steel Industry According to Its Development Plan

Consumer Travel Behaviors and Transport Carbon Emissions: A Comparative Study of Commercial Centers in Shenyang, China

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