An improved approach to estimate direct rebound effect by incorporating energy efficiency: A revisit of China's industrial energy demand

Liu, Hongxun; Du, Kerui; Li, Jianglong

doi:10.1016/j.eneco.2019.02.012

Cited by 69 publications

(20 citation statements)

References 36 publications

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“…The idea of calculating the rebound effect index is to respectively calculate the actual rebound and saving in carbon emissions (or energy consumption) and divide the amount of rebound by the amount saved (Berkhout et al, ; Li & Lin, ; Liu, Du, & Li, ; Saunders, , ; Sorrell & Dimitropoulos, ). The traditional method is as follows:

Re = \frac{σ_{t} ((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}}}{δ_{t} ((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1}}

…”

Section: Methodsmentioning

confidence: 99%

Different effects of technological progress on China's carbon emissions based on sustainable development

Chen

Gao

et al. 2019

Bus Strat Env

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In this paper, we respectively decompose and study different effects of technological progress on carbon emissions in China based on the combination of the logarithmic mean Divisia index method, the Solow residual model, and spatial econometrics. Furthermore, we propose an improved approach to estimate the rebound effect index. By comparing the different effects of technological progress on carbon emissions, our results indicate that China's overall domestic technological progress reduced its carbon emissions over this period. As for the rebound effect index, the estimated results are higher than in previous studies because of the spatial rebound effect, which was ignored by previous studies. Regionally, although the eastern region had high rebound effects, the western region is at the greatest risk from the rebound effects. Finally, we present specific environmental policy proposals for China's sustainable development based on empirical results. KEYWORDS carbon emissions;logarithmic mean Divisia index, rebound effect, spatial econometrics, sustainable development, technological progress

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Re = \frac{σ_{t} ((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}}}{δ_{t} ((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1}}

…”

Section: Methodsmentioning

confidence: 99%

Different effects of technological progress on China's carbon emissions based on sustainable development

Chen

Gao

et al. 2019

Bus Strat Env

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“…As for methods for estimating the rebound effect, they can be divided into two groups. The first is based on microeconomics, where the rebound effect is often calculated by econometric parameter estimation methods (Liu et al, ). The second is based on macroeconomics, where the rebound effect is often calculated by noneconometric methods (Lin et al, ; Lin & Liu, ).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, some scholars have divided the rebound effect of energy consumption into the substitution effect and the income or output effect based on principles of neoclassical microeconomics (Li & Lin, 2017;Schettkat, 2011;Sorrell & Dimitropoulos, 2007). From the perspective of empirical research, an increasing number of scholars have carried out empirical calculations of the rebound effect of total energy consumption in different countries and industries, especially over the past decade (Berkhout, Muskens, & Velthuijsen, 2000;Lin & Xie, 2015;Liu, Du, & Li, 2019;Liu & Lin, 2016;Saunders, 2008Saunders, , 2013Semboja, 1994;Small & Van Dender, 2007;Sorrell & Dimitropoulos, 2007). Although some scholars studied the rebound effect of fossil energy consumption (Hong, Liang, & Di, 2013), few studies paid attention to the rebound effect of nonfossil energy consumption and made further comparisons of fossil and nonfossil energies.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the rebound effects of fossil and nonfossil energy in China based on sustainable development

Chen

Gao

Ding

et al. 2019

Sustainable Development

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Although the rebound effect caused by technological progress has been widely accepted, few studies have estimated and compared the rebound effects of fossil and nonfossil energy separately, because fossil and nonfossil energy consumption have different effects on sustainable development. We use the data envelopment analysis–Malmquist index, logarithmic mean Divisia index, and Jacobian matrix methods to estimate and compare the rebound effects of China's fossil and nonfossil energy during 2006–2014. Empirical results show that nonfossil energy had a higher rebound effect than fossil energy and that technological progress was helpful in decreasing the proportion of fossil energy consumption. Furthermore, we found that technological progress contributed to increases in the relative price of fossil energy, causing nonfossil energy to be more favored. Simultaneously, nonfossil energy produced a substitution effect on fossil energy over the long term. On the basis of the empirical analysis, we also present some environmental policy implications.

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“…Therefore, empirical studies in energy economics literature typically use TPF to model energy demand. Following Ma et al (2009), Wang et al (2018), and Liu et al (2019), this paper also uses the TPF to investigate the degree of substitution in Germany at the industry level. In the following two subsections, we show theoretical model derivations for factor shares in the static and dynamic models, respectively.…”

Section: The Modelmentioning

confidence: 99%

“…This line of research is motivated by different objectives. While the main objective was previously to assess the impact of oil price shocks or to determine the optimal energy taxation for governments, the growing concerns about global warming now dominate the research agenda (Arnberg and Bjorner 2007;Fiorito and van den Bergh 2016;Wang et al 2018;Liu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Towards a carbon-neutral economy: The dynamics of factor substitution in Germany

Alataş

2020

Environ Sci Pollut Res

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This paper investigates the elasticity of substitution between four inputs-capital, labor, energy, and material-with the translog cost approach for a wide range of industries in Germany by incorporating the slow adjustment process in factor substitution. To this end, we take advantage of EU KLEMS database covering a wide range of industries and consider two models. The first is the static model, in which instantaneous and complete substitution adjustments are assumed. The other model, referred to as dynamic, takes into account the slow adjustment process and applies this to the cost share equations which are estimated using Zellner's seemingly unrelated regression. The empirical results suggest that (i) the dynamic models have greater explanatory power than the static models; (ii) the production process at the national or industry level in Germany is mainly characterized by a complementarity or weak substitutability between energy and other inputs, which limits German government's ability to reduce energy use through factor substitution; (iii) among four factor prices, energy demand seems to be more sensitive to changes in the price of material, followed by labor. Hence, an increase in energy prices can be efficient to some extent in order to reduce energy use; (iv) there is a substantial industry heterogeneity in Germany in terms of both input substitution and its adjustment process. Therefore, strategies to mitigate CO 2 emissions through input substitution channel should be designed at the industry level based on the industry-specific needs and peculiarities. It is because well-designed comprehensive policies that consider different structures of industries could help to achieve a carbon-neutral economy for Germany. Keywords CO 2 emissions. Input substitution. Translog cost function. Germany 1 In 2015, CO 2 contributed 73% of global GHG emissions and the largest source of GHG emissions was the fossil fuel combustion (IEA 2018).

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An improved approach to estimate direct rebound effect by incorporating energy efficiency: A revisit of China's industrial energy demand

Cited by 69 publications

References 36 publications

Different effects of technological progress on China's carbon emissions based on sustainable development

Different effects of technological progress on China's carbon emissions based on sustainable development

Analysis of the rebound effects of fossil and nonfossil energy in China based on sustainable development

Towards a carbon-neutral economy: The dynamics of factor substitution in Germany

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