A new approach for assessing the macroeconomic growth energy rebound effect

Jin, Taeyoung; Kim, Jinsoo

doi:10.1016/j.apenergy.2019.01.220

Cited by 51 publications

(35 citation statements)

References 60 publications

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“…First, effects of economic growth and carbon intensity on carbon emissions is respectively represented by

((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}}

and

((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1}

, which is not optimal because the sum of them is not equal to changes in total carbon emissions (

((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}} + ((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1} \neq C_{t - 1} - C_{t}

). Second, the contribution rate of technological progress to economic growth σ t estimated by previous method is uncertain (Jin & Kim, ) because regional technological progress may be ignored. In line with previous studies, if the national technological progress estimated is negative, then the rebound effects estimated will be also negative, which is considered to prove that there is no rebound effect (Lin et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…Second, the contribution rate of technological progress to economic growth σ t estimated by previous method is uncertain (Jin & Kim, 2019) because regional technological progress may be ignored. In line with previous studies, if the national technological progress estimated is negative, then the rebound effects estimated will be also negative, which is considered to prove that there is no rebound effect .…”

Section: An Improved Approach To Calculate the Rebound Effect Indexmentioning

confidence: 99%

“…Hence, technological progress in adjacent areas may have significant influence on local technological progress and local carbon emissions. Third, the traditional approach of calculating the rebound effect index has several shortcomings because the calculation of increased carbon emissions and saved carbon emissions caused by technological progress is not perfect and can be further improved (Jin & Kim, ).…”

Section: Introductionmentioning

confidence: 99%

“…Third, the traditional approach of calculating the rebound effect index has several shortcomings because the calculation of increased carbon emissions and saved carbon emissions caused by technological progress is not perfect and can be further improved (Jin & Kim, 2019). Therefore, we respectively calculate increased and saved carbon emissions caused by technological progress based on the combination of the logarithmic mean Divisia index (LMDI) method, the Solow residual model, and spatial econometrics, which was unlike previous study that did not put both increased and saved carbon emissions caused by technological progress into consideration.…”

mentioning

confidence: 99%

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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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“…First, effects of economic growth and carbon intensity on carbon emissions is respectively represented by

((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}}

and

((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1}

, which is not optimal because the sum of them is not equal to changes in total carbon emissions (

((), Y_{t} - Y_{t - 1}) \frac{C_{t - 1}}{Y_{t - 1}} + ((), \frac{C_{t - 1}}{Y_{t - 1}} - \frac{C_{t}}{Y_{t}}) Y_{t - 1} \neq C_{t - 1} - C_{t}

Section: Methodsmentioning

confidence: 99%

Section: An Improved Approach To Calculate the Rebound Effect Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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

Chen

Gao

et al. 2019

Bus Strat Env

View full text Add to dashboard Cite

show abstract

“…Endogeneity and robustness problems are also risks when adopting econometric methods (Nakamura & Nakamura, ). The shortcomings of the second method arise from uncertainty regarding the contribution rate of technological progress to economic growth (Jin & Kim, ), because regional technological progress may be ignored. In line with previous studies, if the estimated national technological progress is negative, then the estimated rebound effects will also be negative, which is considered to prove that there is no rebound effect (Lin et al, ).…”

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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Material demand and material efficiency for sustainable development in the European Union countries: A stochastic frontier analysis

Alataş

Hiçyılmaz

Karakaya

2023

Sustainable Development

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Materials are of great importance as they are fundamentally integrated into many different critical topics, including green transition, technological progress, industrial emissions, and supply chains. Therefore, the amount of materials we demand and employ in the production process or how efficiently we use them become highly crucial. In this paper, we study the emerging role of materials with a special focus on the key role of material efficiency (ME) in climate mitigation. To this end, we empirically investigate the main determinant of material demand and evaluate the ME performance in the European Union (EU) countries over the period of 1995–2019 based on a four‐component Stochastic Frontier Analysis (SFA) panel data approach that separates unobserved country effects from persistent and transient inefficiency. We obtain the following outcomes. First, material demand function estimates reveal that economic growth and energy consumption are the main drivers of domestic material consumption, highlighting the need for promoting sustainable energy and economic growth. Second, although the SFA‐based ME scores significantly vary across countries, transient inefficiency is the main source of overall inefficiency for most countries, suggesting that there is considerable potential for improvement in ME. While Finland, Sweden, Denmark, Bulgaria, Cyprus, France, and Slovenia improve their ME performance over time, the ME score of some other countries, such as Ireland, Portugal, Italy, Spain, and the Slovak Republic reveal an opposite outcome. Third, the results show that material intensity is not always a good proxy for ME.

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A new approach for assessing the macroeconomic growth energy rebound effect

Cited by 51 publications

References 60 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

Material demand and material efficiency for sustainable development in the European Union countries: A stochastic frontier analysis

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