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Carbon efficiency, technology, and the role of innovation patterns: Evidence from German plant-level microdata

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  • Petrick, Sebastian

Abstract

We describe the determinants of energy intensity, carbon intensity, and CO2 emissions in the German manufacturing sector between 1995 and 2007, applying the LMDI index decomposition technique not to aggregate but to micro data. We trace back changes in total CO2 emissions from manufacturing to changes in activity level, structural change between sectors, structural change within sectors, energy intensity at the firm level, fuel mix, and emission factors. We use a firm data set on energy use from the AFiD-Panel on German manufacturing plants that allows us to analyze energy use at the firm level with unprecedented accuracy. Our results show that heterogeneity among firms within one sector is a driver of energy intensity, carbon intensity, and CO2 emissions. By stressing the importance of competition between firms for energy efficiency improvements, we highlight a factor that has so far been widely ignored. Firm heterogeneity has so far rarely included in index decomposition analyses. Contrary to wide-spread beliefs, energy intensity improvements at the firm level do not play a significant role in reducing emissions. Based on findings from the decomposition analysis, we use sector-level results on the relative importance of improvements in firm-level energy intensity and intra-sectoral structural change to distinguish two different innovation channels: innovation by technology and by entrants. We show that incumbent firms in a number of sectors, including some of the most energy intensive ones, do not significantly improve their energy efficiency. Innovation takes place via new entrants instead, rendering standard policies targeted at firm-level energy efficiency ineffective.

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  • Petrick, Sebastian, 2013. "Carbon efficiency, technology, and the role of innovation patterns: Evidence from German plant-level microdata," Kiel Working Papers 1833, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1833
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    References listed on IDEAS

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    Cited by:

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    2. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2018. "Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model," Ecological Economics, Elsevier, vol. 150(C), pages 315-339.
    3. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    4. Ang, B.W. & Wang, H., 2015. "Index decomposition analysis with multidimensional and multilevel energy data," Energy Economics, Elsevier, vol. 51(C), pages 67-76.
    5. Richter, Philipp M. & Schiersch, Alexander, 2017. "CO2 emission intensity and exporting: Evidence from firm-level data," European Economic Review, Elsevier, vol. 98(C), pages 373-391.
    6. Francesco Lamperti & Andrea Roventini, 2022. "Beyond climate economics orthodoxy: impacts and policies in the agent-based integrated-assessment DSK model," European Journal of Economics and Economic Policies: Intervention, Edward Elgar Publishing, vol. 19(3), pages 357-380, December.
    7. Berner, Anne & Lange, Steffen & Silbersdorff, Alexander, 2022. "Firm-level energy rebound effects and relative efficiency in the German manufacturing sector," Energy Economics, Elsevier, vol. 109(C).
    8. Marco Amendola & Francesco Lamperti & Andrea Roventini & Alessandro Sapio, 2023. "Energy efficiency policies in an agent-based macroeconomic model," LEM Papers Series 2023/20, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2020. "Climate change and green transitions in an agent-based integrated assessment model," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    10. Zhong, Sheng, 2021. "Assessing the drivers of changes in aggregate fuel economy in Massachusetts: The role of vehicle reallocation," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Ma, Chunbo, 2014. "A multi-fuel, multi-sector and multi-region approach to index decomposition: An application to China's energy consumption 1995–2010," Energy Economics, Elsevier, vol. 42(C), pages 9-16.
    12. Xu, X.Y. & Ang, B.W., 2014. "Multilevel index decomposition analysis: Approaches and application," Energy Economics, Elsevier, vol. 44(C), pages 375-382.
    13. repec:hal:spmain:info:hdl:2441/4hs7liq1f49gh9chdf7r17gam6 is not listed on IDEAS
    14. repec:hal:spmain:info:hdl:2441/5vt1fet9fq9o5pkgj2qh2vn1cm is not listed on IDEAS
    15. Rottner, Elisa & von Graevenitz, Kathrine, 2022. "What drives carbon emissions in German manufacturing: Scale, technique or composition?," ZEW Discussion Papers 21-027, ZEW - Leibniz Centre for European Economic Research, revised 2022.

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    More about this item

    Keywords

    index decomposition; industrial energy; energy intensity; carbon intensity; structural change;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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