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Does technology diffusion help to reduce emission intensity? Evidence from organized manufacturing and agriculture in India

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  • Majumdar, Devleena
  • Kar, Saibal

Abstract

This paper measures the ‘emission intensity’ of the fifteen organized manufacturing and agricultural sectors in India. Our primary objective and contribution in this paper are in obtaining a direct relation between technological adoption (greater capitalization) and emission intensity at the industry level over a period of fourteen years between 1996 and 2009. We use the Environmentally Extended Input–Output (EEIO) model to calculate the direct, upstream and total emission intensity generated through the entire supply chain of production and consumption. From the fixed effects panel regression results we note that technological adoption helps to reduce emission intensity across industries, although, beyond a critical level it raises the intensity. Importantly, when better technologies are adopted for production of export goods as against non-traded goods, emission falls in a significant way.

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  • Majumdar, Devleena & Kar, Saibal, 2017. "Does technology diffusion help to reduce emission intensity? Evidence from organized manufacturing and agriculture in India," Resource and Energy Economics, Elsevier, vol. 48(C), pages 30-41.
    • Handle: RePEc:eee:resene:v:48:y:2017:i:c:p:30-41
    DOI: 10.1016/j.reseneeco.2017.01.004
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    4. Xu, Shang & Allen Klaiber, H., 2019. "The impact of new natural gas pipelines on emissions and fuel consumption in China," Resource and Energy Economics, Elsevier, vol. 55(C), pages 49-62.
    5. H. Wang & Chen Pan & P. Zhou, 2019. "Assessing the Role of Domestic Value Chains in China’s CO2 Emission Intensity: A Multi-Region Structural Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(2), pages 865-890, October.
    6. Li, Yingzhu & Su, Bin & Dasgupta, Shyamasree, 2018. "Structural path analysis of India's carbon emissions using input-output and social accounting matrix frameworks," Energy Economics, Elsevier, vol. 76(C), pages 457-469.
    7. Ying Luo & Xiaowen Jie & Xiaoping Li & Liming Yao, 2018. "Ranking Chinese SMEs Green Manufacturing Drivers Using a Novel Hybrid Multi-Criterion Decision-Making Model," Sustainability, MDPI, vol. 10(8), pages 1-23, July.
    8. Wei Zhen & Quande Qin & Lei Jiang, 2022. "Heterogeneous Domestic Intermediate Input-Related Carbon Emissions in China’s Exports," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(3), pages 453-479, March.
    9. Zhu, Bangzhu & Su, Bin & Li, Yingzhu, 2018. "Input-output and structural decomposition analysis of India’s carbon emissions and intensity, 2007/08 – 2013/14," Applied Energy, Elsevier, vol. 230(C), pages 1545-1556.

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    More about this item

    Keywords

    Emission intensity; Input–output; Organized sector; Panel fixed effects; India;
    All these keywords.

    JEL classification:

    • L6 - Industrial Organization - - Industry Studies: Manufacturing
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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