Journal of Productivity Analysis

, Volume 42, Issue 3, pp 355–366

How does a firm’s management of greenhouse gas emissions influence its economic performance? Analyzing effects through demand and productivity in Japanese manufacturing firms

  • Kimitaka Nishitani
  • Shinji Kaneko
  • Satoru Komatsu
  • Hidemichi Fujii

DOI: 10.1007/s11123-014-0388-9

Cite this article as:
Nishitani, K., Kaneko, S., Komatsu, S. et al. J Prod Anal (2014) 42: 355. doi:10.1007/s11123-014-0388-9


This paper analyzes how a firm’s management of greenhouse gas (GHG) emissions affects its economic performance. The theoretical model we derive from Cobb–Douglas production and inverse demand functions predict that in conducting GHG emissions management, a firm will enhance its economic performance because it promotes an increase in demand for its output and improves its productivity. The estimation results, using panel data on Japanese manufacturing firms during the period 2007–2008, support the view that a firm’s GHG emissions management enhances a firm’s economic performance through an increase in demand and improvement in productivity. However, the latter effect is conditional. Although a firm’s efforts to maintain lower GHG emissions improves productivity, efforts to reduce GHG emissions further does not always improve it, especially for energy-intensive firms. Because firms attempting to maintain lower GHG emissions are more likely to improve their productivity, there is a possibility that firms with high GHG emissions can also enhance economic performance by reducing their emissions in the long term, even if additional costs are incurred. In addition, better GHG emissions management increases the demand of environmentally conscious customers because a product’s life cycle GHG emissions in the upper stream of the supply chain influence those in the lower stream, and customers evaluate the suppliers’ GHG emissions management in terms of green supply-chain management.


Greenhouse gas emissions managementEconomic performanceIncrease in demandImprovement in productivityRandom-effects instrumental variables model

JEL Classification


Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Kimitaka Nishitani
    • 1
  • Shinji Kaneko
    • 2
  • Satoru Komatsu
    • 3
  • Hidemichi Fujii
    • 4
  1. 1.Research Institute for Economics and Business AdministrationKobe UniversityKobeJapan
  2. 2.Graduate School for International Development and CooperationHiroshima UniversityHigashi-HiroshimaJapan
  3. 3.School of Global Humanities and Social ScienceNagasaki UniversityNagasakiJapan
  4. 4.Graduate School of Fisheries Science and Environmental StudiesNagasaki UniversityNagasakiJapan