CO2 emissions from forest clear cut in road contruction project

Hyounkyu Lee, Byungil Kim, Hyoungbae Park, Hyoungkwan Kim

Research output: Contribution to conferencePaperpeer-review


Major sources of CO2 release caused by anthropogenic activities include fossil fuel combustion, cement production, and forest clear cut. The first two sources have been recognized as important in the construction industry; efforts have been made to find the causes of and the solutions to the emissions in the fuel combustion and cement production. However, the third major source-forest clear cut-has not yet received its deserving attention in the construction industry, although it may be one of the most important source of CO2 release in various infrastructure projects such as road construction. This study presents a methodology to calculate the amount of CO2 release from forest clear cut in a road construction project. An actual road construction in Korea was used as a case study for quantifying the CO2 release. First, the forest area was divided to three categories (biomass carbon stocks, carbon stocks in dead organic matters, and carbon stocks in soils) depending on the source of CO2 emission, as suggested by IPCC (International Panel on Climate Change). Second, for each category, a methodology was derived to estimate the CO2 emission amount using the gain-loss analysis. Finally, the CO2 emissions from the forest clear cut were compared to those from the fuel consumption by equipment usage in the same case to understand their relative magnitude in CO2 emissions. The study results are expected to be used as a basis for development of CO 2 emission reduction strategies in road construction.

Original languageEnglish
Number of pages6
Publication statusPublished - 2011
Event28th International Symposium on Automation and Robotics in Construction, ISARC 2011 - Seoul, Korea, Republic of
Duration: 2011 Jun 292011 Jul 2


Other28th International Symposium on Automation and Robotics in Construction, ISARC 2011
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Building and Construction


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