Life cycle assessment of carbon emission from natural gas pipelines

With the deterioration of global climate and environment, the carbon emission of the oil and gas industry has been an important issue of global concern. At present, there is a lack of systematic research on carbon emission calculation of natural gas pipeline from construction to disposal. We propose a life cycle assessment method for natural gas pipeline in order to quantify the carbon emissions from construction to disposal. The carbon emissions of natural gas pipeline are divided into four parts in this model: manufacturing, construction, operation, and recycling. For each of the four stages, carbon emissions from material production and construction, facility construction and equipment operation are all detailedly calculated. In addition, methane, the main component of natural gas, has a non-negligible impact on the atmosphere. Therefore, gas leakage from the pipeline system is also considered in this paper. And data from actual pipelines are utilized in the case study. The results show that the carbon emissions of these pipelines are in the range of 26.58–67.14 t CO 2 / (km×10 8 m 3 / a) and 11.41–30.27 t CH 4 / (km×10 8 m 3 / a). Among them, the production process of pipelines contributes the most to carbon emissions, accounting for about 80 % of the total CO 2 emissions. Through sensitivity analysis, we found that pipeline parameter, power emission factor, and compressor operation status turn out to be the main factors affecting carbon emission. With the establishment of this model, potential carbon emission of planned pipelines can be estimated, which has guiding significance for future pipeline construction. • Conduct a comprehensive life cycle inventory analysis on natural gas pipeline system. • Analyze potential CO 2 emission and methane emission in natural gas pipeline system. • Summarize the key parameters affecting the system's total emissions and their sensitivity. • Propose a fitting formula to estimate pipeline emission based on real data.