Evaluating the dynamic environmental impacts of technologies

The impacts of technologies depend on various factors, including the background state of the environment. Here we explore how the intensity and decay of impacts influences the relative attractiveness of technologies. We focus on comparing the impacts of various greenhouse gases, and develop new models to evaluate technologies.

Papers in progress focus on the development of new metrics to compare the impacts of greenhouse gases and technologies to one another. We are also investigating how technology portfolios can optimally adapt to a changing background environmental state.

New metric development: We are working on a revised emissions factors which take into account the timing of use of a technology relative to the nearness of critical thresholds. This work focuses on comparing methane and carbon dioxide emissions across various transportation fuels and electricity generation options (Figure 1).

  • Edwards MR, Trancik JE, Climate Impacts of Energy Technologies Depend on Emissions Timing, to appear in Nature Climate Change.

Figure 1. Assessments of the climate benefits of alternative fuels depend on the time horizon over which impacts are evaluated, for example 100 years (A) or 20 years (B). Alternative fuels emit multiple gases during their life cycle – including methane, which has a high initial climate impact but relatively short atmospheric lifetime (C).  As a result, the impacts of methane-heavy fuels like natural gas are initially high but decay more quickly than those of methane-light fuels like gasoline (D).

Portfolio optimization: In this work we ask how we should value methane and carbon dioxide emissions against one another in a transportation fuel portfolio. We are exploring the possibility of using revised emissions factors to reduce environmental impact while meeting energy demand.

 

Figure 2. Performance of fuel portfolios relative to a climate stabilization target for the sector. Portfolios are constructed by minimizing a measure based on a rescaled radiative forcing. We consider a methane-heavy algae diesel and methane-light soy diesel for our portfolios, and show that an optimal order of use can be identified (solid lines as compared to the dashed lines). The analysis is performed for two time horizons, S1 and S2. The optimal fuel switching year is earlier for the shorter horizon (blue lines) than the later horizon (green lines).