The statistical projection of global GHG emissions from a consumption perspective
Projections of future emissions based on current trends are vital to assess the performance of mitigation efforts and to devise mitigation strategies. However, previous studies are uncertain how the persistence and convergence of mitigation efforts may impact future trajectories from the consumption perspective. Here we assume regional convergence over time after finding strong supporting historical evidence and develop a Markov Chain Monte Carlo (MCMC) Bayesian analysis of historical and projected consumption-based emissions during 1995–2050 via a modified Kaya identity. We find global emissions of 21–94 GtCO2eq yr−1 by 2050, slightly higher than SSP1-2.6 and encompassing SSP3-7.0, resulting in 1.4–2.9 °C of warming above pre-industrial levels mid-way through the century. The median projection shows a 2028 peak of 42 GtCO2eq yr−1 and an annual average decrease of 0.25 GtCO2eq thereafter. Future emission decreases result from the interplay between a rapid reduction driven by decreasing consumption-based emission intensities, especially in clothing and transport, and the change of final demand structure (−1.53 GtCO2eq yr−1) against increases from economic and population growth (+1.33 GtCO2eq yr−1). This highlights the importance in continually updating projections and considering models that reflect convergence and consumption structure changes, in order to better inform climate policy.
To reference this document use:
Consumption-based GHG emissions
Markov Chain Monte Carlo Bayesian analysis
Environment & Sustainability
Sustainable Production and Consumption, 34 (34), 318-329