The long-term relationship between emissions and economic growth for SO 2 , CO 2 , and BC

Published

Journal Article

© 2018 The Author(s). Published by IOP Publishing Ltd. Simplified assumptions regarding the relationship between per capita income and emissions are oftentimes utilized to generate future emission scenarios in integrated assessment models (IAMs). One such relationship is an environmental Kuznets curve (EKC), where emissions first increase, then decline with income growth. However, current knowledge about this relationship lacks the specificity needed for each sector and pollutant pairing, which is important for future emission scenarios. To fill this knowledge gap, we analyze the historical relationship between per capita income and emissions of SO 2 , CO 2 , and black carbon (BC) utilizing widely-used global, country-level emission inventories for the following four sectors: power, industry, residential, and transportation. Based on a modeling setup using long-term growth rates, emissions of SO 2 from the power and industrial sectors, as well as CO 2 from the industrial and the residential sectors, largely follow an EKC pattern. Income-emission trajectories for SO 2 and CO 2 from other sectors, and those for BC from all sectors, do not show an EKC, however. Results across different global inventories were variable, indicating that uncertainties within historical emission trajectories persist. Nonetheless, these results demonstrate that long-term income-emission trajectories of air pollutants are both sector and pollutant specific. Future reference trajectories of SO 2 and BC from three IAMs show earlier estimates of turnover incomes and faster rates of emission declines when compared to historical data. Users of future emission scenarios derived using EKC assumptions should consider the underlying uncertainties in such projections in light of this historical analysis.

Full Text

Duke Authors

Cited Authors

  • Ru, M; Shindell, DT; Seltzer, KM; Tao, S; Zhong, Q

Published Date

  • December 1, 2018

Published In

Volume / Issue

  • 13 / 12

Electronic International Standard Serial Number (EISSN)

  • 1748-9326

International Standard Serial Number (ISSN)

  • 1748-9318

Digital Object Identifier (DOI)

  • 10.1088/1748-9326/aaece2

Citation Source

  • Scopus