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

Abstract

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.

Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide

“…This additional heterogeneity is not captured here, but is small relative to the global differences we estimate. We neglected uncertainty in the input variables, though PM 2.5 concentrations, relative risks, CO 2 emissions, carbon footprints, and city GDP are each uncertain and may vary between existing datasets and inventories 23 . PM 2.5 concentrations are uncertain because much of the world still lacks ground monitoring networks, though most monitors included by Shaddick et al .…”

Reducing global air pollution: the scope for further policy interventions

“…The observed increase and subsequent decline in SO 2 emissions in many industrialized countries during the second half of the twentieth century inspired the 'environmental Kuznets curve' hypothesis, suggesting that environmental degradation tends to get worse as modern economic growth occurs until average income reaches a certain point [5,6]. However, the explanatory power and general validity of this hypothesis has been strongly contested [7][8][9][10][11][12], inter alia, because similar turning points have not yet been observed for other substances, including agricultural ammonia (NH 3 ) and greenhouse gas emissions. Also, the role of (environmental) policy interventions is not explicitly recognized but implicitly subsumed as an autonomous concomitant of economic growth.…”

Analysing the relationship between CO2 emissions and GDP in China: a fractional integration and cointegration approach

“…Mikaylov et al (2018) use a variety of cointegration methods (Johansen, ARDL, DOLS (dynamic ordinary least squares), FMOLS (fully modified ordinary least squares) and CCR (correlation regression estimator)) to test for the existence of an EKC in Azerbaijan and find that economic growth has a positive and statistically significant longrun effect on emissions which implies that the EKC hypothesis does not hold. Ru et al (2018) apply a recently developed methodology based on the long-term growth rates (Stern et al, 2017) to model the income-emission relationship for four sectors (power, industry, residential, and transportation) and three difference types of pollutants (SO 2 (sulfur dioxide), CO 2 , and BC (black carbon)); the analysis uses data for various countries from the global emission inventory developed at Peking University and finds that the results are both sector and pollutant specific. Barassi and Spagnolo (2012) estimate a VAR-GARCH (vector autoregression-generalized autoregressive conditional heteroskedastic) model and find evidence of both mean and volatility spillovers between per capita economic growth and carbon dioxide emissions in Canada, France, Italy, Japan, UK (United Kingdom) and USA (United States of America) over the period 1870-2005. Panel studies supporting the validity of the EKC hypothesis include Martínez-Zarzoso and Bengochea-Morancho ( 2004) for 22 OECD countries; Farhani et al (2014) for 10 Middle East and North African (MENA) countries; Gao and Zhang (2014) for 14 sub-Saharan African countries; Kasman and Duman (2015) for 15 countries which were Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Iceland, Latvia, Lithuania, FYR of Macedonia, Malta, Poland, Romania, Slovak Republic, Slovenia, and Turkey; Pao and Tsai (2011) for Brazil, Russia, India and China; Osabuohien et al (2014) for 50 African countries; Kim (2019) for newly industrialized Asian countries; Apergis and Payne ( 2009) for six Central American countries; Anastacio (2017) for Canada, the and Mexico.…”