Romero-Avila, DiegoOmay, TolgaEconomics2024-07-052024-07-05202311387-585X1573-297510.1007/s10668-022-02566-22-s2.0-85137011245https://doi.org/10.1007/s10668-022-02566-2https://hdl.handle.net/20.500.14411/2429Anthropogenic emissions of reactive gases, aerosols and aerosol precursor compounds are responsible for the ozone hole, global warming and climate change, which have altered ecosystems and worsened human health. Environmental authorities worldwide have responded to these climate challenges through the 2030 Agenda for Sustainable Development. In this context, it is key to ascertain empirically whether emission levels are converging among the countries forming the industrialized world. In doing so, we focus on 23 industrialized countries using a novel dataset with ten series of annual estimates of anthropogenic emissions that include aerosols, aerosol precursor and reactive compounds, and carbon dioxide over the 1820-2018 period. We apply four state-of-the-art panel unit root tests that allow for several forms of time-dependent and state-dependent nonlinearity. Our evidence supports stochastic convergence following a linear process for carbon dioxide, whereas the adjustment is nonlinear for black carbon, carbon monoxide, methane, non-methane volatile organic compounds, nitrous oxide, nitrogen oxides and sulfur dioxide. In contrast, ammonia and organic carbon emissions appear to diverge. As for deterministic convergence, carbon dioxide converges linearly, while black carbon, carbon monoxide, nitrogen oxides, non-methane volatile organic compounds and sulfur dioxide adjust nonlinearly. Our results carry important policy implications concerning the achievement of SDG13 of the global 2030 Agenda for Sustainable Development, which appears to be feasible for the converging compounds.eninfo:eu-repo/semantics/openAccessGHGs emissions convergenceNonlinearitiesState-dependenceStructural breaks2030 Agenda for Sustainable DevelopmentConvergence of GHGs emissions in the long-run: aerosol precursors, reactive gases and aerosols-a nonlinear panel approachArticleQ2Q125111230312337WOS:000844501000003