RESUMO
Empirical evidence suggests that variations in climate affect economic growth across countries over time. However, little is known about the relative impacts of climate change on economic outcomes when global mean surface temperature (GMST) is stabilized at 1.5°C or 2°C warming relative to pre-industrial levels. Here we use a new set of climate simulations under 1.5°C and 2°C warming from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) project to assess changes in economic growth using empirical estimates of climate impacts in a global panel dataset. Panel estimation results that are robust to outliers and breaks suggest that within-year variability of monthly temperatures and precipitation has little effect on economic growth beyond global nonlinear temperature effects. While expected temperature changes under a GMST increase of 1.5°C lead to proportionally higher warming in the Northern Hemisphere, the projected impact on economic growth is larger in the Tropics and Southern Hemisphere. Accounting for econometric estimation and climate uncertainty, the projected impacts on economic growth of 1.5°C warming are close to indistinguishable from current climate conditions, while 2°C warming suggests statistically lower economic growth for a large set of countries (median projected annual growth up to 2% lower). Level projections of gross domestic product (GDP) per capita exhibit high uncertainties, with median projected global average GDP per capita approximately 5% lower at the end of the century under 2°C warming relative to 1.5°C. The correlation between climate-induced reductions in per capita GDP growth and national income levels is significant at the p < 0.001 level, with lower-income countries experiencing greater losses, which may increase economic inequality between countries and is relevant to discussions of loss and damage under the United Nations Framework Convention on Climate Change.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.
RESUMO
Meeting the Paris Agreement's climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning-based extension of the common difference-in-differences approach. We identified 63 successful policy interventions with total emission reductions between 0.6 billion and 1.8 billion metric tonnes CO2. Our insights on effective but rarely studied policy combinations highlight the important role of price-based instruments in well-designed policy mixes and the policy efforts necessary for closing the emissions gap.
RESUMO
Fibroblast growth factor receptor 4 (FGFR4) is thought to be a driver in several cancer types, most notably in hepatocellular carcinoma. One way to achieve high potency and isoform selectivity for FGFR4 is covalently targeting a rare cysteine (C552) in the hinge region of its kinase domain that is not present in other FGFR family members (FGFR1-3). Typically, this cysteine is addressed via classical acrylamide electrophiles. We demonstrate that noncanonical covalent "warheads" based on nucleophilic aromatic substitution (SNAr) chemistry can be employed in a rational manner to generate highly potent and (isoform-)selective FGFR4 inhibitors with a low intrinsic reactivity. Key compounds showed low to subnanomolar potency, efficient covalent inactivation kinetics, and excellent selectivity against the other FGFRs, the kinases with an equivalent cysteine, and a representative subset of the kinome. Moreover, these compounds achieved nanomolar potencies in cellular assays and demonstrated good microsomal stability, highlighting the potential of SNAr-based approaches in covalent inhibitor design.