We present atmospheric radiocarbon concentrations in CO2 integrated samples taken between January 2019 and December 2021 in the Mexico City Metropolitan Area (MCMA) and explain the variations in terms of changes in emission sources associated with the COVID-19 lockdown restrictions imposed from March 2020. Δ14C values for samples collected during 2019 range between –44.15‰ and –13.17‰, with lower values during months with higher fossil fuels consumption and air stagnation, whereas higher values were found for periods with high number of fires around MCMA or wet months with higher contribution of heterotrophic respiration. For samples collected during 2020, Δ14C values range between –17.7‰ and 2.25‰, with an increasing trend immediately after the initial lockdown and higher values obtained for samples collected during lockdown phases 2 and 3 and the period of extremely high epidemic risk. This agrees with the 38% and 52% decrease in gasoline and diesel sales. Once essential activities gradually opened from July 2020, Δ14C follow a decreasing trend as vehicle traffic started to increase again. Δ14C values for samples collected during 2021 range from –32.89‰ to –10.27‰, with the higher value obtained during a period of extremely high epidemic risk with a 30% reduction in gasoline and diesel consumption. Despite the complexity of emission sources in MCMA, from Δ14C variations it was possible to identify changes in fossil CO2 emissions resulting from the significant reduction in vehicle traffic due to the COVID-19 lockdown and the restrictions imposed to control transmission of the disease.

Decision-makers need readily accessible tools to understand the potential impacts of alternative policies on forest cover and greenhouse gas (GHG) emissions and to develop effective policies to meet national and international targets for biodiversity conservation, sustainable development and climate change mitigation. Land change modelling can support policy decisions by demonstrating potential impacts of policies on future deforestation and GHG emissions. We modelled land change to explore the potential impacts of expert-informed scenarios on deforestation and GHG emissions, specifically CO2 emissions, in the Ankeniheny–Zahamena Corridor in eastern Madagascar. We considered four scenarios: business as usual; effective conservation of protected areas; investment in infrastructure; and agricultural intensification. Our results highlight that effective forest conservation could deliver substantial emissions reductions, while infrastructure development will likely cause forest loss in new areas. Agricultural intensification could prevent additional forest loss if it reduced the need to clear more land while improving food security. Our study demonstrates how available land change modelling tools and scenario analyses can inform land-use policies, helping countries reconcile economic development with forest conservation and climate change mitigation commitments.