Effective forest-based climate change mitigation requires our best science.

California's Cap-and-Trade Program sets a limit on the major sources of greenhouse gas emissions and allows a portion of excess emissions to be offset through purchase of credits for climate benefits accrued elsewhere. Badgley et al. (2021, Global Change Biology, https://doi.org/10.1111/gcb.15943) describe how the use of mean forest carbon stocks from ecological supersections can create perverse incentives for project developers, potentially leading to over-crediting and nonadditional offsets. Carbon markets remain a valuable tool in combating climate change, but ensuring projects' additionality is of critical importance to effective carbon mitigation. Badgley's article should serve as a call to action to redouble efforts at integrating the latest carbon science into effective and timely policy solutions.

The Influence of Human Demography on Land Cover Change in the Great Lakes States, USA.

The Great Lakes region contains productive agricultural and forest lands, but it is also highly urbanized, with 32 of its 52 million residents living in nine large metropolitan areas. Urbanization of undeveloped areas may adversely affect the productivity of agricultural and forest lands, and the provision of ecosystem services. We combine demographic and remote sensing data to evaluate land cover change in the region using a two-phase statistical modeling approach that predicts the incidence and extent of land cover change for each of the region's 10,579 county subdivisions. Observed patterns are spatially uneven, and the probability of land cover change is influenced by current land use, human habitation, industry, and demographic change. Pseudo R2 values varied from 0.053 to 0.338 for the first-phase logistic models predicting the presence of land cover change; second-stage beta models predicting the rate of change were more reliable, with pseudo R2 ranging from 0.225 to 0.675. Overall, changes from agriculture or greenspace to development were much more predictable than changes from agriculture to greenspace or vice versa, and demographic variables were much more important in models predicting change to development. Although models successfully predicted the general location of land cover change, and models from before the Great Recession were useful for predicting the location but not the amount of change during the recession, fine-grained prediction remained challenging. Understanding where future changes are most probable can inform planning and policy-making, which may reduce the impact of development on resource production, environmental health, and ecosystem services.