Resilience to specified kinds of disasters is an active area of research and practice. However, rare or unprecedented disturbances that are unusually intense or extensive require a more broad-spectrum type of resilience. General resilience is the capacity of social-ecological systems to adapt or transform in response to unfamiliar, unexpected and extreme shocks. Conditions that enable general resilience include diversity, modularity, openness, reserves, feedbacks, nestedness, monitoring, leadership, and trust. Processes for building general resilience are an emerging and crucially important area of research.

Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4 \(^{\circ }\)C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature–damage relationship, we find a three- to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four- to five-fold.

Over a million species face extinction, highlighting the urgent need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people’s lives. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, conservation area prioritization through artificial intelligence (CAPTAIN), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects significantly more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, further improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resource-limited world.

Under the UN Paris Agreement, countries committed to limiting global warming to well below 2 °C and to actively pursue a 1.5 °C limit. Yet, according to the 2018 Economics Nobel laureate William Nordhaus, these targets are economically suboptimal or unattainable and the world community should aim for 3.5 °C in 2100 instead. Here, we show that the UN climate targets may be optimal even in the Dynamic Integrated Climate–Economy (DICE) integrated assessment model, when appropriately updated. Changes to DICE include more accurate calibration of the carbon cycle and energy balance model, and updated climate damage estimates. To determine economically ‘optimal’ climate policy paths, we use the range of expert views on the ethics of intergenerational welfare. When updates from climate science and economics are considered jointly, we find that around three-quarters (or one-third) of expert views on intergenerational welfare translate into economically optimal climate policy paths that are consistent with the 2 °C (or 1.5 °C) target.