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New approach proposed for defining preconditions for human development Crossing certain biophysical thresholds could have disastrous consequences for humanity Three of nine interlinked planetary boundaries have already been overstepped

Featured paper: See Editorial p553

AbstractA better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) and short-term deviations from these averages (anomalies) both influence tree growth, but the rarity of long-term data integrating climatic gradients with tree censuses has so far limited our understanding of their respective role, especially in tropical systems. Here, we combined 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how tree growth responds to both climate means and anomalies, and how species functional traits mediate these tree growth responses to climate. We showed that short-term, anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both long-term and short-term climate variations. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests, and that species traits can be leveraged to understand these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

This thesis reports on the development and testing of a form of participatory budgeting in which citizens are asked to choose from a set of local authority interventions whilst having to comply with two constraints – one financial and the other relating to greenhouse gas emissions. The project has its roots in the weak performance to date of the local government sector in responding to climate change, despite its considerable influence. It is also informed by the troubled relationship between local authorities and citizens. Participatory budgeting is selected as the starting point because it has been found to draw a larger and more diverse audience than more orthodox forms of citizen participation and because it can present participants with a requirement to trade off priorities. The core of the thesis describes the design and development of “participatory emissions budgeting”, a central aspect being the estimation of emissions attributable to local authority interventions. This culminates in formal trials of the method with citizens, followed by quantitative and qualitative evaluation. The method is then presented to a range of local authority stakeholders to gauge their views concerning its potential application. Participatory emissions budgeting is found to be technically feasible: participants consistently arrive, through deliberation, at choice sets that comply with the constraints set. Whilst they report finding the experience interesting and enjoyable, they are critical of the imposition of an emission constraint, in the context of general scepticism concerning the value or legitimacy of tackling climate change through such a decision-making process. Local authority stakeholders see some value in the method but would not wish to apply it as designed – to decide on the allocation of resources. They would rather use it to support decision making within their organisations, as a market-research or educational tool.

Abstract In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

AbstractData capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.