Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban, suburban, and town environments. This field often entails extensive civic involvement and collaborative partnerships with institutions. Its overarching objectives span a spectrum from preserving water quality, habitat, and biodiversity to mitigating the Urban Heat Island (UHI) effect. The UHI phenomenon, characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage, serves as a focal point in this study. The study focuses on developing a methodological framework that integrates Geographically Weighted Regression (GWR), Random Forest (RF), and Suitability Analysis to assess the Urban Heat Island (UHI) effect across different urban zones, aiming to identify areas with varying levels of UHI impact. The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect. Conducted in various London areas, the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI. By mapping the spatial variability of UHI, the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies. The study’s findings are expected to provide valuable insights for urban planners and policymakers, aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management.

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Investing in projects that support environmental benefits, such as tree harvesting, has the potential to reduce air pollution levels in the atmosphere in the future. However, this kind of investment may increase the current level of emissions. Therefore, it is necessary to estimate how much the policy affects the current level of CO2 emissions. This makes sure the policy doesn’t increase the level of CO2 emissions. This study aims to analyze the effect of the One Billion Trees program on CO2 emissions in New Zealand by employing the 2020 input–output table analysis. This investigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand. The results of this study for the first year of plantation suggest that the policy increases the level of CO2 emissions in all regions, especially in the Waikato region. The direct and indirect impact of the policy leads to 64 kt of CO2 emissions on the demand side and 270 kt of CO2 emissions on the supply side. These lead to 0.19 and 0.74% of total CO2 emissions being attributed to investment shocks. Continuing the policy is recommended, as it has a low effect on CO2 emissions. However, it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.

Allometric equations are fundamental tools in ecological research and forestry management, widely used for estimating above-ground biomass and production, serving as the core foundations of dynamic vegetation models. Using global datasets from Tallo (a tree allometry and crown architecture database encompassing thousands of species) and TRY (a plant traits database), we fit Bayesian hierarchical models with three alternative functional forms (power-law, generalized Michaelis–Menten (gMM), and Weibull) to characterize how diameter at breast height (DBH), tree height (H), and crown radius (CR) scale with and without wood density as a species-level predictor. Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups, whereas the CR–DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms. Although including wood density did not significantly improve predictive performance, it revealed important ecological trade-offs: lighter-wood angiosperms achieve taller mature heights more rapidly, and denser wood promotes wider crown expansion across clades. We also found that accurately estimating DBH required considering both height and crown size, highlighting how these variables together distinguish trees of similar height but differing trunk diameters. Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density, though not always predictive at broad scales, helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures. These findings offer practical pathways for integrating height- and crown-based metrics into existing carbon monitoring programs worldwide.