Synergies or Trade-Offs? Optimizing a Virtual Urban Region to Foster Plant Species Richness, Climate Regulation, and Compactness Under Varying Landscape Composition

Abstract

Ongoing urbanization forces us to reflect on how we can cater for multiple targets when building cities. In this study, we investigate whether compactness as a common urban development strategy, climate regulation as an example for an ecosystem service, and vascular plant species richness as a measure of biodiversity form a synergistic relationship or whether trade-offs exist. We use a genetic algorithm to optimize the spatial allocation of three types of land cover blocks in a stylized urban region. These blocks are categorized as high-, low-density, or park blocks, depending on the proportion of green and built-up cells within each block. We systematically vary landscape composition at the block level, but keep city size constant. Our most important finding is that the relationships of target functions can shift between tradeoff and synergy, and this is shaped by landscape composition. For example, we found a trade-off between species richness and urban compactness in landscapes with large proportions of high-density areas and parks, while they have a synergistic relationship in low-density landscapes. Such dependencies of trade-offs and synergies on landscape composition need to be explored further, which may help address the wickedness of urban planning problems.

Matching Ecosystem Services Supply and Demand through Land Use Optimization: A Study of the Guangdong-Hong Kong-Macao Megacity

“…Landscape configuration can greatly affect ES supply; therefore, it was considered in this study. Landscape richness is highest when ecosystem cells are spread over as many patches as possible, while compactness is maximized when forming a single ecosystem patch [ 28 ]. Landscape richness and heterogeneity were expected to have positive effects on pollination and biodiversity, especially for species that use more than one cover type [ 81 ].…”

“…If a targeted ES supply does not match the corresponding demand (the ES supply is lower than the demand), reducing the ESSD mismatch is defined as an optimization objective. Considering the different factors and objectives involved in the definition of spatial suitability, several objectives (e.g., maximizing the number of beneficiaries of ES, achieving maximum compactness of land use to avoid land fragmentation) may be developed when directly taking into account all the relative factors [ 28 , 64 ]. Multi-objective optimization problems can be solved either by integrating all the objectives into one single function by assigning a weight to each objective, or by generating multiple solutions simultaneously based on Pareto-based methods [ 65 ].…”

“…Numerous optimization algorithms have been applied to optimize land use allocation, including ant colony algorithms [ 24 ], genetic algorithms [ 25 ], nondominated sorting genetic algorithm-II [ 26 ], and artificial immune algorithms [ 27 ]. For example, CoMOLA (Constrained Multi-objective Optimization of Land use Allocation) is a landscape optimization tool that finds optimal options for multiple objectives [ 28 ], and SAORES (a spatially explicit assessment and optimization tool for regional ecosystem services) is designed to integrate assessment and optimization of ES for ecological restoration and management of the Chinese Loess Plateau [ 29 ]. Nonetheless, few studies have integrated optimization into landscape management and planning to reduce or minimize ESSD mismatches.…”

Forested Riparian Zones Provide Important Habitat for Fish in Urban Streams

“…To conceptualizing and planning the optimal configuration of forested riparian zones at the catchment scale (i.e., width, length, position in the stream network), however, requires context-specific considerations and the forecasting of different scenarios. Future urban planning should implement modelling frameworks, such as CoMOLA [86,87], to optimize the spatial allocation and extent of riparian zones along urban stream networks to promote biodiversity, ecosystem functioning, and services to enhance human well-being, thus ultimately helping to create sustainable and livable cities of the future.…”

An Outlier-Robust Point and Interval Forecasting System for Daily PM2.5 Concentration

“…As a result, the optimal distribution determination problem can be converted into solving the maximum value problem. Inspired by Wang et al (2020b), Schwarz et al (2020), andLieß et al (2021), artificial intelligence optimization can be considered a promising technique for searching for the optimal distribution parameters. Based on this idea and considering the limitations of the traditional method, the artificial intelligence-based distribution evaluation is proposed to obtain the optimal distribution in this study.…”