Global projections of future urban land expansion under shared socioeconomic pathways

A Research Agenda for Urban Biodiversity in the Global Extinction Crisis

Rapid urbanization and the global loss of biodiversity necessitate the development of a research agenda that addresses knowledge gaps in urban ecology that will inform policy, management, and conservation. To advance this goal, we present six topics to pursue in urban biodiversity research: the socioeconomic and social–ecological drivers of biodiversity loss versus gain of biodiversity; the response of biodiversity to technological change; biodiversity–ecosystem service relationships; urban areas as refugia for biodiversity; spatiotemporal dynamics of species, community changes, and underlying processes; and ecological networks. We discuss overarching considerations and offer a set of questions to inspire and support urban biodiversity research. In parallel, we advocate for communication and collaboration across many fields and disciplines in order to build capacity for urban biodiversity research, education, and practice. Taken together we note that urban areas will play an important role in addressing the global extinction crisis.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth's system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Analyzing Land-Use Change Scenarios for Ecosystem Services and their Trade-Offs in the Ecological Conservation Area in Beijing, China

It is generally believed that land-use changes can affect a variety of ecosystem services (ES), but the relationships involved remain unclear due to a lack of systematic knowledge and gaps in data. In order to make rational decisions for land-use planning that is grounded in a systematic understanding of trade-offs between different land-use strategies, it is very important to understand the response mechanisms of various ecosystem services to changes in land-use. Therefore, the objective of our study is to assess the effects of land-use change on six ecosystem services and their trade-offs among the ecosystem services in the ecological conservation area (ECA) in Beijing, China. To do this, we projected future land-use in 2030 under three different scenarios: Business as Usual (BAU), Ecological Protection (ELP), and Rapid Urban Development (RUD), using GeoSOS-FLUS model. Then, we quantified six ecosystem services (carbon storage, soil conservation, water purification, habitat quality, flood regulation, and food production) in response to land-use changes from 2015 to 2030, using a spatially explicit InVEST model. Finally, we illustrated the trade-offs and/or synergistic relationships between each ecosystem service quantified under each of the different scenarios in 2030. Results showed that built-up land is projected to increase by 281.18 km2 at the cost of water bodies and cultivated land from 2015 to 2030 under the RUD scenario, while forest land is projected to increase by 152.38 km2 under the ELP scenario. The carbon storage, soil conservation, habitat quality, and the sum of ecosystem services (SES) would enrich the highest level under the ELP scenario. Land-use strategies that follow the ELP scenario can better maintain the ecosystem services and sustainable development of natural and social economic systems.

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

Simulating the Impact of Future Climate Change and Ecological Restoration on Trade-Offs and Synergies of Ecosystem Services in Two Ecological Shelters and Three Belts in China

Development of suitable ecological protection and restoration policies for sustainable management needs to assess the potential impacts of future land use and climate change on ecosystem services. The two ecological shelters and three belts (TSTB) are significant for improving ecosystem services and ensuring China’s and global ecological security. In this study, we simulated land use in 2050 and estimated the spatial distribution pattern of net primary productivity (NPP), water yield, and soil conservation from 2010 to 2050 under future climate change. The results showed that water yield, NPP, and soil conservation exhibited a spatial pattern of decreasing from southeast to northwest, while in terms of the temporal pattern, water yield and NPP increased, but soil conservation decreased. Water yield was mainly influenced by precipitation, NPP was affected by temperature and implementation of ecological restoration, and soil conservation was controlled by precipitation and slope. There was a strong spatial heterogeneity between trade-offs and synergies. In terms of the temporal, with the combination of climate change and ecological restoration, there was a synergistic relationship between water yield and NPP. However, the relationships between water yield and soil conservation, and between NPP and soil conservation were characterized by trade-offs. In the process of ecological construction, it is necessary to consider the differences between overall and local trade-offs and synergies, as well as formulate sustainable ecological management policies according to local conditions. Understanding the response of ecosystem services to future climate change and land use policies can help address the challenges posed by climate change and achieve sustainable management of natural resources.

The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.

Future urban development exacerbates coastal exposure in the Mediterranean

Changes in the spatial patterns and rate of urban development will be one of the main determinants of future coastal flood risk. Existing spatial projections of urban extent are, however, often available at coarse spatial resolutions, local geographical scales or for short time horizons, which limits their suitability for broad-scale coastal flood impact assessments. Here, we present a new set of spatially explicit projections of urban extent for ten countries in the Mediterranean, consistent with the Shared Socioeconomic Pathways (SSPs). To model plausible future urban development, we develop an Urban Change Model, which uses input variables such as elevation, population density or road network and an artificial neural network to project urban development on a regional scale. The developed future projections for the five SSPs indicate that accounting for the spatial patterns of urban development can lead to significant differences in the assessment of future coastal urban exposure. The increase in exposure in the Extended Low Elevation Coastal Zone (E-LECZ = area below 20 m of elevation) until 2100 can vary, by up to 104%, depending on the urban development scenario chosen. This finding highlights that accounting for urban development in long-term adaptation planning, e.g. in the form of land-use planning, can be an effective measure for reducing future coastal flood risk on a regional scale.

A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models

Due to the increase in future uncertainty caused by rapid environmental, societal, and technological change, exploring multiple scenarios has become increasingly important in urban planning. Land Change Modeling (LCM) enables planners to have the ability to mold uncertain future land changes into more determined conditions via scenarios. This paper reviews the literature on urban LCM and identifies driving factors, scenario themes/types, and topics. The results show that: (1) in total, 113 driving factors have been used in previous LCM studies including natural, built environment, and socio-economic factors, and this number ranges from three to twenty-one variables per model; (2) typical scenario themes include “environmental protection” and “compact development”; and (3) LCM topics are primarily growth prediction and prediction tools, and the rest are growth-related impact studies. The nature and number of driving factors vary across models and sites, and drivers are heavily determined by both urban context and theoretical framework.

Spatially-explicit modeling and intensity analysis of China's land use change 2000-2050

Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000-2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000-2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010-2050. The total amount of cultivated land continuously decreases but will not violate the Chinese "Cultivated Land Red-Line Restriction" by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development.

Future Impacts of Land Use Change on Ecosystem Services under Different Scenarios in the Ecological Conservation Area, Beijing, China

Ecosystem services (ES), defined as benefits provided by the ecosystem to society, are essential to human well-being. However, it remains unclear how they will be affected by land-use changes due to lack of knowledge and data gaps. Therefore, understanding the response mechanism of ecosystem services to land-use change is critical for developing systematic and sound land planning. In this study, we aimed to explore the impacts of land-use change on the three ecosystem services, carbon storage (CS), flood regulation (FR), and soil conservation (SC), in the ecological conservation area of Beijing, China. We first projected land-use changes from 2015 to 2030, under three scenarios, i.e., Business as Usual (BAU), Ecological Land Protection (ELP), and Rapid Economic Development (RED), by interactively integrating the Markov model (Quantitative simulation) with the GeoSOS-FLUS model (Spatial arrangement), and then quantified the three ecosystem services by using a spatially explicit InVEST model. The results showed that built-up land would have the most remarkable growth during 2015–2030 under the RED scenario (2.52% increase) at the expense of cultivated and water body, while forest land is predicted to increase by 152.38 km2 (1.36% increase) under the ELP scenario. The ELP scenario would have the highest amount of carbon storage, flood regulation, and soil conservation, due to the strict protection policy on ecological land. The RED scenario, in which a certain amount of cultivated land, water body, and forest land is converted to built-up land, promotes soil conservation but triggers greater loss of carbon storage and flood regulation capacity. The conversion between land-use types will affect trade-offs and synergies among ecosystem services, in which carbon storage would show significant positive correlation with soil conservation through the period of 2015 to 2030, under all scenarios. Together, our results provide a quantitative scientific report that policymakers and land managers can use to identify and prioritize the best practices to sustain ecosystem services, by balancing the trade-offs among services.

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.