Global projections of future urban land expansion under shared socioeconomic pathways

Exposure of protected areas in Central America to extreme weather events

Central America and the Caribbean are regularly battered by megadroughts, heavy rainfall, heat waves, and tropical cyclones. Although 21st-century climate change is expected to increase the frequency, intensity, and duration of these extreme weather events (EWEs), their incidence in regional protected areas (PAs) remains poorly explored. We examined historical and projected EWEs across the region based on 32 metrics that describe distinct dimensions (i.e., intensity, duration, and frequency) of heat waves, cyclones, droughts, and rainfall and compared trends in PAs with trends in unprotected lands. From the early 21st century onward, exposure to EWEs increased across the region, and PAs were predicted to be more exposed to climate extremes than unprotected areas (as shown by autoregressive model coefficients at p < 0.05 significance level). This was particularly true for heat waves, which were projected to have a significantly higher average (tested by Wilcoxon tests at p < 0.01) intensity and duration, and tropical cyclones, which affected PAs more severely in carbon-intensive scenarios. PAs were also predicted to be significantly less exposed to droughts and heavy rainfall than unprotected areas (tested by Wilcoxon tests at p < 0.01). However, droughts that could threaten connectivity between PAs are increasingly common in this region. We estimated that approximately 65% of the study area will experience at least one drought episode that is more intense and longer lasting than previous droughts. Collectively, our results highlight that new conservation strategies adapted to threats associated with EWEs need to be tailored and implemented promptly. Unless urgent action is taken, significant damage may be inflicted on the unique biodiversity of the region.

Substantial terrestrial carbon emissions from global expansion of impervious surface area

Global impervious surface area (ISA) has more than doubled over the last three decades, but the associated carbon emissions resulting from the depletion of pre-existing land carbon stores remain unknown. Here, we report that the carbon losses from biomass and top soil (0-30 cm) due to global ISA expansion reached 46-75 Tg C per year over 1993-2018, accounting for 3.7-6.0% of the concurrent human land-use change emissions. For the Annex I countries of UNFCCC, our estimated emissions are comparable to the carbon emissions arising from settlement expansion as reported by the national greenhouse gas inventories, providing independent validation of this kind. The contrast between growing emissions in non-Annex I countries and declining ones in Annex I countries over the study period can be explained by an observed emerging pattern of emissions evolution dependent on the economic development stage. Our study has implications for international carbon accounting and climate mitigation as it reveals previously ignored but substantial contributions of ISA expansion to anthropogenic carbon emissions through land-use effects.

Urbanization reduces diversity, simplifies community and filter bird species based on their functional traits in a tropical city

Understanding how organisms are coping with major changes imposed by urban intensification is a complex task. In fact, our understanding of the impacts of urbanization on biodiversity is scarce in the global south compared to the north. In this study, we evaluated how bird communities are affected by impact of urban intensification in a tropical city. Thus, we assessed whether increased urban intensification 1) jeopardizes bird diversity (taking into account taxonomic-TD, phylogenetic-PD, and functional-FD dimensions), 2) drives changes in bird community composition and enables the detection of indicator species of such impact, and 3) leads to changes in bird functional traits linked to reproduction, resource acquisition, and survival. We found that urban intensification has a direct impact on the bird community, reducing all three types of diversity. Communities in areas of greater urban intensity are represented by fewer species, and these species are PD and FD less distinct. In addition, we detected at least ten species of areas of lower urban intensity that proved to be more sensitive to urban intensification. With regard to bird traits, we found no significant responses from reproductive, habitat use and feeding variables. Body weight and tail length were the only variables with significant results, with higher urbanization intensity areas selecting for species with lower weights and longer tails. Given the global biodiversity loss we are observing, this information can guide urban managers and planners in designing urban landscapes to maintain biodiversity in cities.

How does land consolidation affect nongrain production? Evidence from county-level data in Jiangsu Province, China

Using county-level panel data for Jiangsu Province from 2008 to 2018, this study adopted a fixed effect model to analyze the impact of land consolidation on crop planting structure, also considering the moderating effect of distance from the city center and the heterogeneous effect of various types of land consolidation. The results revealed that farmland consolidation and land reclamation had a negative impact on the proportion of grain crops (rice, wheat, and corn) cultivated, which declined by 0.0051 % (0.0069 %), 0.0055 % (0.0124 %), and 0.0101 % (0.0123 %) for every 1 % increase in investment, construction area, and newly added arable land from farmland consolidation (land reclamation), respectively, demonstrating that land consolidation has not prevented, or even encouraged nongrain production expansion. The production conditions of reclaimed arable land and land transfer practices following consolidation may be factors affecting these declines. Notably, the negative effect of land consolidation on crop planting structure weakens when the land is further away from the city center. To ensure food security, priority should be given to follow-up management after land consolidation and rational oversight and guidance following land transfer.

Construction of ecological security pattern of arid area based on landscape ecological risk assessment: a case study of the Wu-Chang-Shi urban agglomeration

The construction of ecological security pattern (ESP) holds paramount importance in ensuring regional environment sustainability. This study introduces an innovative approach to ESP construction grounded in landscape ecological risk (LER) assessment, with Wu-Chang-Shi urban agglomeration in Xinjiang, China, serving as a case study. Initially, LER within the area was evaluated using the LER Index (LERI) method. Subsequently, the Geodetector model was employed to discern the relationship between multi-source data and LER. Furthermore, ecological resistance and corridors were delineated utilizing the minimum cumulative resistance (MCR) model. Lastly, the corridors were optimized using the gravity model, finalizing the ESP construction. Study results reveal that LER was always fluctuating from 1990 to 2010, and tended to stabilize from 2010 to 2020. Factor detection underscores the predominant influence of land use on LER, followed by elevation and vegetation distribution. The ESP shows the imperative for improving connectivity of the natural areas that are fragmented by urban land, highlighting the great significance of the woodland-originating corridors. Finally, strategies are proposed to enhance woodland and water coverage, boost landscape diversity in nature reserves, and prioritize ecological conservation in corridor regions. In summation, the study furnishes a framework for analyzing arid regions in Eurasia. Furthermore, the research idea of evaluation-analysis-remodeling also offers insights into environmental management in developing areas with more diverse climate types.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP of synergizing economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000-2005), sharp decline (2005-2009), slow decline (2009-2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005-2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

Exploring future ecosystem service changes and key contributing factors from a "past-future-action" perspective: A case study of the Yellow River Basin

Understanding the impacts of climate change and human activities on ecosystem services (ESs) and taking actions to adapt to and mitigate their negative impacts are of great benefit to sustainable regional development. In this paper, we integrate the System Dynamics Model (SD), the Future Land Use Simulation (FLUS) model, the Integrated Valuation and Trade-offs of ESs (InVEST) model, and the Structural Equation Model (SEM). We select three scenarios, SSP1-1.9, SSP2-4.5, and SSP5-8.5, from the Coupled Model Intercomparison Project 6 (CMIP6) to forecast future changes under these scenarios in the Yellow River Basin (YRB) by 2030. We predict future changes in water yield (WY), carbon storage (CS), soil retention (SR), and habitat quality (HQ) in the YRB. The results show that: (1) Under the SSP1-1.9 scenario, ecological land types such as forests, grasslands, and water bodies are protected and restored to a certain extent; under the SSP2-4.5 scenario, the degree of land spatial development occupies an intermediate state among the three scenarios; and under the SSP5-8.5 scenario, there is an obvious increase in the artificialization of the watershed's land use. (2) Under scenario SSP1-1.9, there is a comprehensive approach to sustainable development that significantly improves all ESs in the watershed, while the SSP5-8.5 and SSP2-4.5 scenarios demonstrate an increase in trade-offs between WY, HQ, and CS, especially in the downstream area. (3) Anthropogenic factors having more significant impacts in the SSP5-8.5 scenario. In this paper, we not only summarize the differences in trade-offs among various ESs but also provide an in-depth analysis of the key factors affecting future ESs, providing new ideas and insights for the sustainable development of ES in the future. In summary, we propose a prioritized development pathway for the future, a reduction of trade-offs between ESs, and an improved capacity to respond to challenges.

Urbanization promotes carbon storage or not? The evidence during the rapid process of China

China's commitment to attaining carbon neutrality by 2060 has galvanized research into carbon sequestration, a critical approach for mitigating climate change. Despite the rapid urbanization observed since the turn of the millennium, a comprehensive analysis of how urbanization influences urban carbon storage throughout China remains elusive. Our investigation delves into the nuanced effects of urbanization on carbon storage, dissecting both the direct and indirect influences by considering urban-suburban gradients and varying degrees of urban intensity. We particularly scrutinize the roles of climatic and anthropogenic factors in mediating the indirect effects of urbanization on carbon storage. Our findings reveal that urbanization in China has precipitated a direct reduction in carbon storage by approximately 13.89 Tg of carbon (Tg C). Remarkably, urban sprawl has led to a diminution of vegetation carbon storage by 8.65 Tg C and a decrease in soil carbon storage by 5.24 Tg C, the latter resulting from the sequestration of impervious surfaces and the elimination of organic matter inputs following vegetation removal. Meanwhile, carbon storage in urban greenspaces has exhibited an increase of 6.90 Tg C and offsetting 49.70% of the carbon loss induced by direct urbanization effects. However, the indirect effects of urbanization predominantly diminish carbon storage in urban greenspaces by an average of 5.40%. The degree of urban vegetation management emerges as a pivotal factor influencing the indirect effects of urbanization on carbon storage. To bolster urban carbon storage, curbing urban sprawl and augmenting urban green spaces are imperative strategies. Insights from this study are instrumental in steering sustainable urban planning and advancing towards the goal of carbon neutrality.

Submicron aerosol pollution in Greater Cairo (Egypt): A new type of urban haze?

Greater Cairo, the largest megacity of the Middle East North Africa (MENA) region, is currently suffering from major aerosol pollution, posing a significant threat to public health. However, the main sources of pollution remain insufficiently characterized due to limited atmospheric observations. To bridge this knowledge gap, we conducted a continuous 2-month field study during the winter of 2019-2020 at an urban background site, documenting for the first time the chemical and physical properties of submicron (PM1) aerosols. Crustal material from both desert dust and road traffic dust resuspension contributed as much as 24 % of the total PM1 mass (rising to 66 % during desert dust events), a figure not commonly observed in urban environments. Our observations showed significant decreases in black carbon concentrations and ammonium sulfate compared to data from 15 years ago, indicating an important reduction in both local and regional emissions as a result of effective mitigation measures. The diurnal variability of carbonaceous aerosols was attributed to emissions emanating from local traffic at rush hours and nighttime open biomass burning. Surprisingly, semi-volatile ammonium chloride (NH4Cl) originating from local open biomass and waste burning was found to be the main chemical species in PM1 over Cairo. Its nighttime formation contributed to aerosol water uptake during morning hours, thereby playing a major role in the build-up of urban haze. While our results confirm the persistence of a significant dust reservoir over Cairo, they also unveil an additional source of highly hygroscopic (semi-volatile) inorganic salts, leading to a unique type of urban haze. This haze, with dominant contributors present in both submicron (primarily as NH4Cl) and supermicron (largely as dust) modes, underscores the potential implications of heterogeneous chemical transformation of air pollutants in urban environments.

The great urban shift: Climate change is predicted to drive mass species turnover in cities

Human experiences with nature are important for our culture, economy, and health. Anthropogenically-driven climate change is causing widespread shifts in biodiversity and resident urban wildlife are no exception. We modelled over 2,000 animal species to predict how climate change will impact terrestrial wildlife within 60 Canadian and American cities. We found evidence of an impending great urban shift where thousands of species will disappear across the selected cities, being replaced by new species, or not replaced at all. Effects were largely species-specific, with the most negatively impacted taxa being amphibians, canines, and loons. These predicted shifts were consistent across scenarios of greenhouse gas emissions, but our results show that the severity of change will be defined by our action or inaction to mitigate climate change. An impending massive shift in urban wildlife will impact the cultural experiences of human residents, the delivery of ecosystem services, and our relationship with nature.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Global scale identification of catchments phosphorus source shifts with urbanization: A phosphate oxygen isotope and Bayesian mixing model approach

Different scenarios of urban expansion can influence the dynamic characteristics of catchments in terms of phosphorus (P). It is important to identify the changes in P sources that occur during the process of urbanization to develop targeted policies for managing P in catchments. However, there is a knowledge gap in quantifying the variations of potential P sources associated with urbanization. By combining phosphate oxygen isotopes from global catchments with a Bayesian model and the urbanization process, we demonstrate that the characteristics of potential P sources (such as fertilizers, urban wastewater, faeces, and bedrock) change as urban areas expand. Our results indicate that using phosphate oxygen isotopes in conjunction with a Bayesian model provides direct evidence of the proportions of potential P sources. We classify catchment P loadings into three stages based on shifts in potential P sources during urban expansion. During the initial stage of urbanization (urban areas < 1.5 %), urban domestic and industrial wastewater are the main contributors to P loadings in catchments. In the mid-term acceleration stage (1.5 % ≤ urban areas < 3.5 %), efforts to improve wastewater treatment significantly reduce wastewater P input, but the increase in fertilizer P input offsets this reduction in sewage-derived P. In the high-level urbanization stage (urban areas ≥ 3.5 %), the proportions of the four potential P sources tend to stabilize. Remote areas bear the burden of excessive P loadings to meet the growing food demand and improved diets resulting from the increasing urban population. Our findings support the development of strategies for water quality management that better consider the driving forces of urbanization on catchment P loadings.

Global distribution of surface soil organic carbon in urban greenspaces

Urban greenspaces continue to grow with global urbanization. The global distribution and stock of soil organic carbon (SOC) in urban greenspaces remain largely undescribed and missing in global carbon (C) budgets. Here, we synthesize data of 420 observations from 257 cities in 52 countries to evaluate the global pattern of surface SOC density (0-20 cm depth) in urban greenspaces. Surface SOC density in urban greenspaces increases significantly at higher latitudes and decreases significantly with higher mean annual temperature, stronger temperature and precipitation seasonality, as well as lower urban greenness index. By mapping surface SOC density using a random forest model, we estimate an average SOC density of 55.2 (51.9-58.6) Mg C ha-1 and a SOC stock of 1.46 (1.37-1.54) Pg C in global urban greenspaces. Our findings present a comprehensive assessment of SOC in global urban greenspaces and provide a baseline for future urban soil C assessment under continuing urbanization.

Identifying the impacts of land use landscape pattern and climate changes on streamflow from past to future

Identifying the individual and combined hydrological response of land use landscape pattern and climate changes is key to effectively managing the ecohydrological balance of regions. However, their nonlinearity, effect size, and multiple causalities limit causal investigations. Therefore, this study aimed to establish a comprehensive methodological framework to quantify changes in the landscape pattern and climate, evaluate trends in streamflow response, and analyze the attribution of streamflow events in five basins in Beijing from the past to the future. Future climate projections were based on three general circulation models (GCMs) under two shared socioeconomic pathways (SSPs). Additionally, the landscape pattern in 2035 under a natural development scenario was simulated by the patch-generating land use simulation (PLUS). The Soil and Water Assessment Tool (SWAT) was applied to evaluate the streamflow spatial and temporal dynamics over the period 2005-2035 with multiple scenarios. A bootstrapping nonlinear regression analysis and boosted regression tree (BRT) model were used to analyze the individual and combined attribution of landscape pattern and climate changes on streamflow, respectively. The results indicated that in the future, the overall streamflow in the Beijing basin would decrease, with a slightly reduced peak streamflow in most basins in the summer and a significant increase in the autumn and winter. The nonlinear quadratic regression more effectively explained the impact of landscape pattern and climate changes on streamflow. The trends in the streamflow change depended on where the relationship curve was in relation to the threshold. In addition, the impacts of landscape pattern and climate changes on streamflow were not isolated but were joint. They presented a nonlinear, non-uniform, and coupled relationship. Except for the YongDing River Basin, the annual streamflow change was influenced more by the landscape pattern. The dominant factors and the critical pair interactions varied from basin to basin. Our findings have implications for city planners and managers for optimizing ecohydrological functions and promoting sustainable development.

Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Improved human greenspace exposure equality during 21st century urbanization

Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts.

The kinetic Ising model encapsulates essential dynamics of land pattern change

A land pattern change represents a globally significant trend with implications for the environment, climate and societal well-being. While various methods have been developed to predict land change, our understanding of the underlying change processes remains inadequate. To address this issue, we investigate the suitability of the two-dimensional kinetic Ising model (IM), an idealized model from statistical mechanics, for simulating land change dynamics. We test the IM on a variety of patterns, each with different focus land type. Specifically, we investigate four sites characterized by distinct patterns, presumably driven by different physical processes. Each site is observed on eight occasions between 2001 and 2019. Given the observed pattern at the time ti we find two parameters of the IM such that the model-evolved land pattern at ti+1 resembles the observed land pattern at that time. The data support simulating seven such transitions per site. Our findings indicate that the IM produces approximate matches to the observed patterns in terms of layout, composition, texture and patch size distributions. Notably, the IM simulations even achieve a high degree of cell-scale pattern accuracy in two of the sites. Nevertheless, the IM has certain limitations, including its inability to model linear features, account for the formation of new large patches and handle pattern shifts.

Investigating non-point pollution mitigation strategies in response to changing environments: A cross-regional study in China and Germany

Climate change and urbanization have altered regional hydro-environments. Yet, the impact of future changes on the pollution risk and associated mitigation strategies requires further exploration. This study proposed a hydraulic and water-quality modeling framework, to investigate the spatiotemporal characteristics of pollution risk mitigation by low impact development (LID) strategies under future Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP) scenarios. Results demonstrated that the LID strategies exhibited an effective performance of pollutant removal in the current hydro-environment, with the removal rates ranging from 33% to 56%. In future climate and urbanization scenarios, the LID performance declined and turned to be uncertain as the greenhouse gas (GHG) emissions increased, with the removal rates ranging from 12% to 59%. Scenario analysis suggested that the LID performance was enhanced by a maximum of 73% through the diversified implementation of LID practices, and the performance uncertainty was reduced by a maximum of 67% through the increased LID deployment. In addition, comparative analysis revealed that the LID strategies in a well-developed region (Dresden, Germany) were more resilient in response to changing environments, while the LID strategy in a high-growth region (Chaohu, China) exhibited a better pollutant removal performance under low-GHG scenarios. The methods and findings in this study could provide additional insights into sustainable water quality management in response to climate change and urbanization.

Spatial-temporal distribution of global production-living-ecological space during the period 2000-2020

Global production-living-ecology space closely corresponds with sustainable development's economic, social and ecological elements. The dataset of global production-living-ecological space in this paper was generated by combining the global land cover obtained using GlobeLand30 and the population density supplied by NASA's Socioeconomic Data and Applications Center in 2000, 2010, and 2020. The verification was carried out using the random sampling function of ArcGIS software on the basis of Google Earth sample images. The overall accuracy of the global production-living-ecological space data in 2020 was 83.94% and the Kappa coefficient was 0.81. The overall accuracy of the global production-living-ecological space data in 2010 was 87.00% and the Kappa coefficient was 0.84. The overall accuracy of the spatial data in 2000 was 86.06% and the Kappa coefficient was 0.83. The dataset fills a gap in the global production-living-ecology space database and will be an essential reference for assessing the coordinated development of sustainable development goals.

Delhi urbanization footprint and its effect on the earth's subsurface state-of-stress through decadal seismicity modulation

Urban land and its expansion have profoundly impacted the global environment, including the stress change in the earth's subsurface, even though urban land is a small fraction of the global land surface. Divulging such effects has never been more important, given the role of stress in determining the safety of the urban population against earthquakes. However, knowledge of this time-dependent non-linear effect of urbanization on the subsurface remains in the gray area. This study focuses on the area surrounding Delhi, the capital city of India, to understand the relative contribution of the building load created by rapid urbanization in exacerbating the subsurface state-of-stress. The results highlight that, since 2010, the modulation in the seismicity rate and the stability of basement thrust faults is linked not only to urbanization but also to decadal groundwater storage. Mounting evidence suggests that the rapid urbanization, and the resulting non-tectonic horizontal compression, stabilize faults in the Aravalli Delhi belt, which are destabilized due to the extensive groundwater extraction. This affects the decadal seismicity trend around the Aravalli Delhi fold belt. Nonetheless, the magnitude of this time-dependent deformation influence on the seismicity modulation remains uncertain. The findings from this study quantify the geomechanical impacts of urbanization in the Delhi area for the first time.

Impact of global urban expansion on the terrestrial vegetation carbon sequestration capacity

Continuous urban expansion has a negative impact on the potential of terrestrial vegetation. Till now, the mechanism of such impact remains unclear, and there have been no systematic investigations. In this study, we design a theoretical framework by laterally bridging urban boundaries to explain the distress of regional disparities and longitudinally quantify the impacts of urban expansion on net ecosystem productivity (NEP). The findings demonstrate that global urban expanded by 37.60 × 10⁴ km² during 1990-2017, which is one of the causes of vegetation carbon loss. Meanwhile, certain climatic changes (e.g., rising temperature, rising CO₂, and nitrogen deposition) caused by urban expansion indirectly boosted vegetation carbon sequestration potential through photosynthetic enhancement. The direct decrease in NEP due to the urban expansion (occupying 0.25 % of the Earth's land area) offsets the 1.79 % increase due to the indirect impact. Our findings contribute to a better understanding of the uncertainty associated with urban expansion towards carbon neutrality and provide a scientific reference for sustainable urban development worldwide.

Projecting global oil palm expansion under zero-deforestation commitments: Direct and indirect land use change impacts

In the last three decades, global production of oil palm has boomed, which has partly come at the expense of tropical rainforests. Recognizing this, many companies operating in the palm oil industry have committed to eliminate deforestation from their operations, often referred to as zero-deforestation commitments (ZDCs). Here, we estimate that if ZDCs are fully adopted and enforced across all sectors and geographies, the global extent of oil palm plantations may be 11 M ha or 40% smaller in 2030 than in a business-as-usual (BAU) scenario that assumes no compliance with ZDCs. As a result of such land-sparing effects, we estimate that 96 M ha of forests are saved from conversion, of which, 17% would otherwise have been converted (directly or indirectly) due to expanding oil palm plantations. Overall, these figures suggest that ZDCs have the potential to deliver major environmental benefits if they are fully adopted and enforced.

Effects of land-use change on carbon emission and its driving factors in Shaanxi Province from 2000 to 2020

Exploring the process of carbon emissions under the "carbon peaking and carbon neutrality goals" can contribute to sustainable economic development. This research takes Shaanxi Province as an example. We elaborated on the spatial and temporal characteristics of land-use change from 2000 to 2020 and adopted the carbon emission model method to calculate land-use carbon emissions, also used urban morphological indicators to reveal the main factors of carbon emission changes. The results show that from 2000 to 2020, the land-use change in Shaanxi Province is mainly reflected in the increase in construction land area and the decrease in agricultural land area. Among them, the construction land area increased by 2192 km², and the agricultural land area decreased by 5006 km². Land-use carbon emissions increased by 1.28 × 10¹¹ kg during this period. Construction land is a major contributor to carbon emissions. The forestland is the main carbon sink. Carbon emissions showed a spatial pattern of "high in the north, low in the south, and concentrated in the middle." Urban form change is the driving factor affecting land-use carbon emissions in Shaanxi Province. The results of the research contribute to the understanding of regional carbon emission mechanisms and provide a scientific basis for reducing carbon emissions.

How to balance land demand conflicts to guarantee sustainable land development

Severe arable land loss and ecological problems raise attention to protect/develop land for food and ecology demand. Spatial conflict appears in front of multidemand for urbanization, food, and ecology. Our study took China as an example and explicitly outlined spatial preference of urbanization, food, and ecology. From the aspect of land amount, there are enough lands to support multidemand with a surplus of agriculture land of 45.5 × 10⁶ ha. However, spatial conflict widely appears among the multidemands. We tested the impacts of different priorities on urban pattern, crop yield, and ecology and found the priority of food > ecology > urbanization gave the best outcome. Our results verified the importance of including priority of land multidemand to avoid confusion and increase efficiency in the implementation of land policies.

Urbanization does not endanger food security: Evidence from China's Loess Plateau

The expansion of construction land due to urbanization is the most rapid land use change in contemporary human history and has always occupied high-quality cropland, posing a severe threat to cropland and food security, it's essential to clarify the impact of urbanization on cropland and food security. This study proposed a research framework based on the regulating role of human activities, used quantifiable complex network analysis to uncover the vital role of urbanization in the evolution of land systems, and combined trajectory analysis of crop yield change to explore the impact of different urbanization modes (urban, town, and township mode) on food security through a continuous observation on 4259 township-level administrative regions of the Loess Plateau from 1990 to 2020. The findings proved that urbanization occupied the greatest land use area of cropland, and the town mode occupied 58.62 % of all urbanization modes encroaching on cropland, which has become a new pattern for advancing the urbanization development in the Loess Plateau. Construction land is more likely to be transferred in than out in the land use transfer network, while other land use types converted to construction land will be difficult to reverse. The Chinese government has implemented pragmatic policies, improved agricultural production techniques, and promoted agricultural intensification, resulting in a considerable increase in crop productivity and crop yield and the achievement of basic crop yield self-sufficiency of the Loess Plateau, so urbanization would not endanger food security. This study not only provides a more systematic research framework for related studies but also provides a theoretical basis for securing food security in other rapidly urbanizing regions of the world.

Dynamic multi-dimensional scaling of 30+ year evolution of Chinese urban systems: Patterns and performance

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory β_c = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Water-climate change extended nexus contribution to social welfare and environment-related sustainable development goals in China

Climate change exacerbates uncertainties in water resource management, water supply, and treatment that are energy intensive and then exert great pressure on climate change mitigation; hence, interrelated and contradictory characteristics within the water-climate change (WC) nexus system are needed to be studied. The nexus thinking and coordination of WC would impact many realistic practices and assist in sustainable socioeconomic development since traditional single-target policies have sometimes been out of function. Hence, the ability to direct water production and use as well as climate change mitigation has become a hotspot recently. Furthermore, we find that there has been no complete research on reviewing the impacts of the WC nexus in different areas on the Sustainable Development Goals (SDGs). Hence, this paper builds a core nexus of WC and then analyzes those effects on social and environmental aspects in many areas, including sewage treatment, energy transition, waste treatment, land management, and ocean management. This paper discusses how WC interlinkages are utilized to realize SDGs in those areas. Moreover, uncertainties derived from exogenous hydrology, climate change, and anthropogenic endogenous systems for realistic problems appeal to gradually increasing concern. Finally, implications offer valuable guidelines for integrated management of water and carbon emissions, as well as sustainable socioeconomic development in the future.

Evaluation and prediction of land use change impacts on ecosystem service values in Nanjing City from 1995 to 2030

Land use changes have a considerable impact on ecosystem services (ESs). In recent years, land use changes caused by urban expansion in Nanjing City have been obvious and are expected to further change in the future. Therefore, it is urgent to quantitatively assess ecosystem service value (ESV) changes caused by previous land use changes and future potential changes in Nanjing. In this study, land use data products based on remote sensing images, Dyna-CLUE model, and the ESV equivalent coefficient method were applied to assess the impact of land use changes on ESVs in Nanjing City over the past 23 years (1995-2018), and to forecast the changes of ESV in 2030. The results indicated that the total ESVs of Nanjing City displayed a trend of first increasing and then declining in 1995-2018. From the land use classification, the water area had the largest ESV in Nanjing, followed by arable land. Additionally, the regulating service value was the highest among the four primary ESs in Nanjing from 1995 to 2030, with the highest value of 13.73 billion yuan in 2015. Among the three forecast scenarios, the ecological protection scenario had the highest total ESV and was followed by the urban expansion and business as usual scenarios. These findings may assist for the scientific decision-making of sustainable land use and ecosystem management in Nanjing City.

An improved hybrid model on source-risk of polycyclic aromatic hydrocarbon in soils of the Yangtze River Delta urban agglomeration

The source, exposure and risks of polycyclic aromatic hydrocarbons (PAHs) in soil environments are of great importance to evaluate soil quality. However, understanding the risks of specific sources of PAHs in soils remains poorly understood. In this study, we determined the source, exposure and risks of PAHs in the Yangtze River Delta urban agglomeration. The source analysis receptor model combined with land use types significantly increased the identification of pollution sources and improved the prediction accuracy of PAH concentrations. There is a strong correlation between the measured and predicted values of high ring PAH. The correlations of BbF, InP and Pyr are 0.947, 0.896 and 0.906 respectively, which is significantly higher than the unmodified model. By combining the ecological risk assessment and health risk assessment models of PAHs, we established an improved mixed source-risk model. The PAHs in urban soils had the highest ecological risk and health risk, with risk probabilities of 56.3 % and 28.2 %, respectively. The average contamination severity index values of PAHs caused by oil combustion sources, coal combustion sources, coking furnace sources, and fuel (biomass, petroleum, and diesel) combustion sources were 0.13, 0.10, 0.16 and 0.17, respectively. The average noncarcinogenic risks of PAHs from oil combustion sources, coal combustion sources, coking furnace sources and biomass, petroleum volatilization and diesel combustion sources to children were 0.12, 0.11, 0.08 and 0.13, respectively. Approximately half of the PAH pollution risk in forestland and grassland soil were associated with the combustion of petroleum fossil fuels. This study quantitatively analyzed the contribution of different PAHs pollution sources in different land types of soils, further calculated the risks of each pollution source to the ecological environment and human health, and proposed corresponding treatment measures, which provided scientific and systematic methods and technologies for soil pollution management in other regions of the world.

Ecological spatial intensive use optimization modeling with framework of cellular automata for coordinating ecological protection and economic development

With the exposure of excessive intensive use of urban and agricultural space, the optimization of intensive use of ecological space provides a new way to coordinate the global problem of spatial conflict between ecological protection and economic development. However, the coupling accuracy of the existing structure-spatial coupling optimization model is low, which cannot provide method support for the optimization of intensive use of ecological space. To solve this problem, we propose a new model of ecological spatial intensive use optimization (ESIUO) based on the non-stationarity of the Markov state transition probability of the dominant ecosystem service functions (DESFs) and their suitability, and with the help of the framework of cellular automata (CA). We took Qionglai City as an empirical study area, and compared the results of this model with those of CA-Markov and CLUE-S models with the same parameters. The results show that: (i) The quantitative structure corresponding to the spatial layout of each dominant ecosystem service function (DESF) optimized by the ESIUO model has the smallest relative error (δ_k≤0.04%) with the optimal quantitative structure. (ii) The layout of DESFs optimized by the ESIUO model maximizes the supply capacity of ecosystem services. The minimum matching degree between the distribution of each DESF and the high-value area of its suitability is 92.06 %, and the spatial distribution is more compact, and the comprehensive effect of spatial layout is the best. Further analysis confirmed that the model can establish the spatial layout of DESFs that can realize the high precision coupling with the optimal quantitative structure of DESFs in terms of quantitative structure, and can support the construction of the layout of intensive use of ecological space to alleviate the pressure of non-ecological space expansion in these areas, and then provide a new way to coordinate ecological protection and economic development.

Analysis of Spatiotemporal Changes of Ecological Environment Quality and Its Coupling Coordination with Urbanization in the Yangtze River Delta Urban Agglomeration, China

It is inevitable that urban agglomeration will have a coercive impact on the regional Ecological Environment Quality (EEQ) as a consequence of high-speed urbanization. Balancing the EEQ and urbanization development has become a problem worthy of attention. In order to objectively evaluate the EEQ of the Yangtze River Delta Urban Agglomeration (YRDUA) and explore the impact of the urbanization process on it, this paper is based on the Modified Remote Sensing Ecological Index (MRSEI) and the Comprehensive Night Light Index (CNLI), respectively, and conducts a quantitative assessment of the YRDUA in China from 2000 to 2020. The results show that: (1) From 2000 to 2020, the MRSEI of the YRDUA first decreased and then increased, and the ecological environment quality degraded first and then improved; however, there were significant differences between regions. The ecological environment quality in the south is obviously better than that in the north, and the ecological environment quality in the north changes more drastically, and the low value area of MRSEI will gradually move downstream as time changes; (2) During the study period, the YRDUA formed a hierarchical and progressive urbanization pattern. The inland urbanization process expanded from east to west along the Yangtze River, and the urbanization process of coastal cities expanded from Shanghai as the center to the north and south with high-intensity urbanization cities concentrated in Shanghai and its surrounding cities and low-intensity urbanization cities distributed in the western part of the urban agglomeration; (3) The Coupling Coordination Degree (CCD) between urbanization and EEQ in the YRDUA has continuously improved with an increase of 28.57% in the past 21 years, and the number of cities with high level coupling continues to rise, while the number of medium level coupling cities and low level coupling cities has declined. As a large-scale and long-term analysis of changes in the EEQ and the urbanization process, this study can provide theoretical support for policymakers to formulate mesoscale development planning, EEQ monitoring, and environmental protection policies.

Increased hydropower but with an elevated risk of reservoir operations in India under the warming climate

Hydropower is a significant contributor to clean global electricity generation; therefore, it plays a crucial role in climate change mitigation. Notwithstanding major hydropower dams in India are in diverse climatic regions and exposed to risks because of the warming climate, potential changes in hydroclimate remain largely unexplored. Using observations and climate projections, we demonstrate the hydroclimatic changes in the upstream catchments and their implications for the hydropower generation of 46 major hydropower dams in India. A warmer (up to 5.0°C) and wetter projected climate with a substantial increase (5.0-33%) in precipitation will lead to an increased (7-70%) inflow to reservoirs of major dams. Increased inflow will enhance (9-36%) the hydropower production for most dams in the future, with a more prominent rise in central India dams. A simultaneous rise in extreme inflow and high reservoir storage conditions is projected under future climate for most dams. However, future climate changes project a favorable hydroclimate for hydropower production, with the associated risks related to extremes.

The impact of land use and land cover changes on the landscape pattern and ecosystem service value in Sanjiangyuan region of the Qinghai-Tibet Plateau

Decades of intensifying human activities have caused dramatic changes in land use and land cover (LULC) in the ecologically fragile areas of the Qinghai-Tibet Plateau, which have led to significant changes in ecosystem service value (ESV). Taking the ecologically fragile Sanjiangyuan region of the Qinghai-Tibet Plateau as the research object, we focused on understanding the impact of LULC changes on the Sanjiangyuan's landscape pattern and its corresponding ESV, which was combined with a Markov-Plus model to predict LULC changes in 2030. The results showed: (1) from 2000 to 2020, the LULC of Sanjiangyuan has changed to varying degrees, respectively. In the central and southern regions where animal husbandry is the mainstay activity, the area of grass land converted to bareland had expanded; (2) from 2000 to 2010, the total regional ESV increased sharply. However, the total amount of ESV decreased from 2010 to 2020; (3) the overall ESV in the study area was observed to be trending down and is expected to decrease by approximately 4.25 billion CNY by 2030; (4) the fragmentation and complexity of regional landscape patterns will negatively affect local ecosystem stability and biodiversity. Overall, there is a strong temporal and spatial correlation between LULC and ESV. This study will provide a reference for the local government to provide targeted and sustainable land management policies, thereby promoting the improvement of the Qinghai-Tibet Plateau regional ecology value.

Spatiotemporal heterogeneity and influencing factors on urbanization and eco-environment coupling mechanism in China

High-quality urbanization is the core for realizing human well-beings, for which reason investigating how the relationship evolves between urbanization and eco-environment is of crucial importance. Differing from the rationale of revealing spatial spillover effects using traditional tests, we consider spatial network characteristics to enrich the notion of local coupling and telecoupling from a relational perspective. First, we adopt coupling coordination degree model (CCDM) and decoupling model (DM) to calculate the urbanization and eco-environment coupling coordination degree (UECCD) and the decoupling index (DI) in 30 provinces and municipalities of China from 2008 to 2017. Second, we use gravity model to construct urbanization and eco-environment coupling coordination network (UECCN), in which provinces are nodes and spatial connection relationships of UECCD are edges between nodes. Third, we introduce social network analysis (SNA) to reveal spatial network characteristics of UECCN without using local spatiotemporal heterogeneity. Finally, we employ spatial econometric model to reveal factors that influence urbanization and eco-environment coupling effect. The major findings and conclusions of this study are summarized as follows. (1) The main subclasses of UECCD and DI are basically uncoordinated patterns with eco-environment lagging and weak decoupling, respectively. (2) Only two spatial agglomeration types of UECCD exist, the high-high (H-H) clustering in Shanghai and the low-low (L-L) clustering in western China, whereas no significant spatial agglomeration effect is observed among most provinces. (3) The distribution characteristics of UECCN are sparse in western China and dense in eastern China, and the spatial correlation strength of UECCN improves. (4) Technological innovation plays a critical role in promoting UECCD, while the total population, per capita disposable income, coupling network structure, and environmental regulations exert significant impact on UECCD. Collectively, we propose to prioritize governance provinces with low UECCD in western China as well as adequately utilize the positive externalities of key node provinces in eastern China. Equally importantly, we suggest that it is also critical to fully exert a driving force of technological innovation on improving the UECCD by promoting renewable energy utilization.

The impacts of spatial resolutions on global urban-related change analyses and modeling

Various global land use/land cover (LULC) products have been developed to drive land-relevant climate and hydrological models for environmental assessments. However, systematic studies remain scarce that assess the uncertainties of using these products. By using a total of 16 commonly used global LULC products, we find a logarithm law of upscaling with the spatial resolution. The law reveals spatial details of urban features will be majorly distorted when using LULC products with coarser resolutions. A tipping point of the law around the 30-m resolution was identified by additional analysis of the 1-m and 10-m local land use dataset. Through the example of assessing crop production loss, we further find that most of these products will yield a significant underestimation of crop production losses, globally and locally. We conclude that the underestimated urban land rooted in most of these products would cause vital impacts on global change analyses and modeling.

Spatiotemporally Explicit Mapping of Built Environment Stocks Reveals Two Centuries of Urban Development in a Fairytale City, Odense, Denmark

The urban built environment stocks such as buildings and infrastructure provide essential services to urban residents, and their spatiotemporal dynamics are key to the circular and low-carbon transition of cities. However, spatiotemporally explicit characterization of urban built environment stocks remains hitherto limited, and previous studies on fine-grained mapping of built environment stocks often focus on an urban area without consideration of temporal dynamics. Here, we combined the emerging geospatial data and historical maps to quantify the spatially and temporally refined stocks of buildings and infrastructure and developed a novel indexing method to track the construction, demolition, and renovation for each building across various historical snapshots, with a case study of Odense, Denmark, from 1810 to 2018. We show that built environment stock in Odense increased from 80 t/cap in 1810 to 279 t/cap in 2018. Their dynamics appear overall in line with urban development of Odense over the past two centuries and well reflect the combined effects of industrialization, infrastructure development, socioeconomic characteristics, and policy interventions. Such spatiotemporally explicit stock mapping offers a physical and resource perspective for measuring urbanization and provides the public and government insight into urban spatial planning and related resource, waste, and climate strategies.

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2 kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20 years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32 kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1 kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.

Population-environment dynamics across world's top 100 urban agglomerations: With implications for transitioning toward global urban sustainability

Urbanization is a long-term global trend critical for shaping human-Earth sustainability in the Anthropocene. In past decades, much progress has been made in researching urban sustainability, particularly global assessments of the big picture and case studies of individual cities. Here we examine the world's top 100 urban agglomerations (UAs) in terms of size-that rank high on sustainability agendas and cover 28% of the global UA area-regarding four broadly concerned challenges: population shrinkage, slum development, greenness loss, and heat exposure. Instead of merely focusing on global/regional "averages" or individual cases, we take one step further to identify the "anomalies" of urban sustainability among the 100 UAs for each dimension and on the whole as multi-dimensional coupled infrastructure systems. Results show: (1) urban population of the 100 UAs increased by 36% during 2000-2020; (2) urban slums occurred in 85% of 34 examined UAs in the Global South; (3) urban greenness declined in the 100 UAs by 8% during 2000-2019; and (4) 79% of the 100 UAs were projected to have less than 30 EHDs per year during 2021-2030. Our findings provide global baselines for place-based problem-driven policymaking for the examined UAs and suggest improving urban green infrastructure as their top policy imperative. Our findings point to a critical research gap in the urban sustainability literature: Studying sustainability transitions of the "abnormally" sustainable UAs identified in this study that had exceptional performances on the four examined sustainability dimensions, e.g., Beijing of China and Milan of Italy.

Smart cities: reviewing the debate about their ethical implications

This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition.

Integrating potential ecosystem services losses into ecological risk assessment of land use changes: A case study on the Qinghai-Tibet Plateau

In recent years, climate change has caused a significant increase in the natural disaster risk on a global scale, posing a great threat to humans and ecosystems. In addition to natural disasters, climate change and human activity-driven land use changes can also increase the ecological risk by reducing the supply of ecosystem services for humans. However, compared with the mature risk assessment framework in the field of natural disasters, the ecological risk of land use change is still a novel concept, and neither the connotation nor the evaluation methods are sufficiently defined. Therefore, with the help of the classic framework in the field of disaster risk assessment, a new framework for assessing the ecological risk of land use change is proposed and applied to the Qinghai-Tibet Plateau of China. The ecological risk of land use changes can be defined as the product of the possibility of land use changes and the hazard (loss of ecosystem services) caused by land use changes. In the future, the possibility of land use change on the Qinghai-Tibet Plateau will be higher in the east and lower in the west; the accompanying hazards are predicted to be higher in the southeast and lower in the central and western regions. The ecological risk of land use changes on the Qinghai-Tibet Plateau will be highest in the southeastern part and along the edges of the plateau.

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020-2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways' (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

Ecosystem service deficits of European cities

Climate change and biodiversity loss are two pressing global environmental challenges that are tightly coupled to urban processes. Cities emit greenhouse gases through the consumption of materials and energy. Urban expansion encroaches on local habitats, while urban land teleconnections simultaneously degrade distant ecosystems. These processes decrease the supply of and increase the demand for ecosystem services inside and outside urban areas. Most cities are in a state of ecosystem services deficit, whereby demand exceeds local supply of ecosystem services. Methods to quantify this deficit by capturing multi-scale and multi-level ecological exchanges are incipient, leaving scholars with a partial understanding of the environmental impacts of cities. This paper deploys a novel method to simulate future urban supplies and demands of two key ecosystem services needed to combat climate change and biodiversity loss - global climate regulation and global habitat maintenance. Applying our model to eight representative European cities, we project growing ecosystems deficits (demand exceeds supply) between 8% and 214% in global climate regulation and 11% and 431% in global habitat maintenance between 2020 and 2050. Variation between cities stems from differing dietary patterns and electricity mixes, which have large implications for ecosystems outside the city. To combat these losses, urban sustainability strategies should complement local restoration with changes to local consumption alongside promoting remote ecological restoration to tackle the multi-level environmental impacts of cities.

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development.

Estimation and Simulation of Forest Carbon Stock in Northeast China Forestry Based on Future Climate Change and LUCC

Forest carbon sinks (FCS) play an important role in mitigating global climate change, but there is a lack of more accurate, comprehensive, and efficient forest carbon stock estimates and projections for larger regions. By combining 1980–2020 land use data from the Northeast China Forestry (NCF) and climate change data under the Shared Socioeconomic Pathway (SSP), the land use and cover change (LUCC) of NCF in 2030 and 2050 and the FCS of NCF were estimated based on the measured data of forest carbon density. In general, the forest area of NCF has not yet recovered to the level of 1980. The temporal change in the FCS experienced a U-shaped trend of sharp decline to slow increase, with the inflection point occurring in 2010. If strict ecological conservation measures are implemented, the FCS of the NCF is expected to recover to the 1980 levels by 2050. We believe that the ecological priority (EP) scenario is the most likely and suitable direction for future development of the NCF. We also advocate for more scientific and stringent management measures for NCF natural forests to unlock the huge potential for forest carbon sequestration, which is important for China to meet its carbon neutrality commitments.

Spatiotemporal Evolution of Cultivated Land Non-Agriculturalization and Its Drivers in Typical Areas of Southwest China from 2000 to 2020

Cultivated land resources are crucial to food security and economic development. Exploring the spatiotemporal pattern of cultivated land non-agriculturalization and its drivers is a prerequisite for cultivated land conservation. This paper used GlobeLand30 data to reveal the spatial and temporal pattern, the shift of the gravity center and the drivers of cultivated land non-agriculturalization by employing spatial analysis, gravity center model and the geographical detector model. The results show a dramatic increase in the non-agriculturalization of cultivated land in the period of 2010–2020 compared to 2000–2010. Spatially, the cultivated land non-agriculturalization mainly occurred in areas with high urbanization levels, such as eastern Sichuan Province and western Chongqing Municipality, while the cultivated land non-agriculturalization in other areas was small-scale and spatially scattered. Furthermore, the speed of cultivated land non-agriculturalization showed spatial unevenness, and the gravity center of cultivated land non-agriculturalization shifted towards the northeast at a distance of 123.21 km. The cultivated land non-agriculturalization was affected by GDP per capita, population density, GDP per unit of land and total retail sales of social consumer goods. The key drivers for the cultivated land non-agriculturalization in the study area were the continuous expansion of urban space and the large-scale cultivation of economic fruit trees. The government should promote small-scale machinery suitable for agricultural cultivation in the mountainous and hilly areas of Southwest China, and appropriately develop economic fruit groves and livestock farming to reduce the phenomenon of cultivated land non-foodization.

Impacts of Land-Use Change on the Spatio-Temporal Patterns of Terrestrial Ecosystem Carbon Storage in the Gansu Province, Northwest China

Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions.