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

An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks

Cement production and its air pollutant and carbon dioxides (CO2) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO2 emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO2 at the county level till 2060. Results show that the cement production will decline from 2327 million metric tons (Mt) in 2015 to 704 Mt. in 2060 under the Shared Socioeconomic Pathways 1 (SSP1). Counties closer to provincial capital will experience greater retirement of cement industry. Likewise, the emissions of air pollutants and CO2 will experience a steady downward trend driven by the declining cement production and the improvement of pollution control technologies. There will be a more significant regional heterogeneity in the reduction of production and emissions at city level compared to the province level. With the clearance for nearly two-thirds of counties, future cement production and emissions will be more intensively distributed in a few cities. The shares of emissions in southwestern regions will grow from 2015 to 2060 while those of eastern regions will continue decreasing. The comparison between the changing spatial distributions of emissions and gross domestic product (GDP) indicates a positive effect of existing policies in reconciling regional economic development and air pollution controls. The outcome could support the analyses on the impact of industrial development on air quality and public health, and the method can be applied widely for other industrial sectors for a more comprehensive understanding of future emission relocation.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Quantifying future water resource vulnerability in a high-mountain third pole river basin under climate change

Understanding the water resource vulnerability (WRV) in global mountain regions under climate change is crucial for water resources management and socio-economic development. However, the WRV in the high-mountain Third Pole region (with quite a few transboundary river basins) remains largely unclear. Here, we have applied a comprehensive assessment framework of WRV to a Third Pole high-mountain river basin (Nujiang-Salween River, NSR) and its dependent downstream. The framework consisted of sensitivity, exposure, adaptability, hazard, and water stress indices, considering climate change, socio-economics, government effectiveness, natural disasters, and water supply capacity of the target river basin. Our results indicate that the downstream area (with intensive human activities) often exhibited significantly higher WRV than the mountain region; while the WRV shows an M-shaped change with increasing elevation, with the highest vulnerability occurring in a relatively low elevation range (e.g., 500-1500 m for the NSR basin). In the near future, we find that the spatial pattern of WRV in the basin is alternately influenced by adaptation, water scarcity, and exposure; whereas climate change serves as the main driver affecting the WRV in the far future. These findings enhance our understanding of the WRV in high-mountain transboundary basins of the Third Pole under global change.

Aspirational nitrogen interventions accelerate air pollution abatement and ecosystem protection

Although reactive nitrogen (Nr) emissions from food and energy production contribute to multi-dimensional environmental damages, integrated management of Nr is still lacking owing to unclear future mitigation potentials and benefits. Here, we find that by 2050, high-ambition compared to low-ambition N interventions reduce global ammonia and nitrogen oxide emissions by 21 and 22 TgN/a, respectively, equivalent to 40 and 52% of their 2015 levels. This would mitigate population-weighted PM2.5 by 6 g/m3 and avoid premature deaths by 817 k (16%), mitigate ozone by 4 ppbv, avoid premature deaths by 252k (34%) and crop yield losses by 122 million tons (4.3%), and decrease terrestrial ecosystem areas exceeding critical load by 420 Mha (69%). Without nitrogen interventions, most environmental damages examined will deteriorate between 2015 and 2050; Africa and Asia are the most vulnerable but also benefit the most from interventions. Nitrogen interventions support sustainable development goals related to air, health, and ecosystems.

Spatiotemporal changes of urban vacant land and its distribution patterns in shrinking cities on the globe

Urban vacant land (UVL) has been an important issue in the urbanization process, especially for shrinking cities. Identifying UVL and analyzing its spatiotemporal characteristics are the foundation for coping with this issue. This study identified UVL in 497 shrinking cities on the globe (10 % of shrinking cities in total) in 2016 and 2021 using manual labeling and deep learning to reflect the distribution patterns of UVL and its spatiotemporal changes. The results reveal that a global expansion of UVL from 2016 to 2021 in 497 shrinking cities, with diverse distribution patterns and varying changes across different regions. As for socioeconomic factors, UVL is related to population shrinkage, and the UVL ratio presents a phased change with the increase of the urbanization rate, revealing an inverted U-shaped relationship between the UVL ratio and the urbanization rate. The distribution patterns of UVL also vary globally in different urbanization phases. This study can provide theoretical and practical insights for improving urban planning and promoting sustainable urbanization.

Exposure of water purification deficit network in response to nitrogen application intensity

Ecosystem services are strongly responsive to changes in land use intensity, especially for the service of water purification, which is highly sensitive to water pollutant emission. Increased nitrogen (N) application to cropland has potential impacts on the supply and demand for water purification through changes in land use intensity. However, there has been a lack of research focusing on the impacts of cropland N application on population exposure to water purification deficit and their cross-regional delivery network. Taking the Dongting Lake (DTL) Basin as an example, this study explored the spatial pattern of N exposure in the DTL Basin from 1990 to 2015 by integrating water purification deficit and population density. Changes in potential N exposure in 2050 were simulated based on population projection data from the Shared Socioeconomic Pathways (SSP1-5). N delivery pathways in the DTL Basin were clarified by constructing the N delivery network. The results showed that N exposure increased significantly with increasing N application in DTL Basin. The DTL surrounding area and lower reaches of the Xiangjiang River Basin had high increases of N exposure (50.2 % and 71.6 %) and high increases in N exposure due to increases in N application per unit (N influence coefficients exceeding 0.5). The lower reaches of the Xiangjiang River Basin with the highest population density had the smallest decrease in N exposure (1.4 %-11.1 %) in the SSP1-5 scenarios. During 1990-2015, the increase of N export to the DTL surrounding area was higher in the lower reach sub-basins of DTL Basin. N application had a stronger impact on N delivery processes in the lower reaches of DTL Basin. Managers should distribute N applications to basins with high N retention and low N export to the DTL surrounding area. This study confirmed the strong response of water purification deficit and its population exposure to N application, and provided decision-making guidelines for water quality enhancement in DTL Basin from a spatial planning perspective.

Rethinking the country-level percentage of population residing in urban area with a global harmonized urban definition

The UN (United Nations) collects global data on the country-level Percentage of Population Residing in Urban Area (PPRUA). However, variations in urban definitions make these data incomparable across countries. This study assesses national defined PPRUA within UN statistics against estimates we derived using global comparable definitions. Refer to the UN's Degree of Urbanization framework, we propose 90 global harmonized methods for estimating PPRUA by combining different configurations of three global population datasets, six urban total population thresholds, and five urban population density thresholds. This approach demonstrated significant variations in country-level PPRUA estimations, with wide 95% confidence intervals using the Z score method. Most national defined PPRUA fall between the upper 95% CI and the median of the estimations, underscoring the need for globally harmonious PPRUA estimates. This study advocates for a reassessment of datasets and thresholds in the future and for investigating urbanization on a scale beyond the country level.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Significantly wetter or drier future conditions for one to two thirds of the world's population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world's current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century's end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.

City-level population projection for China under different pathways from 2010 to 2100

Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.

Are open-source hydrodynamic models efficient in quantifying flood risks over mountainous terrains? An exhaustive analysis over the Hindu-Kush-Himalayan region

The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly.

Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways

Global climate change and rapid urbanization, mainly driven by anthropogenic activities, lead to urban flood vulnerability and uncertainty in sustainable stormwater management. This study projected the temporal and spatial variation in urban flood susceptibility during the period 2020-2050 on the basis of shared socioeconomic pathways (SSPs). A case study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was conducted for verifying the feasibility and applicability of this approach. GBA is predicted to encounter the increase in extreme precipitation with high intensity and frequency, along with rapid expansion of constructed areas, resulting in exacerbating of urban flood susceptibility. The areas with medium and high flood susceptibility will be expected to increase continuously from 2020 to 2050, by 9.5 %, 12.0 %, and 14.4 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In terms of the assessment of spatial-temporal flooding pattern, the areas with high flood susceptibility are overlapped with that in the populated urban center in GBA, surrounding the existing risk areas, which is consistent with the tendency of construction land expansion. The approach in the present study will provide comprehensive insights into the reliable and accurate assessment of urban flooding susceptibility in response to climate change and urbanization.

Using Alternative Sources of Energy for Decarbonization: A Piece of Cake, but How to Cook This Cake?

Few analytical or research works claim that the negative impact of improper use of ASEs may be comparable with that of hydrocarbons and sometimes even greater. It has become a common view that "green" energy (ASE) is clean, safe and environmentally friendly (eco-friendly) in contrast with "black" energy (hydrocarbons). We analyzed 144 works on systemic and/or comparative research of the modern and prospective ASE: biofuels, hydrogen, hydropower, nuclear power, wind power, solar power, geothermal power, oceanic thermal power, tidal power, wind wave power and nuclear fusion power. We performed our analysis within the Spaceship Earth paradigm. We conclude that there is no perfect ASE that is always eco-friendly. All ASEs may be dangerous to the planet considered as a closed and isolated unit ("spaceship") if they are used in an inconsistent manner. This is not in the least a reason to deny them as prospective sources of energy. Using all ASEs in different proportions in various regions of the planet, where their harm to the planet and humanity can be minimized and, on the contrary, their efficiency maximized, would give humanity the opportunity to decarbonize the Earth, and make the energy transition in the most effective way.