Municipal Scale Research Articles

National‐Scale Flood Hazard Data Unfit for Urban Risk Management

AbstractExtreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national‐scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo‐Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo‐Drain estimates of population and property value exposed to 1%‐ and 5%‐annual‐chance hazards diverge at finer scales of governance, for example, by 4‐ to 18‐fold at the municipal scale. FSF and PRIMo‐Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national‐scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.

Skilful probabilistic medium‐range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system

AbstractDengue fever is a source of substantial health burden in Vietnam. Given the well‐established influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a world‐leading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (May–October) ensemble precipitation and 2‐m temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001–2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce district‐level epidemiological forecasts for urban areas where dengue is most prevalent.

Modelling Current-State N- and P-Fluxes into Surface Waters in Germany

For the first time, the AGRUM model consortium—consisting of the agro-economic model RAUMIS, the water balance model mGROWA, the hydrological nutrient transport models DENUZ, WeKu and MEPhos, and the urban emission model MONERIS—was jointly set up throughout Germany (357,000 km2). This provided a nationwide consistent nutrient model to capture the current status of N and P inputs to surface waters from diffuse sources and urban areas. Diffuse nutrient emissions were quantified in high spatial resolution for the input pathways’ groundwater, drainage runoff, and natural interflow (100 m × 100 m), as well as for water erosion and wash-off (25 m × 25 m). The sum of diffuse nutrient inputs to surface waters is about 385,000 metric tons N/yr and ca. 11,530 metric tons P/yr. Urban emissions were quantified either as point source inputs (wastewater treatment plants, industrial direct dischargers) or at municipality scale for different collection and treatment systems, e.g., rainwater sewers or decentralized treatment plants, and sum up to ca. 95,000 t N/yr and 7500 t P/yr. As modelled, total N and P inputs into surface waters correspond well with observed N and P loads in rivers. The model results represent valuable information for water managers, being responsible for the preparation of management plans for the third management cycle of the EC Water Framework Directive spanning from 2021 to 2027.

A web-based urban hydrology model for municipal scale applications

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Multi-Scale Analysis of Spatial and Temporal Evolution of Carbon Emissions in Yangtze River Economic Belt and Study of Decoupling Effects

An in-depth, longitudinal examination of carbon emissions and decoupling within the Yangtze River Economic Belt, supplemented by a dynamic assessment of its evolutional trajectory, provides a scientifically grounded framework and pragmatic value for the drafting of regional carbon emission mitigation strategies. Using the Yangtze River Economic Belt as a context, this study formulates a carbon emission model spanning provincial, city, and county levels. The model serves to uncover the spatiotemporal characteristics of carbon emissions within the Yangtze River Economic Belt from a multi-scalar vantage point. The Tapio decoupling model is then invoked to examine the extent and nature of decoupling between economic advancement and carbon emissions across these disparate scales. The outcomes divulge the following: (1) At the provincial echelon, the progression of carbon emissions born from energy consumption within the Yangtze River Economic Zone presents an escalating then stabilizing trend line. The carbon emissions growth rate transitions from a swift ascension of 8.44 percent initially, subsequently tapering to a moderate increment of 0.42 percent at the period’s culmination. The trajectory of carbon decoupling at the provincial scale tends to be generally propitious. (2) At the municipal scale, the overall carbon emission level shows a gradual upward trend, and then gradually forms a pattern of centripetal aggregation and peripheral diffusion. The decoupling status during the study period is mainly weak and strong decoupling, with the number of weak decoupling showing a fluctuating change in increasing and then decreasing, while the strong decoupling shows a slow and orderly growth trend, and is mainly distributed in most of the municipalities in Jiangsu, Zhejiang, and Shanghai. (3) At the county scale, centripetal aggregation and peripheral diffusion were already present at the beginning of the study period, followed by the gradual expansion and formation of several carbon emission centers of different sizes. The temporal evolution of county-level decoupling is more significant, with weak and strong decoupling dominating the county-scale decoupling during the study period, especially in the upper and middle reaches of the Yangtze River Economic Belt, but the overall trend shows signs of gradual decoupling.

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Abstract. Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

Spatiotemporal Dynamics of CO2 Emissions in China Based on Multivariate Spatial Statistics

With China’s rapid industrialization and urbanization in the process of socio-economic development, the extensive use of energy has resulted in a large amount of CO2 emissions, which puts great pressure on China’s carbon emission reduction task. Through multivariate socio-economic data, this paper proposes an extraction and screening method of multivariate variables based on land-use types, and the downscaled spatial decomposition of carbon emissions at different scales was carried out by using the spatial lag model (SLM). This paper makes up for the shortcomings of previous studies, such as an insufficient modeling scale, simple modeling variables, limited spatio-temporal span of spatial decomposition, and no consideration of geographical correlation. Based on the results of the spatial decomposition of carbon emissions, this paper explores the spatial and temporal dynamics of carbon emissions at different scales. The results showed that SLM is capable of downscaling the spatialization of carbon emissions with high precision, and the continuity of the decomposition results at the provincial scale is stronger, while the differences of the decomposition results at the municipal scale are more obvious within the municipal units. In terms of the spatial and temporal dynamics of CO2 emissions, carbon emissions at both scales showed a significant positive correlation. The dominant spatial correlation types are “Low–Low” at the provincial level, and “Low–Low” and “High–High” at the municipal level. The smaller spatial scope is more helpful to show the geographic dependence and geographic differences of China’s carbon emissions. The findings of this paper will help deepen the understanding of the spatial and temporal changes of carbon emissions in China. They will provide a scientific basis for the formulation of feasible carbon emission reduction policies.

Targeting the “poorest of the poor”: A multi-scale approach

Understanding that poverty presents multidimensional and multi-scale deployments, a methodology is proposed that allows to identify those poorest families among the poor in the State of São Paulo for cash transfer purposes. We propose a method to combine the vulnerability of the family with the other scales, resulting in a more refined classification of the situation of poverty, which is more difficult to be biased by the selfreported register. We added a georeferenced context-based component to differentiate among families with same vulnerability but living in different levels of intraurban and municipal conditions. At the municipal scale, we developed two sub-indexes through Principal Component Analysis (PCA): a) the Municipal Context Sub-Index, and b) the Municipal CadUnico Sub-Index. These sub-indexes were then integrated. On the scale of the Census enumeration area, we considered aspects that can aggravate the poverty condition, such as physical environmental risks and socioeconomic deprivation. On the family-scale, we consider the score defined by the São Paulo Multidimensional Poverty Index (MPI). The final multi-scale classification of families results from the sum of the scores obtained at the family level, the Census enumeration area and the municipality. The indexes were validated internally and externally. We simulated a scenario of hypothetical cash transfer programs using the current tool (MPI-S) and the proposed methodology to benefit up to 10,000 and 100,000 families in the State of São Paulo. We implemented the solution in Power BI tool to allow automatic updates of the indexes to be made in the developed “dashboard”. Results allow us to evaluate the impact of both approaches. The current approach, based only in the multidimensional poverty of the family is more distributed, with a wider geographic distribution, sometimes benefiting only one family in some municipalities or a great number of families in one municipality. The proposed approach tends to concentrate the assistance with greater percentages of families in the CadUnico by municipality. Besides aiding on cash transfer decision, the results obtained on the context and the intensity of municipal poverty can be used for programs aimed at leveraging economic development in the different administrative regions of the State of São Paulo.

Machine learning application for estimating electricity demand by municipality

In order to achieve efficient and low carbon energy systems, attention has been focused on locally distributed energy systems, but a detailed and accurate supply–demand database is essential for simulating and optimizing such local systems. Demand data are generally not maintained at small scales, and it is common to estimate electricity demand by prorating global data from large energy carriers. However, the accuracy of the proration method is questionable. The present study explores the idea of using machine learning as an alternative to the proration method for downscaling electricity demand data based on a neural network model. The input data consist of four categories: weekday/holiday determination data, temperature data, periodic signal data, and demand sector configuration data. The model shows a satisfying generalization capacity for downscaling data from regional to prefectural and municipal scales while capturing local characteristics that are not accounted with the proration method.

Scenario Analysis of Green Infrastructure to Adapt Medium-Size Cities to Climate Change: The Case of Zaragoza, Spain

Planning a well-structured urban green infrastructure (UGI) is essential for cities to counteract the impacts of climate change. Soil carbon and air temperature differences between open and plant-covered sites were used as proxies of carbon sequestration (CS) and temperature regulation (TR) to evaluate the current conditions of UGI in Zaragoza, a medium-sized city in northeastern Spain. Alternative scenarios were constructed, after a stakeholder consultation, at both city and municipal (city plus peri-urban zone) scales, extrapolating the highest values of CS and TR to two groups of UGI types grouped based on the state of their ecological functioning. We employed analysis of variance to compare mean values of CS and TR across diverse scenarios at both city and municipality scales. Statistically significant differences were found in city-scale and municipality-scale scenarios for both CS and TR. Multiplying CS by area did not show significant variation in city scale. Significant differences were found when multiplying TR by area at both scales, with exceptions in certain scenario combinations. These results suggest favoring the restoration of UGI sites in peri-urban zones (such as forests and steppe zones) to increase CS and those in densely urbanized zones (such as urban parks) to provide TR benefits.

Analysis of environmental health and sustainability of Brazilian municipalities in the Baixo Pardo/Grande River Basin

Population growth, the lack of urban planning and poor sanitation infrastructure have persisted in the management of urban space when the core issue is environmental health and the sustainability of municipalities. This paper aimed to evaluate the environmental health and sustainability of the 12 Brazilian municipalities in the Baixo Pardo/Grande (SP) river basin. The exploratory approach and the case study guided the comparative analysis of sustainability through the Sustainable Cities Development Index (100 indicators) and the Brazilian Environmental Health Index (18 indicators) to obtain an integrated classification of these indices on a municipal scale. The analyses highlighted that 100% of studied municipalities reached medium level (score: 51-75) out for health and sustainability, especially to Altair and Orlândia results Bebedouro, Viradouro and Colina require careful monitoring of the following indicators sanitary sewage and vector control, water and socioeconomic resources, and water and socioeconomic resources, respectively. The basic sanitation, health diseases and infant mortality pillars relating to these indices identified seven similar indicators, that is, they measure the same aspects of the analyzes. Then, they were excluded to avoid duplication of data. The least promising results of Sustainable Development Goals (SDGs) were Gender equality (SDG 5), Industry, innovation and infrastructure (SDG 9), Protect life on Earth (SDG 15) and Partnerships and means of implementation (SDG 17). Finally, the great challenges of the Baixo Pardo Grande River Basin pointed out social, technological and connectivity issues between interinstitutional sectors from a regional perspective.

It's not all about drought: What “drought impacts” monitoring can reveal

Drought impacts monitoring has been called the missing piece in drought assessment. The potential to improve drought management is high but uncertain due to rare analyses of impacts datasets, predominantly because there are few impacts monitoring programmes to generate the datasets. Drought impacts monitoring is conducted on the ground in much of Brazil by local observers at monthly and municipality scale to support the Brazilian Drought Monitor. In Ceará state, within drought-prone semiarid northeast Brazil, over 3600 drought impacts reports were completed by agricultural extension officers from 2019 to 2022. We investigated, through manual coding and observer interviews, the reported drought impacts and impact drivers. Analysis provided a catalogue of the experienced impacts and showed that impacts still occur, and are often normalised, during non-drought periods, sometimes as lingering effects of previous droughts. The impact drivers were predominantly non-extreme hydrometeorological conditions or a result of socio-technical vulnerabilities such as insufficient water infrastructure. The normalisation of “impacts” included, in particular: a generally accepted high level of crop losses and consistently low reservoir levels around which the agricultural and domestic systems are adapted. Conventional drought indices often did not align with experienced impact severity, highlighting the limitations of relying solely on these indices for emergency response. Continual impacts monitoring could be extremely valuable anywhere in the world for identifying vulnerabilities and informing proactive measures to reduce drought and other hazard risk, in addition to guiding targeted mitigation efforts.

Spatial Conservation Prioritization for Land in Megacity Facing Climate Change and Biodiversity Loss

Climate change and biodiversity loss are two major threats to the world. Ecosystem conservation is an important issue for humanity, and international intergovernmental science-policy platforms highlight the mutually beneficial relationships among biodiversity conservation, ecosystem services and measures to mitigate climate change. Rapid urbanization has brought various deeply interconnected ecological problems to large cities. Therefore, key areas for conservation must be identified in an integrative manner to maximize conservation effectiveness and meet both ecological and human social needs. Spatial conservation prioritization provides a way to consider different functional needs as a whole in light of certain objectives while highlighting their contradictions and overlaps in spatial utilization. The megacity of Beijing continues to experience frequent extreme weather events despite the implementation of a series of ecological restoration measures. We integrated the potential distributions of 64 critically endangered native species to represent biodiversity and used five main ecosystem services required for climate governance to represent ecosystem services in the city. Using the spatial prioritization software Zonation 5, we assessed the spatial distribution of conservation priorities at the municipal scale and changes in conservation effectiveness in four protection scenarios. The results showed that the existing PAs in Beijing could protect 24.99% of the current distribution of biodiversity and ecosystem services. The conservation effectiveness can reach 78.75% when the PAs are increased to 30%. This efficiency rate is 4.46% lower than that of the optimal scenario, which does not consider the existing PAs. An emphasis on urban rewilding spaces may be the key to overcoming conservation bottlenecks to further enhance the efficiency of ecological conservation. To achieve future conservation targets, policy development may start with urban built-up areas.

Worldwide scaling of waste generation in urban systems

The production of waste as a consequence of human activities is one of the most fundamental challenges facing our society and global ecological systems. Waste generation is rapidly increasing, with corresponding shifts in the structure of our societies, where almost all nations are moving from rural agrarian societies to urban and technological ones. However, the connections between these societal shifts and waste generation have not yet been described. In this study we applied scaling theory to establish a new understanding of waste in urban systems and identified universal scaling laws of waste generation across diverse urban systems worldwide for three forms of waste: wastewater, municipal solid waste, and greenhouse gases. We found that wastewater generation scales superlinearly, municipal solid waste scales linearly, and greenhouse gas emissions scale sublinearly with city size. In specific cases, production can be understood in terms of city size coupled with financial and natural resources. For example, wastewater generation can be understood in terms of the increased economic activity of larger cities, and the deviations from the scaling relationship, indicating relative efficiency, depend on gross domestic product per person and local rainfall. The temporal evolution of these scaling relationships reveals a loss of economies of scale and a general increase in waste production, where sublinear scaling relationships become linear. Our findings suggest general mechanisms controlling waste generation across diverse cities and global urban systems. Our approach offers a systematic framework to uncover these underlying mechanisms that might be key to reducing waste and pursuing a more sustainable future.

Chinese urbanization promoted terrestrial ecosystem health by implementing high‐quality development and ecological management

AbstractHigh‐quality urbanization and a healthy ecosystem are both the material basis for sustainable social development. However, the tie between terrestrial ecosystem health (TEH) and urbanization is still unclear. Therefore, we assessed the spatial and temporal dynamics of urbanization and TEH at 368 cities in China from 2000 to 2020, then explored their spatial interaction and driving mechanisms by spatial autocorrelation analysis and structural equation modeling. The results showed: (1) China's comprehensive urbanization index (UI) improved from 0.08 in 2000 to 0.10 in 2020, contributing by some national urban agglomerations such as Beijing‐Tianjin‐Hebei, Yangtze River Delta, and Pearl River Delta. (2) China's terrestrial ecosystem health index (EHI) also increased from 0.67 to 0.68. Ecosystem vigor improved significantly, while ecosystem organization and resilience both decreased. (3) EHI and UI appeared to be locally spatially dependent, and path dependence was presented at municipal scales. (4) At the national scale, urbanization positively relates to EHI, which was enhanced by social, economic, and topography factors. The dominant drivers on EHI varied among regions, and urbanization improved EHI in all regions except for the southwest. Our study demonstrated that urbanization would promote TEH by implementing high‐quality development and ecological management simultaneously, providing theoretical support for urban sustainable development and ecological management.

Feasibility of financing nature-based solutions for water security through water tariffs: Evidence from South Africa

Abstract In many countries, catchment restoration is underfunded. This study aims to address whether household water pricing could be used as a mechanism for securing funds for catchment restoration. The objectives were to determine households' willingness to pay (WTP) for their existing water use, investigate whether institutional trust and municipal satisfaction influenced WTP, and establish whether aggregate consumer surplus at the municipal scale could cover the costs needed to finance catchment restoration. Surveys were conducted on 502 households in three metropolitan municipalities in the City of Cape Town, eThekwini, and Nelson Mandela Bay. Contingent valuation revealed that average WTP for water was between 12 and 137% more and 32 and 73% more than what households currently pay for water per month in the City of Cape Town and eThekwini, respectively. Satisfaction with municipal service delivery positively influenced WTP, while institutional trust did not. The City of Cape Town, based on the aggregate WTP from the higher income categories, consumer surplus was 779 million South African Rand (ZAR)/year, more than double the estimated cost required to restore the catchment areas supplying water to the city over 30 years. In eThekwini, consumer surplus was equal to the amount needed over 30 years (250 million ZAR/year). These results demonstrate the significant potential to raise water tariffs for higher income households in metropolitan municipalities.

Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding

Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.

Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and Multiscale Geographically Weighted Regression.

Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.

The role of land inequality in the poverty-forest loss nexus patterns: A case study from Chile

The predominant school of thought argues that poverty is a major cause of deforestation, but several studies have begun to contest this assumption by putting forward that a complex web of factors influences this connection and that multi-patterned nexuses can exist. We explore these nexuses taking southern Chile as a case study and using a Path Analysis for modelling. We relied on secondary data at the land property and municipality levels to construct the exogenous (land inequality, farm and forestry subsidies, and firewood consumption) and endogenous variables of the model (poverty, native forest loss, non-native tree afforestation, ecosystem services supply), which runs at the municipality scale (n = 147). We obtained a very good model adjustment as indicated by common goodness of fit measures. The model's path coefficients showed that land inequality (measured with the Gini coefficient) had a positive direct effect on poverty and forest loss. In turn, poverty had a negative direct effect on forest loss, whereas forest loss had a total negative effect on poverty. Thus, poverty per se does not cause forest loss or vice versa, and rather than a single poverty-forest loss nexus, “many-patterned” nexuses arise. We discus three of these patterns, namely i) development that aggravates poverty, ii) land inequality that aggravates forest loss and poverty, and iii) land inequality that “helps” the cause of forest conservation. These results suggest that policies that seek to solve poverty (e.g., farm subsidies) will not necessarily help prevent forest loss, but neither will neoliberal forest and conservation policies alone. As the SDGs state, solving inequality seems to be a necessary condition for both alleviating poverty and ensuring forest conservation.