Shared Socioeconomic Pathways Research Articles

Unveiling key drivers of economy-water system and transforming water use pattern into sustainable development: Inner-Shaan-Ning region in the Yellow River Basin

The economy-water system involves complex interactions between water resources and economic activities; fully understanding these complexities and balancing economic development with water resources consumption remains a significant challenge. This study develops a factorial multi-region input-output CNN-LSTM (FMCL) model through integrating deep learning (convolutional neural network and long short-term memory, CNN-LSTM) and multivariate statistical analysis (factorial design, FD) into a multi-region input-output (MRIO) framework. The FMCL model not only unveils the socioeconomic drivers underlying water consumption patterns, but also quantitatively assess complicated interactions within the economy-water system under multiple scenarios. The FMCL model is then applied to a typical water-scarce region in the Yellow River Basin (three provinces of Inner Mongolia, Shaanxi and Ningxia, abbreviated as Inner-Shaan-Ning region). The main findings are: (i) direct water consumption intensity, industrial structure (agriculture, manufacturing, construction, and accommodation & catering), and per capita household consumption constitute the key socioeconomic drivers of water consumption; (ii) among different shared socioeconomic pathways (SSPs), Inner Mongolia, Shaanxi, and Ningxia would achieve relatively high levels of economy-water sustainability under SSP2, which represents a pathway of moderate socioeconomic development trends; (iii) the interaction between accommodation & catering and per capita household consumption would be significant; compared to the baseline SSP2, interactive policies that focus on key factors would achieve higher economy-water sustainability; (iv) from 2026 to 2050, the optimal interactive policy in Inner-Shaan-Ning region could reduce annual water consumption per unit of GDP by 1.22%, 3.82% and 3.34%, respectively. These findings cannot only reveal the key drivers and their multi-dimensional interactions within the economy-water system, but also facilitate the transformation of water use pattern into sustainable development.

Evaluation of the impact of hydrological changes on reservoir water management: A comparative analysis the CanESM5 model and the optimized SWAT-SVR-LSTM

This research examines the impacts of climate change and socio-economic variables on the hydrological cycle, reservoir water management, and hydropower capacity at the Gezhouba Dam. The Gezhouba Dam serves as a crucial hydroelectric power station and dam, playing a vital role in regulating river flow and generating electricity. In this study, an innovative method is employed, combining the Soil and Water Assessment Tool (SWAT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) models. This model is optimized using the Developed Thermal Exchange Optimizer. This optimized combined model significantly enhances the reliability and precision of the forecasting inflow and reservoir levels. By utilizing the Canadian Earth System Model version 5 (CanESM5), we examine climate variables across various scenarios of Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP). Under the SSP5-RCP8.5 scenario, the most aggressive in terms of emissions, we project a temperature rise of 2.6 % and a precipitation decrease of 2.7 %. This scenario leads to the most substantial changes in the hydrological cycle and altered river flow patterns. The results show a direct correlation between precipitation and inflow (0.952) and a strong inverse correlation between temperature and inflow (0.893). The study predicts significant decreases in all flow metrics, with mean high flow (Q5) periods affecting hydropower generation, especially under the SSP5-RCP8.5 scenario. Additionally, the filling frequency rate (FFR) and mean filling level (MFL) are projected to decrease, with a more pronounced decline in the far future, indicating a potential compromise of the reservoir's water storage and power generation capabilities, especially under the SSP5-RCP8.5 scenario.

Global Assessment of Compound Climate Extremes and Exposures of Population, Agriculture, and Forest Lands Under Two Climate Scenarios

AbstractClimate change is expected to increase the global occurrence and intensity of heatwaves, extreme precipitation, and flash droughts. However, it is not well understood how the compound heatwave, extreme precipitation, and flash drought events will likely change, and how global population, agriculture, and forest will likely be exposed to these compound events under future climate change scenarios. This research uses eight CMIP6 climate models to assess the current and future global compound climate extreme events, as well as population, agriculture, and forestry exposures to these events, under two climate scenarios, Shared Socioeconomic Pathways (SSP), SSP1‐2.6 and SSP5‐8.5 for three time periods: early‐, mid‐, and late‐ 21st century. Climate extremes are derived for heatwaves, extreme precipitation, and flash droughts using locational‐dependent thresholds. We find that compound heatwaves and flash drought events result in the largest increases in exposure of populations, agriculture, and forest lands, under SSP5‐8.5 late‐century projections of sequential heatwaves and flash droughts. Late‐century projections of sequential heatwaves and flash droughts show hot spots of exposure increases in population exposure greater than 50 million person‐events in China, India, and Europe; increases in agriculture land exposures greater than 90 thousand km2‐events in China, South America, and Oceania; and increase in forest land exposure greater than 120 thousand km2‐events in Oceania and South America regions when compared to the historical period. The findings from this study can be potentially useful for informing global climate adaptations.

Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios

The optimal allocation of water resources is crucial for addressing regional water scarcity, adapting to climate change, and promoting sustainable development. This study focused on the Shule River Basin and considered three climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5), which is combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). A water resources optimal allocation model including water supply and demand prediction, multi-objective optimization, decision-making and analysis on optimal water allocation schemes was constructed. This model aims to analyze the optimal allocation of water resources in the Shule River Basin over three planning years (2025, 2030, 2035), considering guarantee rates of 50 % and 75 % under various climate scenarios. The obtained results indicate that: (1) An increasing trend in water demand in the planning year. For instance, under the SSP1–2.6 scenario, the basin's water demand is projected to reach 1.920×10^9 m3, 2.037×10^9 m3, and 2.103×10^9 m3 in 2025, 2030, and 2035, respectively. (2) The total water supply with a guarantee rate of 50 % (75 %) in the planning years is projected to be 1.939 (1.483)×10^9, 1.957(1.502) ×10^9, and 1.974 (1.519) ×10^9 m3, respectively. (3) In the three scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the water demand for domestic and ecology is met in each planning year. There is a slight water shortage in the industrial sector, with a shortage rate ranging from 0 % to 13.41 %. The agricultural sector experiences the highest water shortage, with a rate of 1.93–44.77 %. (4) The water supply structure of each optimal allocation scheme of water resources was optimized. The economic benefits mainly depend on the industrial and ecological sectors. These findings offer valuable insights for optimizing the distribution of regional water resources in response to changing climatic conditions.

Phenological Shifts in Lake Ice Cover Across the Northern Hemisphere: A Glimpse Into the Past, Present, and the Future of Lake Ice Phenology

AbstractLong‐term ice phenology records quantify the effects of climate change on Northern Hemisphere lakes. This study uses lake ice phenological records across a gradient of lake sizes (0.1–31,967.8 km2 in lake surface area) obtained from community science networks. We compiled in situ ice phenological records for 2,499 lakes across 15 countries for an average of 30 years. These data revealed that for the last 50 years (1971–2020), the annual mean duration of lake ice cover decreased at a rate of 9 days per decade, with a regime shift in lake ice phenology in the late 1980s. We projected that at the end of the century (2070–2099), ice duration will decrease by an average of 10 days when compared to the historical time period (1971–2000) for the shared socioeconomic pathway (SSP) 1–2.6 climate scenario (SSP126), 23 days for SSP370, and 28 days for the SSP585. Impending human development can enhance or attenuate lake ice loss, as adaptation strategies can accelerate fossil fuel use, result in conflict, or seek strategies apart from fossil fuel development. These future pathways have critical implications for the future preservation of lake ice cover.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Prediction of long-term photovoltaic power generation in the context of climate change

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585 % increase in the average annual power generation during the future carbon peaking period (2025–2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479 %.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

Regional carbon inequality prediction in China based on shared socioeconomic pathways: A human well-being equity perspective

Carbon inequality is strongly related to economic development and human well-being (HWB) improvements. However, relatively little research has been undertaken to predict future interregional carbon inequality in China from an HWB equity perspective based on scenarios combining shared socioeconomic pathways (SSPs) and carbon reduction policies. The biproportional scaling method named after economist Richard Stone (RAS) was used to predict China's 2020 to 2050 multiregional input–output (MRIO) tables. Then, these MRIO tables were used to simulate future net carbon emissions (NCEs) and net human well-being (NWB) transfers under the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios. China's interregional carbon inequality was predicted under different scenarios to clarify the ideal path for mitigating carbon inequality. In the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios, the total carbon emissions (CEs) clearly decrease, whereas the total HWB clearly increases. Transfers of NCEs between regions increases, and transfers of NWB between regions decreases in each of the four scenarios. According to the mean regional environmental inequality (REI) value, China's interregional carbon inequality is relatively low under the SSP1–1.5 °C and SSP1-NEU scenarios and relatively high under the SSP2–2 °C and SSP5-BAU scenarios. Mitigating interregional carbon inequality is a long-term and arduous task that requires commitments from governments, businesses and society. These findings clarify the optimal path for China to reduce carbon inequality in the future and provide a theoretical basis for government agencies to rationally adjust the current development model. Furthermore, they provide a supportive reference to help other economies achieve equitable and sustainable development.

Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China

Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km2) and grassland (645.741 km2). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration.

Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis

ABSTRACT The generation of hydropower is profoundly influenced by shifts in streamflow patterns induced by climate change. This research examines changes in streamflow and the potential surge in hydropower generation over a span of 35 years (2015–2050) at the Bhakra Dam site within the Upper Sutlej River basin. Employing a deep learning methodology, particularly the long short-term memory (LSTM) model, in conjunction with Coupled Model Intercomparison Project (CMIP) 6 multi-global climate model (GCM), facilitates a thorough analysis of these dynamics. Six out of 14 bias-corrected statistically downscaled datasets (0.25° × 0.25° grid resolution) from CMIP6 multi-GCM were selected based on entropy and combined compromise solution techniques. This innovative approach is utilized to assess streamflows and project potential increases in hydropower at the Bhakra Dam site under shared socioeconomic pathways (SSP) scenarios, specifically SSP245 and SSP585. The results indicate a maximum increase of approximately 15% and 17% in mean monthly streamflow under SSP245 and SSP585, respectively. Moreover, dependability flows calculated at Q50, Q75, and Q90 show respective rises of 13%, 16%, and 17% under SSP245 and 21%, 17%, and 18% under SSP585. The projected hydropower potential exhibits an increase of up to 15.9% and 17.3% under SSP245 and SSP585, respectively.

Evaluation of farmland production potential in key agricultural production areas on the Qinghai-Tibet Plateau under multi-scenario simulation

Predicting changes in future land use and farmland production potential (FPP) within the context of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) is crucial for devising sustainable land use strategies that balance agricultural production and ecological conservation. Therefore, the Huangshui Basin (HSB) in the northeast Qinghai–Tibet Plateau is taken as the study area, and a LUCC–Plus-FPP (LPF) coupling framework based on the SSP–RCP scenarios is proposed to evaluate future land use patterns and FPP changes. On the basis of the predictions of land use changes from 2020 to 2070, the trade-offs in grain production resulting from bivariate changes in farmland and FPP under future scenarios are analyzed. The results indicate that the model has a high simulation accuracy for land use types, with an overall accuracy of 0.98, a kappa coefficient of 0.97, and a figure of merit value of 0.21. Under the SSP245 and SSP585 scenarios, built-up land increases significantly, by approximately 45.89 %. Farmland and grassland conversions contribute the most to increased built-up land. Farmland area consistently decreases by approximately 5 % across all scenarios. The protection of farmland in the study area is difficult to undertake and thus requires much attention. Moreover, under the SSP126 scenario, the FPP of most districts is greater than that in 2020, and the average FPP of the HSB from 2030 to 2070 is greater than that in 2020. In the SSP585 scenario, by 2070, the average FPP of all districts decreases to different degrees compared with that in 2020. Furthermore, the compensated farmland quantities and average FPPs under all the scenarios are significantly lower than the amount of occupied farmland. The results provide a theoretical foundation and data support for farmland protection decision-making and layout optimization in the Qinghai–Tibet Plateau.

Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

ABSTRACT Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.

Analysis of carbon peak achievement at the provincial level in China: Construction of ensemble prediction models and Monte Carlo simulation

As China advances toward its carbon peaking goals, many regions face the challenge of balancing rapid economic growth with sustainable development. Evaluating carbon emissions at the provincial level is crucial for formulating effective strategies to achieve China's carbon peak targets. This study aims to construct an accurate model for predicting carbon emissions and to explore the evolution of these emissions across Chinese provinces, as well as their contributions to national carbon peak targets. Using the Environmental Kuznets Curve (EKC) theory, the 30 provinces were categorized into groups. An ensemble carbon emissions forecasting model was developed by integrating time-series models with multifactor models. Three scenarios were established within the framework of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Monte Carlo simulations were employed to explore potential pathways to achieve carbon peaks. The results indicate that China will reach its carbon emission peak between 2030 and 2031, with peak values expected to range between 11,499.65 and 11,629.51 Mt. Significant differences were observed among the provincial groups in their contributions to carbon peaking. Groups II and III are projected to peak in 2030 and 2022, respectively, while Groups I and IV face greater challenges, with peak years projected between 2032–2035 and 2031–2034, respectively. Four tiers with different emission reduction responsibilities were identified by comparing the peak times of the 30 provinces under the three scenarios, and optimal recommendations for achieving carbon peaks were proposed for each province. The accurate prediction models and Monte Carlo simulations provide reliable results for achieving carbon peak targets across Chinese provinces, offering a scientific basis for optimizing national carbon emission reduction policies.

Senecio inaequidens DC. will thrive in future climate: A case study in a Mediterranean biodiversity hotspot

Monitoring the expansion of invasive non-native plants under current and future climatic conditions is crucial for understanding biodiversity threats, addressing the ecological impact, and developing effective management strategies. This study focuses on modelling the expansion and distribution of Senecio inaequidens DC. on the island of Sardinia (Italy) to address these environmental challenges. The objectives were to identify bio-climatically suitable areas under current conditions, project potential future distribution, and evaluate invasion dynamics on the island to localize suitable areas for effective management strategies.Species data were collected from 1991 to the present, supplemented by global databases and analyzed using an ensemble species distribution model approach. This approach utilized presence data, high-resolution current bioclimatic variables (40 m2), developed explicitly for our study area, and two future scenarios derived from the newly Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socio-economic Pathways (SSP2–4.5 and SSP5–8.5 for 2040 and 2060).The ensemble model's findings suggest a close alignment between the currently documented occurrences of S. inaequidens and its bio-climatically suitable habitats in Sardinia. Moreover, predictions indicate high bioclimatic suitability for S. inaequidens in the western and southwestern coastal regions, contrasting with its known occurrences at higher altitudes. Notably, the model also forecasts high bio-climatic suitability across most small islands surrounding the region and in central-east Sardinia, potentially indicating habitats at lower altitudes compared to current records.Under the SSP2–4.5 scenario, suitable areas are expected to nearly double by 2040 and more than double by 2060, compared to current conditions. Under the SSP5–8.5 scenario, the increase in suitable habitats is projected to be about 83.31% by 2040 and more than double by 2060. These results highlight the species' ability to thrive under climate change, with a more pronounced range expansion under the pessimistic management regime (SSP5–8.5) than under the intermediate one (SSP2–4.5), particularly in the central region of the island. This expansion under the more severe management scenario is particularly alarming as it reflects limited implementation of environmental management policies.The study underscores the potential ecological risk posed by S. inaequidens due to its potential range expansion and ability to invade different habitat types, from coastal regions to mountainous areas, under current and future scenarios. Based on these findings, we propose targeted management actions for monitoring and eradicating the species, leveraging prior information and local experiences to mitigate its impact.

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines

Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the historical evolution and project future scenarios of building material stock (MS) and their spatial distribution across Japan's three major metropolitan areas. Through a comprehensive material flow and stock analysis, the historical accumulation of building materials from 2009 to 2020 was quantified, revealing a dominance of concrete and an increasing overall stock. The contributions of various driving factors to changes in construction areas were explored, identifying population dynamics as the predominant influence. Leveraging shared socioeconomic pathway scenarios (SSPs), this research forecasted building floor area and MS until 2050 under five distinct SSPs. The results indicated an overall reduction across all scenarios, yet with a continued concentration in high-density urban cores. The substantial gap between the highest and lowest projected MS scenarios highlighted opportunities for material conservation and emission reductions through sustainable practices. Sustainable urban development in densely populated regions necessitates a balance between infrastructure provision and environmental conservation, while in sparsely populated areas, the focus shifts to the efficient management and utilization of vacant properties and materials to cope with the impacts of significant population declines. By offering insights into the building floor area and MS implications of Japan's demographic changes, this study underscores the necessity of sustainable urban planning and resource management strategies to navigate the challenges posed by demographic shifts, ultimately contributing to sustainable development and environmental conservation goals.

Differential impact of climate and land use change on habitat suitability of migrant passerines according to habitat preferences

Our study contributes to the ongoing dialogue on the impact of climate and habitat changes on migratory bird species, particularly focusing on how these effects vary based on species’ habitat preferences. We used citizen science data for 22 African-Eurasian migratory bird species and categorized them into five groups based on habitat preferences, following the classification methodology of Atkinson et al. (2014). Using ensemble species distribution modeling (SDM), we projected changes in potentially suitable habitats across Africa from 2040 to 2100 under contrasting climate and land use scenarios. Our results indicate a differential impact of climate and land use changes on habitat suitability, with species preferring habitats with shrubs and trees being the most vulnerable. Conversely, other group species, such as open country-grassland and farmland birds could experience a significant increase in suitable habitat. We anticipate a profound change in habitat suitability, with the western part of South Africa becoming unsuitable for most species, while an increase in suitable habitat is expected in the Sahel. Bioclimatic rather than land use variables emerged as the primary drivers of these changes. The extent of change in habitat suitability will be strongly influenced by the Shared Socio-economic Pathways followed by human societies.

Assessment and forecast of the water-related nature’s contributions to people on the Loess Plateau from a spatial flow perspective

ContextIntegrating the spatial flow perspective, temporal trade-off perspective, and future tendency perspective enables a comprehensive assessment of nature’s contributions to people (NCPs). However, assessments that integrate these perspectives and combine them with regional ecological restoration policy remain limited.ObjectivesBased on the perspective of spatial flow and considering the two dimensions of nature’s contributions and people’s needs, we assessed and forecasted water-related NCPs, including water yield (NCP6), water purification (NCP7), and soil conservation (NCP8), from the Loess Plateau to downstream regions of the Yellow River in China.MethodsTo forecast the future tendencies of the NCPs and their temporal trade-offs, we simulate land use changes based on rapid landscape restoration and slow landscape restoration under five shared socioeconomic pathways (SSPs) from 2020 to 2100.ResultsThe results showed that the spatial distributions of the values of the three NCPs differ greatly from the corresponding values of nature’s contributions. The soil conservation NCP and water purification NCP will decrease under all scenarios, while the water yield NCP will be stable in most of the scenarios except SSP3. The soil conservation NCP showed a faster decrease under R2 compared to R1, with the highest rate observed at 2.11%. Conversely, the water purification NCP and water yield NCP experienced a faster decrease under R1 than under R2, with rates of 8.61% and 14.30% respectively. The rapid landscape restoration will have a more positive impact on the soil conservation NCP, and a more negative impact on the other two water-related NCPs than the slow landscape restoration.ConclusionsThis assessment provides a forecast of the future benefits of landscape restoration from dual dimensions of nature’s contributions and people’s needs, indicating the importance of integrating the future changes in both dimensions when making regional landscape restoration policy.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.