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• We incorporate spatial factors affecting carbon emissions as drivers into the RF-CA model. • We change the constraints in the CA model for land use change prediction. • We predicted the land use change of Shanghai in 2025 under a low-carbon scenario. As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city's overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches. [ABSTRACT FROM AUTHOR]

• Framework to quantify collective and individual impacts of climate change and human activities on hydrological regimes. • Climate change and human activities have opposite effects on minimum flow and pulse indicators in the Ganjiang River bBasin. • Climate change and human activities exhibit a substantial synergistic effect on multiple aspects of hydrological regimes. Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

• This study develops a disaster risk index (DRI) for assessing disaster risks in coastal communities. • Logit Boost ML model was found the optimal model for this research. • Gabura had the highest DRI value of 0.09, ahead of Southkhali (0.06) and Banishanta (0.04) unions. • The study underscores the importance of evolving from traditional approaches to advanced data-driven tools. Communities in disaster-prone coastal areas are more vulnerable to socioeconomic, environmental, and ecological problems. Knowing how disaster-prone coastal communities are, and, ideally, their resilience against current disaster risks, is an essential first step in reducing consequences. Therefore, this study aims to develop a disaster risk index (DRI) for assessing disaster risks in three coastal communities in southwest Bangladesh. Five machine learning (ML) models and holistic approaches were adopted to conduct the analysis. This study utilized ML-based feature selection methods and considered hazard, exposure, and vulnerability components to identify the most influential indicators for evaluating disaster risk. The researchers systematically developed six distinct feature sets using filter-based feature selection methods. Five ML models then evaluate these sets to determine the optimal set of features. Evaluation metrics were used to validate the performance of ML models, such as accuracy, precision, F-measure, recall, and the receiver operating characteristics (ROC) curve. Based on the Logit Boost ML model, the optimal feature set (set 3) had the best accuracy (0.997), precision (0.997), recall (0.997), F-measure (0.997), and ROC (0.996) compared to other feature selection sets. According to the findings, Gabura had the highest DRI value of 0.09, ahead of the Southkhali (0.06) and Banishanta (0.04) unions. The study underscores the importance of evolving from traditional approaches to advanced data-driven tools to understand disaster risks. The paper's findings can provide policymakers in coastal areas with new insight into the disaster risk differences among the inhabitants, leading to more effective disaster management practices and risk reduction strategies. [ABSTRACT FROM AUTHOR]

• The extent of flooding in China is greater than in any other country. • Nearly 66% of China's landmass is submerged by flooding. • Financial toll of flooding accounts for approximately 1.42% of the annual GDP. • Various forms of climatic change contribute to flooding. The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China's landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China's flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding. [ABSTRACT FROM AUTHOR]

• Vegetation response to drought can be inferred from correlating VAI and SPEI. • The strongest accumulation period for vegetation response is 6–8 months. • Trees and irrigated cropland are the least sensitive to drought. • Shrubland and rainfed cropland show the most sensitivity to drought. • The cumulative climate has a higher impact on vegetation than lag effects. Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries. [ABSTRACT FROM AUTHOR]

• This paper provides a comprehensive conceptual analysis of disaster resilience in climate-vulnerable communities. • The research employs the Walker and Avant Method to elucidate the notion of resilience within the framework of climate vulnerability. • A practical framework and a set of ecological indicators are proposed for environmental impact assessments in disaster resilience. • The research provides tangible illustrations of the proposed approaches and indicators in practical contexts. Resilience is a prior term of debate in vulnerability research irrespective of discipline. Nowadays the resilience concept has been used abnormally in many cases without realizing its intended meaning. Therefore, this study clarifies the resilience concept in the context of climate vulnerability. An integrated review of the literature has been done by the PRISMA approach to identify the most relevant documents. Besides Walker and Avant Method has been used to analyze the concept of resilience. The use of concept in different disciplines, historical perspectives, dimensions, attributes, consequences, and relationship with vulnerability and adaptability have been reviewed to clarify it. This study also provides a theoretical and workable definition of resilience from the perspective of climate change and natural hazards. A conceptual framework has been developed with concept mapping, contributing to the existing debate on resilience study. A case analysis has been done by focusing on a climate-vulnerable community living in the riverine islands in Bangladesh. This article shows that socio-ecological systems approach to defining small island disaster resilience has shortcomings. Additionally, the research-policy divide in resilience studies is exacerbated by the paucity of studies that use the participatory approach to identify disaster resilience indicators. The study makes a compelling case for further research on how to turn academic discoveries into a resource that might benefit local people, particularly those on small islands. The study suggests that social community-based resilience should be developed to protect geographically isolated communities. [ABSTRACT FROM AUTHOR]

• The paper analysed a case study on the monitoring system of an Italian food company. • The study creates a tailor-made environmental Key Performance Indicators dashboard to assess corporate sustainability. • The study investigated rewards systems in the frame of Green Human Resource Management practices. • A final dashboard of around 70 indicators was then created and linked to employees' rewards. Assessing environmental performance, measuring the circular economy and quantifying decarbonization have become crucial in green management in recent years. Both scholars and practitioners have focused on different strategies to create performance indicators capable of tackling all these aspects. At the same time, companies have started connecting managers' and employees' incentives to the achievement of corporate goals. We analysed a case study of an Italian food company which uses environmental performance indicators to assess corporate sustainability and reward managers and employees. Through a series of interviews with members from all departments, we identified around 200 different key performance indicators, i.e., performance, commitment, control, reactivity, economic, consent, transversal and multifunctional. A final dashboard of around 70 indicators was then created and linked to employees' rewards. A tailor-made methodology was, thus, used to define target for the company's management by objectives, taking into account the trend of each indicator. This is the first attempt to investigate how a company can create an environmental performance indicator system to assess corporate sustainability and reward employees through management by objectives. Our study further extends not only the academic literature, but also provides a practical and operative contribution to all the companies that aim to create a new key performance indicator system. Lastly, we merged the management and strategies for promoting green practices amongst employees through a reward system with the environmental performance of each organisational department by creating a methodology to define management by objectives. [ABSTRACT FROM AUTHOR]

• The impact of precipitation, slope, and swamp wetland ratio on water yield is not affected by spatial scale and watershed. • Identifying key areas for ecosystem services combined with multiple scenarios can help to take ecological restoration. • The water yield in the Baihe basin is more sensitive on climate change, while the water yield in the Heihe basin is more easily affected by land use changes. • The economic development and ecosystem services can both be ensured by wetland ecological restoration. Water supply is one of the core ecosystem services functions for wetland. Due to the important status of Zoige wetland, assessment of water supply in Zoige wetland is significant for ecological security all over the world. In this paper, Heihe and Baihe basins in Zoige wetland were taken as research objects, the water yield module of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was used to spatially quantify water supply, with the aims of revealing the driving mechanism of water supply under different basins and spatial scale. We observed the spatial pattern change of water yield under baseline and eleven future scenarios and used the ANOVA of repeated measures and multiple linear regression model to explore the patterns and driving factors of water supply in Heihe and Baihe rivers basins. The research results were as follow. (1) The precipitation is main factor to improve water yield, but this intensity is different in different basins and spatial scale. Topography, climate, vegetation, and percentage of wetland conditions have important impacts on water yield services. However, the impacts of these conditions on water yield were various significantly across different watersheds and spatial scales (excluding factors such as precipitation, slope, and percentage of swamp wetland). (2) The spatial pattern of water yield main was affected by land use change and the amount of water yield main was affected by future climate change. (3) Water yield in the Baihe basin was more sensitive to climate change, while the Heihe basin is more sensitive to land use changes. Wetland ecological restoration strategies can effectively improve regional water yield, especially in the Heihe basin. (4) In all scenarios, the southeast regions of the study region were both the main hot spots of water yield. Wetland ecological restoration strategies have added hot spots in the north regions of the Baihe river basin, reduced hot spots in the middle-south regions of the Heihe basin. [ABSTRACT FROM AUTHOR]

[Display omitted] • We develop a framework to estimate future Flood Regulation Service (FRS) dynamics. • The framework considers the joint impacts of both climate change and urbanization. • Future FRS supply–demand imbalance will continue to intensify. • Future impacts of climate change in Baiyangdian Lake Basin cannot be ignored. • Cities should actively adapt to climate change to reduce flood risks in the future. Estimating the supply–demand relationship's future dynamics and trend of Flood Regulation Service (FRS) is important for preventing flood risks and choose an appropriate future development path. Less attention has been given to assessing the future trends in this relationship under the joint influence of climate change and urbanization. This paper aims to quantify future trends in the FRS supply–demand relationship under the joint impacts of climate change and urbanization. To this end, we took the Baiyangdian Lake Basin as an example and developed a "Climate Change-Urbanization-FRS supply and demand" framework by combining future climate scenarios from the ScenarioMIP (Scenario Model Intercomparison Project) dataset and socioeconomic data from the SSPs (Shared Socioeconomic Pathways) to estimate the FRS supply and demand dynamics during the period from 2020 to 2050. The results show that the framework has a reasonable ability to estimate the FRS supply–demand relationship. The correlation coefficients between estimated supply–demand ratios and the historical values exceeded 0.9. The average FRS supply–demand ratio among the four scenarios shows a significantly downward trend between 2020 and 2050. The FRS is expected to decrease by 0.159 (36.6 %), resulting in the imbalanced area increasing from 38.3 km2 (4.1 % of the whole basin) to 11049.7 km2 (36.2 %). We believe that although urbanization is the dominant factor contributing to the intensified imbalance trend (with a contribution of 71.2 %), influences from climate change also cannot be ignored (with a contribution of 28.8 %). Therefore, adaptation actions to climate change should be actively taken to reduce urban flood risks in the future. [ABSTRACT FROM AUTHOR]