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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]

• Literature review papers related to the tourism-energy nexus are analyzed. • Four main groups are identified considering the quantitative methodology used. • Topics and discussions on the nexus between tourism and energy are developed. • Future research is discussed. The high dependence of tourism activities on energy consumption, and particularly on non-renewable energy, is a major barrier to building sustainable tourism models. For this reason, the analysis of tourism and energy use is a growing field of study within sustainability debates. Despite this, there is a gap in the literature and, to the best of our knowledge, there is no previous study that critically reviews the vast literature on the energy-tourism nexus. This paper aims to fill this gap in tourism knowledge. The main objective of this article is to conduct a systematic review of scientific studies with a quantitative empirical basis that examine this nexus, trying to answer the following questions: What are the main methodologies used to approach the tourism-energy nexus? which indicators or variables are relevant?; is there a consensus on the results?; and what are the main lines of discussion on the use of energy in tourism? To this end, we particularly focus on the main methodologies, results and perspectives during the period 2001–2021. A total of 1,189 articles were reviewed by abstract and title, 236 were included for the full review and 163 were finally selected. The results show how the nexus and energy dependence of tourism is assessed mainly from four major methodological groups: G1) econometric models, regressions and other statistical methodologies (67% of the sample); G2) surveys, questionnaires and energy audits (15%); G3) Life Cycle Analysis (13%) and G4) other approaches and environmental impact indicators to assess the relationship between energy and tourism (5%). The analysis between the causal relationships between tourism development, economic growth, energy use, environmental degradation and energy efficiency of accommodations are the main issues evaluated, considering a wide range of indicators/variables. Based on the gaps and limitations found in the literature review on tourism and energy, it is discussed how some findings should be interpreted with caution due to their methodological approaches and differences. Finally, some of the main lines of future research in this field are also presented. [ABSTRACT FROM AUTHOR]

• This paper aims at quantifying the factors affecting vegetation growth in the western of Inner Mongolia, and judging the relative contribution of natural factors and man-made factors. • It is obtained through geodetectors (GD) that Rainfall is the most important driving factor, with the q-value of 0.77 and GDP also having a huge impact on vegetation growth, q-value of 0.64. • Different from most studies, human activity also has a positive effect on vegetation growth. • The relative contribution of human activities reached 51.75%, slightly exceeding the influence of natural factors. Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest. [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]

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.

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.

• This paper aimed to investigate the interactions between land use cover (LUC) and climate change (CC). • The results showed that the interactions between LUC and CC are both direct and indirect. • Arable and rangeland use changes affect CO2 emission in both direct and indirect ways. • Rising rangelands and reducing forest areas have indirectly increased surface temperature. • CC mitigation policies should be followed by considering both direct and indirect effects. Humanity deals with several challenges in this century such as climate change, land use, and land use/cover change (LUCC). Determining the patterns, developments, and consequences of LUCC issues for the livelihoods of people, especially poor people, is very important. Therefore, this paper aims to investigate the interactions between LUCC and climate change over the period of 1966–2015 (50 years) as a complex system at the global level. CO 2 emissions and surface temperature are considered as the main indicators of climate change (CC). The data were analyzed in time-oriented (time-based) and local or place-oriented (country-based) manners. The results showed that arable and rangeland use changes (LUC) affect CO 2 emissions in both direct and indirect ways. However, the direct effect of rangeland use change is positive, and its indirect effect is negative. In addition, deforestation has increased CO 2 emissions indirectly. LUCC can also change the ability of the ecosystem to deliver services to people, including biodiversity and other resources such as food, fiber, water, etc. Therefore, it is critical to determine the patterns, trends, and impacts of LUCC on CC. Thus, CC mitigation policies should be followed by considering both direct and indirect effects. Without a doubt, this will be realized when the decision and policymakers have a better understanding of the structure and interaction between CC, LUCC, and their components as a whole system. [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]

• Collections of basic research for urban spatial planning. • Literatures of urban agglomerations, urban size, urban compactness, and urban form on carbon emission. • Urban spatial structure and carbon emissions research from a multi-ecosystem interaction framework. Carbon emission reduction is one of the key factors in global climate change and should be an important component in urban planning and management. A full understanding of the relationship between carbon emissions and urban spatial structure is necessary. This paper presents the research on this relationship from the perspectives of urban agglomeration, urban size, urban compactness, and urban form. The results revealed that: (1) there is still a lack of research on the spatial structures of urban agglomerations and their relationship with carbon emissions, so it is urgent to conduct indicators system design and empirical research; (2) two-dimensional indicators have frequently been used to describe urban form and urban compactness, but three-dimensional indicators, such as volume ratio and floor height, are also important indicators for policymaking; (3) higher urban complexity, irregularity, and fragmentation were believed to contribute to urban carbon emissions because of the lack of continuity and connectivity. This paper also proposes a comprehensive analytical framework for examining urban spatial structures and carbon emissions. We position the research on the relationship between urban spatial structure and carbon emissions as a bridge between human ecosystems and natural ecosystems. [ABSTRACT FROM AUTHOR]

• A single year was divided into three different seasons. • The impact of human activities and climate change on runoff were quantified. • The main factors of runoff changes caused by human activities were analyzed. The Yongding River has ceased to flow due to the impact of human activities and climate change, inducing environmental degradation. Analyzing the driving factors is vital for environmental restoration and water resources allocation, but it is challenging especially at seasonal scale. In this paper, the monthly abcd model was constructed for the Yongding River basin and two sub-basins, namely Yanghe and Sangganhe, to quantify the relative contribution of climate change and human activities to runoff decrease. The results show that the annual runoff in the basin has been declining significantly since the 1980s, up to 30 mm/year. Human activities are the main influencing factors, with 108% contribution in the entire Yongding River basin, 122% and 98% in Yanghe and Sanggan sub-basins, respectively. The impact of human activities on runoff varies in different seasons, which is highest in the wet season, followed by irrigation season and dry season. The impact of human activities is more pronounced in July-September due to water withdrawal for irrigation and operation rules of reservoirs, which is twice as much as that in April-June. A stricter management system can alleviate the impact of human activities on runoff change. [ABSTRACT FROM AUTHOR]

• A general framework for predicting flow and biodiversity changes under future climate change is proposed. • Discharge is consistently on the rise, reaching a maximum in the late 21st century. • Hydrological regimes change significantly and integrated degree of hydrological variation will reach a moderate level. • Shannon Index values show increasing trends and fluvial biodiversity is improved. Scientific evaluation of the eco-hydrological regimes is of great significance for water resource management and aquatic ecosystem protection. The paper took the western route source areas (WRSA) of the South-to-North Water Diversion Project (SNWDP) as the research region. In order to quantify climate-induced eco-hydrological effects, a general framework was proposed to predict changes in flow regimes and fluvial biodiversity under climate change: The coupling of Global Climate Models (GCMs) and the Distributed Time Variant Gain Model (DTVGM) was used to simulate discharge under different climate conditions. Based on Indicators of Hydrologic Alteration (IHA), changes in eco-hydrological regimes were evaluated by using the Range of Variability Approach (RVA), assessment of overall hydrological change degree (D 0), and Shannon index (SI). Changes in discharge, flow regimes, and fluvial biodiversity of WRSA were analyzed during the projected period (2041–2100) compared to the base period (1960–2014). According to the projections, under four SSPs-RCPs scenarios, the future discharge of WRSA increases significantly compared to the base period. D 0 of WRSA is moderately or severely changed, and hydrological changes are mainly reflected in high flows such as monthly median flows during flood season, maximum flows, and high pulse duration. While flow regimes are disturbed, SI values of WRSA keep rising and the biodiversity is improved. The increase of discharge and SI becomes progressively greater with elevated radiation intensity, and the quantity of severely altered indicators of IHA increases. In conclusion, future hydrological alterations in SNWDP are expected to further intensify with higher levels of climate change, and these changes are generally conducive to the improvement of fish biodiversity. [ABSTRACT FROM AUTHOR]

• Tree ring is an ideal indicator of annual total grain production parameters. • Tree growth synchronized with grain production, and concurrent response to climate. • Reconstruction of total grain productivity based on dendrochronology. • To explore the relationship between total grain sown area and climate change. Climate change affects the growth of regional vegetation; both trees and grain crops often change concurrently, such that the annual radial growth of trees shows consistent inter-annual variations with the total grain crop productivity. However, it remains unclear whether they exhibit concurrent responses to climate factors, and that there lacks of the study on long-term high-resolution variations of grain crops productivity. This paper employs a Pinus massoniana tree-ring series from the Tongbai Mountains to analyze the correlations between tree-ring chronology, local climate data and grain productivity indicators (i.e., total sown grain areas (TSA), total grain outputs (TGO), and average grain yield per hectare (YPH)) of Henan Province in central China. The results indicate that temperature in March and August was the main limiting factor on tree growth, and the best concurrent relationship with tree growth was TSA, which has a correlation of 0.747 (p < 0.001) during 1959–2020. Therefore, a 124-year TSA series in Henan Province was reconstructed using tree-ring data from the Tongbai Mountains, which reveals there were two distinct low periods of total grain sown area in the 1920 s-1930 s and 1980 s-2000 s. There are significant cycles of about 2.57a (p < 0.01), 2.89a (p < 0.05), and 10.95a (p < 0.1), indicating that vegetation growth might be affected by large-scale climate forcing, such as ENSO (2-7a cycle) and sunspot activity (11a cycle). Overall, this study outlines a new approach to understand long-term changes in grain production, which is conducive to grain management and socioeconomic sustainability. [ABSTRACT FROM AUTHOR]

• Mudflats conditions in the UK are unfavourable and managing them is a challenge. • Benthic species and shorebirds can be used as indicators of mudflat conditions. • Shannon's diversity index and AMBI results may provide different results. • A lack of monitoring data for UK mudflats makes effective conservation difficult. Intertidal mudflats are highly productive coastal habitats, serving as a transition zone between terrestrial and marine ecosystems and providing a wealth of ecosystem services, including providing nursery and feeding grounds for many species of conservation and commercial importance. These habitats are important for wintering shorebirds and waders, which depend on them for food, nesting, and migration. Their importance was formally recognised in 1971 following the Ramsar Convention on Wetlands of International Importance, yet their conservation status remains largely uncertain as they are difficult to survey, and a complete understanding of their complex biophysical processes has yet to be established. While intertidal mudflats are found all around the UK coastline, there are few routine field surveys of these habitats, making effective conservation in the face of rising anthropogenic and climate pressures challenging. Marine protected areas (MPAs) across the UK list mudflats and sandflats not covered by seawater at low tide as a designation feature separate from saltmarshes, but they are often managed together – where management exists. A lack of resources has resulted in several paper parks around the UK, where MPAs are designated but not adequately monitored or managed. This study explores the use of population data of benthic species and shorebirds as a proxy for temporal changes in mudflat conditions, focusing on two MPAs in the Irish Sea, Strangford Lough in Northern Ireland, and the Solway Firth in Scotland/England. It finds that the two methods together can provide information useful to support MPA management, but this approach is hampered by poor availability of sufficient data. Such data limitation can impact what it means to achieve or maintain favourable conditions for these habitats. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

• Recent research on the B&R eco-environment is reviewed. • The B&R is likely to face environmental challenges due to its fragile background. • Four key aspects of the ecological environment are studied. • Sustainable development should be promoted under socioeconomic frameworks for the B&R. • Future research directions for improving the B&R eco-environments are proposed. The Belt and Road Initiative (BRI) will play an essential role in boosting the world's economy. However, the main portion of the Belt and Road (B&R) is in arid, semi-arid, or sub-humid fragile ecological regions, which with global climate change and the intensification of human activities will face many pressing environmental challenges in the future. Despite this, in the existing research on the B&R, papers in the field of natural sciences are less than 1.2–4.3% of the total number of papers on the main databases, which is far lower than the coverage in humanities and social sciences. And those focused on the ecological and environmental issues along the B&R are even fewer, so necessary understanding of both the risks and adequate mitigation measures remains unclear. This paper systematically reviews the relevant research on the eco-environments along the B&R, and summarizes it in terms including of spatial patterns and cognition, cost efficiency and evaluation, ecological security and evaluation, ecological footprint and carrying capacity. It further suggests that a better understanding of environmental changes and their effects on the B&R is needed. Additionally, it is essential that the potential positive socio-economic impacts for the future under the BRI are sustainable. Finally, future research directions are proposed to improve for the better eco-environments in B&R regions with consideration of global changes. [ABSTRACT FROM AUTHOR]

• Based on the principles of SPEI, we develop a GDAI. • The relationship between GDAI and AI is closer. • The correlation between GDAI and SPEI is also better. Climate change has increased the risk of drought in both arid and non-arid regions of the globe. Therefore, in order to monitor and evaluate global drought and aridity, based on the principle of standardized precipitation evapotranspiration index (SPEI), this paper selected precipitation and potential evapotranspiration data and used a frequency calculation method to develop a multi-scale global drought aridity index (GDAI) that concurrently considers spatial and temporal variation characteristics: the spatiotemporal standardized precipitation evapotranspiration index. The index was compared with the aridity index (AI) and SPEI. The results showed that the correlation coefficients between GDAI and AI were greater than 0.8 in most areas, and the relationship was closer and could reveal the global aridity and wetness zoning. In addition, the correlation between GDAI and SPEI was also better, which could capture the temporal change characteristics of drought. Moreover, this study also compared the grades of GDAI and SPEI with the actual drought grades in Yunnan from September 2009 to August 2010, and found that GDAI was better than SPEI, and GDAI was the most accurately evaluated at the 3 month time scale, with an accuracy rate of 86.11%. Therefore, this index is more helpful for the assessment and monitoring of global drought and aridity. [ABSTRACT FROM AUTHOR]

The high dependence of tourism activities on energy consumption, and particularly on non-renewable energy, is a major barrier to building sustainable tourism models. For this reason, the analysis of tourism and energy use is a growing field of study within sustainability debates. Despite this, there is a gap in the literature and, to the best of our knowledge, there is no previous study that critically reviews the vast literature on the energy-tourism nexus. This paper aims to fill this gap in tourism knowledge. The main objective of this article is to conduct a systematic review of scientific studies with a quantitative empirical basis that examine this nexus, trying to answer the following questions: What are the main methodologies used to approach the tourism-energy nexus? which indicators or variables are relevant?; is there a consensus on the results?; and what are the main lines of discussion on the use of energy in tourism? To this end, we particularly focus on the main methodologies, results and perspectives during the period 2001–2021. A total of 1,189 articles were reviewed by abstract and title, 236 were included for the full review and 163 were finally selected. The results show how the nexus and energy dependence of tourism is assessed mainly from four major methodological groups: G1) econometric models, regressions and other statistical methodologies (67% of the sample); G2) surveys, questionnaires and energy audits (15%); G3) Life Cycle Analysis (13%) and G4) other approaches and environmental impact indicators to assess the relationship between energy and tourism (5%). The analysis between the causal relationships between tourism development, economic growth, energy use, environmental degradation and energy efficiency of accommodations are the main issues evaluated, considering a wide range of indicators/variables. Based on the gaps and limitations found in the literature review on tourism and energy, it is discussed how some findings should be interpreted with caution due to their methodological approaches and differences. Finally, some of the main lines of future research in this field are also presented.

The Yongding River has ceased to flow due to the impact of human activities and climate change, inducing environmental degradation. Analyzing the driving factors is vital for environmental restoration and water resources allocation, but it is challenging especially at seasonal scale. In this paper, the monthly abcd model was constructed for the Yongding River basin and two sub-basins, namely Yanghe and Sangganhe, to quantify the relative contribution of climate change and human activities to runoff decrease. The results show that the annual runoff in the basin has been declining significantly since the 1980s, up to 30 mm/year. Human activities are the main influencing factors, with 108% contribution in the entire Yongding River basin, 122% and 98% in Yanghe and Sanggan sub-basins, respectively. The impact of human activities on runoff varies in different seasons, which is highest in the wet season, followed by irrigation season and dry season. The impact of human activities is more pronounced in July-September due to water withdrawal for irrigation and operation rules of reservoirs, which is twice as much as that in April-June. A stricter management system can alleviate the impact of human activities on runoff change.

Scientific evaluation of the eco-hydrological regimes is of great significance for water resource management and aquatic ecosystem protection. The paper took the western route source areas (WRSA) of the South-to-North Water Diversion Project (SNWDP) as the research region. In order to quantify climate-induced eco-hydrological effects, a general framework was proposed to predict changes in flow regimes and fluvial biodiversity under climate change: The coupling of Global Climate Models (GCMs) and the Distributed Time Variant Gain Model (DTVGM) was used to simulate discharge under different climate conditions. Based on Indicators of Hydrologic Alteration (IHA), changes in eco-hydrological regimes were evaluated by using the Range of Variability Approach (RVA), assessment of overall hydrological change degree (D0), and Shannon index (SI). Changes in discharge, flow regimes, and fluvial biodiversity of WRSA were analyzed during the projected period (2041–2100) compared to the base period (1960–2014). According to the projections, under four SSPs-RCPs scenarios, the future discharge of WRSA increases significantly compared to the base period. D0 of WRSA is moderately or severely changed, and hydrological changes are mainly reflected in high flows such as monthly median flows during flood season, maximum flows, and high pulse duration. While flow regimes are disturbed, SI values of WRSA keep rising and the biodiversity is improved. The increase of discharge and SI becomes progressively greater with elevated radiation intensity, and the quantity of severely altered indicators of IHA increases. In conclusion, future hydrological alterations in SNWDP are expected to further intensify with higher levels of climate change, and these changes are generally conducive to the improvement of fish biodiversity.

Climate change affects the growth of regional vegetation; both trees and grain crops often change concurrently, such that the annual radial growth of trees shows consistent inter-annual variations with the total grain crop productivity. However, it remains unclear whether they exhibit concurrent responses to climate factors, and that there lacks of the study on long-term high-resolution variations of grain crops productivity. This paper employs a Pinus massoniana tree-ring series from the Tongbai Mountains to analyze the correlations between tree-ring chronology, local climate data and grain productivity indicators (i.e., total sown grain areas (TSA), total grain outputs (TGO), and average grain yield per hectare (YPH)) of Henan Province in central China. The results indicate that temperature in March and August was the main limiting factor on tree growth, and the best concurrent relationship with tree growth was TSA, which has a correlation of 0.747 (p

• We proposed a hybrid approach to evaluate the water quality considering input uncertainty. • Proposed model integrated Monte Carlo simulation, CRITC methods and VIKOR. • The proposed model was applied to Songhua river tributary to verify its effectiveness. • The PLUS model has advantages over the other analyzed models. Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessment methods usually do not consider the uncertainty of water quality data in the collection process, which limits the application of these methods. In order to overcome the above shortcomings, this study constructed a water quality assessment method by integrating Monte Carlo method (MC), CRITIC and VIKOR methods, and applied it to assess the quality of water in the Songhua River tributary. Results indicate that: (1) The water quality assessment of the two sampling points in the study area is level III, which is consistent with the actual situation; (2) This method can overcome the uncertainty caused by sampling error and improve the credibility of water quality evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) and ammonia nitrogen (NH 3 -N) are more evaluation factors related to the evaluation results. When the decision coefficient mechanism λ is taken [0.1–0.5], the outcomes are in line with the real quality of the water. In addition, we recommend that the distribution profile generated based on the measured data should obey the probability distribution density curve that decreases from the middle to the tail of both sides. The findings of this paper can provide a scientific basis for decision makers to carry out water quality restoration and management. [ABSTRACT FROM AUTHOR]

[Display omitted] • Landscape of blue carbon research from the perspective of bibliometrics. • Blue carbon ecosystem was the main research area, but macroalgae deserve attention. • Artificial intervention and methods are potential hotspots in the future research. • Statistics, co-occurrence analysis and thematic evolution are main used methods. Blue carbon sequestration is a nature-based solution to environmental problems caused by climate change and has a significant positive impact on the planet and human well-being. This paper bibliometrically and systematically analyzed 2,613 studies found in the Web of Science Core Collection (WoSCC) from 2003 to 2021 to explore the hotspots and trends of blue carbon research using the Bibliometrix package in R studio and VOSviewer software. The results based on network co-occurrence analysis indicated that mangroves, seagrasses, and salt marshes were the major blue carbon ecosystems (BCEs), and mangrove carbon sinks were the most popular central research area. Furthermore, phytoplankton and microalgae have essential roles in the marine carbon cycle and bioenergy utilization, as revealed by the high proportion of published literature, while the carbon transport mechanisms of seagrass and macroalgae was understudied. Thematic evolution analysis revealed that remote sensing was the most commonly used technology as an emerging research field for ecological monitoring and ecological investigation in blue carbon research in recent years. In addition, increasing carbon sinks by artificial approaches, fishery carbon sinks, and carbon sequestration by macroalgae have been potential hotspots for blue carbon research development. The findings from this study shed light on the trends of blue carbon in the past eighteen years, current research points in the field, and areas that need more research investment going forward. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new repeatable batch processing method is developed for regional SUHI assessment. • A framework is proposed to explain the formation of SUHI effect. • The impacts of most driving factors on SUHI effect are spatial heterogeneous. • More attention should be paid to small cities. The urban heat island (UHI) effect has become a hot topic worldwide because of the double stress of global climate change and rapid urbanization. However, the deficiencies of existing methods hinder the monitoring and assessment of the surface urban heat island (SUHI) effect at the regional scale. This paper presented a new method to assess SUHI intensity, and then applied it in China. The results indicated that: (1) compared with other methods, this new method has some merits like higher accuracy, fewer operational steps, and higher portability. It enables accurate, semi-automated assessment of SUHI intensity at the regional scale. (2) SUHI effect exists in 81.02% of the cities in China. These cities were mainly concentrated in southeastern China, which presented the significant characteristic of spatial agglomeration. (3) Urban-rural differences in relative humidity, vegetation coverage, evapotranspiration, waterbody coverage, and the number of industrial enterprises are the main driving factors for the SUHI effect in China. Furthermore, there are significant interactions among these driving factors. (4) The spatial heterogeneity of the influence directions and intensities of many driving factors is obvious. Therefore, traditional regression models should be utilized more cautiously because they cannot capture the spatial heterogeneity of variables. (5) The hypothesis of "the larger the city size, the higher the SUHI intensity" is not valid, so more attention should be paid to small cities in the future. [ABSTRACT FROM AUTHOR]

Blue carbon sequestration is a nature-based solution to environmental problems caused by climate change and has a significant positive impact on the planet and human well-being. This paper bibliometrically and systematically analyzed 2,613 studies found in the Web of Science Core Collection (WoSCC) from 2003 to 2021 to explore the hotspots and trends of blue carbon research using the Bibliometrix package in R studio and VOSviewer software. The results based on network co-occurrence analysis indicated that mangroves, seagrasses, and salt marshes were the major blue carbon ecosystems (BCEs), and mangrove carbon sinks were the most popular central research area. Furthermore, phytoplankton and microalgae have essential roles in the marine carbon cycle and bioenergy utilization, as revealed by the high proportion of published literature, while the carbon transport mechanisms of seagrass and macroalgae was understudied. Thematic evolution analysis revealed that remote sensing was the most commonly used technology as an emerging research field for ecological monitoring and ecological investigation in blue carbon research in recent years. In addition, increasing carbon sinks by artificial approaches, fishery carbon sinks, and carbon sequestration by macroalgae have been potential hotspots for blue carbon research development. The findings from this study shed light on the trends of blue carbon in the past eighteen years, current research points in the field, and areas that need more research investment going forward.

[Display omitted] • Visualization and quantitative analysis of the global ecological carbon sinks and sources. • Enhance the prediction of carbon sequestration dynamics under climate change. • Climate change potentially affecting the carbon-climate feedback. • Research content is becoming clearer and more systematic. • Keyword clustering the classification of the research into six relatively independent subtopics. Climate change is extensively affecting the global ecosystem, especially the ecosystem carbon sequestration and sequestration potential, which are issues of global concern. The proposed concept of "carbon neutrality" in 2020 has brought ecological carbon sequestration to the forefront. Therefore, the research on climate change and ecosystem carbon sequestration need to be systematically reviewed, summarized, and examined. Based on the Web of Science database, this paper analyzed 4005 articles (1992–2022 year) after rigorous screening via a bibliometrics analysis and then presented the research trends and future research focus of ecological carbon sequestration. The following conclusions are drawn: (1) the research over the last 30 years has steadily improved, and the annual number of publications has increased in a cubic polynomial fashion (R 2 = 0.9937), with 87.57 % of the total publications appearing after 2009. Global Change Biology and Science of the Total Environment are the most influential journals in the field; (2) participation in such research is becoming increasingly common with expanding research areas covering Eurasia, America, Oceania, and Africa. The United States and China are the most productive and influential countries; (3) the diversity of this research is increasing, and the research content is becoming more explicit and systematic. Most research focuses on climate change, carbon sequestration, management, land-use changes, and nitrogen and soil organic carbon; (4) although traditional ecological evaluation techniques were essential early on, remote sensing and modeling have become the primary methods for assessment; (5) keyword clustering allows the classification of the research into six relatively independent subtopics: climate change and terrestrial ecosystems, sequestration and organic-carbon, ecosystem services, nitrogen and carbon cycling, biomass and forest, and blue carbon, which provides a reference for further research on ecological carbon sequestration and carbon neutrality. [ABSTRACT FROM AUTHOR]

[Display omitted] • Current land management can lead to more severe BAP conditions. • Regulating fertilizer application rate can offset HAB risks from extreme climate. • Regulating P was more effective in reducing HAB risks in freshwater watersheds. • Regulating N was more effective in controlling HAB downstream of watershed. This paper was aimed at assessing the risk of HAB occurrence in a highly cultivated watershed and evaluating the effectiveness of N and P fertilizer reduction practices in mitigating this risk under future climate projections. To achieve this objective, a modelling framework was developed for HAB risk assessment under different environmental stressors including climate changes and different land management practices. The modelling framework used a fully distributed physical-based hydrologic model, MIKE SHE, and a hydrodynamic river model, MIKE 11, coupled with ECO-Lab to simulate the fate and transport of different forms of N and P in the Upper Sangamon River Basin (USRB), a highly cultivated watershed in central Illinois. Three different land management scenarios based on the N and P reduction target in "The Gulf Hypoxia Action Plan" were simulated to assess the P nonpoint source behavior and HAB risk level in the USRB from 2020 to 2050 under extreme climate projections. Our results predicted that the HAB risk in USRB is relatively low even with increased bioavailable P (BAP) concentration and longer duration with high BAP concentration due to low water temperature. Reducing N or P application rates by 50% was able to offset the negative effects on HAB occurrence from extreme climate projections apart from spring. Within P and N, regulating P was found to be more effective in reducing HAB risks in the USRB while regulating N was more effective in controlling HAB downstream. Understanding the dynamics of N and P in causing the occurrence of HAB was expected to improve the mitigation measures that we implement at the field level. [ABSTRACT FROM AUTHOR]

• Cropland Reference Ecological Unit (CREU) is a landmass unit that accounts for soil and climate variabilities. • CREU covers an area with presumably similar soil health potential. • A robust comparison of soils at different sites and management can be determined under the framework of CREU. There is a growing consensus on a need for measuring the dynamic soil properties of croplands and even comparisons with a reference state or native land. These measurements and paired comparisons will create the capacity to determine soil health management effects and targets. However, the complex soil heterogeneity and climate variations make soil health potential variable and confound the effects of land-use and management practices and comparisons between soils from different sites. Identifying a discrete landmass unit where all soils have similar health potential will be critical in conducting meaningful comparative studies and measuring the impact of conservation practices. This methodological paper proposes and discusses a land unit, Cropland Reference Ecological Unit (CREU), that accounts for soil genoform and climate variabilities and covers an area with a presumably similar soil health potential. An example CREU has been developed, for one Major Land Resource Area (MLRA) in Nebraska, which is an area delineated based on the standard United States Department of Agriculture – Natural Resources Conservation Service (USDA-NRCS) hierarchical land classification system. This example portrays an actual difference in soil health for different land use and agronomic management practices can be determined by comparing sites under the framework of CREU. Evaluation of management effects on soil health indicators in a CREU will adequately illustrate the beneficial impact of such practices without being confounded by agroecological variations. This proposed framework addresses researchers' current interest in comparing soil health parameters among croplands and reference sites to benchmark soil health measurements, set soil health targets, and determine the effects of different management practices. [ABSTRACT FROM AUTHOR]

• A regional green development index system is established based on the "driver-pressure-state-impact-response" model. • The level of regional green development in Hubei, China, is rising, and the gap between regions is narrowing. • The inter-regional spillover effect of green development is greater than the intra-regional spillover effect. • Technological innovation and the living environment have a positive spillover effect on green development. Green development strives to harmonize economic and social development with environmental protection. It is an innovative development model for people to follow in response to global climate change. This paper first combines the "driver-pressure-state-impact-response" (DPSIR) model with the entropy weighting method to measure the level of green development in Hubei Province from 2010 to 2018. Then, the spatial Durbin model (SDM) is used to analyse in depth the spatiotemporal evolutionary pattern and spatial spillover effects. The results indicate that after 2014, the levels of green development increased significantly and that regional differences gradually shrank. ρ = 0.409 indicates that the green development level has a positive spatial spillover effect, and the spillover effect between regions is greater than that within a region. The spatial spillover effects of the industrial structure and growth efficiency are −2.17 and −8.64, respectively, while the spatial spillover effects of technological innovation and the living environment are 7.35 and 0.32, respectively. Based on our findings, we provide several policy recommendations for developing countries to achieve green, coordinated and sustainable development. [ABSTRACT FROM AUTHOR]

[Display omitted] • As compared to precipitation, soil moisture greatly influences GPP. • There is a solid spatial agreement among PUE and SWUE with a step-wise gradient from southwest to northeast. • PUE and SWUE are relatively low in extremely arid and humid regions. • In relatively moderate areas, PUE and SWUE increase with the abundance of moisture conditions. • Meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Grassland ecosystem dominates in the Mongolian Plateau, mostly located in its arid and semi-arid regions. Although the ecosystem is an important source for agriculture, it is also a fragile system ecologically. This system is one of the most sensitive areas to global climate change. Precipitation (PPT) and soil moisture (SM) are important water sources in the grassland ecosystem, and their changes would greatly affect vegetation growth. This paper generates the precipitation use efficiency (PUE) and soil water use efficiency (SWUE) of Mongolian Plateau grassland based on multi-source remote sensing data to investigate the spatio-temporal distribution pattern and identify the driving factors. Results showed four main findings. Firstly, two water use efficiency (WUE) indicators show a generally increasing trend from 2000 to 2018, with average PUE and SWUE 1.07 gC·m−2·mm−1 and 1.03 gC/kg·H 2 O, respectively. They have similar spatial distribution patterns, consistent with the available water resources, decreasing from northeast to southwest. However, grassland vegetation growth is more sensitive to soil moisture than precipitation, and the dynamic change of SWUE is smoother and more significant than PUE. Secondly, due to the higher species richness, better vegetation biological characteristics and less severe growth environment, meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Thirdly, PUE and SWUE are relatively low in extremely arid and humid regions. In areas with relatively moderate water conditions (PPT in 148–360 mm, SM in 0.14–0.35 cm3/cm3), two indicators increase with the abundance of moisture conditions and reach the maximum. Fourthly, there is a positive linear relationship between PUE (SWUE) with precipitation and a unimodal correlation between PUE (SWUE) with temperature across the entire grassland. However, a varying correlation exists in different grassland ecosystems, especially meadow grassland. Together with analyzing past and future trends, this study provides strong evidence to reflect the impact of global climate change and the management and protection of the grassland ecosystems in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Past LULC changes can be divided into two stages: (I) accelerated evolution stage (1980–2010), and (II) stable evolution stage (2010–2020). • Carbon storage presented a high-low-high distribution pattern from north to south. • Areas with a rapid urbanization process exhibited more obvious changes in carbon storage dynamics. • Rapid LULC dynamics have reduced the carbon sequestration capacity of the ecosystem. • The implementation of ecological conservation could increase the carbon fixation in ecosystems. Land use/land cover (LULC) change has greatly altered ecosystem carbon storage capacity and can eventually profoundly impact global climate change. Characterizing the LULC change and its impact on ecosystem carbon storage in coastal areas is greatly significant to comprehensively understanding the influences of human activities on ecosystems. Based on LULC data, this paper combined CA-Markov and InVEST models to evaluate the past, present, and future LULC change and its impact on ecosystem carbon storage in coastal areas of China. The results showed that past LULC change in coastal areas can be divided into two stages: (I) accelerated evolution stage (1980–2010), and (II) stable evolution stage (2010–2020). Changes in LULC types have led to a downward trend in ecosystem carbon storage, with a cumulative loss of 0.39 Pg and 0.15 Pg during the two stages, respectively. Spatially, carbon storage presented a high-low-high spatial distribution pattern from north to south. Temporally, areas with a rapid urbanization process exhibited more obvious changes in carbon storage dynamics. Compared with the natural change scenario, the areas with LULC types conversion under the ecological conservation scenario in 2050 will be lower due to the limitation of ecological protection, and it caused carbon storage reduction will slow down. Rapid LULC dynamics, that a large area of farmland, unused land and, water have been converted into construction land, reduced the carbon sequestration capacity of the ecosystem. This study will provide reliable references and precise data support for coastal management and decision-making. [ABSTRACT FROM AUTHOR]

• The relationship between the groundwater depth and grassland ecology was analysed; • Groundwater depth affected the composition and diversity of vegetation species; • A reasonable groundwater depth can buffer the impact of climate change on the vegetation to maintain grassland ecological stability; • The control of grassland degradation should include the potential exploitation of groundwater. Grassland degradation in the semiarid area of China is often attributed to overgrazing and expansion of irrigated agriculture without the consideration of groundwater. However, falling groundwater levels in the region have caused many problems for grassland ecology. In this paper,the typical sandy grassland- the Tongliao part of Horqin grassland was selected as the study area to study the spatial distribution of normalized difference vegetation index (NDVI) and the relationship between the NDVI and groundwater depth (GD) of each grassland pixel was studied. The temporal variation of the NDVI and its correlations with precipitation were investigated. The relationship between GD and vegetation species diversity in grassland was analyzed based on the vegetation survey. The results of the relation between GD and NDVI and vegetation species showed that GD can buffer the impact of increase in drought events on the vegetation because GD affected the composition and diversity of vegetation species. As the GD increases, the mean NDVI showed a trend of increasing first and then decreasing, and basically remained unchanged when the GD was greater than 3.5 m. The species diversity of grassland decreased with increasing GD and was not related to GD when GD was greater than 3.5 ∼ 4 m. Falling groundwater levels will cause great changes in the structure of grassland; specifically, azonal vegetation will gradually die and 3.5 m may be the critical GD needed to maintain the incomplete extinction of grassland azonal vegetation in the aeolian sandy soil. Correlation analysis proved that the correlation between NDVI and precipitation was significantly enhanced when GD increased sharply from 3 m to 6 m after 1999. This research indicates that the control of grassland degradation should pay attention to controlling the groundwater exploitation to recover a reasonable groundwater level, which was found to be necessary in the support of diverse and climate resilient of vegetation. [ABSTRACT FROM AUTHOR]

Climate change is extensively affecting the global ecosystem, especially the ecosystem carbon sequestration and sequestration potential, which are issues of global concern. The proposed concept of “carbon neutrality” in 2020 has brought ecological carbon sequestration to the forefront. Therefore, the research on climate change and ecosystem carbon sequestration need to be systematically reviewed, summarized, and examined. Based on the Web of Science database, this paper analyzed 4005 articles (1992–2022 year) after rigorous screening via a bibliometrics analysis and then presented the research trends and future research focus of ecological carbon sequestration. The following conclusions are drawn: (1) the research over the last 30 years has steadily improved, and the annual number of publications has increased in a cubic polynomial fashion (R2 = 0.9937), with 87.57 % of the total publications appearing after 2009. Global Change Biology and Science of the Total Environment are the most influential journals in the field; (2) participation in such research is becoming increasingly common with expanding research areas covering Eurasia, America, Oceania, and Africa. The United States and China are the most productive and influential countries; (3) the diversity of this research is increasing, and the research content is becoming more explicit and systematic. Most research focuses on climate change, carbon sequestration, management, land-use changes, and nitrogen and soil organic carbon; (4) although traditional ecological evaluation techniques were essential early on, remote sensing and modeling have become the primary methods for assessment; (5) keyword clustering allows the classification of the research into six relatively independent subtopics: climate change and terrestrial ecosystems, sequestration and organic-carbon, ecosystem services, nitrogen and carbon cycling, biomass and forest, and blue carbon, which provides a reference for further research on ecological carbon sequestration and carbon neutrality.

Grassland degradation in the semiarid area of China is often attributed to overgrazing and expansion of irrigated agriculture without the consideration of groundwater. However, falling groundwater levels in the region have caused many problems for grassland ecology. In this paper,the typical sandy grassland- the Tongliao part of Horqin grassland was selected as the study area to study the spatial distribution of normalized difference vegetation index (NDVI) and the relationship between the NDVI and groundwater depth (GD) of each grassland pixel was studied. The temporal variation of the NDVI and its correlations with precipitation were investigated. The relationship between GD and vegetation species diversity in grassland was analyzed based on the vegetation survey. The results of the relation between GD and NDVI and vegetation species showed that GD can buffer the impact of increase in drought events on the vegetation because GD affected the composition and diversity of vegetation species. As the GD increases, the mean NDVI showed a trend of increasing first and then decreasing, and basically remained unchanged when the GD was greater than 3.5 m. The species diversity of grassland decreased with increasing GD and was not related to GD when GD was greater than 3.5 ∼ 4 m. Falling groundwater levels will cause great changes in the structure of grassland; specifically, azonal vegetation will gradually die and 3.5 m may be the critical GD needed to maintain the incomplete extinction of grassland azonal vegetation in the aeolian sandy soil. Correlation analysis proved that the correlation between NDVI and precipitation was significantly enhanced when GD increased sharply from 3 m to 6 m after 1999. This research indicates that the control of grassland degradation should pay attention to controlling the groundwater exploitation to recover a reasonable groundwater level, which was found to be necessary in the support of diverse and climate resilient of vegetation.

• The net primary productivity (NPP) significantly increased in marshes of study area. • Summer daytime and nighttime temperatures had an asymmetric effect on marsh NPP. • Increased precipitation in summer and autumn can enhance the NPP of marsh vegetation. The western Songnen Plain has a large area of marshes that are able to modulate the regional carbon cycle. Net primary productivity (NPP) is an important ecological indicator of carbon storage within the marsh ecosystems. Although climate change could have significantly changed marsh vegetation NPP during the past decades, the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh vegetation NPP remain unclear in the western Songnen Plain. In this paper, we assessed the spatiotemporal variation in the NPP of marshes at the western Songnen Plain and evaluated its response to climate change from 2000 to 2020. The NPP of marshes over the western Songnen Plain increased significantly at a rate of 5.69 g C/m2/a during 2000 to 2020, with an average value of about 273.62 g C/m2/a. The largest increase of the NPP of marshes was mainly distributed in the northwest part of the Songnen Plain. Correlation analysis results showed that the increased precipitation in summer and autumn could enhance the NPP of marsh vegetation. By contrast, the increase of mean temperature in summer reduced the NPP of marshes. In addition, we first found the asymmetric impacts of summer minimum temperature (T min) and maximum temperature (T max) on marsh vegetation NPP. Notably, the increase in summer T max could significantly reduce NPP, while the increase in T min could increase NPP. Under the background of global asymmetric warming of daytime and nighttime temperatures, more attention should be paid to the different effects of T max and T min on marsh NPP in the western Sonegnen Plain. The findings of this study can provide theoretical guidance for ecological protection and the adaptive management of marsh vegetation at the western Songnen Plain. [ABSTRACT FROM AUTHOR]

• We analyse the research progress of ecosystem service cascade in recent ten years. • We discuss the advantages and disadvantages of different ecosystem service research frameworks. • We look forward to the future development of ecosystem service cascade. Ecosystem service cascade (ESC) links each component of ecosystem services with social value, building an interdisciplinary research bridge between natural science and social science, which can help decision-makers better integrate the concept of ecosystem services into decision-making. This paper combed the content of the concept of ecosystem services cascade effect, reviewed the research progress of the cascade effect found: ESC due to its structure feature and flexibility, can help researchers to more fully understand the concept of ecosystem services to promote interdisciplinary communication, constantly adjust the structure of the ESC and scope in order to improve the applicability of the model in different research. ESC is widely used in ecosystem service mapping and regional policy regulation. Through literature review, We get the following understanding ①The purpose of research often determines the definition of services and policy preferences, a clear definition of the system will improve the applicability of the ESC framework. ②Assess the wishes and connections of stakeholders and quantify the forms of human participation and impact on each component in the cascade framework, clarify the development characteristics and driving mechanism of Human -Natural relationship, identify the feedback process and quantify the positive and negative feedback cycle.③Quantitatively simulate the impact and regulation of climate change and socio-economic development on regional development under different regional development scenarios. Construct the optimization index system of ESC components to provide schemes and Countermeasures for regional sustainable development. [ABSTRACT FROM AUTHOR]

The western Songnen Plain has a large area of marshes that are able to modulate the regional carbon cycle. Net primary productivity (NPP) is an important ecological indicator of carbon storage within the marsh ecosystems. Although climate change could have significantly changed marsh vegetation NPP during the past decades, the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh vegetation NPP remain unclear in the western Songnen Plain. In this paper, we assessed the spatiotemporal variation in the NPP of marshes at the western Songnen Plain and evaluated its response to climate change from 2000 to 2020. The NPP of marshes over the western Songnen Plain increased significantly at a rate of 5.69 g C/m2/a during 2000 to 2020, with an average value of about 273.62 g C/m2/a. The largest increase of the NPP of marshes was mainly distributed in the northwest part of the Songnen Plain. Correlation analysis results showed that the increased precipitation in summer and autumn could enhance the NPP of marsh vegetation. By contrast, the increase of mean temperature in summer reduced the NPP of marshes. In addition, we first found the asymmetric impacts of summer minimum temperature (Tmin) and maximum temperature (Tmax) on marsh vegetation NPP. Notably, the increase in summer Tmax could significantly reduce NPP, while the increase in Tmin could increase NPP. Under the background of global asymmetric warming of daytime and nighttime temperatures, more attention should be paid to the different effects of Tmax and Tmin on marsh NPP in the western Sonegnen Plain. The findings of this study can provide theoretical guidance for ecological protection and the adaptive management of marsh vegetation at the western Songnen Plain.

• Climate-resilient agriculture (CRA) aims to address hunger and poverty. • We provided six principles for the selection of assessment indicators for CRA. • Developed a framework in quantifying the climate-resilient agriculture. • The assessment framework combined four dimensions and has 71 evaluation indicators. • The framework provides a quantitative tool to evaluate CRA in various scenarios. Climate change threatens agricultural production and leads to an increasing contradiction between food supply and needs. Greenhouse gas (GHG) emissions from agricultural activities will exacerbate climate change in the future. Solving food security has become a major challenge around the world. The development of climate-resilient agriculture (CRA) is critical to address or slow climate change and is also helpful to improve agricultural quality and efficiency. Based on a review of previous studies and a deep understanding of CRA goals, this paper introduced a comprehensive assessment framework to evaluate CRA. This assessment framework combined four dimensions, i.e., agricultural productivity, farmer income, climate adaptability, and the green development level, and has 71 evaluation indicators. The highlights of this framework are to analyze the impacts of climate change on agricultural development and to stress the synergy of mitigation and adaptation strategies. All indicators represent various factors related to the development of CRA and have been used in previous studies. Therefore, the comprehensive assessment framework can be used as an evaluation tool for analyzing the changes in agroecosystem resilience under climate change, which is also helpful to implement scientific management to ensure sustainable agricultural development and food security. [ABSTRACT FROM AUTHOR]

• VFC is consistent with the abrupt change time of precipitation and humidity index. • VFC began to decrease in 1999, and the impact of human activities gradually increased. • MI and NL are the variables that cannot be eliminated in the regression equation. • The climate livestock index can reflect the environmental carrying capacity of local grassland. Climate change and human activities are critical to fractional vegetation cover (FVC). However, until now, their combined effects on FVC have not been well quantified. In this paper we analyze the temporal and spatial variations in the fractional vegetation cover in the Hulun Lake region from 1986 to 2017 and its response to changes in climate parameters and human activities; additionally, the impact of changes in climate parameters and human activities on FVC are discussed. The results showed that according to the Mann-Kendall (M−K) trend test analysis, 65.01% of the FVC in the Hulun Lake region decreased to varying degrees, of which 24.55% showed a significant decrease and only 8.61% increased significantly. Based on the M−K mutation test analysis of FVC and analysis of driving factors from 1986 to 2017, 1999 was the time point at which abrupt changes in FVC, annual precipitation and moisture index occurred in the Hulun Lake region, and water was an important factor affecting FVC. The regression analysis of FVC and the driving factors from 1986 to 2017 showed that the moisture index and the number of livestock could not be eliminated from the regression equation. Additionally, there was a significant correlation between the FVC and the climate livestock index (P < 0.001). Linear regression model constructed by FVC and climatic livestock index: y = 23.016ln(x)-31.454 (R2 = 0.5941), FVC increased with the increase of climatic livestock index. [ABSTRACT FROM AUTHOR]

[Display omitted] • Environmental monitoring is crucial for protecting human and ecosystem health. • Honey bees (Apis mellifera) can serve as global, continuous biomonitoring species. • Colonies are resilient and accumulate or respond to stressors without collapsing. • Long-range foragers transport pollutants, pesticides, and pathogens to their hives. • A. mellifera may serve to monitor emerging threats such as climate change and AMR. Monitoring the environment for pollution, pesticides, and pathogens is crucial for protecting human, agriculture, and overall ecosystem health. Diverse strategies ranging from physical sensors to sentinel species have been used for environmental monitoring. The European honey bee, Apis mellifera , is a globally managed pollinator that can serve as a continuous biomonitoring species. During foraging, honey bees are exposed to contaminants and pathogens and carry them to their hives where they can be detected and quantified. Although individual bees are vulnerable to environmental stressors, the honey bee colony as a whole is more resilient and can accumulate contaminants or respond to them without collapsing. This allows for long-term monitoring of the colony to map contaminants in a geographical area and study ecotoxicology gradients over space and time. In this paper, we review demonstrated and proposed uses of honey bees for environmental monitoring. We focus our discussion on heavy metals, air pollutants, pesticides, and plant pathogens that can be detected in bees and their hive materials including honey, wax, and stored pollen. We present the use of gene expression, microbiome profiling, and other high-throughput methodologies to study dose-dependent exposure and increase detection sensitivity; for example, stored pollen analysis with next generation sequencing can reveal the presence of plant viruses, fungi, and invasive species earlier than traditional detection methods. Finally, we discuss opportunities for using honey bees to monitor emerging threats such as climate change and antimicrobial resistance. This narrative review highlights the versatility and potential utility of the European honey bee as a biomonitoring species for ecosystem health. [ABSTRACT FROM AUTHOR]

Climate change threatens agricultural production and leads to an increasing contradiction between food supply and needs. Greenhouse gas (GHG) emissions from agricultural activities will exacerbate climate change in the future. Solving food security has become a major challenge around the world. The development of climate-resilient agriculture (CRA) is critical to address or slow climate change and is also helpful to improve agricultural quality and efficiency. Based on a review of previous studies and a deep understanding of CRA goals, this paper introduced a comprehensive assessment framework to evaluate CRA. This assessment framework combined four dimensions, i.e., agricultural productivity, farmer income, climate adaptability, and the green development level, and has 71 evaluation indicators. The highlights of this framework are to analyze the impacts of climate change on agricultural development and to stress the synergy of mitigation and adaptation strategies. All indicators represent various factors related to the development of CRA and have been used in previous studies. Therefore, the comprehensive assessment framework can be used as an evaluation tool for analyzing the changes in agroecosystem resilience under climate change, which is also helpful to implement scientific management to ensure sustainable agricultural development and food security.

Monitoring the environment for pollution, pesticides, and pathogens is crucial for protecting human, agriculture, and overall ecosystem health. Diverse strategies ranging from physical sensors to sentinel species have been used for environmental monitoring. The European honey bee, Apis mellifera, is a globally managed pollinator that can serve as a continuous biomonitoring species. During foraging, honey bees are exposed to contaminants and pathogens and carry them to their hives where they can be detected and quantified. Although individual bees are vulnerable to environmental stressors, the honey bee colony as a whole is more resilient and can accumulate contaminants or respond to them without collapsing. This allows for long-term monitoring of the colony to map contaminants in a geographical area and study ecotoxicology gradients over space and time. In this paper, we review demonstrated and proposed uses of honey bees for environmental monitoring. We focus our discussion on heavy metals, air pollutants, pesticides, and plant pathogens that can be detected in bees and their hive materials including honey, wax, and stored pollen. We present the use of gene expression, microbiome profiling, and other high-throughput methodologies to study dose-dependent exposure and increase detection sensitivity; for example, stored pollen analysis with next generation sequencing can reveal the presence of plant viruses, fungi, and invasive species earlier than traditional detection methods. Finally, we discuss opportunities for using honey bees to monitor emerging threats such as climate change and antimicrobial resistance. This narrative review highlights the versatility and potential utility of the European honey bee as a biomonitoring species for ecosystem health.