The decision maker's lament: If I only had some science!

Environmental decision makers lament instances in which the lack of actionable science limits confident decision-making. Their reaction when the needed scientific information is of poor quality, uninformative, unintelligible, or altogether absent is often to criticize scientists, their work, or science in general. The considerations offered here encourage decision makers to explore alternative approaches to alleviate their disappointment. Ironically, many researchers lament the lack of support for the science they wish to deliver and accuse decision makers of failing to realize the value of the scientific studies they propose. Both communities would benefit by remembering that producing actionable science for a pending decision requires knowing the context for that decision beforehand. They may also look inward. Only then will they find answers to the question: What can I do within my own capacity to ensure that the necessary actionable science becomes available and facilitate its use to inform decisions?

Collaboration and engagement with decision-makers are needed to reduce evidence complacency in wildlife management

There exists an extensive, diverse, and robust evidence base to support complex decisions that address the planetary biodiversity crisis. However, it is generally not sought or used by environmental decision-makers, who instead draw on intuition, experience, or opinion to inform important decisions. Thus, there is a need to examine evidence exchange processes in wildlife management to understand the multiple inputs to decisions. Here, we adopt a novel approach, fuzzy cognitive mapping (FCM), to examine perceptions of individuals from Indigenous and Western governments on the reliability of evidence which may influence freshwater fisheries management decisions in British Columbia, Canada. We facilitated four FCM workshops participants representing Indigenous or Western regulatory/governance groups of fisheries managers. Our results show that flows of evidence to decision-makers occur within a relatively closed governance network, constrained to the few well-connected decision-making organizations (i.e., wildlife management agencies) and their close partners. This implies that increased collaboration (i.e., knowledge co-production) and engagement (i.e., knowledge brokerage) with wildlife managers and decision-makers are needed to produce actionable evidence and increase evidence exchange.

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 μg/m3 to 73 μg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Revisiting the EKC framework concerning COP-28 carbon neutrality management: Evidence from Top-5 carbon embittering countries

In the aftermath of the 28th Conference of the Parties (CoP) climate summit in the UAE, the majority of developing countries encounter challenges in attaining their objectives of carbon neutrality for a sustainable economy. The association of economic factors such as economic growth, governance structures, forest area, renewable energy consumption, technological innovation, and urbanization with environmental elements (carbon footprint) is vital for sustainable economic development and environmental management strategies. Therefore, this research reveals this association in five selected high-emitting countries spanning from 1990 to 2022. This research utilizes the Environmental Kuznets Curve (EKC) framework to investigate the interrelationship between these variables. To do so, this study employs the cross-sectional autoregressive distributed lags (CS-ARDL) statistical technique to determine the short- and long-term impacts of the variables under investigation on carbon footprint. In contrast, the mean group (MG) and common correlated effect mean group (CCEMG) have been applied for robustness. The findings revealed that GDP, urbanization, and forest area have positive associations with carbon footprints, whereas GDP square, renewable energy consumption, technological innovation, and governance effectiveness have inverse relationships with carbon footprints. These findings provide all stakeholders with valuable policy recommendations and management advice for accelerating the transition of renewable energy to low-carbon and green growth.

Coincidence of sustainable development indicators for the nekarood watershed with the United Nation's sustainable development goals

The present study evaluated sustainable development indicators of the Nekarood Watershed in Iran using global Sustainable Development Goals (SDGs) indicators. Identifying values and thresholds, justification of optimum values, ranking, and performance assessment of the associated SDGs' indicators were accordingly addressed. Moreover, due to a lack of complete information and ecological conditions, 77 indicators were selected based on compliance with the indicators of global SDGs. These indicators were used to evaluate the development situation of the study area. The indicators were then normalized, weighed, aggregated, and ranked into four categories from achieving to highly challenging. The results showed that among the SDGs, five goals performed above the mean of the global sustainable development goals. So, goals 3 and 1 had the highest performance of 69.82 and 57.97 %, respectively. Likewise, goals 7 and 16 showed the lowest performance beyond the global average of 51.08 and 54.62 %, respectively. SDG3-1-1, SDG3-1-2, SDG3-2-1, and SDG3-2-2 indicators with 100 % performance positively affected SDG3. SDG1-5-1 indicator with 100 % performance also had the most positive effect on SDG1. The performances of nine goals were also lower than the global mean. In this case, the lowest performance was assigned to goal 2, followed by goals 9, 17, 10, and 6, respectively, with 15.24, 19.71, 22.19, 24.98, and 45.78 %. SDG2-4-2, SDG2-4-1, and SDG2-3-1 indicators had the most negative effect on the performance of SDG2. SDG9-2-2 and SDG10-4-1 indicators also had the most negative effect on goals 9 and 10, respectively. The highest performance of the indicators was associated with the Ministry of Health and Medical Education, and the lowest was related to the Ministries of Agriculture Jihad and the Ministry of Industry, Mine, and Trade. The results of the present study verified an overall performance of 36.42 % for the Nekarood Watershed concerning the global SDGs, representing significantly challenging conditions.

Prediction of air pollutant concentrations based on the long short-term memory neural network

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

In search for climate neutrality in ice hockey: A case of carbon footprint reduction in a Finnish professional team

Mitigation actions in all sectors of society, including sports, to limit global warming have become an increasingly hot topic in public discussions and sports management. However, so far, there has been a lack of understanding and practical examples of how these organizations, especially in team sports, can holistically assess and reduce their climate impacts to achieve carbon neutrality. This paper presents a carbon footprint assessment, implemented actions for GHG emission reduction, and offers the example of a professional Finnish ice hockey team that achieved carbon neutrality. The study is based on a life cycle assessment method. The Results show that the team's carbon footprint was reduced from 350 tCO2eq by more than 50% between seasons 2018-2019 and 2021-2022 in the assessed categories. The most GHG emission reductions were achieved in the team's and spectators' mobility and ice hall energy consumption. Furthermore, the team compensated for their remaining emissions to achieve carbon neutrality. Multiple possibilities for further GHG emission reductions were recognized. The majority of the GHG emissions were linked to the Scope 3 category, indicating that co-operation with partners and stakeholders was a key to success in attaining carbon neutrality. This paper also discusses the possible limitations and challenges that sport organizations face in assessing climate impacts and reducing GHG emissions, as well as the prospects of overcoming them. Since there are many opportunities for sports to contribute to climate change mitigation, relevant targets and actions to reduce GHG emissions should be integrated into all sport organizations' management.

Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990-2019)

Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p < 0.05), while CO2 emissions decreased by 24.25 million tonnes (Mt) per year (p < 0.05). The highest reduction in CO2 emissions occurred in Germany (-7.52 Mt CO2 annually), and the lowest in Latvia (-0.087 Mt CO2 annually). The empirical EKC analysis shows an inverted-U shaped relationship between GDP and CO2 emissions in the EU-27. Specifically, a 1% increase in GDP results in a 0.705% increase in carbon emission, while a 1% increase in GDP2 leads to a 0.062% reduction in environmental pollution in the long run (p < 0.01). These findings indicate that economic development within the EU has reached a stage where economic growth positively impacts the environment. Overall, this study provides insights into the effectiveness of environmental policies in mitigating degradation and promoting green growth in the EU 27 countries.

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

Life-cycle comparisons of economic and environmental consequences for pig production with four different models in China

China, the world's largest consumer and producer of pork in the world, is attracting increasing attention due to the environmental impacts of its pig production. Previous studies seldom comprehensively compare the environmental impacts of the pig production system with different models, resulting in different intensities of environmental impacts. We aim to comprehensively evaluate Chinese pig production with different breeding models and explore a more sustainable way for pig production. We use life cycle assessment (LCA) to evaluate and compare environmental impacts of pig production system with four main breeding models in China from 1998 to 2020: domestic breeding, small-scale breeding, medium-scale breeding, and large-scale breeding. The life cycle encompasses fertilizer production, feed production, feed processing, pig raising, waste treatment, and slaughtering. The impact categories including energy consumption (EN), global warming (GWP), acidification (AP), eutrophication (EU), water use (WD), and land occupation (LO) are expressed with "100 kg live weight of fattening pig at farm gate." The results show that driven by governmental support, growing meat demand, and cost advantage, the scale breeding especially large-scale breeding simultaneously yielded greater net economic benefit and less environmental impact compared to other breeding models especially the domestic breeding. Due to mineral fertilizer application, feed production contributed over 50% of the total environmental impacts. Notably, the composition of feeds exerted significant influence on the environmental impacts arising from fertilizer production and feed processing. Furthermore, attributable to the substantial use of electricity and heat, as well as the concomitant emissions, pig raising contributed the largest GWP, while ranking second in terms of AP and EU. Notably, waste management constituted the third-largest EU, AP, and WD. In addition to promote scale breeding, we put forth several sustainable measures encompassing feed composition, cultivation practices, fertilizer utilization, and waste management for consideration.

GIS-based non-grain cultivated land susceptibility prediction using data mining methods

The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) metaheuristic algorithm. In order to, a total of 184 NCL areas were identified based on historical records, and a total of 16 NCL susceptibility conditioning factors (NCLSCFs) were considered, based on both a systematic literature survey and local environmental conditions. The results showed that the XGBoost model optimized by PSO performed well in comparison to other machine learning algorithms; the values of sensitivity, specificity, PPV, NPV, and AUC are 0.93, 0.89, 0.88, 0.93, and 0.96, respectively. Slope, rainfall, fault density, distance from fault and drainage density are most important variables. According to the results of this study, the use of meta-innovative algorithms such as PSO can greatly enhance the ability of machine learning models.

Effect evaluation of different green wastes on food waste digestate composting and improvement of operational conditions

This study attempted to determine the influence of diverse green wastes on food waste digestate composting and the improvement of operational conditions. Various effects of the green wastes (GW), with different types and sizes, initial substrate mixture C/N ratios, compost pile heights, and turning frequencies on the food waste digestate (FWD) composting were examined in the current work. The findings showed that the use of street sweeping green waste (SSGW) as an additive can maintain the thermophilic stage of the FWD composting for 28 days, while the end-product contained the greatest amounts of total phosphorus (TP, 2.29%) and total potassium (TK, 4.61%) and the lowest moisture content (14.8%). Crushed SSGW (20 mm) enabled the FWD composting to maintain the longest thermophilic period (28 days), achieving the highest temperature (70.2 °C) and seed germination index (GI, 100%). Adjusting the initial substrate mixture C/N ratio to 25, compost pile height to 30 cm, and turning frequency to three times a day could enhance the efficiency and improve the fertilizer quality of the co-composting of the FWD and SSGW. This study suggested that co-composting of FWD and SSGW (FWD/SSGW = 2.3, wet weight) is a promising technique for the treatment of municipal solid waste and provided significant theoretical data for the application of composting.

Developing expert scientific consensus on the environmental and societal effects of marine artificial structures prior to decommissioning

Thousands of artificial ('human-made') structures are present in the marine environment, many at or approaching end-of-life and requiring urgent decisions regarding their decommissioning. No consensus has been reached on which decommissioning option(s) result in optimal environmental and societal outcomes, in part, owing to a paucity of evidence from real-world decommissioning case studies. To address this significant challenge, we asked a worldwide panel of scientists to provide their expert opinion. They were asked to identify and characterise the ecosystem effects of artificial structures in the sea, their causes and consequences, and to identify which, if any, should be retained following decommissioning. Experts considered that most of the pressures driving ecological and societal effects from marine artificial structures (MAS) were of medium severity, occur frequently, and are dependent on spatial scale with local-scale effects of greater magnitude than regional effects. The duration of many effects following decommissioning were considered to be relatively short, in the order of days. Overall, environmental effects of structures were considered marginally undesirable, while societal effects marginally desirable. Experts therefore indicated that any decision to leave MAS in place at end-of-life to be more beneficial to society than the natural environment. However, some individual environmental effects were considered desirable and worthy of retention, especially in certain geographic locations, where structures can support improved trophic linkages, increases in tourism, habitat provision, and population size, and provide stability in population dynamics. The expert analysis consensus that the effects of MAS are both negative and positive for the environment and society, gives no strong support for policy change whether removal or retention is favoured until further empirical evidence is available to justify change to the status quo. The combination of desirable and undesirable effects associated with MAS present a significant challenge for policy- and decision-makers in their justification to implement decommissioning options. Decisions may need to be decided on a case-by-case basis accounting for the trade-off in costs and benefits at a local level.

Spatial-temporal differentiation and control strategies of nitrogen environmental loss in China's coastal regions based on flow analysis

Nitrogen pollution emissions from human production and living activities in coastal regions are important topics in the management of environmental pollution in coastal waters. However, to date, there has been relatively little research systematically assessing the environmental loss of nitrogen (NEL) from human activities that negatively affect marine ecosystems. This study categorised emission sources into five subsystems, namely livestock, farming, aquatic, industrial, and residential. Through flow analysis, the anthropogenic emissions of nitrogen in the gas, liquid, and solid phases from 11 coastal provinces in China in 2011, 2015, and 2020 were determined. A nitrogen cost index was constructed by combining the social indicators of each province. The effectiveness of nitrogen emission control since the land-sea coordination and the future challenges for the coastal region were discussed from various perspectives. The results of the study showed that the total NEL that poses a potential threat to marine ecosystems in coastal areas of China has decreased from 18.93 TgN to 14.66 TgN since the proposal of land-sea coordination, with livestock systems and aquatic systems emitting the most. The Bohai and Yellow Seas area were most threatened by nitrogen pollution. Among the three oceanic pathways, liquid-phase nitrogen discharge from each subsystem was effectively controlled, and the control of gas-phase nitrogen emissions is still the most numerous NEL state, although it has had a significant effect. The results of the correlation analysis suggest that NEL flow can characterize the regional management of nutrient-based organic pollutants. Past management tools and environmental investments in China have been more effective in controlling emissions from point and line sources involving artificial facilities, but less direct effect on mariculture. How to control surface source pollution from livestock and aquaculture will be an important challenge to reduce reactive nitrogen emissions in the future.

Twitter dataset on public sentiments towards biodiversity policy in Indonesia

In recent years, biodiversity has emerged as a prominent and pressing topic due to the urgent need to address biodiversity loss and the recognition of its connections to climate change and sustainable development. Additionally, increased public awareness and the consideration of economic factors have further underscored the significance of biodiversity conservation. To investigate the sentiment of the Indonesian people towards biodiversity, we conducted a comprehensive data collection on Twitter, focusing on keywords we have set. We amassed a substantial dataset of 500,000 Indonesian tweets from January 2020 to March 2023. These tweets encompassed a wide range of discussions on biodiversity, including its subdomains such as food security, health, and environmental management. Three annotators labeled each tweet with a sentiment class (positive, negative, neutral), or label none for unrelated tweet. The final label was determined using the majority voting method. The tweets with the final label none and those with undecided sentiment class were considered invalid and excluded in the subsequent process. Before labeling, a team of 18 experts jointly developed a labeling guide. This document served as a reference in labeling. After going through a series of processes, including cleaning (removing duplications, irrelevant tweets, and tweets written other than in Indonesian) and preprocessing, we prepared a dataset containing 13,435 tweets. We measured the inter-annotator agreement level, made several models using different algorithms and the K-Fold cross-validation method, and evaluated the models. The Fleiss' Kappa value of the dataset was 0.62187 as the value of the inter-annotator agreement level, and the F1-score value with the best model using the pre-trained IndoBERT model was 0.7959. The Fleiss' Kappa and F1-score values suggest that the annotators have a substantial comprehension and agreement of how to label a tweet, thus ensuring consistency and reliability of our dataset, and the reusability of our dataset is quite suitable for further research on sentiment analysis on biodiversity, respectively. This dataset will benefit various research, including topic modeling, sentiment analysis, public opinion analysis on Twitter, etc., especially biodiversity-related policies.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.

Integrated social-ecological data for regional natural resource management

Natural resource managers need information about both human and natural systems and interactions between those systems. Much data is available, but mostly from disparate sources and data have often been collected at different time steps and at different geographic scales. We used insights from the literature to select 270 relevant variables, available at national scale, from 33 unique (Australian) data sources. There were numerous with repeat measures, so in total we have 425 variables: 143 specific to 2016, 148 specific to 2021, and 134 available for both periods. We used GIS to summarize the variables spatially based on two geographic boundaries: one describes 63 Natural Resource Management Regions; the other describes 419 (sub) bioregions (formally, IBRA - Interim Biogeographic Regionalisation for Australia). Data deficiencies prevented us from being able to report on all variables for all regions. In the NRM dataset many regions are offshore islands, about which data are not generally available. Moreover, many IBRA regions are small and household level data are not always available at that scale. For analyses requiring a complete dataset at a single time step, our 2021 dataset for NRM regions includes 270 unique variables that describe 56 regions. Our IBRA data includes 214 variables describing 409 regions. To help managers select appropriate data for specific problems/contexts, the metadata file also categorises variables according to (a) whether they pertain to the social or ecological system, or interactions; (b) the segment of society described (where relevant); and (c) the frequency with which data are updated.

To what extent can decommissioning options for marine artificial structures move us toward environmental targets?

Switching from fossil fuels to renewable energy is key to international energy transition efforts and the move toward net zero. For many nations, this requires decommissioning of hundreds of oil and gas infrastructure in the marine environment. Current international, regional and national legislation largely dictates that structures must be completely removed at end-of-life although, increasingly, alternative decommissioning options are being promoted and implemented. Yet, a paucity of real-world case studies describing the impacts of decommissioning on the environment make decision-making with respect to which option(s) might be optimal for meeting international and regional strategic environmental targets challenging. To address this gap, we draw together international expertise and judgment from marine environmental scientists on marine artificial structures as an alternative source of evidence that explores how different decommissioning options might ameliorate pressures that drive environmental status toward (or away) from environmental objectives. Synthesis reveals that for 37 United Nations and Oslo-Paris Commissions (OSPAR) global and regional environmental targets, experts consider repurposing or abandoning individual structures, or abandoning multiple structures across a region, as the options that would most strongly contribute toward targets. This collective view suggests complete removal may not be best for the environment or society. However, different decommissioning options act in different ways and make variable contributions toward environmental targets, such that policy makers and managers would likely need to prioritise some targets over others considering political, social, economic, and ecological contexts. Current policy may not result in optimal outcomes for the environment or society.

Adsorption of trace heavy metals through organic compounds enriched biochar using isotherm adsorption and kinetic models

Trace heavy metals such as copper and nickel, when exceeds a certain level, cause detrimental effects on the ecosystem. The current study examined the potential of organic compounds enriched rice husk biochar (OCEB's) to remove the trace heavy metals from an aqueous solution in four steps. In 1st step, biochar' physical and chemical properties were analyzed through scanning electron microscope (SEM) and Fourier transforms infrared spectroscopy (FTIR). In the 2nd step, two biochar vis-a-vis glycine, alanine enriched biochar (GBC, ABC) was selected based on their adsorption capacity of four different metals Cr, Cu, Ni and Pb (chromium, copper, nickel, and lead). These two adsorbents (GBC, ABC) were further used to evaluate the best interaction of biochar for metal immobilization based on varying concentrations and times. Langmuir isotherm model suggested that the adsorption of Ni and Cu on the adsorbent surface supported the monolayer sorption. The qmax value of GBC for Cu removal increased by 90% compared to SBC (Simple rice husk biochar). The interaction of Cu and Ni with GBC and ABC was chemical, and 10 different time intervals were studied using pseud first and second-order kinetics models. The current study has supported the pseudo second-order kinetic model, which exhibited that the sorption of Ni and Cu occurred due to the chemical processes. The % removal efficiency with GBC was enhanced by 21% and 30% for Cu and Ni, respectively compared to the SBC. It was also noticed that GBC was 21% more efficient for % removal efficiency than the CBC. The study's findings supported that organic compound enriched rice husk biochar (GBC and ABC) is better than SBC for immobilizing the trace heavy metals from an aqueous solution.

Exploring Dynamics of Water, Energy, and Food Systems in Agricultural Landscapes Using Mental Modeling: A Case of Varamin Plain, Iran

This study applies the mental model and cognitive mapping method to involve stakeholders in delineating the mutual relations between sources of water, energy, and food (WEF) production in the Varamin Plain (VP). Through involving farmers and managerial experts, the approach facilitates the deployment of community communication patterns to recognize and comprehend problems and move from single-loop learning to double-loop learning. The dynamic model was driven from the final mental model of the participants to reflect changes in the systems over time. The system dynamic (SD) model incorporates three scenarios for enhancing irrigation efficiency, managing groundwater extraction, and satisfying environmental needs. The results uncovered that the surface and underground water resources of the VP will gradually decrease within the next two decades in the range of 158 and 2700 million cubic meters (MCM) per year. Also, the plain suffers from water insecurity and a 162 MCM shortage. Consequently, focusing on understanding the nexus and nexus governance can enhance resource management and achieve sustainable development goals. Essentially, promoting collaborative governance, such as creating cooperative organizations and implementing double-loop learning, and instituting a water market, regulatory governance, and monitoring laws can improve the state of Varamin Plain's resources. These results carry important policy implications for using mental models to consider dynamics for discussions on participatory management of the WEF system nexus and environmental management.

Environmental commitments and Innovation in China's corporate landscape: An analysis of ESG governance strategies

This study delves into the nexus between corporate ESG commitments-with a spotlight on environmental considerations-and innovation trends in China's corporate sector, leveraging data from Bloomberg's extensive database encompassing over 5102 companies. Our objective was to discern if and how environmental components within the ESG framework serve as precursors to a company's innovative inclinations. Adopting a quantitative methodology, we employed Bayesian Linear Regression and Neural Networks to unearth patterns. Key findings reveal that companies with pronounced environmental commitments within their ESG strategies are not only more innovative but also align more closely with global sustainability benchmarks. Moreover, the role of transparent governance processes in bolstering innovation was evident, highlighting the significance of corporate accountability. The research further underscores the synergy between strategic diversification and innovation, suggesting that an optimal balance in diversification strategies augments a firm's innovative prowess. By integrating traditional accounting insights with cutting-edge data analytics, our study offers a holistic perspective on the environmental and financial ramifications of ESG-driven innovations. This research holds profound implications for academia, industry stakeholders, and policymakers, emphasizing the strategic role of environmental commitments in shaping sustainable and innovative corporate trajectories.

Effect-Directed Analysis Combined with Nontarget Screening to Identify Unmonitored Toxic Substances in the Environment

Effect-directed analysis (EDA) combined with nontarget screening (NTS) has established a valuable tool for the identification of unmonitored toxic substances in environmental samples. It consists of three main steps: (1) highly potent fraction identification, (2) toxicant candidate selection, and (3) major toxicant identification. Here, we discuss the methodology, current status, limitations, and future challenges of EDA combined with NTS. This method has been applied successfully to various environmental samples, such as sediments, wastewater treatment plant effluents, and biota. We present several case studies and highlight key results. EDA has undergone significant technological advancements in the past 20 years, with the establishment of its key components: target chemical analysis, bioassays, fractionation, NTS, and data processing. However, it has not been incorporated widely into environmental monitoring programs. We provide suggestions for the application of EDA combined with NTS in environmental monitoring programs and management, with the identification of further research needs.

Ecological footprints and sustainable environmental management: A critical view of China's economy

Global economies have recently been concerned about sustainable environmental management by reducing emissions and tackling ecological footprints. The rapid economic expansion and investment in traditional manufacturing further raises environmental degradation. China surpasses other emerging economies in the economic growth race yet has remained the top pollution-emitting economy for the last few decades, necessitating scholarly attention. This study examines the influencing factors of ecological footprints in China from the perspective of COP27. Using the extended dataset from 1988 to 2021, this study uses several time series diagnostic tests and verifies the existence of the long-run association between the study variables. Consequently, the non-linear scattered data leads to non-parametric (method of moment quantile regression) adoption. The empirical results indicate that only economic growth is a significant factor in environmental quality degradation in China. However, improving renewable energy usage, research and development, and foreign direct investment reduces the country's ecological footprint. Hence, the latter variables substantially lead to environmental sustainability. The robustness of the results is confirmed via a robust non-parametric estimator and causality test. Based on the empirical results, this study recommends increased investment in research and development, renewable production, and foreign direct investment enhancement.

Integrating infection control and environmental management work systems to prevent Clostridioides difficile infection

Effective infection prevention and control within health care settings requires collaboration and coordination between infection control and environmental management teams. However, the work systems of these teams can be difficult to integrate despite their shared goals. We provide results from a qualitative study of Clostridioides difficile infection prevention in Veterans Affairs facilities regarding challenges in coordination between these teams and opportunities to improve coordination and maximize infection prevention activities.

An integrated methodology for green human resource management in construction industry

Today, by increasing public awareness about environmental issues and pressures from governments and other stakeholders, companies have dealt with environmental challenges more than ever. This paper focuses on environmentally sustainable performance using an integrated methodology based on meta-synthesis, Delphi, and structural equation modeling (SEM) techniques which are utilized in different phases. In the first phase, an in-depth review of green human resources management (GHRM) literature is conducted based on the meta-synthesis method, and as a result, 38 codes are extracted. Next, to adapt and customize the codes with the nature of the construction industry, 2 rounds of Delphi method are implemented to extract the expert judgment from a panel of 15 industry professionals, resulting in 21 codes in 7 categories. To validate the developed methodology, a dataset from 33 Iranian construction companies are collected along with 15 factors in 5 categories determined using SEM. The findings reveal that among 9 main GHRM components extracted from the literature, just 5 components including green recruitment and selection, green performance management, green-reward, green-based employee empowerment, and green training have significant and positive relationships with GHRM. Finally, managerial insights, limitations, and future research directions are discussed.

Making solar electricity dispatchable: A technical and economic assessment of the main conversion and storage technologies

The techno-economic performances of five different solar-electricity conversion technologies (photovoltaic, solar tower, parabolic trough as well as two hybrid PV/CSP systems) associated with three energy storage means (electrochemical, thermal, and thermophotovoltaic) are evaluated thanks to representative models applied to four representative sites around the world. The evaluation is based on the ability to dispatch the power production throughout the year, the ability to maximize energy injection in the electrical grid, and the levelized cost of electricity. It is found that increasing the dispatchability of solar power plants will necessarily lead to the emergence of additional energy losses and important LCOE increase, either because of low round-trip efficiency of the storage system, or because of its high cost of energy capacity. Despite lower energy production for a given collecting area, combination of PV power plants with electrochemical storage or thermal energy storage surprisingly seem to be the most promising paths.

The biological carbon pump, diel vertical migration, and carbon dioxide removal

Carbon dioxide is increasing in the atmosphere promoting the faster environmental change of the Earth's recent history. Several marine carbon dioxide removal (mCDR) technologies were proposed to slow down CO2 in the atmosphere. Technologies now under experimentation are related to the increase in gravitational flux. Other mechanisms such as active flux, the transport performed by diel vertical migrants (DVMs) were not considered. We review the effect of DVMs in the epipelagic realm and the top-down promoted by these organisms upon zooplankton and microzooplankton, and their variability due to lunar cycles. A night source of weak light will increase epipelagic zooplankton biomass due to DVMs avoidance from the upper layers to escape predation, promoting DVMs to export this biomass by active flux once the illumination ceases. This mCDR method should be tested in the field as it will increase the efficiency of the biological carbon pump in the ocean.

Mapping environmental crime to characterize human impacts on islands: an applied and methodological research in Canary Islands

Environmental crimes are a global issue due to the damage they cause to landscapes and ecosystems. This study focused on characterizing environmental crimes in the Canary Islands (Spain). Four categories of environmental crimes related to construction, mining and tilling, solid waste, and liquid waste) were defined and analysed. A total of 28 databases were generated, corresponding to each of the 7 major islands and each environmental crime typology. Each database was linked to information on land use and the socioeconomic and physical characteristics of the territory. For each database, firstly a descriptive statistical analysis was conducted, followed by the generation of a regularized Random Forest model with the aim of identifying characteristics that may be related to the location of environmental crimes. The results showed that, in most cases, proximity to residential accommodations, agricultural areas and industrial zones act as the main explanatory features of the distribution of environmental crimes. Furthermore, a marked pattern of concentration of environmental crimes in the coastal belt of the islands was observed, mainly associated with urban-tourist development since the 1960s and 1970s.

The application of environmental management methods in combating dengue: a systematic review

Among several vector control methods commonly used, environmental management is one of the control measures to mitigate dengue in such vulnerable communities. Since there is no curative treatment for dengue yet, targeted environmental and ecosystem management is increasingly relevant. Hence, this review was conducted to identify the effectiveness of environmental management intervention strategy to reduce dengue cases. We searched PUBMED, CENTRAL, SCOPUS, Web of Science, CIJE, WHO ICTRP, and ClinicalTrials.gov up to January 2021. A total of 521 articles were screened. Only 16 studies were included in this review. There were 6 studies that applied all three types of environmental management interventions (manipulation, modification and behavior), 8 studies applied two types of interventions (manipulation and behavior) and 2 studies applied one type of intervention (manipulation or behavior). All included studies reported reduction of Aedes entomological indices. The studies showed reduction in dengue cases and density of Aedes population through environmental interventions. It is recommended for the health authority to incorporate environmental management intervention in dengue control activities and enhanced the community involvement to ensure sustainability with high impact on dengue reduction.

From Covid-19 to conflict: Does environmental regulation and green innovation improve industrial sector decarbonization efforts and environmental management?

During the COVID-19 period, industrial production was slowed or halted due to COVID restrictions and lockdowns, followed by global tensions and conflicts, which created uncertainties for industrial production. Similarly, during this period, the growth in carbon emissions was seen shrinking. However, it is greatly important to explore whether this shrinking trend will continue or whether a new growth pattern could emerge. Considering this, the research was carried out to investigate the relationship between green innovation initiatives and environmental regulations in the process of environmental management and environmental performance in the industrial sector. To do so, the survey-based research methodology using PLS-SEM was adopted, and data was gathered from 279 managers working in the equipment manufacturing industry. Our empirical findings revealed that environmental regulation and green innovation efforts strengthen decarbonization efforts, which further improve environmental management and environmental performance. The mediating role of decarbonization efforts was found to be prominent among green process innovation, environmental management and environmental performance. Moreover, decarbonization serves as a mediator between green product innovation and environmental management. In contrast, we could not verify that decarbonization mediates the relationship between green product innovation and environmental performance. The key findings are greatly important and provide a fresh roadmap for environmental management in the post-COVID era.

Unlocking green growth challenges: role of green HRM, green career adaptability, and green career success

Multiple industries face challenges in achieving green growth that needs a fix. This research presents an alternative explanation for the acquisition of green growth using the perspective of employees of manufacturing industries. Thus, the study examines the role of green HRM, green career adaptability, and green career success in achieving green growth. Green growth drivers can construct green infrastructures for developing green aspects in economic sectors such as power generation, transportation, and the residential sector. We inquired Chinese SME employees to fill out a closed-ended online survey. PLS-SEM techniques are used to estimate how the study will turn out. According to the results, green career adaptability plays a big part in green HRM and career success. It also plays a significant role in bringing the two together. The results shown that green HRM, adapting to a green career and doing well in a green career, all help green growth in manufacturing SMEs in China. The study's results are strong in their ability to explain. This is especially true in the academic world, where people who can adapt to setbacks and have a green career are likelier to have a good career in organizations that care about the environment. By doing this, the study also helps guide the strategic development goals (SDGs) for climate action and environmental management by acquiring green growth. So, the study makes different suggestions for what to do.

Internet of Things-based sustainable environment management for large indoor facilities

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm's environmental parameters can help to curtail the negative impacts of the environment on chickens' health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O2), carbon dioxide (CO2), carbon monoxide (CO), and ammonia (NH3) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O2 concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO2 levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO2 levels within the control sheds. The NH3 gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).

Are hospital wastewater treatment plants a source of new resistant bacterial strains?

To understand which type of hospital waste may contain the highest amount of antibiotic resistant microorganisms that could be released into the environment, the bacterial strains entering and leaving a hospital wastewater treatment plant (HWTP) were identified and tested for their antibiotic susceptibility. To achieve this goal, samples were collected from three separate sites, inlet and outlet wastewater positions, and sludge generated in a septic tank. After microbiological characterization according to APHA, AWWA, and WEF protocols, the relative susceptibility of the bacterial strains to various antibiotic agents was assessed according to the Clinical and Laboratory Standards Institute guidelines, to determine whether there were higher numbers of resistant bacterial strains in the inlet wastewater sample than in the outlet wastewater and sludge samples. The results showed more antibiotic resistant bacteria in the sludge than in the inlet wastewater, and that the Enterobacteriaceae family was the predominant species in the collected samples. The most antibiotic-resistant families were found to be Streptococcacea and non-Enterobacteriaceae. Some bacterial strains were resistant to all the tested antibiotics. We conclude that the studied HWTP can be considered a source of resistant bacterial strains. It is suggested that outlet water and sludge generated in HWTPs should be monitored, and that efficient treatment to eliminate all bacteria from the different types of hospital waste released into the environment is adopted.

Deep-sea mining: using hyperbaric conditions to study the impact of sediment plumes in the subtidal clam Spisula solida

With the growing interest to exploit mineral resources in the deep-sea, there is the need to establish guidelines and frameworks to support hazard and risk assessment schemes. The present study used a subtidal species of filter-feeding bivalve, the clam Spisula solida, as a proxy to better understand the impacts of sediment plumes in marine organisms under hyperbaric conditions. Four concentrations of suspended sediments (0 g/L, 1 g/L, 2 g/L, and 4 g/L) were used in a mixture with different grain sizes at 4 Bar for 96 h. Functional (filtration rate-FR) and biochemical endpoints (catalase-CAT, glutathione s-transferase-GST, and lipid peroxidation-LPO) were analyzed in the gonads, digestive gland, and gills of S. solida after a 96-h exposure at 4 Bar (the natural limit of the species vertical distribution). The FR showed a decreasing trend with the increasing sediment concentrations (significant effects at 2 and 4 g/L). Additionally, significant changes were observed for some of the tested oxidative stress biomarkers, which were concentration and tissue-dependent, i.e., CAT activity was significantly elevated in gills (1 g/L treatment), and GST was decreased in digestive gland (1 g/L treatment). Overall, the results show that suspended sediments, at 2 and 4 g/L, have negative functional impacts in the bivalve S. solida providing additional insights to improve hazard assessment of deep-sea mining. These findings represent a step forward to ensure the mitigation of the potential negative effects of deep-sea resource exploitation.

Incorporating carbon sequestration toward a water-energy-food-carbon planning with uncertainties

Water-energy-food nexus (WEFN) is the core content in the United Nations 2030 Agenda for Sustainable Development. However, the value of soil and crops' carbon sink function has not yet been fully considered in the managment practices of WEFN system. Here, we developed a water-energy-food-carbon nexus (WEFCN) planning framework that incorporates carbon sequestration and multiple mathematical optimization methods into the practical WEFN management for Henan Province, which is one of major grain-producing areas in China. Uncertainties from multiple objectives, scenarios, and different stakeholder interests are captured. We found that wheat has the largest carbon sequestration, followed by corn and oil-bearing crops, while other crops have implicit carbon sequestration. Since chemical fertilizer produces the most carbon emissions, the usage of chemical fertilizer needs to be reasonably controlled. Overall, the proposed framework supports optimal decision-making for regional-scale WEFCN management and further unlocks the hidden value of agricultural carbon mitigation.

Detection and mapping of the seasonal distribution of water hyacinth (Eichhornia crassipes) and valorization as a biosorbent of Pb(II) in water

In the present research, the presence of water hyacinth (Eichhornia crassipes) on the surface of the San Jose Dam located in the city of San Luis Potosi, S.L.P, Mexico, was monitored and mapped. The monitoring was conducted for 2 years (2018-2020) with remote sensing data from OLI Landsat 8 sensors, based on the normalized difference vegetation index (NDVI). The results demonstrated the capability and accuracy of this method, where it was observed that the aboveground cover area, proliferation, and distribution of water hyacinth are influenced by climatic and anthropogenic factors during the four seasons of the year. As part of a sustainable environmental control of this invasive species, the use of water hyacinth (WH) root (RO), stem (ST), and leaf (LE) components as adsorbent material for Pb(II) present in aqueous solution was proposed. The maximum adsorption capacity was observed at pH 5 and 25 °C and was 107.3, 136.8, and 120.8 mg g-1 for RO, ST, and LE, respectively. The physicochemical characterization of WH consisted of scanning electron microscopy (SEM), N2 physisorption, infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), charge distribution, and zero charge point (pHPZC). Due to the chemical nature of WH, several Pb(II) adsorption mechanisms were proposed such as electrostatic attractions, ion exchange, microprecipitation, and π-cation.

Stratification of stakeholders for participation in the governance of coastal social-ecological systems

Knowledge co-production has become part of an evolution of participatory and transdisciplinary research approaches that are increasingly important for achieving sustainability. To effectively involve the most appropriate stakeholders there is a need for engagement and increasing prominence of stakeholders in environmental management and governance processes. The paper aims at developing and testing a methodology for stratifying stakeholders by (i) classifying organisations involved in coastal and ocean governance by their agency, and (ii) grouping them into organisational archetypes for representation and selection in research processes. Agency was measured by the three dimensions of scale, resources, and power. Each dimension was further elaborated as a set of indicators. The methodology is applied in the context of a research project set in Algoa Bay, South Africa. The stratification of organisations enabled the research team to gain a better understanding of the stakeholder landscape of organisational agency, and thus identify the most relevant stakeholder with which to engage. The use of a hierarchical cluster analysis identified five organisational archetypes in relation to ocean and coastal governance in Algoa Bay. The methodology used in this study proposes an informed and intentional approach to create the conditions under which the co-production of and participation in research processes can take place.

Marine Protected Area Expansion and Country-Level Age-Standardized Adult Mortality

Many countries have adopted targets to increase marine protected areas (MPAs) to limit the degradation of water bodies. Although there is evidence that MPAs can conserve marine life and promote biodiversity, there are limited data on the human health implications of MPAs. Using panel data from 1990, 2000, and 2014, we estimated the country-level associations between MPAs (i.e., percentage of territorial waters designated as marine reserves) and age-standardized mortality (i.e., age-standardized probability of dying between 15 and 60 years from all-causes among ages 15-60/100,000 population) by sex, among 110 countries. We fit mixed-effects linear regression models of mortality as a function of current MPA coverage, gross domestic product growth, year, the prior extent of MPA, electricity coverage, governance, and country-level random effects. We observed a significant inverse association between current MPA coverage and adult mortality. For each 5-percentage-point increase in current MPA coverage, a country had 0.982 times the geometric means of female and male mortality [geometric mean ratio: 0.982 (95% CI 0·976, 0·988)] conditional on past %MPA coverage and other modeled variables. The model showed no significant residual association of mortality with past %MPA conditional on current %MPA and other modeled variables. This is one of the first studies to show a positive association between increasing marine conservation and human health. This macro-level study suggests there may be important co-benefits for human health from expanding MPAs that merit further investigation.

Unveiling green synergies: sustainable performance through human resource management, CSR, and corporate image under a mediated moderation framework

In the twenty-first century, the surge in natural resource consumption and climate change has necessitated stringent measures to ensure ecological integrity and corporate sustainability. Organizations are increasingly adopting green practices, including green human resource management (HRM), green corporate social responsibility (CSR), and the promotion of green firms' image. These strategies are central to fostering sustainable performance and responsible environmental stewardship. These constructs have also elevated consumers' satisfaction and trust in firms' ecological practices. This study employs a quantitative research approach and utilizes convenient sampling methods to explore the ecosystem within the hospitality sector of China. A survey was conducted among middle- and senior-level managers working in top 3-, 4-, and 5-star hotels. The study results demonstrate that green notions are the key to a corporate's sustainable performance. The study reveals that firms' eco-friendly motive ensures the greening of the hotel industry due to their eco-friendly motive. Therefore, companies should be responsible for the environment. They should ensure the implementation of green sustainable practices toward achieving corporate performance.

Geographical distribution and conservation strategy of national key protected wild plants of China

National key protected wild plants (NKPWPs) are considered flagship species for plant diversity conservation in China. Using data for 1101 species, we characterized NKPWPs distribution patterns in China and assessed conservation effectiveness and conservation gaps. In total, 4880 grid cells at a 20 × 20 km resolution were filled with occurrence records for NKPWPs. We identified 444 hotspot grid cells and 27 diversity hotspot regions, containing 92.37% of NKPWPs. However, 43.24% of these hotspot grid cells were fully or partially covered by national nature reserves (NNRs), where 70.21% of species were distributed. Approximately 61.49% of the NKPWPs species were protected by NNRs, but the populations or habitats of 963 species were partially or fully outside of NNRs. With global warming, the overall change in the extent of suitable habitats for NKPWPs is expected to be small, however, habitat quality in some areas with a high habitat suitability index will decrease.

Eutrophication assessment and environmental management perspectives of Tóbari: an arid subtropical coastal lagoon of the Gulf of California

Coastal lagoons are vulnerable to eutrophication processes. In this study, we evaluate the eutrophication process in the restricted, arid subtropical Tóbari coastal lagoon, located in the eastern coast of the Gulf of California, where the main source of nutrient inputs and other pollutants is agricultural wastewater from the Yaqui Valley. The Assessment of Estuarine Trophic Status (ASSETS) model and the Trophic State Index (TRIX) were used to evaluate eutrophication. Overall, ASSETS showed that the Tóbari lagoon has a moderate eutrophication process, with seasonal symptoms of hypoxia, increased phytoplankton biomass, dominance of macroalgae (indicative of nutrient enrichment), and blooms development of potentially harmful algae species. The TRIX showed that the lagoon is mesotrophic most of the year. Challenges of environmental management detected correspond to reducing the input of nutrients and others contaminants from anthropic sources: agriculture, shrimp farming, livestock, and urban zones.

Ecological engineering of iron ore tailings into useable soils for sustainable rehabilitation

Ecological engineering of soil formation in tailings is an emerging technology toward sustainable rehabilitation of iron (Fe) ore tailings landscapes worldwide, which requires the formation of well-organized and stable soil aggregates in finely textured tailings. Here, we demonstrate an approach using microbial and rhizosphere processes to progressively drive aggregate formation and development in Fe ore tailings. The aggregates were initially formed through the agglomeration of mineral particles by organic cements derived from microbial decomposition of exogenous organic matter. The aggregate stability was consolidated by colloidal nanosized Fe(III)-Si minerals formed during Fe-bearing primary mineral weathering driven by rhizosphere biogeochemical processes of pioneer plants. From these findings, we proposed a conceptual model for progressive aggregate structure development in the tailings with Fe(III)-Si rich cements as core nuclei. This renewable resource dependent eco-engineering approach opens a sustainable pathway to achieve resilient tailings rehabilitation without resorting to excavating natural soil resources.

How does environmental policy stringency influence green innovation for environmental managements?

In the struggle to limit global climate change and rising temperatures, the United Nations Climate Change Conference (COP27) was held in Egypt last November. Bringing together nations to recognize climate change as a global concern and to create new "building blocks" to enhance the implementation of the Paris Agreement through actions that can move the world toward a greener, and carbon free future. This study examines a panel of high-income economies from the Organization for Economic Cooperation and Development (OECD) to investigate the empirical linkage between Green Innovations (GI), Disaggregated trade (exports and imports), Environmental policy stringency (EPS), and Consumption-based carbon dioxide emissions from 1990 to 2020. We proceed with the panel cointegration check based on the results of the diagnostic tests. The method of moment quantile regressions (MMQR) is used to investigate the relationships between CCO₂ and various variables in different quantiles. The data show that GI, export, imports, and EPS are major contributors in explaining the substantial variance in CCO₂ emissions in the chosen panel. Specifically, severe environmental rules boost the benefits of green technologies through the use of environmentally friendly technology. Imports, on the other hand, have been determined to be harmful to environmental quality. As a result, member economies should reform their environmental policies to include consumption-based emissions objectives and discourage people' desire for carbon-intensive items from developing countries. This will eventually result in a decrease in consumption-based carbon emissions, assisting in the achievement of true emissions reduction goals and COP27 targets.

Evaluating the effectiveness of coastal environmental management policies in China: The case of Bohai Sea

With marine pollution issues becoming serious and widespread, a series of coastal environmental managemental policies are being carried out worldwide, the effectiveness of which requires comprehensive evaluation. Taking the Bohai Sea (BS) of China as an example, which has been plagued by serious ecological and environmental issues for decades due to terrestrial pollution discharge, this study explored and quantified, for the first time to our best knowledge, the variability of water quality after initiating a dedicated 3-year pollution control action (Uphill Battle for Integrated Bohai Sea Management, UBIBM, 2018-2020) implemented by China's central government, with two water quality indexes of water color (Forel-Ule index, FUI) and transparency (Secchi disk depth, Z_SD, m) from satellite observations. During the UBIBM, a significant improvement in water quality was detected, characterized by a clearer and bluer BS, with Z_SD and FUI improved by 14.1% and 3.2%, respectively, compared with the baseline period (2011-2017). In addition, an abrupt drop in the long-term record (2011-2022) of the coverage area of highly turbid waters (Z_SD≤2 m or FUI≥8) was found in 2018, which coincided with the start of the UBIBM, indicating that the water quality improvement may be attributed to the pollution alleviation of the UBIBM. Independent data of land-based pollution statistics also supported this deduction. (3) Compared with the previous two pollution control actions in the first decade of 21st century, UBIBM was proved to be the most successful one in terms of the achieved highest transparency and lowest FUI during the past two decades. Reasons for the achievement and implications to future pollution control are discussed for a more sustainable and balanced improvement in the coastal environment. This research provides a valuable example that satellite remote sensing can play a vital role in the management of coastal ecosystems by providing effective evaluation of pollution control actions.

Dynamics and Drivers of Land Use and Land Cover Change in the Upper Awash Basin, Central Rift Valley of Ethiopia

This study analyzed the patterns and drivers of LULC dynamics in relation to the expansion of large-scale irrigated agriculture in the Central Rift Valley of Ethiopia from 1972 to 2016. Aerial photographs (1972), Landsat images (1980, 2000) and SPOT5 satellite images (2016) were analyzed using GIS tools to reveal LULC changes, and documentation, key informant interviews and focus group discussions were used to ascertain the biophysical and socioeconomic implications and drivers of these dynamics. The study revealed that cultivated and rural settlement land, and urban built-up areas had expanded at the expense of forestland, woodland, shrubland and grassland. While an increase in the production of cash crops had brought some benefits to smallholder farmers, such as access to irrigation and modern agricultural inputs and technologies, the unregulated conversion of natural vegetation to cultivated land resulted in a loss of biodiversity, deforestation, and reduction of pasture and firewood. We identified that significant LULC changes in the study area were caused by intersecting biophysical, economic, institutional, technological and demographic factors, which reinforced each other with varying magnitudes at different moments in time. These changes were underpinned by one key driver, that is, government agricultural policies that promoted investment in commercial agriculture for national and export markets. The study shows that understanding the complex interaction between the contributing factors and drivers of LULC change is crucial to inform decision-making and policies, in particular, by directing responses towards the underlying drivers of change rather than only the proximate causes.

Drivers for circular economy development: making businesses more environmentally friendly

Stakeholders have been pressuring companies to develop more environmentally friendly strategic and operational solutions. In this sense, companies are seeking alternatives that reduce the negative impacts of organizational activities, Circular Economy (CE) is one of the solutions with the greatest potential for success. Thus, the goal of this paper is to provide drivers for organizations' transition from a linear to a CE. For this reason, content analysis was used as the scientific method, for being appropriate for the interpretation of qualitative data and the identification, clustering, and systematization of themes in a given field of knowledge. In the case of this work, a set of 30 articles with information related to the implementation and development of CE were analyzed, allowing the identification of 19 key elements of CE. These key elements were then grouped and systematized into four drivers: decision-making; capacity and training; sustainable practices; and green supply chain. Scientifically, this work contributes to the improvement and increase of the block of knowledge about the CE, because the drivers can be used to advance the state of the art and as a starting point for the development of new research. In an applied way, the drivers proposed in this article provide a range of actions for managers to make their companies greener and improve their organizational performance, thus contributing environmentally and socially to the planet.

Fertilizer restrictions are not sufficient to mitigate nutrient pollution and harmful algal blooms in the Indian River Lagoon, Florida

In Florida's Indian River Lagoon (IRL), anthropogenic eutrophication has resulted in harmful algal blooms and catastrophic seagrass losses. Hoping to improve water quality, policy makers enacted fertilizer bans, assuming that this would reduce the nitrogen (N) load. To assess the effectiveness of these bans, seawater and macroalgal samples were collected at 20 sites "pre" and ~ five-years "post" bans and analyzed to determine concentrations of dissolved nutrients and stable nitrogen isotope values (δ¹⁵N). Higher concentrations of ammonium and nitrate were observed post-ban and macroalgal δ¹⁵N values increased. A comparison of nutrient concentrations and δ¹⁵N between brown tide (Aureoumbra lagunensis) blooms indicated that the post-ban bloom was more strongly N-enriched with higher δ¹⁵N values than the pre-ban bloom, which had depleted values in the range of fertilizers. These data indicate a primary role of human waste influence in the IRL, suggesting that current management actions have been insufficient at mitigating eutrophication.

Projecting global oil palm expansion under zero-deforestation commitments: Direct and indirect land use change impacts

In the last three decades, global production of oil palm has boomed, which has partly come at the expense of tropical rainforests. Recognizing this, many companies operating in the palm oil industry have committed to eliminate deforestation from their operations, often referred to as zero-deforestation commitments (ZDCs). Here, we estimate that if ZDCs are fully adopted and enforced across all sectors and geographies, the global extent of oil palm plantations may be 11 M ha or 40% smaller in 2030 than in a business-as-usual (BAU) scenario that assumes no compliance with ZDCs. As a result of such land-sparing effects, we estimate that 96 M ha of forests are saved from conversion, of which, 17% would otherwise have been converted (directly or indirectly) due to expanding oil palm plantations. Overall, these figures suggest that ZDCs have the potential to deliver major environmental benefits if they are fully adopted and enforced.

Reclaiming abandoned mine tailings ponds for agricultural use: Opportunities and challenges

Tailings ponds, large man-made structures conceived during the mining process for waste storage, often become deserted post-mining, leaving behind a stark, contaminated landscape. This paper posits that these forsaken tailings ponds can be rejuvenated into fertile agricultural land through adept reclamation efforts. Serving as a discussion paper, it engages in a stimulating exploration of the environmental and health risks linked to tailings ponds. It sheds light on the potential and impediments in the transformation of these ponds into agricultural land. The discussion concludes that despite the substantial hurdles in repurposing tailings ponds for agriculture, there are encouraging prospects with the application of multifaceted efforts.