Environmental Objectives Research Articles

Identifying the optimal type and locations of natural water retention measures using spatial modeling and cost-benefit analysis

Water management has shifted from solely technical and engineering approaches towards nature-based solutions (NBS), like natural water retention measures (NWRM), offering benefits beyond hydrology, such as improved well-being and biodiversity conservation. Determining the best type and location of these measures is challenging due to diverse options with varying benefits and effects depending on measure type and location characteristics. While most studies regarding the optimal allocation and implementation of NBS focus on the urban environment, this study presents a methodology for decision-makers focusing on inter-urban regions with limited data on NWRM implementation. Through hydrological modeling and cost-benefit analysis (CBA), we identify Pareto optimal NWRM sites and types, considering water quantity and quality alongside economic, environmental, and social objectives. We defined optimal locations that seek the most significant reduction of runoff, sediment, and pollutants, whilst optimal NWRM types are defined to seek the most cost-effective measures based on hydrological, ecological, and social criteria. Using the Open Non-point Source Pollution and Erosion Comparison Tool (OpenNSPECT), we simulated increased infiltration in different inter-urban areas and identified the optimal placement. The criteria for selecting suitable NWRM types for the identified areas are derived from the EU Directorate General for the Environment (DG-ENV) NWRM database. The results show different effective areas for reducing runoff, sediment, and pollutants. While one NWRM (natural bank stabilization) was identified as most beneficial for reducing sediment, several measures were selected for runoff reduction. Interestingly, measures with high potential for pollutant reduction seem to offer limited social and biodiversity benefits, suggesting conflicting objectives and highlighting the importance of accounting for multiple criteria. By employing simplified models and qualitative benefit assessments, this paper presents a practical decision-making approach to facilitate NWRM implementation in data-scarce areas.

RaDro: Indoor Drone Tracking Using Millimeter Wave Radar

Core to drone design is its ability to ascertain its location by utilizing onboard inertial sensors combined with GPS data. However, GPS is not always reachable, especially in challenging environments such as indoors. This paper proposes RaDro; a system that leverages millimeter-waves (mmWave) to precisely localize and track drones in indoor environments. Unlike commonly used alternative technologies, RaDro is cost-effective and can penetrate obstacles, a bonus in non-line-of-sight (NLoS) scenarios, which enhances its reliability for tracking objects in complex environments. It does this without the need for tags or anchors to be attached to the drone, achieving 3D tracking with just a single radar point, significantly streamlining the deployment process. Comprehensive experiments are conducted in different scenarios to evaluate RaDro's performance. These include employing different drone models with different sizes to execute a range of aerial manoeuvres across different flight arenas, each with its own settings and clutter, and encountering various LoS and NLoS scenarios in dynamic environments. The experiments aimed to assess the capabilities of the system to extract coarse-grained and fine-grained information for drone detection, motion recognition, and localization. The results showcase precise localization, achieving a 50% reduction in localization error compared to the conventional baseline. This localization accuracy remains resilient even when confronted with interference from other moving sources. The results also demonstrate the system's ability to accurately localize drones in NLoS scenarios where existing state-of-the-art optical technologies cannot work.

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

EFFECTS OF ADOPTING INTERNET OF THINGS (IOT) BASED SMART WASTE BIN IN NIGERIA: A REVIEW OF SCHOLARS POSITIONS

The present inherent hurdles to optimising the Internet of Things (IoT) in the Nigerian economy include maintenance and deployment costs, reluctance to change, technological infrastructure, and connection. In order to meet the urgent demand for creative waste management solutions, this study intends to investigate the effects of implementing IoT-based smart garbage bins in Nigeria. The basis for this quasiexperimental study was replies gathered from scholarly publications in the topic by researchers using a Likert scale. The results show that academics are unified in their support of the use of IoT in waste management, pointing to its potential to lower costs, increase efficiency, lessen environmental risks, and create an eco-friendly atmosphere that stimulates economic growth. In line with environmental objectives, scholarly viewpoints also address challenges such as maintenance expenses, technological know-how, residents' adaptation, cybersecurity risks, and incorrect waste disposal. The study suggests that stakeholders, including individuals and government organisations, work together to maintain personal and community cybersecurity, satisfy technology needs, and establish an eco-friendly environment in light of these results. Even if the study's predictions seem bright, further research is still necessary to solve gaps and investigate unexplored areas of IoT-based waste management in Nigeria.

Data processing in internet of things networks

One of the distinctive features of cloud applications in the Internet of Things (IoT) network is the real-time collection and processing of data about the objects of the physical environment. To solve this problem, with the help of sensors, data about the state of physical objects is collected to form the processing of data streams. The performance of such systems is critically dependent on the ability to securely and efficiently collect, transmit, and analyze data streams between environmental (real-world) objects and information systems. In such a case, it is relevant to study the data stream in Internet of Things networks and the operations that may occur on them. The issue of which platforms to process the operations to be carried out at the same time is the main subject of discussion.

Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system's fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

Green supply chain management for carbon accountability

As disclosure mandates shift their focus towards supply chain carbon accountability, firms are compelled to enhance carbon outcomes throughout their supply chain. The literature suggests that green supply chain management (hereafter, GSCM) plays an essential role in enhancing firms' environmental performance. However, there is a dearth of evidence on the possible informational effects of GSCM on Scope 3 carbon outcomes despite their mutual linkage to supply chains. Using two novel measures of GSCM quality, defined as the extent to which environmental objectives are integrated into supply chain management processes, we find that higher GSCM quality increases the likelihood of a focal customer's Scope 3 carbon disclosures and reduces its Scope 3 carbon footprint. These effects of GSCM quality are realized through improvements in relational trust among customer-supplier relationships and in suppliers' environmental innovation capabilities. Further analysis reveals that the effects of GSCM quality are realized among durable customer-supplier relationships. We identify GSCM quality as an essential and distinct component of corporate environmental governance. These findings highlight the importance of GSCM quality for attaining supply chain carbon transparency and accountability.

Impact orientation and venture capital financing: The interplay of governmental, social impact and traditional venture capital

In addition to traditional venture capital (VC), governmental VC and social impact VC investors have emerged as alternatives to fund entrepreneurial ventures, especially start-ups that incorporate social and/or environmental objectives into commercial operations. Using a sample of 15,510 VC-backed start-ups, we show that impact-oriented ventures are more likely to receive funding from alternative VCs. Both social and environmental orientations increase the chances that a start-up secures funding from impact VCs while social orientation drives results for government VCs. Importantly, we also show that impact-oriented ventures are more likely to secure investment from traditional VCs when impact and governmental VCs co-invest.

Sustainable production scheduling: insights from real-life applications

This study presents an in-depth exploration of sustainable production scheduling within manufacturing systems, focussing on the integration of sustainable objectives and their practical applications. A thorough literature review of 128 articles, adhering to PRISMA guidelines, is complemented by 11 interviews conducted in manufacturing firms. The research identifies a predominant focus on economic and environmental objectives in sustainable scheduling, while the inclusion of social aspects is emerging but remains underdeveloped. Notably, the study highlights a significant gap in real-world applications and a need for empirical studies. It emphasizes the importance of comprehensive sustainability objectives and robust methodologies for practical applications. The goal is to close the gap between theoretical advances and actual implementation of sustainable production scheduling, calling for a concerted effort to produce comprehensive, practical and innovative solutions in this area.

Optimization framework for daylight and thermal environment of retractable roof natatoriums based on generative adversarial network and genetic algorithm

This paper proposes a framework for multi-objective optimization of indoor daylighting and thermal comfort in natatoriums with retractable roofs, using genetic algorithms and building performance simulation. The goal is to balance daylight illumination and thermal comfort under different roof opening states. Efficiency of computational fluid dynamics simulation is improved by a multi-objective optimization framework based on non-dominated sorting genetic algorithms, integrating a generative adversarial network to learn the environmental map. The study analyses the impact of different roof opening ratios on environmental objectives and establishes regression equations to support optimization and accurate decision-making. In the optimal opening state, compared to a fully closed roof, the daylight factor and natural airflow velocity increase by up to 3.9 and 5.3 times, respectively, while the universal thermal climate index decreases by 1.0 °C. The introduction of a generative adversarial network proxy model significantly improves computational efficiency, accelerating natural airflow velocity calculation by 70–100 times and reducing simulation time by 98.6 %. This study expands predictive models from the urban scale to the building scale, enhancing simulation efficiency and promoting the application of deep learning models and genetic algorithms in climate-responsive building environment assessment and prediction.

Behavioural response to boat noise weakens the strength of a trophic link in coral reefs

In oceans, the noise generated by human activities has reached phenomenal proportions, with considerable harmful effects on marine life. Measuring this impact to achieve a sustainable balance for highly vulnerable marine ecosystems, such as coral reefs, is a critical environmental policy objective. Here, we demonstrate that anthropogenic noise alters the interactions of a coral reef fish with its environment and how this behavioural response to noise impairs foraging. In situ observations on the Moorea reef revealed that the damselfish Dascyllus emamo reacts to boat passage by moving closer to its coral bommie, considerably reducing the volume of water available to search for prey. Using boat noise playback experiments in microcosms, we studied D. emamo's behaviour and modeled its functional response (FR), which is the relationship between resource use and resource density, when feeding on juvenile shrimps. Similar to field observations, noise reduced D. emamo's spatial occupancy, accompanied by a lower FR, indicating a reduction in predation independent of prey density. Overall, noise-induced behavioural changes are likely to influence predator-prey interaction dynamics and ultimately the fitness of both protagonists. While there is an urgent need to assess the effect of anthropogenic noise on coral reefs, the ecological framework of the FR approach combined with behavioural metrics provides an essential tool for evaluating the cascading effects of noise on nested ecological interactions at the community level.

Legal Mechanisms for Protecting the Public Interests of the Russian Federation from the Transboundary Effects of ESG Standards

The study notes that ESG standards formulated in the West have no scientific basis, serve as a tool for competitive struggle against Russian companies, and contradict traditional Russian values. The first ESG standard, based on the decarbonization of the economy, does not relate to the environmental protection objectives set by the President of Russia and poses a threat to such constitutionally significant values/national interests of the country as the sovereignty, security and defense capability of Russia. The content of the second Western ESG standard is incompatible with the principles of developing a healthy society. It includes gender perversions presented as new norms, poses a threat to national interests related to morality and public health, and contradicts the meaning of Russian policy defined by the President of Russia, which consists of saving people and increasing human capital as Russia’s main wealth. The third Western ESG standard, which contains a list of non-financial reporting issues related to companies’ disclosure of information on the Internet, poses a threat to such national interests as defense capability and security. Legal mechanisms for the protection of constitutionally significant values of Russia should be based on a system of incentives for Russian economic structures that ensure Russian national interests, through preferences of the Russian state, Russian investment companies and funds. Russia’s development can be sustainable only under conditions of an influx of all Russian resources, primarily gas and oil, which form the basis of the country’s economy. The development of Russia should be based on national/public interests of the Russian Federation as priorities/standards.

The Impact of Sustainable Innovation on Environment, Social and Governance (ESG) Framework: A Moderating Paradigm from Research and Development (R&D) Expenses

In recent years, businesses are giving more and more emphasis to create sustainable innovations that balance economic, environmental, and social objectives. The objective of this paper is to investigate the impacts of sustainable innovation on Environmental, social and governance (ESG) performance. This paper also examines the moderating effect of Research and Development (R&D) expenses on sustainable innovation and ESG. The analysis makes use of information from 34 Bangladeshi listed firms from 2018 to 2022. A content analysis method was applied to collect the panel data from secondary sources. To test the study hypotheses, multiple regression analysis is employed. The result shows that sustainable innovation has a positive impact on ESG performance. In addition, the study also found that R&D has no significant effect on the relationship between sustainable innovation and ESG performance. The results could assist managers and policymakers in developing nations in establishing sustainable innovation strategies to attain ESG performance. This is the yardstick study towards SDGSs to examine the impacts of Sustainable innovation and ESG performance considering R&D expenses as a moderating variable in the context of Bangladesh, an emerging country.

Evaluation on Different Object Selection Visual Feedback in Collaborative Augmented Reality

Collaborative Augmented Reality (CAR) involves multiple users working together in an augmented reality setting to accomplish tasks. In CAR, transparent techniques enable users to see occluded objects in complex environments, yet they often struggle to discern which part of a complex object they have selected. Workspace awareness refers to understanding how others interact within a shared workspace and influence the choice of visualization techniques. This study investigated workspace awareness regarding different object selection visual feedback in co-located CAR. It explored four technique variations: Hi (highlight), Hi-Ray (ray emitting), Hi-View (view switching), and Hi-RV (ray emitting and view switching). Thirty-two participants were divided into 16 pairs, with an instructor demonstrating how to assemble an engine component and a student replicating the process using the four visualization techniques. Results showed the visualization techniques did not affect task completion time significantly. The Hi and Hi-Ray technique had the highest error rate, while the Hi-RV technique had the lowest. However, users preferred the Hi-View technique, which scored highest in interaction, collaboration quality, workspace awareness, and required less workload. To enhance awareness, participants pointed out the weakness in the combination of visual feedback, particularly with the ray emitting technique, which causes confusion when multiple objects are selected due to visual clutter. They suggested that integrating audio feedback could help alert users to their choices, and using color-based indicators could distinguish different actions, such as indicating correct or incorrect placement of virtual objects.

Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China.

China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.

Integrating Entropy Weight and MaxEnt Models for Ecotourism Suitability Assessment in Northeast China Tiger and Leopard National Park

The development of ecotourism in protected areas faces the challenge of balancing conservation and ecotourism. Ecotourism suitability assessments are essential tools for managing tourism in these areas. However, current assessments often overlook biological factors, leading to adverse effects on wildlife. This study uses the Northeast China Tiger and Leopard National Park as a case study to establish a comprehensive assessment system that integrates ecotourism suitability with tiger and leopard habitat suitability, thereby linking ecotourism with wildlife conservation. The primary research methods include ecotourism suitability analysis based on the entropy weight method and habitat suitability analysis using the MaxEnt model. Based on the zoning results of ecotourism and habitat suitability, a comprehensive ecotourism suitability zoning map was produced. This map indicates that areas of very high suitability account for 45.62% of the total area, covering approximately 6152.563 km2, and are primarily located on the edges of village clusters. These areas can be prioritized for developing tourism infrastructure. The comprehensive ecotourism assessment system can balance the development of ecotourism with wildlife conservation, contributing significantly to the coordinated development of economic, social, and environmental objectives.

Examining the environmental consequences of China's mineral wealth and governance in the context of the natural resource curse

This study investigates the environmental consequences of China's mineral wealth and governance within the framework of the natural resource curse. The natural resource curse posits that countries rich in natural resources often experience less economic growth and worse development outcomes than countries with fewer natural resources. This phenomenon is particularly relevant for China, which has experienced significant mineral wealth since the 1990s. The primary aim of this research is to assess how China's mineral wealth and governance structures have influenced environmental outcomes from 1990 to 2022, using an econometric model. The results reveal three key findings: First, regions with higher mineral wealth have experienced more severe environmental degradation, particularly in terms of air and water pollution. Second, governance quality plays a critical role in moderating these effects, with better governance associated with less environmental harm. Third, economic growth driven by mineral wealth does not necessarily translate into improved environmental conditions, highlighting a disconnect between economic and environmental objectives. The policy implication is clear: to mitigate the environmental consequences of mineral wealth, China must strengthen its governance mechanisms and integrate environmental considerations into its resource management strategies.

Nearest neighbor future captioning: generating descriptions for possible collisions in object placement tasks

Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting from their own actions remains insufficient. In this study, we focus on the linguistic explainability of DSRs. Most existing methods do not explicitly model the region of possible collisions; thus, they do not properly generate descriptions of these regions. In this paper, we propose the Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor Language Model for future captioning of possible collisions, which enhances the model output with a nearest neighbors retrieval mechanism. Furthermore, we introduce the Collision Attention Module that attends regions of possible collisions, which enables our model to generate descriptions that adequately reflect the objects associated with possible collisions. To validate our method, we constructed a new dataset containing samples of collisions that can occur when a DSR places an object in a simulation environment. The experimental results demonstrated that our method outperformed baseline methods, based on the standard metrics. In particular, on CIDEr-D, the baseline method obtained 25.09 points, whereas our method obtained 33.08 points.

A regionalized social life cycle assessment of a prospective value chain of second-generation biofuel production

The German Bioeconomy focuses on innovation and in particular on the smart mobilization of resources to produce high-value-added bio-based products, processes and services. In the case of the production of biofuels, the demand for reducing fossil fuel utilization has placed the focus on second-generation biofuel value chains, which mobilize residual streams as their main biomass feedstocks. However, while the environmental impacts of these value chains have been studied using life-cycle approaches, little attention has been given to their social implications. This work aims to fill this gap by assessing the potential social hotspots associated with the production of second-generation biofuel through a prospective case study in Eastern Germany. We use the RESPONSA framework, a regionalized social life cycle assessment developed specifically for assessing value chains of the German Bioeconomy. The results for 22 social indicators reveal that while biofuel production generates employment opportunities, aspects such as working conditions, support for professional qualification, and workers’ participation have an overall poor social performance. The results lead to conclude that socioeconomic issues related to advanced biofuel production persist, exhibiting a socioeconomic disparity, with employment creation being the only area of socioeconomic benefit. Yet, there is a contradiction between job creation and addressing broader social issues in the biofuel industry, which underlines the need for a balanced approach to reconcile environmental objectives with social development. These results provide an opportunity to discuss how to mitigate the negative social impacts of second-generation biofuel production in the region.

A dynamic bi-objective optimization model for a closed loop supply chain design under environmental policies

Given the increasing focus on sustainability and environmental policy constraints, companies are required to redesign their supply chains. This paper explores the optimization of a closed loop supply chain (CLSC) network under both economic and environmental considerations. To achieve this, a bi-objective mixed integer linear model was developed. The proposed model identifies the optimal selection of CLSC facilities and manages both forward and reverse flows between them. The economic objective is reached by minimizing the total CLSC costs, while the environmental objective is satisfied by reducing CO2 emissions throughout the network. Products can be returned throughout their entire life cycle, which is why our model incorporates a dynamic aspect by considering product life cycle phases as time periods for the decision horizon. The model was tested through numerical experiments using a meta-heuristic approach based on the non-dominated sorting genetic algorithm NSGA-II. This algorithm produces a set of Pareto-optimal solutions that balance both objectives effectively. The results showed good performance in terms of computational time and optimization. Pareto solutions offered various options for managers and decision makers aiming for a sustainable closed loop supply chain design.