Environmental Decision Research Articles

Enhancing Decision-Making in Complex Environments: Integrating AHP, Delphi, and Neutrosophic Logic

The integration of the Analytic Hierarchy Process (AHP), the Delphi method, and neutrosophic logic provides a powerful framework for complex decision-making, allowing for an enhanced handling of uncertainties and multiple criteria that characterizes many strategic planning and policy formulation scenarios. AHP’s structured approach helps decompose decision-making into manageable sub-problems, while the Delphi method facilitates expert consensus through iterative rounds, enriching the decision-making process with diverse expert insights. The inclusion of neutrosophic logic allows for better representation and processing of uncertainty, offering a flexible way to handle indeterminate and contradictory information. This robust methodology not only improves the precision of decisions but also adapts to the nuanced requirements of multifaceted decision environments. Future research could benefit from integrating these methods with technological advancements like artificial intelligence to automate and optimize the decision-making process further. Applying this integrated approach in various sectors such as healthcare, environmental management, and urban planning could also provide valuable insights into its effectiveness and scalability.

Innovative Approaches To Data Visualization: Enhancing Decision-making In Corporate Environments

Innovative Approaches To Data Visualization: Enhancing Decision-making In Corporate Environments

The large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets.

We proposed a novel decision-making method, the large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets, to address the challenge of large-scale group multi-attribute decision-making in fuzzy environments. This method concurrently accounted for the membership and non-membership degrees of decision-making experts in fuzzy environments and the corresponding probabilistic value to quantify expert decision information. Furthermore, it applied to complex scenarios involving groups of 20 or more decision-making experts. We delineated five major steps of the method, elaborating on the specific models and algorithms used in each phase. We began by constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights. The following steps involved classifying large-scale decision-making expert groups and selecting the optimal classification scheme based on effectiveness assessment criteria. A global consensus degree threshold was established, followed by implementing a consensus-reaching model to synchronize opinions within the same class of expert groups. Decision information was integrated within and between classes using an information integration model, leading to a comprehensive decision matrix. Decision outcomes for the objects were then determined through a ranking method. The method's effectiveness and superiority were validated through a case study on urban emergency capability assessment, and its advantages were further emphasized in comparative analyses with other methods.

Exploring the performance impact of soft constraint integration on reinforcement learning-based autonomous vessel navigation: Experimental insights

Reinforcement learning has shown promise in enabling autonomous ship navigation, allowing vessels to adapt and make informed decisions in complex marine environments. However, the integration of soft constraints and their impact on performance in RL-based autonomous vessel navigation research remain understudied. This research addresses this gap by investigating the implications of soft constraints in the context of the risk-averse ship navigation problem. Four distinct soft constraint functions are proposed, which are integrated with two widely used RL algorithms, resulting in the creation of eight risk-averse autonomous vessel navigation models. To ensure a comprehensive evaluation of their performance, comparative analyses are conducted across seven virtual digital channel environments. Additionally, a novel metric, known as Large Helm Momentum (LHM), is introduced to quantify the smoothness of autonomous vessel navigation. Through thorough experimentation, key considerations for the design of soft constraint functions in the domain of autonomous ship navigation are identified. A comprehensive understanding of how different soft constraint functions influence autonomous driving behavior has been achieved. Key considerations for designing soft constraint functions in the domain of autonomous ship navigation have also been identified. Five principles, namely the constraint association principle, dominance of hard constraints, reward-balance principle, mapping requirement principle, and iterative improvement principle, are proposed to optimize the design of soft constraint functions for autonomous ship navigation, providing valuable guidance and insights.

ФОРМУВАННЯ ІНВЕСТИЦІЙНОЇ СТРАТЕГІЇ В ПРОЦЕСІ ЕКОНОМІЧНОГО РОЗВИТКУ ПІДПРИЄМСТВА

The article thoroughly analyzes the processes that contribute to the definition and implementation of investment strategies in the context of dynamic company development. The article presents the main methodological approaches to forming an investment strategy, as well as the relationship of this process with the life cycles of enterprises. Significant attention is paid to the integration of the investment strategy with the company’s general strategic planning, considering the economic, political, and social challenges of modernity. The article defines an investment strategy as a system of complex actions aimed at the effective use of resources to achieve the strategic goals of the enterprise. It explores the essence of the investment strategy through the prism of its impact on sustainable growth and development of the enterprise. An analysis of various investment resources is also presented, including material assets, financial investments, intellectual investments, and personnel development. Particular attention is given to the hierarchical structure of the investment policy and its elements according to the period of implementation. The authors identify key principles for forming investment strategies, considering the dynamics of changes in the macroeconomic environment and the need to adapt to rapidly changing business conditions. The article includes research on the relationship between the investment strategy and the general strategy of enterprise development, which allows for adaptation to changes in the external environment and optimization of decisions in the context of different stages of the enterprise’s life cycle. Particularly important is the analysis of strategic and tactical tasks that enable the enterprise to achieve long-term competitive advantages. The article also presents a graphical model that demonstrates the sequence of forming and implementing the investment strategy, starting from defining strategic directions to specifying investment tasks. The conclusions offer recommendations for improving investment management processes that can enhance the overall effectiveness and stability of companies.

Quantifying salinity in calcareous soils through advanced spectroscopic models: A comparative study of random forests and regression techniques across diverse land use systems.

Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions.

Revealing or concealing: The impact of environmental penalty spillover effects on corporate environmental information disclosure

Abstract As a way of environmental protection, environmental administrative punishment affects the environmental decision-making of enterprises to a certain extent. This paper proposes the industry spillover effect of environmental administrative punishment, utilizes the variable factors affecting the quality of corporate environmental information disclosure, and puts forward the hypothesis of the relationship between the spillover effect of environmental punishment and the level and quality of environmental information disclosure, whether it is “displaying” or “disguising”. Companies listed in the heavy pollution industry are selected as research objects to test the relevance of each influencing factor on environmental information disclosure. The double-difference method is used to construct the regression model to verify the influence of government regulation on the level of environmental information disclosure, analyze the impact of environmental information disclosure level on the market value of enterprises, and the relationship between environmental administrative penalties and environmental information disclosure. The regression equation passed the F-test with an adjusted R 2 of 0.425 and the coefficient of EDI of 0.362. It passed the 1% significance level test, indicating that the level of environmental information disclosure is positively related to enterprise value. The higher the level of disclosure of the environment, the higher the market value. Whether the listed company is engaged in legitimacy management or impression management, manipulating environmental disclosure during this period has an impact on the level and quality of environmental disclosure.

Model-based Systems Engineering for Sustainable Factory Design

In the fiercely competitive landscape of modern manufacturing, small and medium-sized enterprises (SMEs) are navigating an intricate web of challenges and opportunities. This environment demands constant adaptation and innovation to remain relevant and profitable while upholding sustainable practices. Nowadays, Model-based Systems Engineering (MBSE) capabilities have evolved to a state where collaboration, handling complexity, and integrating sustainability into design processes are streamlined. By leveraging digital models to holistically represent and analyze complex systems, MBSE empowers designers to systematically incorporate sustainability issues and make informed decisions. This research elucidates the application of MBSE in fulfilling stakeholders' needs, tracing integrated functional requirements, and finding solution alternatives for accelerating the movement toward sustainable factories. In this regard, the Cameo System Modeler is utilized for sustainable factory design decomposition. To facilitate factories' sustainability assessment, the first level of functional requirements is defined based on axiomatic design theory and sustainability metrics which are highlighted in the European Sustainability Report Standard (ESRS). To present the application of Cameo, the Austrian Post sustainability report is analyzed based on assumed sustainability targets. Results show how MBSE can help stakeholders, managers, and designers have a big picture of a sustainable factory in one place and provide a resilient decision-making environment.

Employing Reinforcement Learning to Construct a Decision-Making Environment for Image Forgery Localization

Employing Reinforcement Learning to Construct a Decision-Making Environment for Image Forgery Localization

Approach to the personnel selection in a group decision-making environment based on the use of the MULTIMOORA and PIPRECIA-S methods

Approach to the personnel selection in a group decision-making environment based on the use of the MULTIMOORA and PIPRECIA-S methods

Relaxed Rank Order Centroid Weighting MCDM Method With Improved Grey Relational Analysis for Subcontractor Selection: Photothermal Power Station Construction

This article addresses the problem of subcontractor selection in an uncertain decision-making environment by proposing a new hybrid method and improving a classical multicriteria decision-making (MCDM) method. First, the proposed hybrid method combines subjective and objective weighting methods to estimate the weights of criteria that are represented as grey numbers. The subjective weighting method is the PA weighting method. The objective weighting method is the rank order centroid with slacks (ROCS) weighting method used to compensate for the limitation of the rank order centroid weighting method. Second, grey relational analysis (GRA) is improved by introducing both positive and negative reference (PNR) alternatives instead of a single reference alternative as in the classical GRA. Subsequently, the proposed hybrid grey-point allocation-ROCS weighting method and the GRA-PNR evaluation method are applied to select the most suitable subcontractor for the supply of heliostats for photothermal power station construction. Sensitivity analysis is conducted to verify the robustness of the results. Finally, the technique for order preferences by similarity to an ideal solution with grey values, simple additive weighting with grey relations, and additive ratio assessment with grey criteria scores is applied to validate the participation of the selected subcontractor in the project.

Fusion of Preferences with Linguistic Weighted Power Mean Operator in Complex Decision-Making Environment

This article explores the application of the linguistic 2-tuple computational model in decision-making processes, focusing on its efficiency in managing ambiguous and imprecise linguistic information, which is vital in complex decision-making environments. The main objective is to demonstrate the use of the Weighted Power Mean (WPM) operator for hierarchical aggregation, highlighting its adaptability in reflecting the priority structures of specific problems and preserving the integrity of expert opinions. The model enhances user interaction by minimizing the need for complex numerical conversions, facilitating more intuitive decision-making. The study introduces the methodology of the linguistic 2-tuples, emphasizing their practical application in various decision-making contexts through detailed case studies. It elaborates on the hierarchical aggregation model, discussing the flexibility and potential of the WPM operator to adjust the influence of individual criteria based on their importance. The article also examines potential improvements in aggregation operators to increase their effectiveness and applicability across different scenarios. This comprehensive analysis not only underscores the capabilities of linguistic computational models in modern decision-making environments but also proposes future directions for advancing these techniques to handle increasingly complex information landscapes.

Sustainability scientists’ critique of neoclassical economics

Abstract Non-technical summary Neoclassical economics (NCE) theory and neoliberal economics practice together form one of the principal driving forces of environmental destruction and social injustice. We critically examine ten key hypotheses that form the foundations of NCE, and four other claims. Each fails to satisfy one or more of the basic requirements of scientific practice. Hence, NCE is fundamentally flawed, is irrational in the common meaning of the word, and should not be used as a guide for government policies. Because NCE is socially constructed, it can be replaced with an interdisciplinary conceptual framework that is compatible with ecological sustainability and social justice. Technical summary Neoclassical economics (NCE) is widely regarded as providing theoretical justification for neoliberal notions such as ‘governments should minimize regulation and spending, and hence leave major socioeconomic and environmental decisions to the market’. A large body of literature finds that NCE is largely responsible for environmental destruction and social inequality. As NCE is claimed to be a science and has appropriated terminology (without the content) from physics, we examine critically its basic hypotheses and four other claims from a viewpoint of natural scientists and an ecological economist, each a sustainability researcher. This paper defines NCE in two ways: as a theoretical structure for economics based on (1) the hypotheses of methodological individualism, methodological instrumentalism and methodological equilibration, and (2) the three hypotheses named above together with seven other common hypotheses of NCE. We find that each hypothesis and claim fails to satisfy one or more basic requirements of scientific practice such as empirical confirmation, underlying credible or empirical assumptions, consistency with Earth system science, and internal consistency. Sensitivity analysis is rare and ability to predict is lacking. Therefore, we recommend that neoclassical microeconomics be reformed and neoclassical macroeconomics be abandoned and replaced with a transdisciplinary field such as social ecological economics. Social media summary Conventional economics, a driver of environmental damage and social inequality, fails examination by sustainability scientists.

NAČELO UČEŠĆA JAVNOSTI U DONOŠENJU ODLUKA OD ZNAČAJA ZA ŽIVOTNU SREDINU U MEĐUNARODNOM PRAVU

The public participation principle allows the public to express their views and needs, thus effective implementation of this principle enables citizens and their associations to influence decisions important to them. In the 1990s, there was a growing idea that to make a fair environmental decision, it was essential to involve those affected by the decision. This article researches into the public participation principle in the field of environmental protection, both in theory and in its position within international environmental law. Moreover, it examines the concept and elements of public participation, the notion of the public, and the significance of public participation in realising environmental rights. International environmental treaties analysis reveals that the public participation principle has been widely accepted, and has played a crucial role in their further development.

Public Environmental Concern and Corporate Environmental Governance

We examined the impact of public environmental concerns (PEC) on corporate environmental governance (CEG). Using Chinese listed companies in Shanghai and Shenzhen stock market as our research sample, we find that the higher the regional PEC, the better the level of CEG, and the positive effect of PEC on CEG is more significant in the sample of non-state owned enterprises. An examination of the influence mechanism finds that PEC increase government environmental attention and therefore more stringent government environmental regulations are developed to constrain corporate environmental decisions. This study will help us better understand the unique role of the public in corporate environmental strategy in the Internet era.

Coupling an Autonomous UAV With a ML Framework for Sustainable Environmental Monitoring and Remote Sensing

Many countries face problems in monitoring plant problems and monitoring large environments, as until now, there is no accurate means of aerial monitoring through which concerned parties can benefit from watersheds, monitor large agricultural areas, and make wise environmental decisions regarding them. This paper describes a pioneering approach to develop smart agriculture using multimission drones equipped with dual cognitive modules (brains) that are powered by a machine learning (ML) framework. The first brain uses deep reinforcement learning (DRL) principles to enable autonomous flight, allowing drones to navigate complex agricultural terrain with agility and flexibility. The second brain is responsible for precise and crucial agricultural tasks: counting trees, detecting water locations, and observing and analyzing plants using the Faster R‐CNN algorithm. The system is linked with the ground station for control and command, as well as it includes an Internet of Things (IoT) infrastructure equipped with sensors that collect soil parameters, which then get sent via 5G Wi‐Fi. The dual architecture of the drones, combined with the ground‐level IoT system, creates a comprehensive framework that not only enhances agricultural technologies but also aligns with environmental conservation goals, embodying a paradigm shift towards a greener and more sustainable future. Obtained results show reasonable results with an accuracy rate of 98%.

Typology of intellectual environmental decision support systems

Typology of intellectual environmental decision support systems

Medición de impactos socioeconómicos en estudios ambientales: Una aproximación metodológica

The assessment (ex – ante) of the physical and biological impacts in an Environmental Impact Assessment (EIA) has a methodology reviewed and supported by the majority of the scientific community, such as Environmental Quality Standards (ECA) or Maximum Permissible Limits (MPL). However, what are the parameters to assessment socioeconomic impacts in an EIA? And what indicator to use to measure these impacts? These questions are intended to be answered in this work through a methodological approach, taking current economic theory as support and constructing a set of intensity indicators and their respective parameters. It is expected that this proposal can be a reference for evaluators, consultants and that it be continually reviewed by the scientific community, and that in this way the EIAs are better prepared and can be suitable for making environmental decisions.

A Seat at Whose Table? Analyzing Detroit’s Community Benefit Ordinance as a Tool for Environmental Justice

The Environmental Protection Agency defines environmental justice as the “just treatment and meaningful involvement” of all people in the decisionmaking that affects the environment and human health. Since the origins of the modern American environmental justice movement in the 1980s, activists have emphasized the importance of self-determination. Environmental justice requires that decision making processes center the voices of the individuals impacted by decisions made about the distributions of environmental assets and harms. There is a significant challenge, however, in designing community engagement practices that meaningfully involve community members. Since the 1990s, community benefits agreements have been heralded as an effective tool of community and grassroots control over development projects. In 2016, Detroit became the first city in the country to adopt an ordinance mandating the use of community benefits agreements in qualifying developments. This Note endeavors to analyze Detroit’s community benefits ordinance as a tool for achieving environmental justice by examining where the ordinance succeeds in empowering community control over environmental decision making and falls short of this potential. Examining Detroit’s community benefits ordinance in the context of the larger environmental justice movement provides a rich opportunity to consider what communities stand to gain, and lose, when public participation is funneled through formalized legal processes, and what avenues environmental justice activists can explore to ensure that community members are meaningfully involved in decisions that intimately impact them.

Mapping the changing role of expertise in COVID-19 politics in Europe

During major national crises countries experience a ‘rally round the flag’ effect, where citizens, societal groups and politicians put aside mundane conflicts to stand united against a common threat. This shift to depoliticised policymaking was indeed observed in many European countries during the outbreak of the COVID-19 pandemic from 2020 onwards. Subsequently it noted how politics involving COVID-19 were then (re)politicised, typically when lockdowns were lifted. This article sets out to explore the role of expertise in shaping policy responses during politicised/depoliticised processes. It offers a new typology, classifying four different ideal types of roles for experts as leading, antagonistic, managerial, and auxiliary. Using survey data from political science experts across 31 European countries it analyses knowledge exchanges at different stages throughout the pandemic, and finds a shift from normal policymaking conditions to conditions of politicisation/depoliticisation triggered by the pandemic. The findings demonstrate that the role of experts oscillates depending on the issue at hand and the way that either political actors utilise expertise to legitimise decisions or experts themselves turn into political actors within politicised decision environments. During the pandemic, a dramatic shift of the expert’s role occurred where even in countries whose experts previously had an auxiliary role, leading policy decisions became the norm.