Management Of Renewable Resources Research Articles

Smart design upcycling of post-production display panels into new creativematerials to support the sustainable development of a circular economy inthe furniture industry

Smart design upcycling of post-production display panels into new creative materials to support thesustainable development of a circular economy in the furniture industry. The economy is currently transformingfrom a linear economy to a circular economy. This is increasingly supported by policy, legislation, industry, andacademia. A circular economy means more efficient management of renewable natural resources, which hasimplications for businesses’ bioeconomies, as expressed in the sustainable development goals of the UnitedNations’ 2023 Agenda. Our study contributes to solving a research gap by seeking to answer several questions:How can post-production residues be used to improve a company’s bioeconomy? Can they bring profit toa company while saving natural wood? Is upcycling using the smart design of post-production a helpful solutionfor material and cost savings in the bioeconomy? The outcome of our study is twofold. From a creative inputperspective, we show the creative possibilities of upcycling production residues. For business models, we refinethe conceptualisation of a sustainable SME circular bioeconomy through the profit gained from such a productsolution. Based on our research, we present conclusions and suggestions for small and medium-sized enterprises,scientists, designers, and technologists to help the furniture industry transition from a linear economy toa sustainable circular economy. This promotes company profitability and growth and helps to save natural wood.

Water resource management based on wireless virtualization in wastewater reclamation and utilization in dryland botanical garden

Abstract The research on the management and distribution of renewable water resources mostly adopts the method of combining the intelligent optimization algorithm and the target planning model. In this paper, four water distribution users are used as categories, and the problem of renewable water resources allocation is transformed into a classification problem, which is included in the category of wireless virtualization processing problems, and wireless virtualization is selected to support the allocation of renewable water resources. Experiments show that the water shortage of the water distribution scheme obtained by the method in this paper is lower than that of the optimal water distribution scheme of the traditional intelligent optimization algorithm. It can also be seen from the water distribution cost and water shortage of a single user that the water distribution effect of the method in this paper is better than that of the traditional intelligent optimization algorithm (P<0.05). It shows that the research on the management and distribution method of renewable water resources based on wireless virtualization not only provides new ideas for dealing with configuration problems but also has important significance for alleviating the shortage of water resources in our country and the management and utilization of renewable water resources.

Role of financial performance and natural resources development on economic recovery: Empirical evidence from an Asian perspective

The COVID-19 pandemic has had a significant impact on the world economy, with many countries struggling to recover and seeing a decline in economic activity. This study examines the effects of economic risk, renewable energy, and technological advancement, as well as the relationship between natural resources and the performances of Asian economies from 1990 to 2020. The data were analyzed using the second-generation unit root test, and panel regression analysis demonstrates that the economies under study greatly profit from having access to natural resources. Economic growth is fueled by technological advancements and renewable energy sources, and as one moves up the quantile scale, more environmental resources and sustainable power are being used. Based on the dynamic ordinary least squares report, it was determined that the empirical results were trustworthy. The study emphasizes the significance of effective management of renewable energy sources and natural resources to guarantee ongoing economic growth.

A study on imprecise mathematical model for optimal management and utilization of renewable resource by population

The development and survival of the biological population depend heavily on the availability of food and other materials that may be produced from renewable resources. In real life situation the information reported about the parameters of ecological model system may not be precise due to various issues like insufficient data, error in data collection, wrongly measurement etc. To address all of these challenges, an ecological model system must be developed in an ambiguous setting where the ecological parameters are handled as different kinds of fuzzy numbers. In this article, we present an imprecise mathematical model for optimal management and utilization of renewable resources by population. We introduce the model system in interval environment and apply a parametric form of interval number which makes different form of crisp ecological model. The positivity and boundedness of the proposed model system are checked. The stability criterion of feasible equilibrium points and bionomical equilibrium have been discussed. Moreover maximum sustainable strategy and optimal harvesting strategy are analyzed using interval number. Numerical discussion are provided to verify the result of the proposed system.

COP27 perspective of resources management: From conflict to COVID-19 of emerging countries

Resources management in the modern era is a crucial perspective of a sustainable environment linked with sustainable development. Therefore, it is crucial to re-estimate the resources-environment management nexus in a new setting. Concerning environmental management from the COP27 perspective, economies are taking various economic, financial, and environmental steps to reduce hazardous emissions in the region. Recently, BRICS economies have invested in renewables and enhanced capital formation to accelerate environmental recovery. In this respect, this study tends to examine the influence of electricity from renewable resources (ELREC), resources management (resources rents), research and development (RDEV) and gross fixed capital formation (GFCF) on carbon emissions of the BRICS economies throughout 1989–2021. Using various diagnostic tests, this study confirms the long-run equilibrium association between the variables. This study uses non-parametric estimation approaches and concludes that ELREC and RDEV significantly enhance environmental sustainability. Except for forest and oil resources, the rest of the forms of the resources increase emissions. On the other hand, economic growth and GFCF significantly lead to higher emissions, which degrades the environment. Resources rents also contribute to increasing carbon emissions.

Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing

Hyperspectral remote sensing is an emerging technique to develop new cost- and time-effective geophysical mapping methods. To overcome challenges introduced by plant cover in geothermal systems globally, we hypothesise that foliage can be used as a proxy to map underlying surface geothermal activity and heat-flux due to their capability on elemental uptake from geothermal fluids and host rock/soil. This study shows for the first time that foliar elemental mapping can be used to image geothermal systems using both high-resolution airborne and satellite hyperspectral images.This study has specifically targeted kanuka shrub (kunzea ericoides var. microflora) as a proxy media to develop air- and spaceborne hyperspectral solutions to monitor inaccessible, biologically and culturally sensitive geothermal areas. Using high resolution airborne AisaFENIX and PRISMA hyperspectral data, foliar element maps for Ag, As, Ba and Sb have been developed using Kernel Partial Least Squares Regression and Random Forest classification models to track their foliar distribution and develop a conceptual model for metal and thermal induced changes in plants. Our study shows evidence that the created foliar element maps are in concordance with independent LiDAR-retrieved canopy structure and height as well as temperature effects of the underlying geothermal field. This study has proven air- and spaceborne hyperspectral sensors can indeed capture critical information within the VNIR and SWIR regions (e.g. ∼452, ∼500, ∼670, ∼820, ∼970, ∼1180, ∼1400 and ∼2000 nm) that can be used to identify metal and thermal-induced spectral changes in plants reliably (overall accuracy of 0.41–0.66) with remotely sensed imagery. Our non-invasive method using hyperspectral remote sensing can complement existing practices for exploration and management of renewable geothermal resources through timely monitoring from air- and spaceborne platforms.

Gerardo Budowski: an innovator in land use management

To refer to the life and work of Dr. Gerardo Budowski Wolffgang, the definition he provided about himself is very useful: "Although I have training in agriculture and forestry, I would like to call myself a land use manager" use manager”), where environmental concerns are taken into account towards the sustainable management of natural renewable resources.”
 Of course one could add more qualifiers to Budowski's various contributions in the second half of the 20th century. However, the quote above sums them up very well, and in a few keywords: renewable resources, land use and sustainable management, with its three "legs" or dimensions: environmental, economic and social.
 Budowski's career headed in that direction, somewhat surprisingly, starting from a base with a productive agricultural and forestry focus. For someone with less vision and ability than him, this platform could have led to a very different and probably less extensive journey.

Stochastic growth and regime shift risk in renewable resource management

Renewable resources are affected by both environmental variability, which makes the year-to-year stock growth uncertain, and the risk of irreversible events (e.g., a stock collapse). Little is known about how a renewable resource harvester would optimally respond to the combined effects of both sources of risk. In this paper, we propose a simple dynamic resource model to investigate this issue. For some structures of the harvesting cost function, we find that anticipating a higher variability in biological growth induces a cautious management policy, but only when regime shift risk is accounted for. Accounting for the risk of regime shift may prescribe large changes in management responses to anticipated random changes in biological growth whereas ignoring such a risk prescribes small changes in management. Optimal escapement is not constant but varies across all periods when the planning horizon is finite and the regime shift risk is endogenous.

Do international trade diversification, intellectual capital, and renewable energy transition ensure effective natural resources management in BRICST region

Brazil, the Russian Federation, India, China, South Africa, and Turkey, collectively known as BRICST experienced rapid economic growth in recent years. A third of the world's material is mined to provide these nations' material needs. A lack of effective resource management results in the depletion of natural resources and the emergence of environmental consequences. Therefore, identifying the key drivers that stimulate effective resource management is crucial. This study draws an asymmetric association between trade diversification, intellectual capital, renewable energy transition, and material footprint in BRICST countries from 1990 to 2019. It applies Method of Moments Quantile Regression” (MMQR) to inspect the quantile-based association between variables due to possible non-normality in data. The outcome MMQR illustrates the negative and significant coefficients of trade diversification, intellectual capital, and renewable energy transition at all quantiles (0.1–0.9). It implies that trade baskets with less specialized products increases material footprint, while human capital and transforming energy resources from fossil fuels to renewable energy support sustainable resource management. Similar results are endorsed using other panel estimators and helps in formulating policies to improve progress toward SDG goals 8.4 and 12.2.

Time-consistent renewable resource management with present bias and regime shifts

We investigate the extraction plan of present-biased decision makers managing a renewable resource stock whose growth is uncertain and which could undergo a rapid and significant change when stock falls below a threshold. We show that the Markov-Nash equilibrium extraction policy is unique, time consistent, and increasing in resource stock. An increase in the threshold leads to increased resource extraction, rather than the precautionary reduction in extraction often observed with exponential discounting. An increase in the degree of present bias also leads to an increase in resource extraction. Our analysis suggests that accounting for and appropriately dealing with resource managers’ present bias may be important to understand resource use sustainability.

PROCSIM: An Open-Source Simulator to Generate Energy Community Power Demand and Generation Scenarios

Reducing our carbon footprint is one of the biggest challenges facing humanity in the current millennium. In the last few years, researchers have focused their attention on balancing the demand and supply, thereby allowing better management of renewable energy resources. In this regard, many energy management strategies have been developed. Nevertheless, testing, evaluating, and comparing such approaches in multiple scenarios, and above all, assessing their generalization, is currently a hard, or even impossible, task. Furthermore, analyzing the impact of such strategies in Energy Communitys (ECs) is an underexplored task. This is due to the lack of existing EC datasets and simulators that allow users to evaluate and compare their approaches. Although there are some tools to generate demand and production profiles, they are all developed with a single purpose. To address these challenges, PROCSIM is presented: an open-source simulator designed especially to create energy community datasets for multiple purposes—in particular, to test and evaluate different algorithms and models. It includes integration with a consumption-profiles generator, tools to simulate Solar Photovoltaic (PV) and wind production, a module that generates an EC dataset, and finally, a set of metrics to evaluate the generated community. To conclude, a case study comprised of two experiments is presented. The first experiment shows how an EC dataset can be created using PROCSIM. In the second experiment, an exemplification of how this dataset can be used to evaluate an optimization algorithm is provided, namely, to optimize the control of a battery. Ultimately, it is shown that the simulator can generate energy community power demand and generation scenarios. The scenarios can be fully customized by the user, considering different sizes (power capacity) and numbers of assets, and diverse generation/consumption characteristics. The datasets generated by PROCSIM can be useful for different purposes, such as optimal scheduling of EC generation resources and consumption flexibility, and for designing battery energy storage systems.

Periodic dynamics of a single species model with seasonal Michaelis-Menten type harvesting

In this paper, we formulate a seasonally interactive model between closed seasons and open seasons with Michaelis-Menten type harvesting based on some management and capture methods of renewable resources. It is assumed that the population growth obeys the logistic equation in closed seasons and is captured following Michaelis-Menten type response function in open seasons. We define a length threshold of the closed season T¯⁎, which depends on the harvesting parameter. Under the extinct condition of the corresponding continuous harvesting model, by setting the closed season, theoretical results show that the origin is globally asymptotically stable if and only if T¯≤T¯⁎, and there exists a unique globally asymptotically stable T-periodic solution if and only if T¯>T¯⁎. In particular, under the critical conditions on special harvest parameters, it is found that the T-periodic solution still exists as long as an arbitrary positive close season is formulated. Numerical examples are carried out to confirm the obtained theoretical results. Brief conclusions and discussions on our findings are also provided.

A new proposed model of EMOLUP for assessing of ecological capability of different utilizations and land use planning in Sepidan Township, Iran

The optimum use and appropriate management of renewable resources, with dynamic characteristics, needs to evaluate and classify the ecological capability of environment and its socio-economic conditions. Land use planning (LUP) is an iterative process based on the dialogue amongst all stakeholders aiming at the negotiation and decision for a sustainable form of land use in rural areas as well as initiating and monitoring its implementation. The main objective of this paper is the implementation of integration quantitative model namely EMOLUP (Eco-Socioeconomic Model of Land Use Planning) in Sepidan Township of the Fars province in Iran. Therefore, two main steps were prepared for the new model: I. Ecological capability evaluation of different land uses. This step is composed of the geometric mean method instead of the Boolean and MCE methods. II. Land use planning and prioritizing for the various uses. This step has been composed intersecting ecological capability maps and land use planning, based on two scenarios (economic and social). Then, it was compared with current qualitative and quantitative methods. Also, current land use is used for calibrating and modifying the proposed models. Results show using the geometric mean method is better than Boolean models, and the method of the calibrated geometric mean (with overall accuracy > 63 and kappa index > 0.39 for all land uses) is the best among different used models. Also, results of prioritizing and land use planning showed that quantitative method with two socio-economic scenarios (with an average of EPM erosion model = 0.31) is the best method for land use planning in the study area. We confirmed that the EMOLUP model can contribute to a better understand land use planning in different regions of the world.

The Evolution of Decision-Making Frameworks in Renewable Resource Management

The sustainable management of renewable resources necessitates dynamic and effective decision-making frameworks that can adapt to evolving environmental, technological, and regulatory contexts. This study aimed to explore and document the evolution of decision-making frameworks in renewable resource management, identifying key themes that characterize the processes and outcomes associated with these frameworks. A qualitative research design was employed, focusing solely on semi-structured interviews to gather in-depth insights from professionals involved in renewable resource management. Participants were purposively sampled to ensure a broad representation of expertise and geographic diversity. Data collection continued until theoretical saturation was achieved, ensuring a comprehensive understanding of the decision-making frameworks. Four main themes emerged from the data: Policy Evolution, Decision-Making Processes, Resource Management Practices, and Impact and Outcomes. Each theme was explored through various categories, such as Regulatory Changes, Stakeholder Influence, and Technological Advancements under Policy Evolution; Criteria Development, Process Implementation, and Collaboration and Partnerships under Decision-Making Processes; and similar breakdowns for Resource Management Practices and Impact and Outcomes. The study highlights the complexity and multi-dimensional nature of decision-making in renewable resource management. Effective decision-making frameworks are shown to be crucial for navigating the challenges of sustainability and ensuring the effective management of renewable resources. These frameworks must integrate diverse inputs, adapt to changing conditions, and effectively balance environmental, economic, and social considerations.

Management of Renewable Resources in the Energy Sector in the Context of Ensuring the Economic Sustainability of Energy Systems

The purpose of the study is to substantiate the environmental, economic and financial aspects of the management of renewable resources in the energy sector with an assessment of the strategic development of renewable energy. One has assessed the potential of renewable energy sources in Ukraine, amounting to about 454.4 kWh, which is 59.2 million tons of reference fuel per year. The 2016-2020 analysis revealed that the capacity of the renewable electric power industry was increased to 10,900 MW, and the production of green electricity was up to 26 billion kW, while the generation of thermal energy from renewable resources increased to 5.85 million tons. The development of renewable resources was ensured by the issuance of Green and Sustainability-linked bonds for five years, amounting to 825 million USD with a yield rate of 6.785%. Directions were identified to increase the production of renewable resources in the energy sector and increase their share to 20% in accordance with the Energy Strategy of Ukraine until 2035. Therefore, there will be the demand for green and environmental investments and green bonds, indicating the placement of funds in energy efficient and environmental projects.

Preface for “renewable power generation, energy storage, and resource management challenges and solutions”

Preface for “renewable power generation, energy storage, and resource management challenges and solutions”

Mathematical modeling for anaerobic digestion under the influence of leachate recirculation

<abstract><p>In this paper, we proposed and studied a simple five-dimensional mathematical model that describes the second and third stages of the anaerobic degradation process under the influence of leachate recirculation. The state variables are the concentration of insoluble substrate, soluble substrate, produced hydrogen, acetogenic bacteria and hydrogenotrophic-methanogenic bacteria. The growth rates of used bacteria will be of general nonlinear form. The stability of the steady states will be studied by reducing the model to a 3D system. According to the operating parameters of the bioreactor described by the added insoluble substrate, soluble substrate and hydrogen input concentrations and the dilution rate, we proved that the model can admit multiple equilibrium points and we gave the necessary and sufficient assumptions for their existence, their uniqueness and their stability. In particular, the uniform persistence of the system was satisfied under some natural assumptions on the growth rates. Then, a question was answered related to the management of renewable resources where the goal of was to propose an optimal strategy of leachate recirculation to reduce the organic matter (either soluble or insoluble) and keep a limitation of the costs of the recirculation operation during the process. The findings of this work were validated by an intensive numerical investigation.</p></abstract>

Greed is good: Heuristic adaptations for resilience in renewable resource management

AbstractDecision problems may be subject to objectives or constraints that make the formal model intractable. We propose an adaptive and pragmatic approach to address such nonstandard objectives or constraints, where these are first circumvented for feasibility, then accounted for through heuristics. One example is managing risk and resilience in a natural system facing uncertainty. Our procedure is exemplified in a predator‐prey fisheries system where a reference policy that maximizes expected profits implies a risk of prey stock collapse. The reference policy includes a no‐fishing section for the prey and harvest beyond myopic catch for the predator in parts of state space. We construct heuristic recovery plans, based on the reference policy, to reduce the risk of collapse by partly backing in the auxiliary objective. Under the heuristic policies, system resilience is enhanced with limited economic losses. Via Monte Carlo simulations, we calculate viability probabilities as measures of improved resilience and employ dynamic programming to assess value losses.

Sustainable Management of a Renewable Fishery Resource with Depensation Dynamics from a Control Systems Perspective

Human societies are exploiting natural renewable sources such as fisheries, forests, groundwater basins, rivers, and soil at an increasing intensity. Around the world, these resources are being managed by various institutions or governments. One of the challenges faced by institutions is to develop strategies and policies to effectively manage these renewable resources under social and ecological uncertainties, disturbances, policy implementation difficulties, and measurement errors. In this paper, a fishery is considered as an example and the problem of managing a fishery is approached from a control systems perspective. The justification behind this approach is due to the observation that the problem of managing a renewable resource can be posed as a control systems problem and that the discipline of control systems possesses tools and methods to deal with model uncertainties, external disturbances, measurement errors and implementation issues. For the fishery, a depensation type population dynamics model is considered. Depensatory models are used in social/ecological systems in order to model dynamics of certain species of fish populations. An optimal control strategy based on Pontryagin’s Maximum Principle is derived and its sustainability and robustness properties with respect to parametric uncertainties, measurement errors and disturbances are examined. Finally, a sub-optimal but more robust control strategy is proposed and its robustness properties are provided. The main objective of the paper is to show that a control systems engineering approach can be applied to a social-ecological problem and it can provide easy to implement management strategies, insight, and guidance into the management of renewable resources.

Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long–Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.