Revisiting the relationship between natural resources and environmental quality- can ICT break the "resource curse" in the environment?

Despite the prevalence of discussions on the "resource curse", the impact of natural resources on environmental quality for better or for worse has not been clearly answered, this study aims to answer the question by introducing the role of Information and Communication Technologies (ICT). To that end, by using the Instrumental Variable Generalized Method of Moments (IV GMM) estimator and a sample of 102 developing and emerging economies from 2006 to 2016, this paper studies the impact of ICT on the relationship between natural resources and environmental quality. Specially, the Environmental Performance Index (EPI) captures the environmental quality. The results show that natural resources have a significant negative effect on EPI, specially, EPI decreases by one unit with a 1% increase in natural resource rents. ICT significantly mitigates this adverse effect, and marginal effects analysis further confirms its positive moderate effects. The results proved to be robust by Lewbel 2SLS and Driscoll-Kraay techniques or other robust tests. It is noteworthy that the adverse effect of natural resources on EPI is greater and the mitigating effect of ICT is more pronounced in low-income countries and lower-middle income countries. Overall, these results remind resource-based countries to vigorously develop ICT, and apply intelligent exploration, digital monitoring, or other digital technologies to realize the high-efficiency use of natural resources, reducing environmental pollution and ecological damage.

Recycling thermally deactivated asbestos cement in mortar: A possible route towards a rapid conclusion of the "asbestos problem".

The "asbestos problem" arises from the fact that asbestos is still abundant in many buildings and represents a hazard for human health. Current strategies adopted by law aiming at mitigating this hazard are far from being ideal. A smarter solution would be an energy sustainable detoxification treatment followed by recycling. If adopted, it would preserve the environment from pollution, natural resources from depletion and human health from hazard. Asbestos-cement slates were thermally deactivated through a sustainable process and reused in mortar for plaster applications. We found that the addition up to 7 wt% of the deactivated product does not affect significantly the water demand; does not affect thixotropy, stickiness and spreadability of the plastic mixture; slightly increases the strength of the mortar; does not compromise mechanical properties after aging. Considering the huge amount of traditional mortar employed worldwide, a rapid end of the "asbestos problem" is envisaged.

Beach-cast seagrass wrack: A natural marine resource improving the establishment of dune plant communities under a changing climate.

Seagrass wrack plays multiple ecological roles in coastal habitats but is often removed from beaches and used for economical processing, neglecting its potential role in sustaining dune plant establishment under changing climate scenarios. Rainwater shortage is a major stress for seedlings and reduced precipitations are expected in some coastal areas. We investigated in mesocosm how wrack influenced seedling performance of Cakile maritima, Thinopyrum junceum, and Calamagrostis arenaria under current and reduced precipitation. We also assessed wrack water holding capacity and leachate chemical/physical properties. Wrack stimulated seedling growth while reduced precipitation decreased root development. Wrack mitigated the effects of reduced precipitation on T. junceum and C. arenaria biomass. Wrack retained water up to five-fold its weight, increased water pH, conductivity, and nutrient content. Wrack promotes dune colonization by vegetation even under rainwater shortage. Thus, the maintenance of this natural resource on beaches is critical for improving dune resilience against climate changes.

Microbial biotechnology and beyond: A roadmap for sustainable development and climate mitigation in the transition from fossil fuels to green chemistry.

Our planet, which operates as a closed system, is facing increasing entropy due to human activities such as the overexploitation of natural resources and fossil fuel use. The COP28 in Dubai emphasized the urgency to abandon fossil fuels, recognizing them as the primary cause of human-induced environmental changes, while highlighting the need to transition to renewable energies. We promote the crucial role of microbes for sustaining biogenic cycles to combat climate change and the economic potential of synthetic biology tools for producing diverse non-fossil fuels and chemicals, thus contributing to emission reduction in transport and industry. The shift to 'green chemistry' encounters challenges, derived from the availability of non-food residues and waste (mainly lignocellulosic) as raw material, the construction of cost-effective bioprocessing plants, product recovery from fermentation broths and the utilization of leftover lignin residues for synthesizing new chemicals, aligning with circular economy and sustainable development goals. To meet the Paris Agreement goals, an urgent global shift to low-carbon, renewable sources is imperative, ultimately leading to the cessation of our reliance on fossil fuels.

Affectation of COVID-19 pandemic on the use and abundance of wild resources in Tabasco, Mexico: A qualitative assessment.

Southern Mexico is particularly rich in natural resources, yet unemployment has risen to 8% during the COVID-19 pandemic. The effect of the pandemic on the use and abundance of Tabasco's wild resources was examined through personal surveys. By using Microsoft Forms® with cell phones 1,963 surveys were collected. Cronbach's alpha, Z-value, and chi2 were calculated using the MAXQDA Analytics Pro program. A higher abundance of wild resources before the pandemic than today (57% vs. 11%) was observed. During the pandemic, people referred more to a high use (28%) of resources than to a low use (20%). This caused the low abundance or scarcity of wild products to be greater during the pandemic than before the pandemic (43% vs. 4%). Wild foods and timber were the most used products. The pandemic has produced a greater use of natural resources probably due to the high unemployment rate in rural areas. Future studies of wild products should address the relevant products in the locality and their even sampling. Finding suitable respondents is highly recommended.

Fuzzy cognitive maps for municipal governance improvement.

This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.

Determining the green technology innovation accelator and natural resources towards decarbonization for the EU countries: evidence from MMQR.

Gearing up for green technology innovation (GTI) and natural resources has become even more important in the transition to a zero-emission life, a green economy, and sustainable development goals. This attempt has become a situation that needs to be overpowered much sooner by the European countries, which have encountered challenges in many ways, especially regarding natural resources, energy supply, and the climate crisis. In this vein, the current study follows the novel, robust Method of Moment Quantile-Regression (MM-QR), which successfully yields heterogeneous information structure across quantiles, to examine the determinants of GTI for 15 EU countries over the period of 2003-2018. MM-QR estimation results indicate that the determinants of green technology innovation are heterogeneous across the EU countries. While green growth (GG) has an adverse impact on GTI in middle- and high-GTI countries, the effect of ecological footprint on GTI is positive for countries in the highest-GTI countries. The positive effects of financial development (FD) on GTI are revealed for all countries. Remarkably, environmental taxes have an adverse and positive influence on GTI in the lowest and highest quantile countries, respectively. Finally, renewable energy and greenfield FDI have no effect on GTI. Governments can promote GTI by providing financial resources, in the most immaculate way, to firms that engage in green technology projects, as well as by encouraging these through environmental taxes.

An integrated deep-learning model for smart waste classification.

Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution - a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.

Fresh evidence of the impact of economic complexity, health expenditure, natural resources, plastic consumption, and renewable energy in air pollution deaths in the USA? An empirical approach.

Most plastic waste generated from plastic consumption cannot be recycled and is destroyed by burning. As a result of burning plastics, microplastics spread into the atmosphere, increasing air pollution. Respiratory diseases and chronic health problems are caused by air pollution. Approximately 7 million people die each year due to pollution-related ailments. Therefore, it is crucial to provide empirical evidence rather than approximate estimates of the role of plastic consumption in air pollution-related deaths. Also, understanding the causes of air pollution-related deaths and demonstrating the policies' effectiveness will provide valuable insights for policymakers, the international community, and researchers. This study investigates the effects of plastic consumption, health expenditures, natural resources, economic complexity, and renewable energy on air pollution deaths in the USA from 1995 to 2019 using the novel Fourier Augmented ARDL method. The findings show that plastic consumption, health expenditures, natural resources, and economic complexity increase air pollution deaths, while renewable energy decreases it. Such findings imply that plastic consumption is an essential determinant of air pollution-related mortality, that health policy must be reconsidered, that efficient use of resources is important and that sophisticated economic structures do not always produce the desired results. Overall, policymakers should review health policies to reduce deaths from air pollution and take measures to support green growth using renewable energy and economic complexity tools.

Sustainable building materials-recycled aggregate and concrete: a systematic review of properties, modification techniques, and environmental impacts.

Resource utilization of construction and demolition (C&D) waste has great potential to significantly reduce the consumption of natural resources and improve the environment. Meanwhile, establishing a sound policy system and reducing production are the key ways to solve the problem of C&D waste. Numerous studies on C&D waste, recycled concrete aggregate (RA), and recycled aggregate concrete (RAC) have been reported in the literature, with few systematic summaries. From a global perspective, this paper assessed the current situation of C&D waste and the countermeasure of several major economies. Then, this paper systematically introduces the composition structure and characteristics of RA. Modification techniques from macro and micro perspectives of RA and its effect on RAC were also presented. Paper also reviews the environmental impacts of RA and RAC. The results showed that bonded mortar was the most significant defect of RA than natural aggregate (NA). Thus, RA weakened RAC's microstructure, workability, mechanical properties, and durability. The research on the modification of RA mainly focused on removing bonded mortar and enhancing bonded mortar containing physical or chemical methods. Enhancing bonded mortar was a more effective method than removing bonded mortar. Carbonation and microbially induced calcium carbonate precipitation were highly efficient and environmentally friendly for RA modification. Research progress in quantifying the environmental impacts associated with concrete from waste materials through the LCA methodology is presented. Suggestions and an outlook were given on the critical issues facing RA and RAC. We expect that this work can provide more technical support for C&D waste utilization.

Advancing ethnobiology for the ecological transition and a more inclusive and just world: a comprehensive framework for the next 20 years.

This opinion piece, written by ethnobiologists from different parts of the world, emphasizes the importance of ethnobiology research in advancing contemporary biology, natural resource management, biodiversity conservation, sustainable development, and, especially, contributing to the ecological transition and more just and inclusive world. To achieve these goals, it is essential to develop research and collaborate with social groups that live in close relationship with nature in research activities, such as Indigenous Peoples and Local Communities (IPLC), as well as Afro-descendants and other Marginalized, Minority or Minoritized Communities (AMMC). Ethnobiology can identify and provide locally appropriate solutions to local problems, enabling sustainable resource management at the landscape level. The text explores important aspects that need to be considered to guide the future of ethnobiology in the next 20 years, aiming to integrate and amplify previous discussions held in the discipline and identify points that demand ongoing attention. This paper highlights reflections from diverse researchers, emphasizing how ethnobiology can embrace different perspectives and employ rigorous analysis of complex phenomena toward effective policies and practices. This approach holds the potential to address the challenges the planet is currently facing in the coming decades.

Harnessing fluorescent carbon quantum dots from natural resource for advancing sweat latent fingerprint recognition with machine learning algorithms for enhanced human identification.

Nowadays, it is fascinating to engineer waste biomass into functional valuable nanomaterials. We investigate the production of hetero-atom doped carbon quantum dots (N-S@MCDs) to address the adaptability constraint in green precursors concerning the contents of the green precursors i.e., Tagetes erecta (marigold extract). The successful formation of N-S@MCDs as described has been validated by distinct analytical characterizations. As synthesized N-S@MCDs successfully incorporated on corn-starch powder, providing a nano-carbogenic fingerprint powder composition (N-S@MCDs/corn-starch phosphors). N-S@MCDs imparts astounding color-tunability which enables highly fluorescent fingerprint pattern developed on different non-porous surfaces along with immediate visual enhancement under UV-light, revealing a bright sharp fingerprint, along with long-time preservation of developed fingerprints. The creation and comparison of latent fingerprints (LFPs) are two key research in the recognition and detection of LFPs, respectively. In this work, developed fingerprints are regulated with an artificial intelligence program. The optimum sample has a very high degree of similarity with the standard control, as shown by the program's good matching score (86.94%) for the optimal sample. Hence, our results far outperform the benchmark attained using the conventional method, making the N-S@MCDs/corn-starch phosphors and the digital processing program suitable for use in real-world scenarios.

The great potential of polysaccharides from natural resources in the treatment of asthma: A review.

Despite significant progress in diagnosis and treatment, asthma remains a serious public health challenge. The conventional therapeutic drugs for asthma often have side effects and unsatisfactory clinical efficacy. Therefore, it is very urgent to develop new drugs to overcome the shortcomings of conventional drugs. Natural polysaccharides provide enormous resources for the development of drugs or health products, and they are receiving a lot of attention from scientists around the world due to their safety, effective anti-inflammatory and immune regulatory properties. Increasing evidence shows that polysaccharides have favorable biological activities in the respiratory disease, including asthma. This review provides an overview of primary literature on the recent advances of polysaccharides from natural resources in the treatment of asthma. The mechanisms and practicability of polysaccharides, including polysaccharides from plants, fungus, bacteria, alga, animals and others are reviewed. Finally, the further research of polysaccharides in the treatment of asthma are discussed. This review can provide a basis for further study of polysaccharides in the treatment of asthma and provides guidance for the development and clinical application of novel asthma treatment drugs.

Venture capital, innovation channels, and regional resource dependence: Evidence from China.

Although the literature predominantly emphasises the crucial role of technological innovation in alleviating resource dependence, limited attention has been given to the pivotal role of capital in driving such innovation. As a critical factor in technological advancements and productivity enhancement, venture capital has a substantial function in the utilisation of resources and the development of sustainable energy sources. Drawing upon panel data from 30 provinces in China, this study explores how venture capital and resource dependence are interrelated. Our research reveals that venture capital effectively mitigates regional resource dependence by facilitating increased investment in innovation channels. However, the weakening of regional human resources mitigates venture capital's diminishing impacts on resource dependence. These findings provide valuable insights for countries seeking to reduce their dependence on natural resources and achieve long-term economic sustainability.

GIS-based land suitability analysis for the optimal location of integrated multi-trophic aquaponic systems.

Aquaponics has witnessed global proliferation and a notable enhancement in sustainability in recent years. Consequently, it assumes paramount importance to delineate optimal locations for its implementation, in fact, the success of an aquaponic facility also depends on its geographical placement, necessitating consideration of many variables encompassing natural resources, socioeconomic factors, infrastructural availability and environmental constraints, whether natural or artificial. This paper focuses on the definition and test in the Emilia-Romagna region (Italy) of a GIS-based multi-criteria land suitability assessment model aimed at allowing the diffusion and environmental integration of innovative integrated multi-trophic aquaponic systems. The process has been implemented with a Weighted Linear Combination (WLC) model, where decisions and criteria were selected via a participatory mechanism involving experts in various fields. The region has been subdivided into 50 × 50 m grid cells, with each grid cell being associated with a value ranging from 0 to 8. In this context, a rating of 0 means unsuitability, while a rating of 1 denotes minimal suitability, and the highest rating of 8 designates maximal suitability. Notably, a substantial portion of the surveyed territory has been found to be completely unsuitable for the establishment of aquaponic facilities. More than 86.4% of the remaining suitable areas were rated 6, 7, or 8, affirming the overall favourability of the Emilia-Romagna region for aquaponic installations. Finally, the veracity and robustness of the results have been tested through a one-at-a-time sensitivity analysis, that has proven the appropriateness of the proposed model.

Going green! Factors influencing green competitive advantage of Chinese SMEs: a moderated-mediated perspective.

This research observes the impact of environmental corporate social responsibility (CSR) and green supply chain management (GSCM) on green competitive advantage in Chinese manufacturing SMEs. Top management commitment is used as a mediator between environmental CSR, GSCM, and green competitive advantage. Moreover, a pro-environmental business strategy is used as a moderator between top management commitment and green competitive advantage. A simple random sampling technique was used, and 331 questionnaires were part of the final analysis. PLS-SEM is used for hypothesis testing. The results reveal that environmental CSR does not influence green competitive advantage. GSCM, top management commitment, and pro-environmental business strategy significantly measure green competitive advantage. Top management commitment significantly mediates between environmental CSR, GSCM, and green competitive advantage. Finally, a pro-environmental business strategy significantly moderates between top management commitment and green competitive advantage. This study strives to provide insightful information for strategic managers of Chinese manufacturing SMEs to optimize green competitive advantage, including environmental CSR, GSCM, top management commitment, and pro-environmental business strategy in decision-making. This study adds value to the body of knowledge by concentrating on factors that determine green competitive advantage. This initial research integrates environmental CSR, GSCM, top management commitment, pro-environmental business strategy, and green competitive advantage in a single framework using natural resource-based view (RBV). Furthermore, the study would present various implications for managers and lines for future directions.

Impact of different geopolitical factors on the energy transition: The role of geopolitical threats, geopolitical acts, and geopolitical risks.

In order to better understand the impact of different geopolitical factors on energy transition, the impact of geopolitical threats (war threats, peace threats, military buildups, nuclear threats and terror threats), geopolitical acts (beginning of war, escalation of war and terror acts), and geopolitical risks on energy transition were systematically investigated. Green technologies, natural resource rents and trade openness were incorporated into the analytical framework, and a dynamic panel threshold model was utilized to explore the impact of geopolitical risks on energy transition across different income levels. To this end, data on geopolitical threats, geopolitical acts, geopolitical risks, energy transitions and other key social economic factors for 38 countries from 2000 to 2022 were collected. The heterogeneity simulation results show that there is a negative correlation between geopolitical threats, geopolitical acts, geopolitical risks and energy transition. Moreover, geopolitical threats have more significant hindrance to the energy transition than geopolitical acts. The results of the nonlinear panel simulation show that there is a double threshold effect of geopolitical risks on energy transition. When geopolitical risk crosses the threshold (0.5197), the coefficient decreases to -0.29, which means that the rising geopolitical risk increases the inhibition on energy transition, and the inhibitory effect is slightly weakened after a certain level. Finally, policy implications are offered.

Behaviorally segmented audiences for managing sunscreen chemical pollution risk in protected coastal natural resource areas.

This audience segmentation of visitors at coastal parks in Hawaii and North Carolina addresses an emergent natural resource management concern and risk to aquatic ecosystems: sunscreen chemical pollution. Four audiences were identified that correspond to different behavioral profiles: sunscreen protection tourists, multimodal sun protection tourists, in-state frequent park visitors, and frequent beachgoers who skip sunscreen. The second-largest audience, sunscreen protection tourists, represents 29% of visitors at Cape Lookout National Seashore and 25% at Kaloko-Honokohau National Historical Park. This group ranks of most concern for chemical pollution because they use sunscreen, but not typically mineral formulations or other methods such as protective clothing, and they have lower levels of sunscreen chemical issue awareness. The identification of similar audience segments across regions with differing cultural characteristics and sunscreen regulation status suggests the robustness of the model and its indicator variables, with implications for both environmental protection and public health. Further, coastal visitors' interest in enacting pro-environmental sun protection behaviors during their next park or beach visit indicates the potential for natural resource managers to holistically address risks in both domains through targeted interventions with audiences of most concern.

A novel approach to assessing natural resource injury with Bayesian networks.

Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562-573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Revisiting the Environmental Kuznets Curve (EKC) Hypothesis of Carbon Emissions: Exploring the Impact of Geopolitical Risks, Natural Resource Rents, Corrupt Governance, and Energy Intensity.

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions.