Purpose This review critically synthesizes recent advances on natural preservatives used to enhance the safety, quality and shelf life of meat and meat products. Design/methodology/approach With an increasing number of cases and deaths due to contaminated food, there is a pressing need for effective preservation methods. Concerns about the impact of synthetic preservatives on health have led to growing interest in natural preservatives. Findings Plant-derived essential oils (rosemary, thyme, oregano and cinnamon) showed the strongest antimicrobial effects, typically achieving 1–4 log CFU/g reductions and delaying lipid oxidation by 7–15 days, while flavonoids (green tea, grape seed and propolis) reduced oxidative rancidity by 25–60%. Organic acids such as lactic and citric acid consistently produced 2–3 log reductions in pathogens. Among animal-based preservatives, lysozyme and lactoferrin ensured targeted inhibition of Listeria, Staphylococcus aureus and E. coli. Microbial-derived systems particularly nisin, pediocin and sakacin were the most potent clean-label antimicrobials, delivering 2–6 log reductions showing strong compatibility with ready to eat, fermented and vacuum-packed meats. Originality/value A key originality of this review lies in its cross-category comparative analysis between plant, animal and microbial systems combined with mechanism-based classification, dose response synthesis and shelf-life outcomes, which are seldom integrated in prior reviews. Emerging delivery technologies including nanoemulsions, nanoliposomes, microencapsulation, nano-chitosan coatings and active packaging films were identified as critical enhancers that improve bioactive stability, reduce effective doses and minimize sensory changes.
Purpose This study examines the impact of energy transition (ET) and technological innovation (TI) on environmental quality in G7 economies. Design/methodology/approach Using a balanced panel of 175 observations from 1999–2023, the cross-sectional autoregressive distributed lag (CS-ARDL) model analyzes short- and long-term relationships while accounting for cross-sectional dependence and heterogeneity. Findings Results show that ET and TI significantly reduce carbon emissions, thereby enhancing environmental quality. Practical implications Policies should prioritize renewable energy, energy efficiency and green-oriented TI to achieve meaningful emission reductions. Originality/value By focusing on G7 economies and applying CS-ARDL, the study addresses a literature gap and highlights the importance of sustained investment in sustainable and innovative initiatives for environmental improvement.
Purpose The aim of this paper is to assess the dispersion of gas flaring (GF) emissions in the Hassi R'mel field, Algeria, through a diachronic analysis covering the 2018–2022 period. The objective is to characterize the temporal evolution and spatial extent of pollutant plumes. Design/methodology/approach The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to simulate the dispersion of GF pollutants over a 72-hour period for four representative seasonal scenarios per year. Simulations were initialized using meteorological data from the NOAA Global Data Assimilation System (GDAS) at a 1° spatial resolution. Findings Results reveal summer and spring conditions produce the largest plume extensions. In contrast, winter stability limits plume development and promotes pollutant accumulation near the source. These simulations confirm that GF pollutants from Hassi R'mel frequently extend beyond national borders, influencing air quality over eastern Algeria and the Mediterranean basin. Originality/value This study represents the first diachronic modeling assessment of gas flaring dispersion in Algeria's desert environment. It enables a consistent year to year comparison of plume footprint and demonstrates that seasonal meteorology exerts stronger control on transport extent than interannual variability in flaring volumes. This provides a decision-relevant basis for prioritizing mitigation strategies and for framing progress toward Zero Routine Flaring by 2030.
Purpose This study presents a comprehensive bibliometric and literature-based assessment of global research on microplastic (MP) and nanoplastic (NP) bioaccumulation and toxicity in marine organisms from 2015 to 2025, with particular emphasis on ecological risks, human health implications and emerging mechanistic insights into pollutant transfer across marine food webs. Design/methodology/approach A dataset comprising 1,623 peer-reviewed publications indexed in Scopus was analyzed using VOSviewer to generate collaboration networks, co-citation maps and keyword clusters. Bibliometric findings were complemented by qualitative interpretations of highly cited studies to contextualize major advances in toxicological mechanisms, exposure pathways, model organisms and ecological risk frameworks. Findings Scientific output increased markedly after 2018, driven by advancements in analytical detection techniques and growing regulatory concern. China leads global research output, followed by Italy, India, Spain and the UK, whereas environmentally vulnerable regions such as the Arabian Gulf remain underrepresented. The research focus has evolved from descriptive occurrence studies to mechanistic and molecular-level toxicology, identifying oxidative stress, immunomodulation, gene expression changes, neurotoxicity and NP-specific risks such as tissue penetration and potential blood–brain barrier translocation. Recent literature further reinforces ecological risk assessment frameworks, seafood safety concerns and the need for harmonized monitoring strategies. Methodological limitations, including reliance on English-language Scopus-indexed publications, are acknowledged. Originality/value This study is among the first to integrate systematic bibliometric mapping with qualitative synthesis to identify the principal scientific drivers shaping a decade of MP/NP ecotoxicological research. The findings provide strategic directions for future investigations, including multi-omics approaches, standardized toxicity endpoints, enhanced regional surveillance and policy initiatives aimed at marine ecosystem protection and public health safeguarding.
Purpose This study develops an AI-driven multimodal framework integrating automated waste segregation with energy recovery prediction to support Saudi Vision 2030 sustainability goals. Design/methodology/approach A two-stage vision pipeline (YOLOv9 + Swin Transformer) performs real-time waste detection and classification. Multimodal physicochemical features are modeled using XGBoost, deep neural networks (DNNs), and graph neural networks (GNNs) to predict energy recovery potential. Reinforcement learning (RL) optimizes routing of waste streams to appropriate facilities. Experiments use a Saudi-specific dataset of 50,000 annotated images and 5,000 physico-chemical records. Findings The integrated framework achieved mAP = 94.3% and R2 = 0.96, improving landfill diversion and renewable energy contribution by 12.1% and 15.2%, respectively, compared with baseline models. Research limitations/implications Hazardous waste remains underrepresented in the dataset. Future work will address this via targeted data collection and active learning. Practical implications The framework provides a deployable solution for real-time waste classification and energy estimation in smart-city contexts, with a projected payback period of approximately 1.5 years for a 1,000-bin deployment. Originality/value This study introduces the first Saudi-specific multimodal waste dataset and a unified AI framework bridging waste segregation, energy prediction, and smart-city optimization—an end-to-end solution absent from prior literature.
Purpose This study aims to clarify and systematize the conceptual, theoretical and methodological foundations of three key consumer behaviors, namely pro-environmental behavior (PEB), sustainable consumption behavior (SCB) and circular behavior (CB), which are critical for advancing sustainability transitions. Design/methodology/approach A scoping literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol across three major databases (Scopus, Web of Science and Google Scholar), and the final sample included 258 academic papers published from 2019 to 2025. The selected literature was analyzed through the theory–context–characteristics–methods (TCCM) framework to identify conceptual definitions, theoretical models, behavioral determinants and methodological approaches. Findings The main findings indicate that while PEB, SCB and CB share a common characteristic such as the consumer's conscious intention to reduce environmental impact, there are differences in scope and emphasis. PEB includes activism and public engagement, SCB focuses on consumption and its social dimensions and CB emphasizes consumer acceptance of circular innovations. Research limitations/implications Future research should expand the scope of analysis to include grey literature, adopt longitudinal and experimental designs to validate behavioral models and systematically examine the role of artificial intelligence in shaping sustainability behaviors. Originality/value This is the first study to conduct a comparative scoping review of PEB, SCB and CB using the TCCM framework. It offers a structured synthesis that resolves terminological ambiguities, maps theoretical evolution and identifies gaps in measurement and intervention research.
Purpose This study aims to examine how institutional, financial and cultural factors shape sustainable entrepreneurial ecosystems in North Africa and the Gulf Cooperation Council (GCC), emphasizing their role in advancing the water–energy–food–environment (WEFE) nexus. It investigates how entrepreneurship drives sustainability transitions, resource efficiency and climate resilience in arid and resource-constrained contexts. Design/methodology/approach A comparative qualitative framework is employed, integrating institutional theory, the resource-based view and the entrepreneurial orientation model to analyze the interaction of governance quality, access to sustainable finance and entrepreneurial behavior in promoting WEFE-oriented innovation. The analysis draws on peer-reviewed literature, government strategies and institutional reports published between 2018 and 2024. Findings Results indicate that the GCC benefits from coherent institutions, advanced financial systems and policy-driven innovation supporting renewable energy, water management and circular economy ventures. North African ecosystems remain constrained by fragmented governance and limited green finance, resulting in predominantly necessity-driven entrepreneurship. While both regions increasingly align entrepreneurship with sustainability objectives, WEFE integration is more advanced in the GCC. Practical implications The study highlights the need for institutional reforms, expanded green finance, and entrepreneurship education incorporating WEFE principles. Sustaining progress in the GCC requires deeper private-sector engagement, robust impact assessment of environmental innovation and enhanced cross-regional collaboration to transfer knowledge and best practices. Originality/value This research links entrepreneurial ecosystem development to the WEFE nexus in arid regions, offering a comparative framework illustrating how institutional stability, financial inclusion and innovation-oriented mindsets foster low-carbon, resource-efficient growth aligned with sustainable development goals 6, 7, 8, 9, 12 and 13.
Purpose Across the world, inefficient waste management systems and rapid urbanization have caused the accumulation of waste in drainage channels. Hence, this study addressed the problem of improper solid waste disposal (ISWD) habits of people in urban environments, and its impact on urban flooding. The specific objectives were to examine the impact of ISWD; resident's awareness level; types of solid waste disposed of; and mitigating strategies. Design/methodology/approach Survey and observational were used, with questionnaire and pictures as data collection instruments. Stratified sampling techniques was used to sample of 212 residents in the study area. While percentages, bar and pie charts, and Pearson's Product Moment Correlation (PPMC) were the tools used for data analysis. Findings Findings revealed that awareness level of residents on the contribution of ISWD to urban flooding is below 50%; 48% and 37% of residents' dispose of mostly plastic and cellophanes/nylons respectively; while others accounted for 15%. Findings also showed a high impact of ISWD on urban flooding. Strategies such as proper solid waste management, construction of better drainage channels, and environmental sanitation amongst others were suggested as strategies for mitigating the impact of ISWD on urban flooding. Research limitations/implications Participants may have given desirable responses instead of truthful ones. Secondly, survey research often collect data at surface level, rather than in-depth information, these could affect the reliability of the results. Practical implications The findings have raised a red alarm to urban authorities/governments, on the implications of irresponsible solid waste disposal. It will also highlight the cost of human negligence in waste management to public sectors and experts. Originality/value The authors' declare that this study is an original research that addresses a devastating environmental problem ‘flooding in urban environments.
The study investigates the obscure interaction between trade openness, financial development (FD), economic growth, non-renewable energy use, foreign direct investment (FDI) and environmental sustainability in Oman. The study analyses annual time series data from 1973 to 2021, sourced from the World Bank Indicator and Global Footprint Network. It uses the Augmented Dickey–Fuller and Phillips–Perron tests to check variable stationarity and the autoregressive distributed lag (ARDL) bound cointegration technique to assess long-run relationships. The ARDL model examines dynamic linkages between the dependent and explanatory variables, while the robustness of the results is verified using the fully modified ordinary least squares method. The study finds that FDI enhances Oman's environmental quality in both the short and long run, supporting the pollution halo hypothesis in Oman's case. Economic growth and unclean energy usage increase the ecological footprint (EF) in both the long run and the short run. Moreover, while trade openness reduces the EF in the short run, it leads to environmental degradation in the long run in Oman. This study adopts the alternative encompassing measurement for environmental quality (EF) to determine the dynamic association between FDI, FD, trade, economic growth and unclean energy use and environmental quality simultaneously in the context of Oman, thus providing improved findings to inform insightful policymaking. The literature review revealed a lack of research, especially for Gulf countries, including Oman. Thus, this study is needed.
This study aims to investigate the spatial distribution and seasonal dynamics of toxic metals in water, soil and edible crops cultivated near the El-Qalyubia Drain, Egypt. Where irrigation sources may include reused agricultural drainage water. It aims to identify contamination hotspots, assess ecological risks and evaluate the food safety implications of drainage water reuse. Soil, drainage water and edible plant samples were collected from eight sites during summer and winter. Ecological risks were evaluated using the contamination factor, ecological risk factor, potential ecological risk index and pollution load index. Crop contamination was assessed in accordance with the Codex Alimentarius maximum permissible limits for food safety. Drainage water met Egyptian standards; however, certain soils and crops exhibited elevated contamination, particularly with copper, manganese, cadmium and lead. Wheat, turnip and molokheya frequently exceeded Codex limits for cadmium and lead. Copper enrichment was most pronounced in winter (Cf > 40). The ecological risk index indicated low risk in summer (RI = 33.38) but moderate in winter (RI = 173.42). The study covered one major drain and two seasons, limiting extrapolation to other systems or longer timescales. Broader multi-drain and multi-year studies across diverse agro-ecological zones are needed to strengthen generalizability. Findings underscore the need for soil remediation, crop monitoring and regulatory control to restrict high-accumulating crops in contaminated areas. This is the first integrated, seasonally resolved assessment linking water, soil and crop contamination, revealing inconsistencies between water quality standards and actual food safety outcomes in reuse-based agriculture.
To examine leadership challenges to net-zero emissions and strategies to overcome economic, technological, social and political barriers. Adopts a qualitative study, of secondary data, including scholarly articles, international organization reports and examples, using thematic analysis to identify leadership practices driving climate action and sustainability. The study highlights the critical role of transformational and collaborative leadership in overcoming barriers to net-zero emissions. Key strategies include long-term vision, inclusive leadership and the integration of digital technologies, circular economy models and behavioral science to accelerate sustainability and drive climate action. While offering valuable insights, further research using primary data is needed to explore sector-specific leadership challenges, political leadership’s role and the impact of gender, diversity and inclusion on climate leadership effectiveness. The findings underscore the necessity of adaptive leadership, strategic collaboration and sustainability-focused decision-making for policymakers, business leaders and community organizers, helping them navigate the challenges of transitioning to a net-zero economy and ensuring the long-term success of climate policies. This study stresses the need to address social concerns about the effects of climate action on jobs and communities, advocating for inclusive leadership that promotes wide-ranging engagement to ensure the social legitimacy and equity of net-zero transitions. The research advances the understanding of climate leadership by proposing innovative solutions to achieving net-zero emissions.
This study presents a comprehensive hydrogeochemical assessment of groundwater in the Midyan Basin, northwest Saudi Arabia, with the goal of evaluating its suitability for irrigation and drinking in a hyper-arid environment. A total of 72 groundwater samples were collected from shallow wells and analyzed for major ions and trace metals using ion chromatography and ICP-MS. Hydrochemical facies were determined through Piper and Gibbs diagrams. Water quality indices, including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), and Residual Sodium Carbonate (RSC), Sodium Percentage (Na%), Magnesium Ratio (MR)and Chloro-Alkaline Indices (CAI) were used to assess irrigation suitability. Groundwater is dominated by Ca2+–SO42−–Cl− and Na+–SO42−–Cl− facies, reflecting evaporite dissolution, carbonate weathering and ion exchange. EC ranged from 1,106 to 14,290 µS/cm and TDS from 545 to 7,027 mg/L. About 29% of samples exceeded the WHO TDS limit, and 95% exceeded sodium limits (200 mg/L). Magnesium ranged from 18.8 to 899.3 mg/L, and calcium from 144.6 to 1869.0 mg/L, both often exceeding guideline values. Nitrate exceeded the 50 mg/L limit in 29% of samples, while fluoride concentrations ranged from 0.98 to 2.10 mg/L. Most trace metals were within safe limits, though slightly elevated levels of Mn, Cr, Zn, and As were detected in a few locations, likely due to anthropogenic inputs. Based on EC and SAR values, nearly 40% of samples are unsuitable for irrigation, though 91% fall within acceptable zones for salt-tolerant crops based on the Wilcox diagram. This is the first systematic hydrogeochemical investigation of the Midyan Basin. It reveals significant geogenic and anthropogenic influences on groundwater quality and provides essential data to guide sustainable water management and agricultural planning in arid regions.
This study explores how organizational innovation supports environmental sustainability through digital transformation, green technologies and operational efficiency amid technological, environmental and geopolitical challenges. A qualitative approach and secondary data analysis are used to examine innovation across technological, managerial and business model dimensions, with a focus on integrating sustainability into corporate strategy. The study finds that organizations can align profitability with sustainability by adopting green technologies and digital solutions that enhance efficiency and resilience. However, aligning innovation with sustainability goals remains challenging, particularly in complex regulatory and geopolitical environments. Embedding ecological civilization principles into strategy is vital for long-term competitiveness. Reliance on secondary data and qualitative methods may limit generalizability. Future research should incorporate empirical studies and quantitative analyses to validate these findings. The study offers strategic insights for integrating sustainability into innovation processes. It highlights the need for adaptive corporate strategies to enhance resilience in volatile markets. Promoting sustainability-driven innovation contributes to environmental protection, renewable energy adoption and responsible corporate behavior. This research provides theoretical and practical contributions by examining the role of digital transformation, AI analytics and green technologies in achieving sustainable innovation and competitive advantage.
Efficient water management is a key factor for agriculture in India. Particularly, it is aiding in the estimation of crop water requirements (CWR) to enhance yields and profitability. This study aims to improve crop water demand forecasting through a neural network-based time series model tailored for agricultural applications. A hybrid forecasting model is proposed which combines an Ensemble of Hyperparameter-Tuned Nonlinear Autoregression Neural Network (E-NLARNN) for crop yield prediction with a Generalized Regression Neural Network (GRNN) for prediction error correction. E-NLARNN aggregates forecasts from multiple optimized NLARNNs to improve robustness in crop yield prediction. It combines prediction from multiple hyperparameter-tuned NLARNNs. GRNN models the residual errors to further refine predictions. Five different crop yield datasets were used to demonstrate the effectiveness of the proposed work. Its performance was benchmarked against traditional NLAR and E-NLARNN models using RMSE and R-value metrics. This hybrid model demonstrated significant improvements, achieving RMSE reductions between 9.6% and 19.5% compared to E-NLARNN models and between 19.3% and 30.5% compared to the best TA-NLARNN variants across datasets. It consistently outperformed baseline methods in terms of accuracy and stability. This study presents a novel hybrid neural network approach that integrates ensemble learning and regression-based error correction for agricultural time series forecasting. By enhancing prediction accuracy and offering interpretable insights, the proposed model supports more reliable irrigation planning and sustainable water resource management.
This study validates an adapted version of the Ecobarometer of Andalucia to assess perceptions, knowledge, practices and attitudes toward water management and pollution among university students in Latin America. It aims to provide a reliable tool for analyzing environmental awareness and behaviors related to water conservation. A quantitative, cross-sectional and descriptive design was used. Validity and reliability were assessed through exploratory and confirmatory factor analyses (EFA and CFA), based on online surveys with 96 participants selected by non-probabilistic convenience sampling. Reliability was measured using Cronbach’s alpha, McDonald’s Omega and Jöreskog’s Rho. Discriminant validity ensured distinct construct representation. Six dimensions were validated: information on environmental issues, minimization of personal responsibility, daily environmental practices, perception of responsibility, participation and responsible consumption and domestic water management. While environmental knowledge correlated positively with sustainable practices, the effect was limited. Perceiving external agents as primarily responsible correlated negatively with personal engagement. The findings also revealed moderate concern for water pollution and a high commitment to sustainable practices at the household level. However, a tendency to externalize responsibility to institutions was observed, which may limit personal engagement in broader environmental actions. Self-reporting may introduce bias, and the sample limits generalizability. Future studies should use broader samples and indirect assessments. Longitudinal research is recommended to explore causality. The instrument can support environmental education, assess public policy impact and monitor water sustainability initiatives. The study offers a validated, context-specific tool for assessing water awareness in Latin America, addressing a regional research gap and enabling targeted educational and policy interventions.
This research aims to identify the level of discourses in sustainability reporting and integrate sustainable development goals (SDGs) for Bangladeshi companies listed on the Dhaka Stock Exchange from 2017 to 2022. Fairclough's three-dimensional framework (1995) was employed to analyze sustainability discourse. The study examines audited annual reports from the top 30 listed companies over six years, comprising a total of 180 observations. This study provides exploratory insights into corporate initiatives addressing various SDGs themes. Most companies disclose information about climate change vulnerability, corporate social responsibility, and expenditures on education, health and environmental efforts, yet fail to fully integrate these aspects into their core business activities. The paper focuses on a sample of thirty companies over a six-year period, which may restrict the breadth of findings. Also, a significant portion of SDGs disclosures remain rhetorical, emphasizing corporate messaging strategies rather than conducting a substantive impact assessment. Field-based approaches, such as interviews with senior officials overseeing SDGs implementation would provide deeper insights into corporate motivations and challenges. The results offer valuable insights for managers of multinational and private enterprises, supporting efforts to align business practices with SDGs and contribute to achieving sustainability targets by 2030. This study contributes to the extant body of critical discourse analysis while exploring the integration of SDGs into business activities within the Bangladeshi corporate landscape.
This study explores renewable energy research in Oman, focusing on growth, trends and economic impacts and identifies key contributors, emerging themes and economic ties influencing development. A bibliometric analysis of 204 studies was conducted to evaluate collaboration networks, citation trends and research performance. Additionally, a vector autoregression (VAR) model was used to examine the associations between economic variables such as economic growth, foreign direct investment and energy consumption. The findings indicate a consistent increase in research on renewable energy, with publications increasing by 6.3% annually from 2020. Keyword and trend analyses highlight a growing emphasis on regulatory frameworks, with terms such as “law” emerging. A keyword network analysis revealed that renewable energy research in Oman is significantly influenced by regional economic and environmental factors. The VAR model further supports the role of renewable energy in Oman’s economic growth, showing strong correlations between energy consumption, foreign direct investment (FDI) and economic expansion. The study's data were limited by secondary databases and VAR analysis, suggesting the need for larger datasets and advanced modelling techniques for more accurate results. This study explores renewable energy research and its economic impact in Oman, providing insights for researchers, policymakers and industry stakeholders on sustainable energy transitions and economic growth. It uniquely combines bibliometric and VAR analyses for a country-specific assessment, quantifying interrelationships between energy consumption, investment and economic output. The findings offer a replicable framework for emerging economies, delivering long-term guidance for policy, investment and sustainable energy planning internationally.
In 2023, Saudi Arabia led Gulf Cooperation Council (GCC) trade in low-carbon technology (TLCT), exporting over $1.2 billion in solar panels and technologies. Its focus on TLCT trade drives regional sustainability and economic growth, solidifying its role as the GCC's hub for sustainable trade. The current study uses a modified gravity model to assess the bilateral TLCT between Saudi Arabia and its trading partners of the GCC. The model uses economic size (mass) as the numerator and carbon emissions ratio as the denominator, replacing the traditional distance variable to account for environmental disparities as trade barriers. Cross-section non-linear autoregressive distributed lag model estimates are used to estimate the modified gravity model. The estimates reveal that Saudi Arabia's economic size positively impacts TLCT in both the short and long run, while negative shocks reduce the trade. Similarly, the increase in economic size of trading partners enhances TLCT volumes and vice versa. The higher carbon emissions ratio indicates more emissions in Saudi Arabia relative to its partners, hindering the LCT trade, while a lower ratio facilitates trade by reducing environmental trade barriers. The current study presents novel and distinct contributions to the literature on trade and sustainability. First, it estimates the dynamics of TLCT that have been underexplored in empirical modelling. Second, it modifies the traditional gravity model of trade by introducing the emissions gap as a trade barrier, thereby integrating environmental performance with TLCT. Third, it applies a non-linear modelling approach to capture asymmetric oscillations in the effects of economic size and the emissions gap on TLCT.
Chemical fertilizers are utilized in agriculture to enhance plant growth and boost crop yields. However, they are expensive, and excessive use can reduce the long-term fertility of the soil, negatively impacting plants and the surrounding environment. Thus, this review paper aims to emphasize rhizosphere phosphate-solubilizing bacteria (PSB) as an effective, eco-friendly and natural alternative to chemical fertilizers. PSB improves crop productivity by enhancing soil microbial communities, secreting enzymes, acidifying soil and making phosphorus and other nutrients more available to plants. Relevant research and review articles on rhizosphere PSB and their application in sustainable agricultural practices have been collected from academic journals and various online databases. PSB converts insoluble phosphorus into soluble form, with other nutrients, making them an optimal choice for organic farming and sustainable agriculture. This paper provides a new perspective on soil fertility depletion and illustrates how PSB enhance soil health, increase crop yields and improve plant stress tolerance. The use of PSB can reduce farmers' dependence on costly chemical fertilizers, thereby enhancing economic sustainability. It also promotes food security and strengthens rural livelihoods by offering affordable, efficient and eco-friendly alternatives to chemical fertilizers. This paper examines the mechanisms employed by PSB to transform immobile phosphate compounds into bioavailable forms. It provides a description of the specific genes and enzymes involved in the solubilization process.
This study examines the relationship between energy sector profitability and environmental sustainability in Saudi Arabia, with a focus on carbon emissions and energy efficiency. This study employs a quantitative research design, over the time period of 2010–2023, using ordinary least squares (OLS) regression to analyze the impact of energy sector profitability on environmental sustainability indicators such as carbon emissions and energy efficiency. The emissions model indicates a strong fit (Adj. R2 = 0.68; R2 = 0.82; DW = 2.03; p < 0.05), revealing that improvements in energy productivity (Y1) and overall economic growth (C1) significantly contribute to lowering environmental pressures, while oil-revenue dependency (Y2) and population growth (C2) show no meaningful impact on emissions. The energy-intensity model demonstrates good explanatory power as well (Adj. R2 = 0.65; R2 = 0.80; p = 0.051; DW = 1.58), indicating that reliance on oil revenues (Y2) significantly enhances energy efficiency, while profitability (Y1), economic growth (C1) and population growth (C2) do not exhibit substantial influence. The results of this study can help in strategic planning of the country by aligning economic growth with environmental sustainability goals by utilizing the profitability of the energy industry as a tool to improve energy efficiency and lower carbon emissions. This study supports Saudi Arabia's Vision 2030. Improved efficiency can ease fiscal space for clean-energy investment, supporting SDG 7 and SDG 13. This study uniquely explores how profitability in Saudi Arabia's energy sector influences both emissions reduction and energy efficiency.
