Search Results (3,413)

Soil carbon (C) sequestration is a key component of European climate change mitigation strategies, and it forms part of the Common Agricultural Policy (CAP) and Good Agricultural and Environmental Conditions (GAEC) standards. Using national data for Romania (2005–2024), this paper aims to quantify how crop type, tillage system (conventional, minimum-till and no-till), and nitrogen fertilization influence soil humic carbon (Ch) in wheat, maize, sunflower and rapeseed cropping systems. Carbon inputs from residues, roots, and rhizodeposition were calculated in R based on Intergovernmental Panel on Climate Change (IPCC) coefficients, then tested in Statistical Package for the Social Sciences (SPSS) (factorial ANOVA, multiple regression, Pearson correlations). The results showed that both crop type and tillage systems significantly influence humic carbon values, with the highest values obtained in oilseed crops and in conservation systems (minimum-till and no-till). Among the quantitative factors, nitrogen fertilization had the most pronounced positive effect on carbon fluxes, while yield and precipitation had less influence. The conclusions indicate that the adoption of conservative soil management, in line with CAP objectives and GAEC standards, can support the increase of carbon stocks, with the need for contextual assessment of economic performance and pedoclimatic conditions. Full article

This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance, drawing upon the multi-methodological framework of combining econometrics with state-of-the-art machine learning approaches. Employing Instrumental Variable (IV) Panel data regressions, viz., 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz., Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz., Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model-Based, and Random Forest—were applied to uncover unknown structures and predict the behavior of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), social (S), and governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labor, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing us to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust. Full article

The concept of sustainability in the supermarket sector has emerged as a strategic priority, as companies are required to reduce their environmental footprint and enhance their social and economic performance. The aim of this literature review is to identify, document, and analyze the key performance indicators (KPIs) applied in the sector, with emphasis on environmental, social, and economic dimensions, and to investigate the extent to which technical energy interventions are linked to business and consumer benefits. The methodology was inspired by the general logic of organized search and selection procedures, and for this reason, elements of the PRISMA framework were used, with a search conducted across multiple international scientific databases and selection criteria ensuring the validity and relevance of the sources. The analysis classified the indicators into the following three categories: environmental (e.g., CO₂ emissions, energy consumption), social (e.g., customer satisfaction, corporate image), and economic (e.g., ESG score, return on investment). The study revealed substantial progress made by supermarket chains globally in adopting energy-efficiency technologies, such as LED lighting and renewable energy with proven benefits in reducing consumption and consequently, improving environmental performance. However, a lack of holistic integration between technical interventions and social-economic indicators was identified, limiting the use of KPIs as a strategic tool for guiding specific sustainability strategies. This research concludes that there is a need to develop unified, sector-specific measurement frameworks that integrate environmental, social, and economic parameters, as well as empirical research that quantitatively connects energy strategies with business and consumer performance through comparable indicators in the context of supermarket operations, thereby opening ground for further exploration of the field. Full article

An effective housing policy must ensure affordability for individuals across all income levels by integrating advanced technological innovations with comprehensive socioeconomic strategies. Affordable housing fosters social inclusion, whereas sustainability supports long-term environmental protection and economic stability. The success and long-term sustainability of affordable housing initiatives are heavily influenced by current socioeconomic conditions, emphasizing the need for context-specific, inclusive, and sustainable housing solutions. Benchmarks are crucial in affordable housing to determine if it is climate-positive, aligning with the goals of the United Nations’ Sustainable Development Goal 11.1, which seeks to provide affordable and sustainable housing for everyone by 2030. This study uses the Scopus database to perform a scientometric analysis of 595 publications (2015–2024) on sustainability and affordability in housing. Using R-Studio 2025.05.1 + 513.pro3 and VOSviewer 1.6.20, it examines bibliographic trends, research gaps, and collaboration patterns across countries and journals. This study highlights performance thresholds related to economic, environmental, energy, territorial, and climatic factors. However, cost and ecological objectives can cause conflict with each other practically, and hence a balanced approach including green practices, efficient materials, and subsidies is crucial. There is a need for policymakers to address market gaps to prevent socially exclusive or environmentally harmful outcomes, maintain long-term urban resilience, and ensure sustained urban resilience and equitable access to affordable, sustainable housing by 2030. Integrating sustainable materials, circular and climate-resilient design, smart technologies, inclusive governance, and evidence-based policies is crucial for advancing affordable, equitable, and resilient housing. This approach guides future research and policy toward long-term social, economic, and environmental benefits. The findings and recommendations promote sustainable, affordable housing, emphasizing the need for further research on climate-resilient, energy-efficient, and cost-effective building solutions. Full article

Urban parks are essential components of sustainable cities, providing vital health, social, and environmental benefits. Using weekly smartphone-based visitation data for 182 parks in Las Vegas from 2019 to 2022, this study quantifies how the COVID-19 pandemic altered park use and identifies the socio-economic, environmental, and infrastructural determinants of these changes. Park visitation in Las Vegas showed a marked early pandemic decline followed by uneven recovery, with socially vulnerable neighborhoods lagging behind. Ordinary Least Squares (OLS) and Random Forest (RF) models were used to capture both linear and nonlinear relationships. The RF model explained 81% of the variance in standardized visitation (R² = 0.81, RMSE = 0.0415), substantially outperforming the OLS benchmark (R² = 0.24, RMSE = 0.0656). Domain-specific RF models show that socio-economic variables alone achieve an R² of 0.88, compared with about 0.70 for housing, environmental/health, and lighting variables, while demographic variables explain only 0.39, indicating that social vulnerability is the dominant driver of visitation inequalities. Phase-specific analyses further reveal that RF performance increases from R² = 0.84 before the pandemic to R² = 0.87 after it, as park visitation becomes more strongly coupled with socio-economic and health-related burdens. After COVID-19, poverty, uninsured rates, and asthma prevalence emerge as the most influential predictors, while the relative importance of demographic composition and environmental exposure diminishes. These findings demonstrate that pandemic-era inequalities in park visitation are driven primarily by reinforced socio-economic and health vulnerabilities, underscoring the need for targeted, equity-oriented green-infrastructure interventions in disadvantaged neighborhoods. Full article

The paper focuses on the analysis of the impacts of climate change on the economic performance of the studied brownfield industrial park in the Upper Nitra region of Slovakia. The aim of this paper was to suggest ways of eliminating possible negative environmental burdens and the creation of site reclamation as a result of the termination of mining and the elimination of adverse environmental impacts in the selected region. The intention was to present the main causes of the decline of mining activity, with its environmental, economic and social consequences of the end of mining. The natural conclusion of the mining process is the comprehensive revitalisation of the area affected by mining activities. We have analysed the process of revitalisation of water areas and systems affected by mining activities and quantified the amount of investments needed for this revitalisation. Full article

Urban forests offer a range of environmental, climatic, economic, and social benefits to citizens. However, these effects have not been systematically measured owing to the localized nature of urban forests. This study developed a framework to assess the effects of urban forest ecosystem services and elucidate the service and benefit pathways of its indicators. Two PRISMA-guided scoping reviews were conducted using Web of Science and Scopus to identify English peer-reviewed articles (2015–2024) on the effects of urban forests and indicators. The studies on the urban forest effects were analyzed to systematically code and classify the criteria, effects, methods, and techniques based on the nature-based solutions. In terms of indicators, the ecosystem service cascade was employed to organize indicators across four pathways with structures/function, service, benefit, and value. The review revealed that temperature regulation, air pollution reduction, and carbon sequestration were the most studied effects, followed by social effects; in contrast, economic benefits and sound and noise were the least studied and assessed. Furthermore, indicator pathways were found to vary by effects. Drawing on this scoping review, a standard and expanded indicator assessment framework was developed. The proposed framework provides a decision-support tool to assess urban forest performance based on evidence, facilitating link between biophysical properties and human outcomes. Full article

Building a resilient and efficient supply chain system is critical for sustaining firm operations in an increasingly uncertain global environment. This study examines whether the firm-level exposure to industry-wide robot penetration mitigates firm-level supply chain risks. By adopting Bartik’s instrumental variable approach to decompose industry-level robot data to the firm level (from the International Federation of Robotics, IFR), and using a novel text-mining-based supply chain risk index, constructed via a tailored “supply chain risk” dictionary, to quantify sentences containing both keywords from firms’ annual report MD&A sections, we apply a fixed effects model, and find that robot adoption significantly reduces supply chain risk by enhancing firms’ discourse power and improving supply chain coordination. The effect is more pronounced in firms with higher capital intensity, greater international exposure, stronger regulatory oversight, and better ESG (Environmental, Social, and Governance) performance. By integrating automation adoption with supply chain risk management, this study extends the literature on production economics and supply chain resilience. Our findings reveal that industrial robots, beyond enhancing productivity, function as a risk-mitigating technology that strengthens supply chain stability and operational continuity in volatile global production networks. Full article

This study examines the effects of firm characteristics on environmental, social, and governance (ESG) performance among A-share firms listed in Shanghai and Shenzhen. Drawing on the resource-based view, legitimacy theory, and agency theory, this study examines both direct and indirect mechanisms connecting corporate profitability, firm size, and ownership concentration to enhance ESG performance. This research employs secondary panel data from the CSMAR, DIB, and WIND databases (13,911 observations) and estimates two-way fixed effects models with firm-clustered standard errors. The outcomes show that profitability, firm size, and ESG performance have positive relationships, but ownership concentration is a negative factor in ESG performance. Market share and managers’ risk preferences mediate the relationship between firm characteristics and ESG performance; however, these effects are interpreted as exploratory due to panel data constraints. Internal control enhances these relationships, which emphasize the importance of the process of sustainability itself. The study provides practical insights that managers, policymakers, or investment firms can apply to improve ESG integration accountability in the emerging markets context. Full article

In the context of the growing prominence of socially responsible investment, the debate over whether sustainable corporate practices translate into sustained shareholder value has intensified. As a key tool for aligning their investment portfolios with responsible/sustainable corporate practices, investors rely on listed companies’ Environmental, Social, and Governance (ESG) ratings. This study aims to investigate the long-term impact of ESG practices on the stock performance of listed companies. We perform a Q1 2000–Q1 2025 backtest to analyse the comparative performance of a Best-in-Class ESG portfolio, constructed by the top 30 listed companies with market capitalisations above USD 2 billion ranked by Morningstar Sustainalytics’ ESG Risk Ratings as of 31 March 2025 against the S&P 500 Total Return index. We found that ESG leaders exhibited superior risk-adjusted performance, outperforming the S&P 500 Total Return Index. The BiC portfolios achieved a substantially higher CAGR and Sharpe ratio, while maintaining maximum drawdowns that remained comparable to the benchmark S&P 500 Total Return index. We also found that ESG advantages were more pronounced in market downturns, with the Best-in-Class ESG portfolio showing better CAGR and Sortino ratios. The findings of this study demonstrate that responsible governance and management create benefits for all stakeholders, including investors, society and nature, in the broadest sense of these terms. Full article

Integrating environmental, social, and governance (ESG) reporting with the Sustainable Development Goals (SDGs) is important for achieving corporate sustainability. The rapid evolution of regulations like the Corporate Sustainability Reporting Directive (CSRD), and the fragmented research landscape create uncertainty for strategic planning. This paper addresses the critical gap related to the lack of predictive data into future research trends at the ESG-SDG nexus. The research begins with a bibliometric analysis using two software programs R-Biblioshiny 5.2.0 and VOSviewer 1.6.20, to process data extracted from the Web of Science (Clarivate). Selected key terms regarding sustainability reporting concepts and reporting standards, as well as the engagements of auditors were used to filter the database information. Starting from the bibliometric analysis of 361 publications completed during January 2015–September 2025, the study performs further a quantitative measurement bibliometrics using RStudio 4.5.2 and provides a novel ensemble forecasting model (AutoRegressive Integrated Moving Average, Error, Trend, Seasonal Components, and Linear regression with SDG factors) that cartograph the alignment of the current research field and forecast its evolution. The results reveal that terms regarding reporting “CSRD” and sustainability assurance, “ISSA 5000” are the most dominant research fronts, strongly aligned with SDG 12, 13 and 17. The forecasting model predicts sustained growth in this area. The study contributes by providing a forward-thinking strategic map for researchers, policymakers and businesses, transforming sustainability integration from a compliance task into systematic, data-driven approach for priority setting strategy. Full article

The rising presence of artificial intelligence (AI) across European industries is gradually reshaping how societies manage resources, reduce waste, and pursue long-term sustainability. While researchers widely acknowledge the economic and social implications of AI, they have not yet sufficiently explored its contribution to advancing a circular economy. This study examines how varying levels of AI adoption across EU Member States relate to material footprint, resource productivity, waste generation, and recycling performance. The analysis draws on harmonized Eurostat data from 2023, the most recent year for which complete and comparable indicators are available, enabling a coherent cross-sectional perspective that reflects the period when AI began to exert a more visible influence on economic and environmental practices. By combining measures of AI uptake with key circular economy indicators and applying factor analysis, neural network modelling, and cluster analysis, the study identifies underlying patterns and country-specific profiles. The results suggest that higher AI adoption is often associated with greater resource productivity and more efficient material use. However, its effects on waste generation and recycling remain uneven across Member States. These findings indicate that AI can support circular economy objectives when embedded in coordinated national strategies and supported by robust institutional frameworks. Strengthening the alignment between digital innovation and sustainability goals may help build more resilient, resource-efficient economies across Europe. Full article

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions. Full article

This study examines the impact of Green Entrepreneurial Orientation (GEO), Managerial Environmental Concerns (MECs), and Green Absorptive Capacity (GAC) on the environmental performance of Pakistani SMEs. The Dynamic Capabilities View (DCV) and Natural Resource-Based View (NRBV) demonstrate that innovation focused on sustainability and competitive advantage is enhanced by managerial engagement, knowledge capability, and environmental awareness. A cross-sectional survey involving 350 managers of SMEs in Pakistan utilized covariance-based structural equation modeling (CB-SEM). The MEC-to-GEO direction was insignificant, and this implies that the issue of managerial concern is not a driving force towards the initiation of green entrepreneurial endeavors. The confirmatory factor analysis model for the 350 SMEs demonstrates a satisfactory fit (CFI = 0.947; RMSEA = 0.073), along with reliability and validity. GEO and EP are positively influenced by GAC and MECs, with R² values of 0.204 and 0.526, respectively. The findings indicate that the absorptive and managerial capabilities of SMEs can integrate environmental responsibility into strategic decision-making, exceeding regulatory criteria to foster proactive environmental innovation. The study emphasizes ethical leadership, environmental competitiveness, and social responsibility through green information management and cooperative networks. The sustainability ideas and GEO are enhanced in developing nations by linking global green initiatives with local institutions and cultural contexts. Organizational management and policymakers should promote environmental education, ecological innovations, and sustainable practices within sectors. The limitations of the study include the use of self-reported data and cross-sectoral replication utilizing objective environmental indicators. Full article

Soybean meal has traditionally dominated poultry diets as the protein source. However, its widespread use raises concerns regarding economic costs, environmental impact and social sustainability. As a result, there is growing interest in alternative protein sources, such as canola meal, which may reduce feed costs while sustaining productivity. This review evaluates the potential of canola meal as a sustainable protein source in modern poultry production systems, focusing on nutritional, economic and environmental advantages, as well as the potential implications of canola meal inclusion in reduced-protein diets. Evidence from scientific studies indicates that canola meal’s nutritional profile supports bird growth and production, although higher fiber content and anti-nutritional compounds reduce metabolizable energy, making it more suitable for laying hens than broiler chickens. Processing techniques, enzyme supplementation, fermentation, and modern cultivars have improved both nutritional value and practical utility of canola meal. Performance outcomes differ by species. Broilers exhibit variable growth at high inclusion levels, whereas laying hens are estimated to tolerate up to 20% without affecting laying performance or egg quality; however, data is severely lacking, particularly under the context of modern reduced-protein diets. Economically, canola meal is cost-competitive with soybean meal. From an environmental perspective, substituting imported soybean meal with local canola reduces greenhouse gas emissions, enhances resource efficiency, and supports pollinators. Nevertheless, trade-offs exist, including increased land use, variable digestibility, and potential eutrophication. Incorporating canola meal into reduced-protein diets offers both economic and ecological benefits, though effectiveness depends on the extent of protein reduction and the precision of amino acid formulation. Overall, canola meal offers a sustainable, economically viable, and environmentally responsible protein source for modern poultry production, provided that inclusion levels are adjusted to species-specific requirements and regional conditions. Full article