Search Results (139)

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

This study explores the challenges and opportunities for embedding the Natural Capital Approach (NCA) in policy processes, especially in the framing of the Environmental Improvement Plan (EIP), which is England’s strategic framework for improving the natural environment, including cleaner air and water, healthy soil, thriving wildlife and climate-adapted landscapes. Semi-structured interviews were undertaken with policymakers working in Defra (Department of Environment, Food and Rural Affairs) and its Arm’s Length Bodies (ALBs) organisations to investigate their views on the barriers and enablers to the adoption of the NCA. It has been widely recognised that the NCA provides unifying concepts that are able to connect economists and ecologists, and it can help to embed nature across government departments and supports to make the business case for nature improvement. On the other hand, there are perceived challenges in mainstreaming the NCA in environmental policy processes. There is some lack of agreement on the usefulness of the approach, problems with the oversuse of monetary valuation in policy appraisal, isolation of work, policy processes and government departments and difficulties in the communication of the benefits of the NCA. Recommendations to overcome the barriers include cross-departmental work placements of natural capital scientists, establishing cross-agency natural capital working goups to work on the use of the NCA to frame environment improvement policies, and prioritising the adoption of deliberative approaches to better understand local values on nature that are difficult or even impossible to monetise. Full article

Environmental, Social, and Governance (ESG) performance has become critical for businesses seeking transparency, sustainability, and stakeholder confidence. However, the extant evidence is equivocal regarding its influence on company performance. Corporate governance may play an important role in determining the efficacy of ESG efforts. Hence, this research investigates the relationship between corporate governance and firm performance, focusing on the moderating role of ESG performance, using unbalanced panel data from 370 Malaysian firms for the period from 2012 to 2023. For analysis of the data, fixed effects estimation, Driscoll–Kraay robust standard errors, and a subsample analysis were used. The results reveal that board expertise and independence enhance firm performance, whereas board gender diversity negatively affects market valuation (Tobin’s Q). Similarly, audit committee expertise shows a negative effect. Further, the results reveal that ESG strengthens the role of board expertise but weakens the influence of audit committee independence. This study offers practical and theoretical insights for policymakers, scholars, and stakeholders seeking to balance sustainability with financial performance, bridging a notable gap in the literature by thoroughly examining the relationship between board gender diversity and ESG performance, and how it affects company performance. This reveals new insights into the strategic significance of diverse leadership in promoting both financial and non-financial results by bridging the gap between corporate governance and sustainability. Full article

European football has become a massive business. Keeping football clubs financially viable depends on accurate player valuations, which underpin balancing incoming and outgoing transfers, contract negotiations, and other expenses. Players’ market values are generally available on public platforms. Still, clubs and analysts increasingly rely on data-driven approaches to enable consistent valuation across leagues, to assess the main drivers of players’ market value, and to early identify the most promising players. This study attempts to predict and interpret football players’ market value in five major European football leagues (England, Spain, Italy, Germany, and France) using match-derived performance statistics and players’ general information. The dataset analyzed comprises about 14,000 player–season observations available through the worldfootballR package, which aggregates data from FBref and Transfermarkt. Five regression algorithms were evaluated within a unified machine learning framework. Model performance was assessed on a test set using RMSE and R² metrics. Results show that non-linear machine learning models outperform the linear ones. Gradient boosting and neural networks recorded the best predictive performance. Model interpretation techniques reveal playing-time exposure and player age as the main determinants of predicted market value, highlighting the importance of match involvement and career stage in the valuation of football players. Full article

Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Full article

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

Technological innovation is changing virtually every aspect of business practices and operational procedures. The introduction of large language models and various types of intelligent processing, commonly referred to as artificial intelligence, presents significant change to cope with. In this paper, we suggest an estimation method, based on real options analysis (ROA), that improves the assessment and valuation of intelligent data processing’s impact on organizations. The presented approach can reflect direct and indirect effects from introducing artificial intelligence methods and is therefore better suited than traditional financial metrics for the assessment of contemporary intelligent tools and solutions. Using Monte Carlo simulation and American-style real options, we have estimated two sample use cases to compare the ROA results against other common valuation methods. Numerical experiments indicate that the suggested approach is capable of capturing both the direct and indirect impact of new technologies, which improves relevant financial and management decisions. Full article

The contemporary green transformation of the economy is a strategic imperative for businesses, especially small and medium-sized enterprises (SMEs) operating in the energy market, forcing the integration of sustainable practices in decision-making processes, including investment efficiency assessment. Classic financial tools, such as the internal rate of return (IRR) and net present value (NPV), commonly used in the SME sector, do not always adequately account for environmental, regulatory, and social risks associated with green transformation, as—particularly in the case of IRR—they rely on the assumption of stable cash flows and do not incorporate regulatory uncertainty, environmental externalities, or ESG-related risks into discounting parameters. The aim of the study was to determine the impact of nominal and real discount rates, adjusted for a synthetic measure of green transformation, on investment decisions. The research methodology combines advanced multi-criteria decision-making techniques, specifically TOPSIS and CRITIC, with sustainable finance concepts, offering an innovative approach to investment decision-making in the SME sector. The study shows that integrating environmental factors, when treated as a risk component, increases the cost of capital and reduces the net present value, while maintaining the profitability of the analysed projects. Incorporating green components into the discount rate enhances valuation appropriateness and improves investment risk management, particularly under macroeconomic uncertainty. The main contribution of the study lies in linking a synthetic green transformation indicator with dynamic discount rate adjustment within a multicriteria framework, extending existing ESG-adjusted valuation models by enabling a more structured and data-driven incorporation of environmental transition risk. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

In this study, we propose a structured valuation framework for non-fungible tokens (NFTs), a distinct class of digital assets whose pricing mechanisms remain insufficiently understood. Based on previous empirical studies and illustrative case analyses of three major NFT collections, we synthesize insights from non-cash-flow asset theory, market microstructure, and behavioral finance to construct a four-layer valuation framework consisting of the Asset, Market, Technology, and Ecosystem layers. We identify three NFT-specific mechanisms—verified digital scarcity, pseudonymous signaling, and on-chain herding—that modify or extend traditional valuation paradigms. Empirical evidence from the literature suggests that rarity-driven asset features and social-influence dynamics are dominant price determinants, while wash trading, fragmented liquidity, and platform incentive structures generate persistent distortions in price discovery. Case analyses of CryptoPunks, Bored Ape Yacht Club, and Pudgy Penguins demonstrate how differing risk exposures across the four layers translate into distinct valuation trajectories. With this framework, we obtain a basis for improved risk assessment, regulatory oversight, and business model design in NFT markets. Full article

Land use and land cover (LULC) change strongly influences national carbon dynamics and the effectiveness of forest-based climate mitigation strategies, particularly in mountainous developing countries. This study integrates scenario-based LULC modeling, spatially explicit carbon accounting, and economic valuation to assess how alternative development pathways affect carbon storage and its economic value in Nepal over the 2020–2050 period. LULC projections for four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER), were generated using the TerrSet Land Change Modeler, with 2020 as the baseline. These projections were then used as inputs to the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon Storage and Sequestration model to estimate changes in ecosystem carbon stocks, integrating aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter pools. Carbon stock changes were monetized using a constant carbon price of USD 5/tCO₂e and a 3% discount rate to estimate net present values (NPV). Results reveal strong divergence across scenarios. National carbon storage remains near-neutral under BAU (−0.46% by 2050), declines under RUD (−2.42%) and FDTC (−5.32%), and increases substantially under ALER (+11.74%). These biophysical outcomes translate into contrasting economic values: BAU yields a small negative NPV, RUD and FDTC generate large discounted losses, and ALER produces a strongly positive NPV exceeding USD 800 million by 2050. Spatially, forest and other wooded land dominate national carbon dynamics, while urban expansion and forest degradation drive disproportionate losses. Overall, the study results demonstrate that recovery-oriented land-use pathways offer substantially greater long-term carbon and economic benefits than development trajectories dominated by urban expansion or forest degradation, providing a policy-relevant framework to support Reducing Emissions from Deforestation and Forest Degradation, together with conservation, sustainable forest management, and enhancement of forest carbon stocks (REDD+) planning and long-term mitigation assessment in Nepal. Full article

Integrated landscape (bioregional, territorial) management (ILM) is a model for place-based planning and development that integrates values of healthy nature, regenerative economies, human well-being, and social solidarity. This review paper analyzes the support system for ILM to achieve transformative change, highlighting 20 dimensions in five support sub-systems. Though landscape partnerships (LPs) are now widespread, they have little coordinated support to form and have weak capacities, inadequate long-term operational funding, and limited cultural resonance. Landscape programs have proliferated and gained notable system-level support, but LP coalitions and alliances are just emerging, and there is little coordinated provision of LP support services. Despite widespread developments in the knowledge base, methods, and tools for local ILM design, there is little coordinated system support and limited dedicated work on data and IT, impact assessment, or strategic research. Landscape finance tools and business engagement with LPs are being explored, but economic valuation is inadequate, and little financing has shifted to coordinated landscape investments. In public policy, professional planners and international policy frameworks are adopting ILM, but government policies and tenure systems provide sparse support. High-leverage actions can accelerate progress in each dimension. But to fully realize the transformative potential of ILM will require more coherent support strategies. Full article

This study examines how ESG performance translates into firm value in an Indonesia setting characterized by high information asymmetry, strong political–business linkages, and weak ESG assurance adoption. Using panel data from non-financial firms listed on the Indonesia Stock Exchange over the 2010–2023 period (1700 firm-year observations), we analyze whether political connections and external ESG assurance condition the value relevance of ESG performance. The results show that ESG performance is positively associated with firm value; however, this relationship is highly context-dependent. Political connections significantly strengthen the ESG–firm value relationship, suggesting that politically connected firms are better able to convert ESG engagement into economic value by enhancing legitimacy, reducing regulatory uncertainty, and securing stakeholder support. In contrast, external ESG assurance does not significantly moderate this relationship, reflecting the limited credibility and weak differentiation of assurance practices in Indonesia’s immature sustainability assurance market. These findings highlight that, in emerging markets, ESG disclosures are not uniformly credible and may be subject to political capture or symbolic reporting. ESG creates firm value primarily when it is reinforced by institutional mechanisms that reduce perceived risk and enhance credibility. This study contributes to the ESG literature by demonstrating that the valuation effects of sustainability performance depend not only on ESG outcomes but also on the political and institutional environment in which firms operate, with important implications for regulators, investors, and managers in Indonesia. Full article

Value is the result of the combined, conscious, and creative actions of caring, which promote sustainable prosperity. Despite its centrality in marketing theory, value is treated in the literature as a self-evident, abstract term denoting concepts as diverse as the desire to acquire goods or enjoy services, the benefits derived from using a product, the price of an object, or a customer’s contribution to business profits. This approach leads to amoral marketing decision-making focused on extracting value from stakeholders and accumulating it in the form of shareholder wealth. In this framework, the negative consequences of marketing actions for society and the natural environment are simply dismissed as externalities. This is not sustainable as it degrades the environment and increases wealth and human welfare disparities between individuals, groups, and societies. Drawing on conceptualisations of value from the fields of philosophy, semiotics, and economics, value is here defined as the result of the combined, conscious, and creative actions of caring which promote sustainable prosperity. As such, value is understood to be co-created by the interactions of various stakeholders and positioned as the link between individuals, companies, markets, society, and the natural environment. Marketing theory has traditionally viewed value creation and exchange as the result of dyadic interactions. The socioeconomic and technological milieu of the 21st century, however, creates a business ecosystem characterised by digitalisation, interconnectivity, and decentralisation which means that, the number of participants in value co-creation networks is increasing and potentially tending towards infinity. Consequently, marketing is reconceptualised as the values-driven mechanism for value formation, valuation, symbolism, exchange facilitation, and integration of the resources required for value co-creation and distribution aiming at contributing to sustainable prosperity. Virtuous marketers and mindful marketing practice can ensure the optimal use of resources and the maximisation and equitable distribution of welfare in the present without compromising the ability of future generations to continue to generate and enjoy value. Thus, by placing value at the centre of the business ecosystem, marketing contributes to sustainable prosperity. Full article