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Topic Review Peer Reviewed
Demand for Green Skills in the Latin American Labour Market: Mapping the Available Evidence and Research Gaps
Demand for green skills denotes the requirement, on the part of employers and productive structures, for knowledge, abilities, and dispositions oriented towards reducing the environmental impact of economic activity. It differs from green employment in that it records occupational content rather than jobs. It can be measured by two routes: the occupational route, which infers the skill content of jobs from occupational classifications and taxonomies, and the direct route, which captures the demand expressed by employers or by the labour market through surveys, interviews and the analysis of job advertisements. Sectoral approaches, which classify economic activities, inform on green employment but do not, on their own, reach the level of the skill. In the sources reviewed for Latin America, the category has no statistical existence: none of the 20 countries considered was found to maintain a classification of green skills, the global indicator monitoring target 4.4 of the Sustainable Development Goals measures digital rather than environmental skills, and national green employment estimates vary within a single country by factors of up to 14.2 depending on the methodological decision adopted. The only quantification of training demand identified in the literature reviewed was carried out in Brazil on 2010 data and identified 2,083,241 formal jobs, 4.5% of the country’s formal employment, in occupations whose requirements change as a result of the green transition. This entry delimits the concept against green employment, contrasts the available occupational taxonomies, inventories country-level coverage, documents the mechanisms producing that variation, and identifies the gaps that the available evidence leaves open.
  • 17
  • 13 Oct 2026
Topic Review
Social Agencies
Social agencies are formally organized entities that provide social or human services and mobilize resources to address the needs of individuals, families, groups, or communities [1][2]. They may operate within public, nonprofit, voluntary, or other organizational settings and can provide services related to health, welfare, education, housing, employment, rehabilitation, or other areas of social need [1][3]. Social agencies typically organize personnel, financial resources, facilities, and other assets to plan, deliver, and evaluate social services [2][4]. Their activities may be supported through government funding, grants, donations, service contracts, fees, or other sources of revenue, making financial management an important component of organizational sustainability and accountability [2][4]. Social agencies also interact with governments, community organizations, businesses, and other service providers to coordinate resources and respond to changing social needs [3]. The term therefore encompasses organizations whose primary activities involve the organized provision of services and mobilization of resources to address social needs and improve individual or community well-being.
  • 15
  • 23 Sep 2026
Topic Review
Social Impact Strategies
Social impact strategies are coordinated approaches through which organizations, social enterprises, investors, and public institutions plan and implement activities to generate positive social change while identifying and addressing potential adverse effects [1][2]. They begin with clearly defined social objectives, such as improving access to essential services, promoting social inclusion, creating employment opportunities, or strengthening community well-being, and align these objectives with organizational missions, operational decisions, and resource allocation [1][3]. Such strategies may involve community engagement, cross-sector partnerships, targeted investments, service delivery, and organizational changes designed to address specific social needs [2][3]. Their implementation can be guided by theories of change, measurable indicators, stakeholder feedback, and ongoing impact monitoring to assess progress and inform adjustments [1]. In finance and impact investing, social impact strategies also guide investment selection, portfolio management, and engagement with investee organizations to pursue measurable social outcomes alongside financial objectives. Effective strategies account for the needs of affected populations, the distribution of benefits and risks, available resources, and the sustainability of outcomes over time.
  • 9
  • 10 Oct 2026
Topic Review
Social Welfare Theory
Social welfare theory is a branch of welfare economics that examines how the well-being of individuals can be evaluated and aggregated to assess the welfare of society as a whole [1][2]. It provides conceptual and analytical frameworks for comparing alternative economic and social states, particularly in relation to resource allocation, efficiency, equity, and distributional outcomes [3]. Major approaches include Pareto-based criteria, social welfare functions, and social choice theory, which differ in how they incorporate individual preferences, utility, and ethical judgments into collective evaluations [4]. These frameworks inform economic policy analysis while highlighting the challenges of comparing individual well-being and reconciling competing social objectives.
  • 7
  • 22 Oct 2026
Topic Review
Social Impact Evaluation
Social impact evaluation is the systematic process of assessing the intended and unintended, positive and negative changes in individuals, groups, and communities associated with a policy, program, project, or organizational activity [1]. It examines the nature, extent, and significance of social outcomes, including changes in well-being, access to resources, social inclusion, livelihoods, and community relationships [1]. The evaluation may combine quantitative indicators, such as changes in employment or service access, with qualitative evidence from interviews, observations, and stakeholder experiences to understand how and why changes occur . Depending on the evaluation design, methods may include baseline and follow-up comparisons, surveys, case studies, contribution analysis, and experimental or quasi-experimental approaches [2]. A key consideration is whether observed outcomes can reasonably be attributed to the intervention, while accounting for contextual influences and alternative explanations. Social impact evaluation can inform decisions about resource allocation, program improvement, accountability, and the distribution of benefits and disadvantages among affected populations.
  • 6
  • 10 Oct 2026
Topic Review
Policy Development
Policy development is the process through which public authorities identify policy problems, establish objectives, formulate and assess alternative courses of action, and develop proposals for authoritative decision-making. It involves defining the nature and scope of a public problem, gathering and interpreting relevant evidence, consulting affected stakeholders, assessing the feasibility and likely consequences of policy options, and selecting appropriate instruments, such as legislation, regulation, public expenditure, or information provision [1][2]. The process is shaped by institutional arrangements, political priorities, available resources, competing interests, and administrative capacity. Although policy development is often represented as part of a policy cycle, its stages are not necessarily sequential or clearly separated. New evidence, political negotiations, implementation constraints, and public feedback may lead to the reconsideration of earlier decisions. Policy development is therefore an iterative process that connects problem definition and policy design with formal decision-making, implementation, monitoring, and evaluation [1][3].
  • 6
  • 23 Oct 2026
Topic Review
Shifting Cultivation
Shifting cultivation is an agricultural land-use system in which an area of land is cultivated for a relatively short period and then left fallow while cultivation shifts to another plot. The alternation between cultivation and fallow is a defining characteristic of the system, although the duration of each phase varies with local environmental conditions, farming practices, and land availability [1]. In many traditional systems, land preparation involves clearing existing vegetation, sometimes using fire, followed by crop cultivation and the subsequent regeneration of natural or secondary vegetation during the fallow period [2]. During fallow, vegetation regrowth can contribute to changes in soil nutrients, biomass, and ecological conditions, while the effects of cultivation depend partly on the history of previous cropping cycles and the length and condition of fallow periods [3]. Shifting cultivation is not defined solely by the use of fire, subsistence production, or the movement of farming households; its distinguishing feature is the rotation of cultivated plots through periods of use and fallow [1][2]. It differs from continuous cultivation, in which the same land remains under regular production, and from permanent crop rotation, in which different crops are cultivated sequentially on land that remains in active agricultural use.
  • 5
  • 19 Oct 2026
Topic Review
ESG Integration into Financial Products and Markets
Environmental, Social, and Governance (ESG) integration into financial markets involves incorporating environmental, social, and governance considerations into investment decisions, financial products, and risk management practices. This entry provides an overview of the evolution of ESG integration and examines its application to financial instruments, including ESG-focused indices, exchange-traded funds (ETFs), impact investments, green bonds, sustainability-linked loans, and ESG-related derivatives. It also addresses challenges associated with ESG reporting, data reliability, measurement standardization, and greenwashing.
  • 5
  • 11 Nov 2026
Topic Review Peer Reviewed
Exploring Synthetic Data in Financial Analytics: A Bibliometric Review of Generation Methods, Validation Challenges, and Future Directions
Synthetic data generation has become a strategic capability in financial analytics, promising to reconcile the sector’s appetite for data-hungry machine learning with strict privacy, confidentiality, and regulatory constraints. This review provides a structured, evidence-based account of the field that is both conceptual and empirical. On the conceptual side, it explains how the principal generators work, states their objectives formally, and derives the practical consequences of each mechanism for financial data; it then organises evaluation into an operational fidelity–utility–privacy framework, extends it to incorporate the fairness and model-risk requirements imposed by regulated lending and insurance and the validation constraints that differ by data type, and offers explicit guidance on selecting a generator. On the empirical side, it maps the intellectual structure of the field through a science-mapping study of 313 Scopus-indexed documents (1997–2026) retrieved with a four-block Boolean query executed on 27 July 2026 and screened from 591 retrieved records. Performance analysis reveals sustained exponential growth, at an annual rate of 14.1% to 17.5% depending on how the endpoints are set, with 2026 covering only the first 208 days of the year, and 73.8% of output published since 2023. The corpus has accumulated 4359 citations with an h-index of 35 and pronounced inequality (Gini = 0.737; 26.2% of documents uncited), although 96.3% of the uncited documents appeared in 2025 or 2026 and the Gini falls to 0.599 once the analysis is restricted to documents with at least eighteen months of citation exposure, so these figures reflect the citation window as much as scientific impact. Co-word analysis, a strategic thematic map computed from the equivalence index, and an unsupervised clustering pipeline validated using the silhouette, Davies–Bouldin, and Calinski–Harabasz indices, together with seed, subsample, and specification stability tests, identify five overlapping and provisional research fronts: risk modelling and Monte Carlo simulation; fraud and credit-risk detection under class imbalance; privacy-preserving generation; machine-learning analytics and benchmarking; and biomedical simulation methods transferable to finance. The internal validation indices are weak in absolute terms, so these fronts are best read as an exploratory summary of a densely connected intellectual space rather than as sharply delineated lines of enquiry. We conclude that the field’s binding constraint has shifted from generation to certification: realistic financial data can now be produced, but the evidence that would establish a given synthetic dataset as simultaneously realistic, useful and safe—downstream performance on representative financial tasks, quantified disclosure risk, and robustness across market regimes—is still assembled on a study-by-study basis rather than through shared benchmarks.
  • 4
  • 12 Nov 2026
Topic Review Peer Reviewed
Credit-Delinquency Modelling and the Financial-Cooperative Gap: A Quantitative Review of Determinants, Ensemble Learning and Explainable Artificial Intelligence
Credit delinquency is the central prudential risk of savings and credit cooperatives, yet the methods used to model it have been developed and tested mostly on banks, consumer credit and benchmark datasets. This review uses the broader credit-delinquency literature as a benchmark against which the position of financial cooperatives is measured. A curated corpus of 123 documents (1953–2026), assembled by expert selection and citation tracing and verified against bibliographic registries, was analysed with bibliometric laws, natural language processing (TF–IDF, non-negative matrix factorisation, co-word networks, semantic drift and burst detection), a binomial generalised linear model of paradigm diffusion based on manually verified coding, a citation-free contribution index and a contingency test of coverage deficit. Five phases are identified, with the largest semantic displacements at the 1997 and 2016 boundaries (cosine drift 0.642 and 0.567). Explainability diffuses fastest (odds ratio 1.354 per year, 95% CI 1.202–1.526), and the mean number of paradigms per document rises from 0.91 to 2.36. Cooperative research follows a different path: none of the 12 cooperative documents employs ensemble learning or explainability methods (Fisher exact p = 0.0001), and a reproducible search of 65,610 Semantic Scholar records indicates that cooperative credit-risk studies apply these methods at roughly one quarter of the rate observed elsewhere (0.8% versus 3.0%). The periodisation, the rapid diffusion of explainability and the cooperative coverage deficit hold when the incomplete year 2026 is excluded and under alternative filter thresholds, topic numbers, network and burst thresholds and coding procedures. A conceptual five-layer framework and a research agenda are proposed to guide empirical work on explainable delinquency modelling in cooperatives.
  • 2
  • 12 Nov 2026
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