Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input-output (IO) tables are published with time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This study develops an applied framework that combines IO nowcasting with an accounting-consistent sector-embedding procedure under limited data availability. Using Ireland's national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints. The paper finds that the bioeconomy is structurally heterogeneous rather than a single uniform sector. Aquaculture is strongly transport- and service-linked, anaerobic digestion is more manufacturing-oriented, and plant-based protein production combines agricultural and industrial inputs while showing relatively high import dependence.

This review explores the role of microsimulation modelling in addressing the multidimensional nature of food security, focusing on affordability, nutrition, and environmental sustainability. Using a hybrid approach that combines qualitative textual synthesis with bibliometric analysis, the study offers both depth and breadth in reviewing current literature. Findings reveal that while affordability and nutrition are frequently examined, environmental aspects remain notably underrepresented in microsimulation-based food security analyses. This gap limits the development of integrated policy responses that address both human and planetary health. The review underscores the need for more interdisciplinary, systems-based modelling approaches that align with global sustainability goals. Bridging these methodological and thematic gaps is important for designing effective, equitable, and environmentally responsible food policies.

Carbon taxes are increasingly popular among policymakers but remain politically contentious. A key challenge relates to their distributional impacts; the extent to which tax burdens differ across population groups. As a response, a growing number of studies analyse their distributional impact ex-ante, commonly relying on microsimulation models. However, distributional impact estimates differ across models due to differences in simulated tax designs, assumptions, modelled components, data sources, and outcome metrics. This study comprehensively reviews methodological choices made in constructing microsimulation models designed to simulate the impacts of carbon taxation and discusses how these choices affect the interpretation of results. It conducts a meta-analysis to assess the influence of modelling choices on distributional impact estimates by estimating a probit model on a sample of 217 estimates across 71 countries. The literature review highlights substantial diversity in modelling choices, with no standard practice emerging. The meta-analysis shows that studies modelling carbon taxes on imported emissions are significantly less likely to find regressive results, while indirect emission coverage has ambiguous effects on regressivity, suggesting that a carbon border adjustment mechanism may reduce carbon tax regressivity. Further, we find that estimates using older datasets, using explicit tax progressivity or income inequality measures, and accounting for household behaviour are associated with a lower likelihood of finding regressive estimates, while the inclusion of general equilibrium effects increases this likelihood.

Low-carbon transition in European agriculture requires evidence on how sectoral structures adjust under policy support. In Ireland, fiscal investment through the Organic Farming Scheme has increased over the period 2020–2023 as part of national and EU commitments to sustainable production. The study provides a descriptive economic analysis of growth and structural change in the organic farming sector over this period, with particular attention to the organic beef subsector. The analysis uses data from the Central Statistics Office Census of Agriculture 2020 and the Farm Structure Survey 2023. Growth is measured through volume-based rates of change in conformity with Eurostat farm structure reporting. The results show growth in the number of organic farms by +147%, while certified organic land area expanded by +144%. Growth occurred primarily in beef systems in absolute terms and in sheep systems in relative terms, with smaller increases in dairy, tillage, and mixed farms. Expansion was concentrated among small and medium-sized holdings and in the Northern and Western regions. At the same time, average standard output on organic farms declined by 24% and median output by 16%, reflecting the entry of smaller and more extensive farms with lower economic scale. The study documents the structural adjustment in Ireland’s organic beef sector during a period of expanded support, without inferring causation. The findings provide new evidence on how policy-supported expansion is associated with sectoral structural change and transition toward lower-carbon production systems in a region with historically low levels of organic adoption. Received: 1 October 2025 | Revised: 26 March 2026 | Accepted: 30 April 2026 Conflicts of Interest The authors declare no conflict of interest to this work. This research was supported by the Teagasc Walsh Scholars Programme, Ireland, and the funder had no role in the study design, analysis or interpretation of the findings, or the decision to submit the manuscript for publication. Data Availability Statement The data used in this study are publicly available from the Central Statistics Office of Ireland Farm Structure Survey2023 and Census of Agriculture 2020 . Farm Structure Survey2023 data are available at https://www.cso.ie/en/releasesandpublica tions/ep/p-fss/farmstructuresurvey2023/data/, and Census of Agriculture 2020 data are available at https://www.cso.ie/en/release sandpublications/ep/p-coa/censusofagriculture2020detailedresults/ organics/. Author Contribution Statement Alfred Afeku: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Cathal O' Donoghue: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Kevin Kilcline: Conceptualization, Methodology, Validation, Resources, Writing – Review & Editing, Supervision, Project administration, Funding acquisition.

This study investigates the economic feasibility and profitability of large-scale biomethane production through anaerobic digestion (AD) at plant capacities of 10, 20, and 40 GWh, using grass silage and cattle slurry as feedstocks. A robust deterministic and probabilistic modelling approach was employed to evaluate essential financial indicators, including Levelized Cost of Biomethane (LCOB), Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PBP). Sensitivity analysis observed at changes in the costs of grass silage (+30%) and biomethane (+50%). A Monte Carlo simulation with 10,000 iterations assessed at uncertainty by figuring out mean values, the chance of a positive NPV, and Conditional Value at Risk (CVaR). The results show that there is a good link between scale and financial performance. The cost of capital expenditures (CAPEX) ranged from & euro;2.9 million for 10 GWh to & euro;7.9 million for 40 GWh. As the scale of the plant grew, the cost of modifications and grid connections went down. The 40 GWh AD plant exhibited the strongest performance, achieving an NPV of approximately & euro;22.2 million, an IRR of 30%, an LCOB of 0.079 & euro;/kWh (approximate to & euro;0.79/Nm3), and a payback period of 3.2 years, whereas the 10 GWh plant delivered a lower IRR of 16% and a longer payback period of 6.7 years. A probabilistic investigation confirmed that larger investments are more resilient, with the probability of a positive NPV rising from 81% to 92%.

Food systems account for a large share of global greenhouse gas emissions but remain largely outside formal carbon pricing frameworks. This paper develops a household-level microsimulation model to assess the environmental, distributional, and nutritional impacts of a uniform food carbon tax in Ireland. Weekly food expenditures from the 2015 Irish Household Budget Survey (6864 households) are converted into quantities using CPI-adjusted supermarket prices and linked to emissions intensities from both Processed Life Cycle Assessment (LCA) and Environmentally Extended Input–Output (EE-IO) models. The analysis compares the two emissions-accounting approaches and tests whether aggregation bias alters inequality estimates. Results show that food carbon taxation is regressive: under a €56/tCO2 tax, the poorest decile faces median burdens of about 3.8% of income with LCA and 2.2% with EE-IO, compared with 0.5–1% in the richest decile. Nutritional vulnerability is also concentrated among low-income households, whose protein- and energy-linked food expenditures reach 2.0–2.3 times the population mean as a share of income. Despite notable item-level differences between LCA and EE-IO, both methods yield statistically similar inequality outcomes, and aggregation bias is limited. These findings emphasise that distributional effects are driven more by household income patterns than emissions-accounting choices.

Microsimulation is widely used in economics to analyse the distributional effects of policy and the behavioural responses of heterogeneous agents. In agricultural economics literature, farm-level simulation and bioeconomic models have developed in parallel with farm level microsimulation. These models operate at different scales but combine biological processes with economic and policy factors. The modelling literature provides little systematic assessment of how these approaches address economic and environmental outcomes in pasture-based agricultural systems. This paper addresses the gap through a systematic review of 173 peer-reviewed modelling studies published between 2000 and 2024. The analysis traces temporal and geographical trends in the literature, reviews methodological choices, and assesses how economic and environmental outcomes are modelled. The results shows that farm-level simulation approaches account for largest share of the literature, followed by optimisation models, while microsimulation and macro-scale approaches are less common. Most of the studies were at the farm-level and focus mainly on environmental outcomes, particularly land use and greenhouse gas emissions. Policy modelling concentrated on conventional production systems, while organic and low-input systems are underrepresented. Approximately half of the reviewed studies originate from Europe, and macro-level approaches account for less than 10% in literature. The literature exhibits a persistent pattern: models that handle farm-level heterogeneity seldom connect to sectoral outcomes, while those designed for aggregation are seldom applied to pasture-based systems. This limits the evidence base for policies that require both micro-level behavioural responses and macro-level assessment. Closing this gap will require integrated frameworks that couple farm-scale representation with sectoral or economy-wide models.

Abstract Aquaculture is an increasingly important component of sustainable food systems, but in national input–output (IO) systems it is commonly represented in aggregate form, obscuring differences across production systems. This paper addresses that problem for Ireland by disaggregating the observed aggregate aquaculture sector within BIO2022 into five species-based sub-sectors: penned salmon, land-based finfish, suspended mussels, seabed mussels, and farmed oysters. Using a demand-driven IO framework, the analysis applies an incorporation algorithm to represent these sub-sectors separately while preserving accounting consistency. The paper compares species-level systems in terms of input composition, feed dependence, value-added structure, and economic linkages. It thereby shows how aggregate sectoral treatment can mask structural heterogeneity within aquaculture and limit interpretation of the sector’s economic role. By improving the species-level representation of aquaculture within BIO2022, the paper provides a more precise basis for analysing aquaculture within the Irish bioeconomy and within a lower-carbon food-system context.

Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input–output (IO) tables are published with substantial time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This paper develops an applied methodology that addresses both limitations by combining IO nowcasting with a reduced-dimensional sector-embedding procedure. Using Ireland’s national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal substantial heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints.

Urban regeneration investments, particularly those involving multi-million-euro allocations, require diverse evaluation methodologies to comprehensively assess their impacts. This paper aims to evaluate the effects of a substantial urban regeneration investment in Limerick, a county in west of Ireland, characterized by significant deprivation. The case study examined is the Limerick opera house, a major mixed-use development comprising office space, a public library, hotel accommodation, and leisure facilities. This project offers an exemplary context for analysing a complex urban regeneration initiative characterised by multiple, interrelated objective. Utilizing a hybrid, downscaled multi regional input output (MRIO) analysis alongside impact analysis, this study examines the economic, environmental, and social impacts of the project. The results indicate that the project generated 1676.43 jobs during the construction phase and 2400 jobs in the long-term phase. However, the employment opportunities created did not substantially benefit deprived areas, primarily due to a mismatch between the skill sets required for the jobs and those available within the local population. This study highlights the necessity for supplementary training and upskilling initiatives to effectively target these communities. Future research may focus on refining the model from an environmental perspective, incorporating more robust methodologies for carbon pricing and cost-benefit analysis.

Household carbon emissions in low- and middle-income countries may be systematically underestimated when non-purchased biomass fuels are excluded from empirical analysis. In many such settings, households rely not only on market-based energy sources but also on firewood, dung cakes, and agricultural residues obtained outside formal market transactions. Because household budget surveys (HBS) primarily record purchased consumption, emissions associated with these non-purchased fuels are typically omitted from standard assessments. This issue is especially relevant in Pakistan, where traditional biomass continues to play an important role in household energy use, particularly among poorer and rural households. Using data from the 2018 Pakistan HBS, this paper incorporates carbon emissions from non-purchased fuels to provide a more complete picture of household energy-related emissions. Empirically, the study combines descriptive analysis with econometric modelling and employs a selection-adjusted quantile regression framework to account for both the non-random incidence of non-purchased fuel use and heterogeneity across the distribution of consumption. The results highlight substantial heterogeneity in the determinants of non-purchased fuel use, with household composition, housing characteristics, and access to modern fuels playing different roles across consumption levels. Selection effects are also important, supporting the use of selection-corrected methods. At the lower end of the distribution, reliance on non-purchased fuels is widespread and closely associated with rural residence, lower income, and limited access to modern energy. The findings contribute to a better understanding of the level and distribution of household carbon emissions in Pakistan and highlight the importance of accounting for non-market energy use in distributional environmental analysis.

Equivalence scales are a central component of distributional analysis. They adjust household incomes for size and composition, shaping poverty and inequality measurement. Most work relies on the modified OECD scale from the mid-1990s, despite substantial changes in consumption patterns since. We revisit equivalence scales for 23 European countries using a demand model based on the linear expenditure system and harmonised Household Budget Survey microdata for 2010–2020. We estimate three demand-based scales: a minimum needs scale anchored in subsistence, a utility implicit scale based on welfare equivalence under common preferences, and a utility explicit scale as a sensitivity check. Across specifications, the demand-based estimates generally imply larger economies of scale than the modified OECD scale, especially for larger households. Scales are lower at higher living standards and exhibit a modest downward trend over time. A simulation of 2020–2024 price changes shows that recent inflation is likely to further reduce scales, with stronger impacts for households with children. Regional heterogeneity persists, reflecting differences in prices, preferences and consumption patterns. Recomputing distributional indicators shows that poverty measures are more sensitive than inequality measures to the choice of equivalence scale. The utility implicit scale typically yields the largest increase in measured inequality relative to the OECD benchmark. Overall, our results indicate that equivalence scales used in distributional analysis should be periodically updated and distributional statistics routinely reported under alternative scales.

The systems of direct taxes and cash benefits in the Member States of the European Union vary considerably in size and structure. We explore their direct impacts on cross-sectional income inequality (termed for the purpose of this paper) using EUROMOD, a tax-benefit microsimulation model for the European Union. This relies on harmonised household micro-data representative of each national population together with simulations of entitlements to cash benefits and liabilities for taxes and social contributions. It allows us to draw a more comprehensive – and comparable – picture of the combined effects of transfers and taxes than is usually possible. We decompose the redistributive effect of taxbenefit systems to assess and compare the effectiveness of individual policies at reducing income disparities. We derive results for the 15 old members of the European Union and present them for each country separately as well as for the EU-15 as a whole.

Agricultural cooperative development in Indonesia is closely linked to government intervention. Policymakers have advocated for a federation system that integrates agricultural sector programmes with cooperatives at both national and rural levels. This study aims to assess the effects of such integration on the performance of agricultural cooperatives by analysing data from annual reports of cooperatives for 2022 and 2023. Furthermore, through the use of panel data regression and Data Envelopment Analysis (DEA), framed within Transaction Cost Theory (TCT), Resource Dependence Theory (RDT), and Dynamic Capabilities Theory (DCT), this research explores how federated integration affects cooperative performance. Various types of integrated services were examined to understand how their characteristics and implementation relate to performance outcomes. By linking theoretical frameworks with empirical evidence, this study provides detailed insights into how integration can enhance cooperative performance and underscores the importance of adapting integration strategies to evolving operational environments.

Agriculture is the mainstay of Sub-Saharan Africa's (SSA) economy, yet the sector's productivity is declining due to climate change, extreme weather variability, and population pressure. Despite several initiatives, the adoption of climate-resilient agriculture (CRA) by farmers remains low. Focusing on Kenya, this study utilises an extended Theory of Planned Behaviour (TPB) Model to examine socio-psychological factors influencing farmers in adopting CRA. This fills a knowledge gap, as it goes beyond traditional cost-benefit analyses by considering individual and community-level behaviors, attitudes, and capabilities. The study also contributes to the TPB literature by extending its scope to include ABC, perceived usefulness, perceived barriers/drivers, and farm and farmer characteristics.