Integrating hydrogen into a computable general equilibrium model

Assessing the multidimensional impacts of CCS technology cost reductions: Evidence from a dynamic CGE model

Unveiling losses due to water scarcity and impacts of policy intervention under SSPs: A water-extended dynamic CGE model

With growing consensus on the scale of climate change and its direct impacts on societies and economies, the debate on how direct climate damages cascade through interconnected economic systems eventually leading to profound indirect effects remains open amid limited quantitative evidence. The localised nature of climate risk means that regional economies may face stark differences in how they are impacted by the direct and indirect physical risks. In Europe, the world's fastest warming continent, concentrated local damages spill over asymmetrically into tightly-interconnected regional markets, potentially leading to increased inter-regional inequalities despite the continent's prioritisation of economic unity through its regional cohesion policy. Solid regionalized economy-wide analysis could identify if physical climate risks serve as structural drivers of future regional inequality, offering timely insights for policy interventions to curb exacerbating socio-economic adversities. Here, using an empirical dynamic computable general equilibrium model disaggregated to NUTS2 European regions, we explore a range of regional economic projections from two particularly costly climate-driven hazards: river flooding and sea-level rise. Our methodology captures complex economic feedbacks across granular regions and sectors to estimate both direct and indirect economic repercussions of combined sea-level rise and river flood events by 2100. We find that these climate-induced hazards represent an economically divergent force for the European regions. In contrast to aggregated studies, the resulting regional economic projections reveal wide heterogeneity in combined (direct and indirect) physical risks, with the most affected regions experiencing devastating declines in GDP up to 51% by 2100. Our disaggregated projections capture the effects of two climate-induced hazards with distinct geographical hotspots - coastal and inland - which occur simultaneously though unfold at different rates, enabling a detailed assessment of regional economic inequalities. Low income regions experience by far the highest proportional losses, leading to increases in both between and within-country regional inequality.

This systematic literature review examines whether and under what conditions carbon taxes reduce national and cross-country carbon emissions. Using the PRISMA 2020 framework, Scopus-indexed journal articles published between 1 January 2015 and 1 April 2026 in Business, Management, and Accounting were reviewed. From 119 records, 11 studies met the inclusion criteria and were synthesized qualitatively through structured data extraction and quality appraisal. The findings show that carbon taxes can reduce emissions, but their effectiveness depends on revenue recycling, tax rates, sectoral coverage, exemptions, economic structure, trade exposure, and the distinction between territorial and consumption-based emissions. The review also reveals limited ex post evidence, as existing studies are dominated by CGE, GTAP, and scenario-based models.

• We link an ESM to a CGE model to assess the macroeconomic impacts of the energy transition in the Netherlands. • The modelling framework offers new methodological insights by integrating detailed energy technologies within an economy-wide CGE model. • Although the energy transition yields GDP gains in the long term, it leads to unemployment, driven by structural shifts in the economy. This study examines the macroeconomic impacts of the energy transition in the Netherlands. To capture key energy transition dynamics and improve the assessment of alternative fuels adoption, particularly in hard-to-abate sectors such as steel and chemical production, we incorporate hydrogen-related activities into a Computable General Equilibrium (CGE) model and soft-link it to an Energy System Model (ESM). We evaluate two main scenarios: a business-as-usual (BAU) trajectory and an Energy Transition (ET) pathway aligned with a carbon-neutrality target. A variant of the ET scenario with limited capital inflows (ET-CA) is considered to assess the role of financing in the energy transition. Our results show that replacing fossil fuels with renewable alternatives drives GDP growth in the long term, with GDP 1.7% higher in 2050 under the ET scenario compared to BAU. Cumulative GDP over 2025–2050 increases in ET compared to BAU, while it declines by €64 billion in ET-CA. Unemployment peaks around the mid-transition period in ET and declines thereafter, converging to about 0.2% above BAU by 2050. In ET, welfare losses are initially severe but moderate over time, whereas they remain consistently higher under ET-CA. We argue that the negative impacts observed under the ET scenarios should be weighed against the potential climate-related economic damages omitted in BAU, which could otherwise reduce its apparent macroeconomic advantage. Our analysis underscores the necessity of policy frameworks that balance the socio-economic impacts of the energy transition with its environmental benefits, especially under constrained financing conditions.

The European Union's Deforestation-Free Regulation (EUDR), approved in 2023 and set to take effect in December 2026, mandates plot-level traceability and physical segregation of compliant from non-compliant products in supply chains linked to deforestation. This paper assesses the economic and environmental impacts of EUDR compliance costs on Brazil's soybean supply chain. Using a dynamic multiregional computable general equilibrium (CGE) model with integrated land-use transitions, we simulate counterfactual outcomes from 2026 to 2035 under two policy scenarios: (i) Legal Amazon–only coverage (S1-AMZN) and (ii) nationwide coverage (S2-BRA). Compliance costs are modeled as production-tax equivalents, scaled by the regional share of EU-bound soybean-equivalent exports. Results show cumulative export losses of US$ 85.1 billion (S1-AMZN) and US$ 216.3 billion (S2-BRA), with corresponding real GDP shortfalls of US$ 409.9 billion and US$ 1.167 trillion. Avoided deforestation ranges from 51,466 to 105,899 ha, though leakage effects emerge under S1-AMZN. Estimated abatement costs are US$ 133–207 per ton of CO₂e, while forgone GDP per hectare of avoided deforestation ranges from US$ 58,630 to US$ 171,652. These findings suggest that clearing the EU's soy supply chain of deforestation alone delivers limited environmental benefits at high economic cost, underscoring the need to complement trade-based measures with robust domestic land-use governance and enforcement policies. • EUDR lowers Brazil's GDP slightly and deepens regional income disparities • Soy export losses drive land and labor shifts toward corn, cotton, and other crops • Welfare gains for the poorest and richest, losses for middle incomes • Modest avoided deforestation at high economic cost • National versus Amazon-only enforcement yields similar macro but less leakage

Fuel poverty is a growing concern in the United Kingdom. However, there is limited evidence on how different forms of public spending on fuel poverty mitigation affect fuel-poor households and the wider economy. This study uses a dynamic computable general equilibrium modelling approach to broadly compare two central fuel poverty approaches in the UK: providing time-limited direct energy bill support and funding basic home retrofits of loft and cavity wall insulation. The findings show that direct bill support provides immediate relief for all fuel-poor households and produces a short-lived economic stimulus driven by consumer spending. In contrast, public investment in delivering basic home retrofits initially triggers a construction-led stimulus that is replaced over time by a sustained boost supported by fuel-poor households saving on their energy bills. However, a given budget reaches fewer households, while the transitory increase in demand for constrained construction capacity leads to the displacement of other economic activities, particularly when producers demonstrate perfect foresight of the time-limited nature of public spending on retrofit programmes. The findings also show that the method used to raise public funds matters: taxing higher-income households reduces overall consumption and weakens economy-wide benefits, while taxing energy suppliers exacerbates regressive energy price pressures. Overall, the study highlights concrete trade-offs between immediate income support and longer-term efficiency gains, and shows how capacity constraints, expectations, and financing choices jointly shape the economic and distributional outcomes of fuel poverty mitigation policy.

EI-RIDE is proposed in this paper as an Earthquake Insurance-driven Regional Infrastructure Investment Decision and Economic Resilience Assessment Framework, which addresses the problem that previous studies have seldom integrated insurance mechanisms into infrastructure investment optimisation and economic resilience quantification. The three innovations of EI-RIDE are as follows: A multi-layer seismic risk assessment and insurance pricing model (MSRA-IPM) that dynamically links regional seismic hazards, infrastructure vulnerabilities and insurance coverage features; a game-theoretic infrastructure investment decision model (GTIDM) based on Stackelberg game theory, which considers the strategic interactions among governments, insurers and infrastructure investors under earthquake insurance incentives; and a dynamic economic resilience quantification index (DERQI) that integrates input-output analysis and computable general equilibrium modelling to evaluate the economic resilience of an area in terms of pre-disaster preparation, disaster absorption and post-disaster recovery. Experiments on multi-regional empirical datasets have shown that it is feasible to increase the optimal level of infrastructure investment by 18.6%, reduce expected economic losses by 31.2%, and improve the Economic Resilience Index (ERI) by 24.5% in the presence of earthquake insurance schemes compared with the baseline scenario without insurance.

This paper examines how reductions in transportation costs reshape regional economic and environmental outcomes in Morocco. We simulate a reduction in delivered (purchasers’) costs via a transport-margin efficiency improvement – implemented as a margin-saving technical change in the transport sector – within a province-level Spatial Computable General Equilibrium (SCGE) framework. Simulating a 1% decline in transport costs, we assess the marginal impacts across regions. Results reveal uneven gains: coastal areas along the Casablanca-Tangier axis and emerging hubs like Béni Mellal-Khénifra and Dakhla-Oued Ed Dahab benefit from growth and lower emissions, while traditional centers such as Fès-Meknès and Marrakech-Safi experience stagnation and rising carbon intensity. The findings highlight a partial decoupling of economic and environmental convergence, driven by both production structure and scale effects. Our analysis stresses the importance of territorially differentiated infrastructure policies that balance spatial equity with sustainability, offering insights for place-based development and green transformation strategies.

The increasing frequency and severity of extreme droughts caused by climate change has emerged as a key risk factor exerting complex effects on the overall national economy through a structure of interconnected industries. The Water Input–Output Linear Programming (WIOLP) model was applied to data from 2015 to 2018 to quantitatively assess the effects of drought-induced water use constraints on production and socioeconomic potential losses. By modeling scenarios in which water use decreased by 10% from 100%, changes in the gross output, the value added, the socioeconomic potential loss, and the shadow price by industry were evaluated. Results showed that socioeconomic potential losses increased nonlinearly, with maximum potential losses of 311,118 billion Korean Won (KRW) in 2015 and 355,260 billion KRW in 2018. The shadow price rose from 7311 to 73,186 KRW/m3 in 2015 and from 3291 to 89,586 KRW/m3 in 2018, confirming that the marginal productivity of water increased exponentially under stricter constraints. Industry-level analysis revealed the largest losses in high water use industries (e.g., agriculture, forestry, fisheries, chemicals, and non-metals), whereas electricity, electronics, and machinery sectors maintained relatively stable production. This study demonstrates that the WIOLP model can empirically analyze nonlinear economic ripple effects under resource constraints, overcoming limitations of conventional input–output and computable general equilibrium models.

In 2024, the UK Government introduced a statutory ‘Growth Duty’ on the energy industry regulator Ofgem. One implication is that industry actors must explain how proposed investments might enable sustainable economic growth processes when submitting their business plans as part of the regulated energy price control system. The first instance of this requirement affected the three GB electricity transmission owners (TOs) when submitting business plans in late 2024 for the RIIO-T3 period, which will run from April 2026 through to March 2031. This paper reports results and insights from independent research drawing on the investment plans of one of the three TOs in a set of economy-wide scenario simulations using a dynamic computable general equilibrium (CGE) model of the UK economy. A central finding is that our results indicate that undertaking early, planned investment at pace in anticipation of projected rising electricity demand, in response to the UK Government's electrification policies, is likely to deliver substantially stronger GDP and employment outcomes than a reactionary investment approach. This outcome is due to both an increased scale of earlier investment and the early creation of some excess capacity, which introduces downward marginal pressure on electricity bills. Moreover, where the latter is sufficient to offset the user bill impacts of investment cost recovery, the net outcome for UK households becomes progressive. The commonly expected outcome of cost recovery through energy bills being regressive does, however, manifest if electricity prices do not adjust in a competitive manner. • Electricity network investment is likely to support sustainable economic expansion. • Early anticipatory network investment supports stronger, wider economy expansion. • There is potential for progressive rather than regressive outcomes for households. • Achieving such results requires electricity prices to adjust with network capacity.

Abstract This study analyzes the macro-structural effects of budget-neutral reallocations of government research and development (R&D) in Korea’s machinery and equipment industry using a recursive dynamic computable general equilibrium model named as Technology and Economy Modelling for Innovation Policy assessment (TEMIP) over 2019–2030. Three portfolios are evaluated: a machinery-centric R&D expansion, a dual R&D expansion combining machinery and software/information and communication technology (ICT), and a dual R&D expansion combining machinery and electrical/electronic equipment. Results show that the machinery–software mix generates the strongest aggregate gains, raising real gross domestic product (GDP) by 0.57 per cent and average sectoral total factor productivity (TFP) by 0.41 per cent through ICT-driven spillovers. The machinery–electronics mix achieves slightly smaller gains (GDP + 0.44 per cent, TFP +0 .36 per cent) but fosters the most diversified industrial structure. The machinery-only strategy yields the highest machinery output but the weakest economy-wide impact (GDP/TFP + 0.33 per cent). Labor decomposition indicates that the software-oriented portfolio increases the skill premium, while the hardware-oriented mix sustains more balanced labor contributions. Overall, the findings underscore the efficiency–inclusiveness trade-off and highlight the need for complementary diffusion and workforce policies in R&D allocation.

Water scarcity is increasingly recognized as a systemic economic constraint, with impacts that extend far beyond directly water-using sectors. Computable General Equilibrium (CGE) models constitute a powerful tool for capturing the indirect and structural effects of water scarcity across interconnected markets, yet their application to water resources poses distinctive conceptual, methodological, and data challenges. This paper reviews how water has been conceptualized and operationalized within CGE models, focusing on the treatment of water scarcity, allocation mechanisms, and economic valuation under conditions characterized by weak or missing price signals. Rather than providing an exhaustive catalogue of applications, the analysis compares alternative modelling strategies—embedding water in land, treating water as an independent production factor, representing water implicitly through productivity effects, and modelling water as a produced commodity—highlighting their respective advantages, limitations, and suitability for different policy questions. The review shows that no single approach dominates across contexts: implicit representations are often sufficient for climate-impact assessments, whereas explicit formulations are required to analyse water markets, allocation rules, and infrastructure investments. A central challenge across all approaches is the fundamentally non-market nature of water, which complicates calibration, pricing, and the interpretation of economic rents. Additional difficulties arise from spatial and temporal heterogeneity, basin-level constraints, return flows, and water quality differentiation, which standard CGE structures struggle to represent. The paper also shortly discusses recent advances in hybrid modelling frameworks that couple CGE models with hydro-economic models (HEMs). The paper concludes by outlining key directions for future research, emphasizing the need for improved water accounts, dynamic and seasonal modelling, and closer integration between economic and hydrological modelling communities.

Purpose. This study aims to evaluate the impact of Regional Comprehensive Economic Partnership (RCEP) tariff commitments on China’s staple grain trade by applying the Global Trade Analysis Project (GTAP) model. In addition, it examines the deviations between model-based trade projections and actual trade outcomes to identify the main sources of inconsistency. Methodology. This study applies the GTAP computable general equilibrium model to simulate the impact of tariff adjustments under RCEP. Using CEPII database indicators to update economic conditions to 2022, the study constructs multiple trade scenarios and compares the predicted results with actual trade data from 2022 to 2023 to assess the accuracy and deviations of the predictions. Results. The analysis shows that tariff reductions under RCEP stimulated grain exports, especially rice, but the positive effects diminished over time, while imports of certain products, such as processed rice, declined. Model simulations predicted export growth of 1 to 5% and import decreases of 1.5 to 2%. However, a comparison with actual trade flows indicates that these effects were overstated. External factors – such as fluctuations in international grain prices, domestic production conditions, and agricultural policy adjustments – had a stronger impact, leading to deviations between the predictions and the actual outcomes. Originality. This study focuses on the impact of RCEP on China’s staple grain trade, comparing GTAP model predictions with actual post-RCEP trade data. It provides an empirical analysis of prediction deviations and reveals the limitations of tariff-based trade forecasts in volatile agricultural markets. Practical value. The findings provide empirical evidence for policymakers, highlighting that tariffs alone have a constrained impact on actual grain trade outcomes. The results underscore the necessity of a multidimensional approach that accounts for macroeconomic volatility, environmental conditions, and policy shifts. The study advocates for a balanced approach, combining moderate tariff reductions with flexible trade strategies, to strengthen the resilience of staple grain markets.

Multisectoral economic modelling provided insights to policymakers during the COVID-19 pandemic, including the economy-wide impacts of changes in tourism and the design of policy responses. These models embed assumptions about how firms and households respond to adverse shocks, which are linked to the level of economic resilience they exhibit. This paper illustrates how Input–Output and Computable General Equilibrium models, incorporating different behavioural assumptions, can yield different estimates of static resilience, the economy's ability to maintain function when shocked. Using the example of Scotland in 2020 and simulating an Accommodation demand shock, we show that model specification can rule out responses that directly influence the degree of estimated resilience in an economy. By comparing simulation results with observed changes in value added of the Accommodation sector, we demonstrate that appropriately accounting for resilience mechanisms helps close the gap between simulated and observed data.

Large-scale investments in highway network generate substantial long-term regional benefits, but the mechanisms linking specific construction corridors, design choices, and phasing strategies to macroeconomic outcomes remain opaque to industry decision makers. This study develops an explainable artificial intelligence (XAI) surrogate for a spatial equilibrium, type regional economic model, explicitly tailored to the needs of highway owners, contractors, and program managers. The study examines the U.S. National Highway System and Interstate network across the contiguous 48 states, using corridor segments (typically 10–80 miles) tracked over 2019–2024 to form a segment–year panel. The resulting sample reflects large-scale investments (mean segment length 24.3 miles; mean contract value $215 M), substantial traffic demand (mean AADT 52,000; truck share 19.5%), and measurable performance gains (mean freight travel-time reduction 7.8%; accessibility gain 6.1%). XAI analysis based on SHAP values shows that corridor-level freight travel-time reduction, baseline manufacturing and logistics shares, whereas improved access for structurally lagging counties and service-sector intensity are dominant drivers of welfare gains in rural regions. From a construction-industry perspective, we define a Workload Stabilization Index, capturing fluctuations in annual highway construction volume at the state level. The framework translates complex regional economic modeling into an interpretable, construction-oriented decision tool, enabling agencies and contractors to see why certain corridors deliver higher macroeconomic returns and to design project packages and schedules that jointly optimize economic impact, risk, and industry stability.

Carbon pricing is central to achieving climate targets, yet its economy-wide and distributional effects, particularly through the transport sectors, remain insufficiently understood. This study employs a static multi-regional computable general equilibrium (CGE) model of Japan to evaluate the economic, environmental, and welfare impacts of carbon pricing, with explicit treatment of transport modes. We simulate a business-as-usual baseline and five policy scenarios, including a national emissions cap achieving a 10% reduction in CO₂, a transport exemption scenario, and three alternative revenue-recycling schemes. The results indicate that achieving a 10% national emissions reduction requires a carbon price of approximately 4,153 JPY per ton of CO₂, with a modest aggregate GDP loss. However, impacts vary substantially across regions. Carbon-intensive industrial and cold-climate regions experience larger output and welfare losses, while some manufacturing regions benefit from the interregional reallocation of production. Carbon pricing also induces strong modal shifts away from emission-intensive water and air transport toward rail and road transport. Exempting transport services recovers only a small share of GDP losses but reduces total emissions abatement by nearly 30%, with the loss driven almost entirely by water transport. Revenue recycling further shapes distributional outcomes. Returning tax revenues to source regions widens disparities, while equalizing per capita welfare losses improves fairness but slightly increases national welfare costs. Overall, the findings demonstrate that carbon pricing design matters as much as the price level. Maintaining coverage of emission-intensive transport, while using revenue recycling to address regional and household burdens can improve policy acceptability without undermining mitigation effectiveness.

Limiting global warming to 1.5 °C or 2 °C requires deep decarbonization across energy, economic, and behavioral systems. While hybrid modeling frameworks combining top–down computable general equilibrium (CGE) models with bottom–up energy system models (e.g. TIMES) are well-established, few studies have integrated large-scale behavioral disruptions or quantified their indirect, economy-wide rebound effects. This study addresses this gap by soft-linking a CGE and TIMES model to evaluate the consequences of a 20% reduction in private vehicle demand in Quebec, Canada, a scenario consistent with regional sustainable mobility policies and empirical evidence on car-sharing. The analysis examines key indicators, including greenhouse gas (GHG) emissions, sector-specific energy consumption, and economic metrics like GDP, household incomes, and investments. Results show that behavioral disruption can be a win–win measure, improving economic performance while reducing decarbonization costs. The linked framework reveals sectoral reallocations, with industrial emissions declining and service-sector emissions partially increasing, reflecting rebound effects that evolve over time—from 17% in 2025% to 67% in 2050—consistent with transport rebound effects reported in the literature (16%–92%). Energy savings remain substantial, particularly for fossil fuels, with transportation energy use decreasing by 4%–10% relative to the baseline of 461.6 PJ of which private vehicles accounted for 44.4% in 2021, though increased low-carbon electricity consumption moderates long-term GHG reductions. This study highlights the importance of incorporating behavioral dynamics and rebound effects into prospective decarbonization modeling. It contributes to life cycle systems thinking and provides critical insights for policymakers designing robust, demand-side climate strategies.

Abstract Decision-analytic frameworks under climate uncertainty include Cost-Benefit Analysis (CBA), which maximizes welfare by trading mitigation costs against quantified damages; Cost-Effectiveness Analysis (CEA), used here in its probabilistic form, which minimizes the cost of meeting a predefined temperature target via a chance constraint that accounts for uncertainty when damages cannot be reliably estimated; and Cost-Risk Analysis (CRA), which reinterprets adherence to the temperature target within an unconstrained utility-maximization framework by penalizing the probability of target exceedance via a risk function. This study operationalizes Cost-Benefit-Risk Analysis (CBRA), a novel framework that extends CRA by retaining its risk function while adding an explicit, partial damage function, thereby internalizing quantified impacts and leaving residual, unquantified impacts to be represented by the reduced risk term. In our application, the partial global damage function is derived from a forward-looking, regionally and sectorally disaggregated Computable General Equilibrium (CGE) model. This allows us to assess how much of the precautionary risk embedded in climate targets is captured by explicit economic losses. We implement CBRA in the integrated assessment model MIND, coupling a modified version of the FaIR climate model that accounts for climate sensitivity uncertainty. Our findings reveal that explicit damages from agriculture, labor productivity, and human health explain 58% of the risk captured by a 2 $$^{\circ}$$ C target under a 65% safety level. We demonstrate that when MIND is updated with FaIR, CRA and CEA deliver near-equivalent outcomes (differences of 1.3% in peak emissions, 0.40% in peak temperature, and 0.36% in cumulative emissions), confirming the theoretical equivalence suggested in previous studies. These results suggest that as damage estimates improve, a greater share of precautionary risk is accounted for within cost-benefit models, reducing the need for rigid precautionary targets and narrowing the gap between CBA and CEA. However, uncertainty in climate sensitivity remains a dominant factor, highlighting the need for a more precise understanding of the climate system response to guide policy.