Smarter Prioritization Could Cut Greece’s Climate Plan Bill by 89 Billion Euros

A new modeling study of Greece’s National Energy and Climate Plan (NECP) suggests the country could reach the same 2050 emissions target with fewer measures, lower cumulative emissions during the transition, and roughly 89 billion euros less in implementation costs — if the plan’s measures are prioritized systematically rather than pursued all at once. The research, published in Frontiers in Environmental Economics, couples a detailed simulation of the Greek energy system with two fuzzy multicriteria analysis techniques to rank the plan’s decarbonization interventions under uncertainty.

The European Union’s climate framework requires Member States to cut greenhouse gas emissions by 55 percent by 2030 relative to 1990 levels and reach net-zero by 2050, with each country detailing its pathway in a legally binding National Commitment embedded in its NECP. Greece’s plan includes phasing out lignite-based electricity production by 2028, expanding renewables to cover 35 percent of total energy consumption and 60 percent of electricity generation by 2030, cutting emissions 43 percent below 1990 levels, and improving final energy consumption efficiency by 38 percent. But critics of the plan have pointed to uncertainty in implementation timing, a lack of risk analysis, absent alternative scenarios, and an overwhelming number of interventions whose combined investment requirements are unclear.

The research team, led by Phoebe Koundouri of the Athens University of Economics and Business and the UN Sustainable Development Solutions Network Global Climate Hub, together with Angelos Alamanos, Ioannis Arampatzidis and Stathis Devves, built their analysis on the Low Emissions Analysis Platform (LEAP). This demand-driven simulation tool models energy demand across sectors — residential, industrial, agricultural, services, and transportation — as the product of activity levels and energy intensities, then works out the supply-side transformations, from oil refining to electricity, heat, hydrogen and synthetic fuel generation, needed to meet that demand. Greenhouse gas emissions are calculated automatically using emission coefficients from the IPCC’s Fifth Assessment Report. The model was validated against 2022 data from the IEA, EUROSTAT and the World Bank, with projections drawn from the Greek NECP.

The team simulated three scenarios to 2050. Under a Business-As-Usual case with no policy interventions, energy consumption and emissions remain essentially stable, with only a modest decline in consumption attributable to Greece’s shrinking population. Under the National Commitment scenario, which jointly deploys all 30 alternative measures drawn from the plan, total energy consumption falls by roughly 23 percent by 2050, driven mostly by steep declines in industry (58 percent) and transportation (34 percent). Supply-side changes are equally dramatic: refined petroleum product generation drops by about 63 percent, electricity generation expands, and green hydrogen and synthetic fuels penetrate the mix after 2030, reaching 1.1 Mtoe and 571 ktoe respectively by 2050. Total greenhouse gas emissions from energy generation fall from approximately 26 MtCO2e in 2022 to 5.2 MtCO2e by 2050, and combined demand- and supply-side emissions drop 91.7 percent below the Business-As-Usual level by 2050 — though not to zero, with residual emissions persisting in industry and maritime sectors.

The novelty lies in what came next. The researchers defined four evaluation criteria: cumulative greenhouse gas emissions over 2022–2050, implementation cost based on official NECP estimates, energy supply dependency measured as the ratio of cumulative primary fuel imports to total supply, and cumulative energy consumption over the planning period. They then applied the Fuzzy Analytic Hierarchy Process (AHP) to assign weights to these criteria, capturing vagueness in preference judgements through triangular fuzzy numbers rather than crisp values. A consistency ratio of 0.012 — well below the commonly used 0.10 threshold — confirmed the reliability of the pairwise comparisons. The Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) then ranked the 30 measures by their distance from an ideal and a negative-ideal solution, using the LEAP model’s performance data and propagating both preference and data uncertainty jointly into the final ranking.

Applying this ranking, the team constructed what they call the SDSN scenario by sequentially adding measures in ranked order until the model reproduced the NECP’s projected 2050 emissions level of approximately 10 MtCO2e. The result: the target was reached with 18 of the 30 measures — effectively 16, since two of the selected industrial measures are subsets of another. The prioritized package combines consumption-side measures, such as heat pumps in residential heating, cooking electrification, and accelerated energy savings in industry, with production-side measures including the phase-out of fossil fuels in electricity generation and the termination of oil refinery operations. Notably, some energy-savings measures, such as shifts from private to public transport or from freight trucks to freight trains, were not selected, meaning the scenario reaches the emissions target without substantially reducing total energy consumption.

The efficiency gains are substantial. Cumulative emissions over 2022–2050 amount to 1,107.6 MtCO2e under the National Commitment scenario versus 1,011.3 MtCO2e under the prioritized scenario — an average of about 3.7 MtCO2e fewer emissions per year during the transition. The prioritized pathway also delivers a considerably lower energy dependency ratio, especially in the final two decades, thanks to faster reductions in crude oil and natural gas imports. On cost, the National Commitment scenario is estimated at approximately 427 billion euros for the full period, while the prioritized scenario comes to roughly 338 billion euros, with 66 billion euros of the saving coming from consumption-side interventions and 23 billion from the production side.

The ranking proved robust under a battery of stress tests. With one-at-a-time perturbations of criterion weights by ±10 percent, the Spearman rank correlation with the baseline stayed at or above 0.94, and at most two measures entered or left the top 18. A Monte Carlo analysis of 10,000 weight vectors sampled within the fuzzy weight bounds yielded a median correlation of 0.94, with 13 measures remaining in the top 18 in at least 90 percent of simulations. Cost perturbations of up to ±30 percent for all measures — and up to ±50 percent for hydrogen and synthetic fuel measures, reflecting the greater uncertainty in these technologies — left the ranking nearly unchanged, with a median correlation of 0.98. Comparisons with alternative multicriteria methods, including simple additive weighting, VIKOR and PROMETHEE II, showed strong agreement: simple additive weighting correlated at 0.97 with the fuzzy TOPSIS ranking, sharing 17 of the top 18 measures. However, across the full weight space the median correlation fell to 0.60, and a cost-first stakeholder archetype produced the most divergent ordering (correlation 0.42), underlining that different policy priorities genuinely change which measures matter most.

The authors are careful about what the result does and does not show. The criterion weights in this proof-of-concept application were provided by the modeling team, not by policymakers or stakeholders, so the prioritized package should be read as an indicative set of options to inform policy deliberation rather than a substitute for it. The framework also has structural limitations: Greece was modeled as a single-node system without spatial representation of the transmission grid, so congestion, island interconnection timing and curtailment — a live issue in Greece, where renewable curtailment rose sharply in 2025 — are not captured, although aggregate grid investment costs are included in the cost criterion. Deployment trajectories for hydrogen and synthetic fuels follow the NECP exogenously rather than responding endogenously to costs, and the analysis does not yet include distributional or just-transition effects, such as the regional employment consequences of refinery closures or affordability concerns for rural households switching from biomass to electric heating.

The framework is designed to be portable. Because new weights can be introduced directly, the approach can be co-designed with stakeholders through the SDSN Global Climate Hub’s network of Living Labs, which operate in around 70 countries, and the team plans to extend the analysis to other European countries toward a cooperative European model. Future applications are expected to add just-transition criteria such as employment, GDP and land-use impacts, and to soft-link the framework with spatially explicit power-system models. For now, the Greek case offers a concrete demonstration that how a country sequences and selects its decarbonization investments — not just how many it makes — can determine both the price tag and the emissions trajectory of the transition.

Subject of Research: Multicriteria prioritization of Greek NECP decarbonization measures using LEAP energy system modeling and fuzzy AHP-TOPSIS under uncertainty

Article Title: An integrated energy system modeling and decision support approach under uncertainty for refining National Commitments

Article References: Koundouri, P., Alamanos, A., Arampatzidis, I., & Devves, S. (2026). An integrated energy system modeling and decision support approach under uncertainty for refining National Commitments. Frontiers in Environmental Economics, 5, Article 1943421. https://doi.org/10.3389/frevc.2026.1943421

Image Credits: AI Generated

DOI: 10.3389/frevc.2026.1943421

Keywords: climate neutrality, decarbonization, Greece, NECP, LEAP, energy system modeling, multicriteria analysis, fuzzy AHP, fuzzy TOPSIS, uncertainty, greenhouse gas emissions, energy policy