The Economic Effects of the Green Transition of the Greek Economy: An Input–Output Analysis

Abstract

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition.

1. Introduction

Greece, as an EU member state committed to the European Green Deal, is obliged to align its policies with the long-term European goal of climate neutrality by 2050. At the core of the European environmental protection policy is the increase in the penetration of renewable energy sources (RES), the increase in energy efficiency, and the change in energy consumption behavior. More specifically, the European Council under the Green Deal and the Fit for 55 Package has adopted key policy initiatives, which include the following: (i) the strengthening and expanding of the Emissions Trading System (EU-ETS); (ii) the adoption of the Social Climate Fund (SCF) to address the social and distributional impact of the new emissions trading system for buildings and road transport; (iii) the adoption of the Carbon Border Adjustment Mechanism (CBAM) to ensure that the emissions reduction efforts of the EU are not offset by increasing emissions outside of its borders (carbon leakage); (iv) the adoption of binding annual GHG emissions targets in sectors not covered by EU-ETS (road and domestic maritime transport, buildings, agriculture, waste, and small industries); (v) a binding commitment to reduce emissions and increase removals in the land use and forestry sectors; (vi) the adoption of progressive EU-wide emissions reduction targets for cars and vans for 2030 and beyond; (vii) an increase in the share of renewables in the EU’s overall energy consumption to 42.5% by 2030, with an additional 2.5% indicative top up that would allow the overall share to reach 45%; (viii) a reduction in the final energy consumption at EU level by 11.7% in 2030 compared to projections made in 2020, etc. [1].

While the Fit for 55 package establishes a clear and comprehensive framework for reducing GHG emissions in the European Union by 55% in 2030 compared to 1990 levels and for ultimately achieving a climate-neutral economy in 2050, it provides flexibility for member states to shape the most appropriate policy mix to achieve the above-mentioned targets. In this context, the questions that decision makers have to answer are related to the following: (i) the level of ambition of the energy saving policies in the final demand sectors, which is strongly related to the new capacity of RES and other clean technologies as well storage systems that should be integrated into the energy supply system, (ii) the appropriate mix of RES technologies that should be installed in the power generation sector, (iii) the intensity of policies to reduce energy demand and improve energy efficiency per final demand sector (i.e., buildings, transport, industry, etc.), and (iv) the appropriate mix of policies and measures per final demand sector.

Decision makers in Greece are also facing similar questions. The final version of the National Energy and Climate Plan (NECP) adopts the electrification of end-uses mainly through technological changes (heat pumps, electric vehicles, etc.) and the coverage of energy needs through the large-scale development of RES [2]. On the other hand, the NECP is characterized by relatively low ambition regarding interventions aiming to reduce final energy demand through energy renovations of buildings, the promotion of soft mobility and public transport, the promotion of the circular economy in industry, etc. At the same time, the research community has developed scenarios that prioritize the promotion of energy sufficiency and efficiency, while the development of RES and other clean technologies is adopted on a smaller scale, with a view to covering the substantially lower energy needs at national/regional level resulting from the implementation of ambitious policies in the final demand sectors [3,4,5].

Regardless of the policy mix that is ultimately adopted, decarbonization and climate neutrality of the Greek economy require large-scale investments in clean technologies; an increase in the penetration of RES, green hydrogen, and synthetic fuels in the country’s energy system; increasing the strength of energy security through increased energy storage capacity; an improvement in energy efficiency in residential, tertiary, and industrial sectors; and the electrification of transportation as well. The level of investment required for the adoption and implementation of clean technologies directly affects the economy by increasing the demand for goods and services. While initially affecting specific sectors, this impact eventually spreads indirectly throughout the economy due to intersectoral linkages, creating significant multiplier effects on both production and employment. On the other hand, as many of these technologies are developed outside the Greek economy, their application increases the country’s trade deficit over time [6].

In this context, the purpose of this study is to estimate the impact of green transition policies on the Greek economy. By employing input–output analysis, the direct, indirect, and multiplier effects of the investments for the green transition on production, value added, employment, and imports are estimated for every five-year span in the period 2020–2050. The analysis is based on two different transition scenarios. The former is aligned with the Greek NECP and envisages the large-scale exploitation of RES and clean technologies promoting the electrification of final demand, while other energy-saving interventions are implemented at a rather conservative scale. The latter (CLEVER scenario) prioritizes energy sufficiency and efficiency interventions to ensure energy security and reduced energy demand, while the RES and other clean energy resources are utilized at a lower scale, aiming to cover the reduced demand. For both scenarios, three substitution sub-scenarios are developed regarding the volume of imports of the required technologies.

Recent macroeconomic research on the economic effects of green transition interventions, using input–output analysis (IOA), confirms that large-scale green investments can deliver measurable benefits for growth and employment (

Table 1. Previous research on the economic effects of green transition interventions.

Author(s)	Country	Timescale	Key Results
[13]	Saudi Arabia	Up to 2060	Under Vision 2030 and the Saudi Green Initiative (SGI), the country can sustain annual non-oil GDP growth of 2.6% through 2030 and 2.0% through 2060. This transformation is projected to generate approximately 23 million new jobs by 2060, primarily through expansion in non-oil manufacturing and service sectors. Green bond financing is identified as a key enabler of this transition, with its market share expected to increase from 3.4% in 2020 to about 15% by 2030 (roughly USD 14 billion) and 30% by 2060 (around USD 39 billion).
[10]	Turkey	2020–2030	An evaluation of two investment scenarios in solar and wind energy in Turkey over a ten-year period reveals substantial macroeconomic impacts. The scenarios involve total investments of USD 23.6 billion and USD 62.4 billion, corresponding to 0.3% and 0.9% of Turkey’s 2020 GDP on an annual basis. Findings indicate that these investment levels are associated with annual GDP growth ranging from 0.6% to 1.8% relative to 2020 levels. Over the whole investment horizon, total employment is projected to reach 27.8 million, implying net job creation of approximately 1 million positions attributable to the green energy transition. However, this benefit may be moderated by an increase in imports—estimated to be between USD 7.8 billion and USD 21 billion—driven by the foreign content of renewable energy technologies.
[11]	Turkey	Up to 2053	The production, employment, and CO2 emission multipliers caused by the transition to renewable energy are calculated under two scenarios in Turkey: in the first scenario, employment and CO2 emission multipliers are calculated for every USD 1 million of final demand for all sectors in the Turkish economy with the current use of energy resources. The second scenario analysis was conducted for Turkey’s COP29 target of increasing the share of renewable energy to 69.1% in 2053. “Electricity, gas, steam, and air conditioning supply” has the highest sectoral production multiplier (3.37). Within the production framework with existing energy resources, 828 new employment opportunities will arise in the economy due to a separate USD 1 million final demand for all sectors. A USD 1 million investment in the fossil fuel-based energy sector creates about 14 jobs. Transitioning 69.1% of energy production to renewables would generate 10 new jobs.
[7]	EU27	2017–2022	This study assesses how reshoring production of photovoltaics, wind turbines, batteries, electric motors, and electric vehicles to the EU would affect GDP and employment. The study assumes that the EU ceases imports from non-EU countries and meets this demand only through domestic production. The findings show that reshoring these five technologies would increase EU GDP by EUR 18.4 billion, or 0.13% of EU GDP, and create 242,728 new jobs. The same shift of imports to EU production would have had roughly half of the impact in 2010. The study also finds significant spillover effects on other sectors of the economy, particularly for metal products, wholesale and retail, professional, scientific and technical activities, and administrative and support services.
[20]	Review Paper		A synthesis of twelve international studies covering North America, Europe, and Asia shows that employment from different energy investments varies widely: every additional USD 1 million spent on fossil fuel energy generation supports only 5 full-time jobs, 8 nuclear power jobs, and about 15 jobs in the main renewable energy technologies (wind, solar, small hydro, biomass, and related options), while upgrading the energy efficiency of buildings creates about 22 jobs.
[14]	China	2020–2030	Two scenarios are assessed for Shenzhen, China, for 2030: (i) a business-as-usual baseline and (ii) a green transition pathway. The green transition plan entails an additional CNY 462 billion investment and is projected to generate CNY 799.5 billion in gross output and CNY 311.4 billion in value added. These figures imply multipliers of CNY 1.73 of output and CNY 0.67 of GDP for every CNY 1 invested. The package is also expected to create 1.79 million full-time jobs, about 3.9 jobs per CNY 1 million invested (≈27.9 jobs per USD 1 million).
[16]	Japan	Up to 2030	The study examines the economic and employment effects of renewable energy systems (RES) in Kyushu, Japan, focusing on solar, wind, and biomass power. Employment coefficients highlight indirect, construction, and operational job impacts (person-years per GWh). For Solar PV Power, 0.6856 construction jobs, 0.2101 operational jobs, and 2.905 indirect jobs are created per GWh. Wind power generates 0.6069 construction jobs, 0.12513 operational jobs, and 2.268 indirect jobs per GWh. Biomass power creates 0.6768 construction jobs, 3.162 operational jobs, and 1.870 indirect jobs per GWh. Biomass operation jobs contribute the highest portion (34%), while PV and wind power plants have lower economic contributions (11%, and 7%, respectively).
[12]	Turkey	2018–2030	Two scenarios—the business-as-usual scenario and the transformation scenario—are evaluated. In the transformation scenario, the average annual investment rises by USD 12.3 billion, equivalent to 1.6% of 2018 GDP. This expenditure increases real GDP by 1.1% in 2030 (USD 1.143 trillion versus USD 1.132 trillion under business-as-usual). Labor-market effects are likewise favorable: the transformation scenario yields a net gain of 43,382 full-time jobs, driven by 95,000 new positions.
[15]	China	2030 and 2050	Evaluation of the impacts of China’s power decarbonization on the economy, employment, and greenhouse gas (GHG) emissions for two alternative scenarios. Decarbonization creates inclusive growth in employment (1.02%) and the economy (1.21%). However, the renewable power sector creates 0.43 million jobs, whereas direct and indirect job losses total 2.04 million (fossil fuel power sector) and 19.18 million (construction, machinery manufacturing, chemicals, and coal mining sectors) in 2050 China’s NZEE (net-zero emissions in the electricity sector) scenario. Middle-skilled workers account for 74.46% of total job losses, with a higher impact on males (67.37%) than on females (32.63%). China’s overall economic loss from the transition is approximately EUR 209.36 billion, though renewable power contributes EUR 110.69 billion to economic growth.
[8]	EU27 + UK	Up to 2050	This study analyzes employment shifts in the electricity sector by operation and maintenance versus capital investment and domestic versus international effects—within 165 sectors, across 13 electricity sources, and in 28 countries. In the “ambitious low-carbon scenario.” The results show a growth in labor demand as the share of renewable energy sources increases. In a 100 percent renewable energy scenario, the electricity sector would see total labor demand in the period up to 2050, which is approximately twice as high as the reference scenario. However, the employment created by capital investments would take place only on a temporary basis, signaling a boom in labor demand during the first phase of the transition (approximately until 2030), followed by a decline that continues up to 2050.
[9]	Croatia	2020–2030	Evaluation of two investment plans for Croatia’s transition to the electricity sector: an accelerated transition mobilizing EUR 2.55 billion over the period 2021–2030 and a moderate transition requiring EUR 2.18 billion. In the first scenario, annual expenditures correspond to approximately 0.48% of Croatia’s GDP in 2019 and adds 3500 full-time jobs. The second scenario generates EUR 92 million per year of additional GDP and 3000 jobs. For every EUR 1 million invested, the direct and indirect impacts are EUR 0.336 million of value added and 14 full-time jobs, while import leakages average EUR 0.643 million.
[17]	Korea	Current year	Evaluation of two power generation scenarios for South Korea: the old nuclear power chain and an expanded renewable energy industry. The results show that every USD 1 million invested in renewable energy generates a gross output of USD 1718 million to the entire national economy, compared to USD 1606 million for nuclear power. The pattern remains consistent for income generation: value added increases by USD 0.859 million in the case of renewables and by USD 0.856 million in the case of nuclear power, while wage payments rise to USD 0.174 million versus USD 0.168 million, respectively.
[18]	Italy	2006–2014	Ex-post evaluation of renewable-energy investments in Italy for the period 2006–2014. During the construction phase, the program absorbed EUR 46 billion, yet import leakages reached 61%, meaning that less than two-fifths of the outlay remained in the domestic economy. Each EUR 1 million spent on construction generated just EUR 0.43 million in domestic value added and 7.3 full-time jobs (direct + indirect). By contrast, the operational phase delivered stronger multipliers: every EUR 1 million of operating expenditure produced EUR 0.73 million in value added and 14.5 full-time jobs.
[19]	USA	Current year	Two alternative public spending investments are evaluated: a “brown” baseline that maintains USD 1 billion of annual support for fossil fuel extraction, and a counterfactual “green” reallocation that directs the same outlay either to renewable energy deployment or to energy efficiency. Fossil fuel activities exhibit the lowest employment impact, sustaining only 2.65 full time equivalent jobs per USD 1 million of final demand (0.94 direct; 1.71 indirect). Redirecting the expenditure to renewables raises this ratio to 7.49 FTE jobs/USD 1 million (4.50 direct plus 2.99 indirect), while energy efficiency investments generate 7.72 FTE jobs/USD 1 million (4.59 direct plus 3.13 indirect).
[6]	Greece	2010–2020	A macroeconomic evaluation of Greece’s clean energy investment roadmap for the period 2010 2020. The Green Investment plan amounts to EUR 47.9 billion, corresponding to approximately 2% of 2010 GDP. The study projects an average annual output increase of EUR 9.4 billion, equivalent to 4–5% of baseline GDP, and an output–investment multiplier of 1.96 (partitioned into 45.4% direct, 30.7% indirect, and 23.9% induced effects). Green investments support 108,000 full-time equivalent (FTE) positions annually, or 22.54 FTE job years per EUR 1 million invested.

). Studies published from 2021 onwards for the EU [7,8], Croatia [9], Turkey [10,11,12], Saudi Arabia [13], China [14,15], Japan [16], and Korea [17] report increases in gross domestic product and labor demand when capital is channeled toward RES, energy efficiency upgrading, and related manufacturing. Previous research in Europe and the United States [6,18,19] confirms these patterns while highlighting the mitigating role of leakages from imports and the importance of the domestic supply chain. In addition, a recent cross-country analysis [20] shows that RES projects create approximately 15 jobs per USD 1 million, which is three times the employment effect of fossil fuel investments (approximately 5 jobs). Furthermore, building energy upgrades create about 22 jobs per USD 1 million, a 46% increase over RES investments, highlighting the labor-intensive nature of efficiency interventions.

The present study offers a comparison at the economy-wide level of two alternative green transition paths for Greece: the NECP scenario, which allocates most of the spending to large-scale renewable energy technologies and end-use electrification while only conservatively implementing energy saving measures, and the CLEVER scenario, which reverses these priorities, focusing on energy sufficiency and efficiency, as well as scaling up renewables and other clean resources to meet reduced load. By contrasting the macroeconomic impact of these different strategies—one prioritizing electrification and the other, the energy demand reduction—our analysis provides a new comparative perspective on the debate over the optimal investment mixes for a just and efficient green transition, in relation to most previous studies, which focus on the macroeconomic effects of distinct technologies. In addition, this study analyses quantitatively the effects of different scenarios of import substitution in selected industrial sectors, thus highlighting in quantitative terms the importance of enhancing domestic industry during the green transition, an issue often ignored in relevant studies.

The reminder of this paper is organized as follows: Section 2 describes the analytical framework employed and the main assumptions adopted. Section 3 presents the results from the application of the model. Finally, in Section 4 the main findings of the study are discussed, and conclusions are drawn.

2. Materials and Methods

2.1. Methodological Framework

Investment impact assessment methodologies can be grouped into two main types. Top-down approaches trace cross-sectoral flows of goods and services to capture economy-wide effects, and bottom-up approaches use micro-level data of investment to estimate direct economic impacts. Top-down models offer advantages in capturing the broader systemic implications of the green transition. These models, such as IOA, consider the entire economy as an interconnected structure and are suitable for identifying how an investment in the context of the green transition diffuses through the different stages of the product value chain. This perspective is essential for understanding how a specific investment can boost activity in various domestic or international sectors, including manufacturing, construction, and service. In addition, top-down models can account for the induced effects whereby increased income due to energy savings generates additional consumer spending in various sectors, further boosting economic output and employment [21]. Unlike bottom-up studies that stop at the project boundary—estimating only direct impact—IOA captures economy-wide spillovers.

For the Greek green transition, with its diverse interventions and substantial financial commitments, a top-down evaluation is essential to capture the full macroeconomics and cross-sectoral impacts of the green transition. IOA is suitable for quantifying these dynamics because it systematically integrates inter-sectoral linkages and the interactions between intermediate and final demand, thereby illustrating how variations in investment influence aggregate output, value added, and employment across the entire economy [22]. An increasing number of studies worldwide focus on examining the broader macroeconomic impacts of measures promoting the transition to clean energy at international, national, and regional levels by applying IOA [20,23,24,25]. These studies quantify benefits such as job creation and GDP growth and identify possible challenges, including the necessary reallocation of resources away from traditional energy industries, the effect on foreign trade and the balance of payments, and unequal distribution of benefits. Recent studies examine how green investments affect jobs and value added, showing that investments in renewables, energy efficiency, and sustainable agriculture generate significant job gains, both directly within green sectors and indirectly across machinery and equipment suppliers [26,27,28]. A top-down approach such as IOA provides comprehensive accounting of all multiplier effects, facilitating policies that maximize economic and environmental returns.

The basic analytical tool of the IOA is the double-entrance input–output matrix (

Table 2. Input–output matrix for an economy with $n$ sectors.

		Sector 1	…	Sector n	Final Demand	Total Output
Domestic production	Sector 1	$Z_d$			$f_d$	$x$
	…
	Sector n
	Value added	$v$
Imports	Sector 1	$Z_m$			$f_m$
	…
	Sector n
	Total input	$x^T$

), which displays the intersectoral commodity flows in the economy and the sectoral structure of final demand. As analytically described in [29], in an economy divided into $n$ sectors of economic activity, the matrix $Z_d\in {\mathbb{R}}^{n\times n}$ represents the domestically produced intermediate demand, and the matrix $Z_m\in {\mathbb{R}}^{n\times n}$ represents the imported intermediate demand. The final demand, denoted by the vector $f\in {\mathbb{R}}^{n\times 1}$, is also expressed as $f=f_d+f_m$, where the vector $f_d\in {\mathbb{R}}^{n\times 1}$ represents the final demand satisfied by domestic production, and the vector $f_m\in {\mathbb{R}}^{n\times 1}$ indicates the final demand satisfied by imports. The total output of the economy is denoted by the vector $x\in {\mathbb{R}}^{n\times 1}$. Then, the matrix $A_d\in {\mathbb{R}}^{n\times n}$, known as the matrix of domestic technical coefficients, is defined as $A_d=Z_d\cdot X^{-1}$, where $X\in {\mathbb{R}}^{n\times n}$ is a diagonal matrix whose diagonal elements are the elements of vector $x$.

The total output of the economy is expressed by Equation (1):

$$x=A_d\cdot x+f_d\Rightarrow x={(I_n-A_d)}^{-1}\cdot f_d$$

(1)

where $I_n$ is the $n\times n$identity matrix. The matrix ${\left(I_n-A_d\right)}^{-1}$ is the well-known Leontief inverse matrix.

Following a similar approach, the matrix $A_m\in {\mathbb{R}}^{n\times n }$of technological coefficients for imported inputs is defined as $A_m=Z_m\cdot X^{-1} ,$and the demand for imports $M$, $M \in {\mathbb{R}}^{n\times 1}$ is used to satisfy the final demand $f_d$ expressed in Equation (2):

$$M=A_m\cdot x\Rightarrow M=A_m\cdot {(I_n-A_d)}^{-1}\cdot f_d$$

(2)

The vertical linkages (or output multipliers) of the economy are given by the row-vector $b\in {\mathbb{R}}^{1\times n}$, according to Equation (3). The element $b_j$ of $b$ captures the direct and indirect output generated in the economy because of a one-unit increase in final demand in sector $j$; in other words, it quantifies sector $j$’s dependence on all other sectors of the economy.

$${b=1_n^T\cdot (I_n-A_d)}^{-1}$$

(3)

Similarly, the row vector $b_l\in {\mathbb{R}}^{1\times n}$ gives the employment multipliers of the economy, and the row vector $b_v\in {\mathbb{R}}^{1\times n}$ gives the value-added multipliers, according to Equations (4) and (5), respectively:

$${b_l=l\cdot (I_n-A_d)}^{-1}$$

(4)

$${b_v=v\cdot (I_n-A_d)}^{-1}$$

(5)

where $v\in {\mathbb{R}}^{1\times n}$ is the vector of value-added intensity (value added per unit of sectoral output), and $l\in {\mathbb{R}}^{1\times n}$ is the employment intensity vector (employment per sectoral output unit). The typical element of $b_l$ shows the change in total (direct plus indirect) employment in the economy caused by a change in final demand for sector $j$, while the typical element of $b_v$ shows the change in total (direct plus indirect) value added in the economy caused by a change in final demand for sector $j$.

Finally, the row vector of intermediate-input multipliers $b_m\in {\mathbb{R}}^{1\times n}$is defined by Equation (6):

$${b_m=1_n^T\cdot A_m\cdot (I_n-A_d)}^{-1}$$

(6)

The typical element of $b_m$ shows the change in the economy’s intermediate imports caused by a change in final demand for sector $j$.

These multipliers are then applied to estimate the impacts of an investment vector $f\in {\mathbb{R}}^{n\times 1}$ on output (${{\rm X}}_{impact}\in {\mathbb{R}}^{1\times n}$), employment ($L_{impact}\in {\mathbb{R}}^{1\times n}$), value added ($V_{impact}\in {\mathbb{R}}^{1\times n}$), and imports (${IM}_{impact}\in {\mathbb{R}}^{1\times n}$), as follows:

$${{{{\rm X}}_{impact}= b}_l\cdot F=(I_n-A_d)}^{-1}\cdot \widehat{f}$$

(7)

$${{VA}_{impact}{=b}_v\cdot F=v\cdot (I_n-A_d)}^{-1}\cdot \widehat{f}$$

(8)

$${L_{impact}{=b}_l\cdot F=l\cdot (I_n-A_d)}^{-1}\cdot \widehat{f}$$

(9)

$${{IM}_{impact}{=b}_m\cdot F=1_n^T\cdot A_m\cdot (I_n-A_d)}^{-1}\cdot \widehat{f}$$

(10)

Moreover, the induced effects of the investment sector, i.e., the additional economic activity generated by the resulting increase in household income, are estimated as follows: first, household wage income is estimated based on the change in employment induced by the examined investments. Next, the household saving rate is applied to the additional income to isolate the portion allocated to consumption. Finally, the sectoral distribution of domestic consumption is obtained by applying the input–output table’s shares of total consumption between domestic production and imports [30]. Following the analysis described above, this additional consumption generates induced impacts on output, value added, and employment. Accordingly, IOA provides the quantitative foundation for measuring the economy-wide transmission effects of green-transition investments in Greece. Moreover, it enables the identification of sectoral interdependencies and the estimation of direct, indirect, and induced impacts arising from these investments.

2.2. Construction of the Investment Vectors—Main Assumptions

2.2.1. The Main Transition Interventions

Following the priorities of the two alternative scenarios, the main measures and technologies for the country’s green transition are selected with a horizon of 2050 (

Table 3. Main decarbonization technologies by intervention sector.

Sectors	Measures	Technologies
Power Generation	Increasing the penetration of RES in electricity production	Photovoltaics	Utility scale
			Rooftop
		Wind parks	Onshore
			Offshore
		Other RES	Geothermal power stations
			Biogas power plants
		Hydroelectric power stations
		Natural gas power plants
	Increasing electricity storage capacity	Batteries
		Pumped storage stations
	Developing the electricity transmission and distribution network	Voltage transformers
		Conductors
		Pylons
Synthetic Fuels	Increasing the penetration of H2 and gaseous synthetic fuels in the country’s energy mix	Electrolysis
		Direct air capture to CO2 production (DAC to CO2)
		Power to fuels
Energy Efficiency	Upgrading energy of residential/non-residential buildings	External shell thermal insulation—thermal facades, energy-efficient PVC and aluminum frames, double-triple glazing
		Heat pumps—fan coils
		Energy-efficient electrical appliances
	Increasing energy efficiency in industrial sector	Energy consumption control systems
Transportation	Replacing conventional vehicle fleet (petrol-diesel engines)	Electric vehicles/hydrogen and synthetic fuel vehicles
		Charging stations
	Electrifying railway network	Overhead power lines
		Electric trains
	Reducing the carbon footprint of coastal and air transport	Electric-hybrid ships/airplanes
		Electrification/synthetic fuel supply infrastructure

). The first scenario is based on the assumptions and the projections of the NECP [2], which is the country’s official strategic document outlining the goals and the path for transitioning toward climate neutrality by 2050. The second scenario is based on the assumptions and forecasts of the CLEVER scenario, which was developed under the coordination and supervision of the négaWatt organization in France. A bottom-up approach was followed for its formulation, based on consultation between 25 different partners from 20 European countries, including Greece, research institutions, civil society organizations, etc., with the aim of synthesizing the different national objectives into a common European plan [31]. The two alternative scenarios differ in the way in which the intended transition will be achieved. The NECP assumes that the transition will be achieved without any considerable differentiation in energy consumption patterns. Given that the NECP predicts positive growth rates until 2050, energy consumption is expected to increase exponentially in the future because of the increasing economic activity. Therefore, in the NECP scenario, ever-increasing investments in RES technologies are required for energy supply to meet increasing future demand, as well as spending on clean technologies to mitigate the environmental effects of energy-intensive and polluting uses. In contrast, CLEVER proposes a different path for the green transition, with a key feature of reducing energy demand, as well as the demand for energy-intensive and polluting uses. Therefore, in the CLEVER scenario, investments in clean technologies for energy production are contained compared to those of the NECP scenario.

The intervention areas on which the transition measures focus are the following:

• Electricity generation;
• The production of synthetic fuels;
• The promotion of energy efficiency in buildings;
• An enhancement in energy efficiency in industry;
• Intervention in the transportation sector.

2.2.2. The Cost of Transition Measures and Technologies

The cost of each intervention related to the green transition is presented in Table 3 and in detail in

Table A1. Penetration and total investment in green transition interventions per five-year period—NECP scenario.

				2025	2030	2035	2040	2045	2050
Power Generation	Photovoltaics	Utility scale	Penetration (GW)	7.4	11.9	16.4	22.4	31.7	36.3
			CapEx (bill. EUR)	3.2	2.9	2.5	3.9	4.2	3.7
			OpEx (bill. EUR)	0.5	0.9	1.1	1.4	1.8	2.2
			Total investment (bill. EUR)	3.7	3.8	3.6	5.3	6	5.9
		Rooftop	Penetration (GW)	0.8	1.5	2.3	3	3.5	4
			CapEx (bill. EUR)	0.5	1	0.9	1	0.9	1
			OpEx (bill. EUR)	0	0.1	0.1	0.1	0.2	0.2
			Total investment (bill. EUR)	0.5	1.1	1	1.1	1.1	1.2
	Wind parks	Onshore	Penetration (GW)	6	7.6	8.5	9.2	11.8	11.9
			CapEx (bill. EUR)	2.3	1.9	1.6	1.3	3.8	1.6
			OpEx (bill. EUR)	0.7	0.9	1	1.1	1.2	1.3
			Total investment (bill. EUR)	3	2.8	2.6	2.4	5	2.9
		Offshore	Penetration (GW)	0	1.9	6.2	9.8	15.4	17.3
			CapEx (bill. EUR)	0	5.1	10.9	8.7	13.1	4.3
			OpEx (bill. EUR)	0	0.4	1.5	2.8	4.1	5.2
			Total investment (bill. EUR)	0	5.5	12.4	11.5	17.2	9.5
	Other RES	Geothermal power stations	Penetration (GW)	0.2	0.2	0.4	0.6	0.7	0.7
			CapEx (bill. EUR)	0.2	0.1	0.5	0.3	0.3	0.2
			OpEx (bill. EUR)	0.1	0.2	0.2	0.3	0.4	0.4
			Total investment (bill. EUR)	0.3	0.3	0.7	0.6	0.7	0.6
		Biogas power plants	Penetration (GW)	0.3	0.4	0.9	1.2	1.3	1.4
			CapEx (bill. EUR)	0.3	0.1	0.8	0.6	0.4	0.2
			OpEx (bill. EUR)	0.1	0.3	0.5	0.7	0.9	1
			Total investment (bill. EUR)	0.4	0.4	1.3	1.3	1.3	1.2
	Hydroelectric power stations		Penetration (GW)	3.1	3.8	3.8	3.8	3.8	3.9
			CapEx (bill. EUR)	0	1.8	0	0	0	0.1
			OpEx (bill. EUR)	1.2	1.4	1.5	1.5	1.5	1.5
			Total investment (bill. EUR)	1.2	3.2	1.5	1.5	1.5	1.6
	Natural gas power plants		Penetration (GW)	6.9	7.7	5.7	5.2	2.8	4.2
			CapEx (bill. EUR)	0.7	0.4	0	0	0	0.6
			OpEx (bill. EUR)	1.7	1.7	1.4	1	0.7	0.6
			Total investment (bill. EUR)	2.4	2.1	1.4	1	0.7	1.2
	Storage	Batteries	Penetration (GW)	1.9	3.1	3.6	8.8	19.1	22.6
			CapEx (bill. EUR)	0.5	0.4	0.2	1.3	2.5	0.8
			OpEx (bill. EUR)	0.2	0.5	0.6	1	2.2	2.9
			Total investment (bill. EUR)	0.7	0.9	0.8	2.3	4.7	3.7
		Pumped storage stations	Penetration (GW)	1.4	2.2	2.2	2.2	2.2	2.2
			CapEx (bill. EUR)	2.2	2.5	0	0	0	0
			OpEx (bill. EUR)	0.1	0.2	0.2	0.2	0.2	0.2
			Total investment (bill. EUR)	2.3	2.7	0.2	0.2	0.2	0.2
	Transmission and distribution network		Total investment (bill. EUR)	1.4	2	5.2	3.9	2.6	3.6
	Total investment in power generation (bill. EUR)			15.9	24.8	30.7	31.1	41	31.6
H2—Synthetic Fuels		Electrolysis	Penetration (GW)	0	0.3	7	12.5	17.4	20.7
			CapEx (bill. EUR)	0	0.2	4.7	3.6	2.9	1.8
			OpEx (bill. EUR)	0	0.1	0.7	1.6	2.4	2.9
			Total investment (bill. EUR)	0	0.3	5.4	5.2	5.3	4.7
		Power to fuels	Penetration (GW)	0	0.3	1	1.9	3	4
			CapEx (bill. EUR)	0	0.2	0.6	0.8	0.7	0.6
			OpEx (bill. EUR)	0	0.1	0.1	0.2	0.4	0.5
			Total investment (bill. EUR)	0	0.3	0.7	0.9	1.1	1.1
		DAC to CO2	Annual potential (ΜtCO2)	0	0	0.5	2.7	3.3	5.6
			CapEx (bill. EUR)	0	0	0.1	0.4	0.1	0.3
			OpEx (bill. EUR)	0	0	0	0.1	0.1	0.2
			Total investment (bill. EUR)	0	0	0.1	0.5	0.2	0.5
	Total investment in the production of synthetic fuels (bill. EUR)			0	0.6	6.2	6.6	6.6	6.3
	Residences		No. of renovated residences (5-year period, thousands)	289	396	454	402	392	418
			External shell renovation cost (bill. EUR)	2.8	3.1	3.9	3.8	3.6	4.3
			Cost of cool-heating devices (bill. EUR)	6.8	4.7	6.9	6.8	7.9	6.8
			Cost (differential) of electrical appliances (bill. EUR)	0.4	1.1	1.1	1.3	1.4	1.4
			Total investment (bill. EUR)	10	8.9	11.9	11.9	12.9	12.5
Buildings’ Energy Efficiency Upgrade	Services buildings		No. of renovated building (5-year period, thousands)	11	12	26	13	24	19
			External shell renovation cost (bill. EUR)	0.3	0.4	0.7	0.4	0.7	0.6
			Heat pumps cost (bill. EUR)	0.2	1.2	0.9	0.8	0.3	0.3
			Total investment (bill. EUR)	0.5	1.6	1.6	1.2	1	0.9
	Total investment in the energy upgrade of residential and tertiary sector buildings (bill. EUR)			10.5	10.5	13.5	13.1	13.9	13.4
Industry	Total investment in the energy upgrade of industrial sector (bill. EUR)			0.6	1.3	1.7	1.9	1.2	0.8
Transportation	Vehicles	Passenger vehicles	Stock of battery electric vehicles (thousands)	34.8	178.1	592.8	1317.4	2307.7	3696.6
			Total (differential) purchase expenditures (bill. EUR)	0.5	1.4	2.9	3.4	3.5	5
			Stock of plug-in hybrid electric vehicles (thousands)	34.8	87.7	65.9	0	0	0
			Total (differential) purchase expenditures (bill. EUR)	0.3	0.5	0.2	0	0	0
			Stock of hydrogen (fuel cell electric) vehicles (thousands)	0	0	52	459.4	630.6	673.3
			Total (differential) purchase expenditures (bill. EUR)	0	0	2.1	15.7	6.5	3.6
		Light-duty trucks	Stock of battery electric vehicles (thousands)	3.6	17	45	92	162.6	261.3
			Total (differential) purchase expenditures (bill. EUR)	0	0.1	0.1	0.2	0.4	0.6
		Total (differential) expenditures for vehicle purchase (bill. EUR)		0.8	2	5.3	19.3	10.4	9.2
		Charging stations	Penetration (GW)	0.1	0.3	0.9	1.8	3.2	5.2
			CapEx (bill. EUR)	0.1	0.2	0.4	0.8	1.1	1.5
			OpEx (bill. EUR)	< 0.1	< 0.1	0.1	0.1	0.2	0.3
			Total investment (bill. EUR)	0.1	0.2	0.5	0.9	1.3	1.8
	Railway network		Total investment in the electrification of railway network (bill. EUR)	1.3	0.9	0	0	0	0
	Rest of transportation		Total investment in the decarbonation of the rest of transport (bill. EUR)	34.2	35.6	41	42	50	50
Expenditures Reduction		Solid fuels	Electricity generation (ΤWh)	9.6	0	0	0	0	0
			Production cost reduction (bill. EUR)	0.4	−0.9	0	0	0	0
		Oil	Final demand (ktoe)	8687	7243	4643	2042	1044	188
			Reduction in consumption expenditures (bill. EUR)	1.3	−2.2	−3.9	−3.9	−1.5	−1.3
		Natural gas	Final demand (ktoe)	1077	788	616	682	89	8
			Reduction in consumption expenditures bill. EUR)	< 0.1	−0.3	−0.2	< 0.1	−0.6	−0.1
	Total expenditure reduction (bill. EUR)			1.7	−3.4	−4.1	−3.9	−2.1	−1.4

and

Table A2. Penetration and total investment in green transition interventions per five-year period—CLEVER scenario.

						2025	2030	2035	2040	2045	2050
Power Generation	Photovoltaics	Utility scale		Penetration (GW)		5.7	8.5	10.2	12.0	13.5	14.7
				CapEx (bill. EUR)		2.2	1.9	1	1.9	0.9	2.4
				OpEx (bill. EUR)		0.4	0.6	0.7	0.8	0.9	0.9
				Total investment (bill. EUR)		2.6	2.5	1.7	2.7	1.8	3.3
		Rooftop		Penetration (GW)		0.8	1.5	2.3	3	3.5	4
				CapEx (bill. EUR)		0.5	1	0.9	1	0.9	1
				OpEx (bill. EUR)		0	0.1	0.1	0.1	0.2	0.2
				Total investment (bill. EUR)		0.5	1.1	1	1.1	1.1	1.2
	Wind parks	Onshore		Penetration (GW)		7	9	10.5	11	11.5	12
				CapEx (bill. EUR)		3.3	2.2	2.1	1.1	2.1	1.9
				OpEx (bill. EUR)		0.8	1	1.2	1.3	1.3	1.3
				Total investment (bill. EUR)		4.1	3.3	3.3	2.4	3.4	3.2
		Offshore		Penetration (GW)		0	1.5	1.8	2	2.5	3
				CapEx (bill. EUR)		0	4	0.6	0.6	1.2	1.1
				OpEx (bill. EUR)		0	0.3	0.6	0.6	0.7	0.9
				Total investment (bill. EUR)		0	4.3	1.2	1.2	1.9	2
	Other RES	Geothermal power stations		Penetration (GW)		0.0	0.2	0.3	0.4	0.5	0.5
				CapEx (bill. EUR)		0	0.4	0.2	0.2	0.2	0
				OpEx (bill. EUR)		0	0.1	0.2	0.2	0.3	0.3
				Total investment (bill. EUR)		0.3	0.5	0.4	0.4	0.5	0.3
		Biogas power plants		Penetration (GW)		0	0	0.3	0.5	0.6	0.8
				CapEx (bill. EUR)		0	0	0.5	0.4	0.2	0.3
				OpEx (bill. EUR)		0	0	0.1	0.3	0.4	0.5
				Total investment (bill. EUR)		0	0	0.6	0.7	0.6	0.8
	Hydroelectric power stations			Penetration (GW)		3.5	4	4	4.7	4.7	4.7
				CapEx (bill. EUR)		0.3	1.3	0	1.3	0	0
				OpEx (bill. EUR)		1.4	1.5	1.6	1.7	1.8	1.8
				Total investment (bill. EUR)		1.7	2.8	1.6	3	1.8	1.8
	Natural gas power plants			Penetration (GW)		6.9	7.7	5.7	5.2	2.8	4.2
				CapEx (bill. EUR)		0.7	0.4	0	0	0	0.6
				OpEx (bill. EUR)		1.7	1.7	1.4	1	0.7	0.6
				Total investment (bill. EUR)		2.4	2.1	1.4	1	0.7	1.2
	Storage	Batteries		Penetration (GW)		1.7	2.6	3.7	4.6	8.9	10.0
				CapEx (bill. EUR)		0.5	0.2	0.3	0.2	1	0.3
				OpEx (bill. EUR)		0.2	0.5	0.6	0.7	1	1.3
				Total investment (bill. EUR)		0.7	0.7	0.9	0.9	2	1.6
		Pumped storage stations		Penetration (GW)		1.4	2.2	2.2	2.2	2.2	2.2
				CapEx (bill. EUR)		1.4	1.6	0	0	0	0
				OpEx (bill. EUR)		0.1	0.2	0.2	0.2	0.2	0.2
				Total investment (bill. EUR)		1.5	1.8	0.2	0.2	0.2	0.2
	Transmission and distribution network			Total investment (bill. EUR)		0.9	1.4	3.5	2.6	1.8	2.4
	Total investment in power generation (bill. EUR)					14.4	20.4	15.8	16.2	15.6	18.1
H2—Synthetic Fuels		Electrolysis		Penetration (GW)		0.1	0.5	1.1	2.1	2.7	3.3
				CapEx (bill. EUR)		1	0.2	0.5	0.6	0.3	0.3
				OpEx (bill. EUR)		<0.1	0.1	0.2	0.3	0.4	0.5
				Total investment (bill. EUR)		0.1	0.3	0.7	0.9	0.7	0.8
		Power to fuels		Penetration (GW)		0	0	0.3	0.5	0.6	0.8
				CapEx (bill. EUR)		0	0	0.2	0.2	0.1	0.1
				OpEx (bill. EUR)		0	0.1	<0.1	<0.1	0.1	0.1
				Total investment (bill. EUR)		0	0	0.2	0.2	0.2	0.2
		DAC to CO2		Annual potential (ΜtCO2)		0	0	0.1	1.3	2.1	4.5
				CapEx (bill. EUR)		0	0	<0.1	0.2	0.1	0.4
				OpEx (bill. EUR)		0	0	<0.1	0.1	0.1	0.1
				Total investment (bill. EUR)		0	0	<0.1	0.3	0.2	0.5
	Total investment in the production of synthetic fuels (bill. EUR)					0.1	0.3	0.9	1.4	1.1	1.5
Buildings’ Energy Efficiency Upgrade	Residences			No. of renovated residences (5-year period, thousands)		25	646	672	688	700	596
				External shell renovation cost (bill. EUR)		0.7	19.3	20	20.5	20.8	17.8
				Cost of cool-heating devices (bill. EUR)		2.9	5.3	4.5	4.4	4.3	3.4
				Cost (differential) of electrical appliances (bill. EUR)		0.4	1.1	1.1	1.3	1.4	1.4
				Total investment (bill. EUR)		4	25.7	25.6	26.2	26.5	22.6
	Service buildings			No. of renovated building (5-year period, thousands)		8	23	32	32	32	32
				External shell renovation cost (bill. EUR)		0.3	0.6	0.9	1	0.9	1.1
				Heat pumps cost (bill. EUR)		0.3	0.7	0.9	0.8	0.8	0.8
				Total investment (bill. EUR)		0.6	1.5	1.8	1.8	1.7	1.9
	Total investment in the energy upgrade of residential and tertiary sector buildings (bill. EUR)					4.6	27.2	27.4	28	28.2	24.5
Industry	Total investment in the energy upgrade of industrial sector (bill. EUR)					0.6	1.3	1.7	1.9	1.2	0.8
Transportation	Vehicles	Private vehicles	Cars	BEV	Stock of battery electric vehicles (thousands)	105.5	489.2	898.9	1492.2	2043.1	3465.4
					Total (differential) purchase expenditures (bill. EUR)	1.6	3.8	2.9	3.1	3.1	5.8
				Synthetic gas/Hydridic	Stock of gas/methane vehicles (thousands)	0.1	0.2	0.3	0.3	0.3	0.2
					Total (differential) purchase expenditures (bill. EUR)	2.6	4.8	3.9	3.6	3.7	6.4
			Motorcycles	Battery electric	Stock of battery electric motorcycles (thousands)	105.1	210.3	315.4	420.6	525.7	736
					Total (differential) purchase expenditures (bill. EUR)	0.3	0.4	0.8	0.5	0.4	0.5
				Synthetic gas/methane	Stock of gas/methane motorcycles (thousands)	105.1	210.3	315.4	420.6	525.7	736
					Total (differential) purchase expenditures (bill. EUR)	0.2	0.2	0.5	0.2	0.1	0
		Light-duty trucks	Battery electric		Stock of battery electric vehicles (thousands)	17.9	46.7	114.3	189.2	263	367.1
					Total (differential) purchase expenditures (bill. EUR)	0.3	0.3	0.4	0.4	0.5	1
			Synthetic gas/methane		Stock of gas/methane vehicles (thousands)	0	0	0	0	0	0.2
					Total (differential) purchase expenditures (bill. EUR)	0	0	0	0	0	0.2
		Heavy-duty trucks	Battery electric		Stock of battery electric vehicles (thousands)	7.5	15	22.5	30	37.5	45
					Total (differential) purchase expenditures (bill. EUR)	0.2	0.2	0.1	0.1	0.1	0.2
			Synthetic gas/methane		Stock of gas/methane vehicles (thousands)	30	60	90	120	150	180
					Total (differential) purchase expenditures (bill. EUR)	0.5	0.6	0.5	1	1.4	1.9
		Buses	Battery electric		Stock of battery electric vehicles (thousands)	2.1	3.5	4.9	6.3	7.6	10.4
					Total (differential) purchase expenditures (bill. EUR)	0.3	0.2	0.1	<0.1	0.1	<0.1
			Synthetic gas/methane		Stock of gas/methane vehicles (thousands)	5.2	7	8.7	10.4	12.2	15.6
					Total (differential) purchase expenditures (bill. EUR)	0.8	0.1	0.1	<0.1	0.2	0.2
		Total (differential) expenditures for vehicle purchase (bill. EUR)				5.2	6.7	6.4	5.9	6.6	10.5
		Charging stations		Penetration (GW)		0.3	0.9	1.6	2.5	3.4	5.6
				CapEx (bill. EUR)		0.2	0.5	0.6	0.7	0.7	1.7
				OpEx (bill. EUR)		<0.1	0.1	0.1	0.2	0.2	0.4
				Total investment (bill. EUR)		0.2	0.6	0.7	0.9	0.9	2.1
	Railway network			Total investment in the electrification of railway network (bill. EUR)		1.3	0.9	0	0	0	0
	Rest of transportation			Total investment in the decarbonation of the rest of transport (bill. EUR)		34.2	35.6	41	42	50	50
Expenditures Reduction		Solid fuels		Electricity generation (ΤWh)		9.6	0	0	0	0	0
				Production cost reduction (bill. EUR)		0.4	−0.9	0	0	0	0
		Oil		Final demand (ktoe)		8687	7243	4643	2042	1044	188
				Reduction in consumption expenditures (bill. EUR)		1.3	−2.2	−3.9	−3.9	−1.5	−1.3
		Natural gas		Final demand (ktoe)		1.077	788	616	682	89	8
				Reduction in consumption expenditures bill. EUR)		<0.1	−0.3	−0.2	<0.1	−0.6	−0.1
	Total expenditure reduction (bill. EUR)					1.7	−3.4	−4.1	−3.9	−2.1	−1.4

(Appendix A).

The costs of power generation and green hydrogen and synthetic fuel production interventions are broken down into capital expenditures (CapEx) and operational expenditures (OpEx). It is assumed that both CapEx and OpEx per unit of installed capacity for all RES technologies decline over time due to the economies of scale achieved during their implementation, as the technologies mature and experience with them increases [32,33,34].

Regarding the cost of the electricity transmission and distribution network, the respective cost estimates of the NECP [2] are adopted in this study, excluding the corresponding CapEx of energy storage stations (batteries and pumped storage), since they are calculated separately below.

The total cost for the energy upgrade of residential and non-residential buildings includes the costs of upgrading their external envelope, replacing traditional heating systems with heat pumps, and installing low-energy light bulbs and electric appliances. It is assumed that only 15% of the total average purchase or purchase and installation cost of air conditioners, lamps, and electrical appliances is attributable to the green transition objective (in essence, 15% of the total average purchase or purchase and installation cost attributable to the green transition corresponds to the additional (differential) cost of purchasing a low-energy-consumption electrical appliance instead of a conventional one).

The cost of reducing the carbon footprint of the vehicle fleet corresponds to the differential cost of replacing conventional internal combustion engine vehicles (petrol or diesel) with mainly electric vehicles or with hydrogen/synthetic fuel engine vehicles (the CLEVER scenario provides predictions for more vehicle types than the NECP. Specifically, in addition to the forecasts of future penetration of electric and gas passenger cars and light-duty trucks provided in the NECP, in the CLEVER scenario, the forecasts also involve the number of electric and gas synthetic fuel passenger cars, motorcycles, heavy trucks, and buses for each five-year horizon until 2050). As for the cost of electric charging stations, this is broken down into CapEx and OpΕx, the unitary prices of which (in EUR/kW and EUR/kW/y, respectively) were derived from the literature [35].

In the NECP [2], there is specific reference to the projects that are scheduled to be completed to achieve the goal of reducing greenhouse gas emissions in the railway transport sector. The cost of electrifying the railway network corresponds to the cost of these projects. Regarding the cost of interventions for the decarbonization of the transport sector (except the rail network), this also comes from the NECP [2] (the NECP [2] provides estimates for the average annual investment expenditures (in billion EUR₂₀₂₀), aiming to reduce GHG emissions in the transport sector for every five-year period until 2050).

Finally, the cost of interventions for the green transition includes the reduction in expenditures entailed by the shift from traditional forms of energy production (natural gas, solid fuels, and oil) to clean energy sources.

2.2.3. The Penetration of Transition Measures and Technologies

The penetration of the green transition interventions in the two scenarios is determined based on the predictions of energy requirements overall and by sector and use [2] (see

Table 4. Penetration, investment costs, and operation and maintenance (O&M) costs of the green interventions included in the scenarios examined.

Interventions			Average Unitary Cost (2020–2050)		NECP Scenario		CLEVER Scenario
			Investment	O&M	Penetration (Up to 2050)	Total Investments (in bill. EUR)	Penetra-tion (Up to 2050)	Total Investments Required (in bill. EUR)
Photovoltaics a	Utility scale		536 EUR/kW	15.7 EUR/kW/y	36.3 GW	28.4	14.7 GW	14.7
	Rooftop		1116 EUR/kW	15.0 EUR/kW/y	4.0 GW	6.1	4.0 GW	6.1
Wind parks a	Onshore		915 EUR/kW	24.5 EUR/kW/y	11.9 GW	18.6	12 GW	19.7
	Offshore		2554 EUR/kW	73.2 EUR/kW/y	17.3 GW	56.1	3.0 GW	10.8
Other RES b			1879 EUR/kW	153 EUR/kW/y	2.1 GW	9.2	1.3 GW	4.9
Hydroelectric power stations			2113 EUR/kW	77.9 EUR/kW/y	3.9 GW	10.4	4.7 GW	12.5
Storage	Batteries		256 EUR/kW	36.4 EUR/kW/y	22.6 GW	13.1	10.0 GW	6.8
	Pumped storage stations c		3147/1950 EUR/kW	18.7 EUR/kW/y	2.2 GW	5.8	2.2 GW	4.0
Natural gas power plants			438 EUR/kW	36.5 EUR/kW/y	7.7 GW	8.6	7.7 GW	8.6
Electrolysis d			713 EUR/kW	37.1 EUR/kW/y	20.7 GW	20.8	3.3 GW	3.4
Power to Fuels e			798 EUR/kW	34.1 EUR/kW/y	4.0 GW	4.0	0.8 GW	0.8
DAC to CO2 f			253 EUR/tCO2/y	8.84 EUR/tCO2/y	5.6 Mt CO2	1.4	4.5 Mt CO2	1.0
Transmission and distribution network g						18.8		12.5
Energy upgrade h	Residential buildings	External shell renovation i	101/336 EUR/m2		2.4 mill. dwellings	68.0	3.5 mill. dwellings	130.7
		Heat-pumps j	9100/6800 EUR/unit		3.9 mill. units	34.2	3.5 mill. units	21.8
		A/C	667 EUR/unit		5.1 mill. units	5.7	3.6 mill. units	3.1
		Electrical appliances k	27 EUR/unit		87.6 mill. units	6.7	87.6 mill. units	6.7
	Service buildings	External shell renovation l	50.4 EUR/m2		0.1 mill. buildings	6.9	0.2 mill. buildings	9.2
		Heat-pumps j	64/48 EUR/m2		229,500 units	3.7	229,500 units	4.4
	Industrial sector m					7.5		7.5
Vehicles n	Cars		7000 EUR/vehicle		4.4 mill.	45.4	3.7 mill.	25.0
	Motorcycles		1800. EUR/vehicle				1.5 mill.	4.2
	Light-duty trucks		7400 EUR/vehicle		261,300	1.6	611,800	3.0
	Heavy-duty trucks		15,400 EUR/vehicle				225,000	6.8
	Buses		76,700 EUR/vehicle				26,000	2.4
Charging stations			800 EUR/kW	16.0 EUR/kW/y	5.2 GW	4.8	5.6 GW	5.4
Transportation infrastructure o						255		255
Expenditures’ reduction (-)						13.1		13.1

^a Capital and O&M costs of the RES power stations are based on [32,33,34]. ^b Capital and O&M costs of “Other RES” consist of the weighted average respective costs of geothermal power stations and biogas power plants. ^c The 1st number corresponds to the cost used in NECP scenario, and the 2nd number refers to the cost unsed in the CLEVER scenario. The capital unit cost of pump storage stations in the CLEVER scenario is assumed to be the differential unit cost of converting a hydroelectric power station to a pumped storage station [36]. ^d Capital and operating unit cost for electrolytic hydrogen production plants is based on [37]. The operating costs of hydrogen production plants do not include electricity costs, as these costs are included in the electricity generation costs. ^e Capital and operating unit costs of PtF are based on [38]. They do not include the capital and operating costs of hydrogen production plants. ^f Capital and operating unit costs of DAC to CO₂ are based on [39,40]. ^g From the total investments required for the development of the transmission and distribution network (coming from the NECP [2]), the total capital expenditures for storage infrastructures were extracted. ^h Individual costs were estimated from market prices. ⁱ External shell’s renovation unit cost in the CLEVER scenario (2nd number) concerns deep renovations, while the respective cost in the NECP scenario (1st number) concerns shallow renovations. ^j Unit cost of heat-pumps in the CLEVER scenario *2nd number) is assumed to be 75% of the respective cost in the NECP scenario (1st number) due to resizing after renovation activities. ^k Electrical appliances’ unit cost refers to the differential cost of replacing electric appliances with energy-efficient ones. ^l External shell’s renovation unit cost in service buildings is assumed to be half of the respective cost in residential buildings due to economies of scale. ^m NECP [2] ⁿ Unit costs per type of vehicle were estimated based on [41] and market prices. Along with the total investments required, they refer to the differential costs of replacing conventional vehicles with electric or hydrogen/synthetic fuel engine vehicles. ^o The cost of electrifying the railway network is included. The relevant projects and their corresponding costs are available in the NECP and on the ERGOSE website [42], respectively.

, and more analytically Table A1 and Table A2, Appendix A).

The penetration of RES technologies for power generation and green hydrogen production is derived directly from the two scenarios under consideration. The NECP [2] projects that by 2050, about 90% of electricity will come from utility-scale photovoltaic parks and offshore wind farms. In the CLEVER scenario, the installed capacities of the RES technologies are significantly lower than in the NECP scenario because of the increased energy efficiency rates adopted in the final demand sectors.

In this context, a greater number of residential buildings are upgraded in the CLEVER scenario compared to the NECP scenario by 2050, focusing on deep renovations instead of shallow upgrades. Moreover, in the NECP [2], 91% of residences are expected to cover their thermal needs with heat pumps by 2050. The respective penetration of heat pumps in the case of tertiary sector buildings is expected to approach 90%. The CLEVER scenario has a goal that from 2030 onwards, the thermal needs of residences will be covered exclusively by heat pumps.

Regarding the penetration of electric plug-in hybrids and synthetic fuel vehicles, the CLEVER scenario extends NECP forecasts by including projections for electric and gas synthetic fuel vehicles—passenger cars, motorcycles, heavy trucks, and buses—for every five-year horizon through 2050.

As for the installed capacity of electric charging stations, it is calculated in both scenarios from the number of electric and hybrid vehicles, assuming that, on an annual basis, the installed output power of publicly accessible charging stations amounts to at least 1.3 kW for each pure electric vehicle in circulation (passenger or van) and 0.8 kW for each plug-in hybrid vehicle in circulation.

2.2.4. Sectoral Distribution Matrix

After analyzing the main measures, technologies, and works involved in the decarbonization of the Greek economy, the sectoral distribution matrix was constructed, which matches these technologies/works with the sectors in which they occur, to construct the corresponding investment vectors for future analysis horizons (

Table A3. Sectoral distribution matrix (%).

Id.	Interventions	Sector Cost	Agriculture, Hunting, Forestry	Chemical and Chemical Products	Basic Metals	Fabricated Metal Products	Electrical Equipment	Machinery and Equipment, Nec	Manufacturing Nec; Repair and Installation of Machinery and Equipment	Electricity, Gas, Steam, and Air Conditioning Supply	Water supply, Sewerage, Waste Management, and Remediation Activities	Construction	Retail Trade	Land transport and Transport via Pipelines	Financial Service Activities	Real Estate Activities	Professional, Scientific, and Technical Activities	Administrative and Support Service Activities	Imports
1.1	Utility scale photovoltaics	Construction				10.4	27.8		20.7			19.0					5.0		17.2
1.2		Operation and maintenance					7.3		16.7						10.0	15.0	38.7	8.3	4.1
2.1	Rooftop photovoltaics	Construction				8.6	38.4		10.0			15.0					5.0		22.9
2.2		Operation and maintenance					8.5		19.7							5.0	53.7	8.3	4.8
3.1	Onshore wind parks	Construction					2.6	37.1				19.0					6.0		35.3
3.2		Operation and maintenance					3.4		56.3								34.0	4.3	1.9
4.1	Offshore wind parks	Construction					2.1	24.0	1.7			49.0							23.2
4.2		Operation and maintenance					10.7		30.0			16.0				5.0	27.3	5.0	6.0
5.1	Geothermal power stations	Construction					18.6	8.0				49.7					6.0		17.7
5.2		Operation and maintenance					5.6	10.5	28.3								38.0	5.0	12.7
6.1	Biogas power plants	Construction					15.4	17.2	12.0			31.0							24.4
6.2		Operation and maintenance	43.0				3.8	2.1	9.3		5.0						31.0	1.7	4.1
7.1	Hydroelectric power stations	Construction					8.9	2.7	6.9			74.0							7.5
7.2		Operation and maintenance					6.9	5.5	26.1							8.7	37.7	6.2	8.9
8.1	Batteries	Construction				3.5	51.2		16.0										29.3
8.2		Operation and maintenance					12.4		22.3							6.0	40.0	12.3	6.9
9.1	Pumped storage stations	Construction					1.7	25.1	1.3			48.0							23.9
9.2		Operation and maintenance					5.3	10.1	22.7								45.5	4.2	12.2
10.1	Natural gas power plants	Construction					31.7	14.5	16.8			6.0							31.0
10.2		Operation and maintenance					11.1		17.4	29.3						4.3	26.8	4.7	6.3
11.1	Electrolysis	Construction			2.8	9.8	28.1	1.3	28.3			7.0					3.0		19.7
11.2		Operation and maintenance					12.3	1.6	61.7		3.1						11.2	1.7	8.4
12.1	Power to fuels	Construction			2.7	6.5	10.1	20.9	21.1			12.0							26.8
12.2		Operation and maintenance					9.1	5.0	35.3		9.5					7.6	19.1	4.7	9.7
13.1	DAC to CO2	Construction					13.3	21.7	20.8			17.0							27.3
13.2		Operation and maintenance					5.8	5.5	41.3		5.8			16.3			11.3	5.7	8.3
14.1	Transmission and distribution network	Construction					64.1												35.9
14.2		Operation and maintenance			28.1	36.4	1.7		15.8										18.1
15.1	Resident energy upgrade	Renovation		25.9		17.3						20.0					15.0		21.8
15.2		Heat pumps		0.6	4.3	2.6	26.9	2.6	33.0								10.0		20.0
15.3		Air conditioners					64.1												35.9
15.4		Electrical appliances					64.1												35.9
16.1	Energy upgrade of service buildings	Renovation		25.9		17.3						20.0					15.0		21.8
16.2		Heat pumps		0.6	4.3	2.6	26.9	2.6	33.0								10.0		20.0
17	Industrial sector			0.6	4.3	2.6	26.9	2.6	33.0								10.0		20.0
18.1	Charging stations	Construction					32.0		12.5			35.0		2.5					18.0
18.2		Operation and maintenance					3.0	1.2	38.8				1.9			9.4	42.8		2.8
19	Vehicles																		10
20	Electrification of railway network					14.7	17.3		14.0			32.0					10.0		12.0
21.1	Transportation	Public transport vehicle fleet replacement																	100.0
21.2		Ship conversion																	100.0
21.3		Electrical equipment					42.7		33.3										24.0
21.4		Mechanical equipment						34.8	33.3										31.8
21.5		Construction/civil engineering projects										100.0
21.6		Licenses/permits-Assessments															100.0

, Appendix B). The elements of each row of the matrix sums to unity, while the ratio of the sum of the elements of each column (which corresponds to a sector of the Greek economy) to the total sum of the elements of the matrix corresponds to the percentage of total expenditures that occurs in this sector.

Most of the expenditures related to green transition technologies and measures correspond to the manufacturing sectors of electrical equipment manufacturing (D27), machinery and equipment manufacturing nec, and repair and installation of machinery and equipment (D31T33). Also, an equally large percentage of the expenditure corresponds to the construction sector and involves the cost of constructing the infrastructure of power plants, green hydrogen, synthetic fuels, and carbon dioxide production units, as well as the remaining civil engineering works required for the implementation and realization of the green transition measures in the final demand sectors. A significant percentage of the expenditure also corresponds to the sector of professional, scientific, and technical activities (D69T75).

2.2.5. Substitution Sub-Scenarios

To investigate the extent to which the level of imports of the required technologies affects the economic effects of investments for the green transition, for each of the two main scenarios, three substitution sub-scenarios were developed (i.e., an optimistic, a moderate, and a pessimistic one), assuming different future import rates of the sectors of manufacture of chemicals and products (D20), manufacture of basic metals (D24), manufacture of fabricated metal products, except machinery and equipment (D25), manufacture of electrical equipment (D27), and manufacture of machinery and equipment nec (D28). In the optimistic sub-scenario, imports to satisfy final demand in each of these sectors are assumed to decrease by 80% throughout the period 2020–2050. The corresponding reductions in the moderate and pessimistic sub-scenarios are 60% and 15%, respectively. For the intermediate time horizons, import substitutions in each sector are modeled as an exponential decay, implying constant percentage reductions in imports every five years, equal to 27.52% in the optimistic sub-scenario, 16.74% in the moderate sub-scenario, and 3.20% in the pessimistic one (Figure 1).

3. Results

To estimate the direct and indirect economic effects in both scenarios, and specifically the effects on production, value added, and employment, Equations (7)–(10) were applied, respectively. The effects were estimated for all three sub-scenarios (optimistic, moderate, and pessimistic) and for all five-year horizons of analysis. The input–output table for the Greek economy and the year 2018 were adopted.

3.1. Direct and Indirect Effects

The direct and indirect effects of the green transition interventions for both the NECP and the CLEVER scenarios, and specifically their effects on production, value added, intermediate, final and total imports, and employment are presented in absolute figures in Appendix C (

Table A4. Direct and indirect effects on output, value added, imports, and employment—both scenarios.

Effect	Sub-Scenario	2025	2030	2035	2040	2045	2050
NECP Scenario
Output (in billions of EUR)	Optimistic	10.3	14.2	20.4	22.6	29.0	27.6
	Moderate	10.3	13.4	18.9	20.8	26.9	25.8
	Pessimistic	10.3	12.5	16.7	17.8	22.6	21.9
Value added (in billions of EUR)	Optimistic	4.7	6.5	9.4	10.4	13.1	12.6
	Moderate	4.7	6.2	8.8	9.7	12.2	11.9
	Pessimistic	4.7	5.8	7.9	8.5	10.5	10.3
Imports of intermediate products (in billions of EUR)	Optimistic	1.6	2.1	3.0	3.4	4.8	4.5
	Moderate	1.6	1.9	2.7	3.0	4.3	4.0
	Pessimistic	1.6	1.7	2.1	2.3	3.3	3.1
Imports of final products (in billions of EUR)	Optimistic	6.1	5.5	6.1	7.9	5.9	4.7
	Moderate	6.1	6.0	7.1	9.1	7.4	5.8
	Pessimistic	6.1	6.6	8.5	11.0	10.2	8.5
Total imports (in billions of EUR)	Optimistic	7.7	7.6	9.1	11.3	10.7	9.2
	Moderate	7.7	7.9	9.7	12.1	11.6	9.9
	Pessimistic	7.7	8.3	10.7	13.3	13.4	11.5
Employment (in thousands of employees)	Optimistic	143.6	195.8	275.3	300.4	379.2	361.3
	Moderate	143.6	189.7	262.8	285.9	361.0	346.9
	Pessimistic	143.6	182.1	244.7	261.6	325.7	314.7
CLEVER Scenario
Output (in billions of EUR)	Optimistic	6.7	15.6	17.3	19.4	19.9	20.3
	Moderate	6.7	14.7	16.0	17.9	18.3	18.8
	Pessimistic	6.7	13.6	14.0	15.3	15.2	15.4
Value added (in billions of EUR)	Optimistic	3.5	7.4	8.0	8.9	9.1	9.3
	Moderate	3.5	7.0	7.5	8.2	8.4	8.7
	Pessimistic	3.5	6.5	6.7	7.2	7.2	7.4
Imports of intermediate products (in billions of EUR)	Optimistic	0.5	2.0	2.5	3.0	3.1	3.1
	Moderate	0.5	1.8	2.2	2.7	2.8	2.8
	Pessimistic	0.5	1.6	1.8	2.1	2.1	2.0
Imports of final products (in billions of EUR)	Optimistic	6.4	7.1	5.6	4.6	4.0	4.2
	Moderate	6.4	7.7	6.5	5.6	5.1	5.2
	Pessimistic	6.4	8.4	7.7	7.3	7.1	7.5
Total imports (in billions of EUR)	Optimistic	6.8	9.1	8.1	7.6	7.1	7.3
	Moderate	6.8	9.5	8.7	8.3	7.8	8.0
	Pessimistic	6.8	10.0	9.5	9.4	9.2	9.4
Employment (in thousands of employees)	Optimistic	112.7	218.7	228.9	250.6	253.9	260.7
	Moderate	112.7	211.7	218.8	238.8	241.7	249.0
	Pessimistic	112.7	202.9	204.1	219.0	217.9	222.7

). It is noted that final imports are derived directly from the investment vectors. However, their analysis is necessary, as the sum of final and intermediate imports constitutes the burden on the country’s trade balance, and in relation to the country’s corresponding macroeconomic data of the year 2023, as shown in

Table 5. Direct and indirect effects in value added, imports, and employment compared to 2023—both scenarios.

Effect	Sub-Scenario	2025	2030	2035	2040	2045	2050
NECP Scenario
Contribution of value added to GDP (% of GDP2023)	Optimistic	2.4%	3.3%	4.8%	5.3%	6.7%	6.5%
	Moderate	2.4%	3.2%	4.5%	5.0%	6.3%	6.1%
	Pessimistic	2.4%	3.0%	4.1%	4.4%	5.4%	5.3%
Contribution to employment (% of employment2023)	Optimistic	3.5%	4.8%	6.8%	7.4%	9.4%	8.9%
	Moderate	3.5%	4.7%	6.5%	7.1%	8.9%	8.6%
	Pessimistic	3.5%	4.5%	6.0%	6.5%	8.0%	7.8%
Contribution to net exports of goods (% of GDP2023)	Optimistic	−3.4%	−3.2%	−3.6%	−4.5%	−3.9%	−3.2%
	Moderate	−3.4%	−3.4%	−4.0%	−5.0%	−4.5%	−3.7%
	Pessimistic	−3.4%	−3.6%	−4.5%	−5.8%	−5.5%	−4.7%
CLEVER Scenario
Contribution of value added to GDP (% of GDP2023)	Optimistic	1.8%	3.8%	4.1%	4.6%	4.7%	4.8%
	Moderate	1.8%	3.6%	3.9%	4.2%	4.3%	4.5%
	Pessimistic	1.8%	3.4%	3.5%	3.7%	3.7%	3.8%
Contribution to employment (% of employment2023)	Optimistic	2.8%	5.4%	5.6%	6.2%	6.3%	6.4%
	Moderate	2.8%	5.2%	5.4%	5.9%	6.0%	6.1%
	Pessimistic	2.8%	5.0%	5.0%	5.4%	5.4%	5.5%
Contribution to net exports of goods (% of GDP2023)	Optimistic	−3.1%	−3.8%	−3.2%	−2.9%	−2.6%	−2.7%
	Moderate	−3.1%	−4.0%	−3.6%	−3.3%	−3.1%	−3.1%
	Pessimistic	−3.1%	−4.3%	−4.1%	−4.0%	−3.9%	−4.0%

. The effects on the net export of goods express the additional trade balance deficit that is expected to be created, taking into account both imports required to meet the final and intermediate demand, and the newly generated value added as well.

As can be seen in Table A4 and Table 5, the investment spending for the green transition in the NECP scenario is expected to increase both GDP and employment in the Greek economy. The impact on GDP and employment increases over time, following the increase in investment spending. Similar results are obtained in the CLEVER scenario. In the latter, the impacts of investment spending on GDP and employment are moderate compared to the NECP scenario, as the CLEVER scenario places emphasis on increasing future energy efficiency and, consequently, investments in clean technologies for energy production and expenditures to cover ancillary uses and activities are limited compared to those of the NECP scenario. At the same time, however, in both scenarios, the imports required to meet the intermediate and final demand will eventually lead to a deterioration in the country’s trade balance, as expressed by the net exports as a percentage of GDP.

Moreover, according to the results, in both scenarios, there is a high differentiation between the optimistic and the pessimistic sub-scenarios in terms of their outcomes, indicating that policies aiming to support the sectors involved in the investments for the green transition can be particularly effective in strengthening the effects on the examined macroeconomic variables (Figure 2). Especially in the CLEVER scenario, this differentiation is increased, compared to the NECP scenario, as we approach the year 2050, a finding that demonstrates the necessity for the implementation of effective policies to support domestic industrial sectors involved in the green transition.

3.2. Sectoral Analysis

The sectoral distribution of the effects on output, value added, and employment do not vary significantly over time and per sub-scenario in both scenarios (see

Table A7. Total effects on output (in millions of EUR per year over every five-year horizon)—NECP scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D41T43	1687.93	2863.80	3806.64	3796.92	5071.81	4283.52	1687.93	2862.35	3803.62	3793.43	5067.62	4280.03	1687.93	2860.53	3799.23	3787.56	5059.46	4272.18
D31T33	1970.12	2182.05	3030.25	3295.25	3834.21	3919.29	1970.12	2179.81	3025.52	3289.86	3827.09	3913.95	1970.12	2177.01	3018.65	3280.79	3813.24	3901.92
D69T75	1388.61	1790.47	2352.25	2678.77	3361.42	3559.77	1388.61	1767.13	2303.07	2622.29	3290.18	3503.71	1388.61	1737.77	2231.60	2527.33	3151.68	3377.56
D27	1115.49	1791.16	2695.21	3311.35	3969.22	3688.02	1115.49	1531.16	2215.08	2713.02	3288.33	3106.75	1115.49	1204.14	1517.47	1707.50	1964.53	1798.47
D45T47	586.44	822.15	1176.37	1295.44	1665.28	1557.31	586.44	776.43	1082.73	1186.10	1531.58	1448.89	586.44	718.91	946.65	1002.31	1271.63	1204.91
D25	511.00	715.77	1178.65	1190.13	1303.88	1278.63	511.00	689.03	1115.22	1118.75	1225.33	1208.09	511.00	655.39	1023.06	998.79	1072.65	1049.38
D68	404.42	559.62	770.99	872.33	1109.99	1133.62	404.42	544.91	740.17	836.76	1065.47	1098.40	404.42	526.41	695.37	776.98	978.91	1019.14
D24	368.25	576.35	1028.41	1115.68	1246.99	1229.07	368.25	518.82	895.03	963.46	1082.61	1082.17	368.25	446.47	701.19	707.62	762.96	751.61
D28	116.58	628.19	1356.66	1599.27	2551.67	1949.99	116.58	451.21	965.10	1167.20	1923.35	1520.69	116.58	228.49	395.86	440.62	701.85	554.89
D35	337.53	362.59	553.76	618.65	560.56	658.63	337.53	340.46	505.22	562.93	495.50	604.00	337.53	312.63	434.65	469.28	369.01	481.09
Others	1793.84	1881.20	2482.31	2854.85	4373.60	4342.17	1793.84	1769.68	2250.27	2592.36	4058.43	4069.10	1793.84	1629.40	1913.01	2151.13	3445.65	3454.69

,

Table A8. Total effects on value added (in millions of EUR per year over every five-year horizon)—NECP scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D31T33	982.76	1088.47	1511.59	1643.78	1912.62	1955.07	982.76	1087.36	1509.23	1641.08	1909.07	1952.40	982.76	1085.96	1505.80	1636.56	1902.16	1946.40
D69T75	824.28	1062.83	1396.30	1590.12	1995.34	2113.08	824.28	1048.97	1367.11	1556.59	1953.06	2079.80	824.28	1031.54	1324.68	1500.22	1870.84	2004.92
D41T43	559.13	948.65	1260.97	1257.75	1680.06	1418.94	559.13	948.17	1259.97	1256.59	1678.67	1417.78	559.13	947.57	1258.51	1254.65	1675.97	1415.18
D68	364.23	504.01	694.38	785.64	999.69	1020.97	364.23	490.76	666.62	753.61	959.59	989.25	364.23	474.10	626.27	699.77	881.64	917.86
D45T47	333.68	467.80	669.35	737.10	947.54	886.11	333.68	441.79	616.07	674.89	871.47	824.42	333.68	409.06	538.64	570.31	723.55	685.59
D27	314.62	505.20	760.18	933.96	1119.52	1040.20	314.62	431.86	624.76	765.21	927.47	876.26	314.62	339.63	428.00	481.60	554.09	507.26
D25	190.53	266.87	439.46	443.74	486.15	476.74	190.53	256.91	415.81	417.13	456.87	450.44	190.53	244.36	381.45	372.40	399.94	391.26
D64T66	171.77	246.32	351.94	387.58	499.32	477.15	171.77	236.20	330.97	363.23	469.18	453.04	171.77	223.47	300.50	322.31	410.59	398.80
D35	159.39	171.22	261.49	292.13	264.70	311.01	159.39	160.77	238.57	265.82	233.98	285.22	159.39	147.63	205.25	221.60	174.25	227.18
D29	104.60	110.95	0.58	0.63	0.81	0.75	104.60	110.93	0.54	0.59	0.76	0.71	104.60	110.91	0.49	0.52	0.65	0.61
Others	712.47	1114.84	2020.52	2293.77	3154.70	2869.54	712.47	982.35	1731.51	1971.35	2724.55	2545.36	712.47	815.65	1311.40	1429.26	1888.28	1815.98

,

Table A9. Total effects on employment (in employees per year over every five-year horizon)—NECP scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D41T43	30,670	52,036	69,168	68,992	92,157	77,833	30,670	52,010	69,113	68,928	92,081	77,770	30,670	51,977	69,033	68,822	91,932	77,627
D31T33	38,933	43,121	59,883	65,120	75,771	77,452	38,933	43,077	59,790	65,013	75,630	77,346	38,933	43,021	59,654	64,834	75,356	77,109
D69T75	28,856	37,207	48,881	55,666	69,852	73,974	28,856	36,722	47,859	54,492	68,372	72,809	28,856	36,112	46,374	52,519	65,493	70,187
D45T47	9720	13,627	19,498	21,471	27,601	25,811	9720	12,869	17,946	19,659	25,385	24,014	9720	11,915	15,690	16,613	21,076	19,971
D27	7772	12,479	18,778	23,071	27,654	25,695	7772	10,668	15,433	18,902	22,911	21,645	7772	8390	10,573	11,897	13,687	12,530
D25	4817	6748	11,111	11,219	12,292	12,054	4817	6496	10,513	10,547	11,551	11,389	4817	6178	9644	9416	10,112	9893
D29	4160	4412	23	25	32	30	4160	4412	22	23	30	28	4160	4411	19	21	26	24
D28	1031	5557	12,002	14,148	22,573	17,251	1031	3992	8538	10,326	17,015	13,453	1031	2021	3502	3898	6209	4909
D16	2752	3164	4393	4771	5621	5653	2752	3140	4343	4713	5549	5596	2752	3110	4271	4617	5408	5468
D77T82	1663	2310	3162	3837	5144	5700	1663	2267	3073	3734	5014	5598	1663	2213	2943	3560	4762	5368
Others	13,241	15,098	28,374	32,114	40,490	39,838	13,241	14,059	26,171	29,597	37,498	37,297	13,241	12,753	22,970	25,365	31,682	31,581

,

Table A10. Total effects on intermediate imports (in millions of EUR per year over every five-year horizon)—NECP scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D24	488.04	759.92	1171.94	1349.27	1634.95	1516.75	488.04	677.94	1009.86	1154.33	1406.21	1326.77	488.04	574.83	774.33	826.68	961.50	899.20
D45T47	135.54	182.04	265.15	299.21	399.81	374.65	135.54	167.42	235.57	264.42	357.88	340.19	135.54	149.04	192.59	205.93	276.36	262.64
D25	98.91	154.55	221.39	234.87	301.34	267.34	98.91	148.00	207.89	219.13	281.60	251.84	98.91	139.77	188.25	192.68	243.23	216.96
D20	95.56	141.31	218.76	244.10	296.50	291.03	95.56	128.81	193.31	214.74	262.51	260.79	95.56	113.07	156.33	165.39	196.43	192.73
D64T66	86.48	124.34	177.77	193.77	249.73	234.09	86.48	119.23	167.18	181.46	234.54	221.91	86.48	112.79	151.80	160.78	204.99	194.50
D16	103.70	119.27	165.66	179.93	212.03	213.17	103.70	118.34	163.73	177.70	209.22	210.96	103.70	117.18	160.92	173.96	203.76	205.99
D23	66.55	110.95	150.52	153.26	202.90	173.56	66.55	109.53	147.69	149.91	198.81	170.26	66.55	107.74	143.59	144.27	190.85	162.82
D27	60.10	96.18	140.47	159.46	202.32	182.73	60.10	87.88	124.29	139.98	178.78	163.68	60.10	77.44	100.77	107.24	133.01	120.82
D19	65.08	71.56	105.19	118.78	166.51	166.17	65.08	65.88	93.12	105.10	150.13	152.21	65.08	58.74	75.58	82.09	118.28	120.79
D22	41.11	62.23	91.72	101.41	132.52	121.04	41.11	58.14	83.06	91.53	119.47	111.25	41.11	53.00	70.46	74.92	94.10	89.23
Others	380.75	272.69	317.18	408.95	966.97	918.57	380.75	235.81	239.83	320.07	853.64	830.32	380.75	189.41	127.41	170.66	633.31	631.75

,

Table A11. Total effects on employment by occupation (employees per year over every five-year horizon)—NECP scenario.

Occupation (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
71	18,460	30,148	40,514	40,869	54,051	46,418	18,460	30,033	40,264	40,588	53,670	46,139	18,460	29,889	39,899	40,115	52,930	45,511
75	12,728	14,387	20,019	21,801	25,542	25,878	12,728	14,308	19,861	21,613	25,313	25,694	12,728	14,208	19,632	21,298	24,870	25,279
21	10,481	14,455	19,902	22,016	28,040	27,766	10,481	14,113	19,180	21,192	26,978	26,944	10,481	13,683	18,131	19,807	24,914	25,095
74	9200	13,594	19,888	21,197	27,212	24,320	9200	13,107	18,915	20,044	25,779	23,189	9200	12,494	17,502	18,107	22,993	20,644
72	11,034	13,583	19,967	21,595	27,175	25,007	11,034	12,798	18,242	19,656	24,584	23,088	11,034	11,810	15,733	16,397	19,547	18,770
24	8032	10,440	13,920	15,625	19,559	20,281	8032	10,285	13,594	15,251	19,095	19,908	8032	10,090	13,119	14,622	18,193	19,066
41	7724	10,705	14,648	16,236	20,226	19,585	7724	10,268	13,777	15,203	18,991	18,563	7724	9719	12,511	13,468	16,588	16,264
Others	65,956	88,446	126,415	141,095	177,382	172,035	65,956	84,798	118,968	132,386	166,624	163,421	65,956	80,208	108,145	117,747	145,710	144,038

,

Table A12. Total effects on output (in millions of EUR per year over every five-year horizon)—CLEVER scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D69T75	3260.45	6928.31	7268.22	7847.39	8025.57	8171.67	3260.45	6843.81	7144.27	7703.58	7874.97	8029.05	3260.45	6737.50	6964.12	7461.84	7582.17	7708.15
D31T33	3994.96	5335.25	5541.05	5753.49	5560.27	5734.49	3994.96	5329.25	5532.64	5743.85	5549.90	5724.46	3994.96	5321.69	5520.42	5727.65	5529.76	5701.90
D41T43	2467.74	5309.35	4820.74	5318.36	5296.25	5392.48	2467.74	5305.82	4815.35	5312.02	5289.71	5386.45	2467.74	5301.38	4807.50	5301.37	5277.00	5372.90
D68	1372.98	2856.01	3064.10	3343.84	3452.64	3570.15	1372.98	2777.27	2948.78	3209.98	3313.17	3437.56	1372.98	2678.21	2781.16	2984.96	3042.02	3139.21
D45T47	1089.19	2567.88	2823.88	3196.10	3277.40	3334.91	1089.19	2405.34	2583.66	2915.02	2987.48	3062.35	1089.19	2200.86	2234.53	2442.56	2423.83	2449.07
D25	606.45	2259.37	2599.05	2753.60	2695.94	2690.53	606.45	2178.97	2465.79	2598.18	2541.31	2543.54	606.45	2077.77	2272.19	2336.99	2240.81	2212.79
D27	1377.71	2290.90	2603.34	3056.62	3094.79	3303.31	1377.71	1958.90	2140.21	2505.30	2564.57	2783.09	1377.71	1541.31	1467.30	1578.77	1533.67	1612.20
D64T66	619.27	1352.66	1461.62	1626.87	1667.76	1712.26	619.27	1292.04	1372.62	1523.23	1560.39	1610.49	619.27	1215.78	1243.28	1349.01	1351.65	1381.50
D20	64.22	1610.63	2338.39	2878.58	3267.18	3048.99	64.22	1279.76	1796.16	2227.97	2580.39	2468.61	64.22	863.52	1007.98	1134.33	1244.98	1163.03
D28	261.18	1283.43	1605.58	1894.09	2440.92	2720.86	261.18	923.00	1144.66	1385.05	1842.12	2122.54	261.18	469.42	474.59	529.03	678.01	776.48
Others	2285.47	4999.71	6031.43	6618.50	6568.60	7006.50	2285.47	4706.41	5583.20	6101.23	6049.45	6503.13	2285.47	4337.44	4931.76	5231.77	5040.07	5370.46

,

Table A13. Total effects on value added (in millions of EUR per year over every five-year horizon)—CLEVER scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D69T75	652.09	1385.66	1453.64	1569.48	1605.11	1634.33	652.09	1368.76	1428.85	1540.72	1574.99	1605.81	652.09	1347.50	1392.82	1492.37	1516.43	1541.63
D31T33	798.99	1067.05	1108.21	1150.70	1112.05	1146.90	798.99	1065.85	1106.53	1148.77	1109.98	1144.89	798.99	1064.34	1104.08	1145.53	1105.95	1140.38
D41T43	493.55	1061.87	964.15	1063.67	1059.25	1078.50	493.55	1061.16	963.07	1062.40	1057.94	1077.29	493.55	1060.28	961.50	1060.27	1055.40	1074.58
D68	274.60	571.20	612.82	668.77	690.53	714.03	274.60	555.45	589.76	642.00	662.63	687.51	274.60	535.64	556.23	596.99	608.40	627.84
D45T47	217.84	513.58	564.78	639.22	655.48	666.98	217.84	481.07	516.73	583.00	597.50	612.47	217.84	440.17	446.91	488.51	484.77	489.81
D25	121.29	451.87	519.81	550.72	539.19	538.11	121.29	435.79	493.16	519.64	508.26	508.71	121.29	415.55	454.44	467.40	448.16	442.56
D27	275.54	458.18	520.67	611.32	618.96	660.66	275.54	391.78	428.04	501.06	512.91	556.62	275.54	308.26	293.46	315.75	306.73	322.44
D64T66	123.85	270.53	292.32	325.37	333.55	342.45	123.85	258.41	274.52	304.65	312.08	322.10	123.85	243.16	248.66	269.80	270.33	276.30
D20	12.84	322.13	467.68	575.72	653.44	609.80	12.84	255.95	359.23	445.59	516.08	493.72	12.84	172.70	201.60	226.87	249.00	232.61
D28	52.24	256.69	321.12	378.82	488.18	544.17	52.24	184.60	228.93	277.01	368.42	424.51	52.24	93.88	94.92	105.81	135.60	155.30
Others	457.09	999.94	1206.29	1323.70	1313.72	1401.30	457.09	941.28	1116.64	1220.25	1209.89	1300.63	457.09	867.49	986.35	1046.35	1008.01	1074.09

,

Table A14. Total effects on employment (in decades per year over every five-year horizon)—CLEVER scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D41T43	27,073	58,247	52,887	58,346	58,103	59,159	27,073	58,208	52,827	58,276	58,031	59,093	27,073	58,159	52,741	58,159	57,892	58,944
D69T75	22,828	48,508	50,888	54,943	56,191	57,214	22,828	47,917	50,021	53,937	55,137	56,215	22,828	47,173	48,759	52,244	53,087	53,969
D31T33	31,653	42,272	43,903	45,586	44,055	45,436	31,653	42,225	43,836	45,510	43,973	45,356	31,653	42,165	43,739	45,381	43,813	45,177
D45T47	6345	14,960	16,451	18,620	19,093	19,428	6345	14,013	15,052	16,982	17,404	17,841	6345	12,822	13,018	14,230	14,121	14,268
D25	3067	11,425	13,143	13,924	13,633	13,605	3067	11,019	12,469	13,138	12,851	12,862	3067	10,507	11,490	11,818	11,331	11,190
D27	6806	11,318	12,862	15,101	15,290	16,320	6806	9678	10,574	12,377	12,670	13,750	6806	7615	7249	7800	7577	7965
D28	1064	5226	6538	7713	9939	11,079	1064	3758	4661	5640	7501	8643	1064	1911	1933	2154	2761	3162
D16	2225	3167	3286	3449	3367	3470	2225	3137	3243	3398	3314	3420	2225	3100	3180	3313	3210	3307
D20	151	3786	5496	6766	7679	7167	151	3008	4222	5237	6065	5802	151	2030	2369	2666	2926	2734
D29	2579	2741	18	20	21	21	2579	2741	17	19	19	20	2579	2740	16	17	17	17
Others	8943	17,058	23,435	26,112	26,574	27,771	8943	16,010	21,863	24,292	24,724	25,991	8943	14,691	19,578	21,233	21,126	21,984

,

Table A15. Total effects on intermediate imports (in millions of EUR per year over every five-year horizon)—CLEVER scenario.

Sector (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
D24	398.58	772.83	870.92	980.24	990.20	1046.37	398.58	695.63	760.27	850.81	862.72	920.73	398.58	598.52	599.49	633.28	614.87	637.99
D20	67.35	222.85	278.84	324.84	347.08	340.88	67.35	195.58	235.85	273.68	293.99	293.99	67.35	161.27	173.37	187.70	190.78	188.49
D45T47	58.73	190.95	227.83	269.46	279.05	278.40	58.73	172.73	200.82	237.77	246.60	247.93	58.73	149.80	161.58	184.51	183.52	179.38
D25	81.12	173.45	175.96	194.15	196.65	202.54	81.12	166.52	166.07	182.70	184.76	191.02	81.12	157.79	151.70	163.45	161.65	165.08
D64T66	62.36	139.11	148.80	165.92	169.47	173.63	62.36	132.95	139.73	155.36	158.54	163.28	62.36	125.19	126.57	137.61	137.29	140.00
D23	57.96	123.35	115.52	128.01	128.31	131.09	57.96	121.80	113.30	125.41	125.65	128.55	57.96	119.85	110.07	121.04	120.48	122.82
D16	83.84	119.44	123.99	130.16	127.09	130.97	83.84	118.29	122.30	128.19	125.03	129.03	83.84	116.83	119.84	124.89	121.01	124.65
D27	49.18	98.62	104.13	117.89	120.78	126.68	49.18	90.72	93.15	105.03	107.83	114.00	49.18	80.79	77.18	83.40	82.65	85.46
D19	−3.36	81.20	115.84	142.34	149.58	144.41	−3.36	70.17	98.53	121.91	128.29	125.34	−3.36	56.30	73.37	87.56	86.89	82.44
D22	31.32	69.52	74.43	82.68	86.27	89.40	31.32	65.10	68.23	75.57	78.62	82.01	31.32	59.53	59.23	63.63	63.77	65.38
Others	−404.69	52.40	269.51	496.97	542.13	436.93	−404.69	8.04	205.13	422.32	463.49	362.39	−404.69	−47.78	111.57	296.85	310.60	194.67

and

Table A16. Total effects on employment by occupation (employees per year over every five-year horizon)—CLEVER scenario.

Occupation (Code)	Optimistic Scenario						Moderate Scenario						Pessimistic Scenario
	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050	2025	2030	2035	2040	2045	2050
71	16,061	33,437	30,882	33,936	33,830	34,522	16,061	33,313	30,709	33,739	33,615	34,313	16,061	33,158	30,458	33,410	33,198	33,844
21	8376	17,959	18,848	20,570	21,085	21,516	8376	17,519	18,205	19,824	20,293	20,773	8376	16,965	17,271	18,570	18,754	19,101
72	7999	15,123	16,743	18,105	18,527	19,131	7999	14,280	15,555	16,755	17,064	17,699	7999	13,221	13,828	14,486	14,221	14,475
75	10,326	14,194	14,752	15,421	14,971	15,443	10,326	14,110	14,632	15,281	14,828	15,305	10,326	14,004	14,458	15,045	14,551	14,996
74	7634	14,417	14,803	16,323	16,422	17,008	7634	13,942	14,143	15,556	15,633	16,237	7634	13,344	13,185	14,267	14,100	14,503
24	6284	13,222	13,902	15,062	15,361	15,632	6284	13,005	13,576	14,680	14,963	15,262	6284	12,732	13,101	14,039	14,189	14,431
41	5919	12,338	12,991	14,346	14,605	14,955	5919	11,797	12,194	13,407	13,649	14,057	5919	11,118	11,036	11,831	11,788	12,035
Others	50,133	98,019	105,987	116,818	119,143	122,464	50,133	93,746	99,770	109,563	111,643	115,346	50,133	88,370	90,736	97,368	97,062	99,332

, Appendix D). For convenience, in this section, the results for the moderate sub-scenario for the year 2030 are discussed (

Table 6. Sectors with the highest direct and indirect effects for the moderate sub-scenario in 2030—both scenarios.

Output		Value Added		Employment
NECP Scenario
D41T43	21.3%	D31T33	17.5%	D41T43	27.4%
D31T33	16.2%	D69T75	16.9%	D31T33	22.7%
D69T75	13.2%	D41T43	15.3%	D69T75	19.4%
D27	11.4%	D68	7.9%	D45T47	6.8%
D45T47	5.8%	D45T47	7.1%	D27	5.6%
D23D25	5.1%	D27	7.0%	D25	3.4%
D68	4.1%	D25	4.1%	D29	2.3%
D24	3.9%	D64T66	3.8%	D28	2.1%
D28	3.4%	D28	3.2%	D16	1.7%
D35	2.5%	D35	2.6%	D77T82	1.2%
Other sectors	13.2%	Other sectors	14.5%	Other sectors	7.4%
CLEVER Scenario
D69T75	19.6%	D69T75	18.4%	D41T43	27.5%
D31T33	15.2%	D31T33	16.3%	D69T75	22.6%
D41T43	15.2%	D41T43	15.4%	D31T33	19.9%
D68	7.9%	D68	8.1%	D45T47	6.6%
D45T47	6.9	D45T47	8.0%	D25	5.2%
D25	6.2%	D25	6.5%	D27	4.6%
D27	5.6%	D27	5.2%	D28	1.8%
D64T66	3.7	D64T66	3.3%	D16	1.5%
D20	3.7%	D20	3.1%	D20	1.4%
D28	2.6%	D28	2.1%	D29	1.3%
Other sectors	13.4%	Other sectors	13.6%	Other sectors	7.6%

).

Starting with the sectoral total effects on output, in both scenarios, these occur almost in the same limited number of sectors, a reasonable finding, since in both scenarios, the initial investment vectors were constructed from the same sectoral distribution matrix. Specifically, in both scenarios, half or more of the total effects on output are concentrated in the sectors of construction (D41T43); manufacturing nec; repair and installation of machinery and equipment (D31T33); and professional, scientific, and technical activities (D69T75). A significant share of the total effects on output also occurs in the sectors of electrical equipment (D27), wholesale and retail trade (D45T47), fabricated metal products (D25), real estate activities (D68), and machinery and equipment nec (D28). Overall, the total effects that occur in the above sectors account for nearly 87% of the total effects on output.

Similarly, in both scenarios, almost 50% of the total effects on value added is concentrated in the sectors of construction (D41T43); manufacturing nec; repair and installation of machinery and equipment (D31T33); and professional, scientific, and technical activities (D69T75). Another 35% of the total value added generated occurs in the sectors of real estate activities (D68), wholesale and retail trade (D45T47), electrical equipment (D27), fabricated metal products (D25), and financial and insurance activities (D64T66).

Moreover, in both scenarios, nearly 70% of the total effects on employment are concentrated again in the sectors of construction (D41T43); manufacturing nec; repair and installation of machinery and equipment (D31T33); and professional, scientific, and technical activities (D69T75).

The indirect effects of the investments on output, value added, and employment reflect the additional output, value added, and employment generated in the economy because of the additional intermediate demand created by direct investments. In the NECP scenario and the moderate sub-scenario for the year 2030, the indirect effects on output, value added, and employment account for 37.2%, 41.6%, and 27.7% of the total generated output, value added, and employment, respectively. In the CLEVER scenario, the corresponding effects contribute 38.2%, 42.5%, and 27.8% to the generated output, value added, and employment, respectively.

An important aspect of an economy’s investment plans is their impact on the country’s trade deficit. Investments directed in the manufacturing sectors, as is the case with investments toward the green transition, may affect the country’s dependence on imported goods and services, as they are related to the use of specialized equipment and high-tech products, which, in the Greek economy, are mainly covered by imports [43]. Regarding the NECP scenario, over 30% of the leakages are directed to the sector of basic metals (D24) and are followed by percentages of less than 10% in several other sectors.

The implementation of the investment spending in the CLEVER scenario will also result in an increase in imports due to the increased demand for imported intermediate inputs. According to the results of the moderate sub-scenario for 2030, the ratio of imported intermediate inputs to planned domestic investment expenditure is 0.19, meaning that for every EUR 1 million of domestic investment expenditure, approximately EUR 0.19 million is directed to imports. More than 30% of the leakages occur again in the sector of basic metals (D24) and are followed by percentages of less than 10% by several other sectors.

Overall, these findings suggest that investments in the green transition, while boosting growth, imply a non-negligible and heterogeneous import cost, with most imports being capital or intermediate goods, and a significant share of final imports consists of electric vehicles.

3.3. Effects by Occupation

Rapid technological developments and socioeconomic changes are bringing about significant restructuring in the characteristics of employment, creating new needs in knowledge and skills. This process leads to radical changes in occupations and the required skills over time. These changes occur on both the supply and demand sides of the labor market, with intensity in sectors that integrate technological innovations more quickly. For this reason, it is important to identify occupations and skills with high potential in an economy, as well as their linkages to the sectors of economic activity in which they are most concentrated.

In both scenarios, the occupations with the greatest contribution to employment are those of building and related trades workers, excluding electricians (71); food processing, woodworking, garment, and other craft and related trades workers (75); science and engineering professionals (21); electrical and electronics trades workers (74); metal, machinery, and related trades workers (72); business and administration professionals (24); and general and keyboard clerks (41). Most occupations with high expected demand increase belong to the general category of craft and related trades workers, which accounts for 41% of the generated demand in the NECP scenario and 39.2% in the CLEVER scenario.

3.4. The Induced Effects of the Green Transition

To estimate the induced effects, the additional income generated in households due to both direct and indirect effects is determined for each examined scenario. This income is then distributed across the sectors of economic activity of the Greek economy based on the sectoral distribution of households’ domestic consumption. In this way, a new final demand vector emerges, which, due to the additional income, causes a disturbance in the economy and leads to an increase in output, value added, and income as well. It turns out that the induced effects are significant for all macroeconomic measures, as, if added to the direct and indirect effects, they would increase the total output by approximately 20%, increase the total value added by approximately 33%, and increase the total employment by approximately 15% (the induced effects for both scenarios in absolute figures are presented in Appendix C,

Table A5. Induced effects of the scenarios examined.

	Sub-Scenario	2025	2030	2035	2040	2045	2050
NECP Scenario
Output (in billion euro)	Optimistic	1.96	2.69	3.86	4.25	5.39	5.14
	Moderate	1.96	2.57	3.62	3.97	5.04	4.87
	Pessimistic	1.96	2.43	3.27	3.51	4.35	4.25
Value added (in billion euro)	Optimistic	1.57	2.00	2.70	2.93	3.61	3.47
	Moderate	1.57	1.93	2.56	2.77	3.40	3.30
	Pessimistic	1.57	1.85	2.35	2.49	3.00	2.93
Employment (in thousands of employees)	Optimistic	21.83	29.75	42.58	46.83	59.27	56.57
	Moderate	21.83	28.49	39.94	43.80	55.43	53.56
	Pessimistic	21.83	26.90	36.12	38.70	47.96	46.79
CLEVER Scenario
Output (billion euro)	Optimistic	1.47	3.04	3.28	3.62	3.70	3.80
	Moderate	1.47	2.90	3.08	3.38	3.46	3.57
	Pessimistic	1.47	2.73	2.79	2.99	2.98	3.04
Value added (in billion euro)	Optimistic	1.28	2.21	2.36	2.56	2.61	2.67
	Moderate	1.28	2.13	2.24	2.42	2.46	2.53
	Pessimistic	1.28	2.03	2.06	2.19	2.18	2.22
Employment (in thousands of employees)	Optimistic	16.51	33.60	36.24	39.91	40.84	41.92
	Moderate	16.51	32.09	34.03	37.35	38.16	39.37
	Pessimistic	16.51	30.18	30.83	33.05	32.94	33.65

).

Table 7. Induced effects of the NECP and CLEVER scenarios examined, expressed as a percentage of GDP and employment of the Greek economy in 2023.

	Sub-Scenario	2025	2030	2035	2040	2045	2050
NECP Scenario
Contribution of value added to GDP (% of GDP2023)	Optimistic	0.81%	1.03%	1.39%	1.51%	1.86%	1.78%
	Moderate	0.81%	0.99%	1.32%	1.42%	1.75%	1.70%
	Pessimistic	0.81%	0.95%	1.21%	1.28%	1.54%	1.51%
Contribution to employment (% of employment2023)	Optimistic	0.41%	0.83%	0.89%	0.98%	1.01%	1.03%
	Moderate	0.41%	0.79%	0.84%	0.92%	0.94%	0.97%
	Pessimistic	0.41%	0.74%	0.76%	0.81%	0.81%	0.83%
CLEVER Scenario
Contribution of value added to GDP (% of GDP2023)	Optimistic	0.76%	1.56%	1.69%	1.86%	1.90%	1.95%
	Moderate	0.76%	1.49%	1.58%	1.74%	1.78%	1.83%
	Pessimistic	0.76%	1.40%	1.43%	1.54%	1.53%	1.56%
Contribution to employment (% of employment2023)	Optimistic	0.41%	0.83%	0.89%	0.98%	1.01%	1.03%
	Moderate	0.41%	0.79%	0.84%	0.92%	0.94%	0.97%
	Pessimistic	0.41%	0.74%	0.76%	0.81%	0.81%	0.83%

presents the induced value added and employment as a percentage of GDP and total employment, respectively, for the year 2023. It follows that the estimation of the induced effects is particularly important and should not be underestimated during the economic evaluation of the investments. Finally, it should be noted that the sectoral distribution of the induced effects differs from the sectoral effects of the green investments, as it is determined by the structure of domestic consumption. The sectors with the highest induced effects for all the examined macroeconomic measures are these of wholesale and retail trade (D45T47); real estate activities (68); accommodation and food service activities (D55T56); financial and insurance activities (D64T66); food products, beverages, and tobacco (D10T12); crop and animal production, hunting, and related service activities (A1T3); electricity, gas, steam, and air conditioning supply (D35); telecommunications (D61); human health and social work activities (D86T88); and education (D85).

3.5. Additional Socioeconomic Impacts of Energy Savings in Households

In both the NECP and CLEVEL scenarios, a series of interventions aims to reduce future energy demand and improve energy efficiency. After the amortization of the respective investments, mainly households but also private companies will have additional resources available that can be reinvested in the economy (Figure 3).

In this section, the impact on the economy of the additional disposable income of households is examined after the amortization (for which a five-year period is assumed) of the investments for energy efficiency improvements. The cost savings are expected to result in an increase in consumption, which will have a multiplier effect on the economy by creating additional output, value added, and employment as well. The sectoral vector of new consumption is constructed based on household consumption data from the input–output table of 2018, which takes into account the distinction between domestically produced and imported consumer goods. Namely, household consumption includes 83% domestic products and 17% imported products (the sectors of electricity generation and petroleum are not included in the vector of new household consumption expenditures, as these are the sectors from which the savings arise). The results for both scenarios are presented in Appendix C,

Table A6. Effects on the economy from the increase in household consumption spending attributed to the reduction in household energy costs.

	2025	2030	2035	2040	2045	2050
NECP Scenario
Savings (in billions of EUR)	-	-	0.5	2.2	3.9	5.2
Consumption (in million EUR)	-	-	0.4	2.1	3.5	4.7
Domestic consumption (in billions of EUR)	-	-	0.4	1.9	3.2	4.3
Impact on production (in billions of EUR)	-	-	0.6	2.7	4.6	6.1
Impact on value added (in billions of EUR)	-	-	0.3	1.6	2.7	3.6
Impact on employment (in thousands of employees)	-	-	4.9	23.4	40.0	52.8
CLEVER Scenario
Savings (in billions of EUR)	-	1.2	3.2	4.9	6.2	7.2
Consumption (in million EUR)	-	1.2	3.0	4.6	5.7	6.5
Domestic consumption (in billions of EUR)	-	1.1	2.8	4.2	5.2	5.9
Impact on production (in billions of EUR)	-	1.5	3.9	5.9	7.4	8.4
Impact on value added (in billions of EUR)	-	0.9	2.3	3.4	4.3	4.9
Impact on employment (in thousands of employees)	-	12.8	32.8	49.4	61.9	70.4

, for the time horizons under analysis with positive savings, while

Table 8. Effects of energy saving spending as a percentage of GDP and employment of the Greek economy in 2023.

	2025	2030	2035	2040	2045	2050
NECP Scenario
Contribution of value added to GDP (% of GDP2023)	-	-	0.17%	0.82%	1.40%	1.85%
Contribution to employment (% of employment2023)	-	-	0.12%	0.58%	0.99%	1.30%
CLEVER Scenario
Contribution of value added to GDP (% of GDP2023)	-	0.46%	1.18%	1.77%	2.22%	2.53%
Contribution to employment (% of employment2023)	-	0.32%	0.81%	1.22%	1.52%	1.73%

presents the results in value added and employment as a percentage of GDP and total employment in Greece in 2023. The impacts of household energy savings on the economy are significant, and their importance increases over time with the increase in the penetration of clean energy technologies in production and consumption. The sectoral distribution of these effects follows the distribution of the induced effects; that is, the effects are concentrated on the sectors where household consumption is directed.

3.6. Multipliers per Type of Intervention and by Sub-Scenario

Finally, the unit multipliers per type of intervention of the two scenarios under analysis are presented, which reflect the additional economic activity and employment created in the economy per EUR one million directed at a specific measure or technology (unit multipliers by investment category are a particularly useful tool for analyzing the performance and economic impact of various investments, as they offer a means of assessing the socioeconomic benefit of an investment and can ensure that decision makers allocate their investment resources more effectively). The unit multipliers of the individual interventions are equal in both scenarios, since they express investments that are made in a similar economic environment, while the unit multipliers of different interventions may differ significantly, as they depend on their degree of dependence on imports, the size and interconnections of the sectors to which the interventions are directed, and the level of technology and labor specialization of the sectors associated with them.

The unit multipliers are estimated for the period 2020–2040; therefore, they express the expected average impact of the corresponding investment expenditures. In the cases of interventions whose cost is broken down into CapEx and OpEx, the unit multipliers for the specific intervention are calculated for both types of costs as well.

Figure 4a–d present the unit multipliers for all interventions for the moderate sub-scenario in the period 2020–2040. The figures show that the impacts of individual interventions differ significantly, while the scope of their impact on the examined macroeconomic characteristics varies.

As can be seen in Figure 4a, the dependence of individual interventions on imports, i.e., the share of imports per unit of investment, ranges from 0% (in construction, permits/studies) to 100% (in vehicle replacement), with an average value of 28.6%. That is, for every EUR 1 million of expenditure, for some of the interventions included in the scenarios under analysis, an average of EUR 286,000 will be channeled into exports. In fact, in all cases, the operation of the interventions creates smaller demand for imports than their construction, a fact that demonstrates the high dependence of the Greek economy on imported industrial products.

Figure 4b shows that the production multipliers, i.e., the value of gross product created per unit of investment, range from 0 (for the replacement of public transport fleet vehicles, which includes imports only) to 2.786 (for the renovation of homes), with an average value of 1.521, meaning that for every EUR 1 million of expenditure, a gross product of EUR 1521 million is produced per year. In the case of production multipliers, the operation of interventions creates greater multiplier effects on production than construction, because of the high degree of dependence of construction on imports.

In Figure 4c, the value-added multipliers, i.e., the value added created per unit of investment, are presented, which range from 0 (for the replacement of a public transport fleet that includes only imports) to 1.525 (for the renovation of homes), with an average value of 0.788. Value-added multipliers are particularly critical, as they capture the impact of investment on GDP. The results show that for every EUR 1 million of expenditure, a gross product of EUR 788,000 per year is generated on average for some of the interventions. In the case of value-added multipliers, it appears that the operation of the interventions creates higher multiplier effects on value added than construction, because of the latter’s high degree of dependence on imports.

As shown in Figure 4d, the employment multipliers, i.e., the new employment created per EUR 1 million of investment in the action, range from 0 people (in the replacement of vehicles, which includes only imports) to 30.8 people (for the operation of biogas plants), with an average value of 19 people. This means that per EUR one million of expenditure, 19 new jobs are created annually. In the case of employment multipliers, it appears that the operation of the interventions creates higher multiplier effects on employment than construction.

Finally,

Table 9. Value-added and employment multipliers (impact per EUR1 million of investment) for the scenarios and sub-scenarios examined.

	Sub-Scenario	2025	2030	2035	2040	2045	2050
NECP Scenario
Value-added multiplier	Optimistic	0.956	0.953	0.965	1.04	1.047	1.109
	Moderate	0.956	0.964	0.982	1.065	1.072	1.137
	Pessimistic	0.956	0.983	1.015	1.122	1.135	1.209
Employment multiplier	Optimistic	25.1	25.28	24.78	24.56	24.11	24.22
	Moderate	25.1	25.88	25.6	25.4	24.86	24.86
	Pessimistic	25.1	26.73	27.08	27.22	26.74	26.71
CLEVER Scenario
Value-added multiplier	Optimistic	1.213	1.152	1.239	1.282	1.339	1.39
	Moderate	1.213	1.173	1.273	1.323	1.385	1.436
	Pessimistic	1.213	1.203	1.336	1.41	1.502	1.572
Employment multiplier	Optimistic	31.61	27.07	27.11	27.38	27.97	28.73
	Moderate	31.61	27.89	28.23	28.57	29.21	29.93
	Pessimistic	31.61	29.09	30.25	31.13	32.41	33.52

reports the value-added and employment multipliers for each sub-scenario. The value-added multiplier indicates the total EUR of gross value added generated per EUR 1 of investment, while the employment multiplier shows the total jobs supported per EUR 1 million of investment, accounting for direct, indirect, induced, and energy-saving effects. Concerning value-added multipliers, the CLEVER scenario clearly outranks the NECP scenario across all time horizons. Specifically, in 2025, the overall multiplier under CLEVER is EUR 1.213 of value added per EUR 1 of investment, compared to 0.956 under NECP; by 2050, it rises to 1.572 versus 1.209, respectively. A similar pattern holds for employment multipliers. Whereas NECP achieves roughly 25 jobs per EUR 1 million of investment, CLEVER exceeds 30 jobs per EUR 1 million. This gap reflects higher induced-contribution multipliers and the impact of energy savings under CLEVER. Thus, CLEVER achieves higher value added per euro invested while significantly enhancing job creation relative to NECP.

4. Conclusions and Discussion

In recent years, it has been widely recognized that the promotion of clean technologies (i.e., RES, energy-saving technologies, etc.) contributes not only to addressing climate change but also to achieving other broader sustainable development goals. The purpose of this study is to estimate the effects of green transition interventions on the Greek economy. By employing input–output analysis, the direct, indirect, and multiplier effects of the investments for the green transition on output, value added, employment, and imports are estimated for every five-year span until 2050.

The analysis is based on two different transition scenarios: in the former, aligned with the National Energy and Climate Plan (NECP), the transition is pursued through increased investments in clean technologies for promoting the electrification of the final demand and the large-scale development of RES, primarily in power generation, but also in the final demand sectors. The latter (scenario CLEVER) proposes a different approach for the green transition, prioritizing the increased penetration of energy sufficiency and efficiency options to ensure energy security and reduce energy demand, which is then covered by RES. In general, the NECP scenario causes greater direct and indirect impacts on the economy, increasing GDP by 2.4–6.7% in the various horizons of the analysis compared to 2023. Correspondingly, employment increases by 3.5–9.4% during the evolution of investments. The direct and indirect effects of the CLEVER scenario are also significant, but smaller than the NECP, given that the investments of CLEVER are also smaller. Specifically, they increase GDP by 1.8–4.8% and employment by 2.8–6.4% compared to 2023 levels in the various time horizons under analysis. In terms of their total induced effects, the CLEVER scenario generates greater impacts on the economy, as it places greater emphasis on energy saving, and therefore, households, after the amortization of the implemented investments, have additional disposable income for consumption. Specifically, the induced impact of the NECP scenario increases GDP and employment compared to 2023 levels by 0.8–3.6% and 0.4–2.3%, respectively, while the corresponding figures for the CLEVER scenario are 0.8–4.5% and 0.4–2.8%. These results demonstrate that the CLEVER scenario generates greater value added per EUR invested and significantly increases employment outcomes relative to NECP, thereby emphasizing the importance of induced multipliers and energy-saving measures in optimizing the socioeconomic impact of green-transition investments. In other words, even though the NECP scenario requires greater investments compared to CLEVER, the total macroeconomic impacts of the two scenarios in question do not differ significantly due to the structure of the Greek economy (i.e., the increased role of services and construction activities and relatively limited contribution of the industrial sectors) and the CLEVER orientation to prioritize actions that can be implemented to a greater extent by domestic companies.

For each of the scenarios of analysis, three alternative sub-scenarios are examined that assume different degrees of import substitution in selected industrial sectors. The results of the analysis show significant differentiations among the scenarios/sub-scenarios, which indicate that policies aiming to support domestic industrial sectors involved in investments for the green transition are important and may contribute significantly to maximizing the social benefits of green transition, thus facilitating the acceptance of the associated investments by civil society.

Mainly, the NECP scenario, and to a lesser extent the CLEVER scenario, requires the commitment of significant funds for their implementation. These investments lead to a deterioration of the country’s trade balance, as expressed by net exports as a percentage of GDP. That is, both the final and intermediate imports required lead to a significant deterioration in the trade balance in both scenarios, but primarily in the NECP one.

Of paramount importance are the results of the analysis made individually for each intervention considered in the two scenarios. Specifically, among the main RES technologies included in the two scenarios under consideration, the installation of photovoltaics has a greater impact on the economy than wind energy, as a larger part of the required investments is made by domestic companies. The economic footprint of residential photovoltaics is even greater as their operation contributes to energy savings in households and therefore to the creation of additional income for consumption. Other RES technologies, as well as new and synthetic fuels, are characterized by relatively greater macroeconomic impacts per monetary unit of investment. However, their overall footprint in the economy is relatively small due to their limited penetration.

The interventions aimed at the energy upgrade of residential buildings (i.e., insulation of the building shell and installation of heat pumps) create relatively greater impacts on the economy than the main RES technologies. This is partly attributed to the resulting energy savings and to the additional income available to households for consumption after the amortization of these interventions. It is also worth mentioning that these impacts are underestimated in the context of our analysis, as they extend over the entire life of the interventions, which, in some cases, exceeds the time horizon of the analysis. The macroeconomic implications of the interventions examined for the non-residential buildings have a significant but smaller economic footprint compared to the corresponding measures in residential buildings, as the secondary impacts on the economy from increased energy savings are not taken into account.

Regarding interventions in the transport sector, the promotion of electromobility has a very small footprint on the economy per unit of investment for countries like Greece, as a significant portion of the invested capital is directed abroad. On the contrary, the contribution of measures aimed at developing and strengthening transport infrastructure, such as the electrification of railways, the strengthening of public transport systems, the installation of electric chargers, etc., is particularly important. From this perspective, it is of paramount importance that future revisions of the national energy and climate plan place greater emphasis on promoting public transport and railways, increasing their share in the overall transport work.

Finally, in industry, the economic impact of the interventions under consideration is relatively modest and depends on the type of interventions required in each industrial sector.

These results are consistent with the findings of other studies (see, for example, the review paper [20]) that shows that the macroeconomic impacts of interventions aiming to improve energy efficiency are relatively greater than those aimed at promoting cleaner technologies in the energy production sector. However, this may not be the case in economies with a strong industrial base (see, for example, ref. [19] for an analysis in the US).

European climate policies set quite strict and clear targets for reducing GHG emissions and building a carbon-neutral economy by 2050. However, member states have some flexibility in choosing the mix of policies and measures they will adopt in order to achieve these targets For economies like Greece that rely to a significant extent on imports of technological equipment, the green transition should prioritize actions to improve energy sufficiency and efficiency, limiting to some extent the required investments for new RES units and energy storage installations. Upgrading the existing building stock through energy renovation programs contributes not only to significant reductions in energy-related GHG emissions but also to economic growth through increased GDP, creation of new jobs, etc. In the transport sector, electromobility needs to be combined with other measures promoting soft mobility, railways, and public transport, with the aim of covering a significant portion of the transport work that is now satisfied by private vehicles.

Of course, the results are subject to the limitations of the applied methodology. IOA is characterized by certain assumptions that limit its results. In particular, it assumes homogeneity (firms in each sector have identical production technologies, neglecting technological differentiation within sectors), linearity (implying stable technological coefficients, according to the Leontief production function, and ignoring the presence of economies of scale or possible input substitution), and stable prices and wages (therefore not capturing market adjustments caused by price adjustments, constraints on factor supply or wage elasticities, and cost dynamics) [22].

A significant innovation of this study is its treatment of the green transition scenarios for the Greek economy. The large-scale increase in demand for imports is assumed to lead to the development of specific domestic sectors that will substitute for part of these imports, with the extent of substitution detailed in the three sub-scenarios developed. Accordingly, the improvement in terms of trade for the country has been considered, at least in sectors with high participation in the planned investments. However, for all other sectors, the import propensities are considered exogenous, and in the context of the IOA, the applied methodology cannot endogenously capture the exchange rate fluctuations or other changes in the terms of trade.

Consequently, while the results of the IOA provide important insights into the overall and sectoral impacts of the green transition, they may overlook dynamic impacts, such as structural changes, technological developments, price fluctuations, and external trade effects, which are particularly important for large-scale or long-term investments, as shown in this research. Dynamic or computational general equilibrium models could be utilized in future studies to better represent these complexities and produce more reliable findings.