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Review

Regional-Scale Energy Modelling for Developing Strategies to Achieve Climate Neutrality

by
Janusz Zyśk
,
Artur Wyrwa
,
Maciej Raczyński
*,
Marcin Pluta
and
Wojciech Suwała
Faculty of Energy and Fuels, AGH University of Science and Technology, 30-059 Kraków, Poland
*
Author to whom correspondence should be addressed.
Energies 2025, 18(7), 1787; https://doi.org/10.3390/en18071787
Submission received: 13 February 2025 / Revised: 17 March 2025 / Accepted: 26 March 2025 / Published: 2 April 2025
(This article belongs to the Special Issue Renewable Energy and Nearly-Zero Emissions Energy Systems)
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Abstract

:
In this work, complex energy and greenhouse gas emissions analysis on a regional scale was presented. In the framework of analysis, the 3E (Energy–Economy–Environment) class optimisation model was developed. The objective function includes the costs of energy in the medium and long term. The model covers the following sectors: energy, industry, agriculture, households, tertiary, transport, and forestry. Data such as (i) potential of individual technologies, (ii) potential of renewable energy sources, (iii) technology and fuel costs, (iv) the legal environment, and (v) sectoral goals indicated in strategic documents at various levels (regional, national, European) were implemented into the model. The aim of the study was to indicate the optimal paths to achieve climate neutrality for a selected coal region of Europe (Małopolska Voivodeship, Poland). For this purpose, four scenarios were developed, with different goals and activities. The base year for the research is 2020, and the scenarios were developed until 2050. The research we carried out allowed us to obtain results on greenhouse gas emissions, fuel consumption, decommissioning old technologies and installing new ones, and costs system development by 2050.

1. Introduction

To mitigate climate change, humanity must reduce greenhouse gas emissions. The Paris Agreement stipulates that its signatories will work toward this goal. The European Union has set an ambitious target of reducing greenhouse gas emissions by 55% by 2030 compared to 1990 levels and achieving climate neutrality by 2050. As of 2020, coal mining was still active in 41 regions across 12 EU Member States, providing approximately 180,000 jobs [1]. In Poland, there are six coal-mining regions, one of which is the Małopolska Voivodeship (Lesser Poland Voivodeship). This administrative region is one of sixteen in Poland, with a population of 3.4 million people (about 9.2% of the country’s total). Its capital is Kraków. Małopolska still has two operating hard coal mines, which produced 66 PJ of coal in 2020. The region also hosts four coal-fired power plants and combined heat and power (CHP) plants with a total capacity of 2496 MW. In 2020, approximately 4000 people were employed in coal mines, while 1200 worked in coal-fired power plants and CHP facilities [2]. In terms of emissions, Małopolska released 16.4 Mt of CO2, 54.5 kt of CH4, and 1.76 kt of N2O in 2020. This represents a significant reduction compared to 1990, when emissions were 23.4 Mt of CO2, 102.5 kt of CH4, and 4.71 kt of N2O [3,4]. These reductions were observed across the energy, industrial, residential, service, and agricultural sectors. However, a substantial increase in emissions has been recorded in the transport sector.
Małopolska has been actively working to reduce greenhouse gas emissions and transition away from coal. In 2020, the region developed a Regional Climate and Energy Action Plan to support this effort [3]. The plan sets a target of a 40% reduction in greenhouse gas emissions by 2030 compared to 1990 levels and aims to achieve climate neutrality by 2050. It includes measures to reduce emissions and adapt to climate change. A second document, Assumptions for the Territorial Plan of the Just Transition of Western Małopolska, analyses the western part of the voivodeship dependence on coal and proposes strategies to mitigate the socio-economic impacts of the energy transition in the region [2].
In the literature, a large variety of energy planning models exist for developing and evaluating energy scenarios [5,6,7,8,9,10,11]. Most of those models operate at the national level. A review of national and regional deep decarbonisation studies conducted in 2022 found that out of 46 models, none covered an area smaller than a country [12]. Some models were developed for the entire EU or a group of countries, such as Scandinavia. The model on the regional scale was developed for Piedmont region in Italy. The TEMOA-Piedmont is the first energy system model of the region, developed within the TEMOA optimisation tool. The model takes into account the following (i) sectors: residential, commercial, agriculture, power, industry, and transport and (ii) fuels: electricity, natural gas, diesel, LPG, oil, gasoline, and jet kerosene [13]. A complex regional model was developed for the Groningen Province in the northern Netherlands [14]. Its modelling method is based on the use of the optimisation model OPERA—a Dutch-based national integrated energy system model. The model includes households, services, industry, and agriculture sectors. EnergyScope TD is designed for the strategic energy planning of urban and regional energy systems [5]. The model includes various branches such as power to gas or vehicle-to-grid. The model was applied to the Swiss energy system. The reference energy system of the AIST-TIMES model for Japan includes primary energy supply (fossil fuels, nuclear, RES, hydrogen, and ammonia), conversion and delivery (power plants, CHP, refineries, CO2 removal), final energy consumption, and energy service demand (industry, commercial and residential, transportation) [15]. The EnergyPLAN energy system analysis model, which contains a component of electricity, heat, and hydrogen, was used to analyse a system based 100% on RES in Denmark [16]. EnergyPLAN software was also used on a local alpine scale in Northern Italy [17]. The Smart Islands method for defining energy planning scenarios takes into account electricity, heating, cooling, transport, water, waste, and wastewater [18]. The bottom-up EFOM-based energy system optimisation model was used on a regional scale in the Apulia region in Southern Italy [19]. The model allows for the development of scenarios for the region and calculates the costs resulting from their implementation. A large-scale integrated modelling system (IMS) was applied to support the impact analysis of climate change and the adaptation planning of the energy management system in the Canadian Province of Manitoba. The results show that energy allocation/production patterns in the residential, commercial, and power-generation subsectors would be sensitive to climate change. It was concluded that the adaptation programs should respond to different levels of climate change impacts on energy supply and demand due to high dependence on RES in the province [20]. The Regional Energy Deployment System (ReEDS) is used for modelling the energy systems of U.S. regions [21]. It takes into account technology, costs, and political conditions. Many regional models are limited to electricity or heat from renewable sources only, and other energy carriers are not taken into account [22,23]. A significant number of energy models take demand-side management into account [24,25]. In the model, we do not take into account the dynamic stability of energy systems; however, such studies may be the next step in the development of the model [26]. Due to the non-linear characteristics of energy flows in the grid, a linear model such as TIMES can only model them in a simplified way. As such, it was not implemented in this work.
The objective of this study was to develop a comprehensive energy model for Małopolska voivodship and to create scenarios do reduce greenhouse gas emissions (CO2, CH4, and N2O), including achieving climate neutrality in the region. The research findings will help define the technological, economic, and environmental conditions for the region’s energy transition up to 2050.
After reviewing the available studies, and to the best of the authors’ knowledge, this is the first complete regional-scale model encompassing all human activities in a European region still largely dependent on coal. The model is based on TIMES (The Integrated MARKAL-EFOM System) model generator.

2. Model

The Regional Scale Energy Model TIMES-Malopolska was developed using the TIMES (The Integrated MARKAL-EFOM System) model generator. It was designed to conduct in-depth energy and environmental analyses for the energy sector [27,28,29,30,31,32,33]. Models based on the TIMES generator belong to the class of 3E models (energy, economy, environment) because they account for these three key factors. TIMES-based models have been developed for global, national, and local scales [34,35,36,37,38,39,40,41,42]. The concept of the TIMES model and its application to studying the Polish energy system has been described in the authors’ previous works [43,44]. The model presented in this work was developed for a specific region and has been extended to include sectors beyond the energy sector. Specifically, it covers transport, industry, buildings, agriculture, and forestry (Figure 1). The model accounts for regional energy flows, greenhouse gas emissions, and the activities of all relevant sectors, including (i) the energy sector (production and distribution of heat, cold, and electricity); (ii) residential, service, and public buildings; (iii) transport (road and rail, passenger, freight, and special-purpose vehicles); (iv) economy (industry and waste management); (v) agriculture (livestock, fertilisation, manure management); and (vi) forestry and land use. The model considers the following fuel carriers: hard coal, crude oil and its derivatives, natural gas, other gases, renewable energy sources (solar, hydro, wind, biomass, and biogas), non-renewable waste, district heating, and electricity. The energy carriers within the model are shared between all sectors. All sectors compete for these commodities, and the model selects the optimal (most cost-effective) solutions across all sectors to achieve the required goals. Therefore, the model chooses between different actions in different sectors.
The modelling was conducted from 2025 onwards, in five-year intervals until 2050. The base year for the model was 2020. The model includes four representative weeks (one for spring, summer, autumn, and winter) with a three-hour resolution, resulting in a total of 224 timeslices. For each timeslice, temporal data such as electricity demand and weather-dependent RES potential are given. At annual level, factors such as (i) the potential of individual technologies, (ii) the total potential of renewable energy sources, (iii) technology and fuel costs, (iv) the legal framework, and (v) sectoral targets outlined in strategic documents at various levels (regional, national, and European) were incorporated into the model.

2.1. General Assumptions

For the base year 2020, energy and emission balances were developed to evaluate the input data used in the model. The energy and emission balances also were used as the starting point for the scenarios considered. The estimated regional primary production included 66.0 PJ of hard coal, 4.0 PJ of natural gas, 22.7 PJ of renewable energy sources (RES), 3.6 PJ of non-renewable waste, and 0.3 PJ of oil. Imports amounted to 78.4 PJ of oil and petroleum products, 50.1 PJ of natural gas and other gases, 27.4 PJ of hard coal, and 27.9 PJ of electricity [45]. The following annual regional RES potential was incorporated into the model: 5.1 PJ from geothermal energy, 3.0 PJ from solar collectors, 30.9 PJ from photovoltaics (PV), 0.5 PJ from hydropower, 5.6 PJ from wind energy, 16.9 PJ from biomass, and 3.6 PJ from biogas [46]. The prices of CO2 emission allowances under the EU ETS were assumed to be 80 EUR/t in 2025–2030, 120 EUR/t in 2035, and 250 EUR/t from 2040 onwards [47,48]. It is also worth noting that the process of obtaining input data for the model took over two years. In Poland, there is a significant lack of regional-level data, which is why the work began with the development of a comprehensive regional emissions and energy balance. The input data were gathered from numerous databases across various institutions, as well as from studies and reports available for the region. Data, parameters, and model assumptions were thoroughly discussed with experts, officials, other scientists, and representatives of the sectors included in the model. A special council was established and spent over eight months assessing and discussing the assumptions, input data, and results of the work. The model includes dozens of input data points, and their acquisition and calculation were complex. The most important parameters and data sources are described in the remainder of the article.

2.2. Energy Sector

The model individually considers the largest power plants, heating plants, and CHP plants (with thermal power above 10 MW) in the voivodeship as of 2020. This includes 26 units with a total thermal capacity of 3644 MW, comprising one coal-fired power plant; three coal-fired CHP plants; one waste-to-energy power plant; and 20 heating plants fuelled by hard coal, natural gas, fuel oil, and biomass, as well as one thermal heating plant. The model also includes the region’s only pumped-storage power plant, with a turbine electric power of 92 MWe and a pump electric power of 89 MWe. Additionally, a set of new energy technologies has been considered, each characterised by specific technical and economic parameters, as outlined in Table 1 and Table 2. Given the rapid development of renewable energy technologies, it has been assumed that their unit investment costs will follow a downward trend towards 2050. The largest decrease in investment costs is expected for photovoltaic installations, which are currently among the fastest growing renewable energy technologies.
The transmission and distribution network is divided into three voltage levels: (i) highest and high voltages (above 60 kV), (ii) medium voltages (above 1 kV, up to 60 kV), and (iii) low voltages (up to 1 kV). Power, CHP, and heating plants, both public and industrial, produce electricity at medium and high voltage, whereas electricity from RES can be produced at any voltage. Electricity is imported at high voltage. The electricity produced or imported is directed to demand sectors or to a process representing a lower voltage network. For simplicity, the model does not represent flows from a lower voltage network to a higher voltage network. In demand sectors, the input can be electricity at all voltage levels, depending on the characteristics of the load devices in the sector. However, two assumptions are made: the household and agricultural sectors draw energy exclusively from the low-voltage network, and the transport and services sectors do not use high-voltage electricity. The cost of electricity is influenced by distribution and transmission prices, which were calculated based on the tariffs of electricity distributors operating in the region.

2.3. Buildings (Household and Tertiary Sectors)

Apartments in the household sector have been divided into three main types according to the buildings in which they are located: (i) apartments in single-family detached houses, (ii) apartments in single-family terraced houses, and (iii) apartments in multi-family buildings. Their shares in the total number of apartments in the region, estimated at 1240 thousand, were 38%, 6%, and 56%, respectively [51,52,53,54,55,56]. Each type of apartment is characterised by various parameters such as area, number and area of windows, height, floor and roof area, and cubic capacity [57,58]. These three types of buildings are also classified according to their construction period and the time of possible thermal modernisation, which influences the heat transfer coefficients of the building envelope [57,58,59]. The costs of possible modernisation were calculated based on the unit costs of replacing windows and insulating walls, floors, and roofs [58].
The tertiary sector is the second segment of the building sector, after households. In the model, it includes public buildings and buildings providing commercial services. Public buildings include (i) publicly accessible cultural facilities, (ii) museum and library buildings, (iii) school and research institution buildings, (iv) hospital and healthcare facility buildings, (v) physical culture buildings, and (vi) buildings intended for religious worship and religious activities. Commercial service buildings include (i) hotel buildings, (ii) tourist accommodation buildings, (iii) office buildings, and (iv) commercial and service buildings [58,59,60,61,62]. The estimated area of commercial service buildings is 10.6 million m2, while public buildings cover 6.0 million m2.
For the household and tertiary sectors, the energy demand for space heating, hot water preparation, lighting, food preparation, and powering household appliances and audio/video devices was estimated. The model includes the following technologies for space heating and domestic hot water preparation: electric; gas; biomass; oil; coal; and LPG boilers, heat pumps (ground and air), and district heating [55,62,63,64,65].

2.4. Transport

The transport sector is represented in the model with a division into passenger transport (in passenger-kilometres), freight transport (in ton-kilometres), and special transport (e.g., police, ambulances, measured in vehicle-kilometres). The model does not include air transport, as there is only one airport in the province, meaning no air travel takes place within the region. Passenger transport includes passenger cars, buses up to 3.5 t GVM, buses over 3.5 t GVM, motorcycles, local passenger trains, long-distance passenger trains, and trams. Freight transport is carried out using trucks up to 3.5 t GVM, trucks over 3.5 t GVM, and freight trains. Special-purpose vehicles are classified as light vehicles (up to 3.5 t GVM) and heavy vehicles (over 3.5 t GVM). Local trains cover passenger transport within the province, meaning that both the starting and destination stations are located in Małopolska. Long-distance trains include passenger traffic where at least one station (starting or ending) is outside Małopolska. Vehicles in the model can use various fuels. For example, passenger cars can run on petrol, diesel, liquefied petroleum gas (LPG), electricity (BEV—battery electric vehicle; PHEV—plug-in hybrid electric vehicle), compressed natural gas (CNG), biofuels, and hydrogen. The transport sector was described using the following data: the number of vehicles by category and fuel type, average annual mileage, unit fuel consumption, average number of passengers or goods transported, transport work, calorific values, and CO2 emission factors for fuels [55,66,67,68,69,70,71,72,73,74,75,76,77,78], For example, gasoline-powered passenger cars were assumed to have an average mileage of 6000 km per year, an average fuel consumption of 6 L per 100 km, and an average occupancy of 1.1 people per vehicle. In 2020, passenger transport activity amounted to 25,761 million passenger-kilometres, freight transport to 33,170 million ton-kilometres, and special-purpose vehicles to 202 million vehicle-kilometres. The vehicle costs included in the model are based on data for European models [60,72]. In general, combustion engine cars (petrol, diesel) are expected to maintain constant prices until 2050, while electric and hydrogen cars will become cheaper between 2025 and 2050. The costs of energy carriers were calculated based on imported resources (e.g., crude oil) or domestic production (e.g., electricity in Poland). It was assumed that one charging station will be required for every 10 electric cars [79]. The lifespan of passenger cars was set at 25 years, to reflect the average vehicle age of 16–17 years in Poland. The lifespan of other vehicles was assumed to be 12 years for road vehicles and 20 years for rail vehicles.

2.5. Economy

The economy sector is characterised by a wide variety of processes and technologies. Many large factories operate unique production processes and technologies on a regional scale. In such cases, aggregation is applied in TIMES-class models. Energy transformation is modelled by considering changes in the structure of the final energy carrier use. The model estimates the costs of these changes for individual industries over different time periods. Here, costs refer primarily to changes in production technologies rather than the costs of energy generation or procurement. It was assumed that in the initial phase (up to 2030), changes will focus on low-cost technologies, primarily by improving energy efficiency, while high-cost investments will be introduced in later stages [80,81,82,83,84,85,86]. Taking into account results from other sectors and literature data, the investment costs associated with CO2 reduction were estimated at 4200 EUR/t for the period 2025–2030, 6000 EUR/t for 2030–2040, and 8800 EUR/t for 2040–2050. Due to the complexity of industrial operations, it is not feasible to estimate cost changes for individual plants, but rather for aggregated industrial groups. This sector also includes waste management process (storage, waste incineration, sewage treatment) [70].
A detailed analysis was conducted on 349 industrial facilities with the highest greenhouse gas emissions, each emitting more than 100 tons of CO2 in 2020. These facilities accounted for approximately 93% of the total emissions in the sector at the voivodeship level. Further analysis of individual industrial units was performed based on the following categorisation and assumptions:
  • The three largest CHP plants were individually modelled, while smaller CHP plants (below 10 MW of electrical capacity) were aggregated by fuel.
  • One industrial heating plant with a thermal capacity over 10 MW was individually modelled, while other heating plants were aggregated by fuel.
  • The only cement plant operating in the region was individually modelled.
  • Food and beverage production and processing industries (meat, fruit, beverages, snacks, dairies) were aggregated into a single group.
  • The chemical industry (chemicals, medicines, fertilisers, industrial gases, cleaning agents) was aggregated into another group.
  • The paper and wood processing industry (wood products, paper, cardboard) was aggregated separately.
  • The metal production and processing industry (steel processing, aluminium) was aggregated into its own group.
  • The mineral industry (excluding cement plants), including construction materials, ceramics, and aggregates, was grouped.
  • The machinery and equipment production industry (refractory products, fittings, car parts, electronics, machinery) was categorised as another group.
  • All other industrial units not included above, as well as facilities that emitted less than 100 tons of CO2 in 2020, were aggregated into the final group. This includes waste management, general industry, building construction, hygiene products, printing, tobacco, mining, and window production.

2.6. Agriculture

The agriculture sector of the Małopolska region was modelled by considering the following factors: (i) methane emissions from livestock (cattle, sheep, pigs, goats, horses) due to enteric fermentation, (ii) methane (CH4) and nitrous oxide (N2O) emissions from manure management, (iii) nitrous oxide emissions from fertilisation, (iv) emissions resulting from the burning of agricultural residues, (v) CO2 emissions from liming and the use of other carbon-containing fertilisers, (vi) energy consumption by tractors and self-propelled combine-harvesters, and (vii) emissions related to energy use in agricultural buildings and equipment [70,87,88,89,90,91].
Meat production is highly volatile, and the future of livestock populations is difficult to predict [88]. European-level studies suggest that the population of cattle and pigs will decline by 0.8% per year between 2021 and 2030 [92]. In contrast, the population of sheep and goats is expected to increase slightly by approximately 0.3% per year during the same period. Due to the lack of national and European-level plans for future livestock populations, the model assumes that livestock numbers remain constant at 2020 levels.

2.7. Land Use and Forestry

In 2020, forests in Małopolska covered 434,482 ha, accounting for 28.75% of the total area of the province [70]. Afforestation efforts in the region included 19.32 ha in 2018, 54.53 ha in 2019, and 27.54 ha in 2020. In addition, approximately 1300 ha of forest is renewed annually through reforestation after logging. The model applies two CO2 absorption coefficients for forests: old forests (over 5 years old): 2.224 t CO2/ha and young forests (≤5 years old): 3.916 t CO2/ha [91]. The cost of afforesting one hectare was estimated at 4500 EUR [93].

3. Scenarios

Four scenarios (Stagnation, National, Małopolska, and Optimistic) were developed, considering the political and regulatory environment as well as the goals established at the regional, national, and EU levels [3,49,94,95,96,97,98]. The Stagnation scenario represents a situation where Małopolska does not take climate action, while other regions and countries continue their energy transition. The National scenario aligns with Poland’s national plans. The Małopolska scenario reflects the voivodeship’s specific climate and energy goals resulting from regional policies. The Optimistic scenario corresponds to the most ambitious EU-level targets. The main objectives for each scenario are summarised in Table 3. The energy efficiency targets and the future energy consumption levels were based on forecasts from the PRIMES_2007 model [99].
Detailed sectoral objectives for the transport sector included, in the case of the (i) Stagnation scenario, the failure to achieve the objectives set out in strategic documents and small changes in the number of journeys and vehicles, and in the (ii) National scenario, 100% zero-emission vehicles in 2030 in the public transport fleet in cities with more than 100,000 inhabitants, a reduction in the average CO2 emissions of new passenger cars by 37.5% and new light commercial vehicles by 31% compared to 2021, and a 14% share of renewable energy sources in this sector. The Małopolska and Optimistic scenarios assumed the same targets for 2030 as the National scenario, and additionally, in the case of the (i) Małopolska scenario, a reduction in energy consumption by at least 5% compared to 2020 and an increase in the use of electric cars by at least 100% compared to 2020, and in the case of (ii) the Optimistic scenario, a reduction in energy consumption by at least 10% compared to 2020, an increase in the use of electric cars by at least 200% compared to 2020, and the introduction of hydrogen technologies. In the Optimistic scenario for 2035, a ban on the registration of new fossil fuel cars was implemented. For the building sector, the requirement for the share of insulated buildings in the total housing stock will be 65%, 70%, 80%, and 85% for the scenarios, respectively: Stagnation, National, Małopolska, Optimistic. In the National scenario, the ban on the use of coal in households was assumed for cities in 2030 and for villages in 2040, whereas in the Małopolska and Optimistic scenarios, this ban was introduced in 2030 for both cities and villages. A requirement for the local governments to ensure in their own public buildings (i) 100% of electricity from RES in 2027 in the Małopolska scenario and (ii) 100% of electricity and heat from RES in 2030 in the Optimistic scenario was implemented. In the National scenario, the energy sector was characterised by the following goals for 2030: (i) at least 85% of heating or cooling systems with an ordered capacity exceeding 5 MW will meet the criteria for an energy-efficient heating system, (ii) starting to use hydrogen as an energy carrier for energy storage processes. In the Małopolska scenario, the following goals were defined: (i) the use of energy storage facilities and (ii) at least 90% of heating or cooling systems with an ordered capacity exceeding 5 MW will meet the criteria for an energy-efficient heating system. For 2050, the goal was set that the RES potential in the province in electricity production will be used at 60%, 80%, and 100%, respectively, in the National, Małopolska, and Optimistic scenarios. In the economy sector, the following targets were set for 2030: (i) the level of preparation for reuse and recycling of municipal waste of at least 60% by weight in the case of the National scenario, (ii) increasing the share of renewable energy sources by 5 percentage points compared to 2020, (iii) reducing the use of coal by at least 40% compared to 2020 in the case of the Małopolska scenario. The Optimistic scenario includes the following targets for economy sector: (i) increasing the share of renewable energy sources by 10 percentage points compared to 2020, (ii) reducing the use of coal by at least 50% compared to 2020, (iii) reducing the use of gas by at least 20% compared to 2020, (iv) using hydrogen as a fuel with a share of at least 5%. In agriculture, goals were set to improve energy efficiency by 2030 compared to 2020, by 10% in the Małopolska scenario and 20% in the Optimistic scenario. In the forestry sector, the target for forest area growth in 2030 compared to 2020 was set at 1.4, 1.8, and 2.4 percentage points for the National, Małopolska, and Optimistic scenarios, respectively.
Each of the above assumptions is implemented in the model as a constraint. These constraints are based on political assumptions and legal regulations. These constraints apply either to processes (e.g., no investment in new internal combustion engines after 2035 in the Optimistic scenario) or to commodities (e.g., total CO2).

4. Results

The research carried out provided various results, including greenhouse gas emissions, fuel consumption, decommissioning of existing technologies and installation of new ones, electricity production by fuel and technology, and system development costs up to 2050.
In 2020, approximately 16 Mt of CO2 was emitted in the Małopolska Voivodeship. In the Stagnation scenario, emissions drop to 10 Mt by 2050, while in the National scenario, they fall to 6 Mt (Figure 2). Despite the lack of explicit reduction targets for 2050 in these scenarios, the model still decreases emissions due to economic and environmental conditions, as low- or zero-emission technologies prove cost-competitive with fossil-fuel-based alternatives. The model achieves the fastest emission reductions in the building sector, where costs are relatively low, while the transport sector experiences the slowest decline due to the gradual replacement of the vehicle fleet. Poland has a significant number of old cars, and many people purchase used vehicles from Western Europe, further slowing the transition. In both the Małopolska and Optimistic scenarios, climate neutrality is achieved, with emissions from transport and agriculture remaining lower than the CO2 absorption capacity of forests. In 2050, in the Małopolska and the Optimistic scenario, CO2 absorption is higher than its emissions. This is because, to achieve climate neutrality in 2050, the model must offset the emissions of other greenhouse gases, primarily CH4, through CO2 absorption (Figure 2).
In the Stagnation scenario, CH4 emissions from the buildings are only slightly reduced (Figure 3). In other scenarios, there is a rapid decrease in landfill emissions. Waste is directed towards recycling, and existing landfills lose their CH4 emission potential over time, while also being equipped with methane capture installations. In every scenario, enteric fermentation (directly related to the animal population) remains at the same level. These emissions are balanced by CO2 absorption in forests.
In all scenarios, a phase-out of coal is observed in electricity production, driven primarily by the price of CO2 in the EU ETS. This is particularly evident in the Stagnation scenario, where, despite the absence of explicit emission reduction targets, the high cost of CO2 allowances leads to a decline in coal use (Figure 4). Local renewable energy sources are insufficient to meet the electricity demand of the Voivodeship, resulting in significant energy imports. The Stagnation scenario, especially between 2040 and 2050, is marked by a situation where coal-based energy becomes too expensive due to emission costs, while renewable energy production remains underdeveloped.
In the Stagnation scenario, despite the absence of specific targets, the consumption of petrol and diesel decreases (Figure 5). The gradual replacement of combustion vehicles with electric ones occurs mainly because by 2035, the costs of both technologies will become nearly equal. In the National scenario, this transition is further driven by cost considerations and energy efficiency targets. In the Małopolska and Optimistic scenarios, electricity consumption in the transport sector surpasses fossil fuel use by 2050, with new combustion car registrations banned from 2035. However, even in these ambitious scenarios, fossil fuels are not completely eliminated by 2050. Instead, CO2 emissions are offset by forest absorption, enabling the province to achieve net-zero emissions. It is important to note that electric vehicles are significantly more efficient than combustion cars, leading to a substantial reduction in overall energy consumption in the sector. The slow pace of fleet replacement in all scenarios is due to the fact that relatively few new cars are purchased in Poland, with many vehicles imported from other countries at an average age of about 11 years. As a result, the average age of cars in Poland remains around 16 years and has increased in recent years.
The investment costs incurred by the energy sector are lower than those of the household, transport, or industrial sectors (Figure 6). In the transport sector, costs are particularly high due to vehicle replacements. However, it is important to consider that people replace vehicles regardless of climate goals. Although appropriate policies may encourage the adoption of alternative technologies or public transport, a significant portion of society will continue to purchase and replace private cars. In the Optimistic scenario, the cumulative investment costs between 2025 and 2050 amount to EUR 14 billion (2020 prices) for the energy sector, EUR 16 billion for buildings, EUR 43 billion for transport, and EUR 21 billion for the economy. In the Stagnation scenario, the corresponding values are EUR 5 billion, EUR 12 billion, EUR 67 billion, and EUR 7 billion, respectively. In the Stagnation scenario, vehicle-related expenditures are significantly higher than in the Optimistic scenario. This is mainly because, in the Optimistic scenario, the number of personal journeys decreases in favour of public transport, leading some people to forgo private car ownership.

5. Discussion

The article presents a system-based analysis of the energy transformation process in the Małopolska province in the perspective of 2050. The dedicated Regional-Scale Energy Model, minimising the total costs of meeting the demand for energy carriers in the following sectors: energy, economy, transport, agriculture, and construction, while considering various constraints resulting from the goals set in the scenarios, was used. The model accurately reflects the situation of the province in 2020, incorporating all data influencing greenhouse gas emissions in both the base year and the future. Four scenarios were analysed:
  • Stagnation—A scenario assuming no implementation of climate goals, in which the economy transforms at its current pace, and renewable energy sources and energy efficiency are not promoted. However, in this scenario, existing mechanisms, such as the costs of CO2 emission allowances, are maintained.
  • National—A scenario assuming that climate goals in the Małopolska province align with those set for Poland as a whole in the National Plan for Energy and Climate, which targets a 7% reduction in CO2 emissions for non-ETS sectors compared to 2005 levels.
  • Małopolska—A scenario based on the targets set for the Małopolska province in the Regional Climate and Energy Action Plan. It assumes a 40% reduction in greenhouse gas emissions by 2030 compared to 1990 and aims at climate neutrality in the province by 2050.
  • Optimistic—A scenario assuming a 55% reduction in greenhouse gas emissions by 2030 compared to 1990, along with the implementation of the “Fit for 55%” package, ultimately achieving climate neutrality in the province by 2050.
The model-based studies provided quantitative results demonstrating the impact of various factors that influence system performance. Climate policy and its implementation play a fundamental role in the region’s energy transition, including the emissions trading system, which imposes the obligation to purchase CO2 emission allowances in ETS sectors, as well as in non-ETS sectors such as households and transport.
The results of the Stagnation scenario confirm that despite the absence of climate goals, the high prices of CO2 emission allowances led to a significant reduction in greenhouse gas emissions, primarily in the energy sector. Although the renewable energy potential in the voivodeship is insufficient to fully meet electricity demand, the results indicate that failing to invest in renewable energy sources (RES) results in a substantial increase in electricity imports and the shutdown of generating units in Małopolska. Beyond the negative impact on energy security, inaction on climate goals may also lead to unemployment in the energy sector due to a lack of development of RES and an uncompetitive economy driven by the high costs of imported energy.
In both the Małopolska and Optimistic scenarios, the greenhouse gas reduction targets for 2030 and 2050 (climate neutrality) were achieved. This was made possible by an increased share of renewable energy sources and the construction of a small modular nuclear reactor in the energy generation sector. Additionally, these scenarios saw the electrification of key energy-consuming sectors, including buildings, where heat pumps were widely used; transport, which saw the adoption of electric vehicles; and industry, where process heating was increasingly generated through electric and hydrogen-based technologies. Despite investments in energy-saving measures, such as the ongoing thermal modernisation of buildings in households and the service sector, none of the scenarios fully achieved their efficiency improvement targets. According to PRIMES 2007 forecasts for Poland and the energy efficiency goals set for 2030, final energy consumption in the province should have amounted to 205 PJ in the National scenario (with a target of 23% efficiency improvement), 180 PJ in the Małopolska scenario (32.5%), and 150 PJ in the Optimistic scenario (40%). However, the model results for 2030 showed that actual final energy consumption reached 216 PJ in the National scenario, 199 PJ in the Małopolska scenario, and 184 PJ in the Optimistic scenario.
These findings highlight the significant challenges in improving energy efficiency and the urgent need for holistic, cross-sectoral pro-efficiency measures. Strengthening and expanding support instruments will be crucial in addressing this issue. In addition to technological advances and efficiency improvements, greenhouse gas emissions were also reduced through changes in the agricultural sector, particularly in livestock breeding and crop cultivation practices, including improved manure management and optimised fertilisation techniques. Importantly, the results indicate that these measures do not compromise food security in the province.
In all scenarios, the district heating sector is undergoing a transformation towards efficient district heating systems, in accordance with the requirements of the EED directive. To fulfil these requirements, coal-fired CHP plants are being replaced by sources fired by biomass, natural gas, and alternative fuels such as hydrogen. In large cities such as Krakow, SMR technology is being installed, if a scenario permits it. Geothermal and large-scale heat pumps are also sources of district heating, especially in smaller district heating systems. Solar collectors, due to their low potential due to the inaccessibility of land around urban areas, are not a significant source of district heat.
The results obtained allow for several key conclusions to be drawn, including
  • In the Stagnation scenario, by 2050, 65% of electricity demand will be met through imports, as fossil fuel power plants will become unprofitable and renewable energy sources will remain underdeveloped.
  • If the voivodeship aims at energy self-sufficiency, it must invest in nuclear power, as the potential of renewable energy sources will cover at most 45% of the electricity demand.
  • The population density of the region is nearly twice that of the national average, and its largely mountainous terrain, including the Tatra Mountains, limits the potential for onshore wind energy development. Furthermore, due to its inland location, the model does not consider offshore wind energy.
  • The use of small modular reactors (SMRs) in Poland remains a topic of debate due to the lack of existing commercial units, uncertain costs, and challenges related to social acceptance.
  • If nuclear technology is excluded from the model, investments shift toward improving energy efficiency and greater imports of electricity.
  • Livestock emissions can be offset through new afforestation efforts.
  • In the building sector, energy consumption could be reduced by 45% between 2020 and 2050.
  • However, in other sectors, achieving efficiency improvements is more challenging. Sector-specific targets alone are insufficient to meet the assumed energy efficiency goals for the region.
  • If the voivodeship does not implement climate policies and targets in the coming years, as illustrated in the Stagnation scenario, it will face significant challenges in maintaining economic competitiveness due to reliance on imported energy and high-carbon-footprint products.
  • The model does not prioritise investments in electricity storage, as the region’s limited renewable energy potential means that most of the energy from these sources is consumed in real time.
It is important to note that models of this kind do not account for social factors, such as individual preferences and behavioural choices. In many cases, decisions are not based solely on cost but also on other considerations, such as convenience. The developed tool will be used to update the Regional Climate and Energy Action Plan for the Małopolska Voivodeship. To ensure optimal solutions, the model and its results are widely consulted with various stakeholder groups. Additionally, the system can be adapted and applied to other regions. The model was validated for the base year 2020. Current plans and legislation underpin the scenarios introduced in the model. The model was developed using the TIMES model generator, a widely used and recognised energy modelling framework that is used by many research groups, including for policy making at EU level.

Author Contributions

Conceptualisation, J.Z., A.W., M.R. and M.P.; methodology, J.Z., A.W. and M.R.; software, J.Z., A.W., M.R. and M.P.; validation, J.Z.; formal analysis, J.Z., M.R. and M.P.; investigation, J.Z.; resources, J.Z., A.W. and M.R.; data curation, J.Z., A.W. and M.R.; writing—original draft preparation, J.Z., A.W. and M.R.; writing—review and editing, J.Z., A.W. and M.R.; visualisation, J.Z. and M.P.; supervision, J.Z., A.W. and W.S.; project administration, J.Z. and W.S.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work received financial support from the funding of AGH University of Science and Technology, Faculty of Energy and Fuels (grant number 16.16.210.476). Part of the research was conducted in the frame of the project “Implementation of the Regional Climate and Energy Action Plan for the Małopolska Voivodeship” LIFE-IP EKOMALOPOLSKA/LIFE19 IPC/PL/000005 co-financed by the LIFE financial instrument within the framework of European Union funds and the National Fund for Environmental Protection and Water Management.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Coal Regions in Transition. Overview 2020; European Investment Bank: Luxembourg, 2020. [Google Scholar]
  2. Pietras-Goc, B.; Motak, E. Assumptions for the Territorial Plan of the Just Transformation of Western Małopolska. Appendix No. 1 to the Information for the Meeting of the Małopolska Province Board on 11 March 2021. 2021. Available online: https://www.malopolska.pl/file/news/2021/2021_03_11_Zal_1_Zalozenia_TPST_MZ.pdf (accessed on 20 February 2025). (In Polish).
  3. UMWM Regional Climate and Energy Action Plan. Annex No. 1 to Resolution No. 228/20 of the Małopolska Province Board of 18 February 2020; Marshal’s Office of the Małopolska Voivodeship: Kraków, Poland, 2020; Available online: https://bip.malopolska.pl/api/files/2311440 (accessed on 5 March 2024). (In Polish)
  4. Sowiżdżał, A.; Zyśk, J. Diagnosis of the Current Situation for the Purposes of Preparing the Regional Climate and Energy Plan for the Małopolska Voivodeship; AGH University of Krakow: Kraków, Poland, 2020. (In Polish) [Google Scholar]
  5. Limpens, G.; Moret, S.; Jeanmart, H.; Maréchal, F. EnergyScope TD: A Novel Open-Source Model for Regional Energy Systems. Appl. Energy 2019, 255, 113729. [Google Scholar] [CrossRef]
  6. Connolly, D.; Lund, H.; Mathiesen, B.V.; Leahy, M. A Review of Computer Tools for Analysing the Integration of Renewable Energy into Various Energy Systems. Appl. Energy 2010, 87, 1059–1082. [Google Scholar] [CrossRef]
  7. Pfenninger, S.; Hirth, L.; Schlecht, I.; Schmid, E.; Wiese, F.; Brown, T.; Davis, C.; Gidden, M.; Heinrichs, H.; Heuberger, C.; et al. Opening the Black Box of Energy Modelling: Strategies and Lessons Learned. Energy Strategy Rev. 2018, 19, 63–71. [Google Scholar] [CrossRef]
  8. Prina, M.G.; Lionetti, M.; Manzolini, G.; Sparber, W.; Moser, D. Transition Pathways Optimization Methodology through EnergyPLAN Software for Long-Term Energy Planning. Appl. Energy 2019, 235, 356–368. [Google Scholar] [CrossRef]
  9. Moret, S.; Codina Gironès, V.; Bierlaire, M.; Maréchal, F. Characterization of Input Uncertainties in Strategic Energy Planning Models. Appl. Energy 2017, 202, 597–617. [Google Scholar] [CrossRef]
  10. Gong, J.W.; Li, Y.P.; Lv, J.; Huang, G.H.; Suo, C.; Gao, P.P. Development of an Integrated Bi-Level Model for China’s Multi-Regional Energy System Planning under Uncertainty. Appl. Energy 2022, 308, 118299. [Google Scholar] [CrossRef]
  11. Liu, Y.; Yu, S.; Zhu, Y.; Wang, D.; Liu, J. Modeling, Planning, Application and Management of Energy Systems for Isolated Areas: A Review. Renew. Sustain. Energy Rev. 2018, 82, 460–470. [Google Scholar] [CrossRef]
  12. Borasio, M.; Moret, S. Deep Decarbonisation of Regional Energy Systems: A Novel Modelling Approach and Its Application to the Italian Energy Transition. Renew. Sustain. Energy Rev. 2022, 153, 111730. [Google Scholar] [CrossRef]
  13. Kavei, F.; Nicoli, M.; Quatraro, F.; Savoldi, L. The Energy Transition in the Age of Open Science: Call for Regional Modelling Solutions 2024. In Proceedings of the CUE2024—Applied Energy Symposium and Forum, Shenzhen, China, 11–13 May 2024. [Google Scholar]
  14. Sahoo, S.; Van Stralen, J.N.P.; Zuidema, C.; Sijm, J.; Faaij, A. Regionally Integrated Energy System Detailed Spatial Analysis: Groningen Province Case Study in the Northern Netherlands. Energy Convers. Manag. 2023, 277, 116599. [Google Scholar] [CrossRef]
  15. Gonocruz, R.A.; Ozawa, A.; Kudoh, Y. Japan’s Energy Transition Scenarios to Achieve Carbon Neutrality under Multiple Energy Service Demand: Energy System Analysis Using the AIST-TIMES Model. Appl. Energy 2025, 383, 125300. [Google Scholar] [CrossRef]
  16. Lund, H.; Mathiesen, B.V. Energy System Analysis of 100% Renewable Energy Systems—The Case of Denmark in Years 2030 and 2050. Energy 2009, 34, 524–531. [Google Scholar] [CrossRef]
  17. Bellocchi, S.; De Iulio, R.; Guidi, G.; Manno, M.; Nastasi, B.; Noussan, M.; Prina, M.G.; Roberto, R. Analysis of Smart Energy System Approach in Local Alpine Regions—A Case Study in Northern Italy. Energy 2020, 202, 117748. [Google Scholar] [CrossRef]
  18. Mimica, M.; Krajačić, G. The Smart Islands Method for Defining Energy Planning Scenarios on Islands. Energy 2021, 237, 121653. [Google Scholar] [CrossRef]
  19. Cormio, C.; Dicorato, M.; Minoia, A.; Trovato, M. A Regional Energy Planning Methodology Including Renewable Energy Sources and Environmental Constraints. Renew. Sustain. Energy Rev. 2003, 7, 99–130. [Google Scholar] [CrossRef]
  20. Cai, Y.P.; Huang, G.H.; Tan, Q.; Liu, L. An Integrated Approach for Climate-Change Impact Analysis and Adaptation Planning under Multi-Level Uncertainties. Part II. Case Study. Renew. Sustain. Energy Rev. 2011, 15, 3051–3073. [Google Scholar] [CrossRef]
  21. Miller, G.C.; Quashnick, J.; Hebert, V. Reaction Rate of Metallic Mercury with Hydroxyl Radical in the Gas Phase. Abstr. Pap. Am. Chem. Soc. 2001, U47, 221. [Google Scholar]
  22. Mutani, G.; Todeschi, V.; Tartaglia, A.; Nuvoli, G. Energy Communities in Piedmont Region (IT). The Case Study in Pinerolo Territory. In Proceedings of the 2018 IEEE International Telecommunications Energy Conference (INTELEC), Turino, Italy, 7–11 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–8. [Google Scholar]
  23. Rabe, M.; Streimikiene, D.; Drożdż, W.; Bilan, Y.; Kasperowicz, R. Sustainable Regional Energy Planning: The Case of Hydro. Sustain. Dev. 2020, 28, 1652–1662. [Google Scholar] [CrossRef]
  24. Lyden, A.; Pepper, R.; Tuohy, P.G. A Modelling Tool Selection Process for Planning of Community Scale Energy Systems Including Storage and Demand Side Management. Sustain. Cities Soc. 2018, 39, 674–688. [Google Scholar] [CrossRef]
  25. Campillo, J.; Dahlquist, E.; Wallin, F.; Vassileva, I. Is Real-Time Electricity Pricing Suitable for Residential Users without Demand-Side Management? Energy 2016, 109, 310–325. [Google Scholar] [CrossRef]
  26. Zhang, H.; Zhai, X.; Zhang, J.; Bai, X.; Li, Z. Mechanism Analysis of the Effect of the Equivalent Proportional Coefficient of Inertia Control for a Doubly Fed Wind Generator on Frequency Stability in Extreme Environments. Sustainability 2024, 16, 4965. [Google Scholar] [CrossRef]
  27. IEA-ETSAP TIMES. Available online: https://iea-etsap.org/index.php/etsap-tools/model-generators/times (accessed on 20 February 2025).
  28. Loulou, R.; Goldstein, G.; Noble, K. Documentation for the MARKAL Family of Models; Energy Technology Systems Analysis Programme: Paris, France, 2004. [Google Scholar]
  29. Amorim, F.; Pina, A.; Gerbelová, H.; Pereira Da Silva, P.; Vasconcelos, J.; Martins, V. Electricity Decarbonisation Pathways for 2050 in Portugal: A TIMES (The Integrated MARKAL-EFOM System) Based Approach in Closed versus Open Systems Modelling. Energy 2014, 69, 104–112. [Google Scholar] [CrossRef]
  30. Balyk, O.; Andersen, K.S.; Dockweiler, S.; Gargiulo, M.; Karlsson, K.; Næraa, R.; Petrović, S.; Tattini, J.; Termansen, L.B.; Venturini, G. TIMES-DK: Technology-Rich Multi-Sectoral Optimisation Model of the Danish Energy System. Energy Strategy Rev. 2019, 23, 13–22. [Google Scholar] [CrossRef]
  31. Loulou, R.; Goldstein, G.; Kanudia, A.; Lettila, A.; Remme, U. Documentation for the TIMES Model. PART I 2016; Energy Technology Systems Analysis Programme: Paris, France, 2016. [Google Scholar]
  32. Loulou, R.; Lettila, A.; Kanudia, A.; Remme, U.; Goldstein, G. Documentation for the TIMES Model. PART II 2016; Energy Technology Systems Analysis Programme: Paris, France, 2016. [Google Scholar]
  33. Gargiulo, M. Getting Started with TIMES-VEDA; Energy Technology Systems Analysis Programme: Paris, France, 2009. [Google Scholar]
  34. Muehlich, P.; Hamacher, T. Global Transportation Scenarios in the Multi-Regional EFDA-TIMES Energy Model. Fusion Eng. Des. 2009, 84, 1361–1366. [Google Scholar] [CrossRef]
  35. Krakowski, V.; Assoumou, E.; Mazauric, V.; Maïzi, N. Feasible Path toward 40–100% Renewable Energy Shares for Power Supply in France by 2050: A Prospective Analysis. Appl. Energy 2016, 171, 501–522. [Google Scholar] [CrossRef]
  36. Panos, E.; Kober, T.; Wokaun, A. Long Term Evaluation of Electric Storage Technologies vs Alternative Flexibility Options for the Swiss Energy System. Appl. Energy 2019, 252, 113470. [Google Scholar] [CrossRef]
  37. Danyang, L.; Wenying, C. Prospective Influences of the Substitution of Electric Vehicles for Liquid Vehicles: TIMES Modeling of the Global Energy System. Energy Procedia 2019, 158, 3782–3787. [Google Scholar] [CrossRef]
  38. Kimuli, I.; Goldstein, G.; Lubwama, M.; Kirabira, J.B.; Sebbit, A. Energy Scenarios for Greater Kampala Metropolitan Area towards a Sustainable 2050: A TIMES-VEDA Analysis. Smart Energy 2023, 10, 100099. [Google Scholar] [CrossRef]
  39. Allena-Ozolina, S.; Pakere, I.; Jaunzems, D.; Freimanis, R.; Blumberga, A.; Bazbauers, G. Passenger Transport Shift to Green Mobility—Assessment Using TIMES Model. Environ. Clim. Technol. 2022, 26, 341–356. [Google Scholar] [CrossRef]
  40. Allena-Ozolina, S.; Pakere, I.; Jaunzems, D.; Blumberga, A.; Bazbauers, G. Integrated MARKAL-EFOM System (TIMES) Model for Energy Sector Modelling. In Proceedings of the 2020 IEEE 61th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia, 5–7 November 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1–7. [Google Scholar]
  41. Tortorella, M.M.; Di Leo, S.; Cosmi, C.; Fortes, P.; Viccaro, M.; Cozzi, M.; Pietrapertosa, F.; Salvia, M.; Romano, S. A Methodological Integrated Approach to Analyse Climate Change Effects in Agri-Food Sector: The TIMES Water-Energy-Food Module. Int. J. Environ. Res. Public Health 2020, 17, 7703. [Google Scholar] [CrossRef]
  42. Allena-Ozolina, S.; Jaunzems, D.; Pakere, I.; Blumberga, A.; Bazbauers, G. Decarbonisation Pathways of Industry in TIMES Model. Environ. Clim. Technol. 2021, 25, 318–330. [Google Scholar] [CrossRef]
  43. Wyrwa, A.; Suwała, W.; Pluta, M.; Raczyński, M.; Zyśk, J.; Tokarski, S. A New Approach for Coupling the Short- and Long-Term Planning Models to Design a Pathway to Carbon Neutrality in a Coal-Based Power System. Energy 2021, 239, 122438. [Google Scholar] [CrossRef]
  44. Pluta, M.; Wyrwa, A.; Zyśk, J.; Suwała, W.; Raczyński, M. Scenario Analysis of the Development of the Polish Power System towards Achieving Climate Neutrality in 2050. Energies 2023, 16, 5918. [Google Scholar] [CrossRef]
  45. Zyśk, J.; Wyrwa, A.; Raczyński, M.; Pluta, M.; Michalska, S.; Wyrwa, E.; Olkuski, T.; Suwała, W. Energy and Emission Balance of the Małopolskie Voivodeship in 2020. Min. Inform. Autom. Electr. Eng. 2023, 1, 7–12. [Google Scholar] [CrossRef]
  46. Sowiżdżał, A.; Tomaszewska, B.; Pająk, L.; Kaczmarczyk, M.; Luboń, W.; Pełka, G.; Hałaj, E.; Hajto, M.; Brawiak, K.; Chmielowska, A.; et al. Assessment of the Renewable Energy Potential in the Małopolska Region; AGH University of Krakow: Krakow, Poland, 2022. (In Polish) [Google Scholar]
  47. Conclusions from Forecast Analyses for the Energy Sector; Ministry of Climate and Environment: Warsaw, Poland, 2021. (In Polish)
  48. MCE Scenario 3. For Pre-Consultation on the Update of KPEIK/PEP2040. Analysis for the Power Sector Taking into Account the Change of the Political and Economic Situation After the Russian Invasion of Ukraine; Ministry of Climate and Environment: Warsaw, Poland, 2023. (In Polish)
  49. Polish Energy Policy Until 2040. 2021. Available online: https://www.gov.pl/web/climate/energy-policy-of-poland-until-2040-epp2040 (accessed on 3 June 2022). (In Polish)
  50. Pluta, M.; Wyrwa, A.; Suwała, W.; Zyśk, J.; Raczyński, M.; Tokarski, S. A Generalized Unit Commitment and Economic Dispatch Approach for Analysing the Polish Power System under High Renewable Penetration. Energies 2020, 13, 1952. [Google Scholar] [CrossRef]
  51. GUS. Statistical Yearbook of the Republic of Poland 2020; Central Statistical Office: Warsaw, Poland, 2020. (In Polish) [Google Scholar]
  52. Population Forecast for the Małopolska Voivodeship until 2050; The Małopolska Regional Research Centre and the Statistical Office in Kraków: Krakow, Poland, 2015.
  53. GUS. Statistical Yearbook of the Małopolska Voivodeship; Editorial Team of the Statistical Office in Krakow: Krakow, Poland, 2021. (In Polish) [Google Scholar]
  54. Potyra, M. Household Forecast for 2016–2050; Central Statistical Office, Department of Demographic and Labor Market Research: Warsaw, Poland, 2016. (In Polish) [Google Scholar]
  55. GUS. Energy Consumption in Households—Estimated Data for 2020; Central Statistical Office: Warsaw, Poland, 2020. (In Polish) [Google Scholar]
  56. GUS. Housing and Municipal Infrastructure in 2020; Central Statistical Office: Warsaw, Poland, 2021. (In Polish) [Google Scholar]
  57. Institut Wohnen und Umwelt GmbH. Residential Building Typology. Available online: https://episcope.eu/building-typology/country/pl/ (accessed on 3 June 2022).
  58. Chiodi, A.; De Miglio, R.; Gargiulo, M.; Kanudia, A.; Nijs, W.; Politis, S.; Ruiz Castello, P.; Zucker, A. JRC-EU-TIMES 2017 Upgrade—Buildings and Heating and Cooling Technologies; Publications Office of the European Union: Luxembourg, 2017. [Google Scholar]
  59. Szczukocka, A. Statistical Assessment of the Importance of the Services Sector in the Economy; Publishing House of University of Łódź: Łódź, Poland, 2013. [Google Scholar]
  60. Nijs, W.; Ruiz, P. 01_JRC-EU-TIMES Full Model. 2019. Available online: https://data.jrc.ec.europa.eu/dataset/8141a398-41a8-42fa-81a4-5b825a51761b (accessed on 3 June 2022).
  61. Peryt, S.; Wnuk, R.; Berent-Kowalska, G.; Nowakowski, P. Energy Efficiency 2010–2020; Statistics Poland: Warsaw, Poland, 2022. [Google Scholar]
  62. Main Office of Building Supervision Central Register of Building Emissions. Available online: https://www.gunb.gov.pl/podmenu/1713 (accessed on 8 June 2022).
  63. Minister of Transport, Construction and Maritime Economy Regulation of the Minister of Transport, Construction and Maritime Economy of 5 July 2013 Amending the Regulation on Technical Conditions to Be Met by Buildings and Their Location; Journal of Laws of The Republic of Poland: Warsaw, Poland, 2013. Available online: https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU20130000926/O/D20130926.pdf (accessed on 5 June 2023). (In Polish)
  64. GUS. The Situation of Households in 2020 in the Light of the Results of the Household Budget Survey; Central Statistical Office: Warsaw, Poland, 2021. (In Polish) [Google Scholar]
  65. GUS. Household Budgets in 2020; Central Statistical Office: Warsaw, Poland, 2020. (In Polish) [Google Scholar]
  66. Waśkiewicz, J.; Pawlak, P. Expert Forecasts of Changes in the Road Transport Sector Activity (in the Context of the Act on the Greenhouse Gas and Other Substances Emission Management System) Tabulated List; Motor Transport Institute: Warsaw, Poland, 2017. (In Polish) [Google Scholar]
  67. Eurostat Passenger Road Transport on National Territory, by Type of Vehicles Registered in the Reporting Country. Available online: https://ec.europa.eu/eurostat/databrowser/view/ROAD_PA_MOV/default/table?lang=en%20 (accessed on 20 June 2020).
  68. Eurostat Railway Transport Measurement—Passengers. Available online: https://ec.europa.eu/eurostat/databrowser/view/rail_pa_quartal/default/table?lang=en (accessed on 1 September 2022).
  69. Eurostat Goods Transported by Type of Transport. Available online: https://ec.europa.eu/eurostat/databrowser/view/RAIL_GO_TYPEPAS__custom_3599985/default/table?lang=en (accessed on 5 June 2022).
  70. GUS Central Statistical Office, Local Data Bank. Available online: https://bdl.stat.gov.pl/bdl/dane/podgrup/temat (accessed on 20 January 2024).
  71. State of the City Report 2020; Krakow City Hall Department of Strategy, Planning and Investment Monitoring Department of Strategic Planning and Analysis: Krakow, Poland, 2021. (In Polish)
  72. European Commission TREMOVE: An EU-Wide Transport Model. Available online: https://ec.europa.eu/environment/archives/air/models/tremove.htm (accessed on 12 February 2022).
  73. Road Transport in Poland in 2018 and 2019; Central Statistical Office: Warsaw, Szczecin, 2021. (In Polish)
  74. Eurostat Road Traffic by Type of Vehicle (Million Vkm). Available online: https://ec.europa.eu/eurostat/databrowser/view/road_tf_road/default/table?lang=en&category=road.road_tf (accessed on 26 March 2025).
  75. Zieliński, J.; Tutka, P.; Kunikowski, P.; Szyszło, A. Synthesis of the Results of the 2020/21 GPR on the Rural National Road Network; General Directorate for National Roads and Motorways, Heller Consult sp. z o.o.: Warsaw, Poland, 2021. (In Polish) [Google Scholar]
  76. Zieliński, J.; Tutka, P.; Kunikowski, P.; Szyszło, A. Synthesis of the Results of the 2020/21 GPR on the Provincial Road Network Outside the City; General Directorate for National Roads and Motorways, Heller Consult sp. z o.o.: Warsaw, Poland, 2021. (In Polish) [Google Scholar]
  77. Railways in the Voivodeships—Use and Transport Policy; Office of Rail Transport: Warsaw, Poland, 2019. (In Polish)
  78. Eurostat Summary of Quarterly Road Freight Transport by Type of Operation and Type of Transport. Available online: https://ec.europa.eu/eurostat/databrowser/view/road_go_tq_tott/default/table?lang=en (accessed on 2 February 2022). (In Polish).
  79. International Council on Clean Transportation. Available online: https://theicct.org/ (accessed on 21 March 2025).
  80. Gillingham, K. Carbon Calculus. For Deep Greenhouse Gas Emission Reductions, a Long-Term Perspective on Costs Is Essential. In Finance & Development; International Monetary Fund: Washington, DC, USA, 2019. [Google Scholar]
  81. Jin, Y.; Chen, B. Comparison of Potential CO2 Reduction and Marginal Abatement Costs across in the China and Korea Manufacturing Industries. J. Innov. Knowl. 2022, 7, 100172. [Google Scholar] [CrossRef]
  82. Lameh, M.; Al-Mohannadi, D.M.; Linke, P. Minimum Marginal Abatement Cost Curves (Mini-MAC) for CO2 Emissions Reduction Planning. Clean Technol. Environ. Policy 2022, 24, 143–159. [Google Scholar] [CrossRef]
  83. Ocena Potencjału Redukcji Emisji Gazów Cieplarnianych w Polsce Do Roku 2030. Podsumowanie; McKinsey & Company Poland: Warsaw, Poland, 2009.
  84. Peryt, S. Trendy Efektywności Energetycznej Polskiej Gospodarki z Wykorzystaniem Narzędzi ODYSSEE; GUS: Warsaw, Poland, 2021. [Google Scholar]
  85. Engel, H.; Purta, M.; Speelman, E.; Szarek, G.; Pluijm, P. Carbon-Neutral Poland 2050; McKinsey & Company: Warsaw, Poland, 2020. [Google Scholar]
  86. Zibunas, C.; Meys, R.; Kätelhöna, A.; Bardowa, A. Cost-Optimal Pathways towards Net-Zero Chemicals and Plastics Based on a Circular Carbon Economy. Comput. Chem. Eng. 2022, 162, 107798. [Google Scholar] [CrossRef]
  87. Agricultural Census 2020; Central Statistical Office: Warsaw, Poland, 2023. (In Polish)
  88. Livestock in 2020; Central Statistical Office: Warsaw, Poland, 2021. (In Polish)
  89. Dong, H.; Mangino, J.; McAllister, T.A.; Hatfield, J.L.; Johnson, D.E.; Lassey, K.R.; Aparecida de Lima, M.; Romanovskaya, A. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. Chapter 10: Emissions from Livestock and Manure Managemen; IPCC: Geneva, Switzerland, 2006. [Google Scholar]
  90. Facts about Fur Farming in Poland; Association of Entrepreneurs and Employers: Warsaw, Poland, 2020.
  91. KOBIZE A Package of Tables with Data on Greenhouse Gas Emissions in the Common Reporting Format (CRF); It Contains Detailed Emission Data for the Years 1988–2021. The Data Was Reported to the UNFCCC Secretariat on 28.03.2023; National Centre for Emission Balancing and Management: Warsaw, Poland, 2023.
  92. EC. EU Agricultural Outlook for Markets, Income and Environment, 2021–2031; European Commission, DG Agriculture and Rural Development: Brussels, Belgium, 2021. [Google Scholar]
  93. National Afforestation Programme; Ministry of the Environment: Warsaw, Poland, 2023. (In Polish)
  94. UMWM Air Protection Program for the Małopolska Province. Małopolska in a Healthy Atmosphere. Annex No. 2 to Resolution No. XXV/373/20 of the Małopolska Regional Assembly of 28 September 2020; Official Journal of the Małopolska Voivodeship: Warszawa, Poland, 2020. Available online: https://edziennik.malopolska.uw.gov.pl/WDU_K/2020/6337/akt.pdf (accessed on 5 January 2024). (In Polish)
  95. MIiS. Polish Hydrogen Strategy until 2030, Annex to Resolution No. 149 of the Council of Ministers of 2 November 2021; Ministry of Climate and Environment: Warsaw, Poland, 2021. (In Polish) [Google Scholar]
  96. MAP. National Energy and Climate Plan 2021–2030. Assumptions and Goals, Policies and Actions; Ministry of State Assets: Warsaw, Poland, 2019. [Google Scholar]
  97. MVS. Development Strategy of the Voivodeship “Małopolska 2030”. Annex to Resolution No. XXXI/422/20 of the Regional Assembly of the Małopolska Voivodeship of 17 December 2020; Małopolska Voivodeship Sejmik: Kraków, Poland, 2020. [Google Scholar]
  98. Long-Term Building Renovation Strategy. Supporting the Renovation of the National Building Stock. Annex to Resolution No. 23/2022 of the Council of Ministers of 9 February 2022. 2022. Available online: https://energy.ec.europa.eu/system/files/2022-06/PL%202020%20LTRS%20_%20EN%20version.pdf (accessed on 15 February 2022).
  99. European Commission PRIMES Energy System Model. Available online: https://web.jrc.ec.europa.eu/policy-model-inventory/explore/models/model-primes (accessed on 15 February 2022).
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Figure 1. General diagram of the TIMES-Malopolska model.
Figure 1. General diagram of the TIMES-Malopolska model.
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Figure 2. CO2 emissions in sectors according to the scenarios analysed.
Figure 2. CO2 emissions in sectors according to the scenarios analysed.
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Figure 3. CH4 emissions from various processes (manure management, enteric fermentation, and field burning of agricultural residue processes are represented in the agricultural sector, wastewater treatment and landfill processes are represented in the economy sector) and senator (building) according to the scenarios analysed.
Figure 3. CH4 emissions from various processes (manure management, enteric fermentation, and field burning of agricultural residue processes are represented in the agricultural sector, wastewater treatment and landfill processes are represented in the economy sector) and senator (building) according to the scenarios analysed.
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Figure 4. Annual electricity production by fuel/technology and electricity imports according to the scenarios analysed.
Figure 4. Annual electricity production by fuel/technology and electricity imports according to the scenarios analysed.
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Figure 5. Annual energy carriers’ consumption by transport sector according to the scenarios analysed.
Figure 5. Annual energy carriers’ consumption by transport sector according to the scenarios analysed.
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Figure 6. Annual investment costs according to the scenarios analysed.
Figure 6. Annual investment costs according to the scenarios analysed.
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Table 1. Characteristics of selected new technologies for the production of electricity and heat [44,48,49,50]. PC—pulverised coal; FBC—fluidised bed combustion; CHP combined heat and power; OCGT—open cycle gas turbine; CCGT—combined cycle gas turbine; CCS—carbon capture and storage; SMR—small modular reactor. * The first value indicates the investment/fixed cost in the first year in which the technology can be put into use (Start), the second the investment, or fixed cost in 2050.
Table 1. Characteristics of selected new technologies for the production of electricity and heat [44,48,49,50]. PC—pulverised coal; FBC—fluidised bed combustion; CHP combined heat and power; OCGT—open cycle gas turbine; CCGT—combined cycle gas turbine; CCS—carbon capture and storage; SMR—small modular reactor. * The first value indicates the investment/fixed cost in the first year in which the technology can be put into use (Start), the second the investment, or fixed cost in 2050.
Fuel/
Technology		StartInvestment CostsFixed CostVariable Costs (Fuel Costs Excluded)Net EfficiencyTechnical LifespanCO2 Emission Factor
YearEUR/kWEUR/kWEUR/kW%Yearskg/GJ
Hard coal—PC20251658440.89464094.19
Hard coal—PC + CCS20353014751.44384011.30
Hard coal—IGCC20252261581.39484094.19
Hard coal—IGGC + CCS20353265792.03404011.30
Hard coal—CHP20252261480.8930/804094.19
Hard coal—CHP + CCS20353517762.7922/754012
Natural gas—CCGT2025754180.5158–623055.82
Natural gas—CCGT + CCS20351357391.1350–52306.70
Natural gas—OCGT2025502160.39403055.82
Natural gas—CHP CCGT20251013250.3634/803055.82
Nuclear—SMR (with heat recovery)20305523–4380 *881.7133/60600
Wind onshore20251367–1107 *35–31 *––250
PV
(roof)		2025818–647 *10–8 *––250
PV (ground)2025758–593 *16––250
Hydro (small)2025197675––600
Biomass—CHP20252915–2718 *1210.8930/80300
Biogas—CHP202526631080.5737/85250
Hard coal—heating boiler202535210.39903094.19
Natural gas—heating boiler202515110.02963055.82
Heating oil—heating boiler202520110.14953074.10
Biomass—heating boiler202550210.0990300
Electricity—heating boiler202511810.1199300
Electricity—large-scale heat pump202582710.11350200
Geothermal energy—heating plant2025125310.1195400
Table 2. Characteristics of selected new electricity storage technologies [44,48,49,50]. CAES—compressed air energy storage. * The first value indicates the investment cost in the first year in which the technology can be put into use (Start), the second the investment cost in 2050. ** The devices work together, i.e., an electrolyser produces hydrogen for storage needs, and the fuel cell is powered only by stored hydrogen.
Table 2. Characteristics of selected new electricity storage technologies [44,48,49,50]. CAES—compressed air energy storage. * The first value indicates the investment cost in the first year in which the technology can be put into use (Start), the second the investment cost in 2050. ** The devices work together, i.e., an electrolyser produces hydrogen for storage needs, and the fuel cell is powered only by stored hydrogen.
Fuel of TechnologyStartInvestment CostsFixed CostVariable Costs (Fuel Costs Excluded)Net EfficiencyTechnical Lifespan
yearEUR/GJEUR/GJEUR/GJ%years
Lithium-ion battery202592,700–39,730 *––9010
Lead–acid battery202555,620–50,323 *––8010
Compressed air energy storage202534,961–29,664 *––5560
Pumped-storage hydroelectricity2025110,741––7560
Compressed hydrogen storage **20252877––8530
yearEUR/kWEUR/kWEUR/GJ%years
Electrolyzer—hydrogen production **2025558–200 *811.866230
Hydrogen cells (without heat recovery) **202530,023–698 *15144.184410
Table 3. Storyline and main objectives for the scenarios considered.
Table 3. Storyline and main objectives for the scenarios considered.
StagnationNationalMałopolskaOptimistic
Climate
No climate targets2030: CO2 emission reduction for non-ETS sectors 7% compared to year 2005 2030: Reducing GHG at least 40% (compared to year 1990)
2050: climate neutrality		2030: Reducing GHG by at least 55% (compared to year 1990),
2050: climate neutrality
Energy efficiency
2030: Remaining unchanged energy consumption at the 2020 level2030: Improvement of energy efficiency 23% in relation to primary energy consumption compared to PRIMES 2007 forecast2030: Improvement of energy efficiency by 32.5% in relation to primary energy consumption compared to PRIMES 2007 forecast2030: At least 40% improvement in energy efficiency compared to PRIMES 2007 forecast
Renewable energy sources
2030: Share of RES up to 20% of gross final energy consumption2030: Share of RES at least 23.5% of gross final energy consumption2030: Share of RES at least 32% of gross final energy consumption2030: Share of RES at least 40% of gross final energy consumption
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Zyśk, J.; Wyrwa, A.; Raczyński, M.; Pluta, M.; Suwała, W. Regional-Scale Energy Modelling for Developing Strategies to Achieve Climate Neutrality. Energies 2025, 18, 1787. https://doi.org/10.3390/en18071787

AMA Style

Zyśk J, Wyrwa A, Raczyński M, Pluta M, Suwała W. Regional-Scale Energy Modelling for Developing Strategies to Achieve Climate Neutrality. Energies. 2025; 18(7):1787. https://doi.org/10.3390/en18071787

Chicago/Turabian Style

Zyśk, Janusz, Artur Wyrwa, Maciej Raczyński, Marcin Pluta, and Wojciech Suwała. 2025. "Regional-Scale Energy Modelling for Developing Strategies to Achieve Climate Neutrality" Energies 18, no. 7: 1787. https://doi.org/10.3390/en18071787

APA Style

Zyśk, J., Wyrwa, A., Raczyński, M., Pluta, M., & Suwała, W. (2025). Regional-Scale Energy Modelling for Developing Strategies to Achieve Climate Neutrality. Energies, 18(7), 1787. https://doi.org/10.3390/en18071787

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