The European Union’s Energy Policy Efforts Regarding Emission Reduction in Cities—A Method Proposal

Abstract

Decisions regarding waste and emission management systems are subject to many sustainability criteria, including environmental, social, and economic criteria. The problem is the multidimensionality of the energy transformation and its reading from different perspectives. This article aims to assess the effectiveness of the municipal energy policy. The VIKOR multicriteria analysis approach to modeling and Criteria Importance Through Intercriteria Correlation were chosen for the method. The approach made it possible to create a ranking and choose a compromise solution. The analyses were carried out for four areas of intervention (ETS tariffs), in which a set of four general criteria and twelve specific criteria were distinguished, and based on the weights assigned, rankings were created highlighting the activities that have the greatest impact on low emission in urban areas. Based on the analyses, it was found that the most significant impact on reducing emissions in urban areas has led to decisions to increase investments in renewable energy sources and promote the reduction in household energy consumption.

1. Introduction

Background

In support of the fight against climate change, the European Union (EU) has set ambitious new targets for reducing greenhouse gas emissions. The EU wants to achieve climate neutrality by 2050, and this goal is enshrined in European climate law and the interim target of reducing emissions by 55% by 2030. The most effective ways to reduce greenhouse gas emissions in all partner countries should be selected to achieve the goal. Reducing air pollution and reducing greenhouse gas emissions are usually closely related. Actions and policies to combat environmental pollution are having an effect (the number of Europeans dying prematurely due to poor air quality is halved compared to the early 1990s) [1]. European industry is becoming greener and causing less pollution of air and water, and advanced wastewater treatment methods are increasingly widely used [2]. Through the provisions of the climate policy of individual Member States, the European Union strives to generate heat and electricity and move and produce food withoutharmful emissions of pollutants that affect people and the environment, as an inevitable by-product of development. The low emission dependency model is shown in Figure 1.

Achieving ambitious environmental goals and ensuring secure and affordable energy for current and future generations require clear strategies across all energy sectors [3]. The targets cover various sectors of the economy (

Table 1. Sectors of greenhouse gas emitters in the EU, Source: report “Trends and projections in Europe 2021, 2022” [1,2] [own elaboration].

The Name of the Economic Sector	Emission Reduction (Goal Achieved)	EU Emissions Trading Scheme (EU ETS)	CO2 Reduction in Million Tons
Energy systems	yes	yes	657
Construction (energy for heating/cooling buildings)	yes	no	215
Industry	yes	yes	332
Transportation	no	no	50
Agriculture	yes	no	100
Change of usage	yes	no	-
Waste	yes	no	-

), including heating and cooling as well as transport, which contribute significantly to energy consumption, greenhouse gas emissions, and local pollution. In addition, in most countries, they relate to primary energy, i.e., to a large extent fossil fuels, and are probably more challenging to decarbonize than electricity.

Because the EU partner countries deal with emission reductions and their implementation differently, different paths and approaches should be distinguished, which in a given country enable the best reduction effect to be achieved [4,5]. Among the initiatives the EU took to accomplish these goals is the Effort Sharing Regulation, which is being updated as part of the Fit for 55 legislative packages. According to data from the European Statistical Office Eurostat [6], greenhouse gas emissions in EU countries have fallen over the last three decades in seven out of eight EU sectors. The data relate to 1990–2020—when the European Union reduced its emissions by the equivalent of more than one and a half million tonnes of CO₂ [6] and achieved the emission targets set by 2020. Many countries have already completed the reduction target (or even exceeded it), but Poland is not one of them [2]. In this context, it is advisable to undertake research and analyses indicating the most favorable development paths and their recommendation in countries that have not achieved the assumed goals.

In carrying out the research assumptions, the authors identified the activities with the greatest potential for reducing pollutant emissions, supported by data from publicly available European statistical databases. This identification made it possible to pose research questions, presented in detail later in the article, regarding the possibility of formulating provisions in program documents conducive to taking measures to increase emission reduction. In this context, it is also worth mentioning the activities related to reforming the ETS system, which is part of the published Emissions Trading System (EU ETS). This system is to implement new tools to support the Old Continent in reducing greenhouse gas emissions by at least 55% by 2030 (compared to 1990). Everything indicates that CO₂ emissions charges will be the most crucial tool to achieve the 55% target emission reductions in 2030 (fit for 55). Therefore, the steps are related to the reform of the EU, ETS, and the EEA to cover emissions from transport and buildings with new CO₂ charges. The purpose of this system will be to encourage investment to reduce emissions and avoid paying for allowances [7]. In this context, unfortunately, Poland may quickly lose its competitiveness. Therefore, in addition to compensation for energy-intensive industries, all proceeds from the sale of CO₂ emissions should already be allocated to reducing emissions and fighting energy poverty.

By creating plans, decision makers and communal and municipal authorities want to prepare the best strategy for achieving their goals, using existing resources while taking care of the environment and quality of life [8,9,10,11,12]. The provisions in the plans present the concept of measures and conditions to reduce greenhouse gas emissions from the city area [13]. The increase in energy consumption from renewable energy sources and the reduction in CO₂ emissions are essential elements of sustainable development for the European Union countries, including Poland. The importance of this subject is evidenced by the fact that EU countries strive to achieve goals that are often contradictory, such as the simultaneous reduction in CO₂ emissions and environmental protection, along with the fulfillment of stricter environmental standards and development related to increasing energy consumption from renewable sources.

When making decisions of a complex nature, the decision maker most often uses not one but many criteria, such as establishing a set of criteria determining the achievement of the values of the assumptions included in the low-emission economy plans, in the form of slogans of programs that financially support the directions of the sustainable development of individual cities. Optimization often concerns maximizing one or several preferences while minimizing the risk of the choice [14]. When faced with realistic decision-making dilemmas, the decision maker usually gives his subjective decisions specific reference points when assessing them [15]. Therefore, the proposed approach to evaluate energy policy measures was based on using a broad spectrum of real data and decision criteria arranged in several layers. This article’s contribution to research in the field of energy policy, therefore, covers several contexts:

-

Analyzes changes in the reduction in emissions and energy consumption in the EU and Poland and the introduction of RES in these areas in 2014–2020;

-

Proposes a combination of the method of determining weights and evaluating solutions in terms of multiple criteria;

-

Identifies activities with the most significant potential for reducing emissions in urban areas and proposing a possible development path, allowing for more effective support for energy policy.

2. Literature Review

Urban and suburban areas currently contain over 75% of Europe’s population [16]. In comparison, cities account for two-thirds of the world’s energy consumption [17] and about 75% of energy-related CO₂ emissions [18]. The pursuit of the sustainable development of cities and the search for solutions to reduce CO₂ emissions are the fundamental directions in the documents of EU countries. These activities are consistent with controlling and combating climate change, which is now the overriding goal [19]. Reducing CO₂ emissions plays a leading role in the current dispute about environmental protection and sustainable development. Still, it must be associated with the simultaneous support of economic growth [20]. Possible solutions include investments in renewable energy sources, directing activities toward the green economy [21,22,23], and a constant reduction in energy consumption by introducing modern technologies and properly shaping energy policy [24,25]. A comprehensive approach that focuses on new ventures and already implemented projects needing modernization, especially in the public utility sector, can be a significant step toward achieving the set objectives [26,27]. An appropriate low-emission policy, implemented at the local level, should activate the inhabitants to take actions aimed at achieving benefits by improving the condition of the environment [28,29,30]. Responsible urban policy necessitates implementing direct measures, achieved through the collaboration of multiple institutions at various levels, to facilitate proper modernization and enhance current buildings’ energy efficiency [31].

The rapid urbanization of cities has forced the need to solve energy problems through provisions in various documents and the improvement of technological solutions. Tools to support decision making enable a comprehensive policy on energy use and the availability of investment financing [32,33]. Many authors point to the fact that a proper assessment of the potential consequences of the choices can support the decision-making process in preventing climate change by introducing devices for obtaining renewable energy [34,35]. Sustainable city management is defined as balancing the emphasis on biodiversity and the functional needs of the community combined with optimizability and financial feasibility.

Public participation and consultation are becoming essential in spatial planning and decision-making systems, especially in issues related to pollution reduction. Promoting sustainable development and low-emission solutions is a challenge for expanding knowledge between society, user preferences, and the environment [36,37]. Differences in society regarding the perception of the world depend on the level of knowledge transferred and the development of information and communication technologies (ICT) created primarily to support decision makers [38,39]. The publication [40] describes how GDP growth and urbanization can reduce CO₂ emissions by properly selecting heating systems or investments in renewable energy sources [41].

Other studies show the creation of simulation tools for spatial planning [42]. Such visualization technologies are excellent for presenting results and supporting environmental planning models. The energy behavior of Polish consumers, especially their motivations and attitudes, was examined by [43]. The authors conducted an expert segmentation of respondents in terms of their incentives to save energy based on the results of their study. The psychological and financial factors were the most important for the respondents. Research on predicting the assessment of sustainable development at the stage of designing new facilities and their use in the decision-making process is already introduced to the state of the art, which opens up new possibilities for practical applications [44,45]. For this reason, the publication’s authors indicate the importance of developing techniques for assessing socio-economic systems, especially considering trends and individual and social preferences and trying to determine the quantity and quality of the consumption environment using the market price. The article by Azaza et al. [46] described how dynamic visualization of energy mapping can improve the understanding of energy flows (including the choice of heating systems, investing in energy efficiency measures and renewable energy sources) and help stakeholders plan the development of a new area through the construction of commercial or residential buildings.

The global scope of markets for energy, commodities, money, and environmental reserves, as well as people’s mobility and production systems, requires that international politics become increasingly powerful, coordinated, and whole. Uğurlu et al. [47], comparing recent progress in renewables, found that some EU countries have exceeded their national 2020 targets, while others must make additional efforts. The most significant change in electricity generation is in solar energy technology, the cost of which is falling. In conclusion, the EU’s success factors in achieving the 2020 targets indicated the benefits of increasing the use of renewable energy and improving energy efficiency policy efforts. Goals explain the course of action, clearly identifying the dimensions that make up the overall goal. Each goal is multidimensional and complex, and their relationship is weak or strong.

Nevertheless, the objectives are closely related, and progress toward one will likely reflect progress toward the other. Brahmi et al. [48], in a review article, examined the role that technological innovation plays in the financial enablement of achieving climate neutrality. The increase in multidimensionality resulting from the loss of coherence makes the policy objectives much more difficult to achieve. Aldieri et al. [49] determined the impact of climate change on agricultural productivity, proving that land use spillover harms the efficiency index. Whereas the range of activities and their increasing spectrum, often unrelated, increase the risk of failure to achieve goals and the effort of all stakeholders [50]. Esposito and Brahmi [51] assessed the contribution of innovation to decarbonization and the fight against the climate crisis, paying particular attention to the role of the renewable energy community. They are among the measures that can guarantee environmentally friendly wealth, aimed at maximizing profit and creating economically sustainable models [51].

Decisions regarding low-emission pollution management systems are subject to many sustainability criteria, including environmental, social, and economic criteria [52,53]. With the current state of knowledge, complex decision problems can be solved using mathematical and statistical methods and economic theories with the support of techniques and information technologies that allow for automatic calculation and estimation of the solution to decision problems [54,55]. One of the most accurate and widely used decision-making methods is multicriteria decision-making (MCDM), which has a variety of applications in various disciplines and areas, from economics and finance to engineering design and medicine [56]. Torkayesh et al. [57] presented the application of MCDM methods to waste management problems and normalization and weighting methods to transform the environmental impact categories for scenarios. The related LCA and MCDM approaches, relevant sustainability criteria, and standards have enabled a set of recommendations for conducting an integrated LCA–MCDM study. Furthermore, MCDM is widely used in risk-based decision making [58] and can be used to prioritize among multiple alternatives and access the best solution.

The stages of MCDM methods may vary depending on the algorithms used. Still, the main elements of the approach are consistent for all. They identify the criteria adopted for the evaluation, determine the criteria weights, and aggregate the obtained solutions and their hierarchization [59]. One of the weighting methods used in this work is the Criteria Importance Through Intercrieria Correlation (CRITIC) method. That is a popular method of determining weights for individual criteria, which is an objective method that considers the intensity of differences between the criteria [58,60]. The idea of the method is based on two main assumptions, the first of which is to determine the intensity of the contrast in the decision model between the possible decisions. The second assumption is the assessment of contradictory relationships between decision criteria. At the same time, it is assumed that qualitative attributes need not be independent [60]. The above procedure was used in the article [61] to identify the optimal configuration of the autonomous elevator/PV/hydrogen system using the MCDM method to determine the weight of the three goals to be assessed. In turn, Wu et al. evaluated the safety of urban transport operations based on the CRITIC method. Their research found that it is an effective tool for planning and managing operational safety for urban rail transport [62]. Other examples of using the method of determining the CRITIC weights are in the analysis and assessment of hydrological safety in mines [63], the selection of industrial waste management techniques [64], and a comprehensive evaluation of the safety of trains under the influence of a hydroelectric power plant [65]. The method was also used to identify the best ways to solve the problem of the limited use of renewable energy in public buildings [66]. The thesis [67] proposed the CRITIC method to select RES system solutions. The authors emphasize that this approach clarifies the ambiguity in the evaluation process, provides insight into multicriteria decision-making problems, and may help decision makers to understand the evaluation accuracy.

One of the methods for the multicriteria evaluation of complex decision-making problems is the VIKOR method [68]. Similarly to TMAI or TOPSIS, it is based on measuring the distance of the tested variant from the ideal scenario. The VIKOR method makes it possible to determine the compromise ranking of variants, the optimal compromise solution, and an assessment of the stability of the compromise solution determined based on the original set weights [15]. The method can use “positive ideal solution (PIS)” and “negative ideal solution (NIS)” as benchmarks. PIS and NIS are created directly from the evaluation scores (ERs) of all alternatives based on their various attributes, as presented by Mishra et al. in [69]. They developed a novel approach to multi-attribute decision-making (MADM) based on fragmented undecided fuzzy sets (FHFS) and a modified VIKOR method to deal more effectively with practical MADM applications. In the approach of Hadi et al. [70] and in the work of Senapati et al. [71], the presented MADMs have disadvantages in that they do not distinguish the ranked order of the alternatives and cannot achieve a good, ranked order—hierarchical in some situations. An integrated simulation model combining the Monte Carlo, CRITC, and VIKOR methods has been proposed for water quality assessment, considering the input data’s uncertainty [72]. In [73], Chen proposed a creative T-SF VIKOR methodology for modeling trade-off rankings in multicriteria analysis, which provided an excellent representation of the information space in response to complex, realistic environments [73]. The thesis [74] proposed using the BWM–CRITIC–VIKOR method to create an integrated energy system plan conducive to improving the energy efficiency of the system and the profitability of project investments. The authors of the research indicate that the choice of the energy system is influenced by the initial value of the acquisition, the degree of use of renewable energy, and the general degree of energy use. Both methods use fuzzy sets. Research has shown that the BWM–CRITIC–VIKOR method’s effectiveness is much more time efficient than the traditional analytical hierarchical process, which is evident in complex decision-making problems [74]. On the other hand, the T-SF VIKOR method is characterized by good applicability, stability, and the effectiveness of the compromise selection results in a fuzzy environment [73]. On the other hand, Deveci et al. proposed using the VIKOR method to prioritize benefits resulting from the investments and implementations of intelligent transport systems [75].

Environmental goals (including those defined as SDG 13 “Climate Action”—Eurostat 2022) are the most complex of all sustainable development goals. The progress toward the EU’s Sustainable Development Goals is quantified and monitored using a set of indicators for each purpose. Coscieme’s et al. analysis [50] showed a lack of consistency within and between the environmental policy objectives in particular [50]. The different dynamics of historical emission reduction in other economies of the Member States and individual sectors of the economy are noticeable. However, there is a lack of studies evaluating the actions taken by the EU Member States and the processes of achieving the goals and supporting decision making, as well as studies on the prioritization of measures aimed at reducing emissions and translating the activity into proceedings with leverage effects for the economy of the Member State and recognized methods supporting decision makers who create records of energy and environmental plans. In the article, the authors, based on publicly available data (Eurostat and Environmental reports) for individual European Union countries and the entire EU, decided to rank the activities with a significant potential for reducing pollutant emissions. At the same time, they posed research questions in the form of:

• How to construct and formulate the provisions of program documents, including, for example, city planning documents, so that they can serve as leverage measures in the energy policy (Q1)?
• What actions should be taken to ensure the most significant effect of actions aimed at reducing emissions (Q2)?
• Considering the EU policy path, including the need to reduce emissions, the research questions posed result from previous research and a review of the current literature. The proposed assessment method, combining the determination of criteria weights with the CRITIC method and making decisions based on a multicriteria analysis with the VIKOR method, is an approach that allows the search for an optimal and stable compromise solution. It is worth emphasizing that the CRITIC method used to determine the weights is an objective method, taking into account the intensity of differences between the criteria, and is not burdened with the subjective assessment of the decision maker. The weights in this method are determined based on real data showing changes in energy management in the EU. On the other hand, the VIKOR method, identifying a compromise solution based on such weights, has an advantage over other methods. It ensures the maximum utility of the compromise solution with minimal opposition from opponents. It also avoids the shortcoming of other ranking methods of not selecting the most advantageous variant because no solution would outperform the others.

3. Materials and Methods

The transition to low-carbon energy systems is a complex process that entails radical technological changes, reductions in greenhouse gas emissions, and benefits for local economies. Still significant are investments considering the need to build capacity, strengthen supply chains, and support the legal framework [76]. Developing a sustainable urban system strategy is difficult due to its complexity. One of the possibilities of supporting decision makers is using energy system optimization models (ESOM—Energy System Optimization Models). Scheller et al. [77] reviewed selected ESOMs with a high level of modeling detail and, thus, with a high spatial, temporal, and contextual resolution, which can be used to support the decision-making process in municipalities [77]. The transition to low-emission energy systems is a complex energy problem that requires a transformational process in the economy, including changes in many dimensions, including social ones [78]. This statement involves a consideration of factors that go beyond technology and economics. Multicriteria analysis, including the VIKOR method, is increasingly used to solve the problem of the multidimensionality of the energy transformation. In the case of a multicriteria problem with quantitative weights, without considering uncertainty, for the ordering and selection of alternatives, the publication of Wątróbski et al. [79] suggests methods that assume the multiplicity of attributes.

Economic development in the world causes an increase in energy demand; at the same time, it is crucial to conduct energy planning with sustainable energy. An appropriate use of resources requires an optimal approach in the decision-making process and is, therefore, a promising direction for future research in modeling [80,81]. The proposed research methodology used to assess the effects of decisions made in energy planning is presented in Figure 2 and consists of the following phases:

• Phase 1—analysis of source documents based on Eurostat data and EEA reports;
• Phase 2—identification of weights for individual criteria using the CRITIC method (Criteria Importance through Inter-criteria Correlation);
• Phase 3—construction of a ranking of variants and achievements based on actions and recommendations for the following perspective of achieving goals, along with weighting based on the results of reducing greenhouse gas emissions for the entire EU economy;
• Phase 4—proposal of activities in the field of energy planning in urban areas based on the results of research and analysis.

3.1. Data

Energy plays a crucial role in the functioning of modern economies and is naturally linked to other sustainable development goals; therefore, data on energy consumption are collected and aggregated at the EU level and, for the article, were taken from Eurostat 2021, 2022. Data for the category “Environment and Energy” consist of a set of indicators (Environment-113, Energy-141) as in the Sustainable Development Programme, which is a continuation of the Millennium Development Goals, organized in 2015, in which 193 member countries of the United Nations (UN) adopted 17 Goals Sustainable Development (SDGs) by 2030, including 169 subheadings, to eradicate poverty in all its dimensions in the world and create prosperity for all humanity [81].

Eurostat is the European Union (EU) statistical office situated in Luxembourg. Eurostat produces environmental statistics, accounts, and indicators. The data describe the state of the environment and its interaction with human activities, mainly production and consumption. The EU inventory is consistent with the national air pollution inventories compiled by the EU Member States. In line with the growing interest in the role of climate change resilience in the EU, the publications of the European Environment Agency are on this subject [1,2]. Based on the EEA, the studies present four areas of intervention: greenhouse gas emissions, final energy consumption, primary energy consumption, and the share of energy from renewable sources [1,2]. In these areas, ETS tariffs will be implemented considering the climate impact of global processes that are deeply interconnected and are not limited to national borders in EU countries, as per the 2020 and 2030 targets. EU intervention areas correspond to the second layer B1-B4, shown in Figure 3. This article used Eurostat data and the European Environment Agency (EEA) reports for 1990–2020. Data in the field of environment and energy were selected, corresponding to the third layer containing the A1-A12 criteria. Selected data description, relevance, and indicators are given in

Table 2. Analysis of source documents for identification of criteria A1–A12, source based on the results of [12] [own elaboration].

	A	Data Description	Indicator
Environment	A1	Air pollutants by source sector (source: EEA) Particulates < 2.5 µm	The dataset includes data on air pollutants: sulfur oxides (SOx), ammonia (NH3), nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOCs), particulate matters (PM10, PM2.5), Lead (Pb), Cadmium (Cd), Mercury (Hg), Arsenic (As), Chromium (Cr) Copper (Cu), Nickel (Ni), Selenium (Se), and Zinc (Zn), as reported to the European Environment Agency (EEA).
	A2	Air pollutants by source sector (source: EEA) Particulates <10 µm
Energy	A3	Wind	Eurostat’s methodology is based on the physical energy content method and is measured as gross electricity production for those where electricity is the primary energy form, according to obligations in Regulation (EC) No 1099/2008.
	A4	Solar thermal
	A5	Solar photovoltaic
	A6	Ambient heat (heat pumps)
	A7	Biogases
	A8	Energy productivity (nrg_ind_ep)	The indicator is part of the EU Sustainable Development Goals (SDG) indicator set, “Energy intensity measured in terms of primary energy and GDP”.
	A9	Energy imports dependency	The indicator is part of the EU Sustainable Development Goals (SDG) indicator set on affordable and clean energy.
	A10	Energy efficiency (nrg_ind_eff)	The dataset covers indicators for monitoring progress toward energy efficiency targets implemented by Directive 2012/27/EU.
Sustain. Develop.	A11	Final energy consumption in households per capita (sdg_07_20)	The indicator is part of the EU Sustainable Development Goals (SDG) indicator set and is embedded in the European Commission’s Priorities under the ‘European Green Deal’.
Crosscut. topics	A12	Final energy consumption in transport by type of fuel	The indicator considers total energy consumption in transport in PJ from 1990 onwards; modes included sea transport, air transport, domestic and international, inland navigation, rail transport, and road transport.

.

The research analyzed data presented in Eurostat, including the results of the available aggregate indexes, indicating the cause-and-effect relationship with the primary data, and then integrated them into a system framework based on hierarchical indexes. Another source document was report [2], which examined historical trends, recent progress, and projected future paths to mitigate climate change through greenhouse gas emissions reduction, renewable energy gains, and improved energy efficiency. The report is based on data provided by EU Member States. Its conclusion is to better align climate change mitigation and energy targets for energy sources through the planned phasing out of fossil fuel subsidies [82] and new national targets in 2023 and 2024.

Keirstead et al. [83] reviewed several articles and identified five key areas of practice: technology design, building design, urban climate, systems design, and policy assessment. He proposed a theoretical definition of the urban energy system model, drawing attention to the sixth field, land use and transport modeling, which, although directly relevant to urban energy use, has been somewhat omitted from the existing literature [83]. Despite the amount of time since this review, agriculture, transport, and land use are still outside the ETS (impact assessment) valuation methods in EU legislation. Due to the uncertainty of the data in the article, that aspect of energy management has also been omitted.

3.2. Identification of the Weights of Individual Criteria

Based on the analysis of source documents available as part of Eurostat data and EEA reports, criteria affecting the reduction in CO₂ emissions were identified. After determining what criteria have been adopted for the further evaluation of solutions that can be used in energy planning, the weights of the criteria should be chosen. Since it should not assume that all criteria are equally important and have the same impact on reducing emissions while optimizing the decision-making process, a method should be used that allows the appropriate weighting of individual criteria to be objectively determined. The CRITIC method is an objective weighting method, with the additional advantage of considering the degree of variability between criteria and the contradictory relationship maintained by individual decision criteria. As a result, it allows for assigning greater weights to those criteria with greater variability between alternative decisions and thus provides significant information to the decision-making process [84]. For these reasons, this method is applicable to real-world problem solving, such as the research presented in this paper. The CRITIC method [85] requires building a decision matrix, X, which contains elements x_ij, being the value of the j-th criterion in the i-th evaluation indicator. The matrix was defined as:

$$X=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ x_{m1}& x_{m2}& \cdots & x_{mn}\end{array}\right].$$

(1)

Because the elements $x_{ij}$ may occur in different units, a normalization process has been carried out according to dependencies:

$$x_{ij}^{*}=\frac{x_{ij}-{\overline{x}}_j}{{\sigma }_j}$$

(2)

where:

${\overline{x}}_j$—mean value calculated as ${\overline{x}}_j=\frac{1}{m}{\displaystyle \sum }_{i=1}^m{x}_{ij}$and ${\sigma }_j$—standard deviation ${\sigma }_j=\sqrt{\frac{1}{m-1}{\displaystyle \sum }_{i=1}^m{\left(x_{ij}-{\overline{x}}_j\right)}^2}$.

It is worth emphasizing that choosing the normalization formula from the point of view of the purpose of the statistical comparative analysis should ensure that the order of magnitude of the variables is brought to a state of comparability. Moreover, in comparative analyses, it is recommended to choose procedures that give stable ranges of the variation of normalized variables. At the same time, it is essential to consistently apply the method because changing the variable transformation procedure often causes a modification of research results unrelated to a change in the data structure.

A standard value matrix was created in this way, X*. Then, the coefficients of variation were calculated, $v_j,$ for each j-th criterion as:

$$v_j=\frac{{\sigma }_j}{{\overline{x}}_j}$$

(3)

and independence coefficients of individual criteria, ${\delta }_j,$ based on the formula:

$${\delta }_j={\displaystyle \sum }_{j=1}^n\left(1-r_j\right)$$

(4)

where $r_j$ is the Person’s correlation coefficient.

After calculating the coefficient of variation (Equation (3)) and the independence factor (Equation (4)), a comprehensive factor should be determined, $C_j,$ for each criterion:

$$C_j=v_j{\displaystyle \sum }_{j=1}^n\left(1-r_j\right)$$

(5)

and, as a result, the weight, w_j, of each criterion adopted for the analysis were calculated as:

$$w_j=\frac{c_j}{{{\displaystyle \sum }}_{j=1}^n{C}_j}$$

(6)

3.3. The Variants Ranking Construction

Using the VIKOR method allows for creating a ranking and selecting a compromise solution (scenario) from among many alternatives, considering conflicting decision criteria. The process is carried out using a multicriteria ranking coefficient defined as the distance between a specific option and the best-case solution [86]. The approach involves the construction of the final ranking of solutions using the VIKOR method based on the proximity coefficient, and using the parameter of strategy validity of most criteria allows you to balance the distance. It should also be noted that this method evaluates the spread of the tested solution from the optimal solution and indicates a solution acceptable to decision makers while raising the least objections from potential opponents. This approach is essential in matters related to the shaping of energy policy. The use of the VIKOR method to assess energy planning scenarios requires the following algorithm [87,88]:

• Step 1: construction of the decision matrix Q.

Decision matrix Q, defined as Equation (7), contains information on the criteria affecting the reduction in CO₂ emissions and is as follows:

$$Q=\left[\begin{array}{cccc}{q}_{11}& q_{12}& \cdots & q_{1n}\\ q_{21}& q_{22}& \cdots & q_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ q_{m1}& q_{m2}& \cdots & q_{mn}\end{array}\right]$$

(7)

where:

m—number of alternative solutions, n—number of criteria.

• Step 2: determination of the maximum, $q_j^{+},$ and the minimum, $q_j^{-},$ values.

The maximum and minimum values in terms of individual criteria are determined as:

$$q_j^{+}=\underset{j}{\underbrace{\max }}{q}_{ij}\mathrm{and}{q}_j^{-}=\underset{j}{\underbrace{\min }}{q}_{ij}$$

(8)

• Step 3: Calculation of metrics Si and Ri

The values of the S_i and R_i metrics, determined based on Equation (9) and Equation (10), are defined as follows:

$$S_i={\displaystyle \sum }_{j=1}^n\frac{w_j\left(q_j^{+}-q_{ij}\right)}{q_j^{+}-q_j^{-}}$$

(9)

$$R_i=\underset{j}{\underbrace{\max }}\left[\frac{w_j\left(q_j^{+}-q_{ij}\right)}{q_j^{+}-q_j^{-}}\right]$$

(10)

The sum of the weighted, normalized distances of the ith solution from the best-case solution defines Equation (9). Whereas Equation (10) defines the maximum length of the weighted, normalized assessment selected from the evaluations of the i-th solution in terms of all analyzed criteria.

• Step 4: hierarchization of the Si and Ri metrics and calculation of the proximity index Ti

It should be noted that the smaller the value of the S_i metric, the better the solution. A similar remark also applies to the R_i metric. Therefore, action needs to be taken:

$$S^{-}=\underset{i}{\underbrace{\min }}{S}_i\mathrm{and}{S}^{+}=\underset{i}{\underbrace{\max }}{S}_i$$

(11)

$$R^{-}=\underset{i}{\underbrace{\min }}{R}_i\mathrm{and}{R}^{+}=\underset{i}{\underbrace{\max }}{R}_i$$

(12)

Based on the values defined in Equations (11) and (12), the proximity factor, $T_i,$ can be calculated as:

$$T_i=v\frac{S_i-S^{-}}{S^{+}-S^{-}}+\left(1-v\right)\frac{R_i-R^{-}}{R^{+}-R^{-}}$$

(13)

where $v\in \left[0,1\right]$—the weight indicates the importance of most criteria’s strategy, while the difference (1—v) determines the veto strength.

By analyzing Equation (12), it should be noted that the solution is more favorable the smaller the values of the components of the sum Equation (13), and thus the smaller the value of the proximity index T_i. It is possible to build a ranking of the importance of individual solutions (scenarios) using the proposed algorithm and, at the same time, suggest actions that will be the best in terms of the adopted criteria, have the most significant potential, and whose introduction will have a positive impact on reducing CO₂ emissions.

3.4. Making Decisions in the Field of Energy Planning

The assessment results of the importance of individual scenarios can support the decision-making process regarding optimal energy planning, which aims to reduce CO₂ emissions, with particular emphasis on urban areas. The developed ranking of solutions allows for identifying solutions with the highest degree of optimality and will enable decision makers to use measures supporting low emissions. In the case of energy planning, the best solutions were identified based on the criteria presented in Figure 3. The methodology presented earlier provided information on the weights and importance of the criteria, which can be considered the basis for taking action in optimal energy planning.

4. Results

Based on the assumed model, the activities with the most significant impact on reducing emissions have been identified. Due to the analysis of urban policy documents, these were attributed to the economic sectors related to agriculture and land use change (e.g., forest land) because they are not monitored using ETS (see Table 1 and Table 2). Attention was focused on increasing energy efficiency, increasing the share of renewable energy in the consumption balance, long-term energy storage, decarbonizing, reducing the demand for energy for heating and cooling buildings, supporting the circular economy (transport excluding aviation and water transport), and support for a sustainable mobility system.

4.1. Data Identification for the Model

Based on the analysis of the available documents and literature, two groups of criteria have been distinguished that are involved in identifying the activities with the greatest potential in creating energy plans. First is a group of general criteria (B1–B4, second-level criteria), and a correctly identified and detailed second group of specific criteria (A1–A12, third-level criteria). The third-level criteria for B2—Renewable Energy—were those with the greatest potential for widespread use in most urban conditions. Ulpiani et al. [89] noticed an increase in the diversity of renewable energy technologies used, e.g., photovoltaic, hydrogen, and storage systems, while emphasizing that over 54% of the new renewable energy capacity comes from PV systems and 32% from wind turbines. Jacobson et al. [90] indicated that the wind and the sun are the only two sources of electricity with sufficient resources to power the world independently. In the same article, the authors confirmed the efficiency of using heat pumps to heat and cool buildings.

The diagram linking the second and third-level criteria is shown in Figure 3. The values of the criteria adopted for the analysis were determined using EEA reports and monitored by the EU in the form of four (aggregating Member States’ GHG emission projections) expert panels, as a result of which the criteria assignment was obtained. The values of all the analyzed criteria have been summarized in

Table 3. Quantitative data accepted for analysis for criteria B1–B4. Source: report [2] [own elaboration].

No.	Second-Level Criteria	2014		2020
		EU	PL	EU	PL
1.	B1 GHG emissions in effort sharing decision [Million tonnes of CO2 equivalent]	2153.75	181.54	2079.17	205.18
2.	B2 Renewables and biofuels [Thousand tonnes of oil equivalent]	4837.397	229.722	9311.277	276.825
3.	B3 Primary energy consumption [Thousand tonnes of oil equivalent]	1,330,456.777	89,494.282	1,235,570.620	96,859.153
4.	B4 Final energy consumption [Thousand tonnes of oil equivalent]	938,787.852	61,547.437	905,908.931	71,144.609

and

Table 4. Quantitative data accepted for analysis for criteria A1–A12, Source: based on [2] [own elaboration].

No.	Second-Level Criteria	Third-Level Criteria	2014		2020
			EU	PL	EU	PL
1.	B1 GHG emissions in effort sharing decision	A1 Air pollutants and particulates < 2.5 µm [tonne]	1,397,901	309,257	1,184,667	254,533
		A2 Air pollutants and particulates < 10 µm [tonne]	2,048,644	401,658	1,807,443	340,426
2.	B2 Renewables and biofuels	A3 Energy balance and wind [Thousand tonnes of oil equivalent]	19,119.231	659.985	34,177.220	1358.560
		A4 Solar thermal [Thousand tonnes of oil equivalent]	4124.383	34.752	4480.347	80.144
		A5 Solar photovoltaic [Thousand tonnes of oil equivalent]	7628.381	0.593	12,048.561	168.350
		A6 Ambient heat (heat pumps) [Thousand tonnes of oil equivalent]	5569.017	109.316	13,212.792	298.111
		A7 Biogases [Thousand tonnes of oil equivalent]	12,680.749	207.438	14,686.834	322.398
3.	B3 Primary energy consumption	A8 Energy productivity and euro per kilogram of oil equivalent [KGOE]	7.676	4.232	8.569	4.717
		A9 Energy imports dependency[Percentage]	54.421	29.415	57.458	42.760
4.	B4 Final energy consumption	A10 Energy efficiency and final energy consumption [Energy indicators 2020–2030]	938.79	61.55	905.91	71.14
		A11 Final energy consumption in households per capita [kilogram of oil equivalent KGOE]	529 (b)	501	555 (ep)	557 (ep)
		A12 Final energy consumption in transport [Thousand tonnes of oil equivalent]	268,810.037	15,804.963	251,440.954	21,778.636

(b) break in time series (ep) estimated provisional.

.

Considering the data for 2014–2020, the total B1 (GHG) emission in the EU decreased, while in Poland, it increased. In the same period, the use of B2 (renewables and biofuels) in the EU increased by approx. 92%, while in Poland, it increased by only 20%. In the EU, in the analyzed period, the consumption of B3 (primary energy consumption) decreased by 8%, while in Poland, it increased by 8%. B4 (final energy consumption) consumption in the EU decreased by approx. 4%, while in Poland it increased by 15%. Because of the above observations, it can be seen that Poland still has the potential to reduce the consumption of final and primary energy (B3 and B4) through, among other strategies, introducing renewable energy in cities.

4.2. Determination of Criteria Weights Using the CRITIC Method

The CRITIC method, the algorithm presented in Section 3.2, was used to determine the weights of individual criteria. The values of the weights for the Second- and Third-level criteria have been summarized in

Table 5. Weights for Second-level criteria and Third-level criteria [own elaboration].

Second-Level Criteria	Weights w	Third-Level Criteria	Weights w
B1 GHG emissions in effort sharing decision[Million tonnes of CO2 equivalent]	0.232	A1 Air pollutants and particulates < 2.5 µm [tonne]	0.060
		A2 Air pollutants and particulates < 10 µm [tonne]	0.057
B2 Renewables and biofuels [Thousand tonnes of oil equivalent]	0.286	A3 Energy balance and Wind [Thousand tonnes of oil equivalent]	0.039
		A4 Solar thermal [Thousand tonnes of oil equivalent]	0.040
		A5 Solar photovoltaic [Thousand tonnes of oil equivalent]	0.038
		A6 Ambient heat (heat pumps) [Thousand tonnes of oil equivalent]	0.035
		A7 Biogases [Thousand tonnes of oil equivalent]	0.040
B3 Primary energy consumption [Thousand tonnes of oil equivalent]	0.241	A8 Energy productivity and euro per kilogram of oil equivalent [KGOE]	0.132
		A9 Energy imports dependency [Percentage]	0.162
B4 Final energy consumption [Thousand tonnes of oil equivalent]	0.241	A10 Energy efficiency and final energy consumption [Energy indicators 2020–2030]	0.045
		A11 Final energy consumption in households per capita [kilogram of oil equivalent KGOE]	0.309
		A12 Final energy consumption in transport [Thousand tonnes of oil equivalent]	0.045

and

Table 6. Weights for Third-level criteria in conjunction with Second-level criteria [own elaboration].

Second-Level Criteria	Weights w	Third-Level Criteria	B1	B2	B3	B4	Weights w
1	2	3	4	5	6	7	8
B1	0.232	A1	0.488				0.060
		A2	0.512				0.057
B2	0.286	A3		0.202			0.039
		A4		0.208			0.040
		A5		0.197			0.038
		A6		0.182			0.035
		A7		0.210			0.040
B3	0.241	A8			0.552		0.132
		A9			0.449		0.162
B4	0.241	A10				0.112	0.045
		A11				0.775	0.309
		A12				0.112	0.045

and presented graphically in Figure 4. By analyzing the weights for the Second-level criteria, it can be seen that the greatest impact on reducing CO₂ emissions in terms of activities carried out in the EU and Poland comes from the B2 criterion (renewables and biofuels), with a designated weight of 0.286. These remarks are also confirmed by the results of earlier research by the authors [91] and other researchers [92,93,94]. Next, the decisions taken to reduce emissions are influenced by actions related to limiting the final energy production and consumption (B3, B4), the weight of which was 0.241. Considering the detailed Third-level criteria, criterion A11 (final energy consumption in households per capita) has the highest weight, for which the weight was 0.309. Criteria A9 (dependence on energy imports), with a weight of 0.162, and A8 (energy productivity), with a weight of 0.132, turned out to be slightly less critical. In the context of the conducted analyses, criterion A6 (heat pumps) was given the lowest weight, amounting to 0.035. Based on the analyses (and based on data obtained from Eurostat), allowing for the determination of the weights of individual criteria from the Third-level criteria, it can be concluded that the energy policy pursued so far in the EU and Poland leads to emission reduction in activities aimed at reducing final energy consumption in households per capita (A11). On the other hand, introducing heat pumps into urban areas has the smallest emission reduction potential (A6).

Table 6 presents the results of determining the weights of individual criteria in three aspects: general analysis of Second-level criteria weights (column 2), available analysis of Third-level criteria weights (column 8), and analysis of Third-level criteria weights compared to Second-level criteria (columns 4–7). Based on the obtained results, it is possible to determine the emission reduction potential for individual activities from the Third-level criteria and Second-level criteria presented earlier and the possibility of Third-level criteria analyzed in conjunction with the Second-level criteria. In the case of criterion B1 (GHG emissions in effort sharing decision), air pollutants and particulates < 10 µm (A2) will have the greatest impact. For criterion B2 (renewables and biofuels), the introduction of energy balance and wind (A3), solar thermal (A4), and biogases (A7) will be the most important. In criterion B3 (primary energy consumption), the most important activities are energy productivity and euro per kilogram of oil equivalent (A8). In contrast, criterion B4 is the limitation—final energy consumption in households per capita (A11). Analyzing the data contained in Table 6, it can also be seen that both in detailed analyses and in activities included in the Second-level criteria, activities related to energy production and import have a great potential for reducing emissions (A8, A9) and, very importantly, final energy consumption in households (A11). These insights prove the need to diversify energy production by introducing RES in cities and taking measures to reduce household energy consumption.

4.3. Making Decisions Affecting the Reduction in Emissions with the Use of VIKOR

Using the VIKOR method, the impact of the Second-level criteria on reducing emissions was estimated. In estimating the effects of the criteria on the results, knowledge was used in the form of numerical values illustrating emission reductions resulting from specific actions and energy decisions. Figure 5 presents the values of the S_i metric, which, as shown in Section 3.3, indicates the quality of matching the decision to the achieved goal. The lower the metric value, the better the emission reduction results. Based on the designated metric, in the next step, the proximity factor, T_i, was determined using Equation (13). The smaller coefficient values prove that the applied solutions have already produced visible effects.

On the other hand, activities with a high proximity factor still have the potential to reduce emissions, and this is a place (gap) for intensifying activities. A hierarchy of actions was created based on the analysis carried out, which is graphically presented in Figure 6. It can be seen that the actions that brought the best effect in the analyzed period (2014–2020) are actions under criterion B1. The B2 (renewable energy) criterion has the greatest importance and potential for further steps in energy planning to reduce emissions. Therefore, decision makers should pay special attention to activities promoting and supporting investments and RES development decisions.

The Third-level criteria were also analyzed using the VIKOR method to create a hierarchy of detailed action. Similarly to the Second-level criteria group, the value of the Si metric (Figure 7) and the Ti proximity coefficient (Figure 8) were determined. Furthermore, in this case, the lower the values of the Si metric and the Ti proximity coefficient, the better the effects of the actions taken that have already been achieved; the higher these values are, the greater the potential of the proposed activities. Analyzing the obtained results, it can be concluded that actions with the greatest untapped potential fall under the A11 (final energy consumption in households per capita), A9 (dependence on energy imports), and A8 (energy productivity) criteria. As a result of the research, the authors identified a significant potential for measures to minimize household energy consumption (Figure 9). Based on our results, it is possible to recommend actions for the provisions of the energy policy aimed at supporting households in investing in renewable energy sources, replacing heat sources with less energy-intensive ones, or activities promoting energy saving.

5. Discussion

The European energy system must be fundamentally transformed, especially in terms of carbon dioxide emissions, to meet the commitment of the EU energy strategy. Renewable energy, as the main “drive” of the transformation, introduces a new dynamic to existing energy systems based on fossil fuels. However, this approach also entails risks and uncertainties related to supply fluctuations and the decentralized production of energy equipment [95] and leads to the need for flexible policymaking. However, at the same time, there is a need for a stable framework to achieve sustainable changes in production and consumption behavior. However, the design of such policies remains challenging due to the interdependence of different pathways, the interactions of market imperfections, and uncertainty about the possible economic, environmental, and social impacts [96]. This approach also has the dimension of designing and integrating various sectoral policies, which we aimed to show in this article (Table 1 and

Table 7. A simplified diagram based on trends and protections in Europe presenting recommended support activities in sectors. Source: report [1] [own elaboration].

Selected the Economic Sectors	EU Recommended Support Activities
Energy systems	Increasing the energy efficiency
	Increasing the share of energy from renewable sources
	Reduction in greenhouse gas emissions resulting from energy consumption
Construction (energy demand for heating and cooling buildings)	Low-emission energy sources
	Decarbonisation of the energy system
	Increasing the amount of electricity from renewable sources
	Changing consumer preferences
	Reduction in consumption energy consumption in households
	Long-term energy storage
Industry	Circular economy
	Increasing the amount of electricity from renewable sources
	Long-term energy storage
	Support for infrastructure for transport, storage, and use of hydrogen and CO2
	Decarbonisation of carbon-intensive sectors (steel and concrete production)
	Digital services and supporting smart energy systems. Design and production and in the sharing economy
Transportation	Supporting a sustainable mobility system
	Strengthening public transport
	Increasing the number of zero-emission cars
	Increase in the activity of rail transport
	Increase in operating costs of ICE-powered cars (CO2 emissions tax imposed)
	Falling prices of low-emission vehicles

).

Energy system models are essential for generating various insights and analyses on energy supply and demand [97,98]. As real experimentation with systemic changes is impossible, computer models can function as tools to enable policymakers to explore different decarbonization options and policies in virtual ‘laboratories’ and generate an understanding of the policy domain. These models are deliberate, mathematical simplifications of reality.

Stoeglehner and Abart-Heriszt [99] proposed the development of energy planning for the Styrian climate and energy strategy as an integral part of spatial planning, which aims to anchor an integrated spatial and energy concept in municipalities. The energy and greenhouse gas inventory are based on a model for determining energy consumption and emissions at the municipal level [99].

Zhang et al. [100] propose an integrated optimization and multi-scale input–output (OMIO) model integrating interval programming, chance constraint programming, and integer programming within the input–output framework. This approach is intended to provide a complex policy analysis. Optimized decision variables, carrying precarious information from the energy system, have been identified as connectivity points triggering changes in economic and environmental impacts [100].

Süsser et al. [101] empirically investigated whether, how, and when models influence the policymaking process and whether, how, and when policymakers influence the design, use, and results of energy modeling. They showed that models are used and influence policymaking, notably through impact assessments and supported target setting, and that policymakers influence models and modelers by influencing data and assumptions, the scope of research, and decision on how to use modeling results [102].

The obtained results in the form of a hierarchy of actions bringing potentially the most excellent benefits to the low emission policy in the future are correlated with the EU recommendations (Table 7). It can therefore be concluded that decision makers implementing the EU guidelines can identify those activities that will be most beneficial in the case of their energy policy in cities. There is a growing body of research on what a coherent policy framework that enables lower levels of demand for energy services might look like, using all kinds of policy instruments from economic incentives, investment in alternative infrastructure, education, to information, research and development, to official recommendations, rules, and regulations in various sectors. Zell-Ziegler et al. [102] covered a total of 27 NECPs (excluding Great Britain) and 15 LTSs (excluding Poland), at the same time showing that financial incentives and fiscal instruments are most often related to the mobility sector and that information is most often related to the construction sector. Financial incentives/taxes are used cross-sectorally [102]. The suggestions from previous research on the need for change in all industries failed to materialize buildings (e.g., reducing usable floor space per capita and changes in heating/cooling temperatures), transport (reducing air travel distances), and manufacturing (reducing the amount of production while increasing the durability of products) are not included in the national records of energy policies [103]. The current policy (including EU governments) does not provide a more significant role for regulatory instruments in the energy policy of cities. In many cases, these are more intentional or visionary measures, showing that the document’s authors see the need for change but have not yet defined specific policy actions [102].

This article proposes an assessment of energy policy effectiveness using the Criteria Importance Through Intercriteria Correlation method and the VICKOR method, prioritizing activities carried out in EU countries to reduce energy consumption and protect the climate. The analyses were carried out for four areas of intervention (ETS tariffs), in which a set of 12 criteria was distinguished. Based on the weights assigned, diagrams were created highlighting the activities that significantly impact low emissions for the city model. In this way, effective energy policy strategies with tremendous future potential were identified based on quantitative indicators.

Considering the future potential for CO₂ reduction, the most favorable actions of a general nature should concern decisions introduced in the EU and Poland on renewables and biofuels (criterion B2). Specific activities with the most significant future potential include an improvement in the final energy consumption in households per capita (criterion A11), actions to make Europe independent of energy imports (criterion A9), and decisions to improve energy productivity (criterion A8).

The limitation of the proposed approach is its use based on previously aggregated data, i.e., the validation of the results of decision-making processes after its implementation in a given area. However, the authors believe such a static approach is necessary, allows for evaluating activities, and reduces the risk of possibly making incorrect decisions in the future. Furthermore, due to the need to introduce measures to reduce energy consumption and monitor progress in this area, the authors plan to conduct future research considering a wider set of criteria and using a fuzzy, expert approach to assessing the decisions made in energy planning. Finally, the authors plan to analyze interesting issues in other sectors of the economy, particularly industry and transport.

6. Conclusions

Comprehensive and transparent assessments are required to select diversified energy portfolios and the energy transition, reflecting the complexity and multidimensionality of the transition to low-carbon energy systems. Clean energy technologies have traditionally been judged on their techno-economic performance. This article proposes a multicriteria evaluation framework for integrated indicators (available in Eurostat) for assessing desirability in urban policy. The framework includes the integrated measures used in the EU to monitor emissivities such as GHG emissions, renewables, biofuels, primary energy consumption, and final energy consumption. The combined use of these indicators in the proposed multicriteria framework provides a comprehensive and transparent assessment that supports informed decision making.

The share of cities in global energy consumption is also steadily increasing. There is no indication that this trend will change as population growth, an increasing demand for building services and comfort levels, and an increasing time spent in buildings will ensure a further upward trend in energy demand in the future. Therefore, understanding how cities use energy becomes extremely important to mitigate the growing ecological problems, the increase in global energy consumption, the increase in emissions, and the impossibility of achieving climate neutrality.

A critical ranking developed using the methods proposed in this article helps identify low-efficiency activities and provides information for introducing priority decisions and effective energy management in urban areas. Furthermore, data from conducted analyses can help decision makers optimize decision making to reduce the risk of making mistakes and reduce the unnecessary costs of inappropriate decision making. The advantage of the proposed approach is primarily the method of determining the weights, which are calculated by considering the intensity of the criteria and their interrelationships. The solution allows us to determine the internal correlation between the assessed measures and to quantify the impact of individual measurements on improving the efficiency of urban areas, together with a proposal of the future measures with the most significant potential for CO₂ reduction. Thanks to the structure of the provisions of program documents, including, e.g., city planning documents, which prefer the most beneficial actions, the energy policy can be supported more effectively, which is the answer to the question (Q1). The sectoral actions recommended by the EU are presented in the table above (Table 7). The activities that, in correlation with the results presented in the article, have the most excellent effect are in bold. It is also the answer to the question (Q2)—what actions should be taken to ensure the most remarkable effectiveness of activities to reduce emissions?