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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1220325</article-id>
<article-id pub-id-type="doi">10.3389/fenvs.2023.1220325</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>New directions for the realisation of SDGs given the economic and welfare costs incurred by air pollution</article-title>
<alt-title alt-title-type="left-running-head">Jasi&#x144;ska-Biliczak and Ikwuwunna</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fenvs.2023.1220325">10.3389/fenvs.2023.1220325</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Jasi&#x144;ska-Biliczak</surname>
<given-names>Anna</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2308578/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ikwuwunna</surname>
<given-names>Eugene</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Institute of Economics and Finance</institution>, <institution>Faculty of Economy</institution>, <institution>Opole University</institution>, <addr-line>Opole</addr-line>, <country>Poland</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Cognizant Technology Solutions</institution>, <addr-line>Cracow</addr-line>, <country>Poland</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>School of Engineering and Engineering Technology (SEET), Federal University of Technology Owerri (FUTO)</institution>, <addr-line>Owerri</addr-line>, <country>Nigeria</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1984975/overview">Artur Badyda</ext-link>, Warsaw University of Technology, Poland</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1707389/overview">Kai Liu</ext-link>, Shandong Normal University, China</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/633032/overview">Muhammad Farooq</ext-link>, University of Engineering and Technology, Lahore, Pakistan</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2391789/overview">Viktorija Skvarciany</ext-link>, Vilnius Gediminas Technical University, Lithuania</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Anna Jasi&#x144;ska-Biliczak, <email>anna.jasinska-biliczak@uni.opole.pl</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>19</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>11</volume>
<elocation-id>1220325</elocation-id>
<history>
<date date-type="received">
<day>10</day>
<month>05</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>08</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Jasi&#x144;ska-Biliczak and Ikwuwunna.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Jasi&#x144;ska-Biliczak and Ikwuwunna</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Air pollution is a multidisciplinary research field, which affects sustainable development (SD) and Sustainable Development Goals (SDGs). Its multi-level cause and effect shows the direct influence of air pollution on human health, standards and costs of living, health treatment, and the economies of states. Poor air quality, as an SDG factor, may affect both lower wellbeing and economic growth. As developed economies have faced problems due to poor air quality, they have worked out solutions based on their technological engagement. However, emerging economies are still facing poor air quality as a main problem affecting their economic growth. The particulate matter factor, according to the air quality study, was used as the basis for comparative and statistical research. The findings of the study indicate that air pollution causes a serious impact relating to workforce productivity, absence from work, mortality, and even lower crop yields. There is an obvious research gap in the investigated field of comparative studies considering air pollution as an SD factor, understood as an economic concept. Therefore, the current study is proposed to fulfil this gap, contribute to the knowledge base of the factors affecting human development, and expand the statistical data based on the findings from a comparison between Poland and Germany on their performance in air quality tests. This is a novel approach pointing to air pollution as the proposed focus of research for the realisation of SDGs.</p>
</abstract>
<kwd-group>
<kwd>sustainable development goals</kwd>
<kwd>air pollution</kwd>
<kwd>sustainable development</kwd>
<kwd>socio-economy</kwd>
<kwd>welfare</kwd>
<kwd>economic costs</kwd>
</kwd-group>
<contract-num rid="cn001">Grant Number: 000-6-69-0</contract-num>
<contract-sponsor id="cn001">Ministerstwo Edukacji i Nauki<named-content content-type="fundref-id">10.13039/501100004569</named-content>
</contract-sponsor>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Environmental Economics and Management</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>The relationship between economic development and environmental pollution has been the subject of intense research (<xref ref-type="bibr" rid="B14">Fodha and Zaghdoud, 2010</xref>; <xref ref-type="bibr" rid="B20">Landrigan, 2016</xref>; <xref ref-type="bibr" rid="B37">Tan et al., 2021</xref>; <xref ref-type="bibr" rid="B38">Torkayesh et al., 2022</xref>). In addition, policymakers have long attempted to promote and sustain economic growth (<xref ref-type="bibr" rid="B34">Sultana et al., 2022</xref>) as the basis for the stable and prosperous development of societies. Furthermore, for the development to be sustainable, it must consider social, ecological, and economic issues; the basis of living and non-living resources; and the long- and short-term benefits and drawbacks of alternative actions (Sustainable Development: Evaluation of the Concept and its Economic, Ethical, Political and Cultural Dimension, n.d.). This is typically thought to operate at the crossroads of environmental, economic, and social concerns.</p>
<p>Economic growth, based on the concept of sustainable development, enables emerging economies to gradually close the gap with more developed economies. Activities that develop an economy in transition may include increasing the standards of living, developing a competitive industrial and commercial base, and improving the infrastructure. Economic growth may also consider access to resources, such as solar energy, clean soil, and air. Moreover, developed economies are not free from the challenges of Sustainable Development Goals (SDGs)&#x2014;they still reduce the influence of their economics on the natural environment.</p>
<p>Agenda 2030 of the SDGs of the United Nations (<xref ref-type="bibr" rid="B35">Sustainable Development Goals, 2022a</xref>) points to goals such as Goal 3, which aims at improving healthcare and wellbeing; Goal 6, which aims at improving access to clean water and sanitation; Goal 8, which is concerned with decent work and economic growth; Goal 10, which aims at reducing equalities; Goal 12, which is regarding responsible consumption and production; and Goal 13, which emphasises climate action for achieving sustained economic growth. Creativity, know-how, technology, and financial resources from all societies are needed to achieve SDGs in every context (Sustainable Development Goals. The SDGs in action. <xref ref-type="bibr" rid="B36">Sustainable Development Goals, 2023</xref>). Economic development is not promoted by human or technological capital; instead, it is fuelled by natural resources (<xref ref-type="bibr" rid="B26">Rahim et al., 2021</xref>). Furthermore, by 2030, SDG 3.9 aims at substantially reducing the morbidity, mortality, and illnesses caused by hazardous substances and pollution and contamination of air, water, and soil (<xref ref-type="bibr" rid="B40">UNDP, 2022b</xref>).</p>
<p>In the European Union, air pollution is considered to be the greatest hazard to the health of the EU residents (<xref ref-type="bibr" rid="B45">Widera et al., 2022</xref>). According to Dr Maria Neira, WHO Director, Department of Environment, Climate Change and Health, air pollution is one of the world&#x2019;s biggest public health issues (<xref ref-type="bibr" rid="B44">WHO, 2022</xref>). Poisonous air may affect the proper functionality of the immune system and increase the risk of chronic illnesses. Furthermore, it causes vast comprehensive socio-economic disruption, direct and indirectly, positive and negative effects on the environment, like reduction in the water and air quality, ecological restoration, and noise reduction (<xref ref-type="bibr" rid="B7">Chakraborty and Maity, 2020</xref>). <xref ref-type="table" rid="T1">Table 1</xref> presents the air pollution levels in the two countries examined.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Air and GHG emissions.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Year</th>
<th align="center">Germany</th>
<th align="center">Poland</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2000</td>
<td align="center">9.970</td>
<td align="center">7.570</td>
</tr>
<tr>
<td align="center">2001</td>
<td align="center">10.200</td>
<td align="center">7.550</td>
</tr>
<tr>
<td align="center">2002</td>
<td align="center">10.030</td>
<td align="center">7.390</td>
</tr>
<tr>
<td align="center">2003</td>
<td align="center">10.060</td>
<td align="center">7.680</td>
</tr>
<tr>
<td align="center">2004</td>
<td align="center">9.880</td>
<td align="center">7.770</td>
</tr>
<tr>
<td align="center">2005</td>
<td align="center">9.680</td>
<td align="center">7.760</td>
</tr>
<tr>
<td align="center">2006</td>
<td align="center">9.840</td>
<td align="center">8.080</td>
</tr>
<tr>
<td align="center">2007</td>
<td align="center">9.470</td>
<td align="center">8.040</td>
</tr>
<tr>
<td align="center">2008</td>
<td align="center">9.580</td>
<td align="center">7.910</td>
</tr>
<tr>
<td align="center">2009</td>
<td align="center">8.950</td>
<td align="center">7.570</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">9.450</td>
<td align="center">7.980</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">9.110</td>
<td align="center">7.870</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">9.270</td>
<td align="center">7.710</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">9.470</td>
<td align="center">7.600</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">8.930</td>
<td align="center">7.260</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">8.930</td>
<td align="center">7.350</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">8.920</td>
<td align="center">7.630</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">8.700</td>
<td align="center">7.960</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">8.380</td>
<td align="center">7.920</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">7.750</td>
<td align="center">7.490</td>
</tr>
<tr>
<td align="center">2020</td>
<td align="center">7.040</td>
<td align="center">6.980</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: Authors&#x2019; elaboration based on OECD data indicators for CO<sub>2</sub> emissions (<xref ref-type="bibr" rid="B23">OECD, Environmental Performance Reviews: Finland, 2021</xref>). Carbon dioxide (CO<sub>2</sub>) in tonnes <italic>per capita</italic> for the year 2000&#x2013;2020 (inclusive).</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>This study aims to formulate a new direction for the realisation of SDGs through an investigation of the role of air pollution as a socio-economic factor in the environmental dimension of sustainable development economic theory. It contemplates air pollution as a factor that influences welfare and has economic costs. However, this exploratory attempt is limited to two countries (Germany and Poland) and is only concerned with data for the year 2020. The results add to the economic knowledge of the development of sustainable development as a concept, especially its environmental dimensions and best practices. Furthermore, this study aims to provide knowledge of current trends in both emerging and developed economies; however, it may be limited by the specificity of the economies on which it focused during the research. The conclusions show that there is a need to conduct research in other economies to fully recognise this problem.</p>
</sec>
<sec id="s2">
<title>2 Literature review</title>
<p>Sustainable development means different things to different people, such as ecologists, environmental planners, economists, and activists. The discussion on sustainable development is partly interesting because the concept has been borrowed from both natural and social sciences (<xref ref-type="bibr" rid="B28">Redclift, 1991</xref>). The literature indicates that different economies have different policies and standards, particularly related to the environment. Developed economies typically adopt stricter environmental control policies and advanced technologies to reduce environmental pollution (<xref ref-type="bibr" rid="B1">Abdouli and Hammami, 2017</xref>). The extant body of literature concerning air pollution can be divided into the following areas: sustainable development as a socio-economic concept and air pollution along an environmental dimension.</p>
<p>Sustainable development is a resource-use pattern that attempts to meet human needs while maintaining the environment; therefore, these needs can be addressed not just now but also in the future. The literature highlights a crucial matter concerning the future of humans (<xref ref-type="bibr" rid="B49">Zhao et al., 2020</xref>). As an economic concept, sustainable development is characterised by three dimensions: economic, social, and environmental. Integrating these into public policies presents specific challenges at every stage of the policy cycle (<xref ref-type="bibr" rid="B12">ESCAP and Scientific, 2015</xref>). <xref ref-type="fig" rid="F1">Figure 1</xref> presents sustainable development using the intersecting circle and concentric circle approaches.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Representation of sustainability as three intersecting circles and its alternative depictions using literal pillar and a concentric circle approach. Source: Three pillars of sustainability: in search of conceptual origins (<xref ref-type="bibr" rid="B24">Purvis et al., 2019</xref>).</p>
</caption>
<graphic xlink:href="fenvs-11-1220325-g001.tif"/>
</fig>
<p>Thus, air pollution can be classified as an environmental factor. According to the literature, air quality is a major environmental concern because of rapid changes in pollutant emissions driven by complex and intensive human activities (<xref ref-type="bibr" rid="B37">Tan et al., 2021</xref>). In addition to climate change, air pollution and quality are key challenges for cities globally and have significant impact on human health and economic development (<xref ref-type="bibr" rid="B21">Liu et al., 2019</xref>). The phenomenon has also been linked to environmental and population health (<xref ref-type="bibr" rid="B43">Watt et al., 2015</xref>; <xref ref-type="bibr" rid="B30">Shindell et al., 2017</xref>). The description of air pollution has gradually developed to be based on the physical and chemical characteristics of specific pollutants, and the focal point has gradually shifted from single to multiple contaminants (<xref ref-type="bibr" rid="B9">Dominici et al., 2010</xref>; <xref ref-type="bibr" rid="B6">Carvalho et al., 2019</xref>). Many developed countries with superior air quality have implemented pollution control measures, while developing countries with large populations are entering or about to enter the stage of severe air pollution (<xref ref-type="bibr" rid="B18">Han et al., 2018</xref>). <xref ref-type="table" rid="T2">Table 2</xref> presents the Global Burden of Diseases (GBD) 2017 risk factor hierarchy based on air pollution.</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>
<xref ref-type="bibr" rid="B15">GBD 2017</xref> risk factor hierarchy and its accompanying exposure definitions, theoretical minimum risk exposure levels, and data representativeness index for each risk factor, pre-2007, 2007&#x2013;17, and the total (across all years)&#x2014;air pollution.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">1</th>
<th align="left">Risk factor</th>
<th align="left">Exposure definition</th>
<th align="left">Theoretical minimum risk exposure level</th>
<th align="left">Data representativeness index</th>
<th align="left">Risk factor</th>
<th align="left"/>
<th align="left"/>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">2</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left">Before 2007</td>
<td align="left">2007&#x2013;17</td>
<td align="left">Total</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">Air pollution</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left">100.0%</td>
<td align="left">100.0%</td>
<td align="left">100.0%</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">Particulate matter pollution</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left">82.9%</td>
<td align="left">88.6%</td>
<td align="left">96.4%</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left"/>
<td align="left">Ambient particulate matter pollution</td>
<td align="left">Annual average daily exposure to outdoor air concentrations of particulate matter with an aerodynamic diameter of &#x2264;2.5&#xa0;&#x3bc;m (PM<sub>2.5</sub>), measured in &#x3bc;g/m<sup>3</sup>
</td>
<td align="left">Joint theoretical minimum risk exposure level, for both household and ambient particulate matter pollution, is a uniform distribution between 2.4 and 5.9&#xa0;&#x3bc;g/m<sup>3</sup>, with the burden being attributed proportionally between household and particulate matter pollution on the basis of the source of PM<sub>2.5</sub> exposure in the excess of the theoretical minimum risk exposure level</td>
<td align="left">17.1%</td>
<td align="left">57.0%</td>
<td align="left">58.0%</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left"/>
<td align="left">Household air pollution from solid fuels</td>
<td align="left">Individual exposure to PM<sub>2.5</sub> due to the use of solid cooking fuel</td>
<td align="left">Ambient particulate matter pollution</td>
<td align="left">82.9%</td>
<td align="left">63.4%</td>
<td align="left">85.5%</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">Ambient ozone pollution</td>
<td align="left"/>
<td align="left">Seasonal (6-month period with the highest ozone level) 8-h daily maximum ozone concentrations, measured in ppb</td>
<td align="left">Uniform distribution between 29.1 and 35.7&#xa0;ppb</td>
<td align="left">100.0%</td>
<td align="left">100.0%</td>
<td align="left">100.0%</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>The data representativeness index was calculated as the percentage of locations for which we had data for a given time period. PM<sub>2.5</sub>, particulate matter with an aerodynamic diameter smaller than 2.5&#xa0;&#x3bc;m, measured in &#x3bc;m/m<sup>3</sup>. ppb, parts per billion. Source: <xref ref-type="bibr" rid="B15">GBD, 2017</xref> Risk Factor Collaborators, 2018.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Reducing air pollution, including greenhouse gas (GHG) emissions originating from the extensive use of fossil fuels, is critical for European countries to improve their environment and achieve carbon neutrality by the middle of this century. To optimally reduce air pollutants and mitigate climate change, national and European Union level regulations and international agreements, such as the SDGs, Kyoto Protocol, and Paris Climate Agreement, must be included in these strategies (<xref ref-type="bibr" rid="B38">Torkayesh et al., 2022</xref>). Understanding the extent to which air pollution influences the process of achieving the SDGs may help estimate the benefit of reducing air pollution, so as to improve the attempts at sustainable development and for evaluating the necessity and urgency required to reduce air pollutants and, thus, improve air quality (<xref ref-type="bibr" rid="B49">Zhao et al., 2020</xref>). As a large number of people are adversely affected by pollution, the following question arose: &#x201c;Should environmental concerns not encompass social concerns?&#x201d; Consequently, not only have the studies in environmental and social fields begun to dovetail, but the study of environmental economics itself has emerged (<xref ref-type="bibr" rid="B29">Rogers et al., 2012</xref>).</p>
</sec>
<sec id="s3">
<title>3 Hypotheses</title>
<p>First, a desk research was conducted. The data used as the base of the research were secondary and included the environmental indicators generated from the 2020 Organisation for Economic Cooperation and Development (OECD). As the comparative method was chosen to verify the hypotheses and the practical aim of the study was to compare emerging and developed economies, Poland and Germany were chosen for analysis. The statistical method, such as the multiple correlation test, was used.</p>
<p>From the aggregate Air Quality Index (AQI), based on the combined effects of five criteria pollutants (CO, SO<sub>2</sub>, NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub>) and considering European standards, the particulate matter pollutant, as the most common and measured pollutant in most countries, was chosen for analysis. The usefulness of this factor has been confirmed in previous studies (<xref ref-type="bibr" rid="B46">Zeger et al., 2000</xref>; <xref ref-type="bibr" rid="B3">Beloconi et al., 2016</xref>; <xref ref-type="bibr" rid="B32">Stafoggia et al., 2017</xref>).</p>
<p>The literature indicates that Sweden, Latvia, France, Lithuania, Hungary, and Italy were ranked as the top six countries with the lowest emissions. However, Finland, Poland, the Czech Republic, Luxembourg, and Estonia had the lowest overall rankings and highest <italic>per capita</italic> emissions (<xref ref-type="bibr" rid="B38">Torkayesh et al., 2022</xref>). The rankings influenced the choice of countries (economies) to be compared: Poland is an emerging economy with one of the highest emissions, and Germany is a developed economy focussing on green economy development. In addition, strong economic bonds exist between these two economies.</p>
<p>The proposed methodology consists of the following steps: desk research and data analysis using comparative and statistical methods, in which the use of public statistics data is justified.</p>
<p>To investigate air pollution as a socio-economic factor and to examine its role in the environmental dimension of the sustainable development economic theory concept, based on the aforementioned discussion, the following hypothesis (H) was formulated:</p>
<p>H: Air pollution plays a crucial role in realising SDGs and allows developing sustainable development as an economic concept.</p>
<p>In addition, the following supporting hypotheses were established:</p>
<p>
<statement content-type="h1" id="H1">
<label>H1:</label>
<p>Air pollution is a crucial environmental factor, which helps formulate new directions for SDGs&#x2019; realisation.</p>
</statement>
</p>
<p>
<statement content-type="h2" id="H2">
<label>H2:</label>
<p>The environmental dimension of sustainable development is becoming increasingly important and is strongly related to healthcare costs.</p>
</statement>
</p>
<p>
<statement content-type="h3" id="H3">
<label>H3:</label>
<p>Air pollution has serious economic implications, particularly at the national level.</p>
</statement>
</p>
</sec>
<sec id="s4">
<title>4 Results and findings</title>
<p>Here, we consider the impact of generated waste and its collection on the economic, social, and environmental dimensions and the waste collected and treated by or for municipalities. It covers wastes from trade and commerce, hospitals, office buildings, and households, including bulky waste, garden waste, street sweepings, market cleansing waste, and contents of litter containers. These exclude wastes from municipal sewage networks and treatment, as well as waste from construction and demolition activities. This indicator is measured in thousands of tonnes and kilogrammes <italic>per capita</italic> (<xref ref-type="bibr" rid="B23">OECD, Environmental Performance Reviews: Finland, 2021</xref>) examined.</p>
<p>At the same time, the purpose of the air, GHG emissions, and CO<sub>2</sub> data was to determine the gross direct emissions from human activities in both Poland and Germany. <xref ref-type="fig" rid="F2">Figure 2</xref> shows the indicators of CO<sub>2</sub> emissions for both countries during the same period.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Indicators of CO<sub>2</sub> emissions in Germany and Poland during 2000&#x2013;2020. Source: OECD data indicators for CO<sub>2</sub> emissions.</p>
</caption>
<graphic xlink:href="fenvs-11-1220325-g002.tif"/>
</fig>
<p>
<xref ref-type="fig" rid="F2">Figure 2</xref> shows the position of examined countries at the background of other OECD countries (grey lines).</p>
<p>The data expressed in CO<sub>2</sub> equivalents refer to gross direct emissions from human activities. CO<sub>2</sub> refers to the gross direct emissions from fuel combustion only, and these data are provided by the International Energy Agency (IEA); air and GHG emissions are measured in thousand tonnes, tonnes <italic>per capita</italic>, or kilogrammes <italic>per capita</italic>, except for CO<sub>2</sub>, which is measured in million tonnes and tonnes <italic>per capita</italic> (<xref ref-type="bibr" rid="B2">Air and emissions, 2022</xref>). For a comparative study, we also compared the data on population exposure to fine particulates in these two countries. This comparison is presented as a percentage in <xref ref-type="table" rid="T3">Table 3</xref>.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Air pollution exposure of the population of Poland and Germany, at the OECD background, exposed to fine particulates (PM<sub>2.5</sub>), with concentrations exceeding the WHO guidelines (10&#xa0;&#x3bc;g per cubic metre) in the percentage of the population in years 2000, 2005, and 2010&#x2013;2019.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Year</th>
<th align="center">Germany</th>
<th align="center">Poland</th>
<th align="center">OECD</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2000</td>
<td align="center">99.8</td>
<td align="center">100</td>
<td align="center">87.5</td>
</tr>
<tr>
<td align="center">2005</td>
<td align="center">99.3</td>
<td align="center">100</td>
<td align="center">84.2</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">99.6</td>
<td align="center">100</td>
<td align="center">74.5</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">99.6</td>
<td align="center">100</td>
<td align="center">76</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">98.3</td>
<td align="center">100</td>
<td align="center">71.5</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">98.6</td>
<td align="center">100</td>
<td align="center">69.9</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">91.9</td>
<td align="center">100</td>
<td align="center">66.6</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">95.5</td>
<td align="center">100</td>
<td align="center">66.2</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">88.3</td>
<td align="center">100</td>
<td align="center">61.6</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">87</td>
<td align="center">100</td>
<td align="center">62.2</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">88.7</td>
<td align="center">100</td>
<td align="center">62.5</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">86.8</td>
<td align="center">100</td>
<td align="center">61.7</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: OECD environmental statistics, air quality, and Health: exposure to PM<sub>2.5</sub> fine particles&#x2014;countries and regions (<xref ref-type="bibr" rid="B23">OECD, Environmental Performance Reviews: Finland, 2021</xref>) (Environment at a Glance&#x2014;Climate Change, Compare Your Country, n.d.).</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>These data are used to find out the percentage of the population that is being exposed to fine particulate (PM<sub>2.5</sub>) concentrations exceeding WHO guidelines (10&#xa0;&#x3bc;g per cubic metre) and its effect on the population in Poland and Germany. It represents the percentage of the population exposed to fine particulate (PM<sub>2.5</sub>) in the air, which increases the risk of respiratory and cardiovascular diseases in particular. These data indicate the exposure of the population to fine particulate concentrations more than 10&#xa0;&#x3bc;g/m<sup>3</sup> and are expressed as annual averages.</p>
<p>The data reveal interesting observations. For example, the emerging economy (Poland) made no progress during the 20-year period, while OECD countries and the developed economy (Germany) made progress on this count during the same period. This is because the developed economy, Germany, made investments in research and development and new technologies, as well as green energy. The economic and social costs incurred by air pollution are mainly related to a shorter life expectancy (real and expected) and health disorders (also understood as healthcare social costs) in the emerging economy (Poland). The social cost of air pollution is also related to mortality, as shown in <xref ref-type="table" rid="T4">Table 4</xref>.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Mortality attributed to exposure to fine particulates (PM<sub>2.5</sub>), with the annual deaths per million inhabitants during 2000&#x2013;2019.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Year</th>
<th align="center">Germany</th>
<th align="center">Poland</th>
<th align="center">OECD</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2000</td>
<td align="center">505</td>
<td align="center">836</td>
<td align="center">380</td>
</tr>
<tr>
<td align="center">2001</td>
<td align="center">482</td>
<td align="center">813</td>
<td align="center">369</td>
</tr>
<tr>
<td align="center">2002</td>
<td align="center">468</td>
<td align="center">793</td>
<td align="center">361</td>
</tr>
<tr>
<td align="center">2003</td>
<td align="center">454</td>
<td align="center">787</td>
<td align="center">352</td>
</tr>
<tr>
<td align="center">2004</td>
<td align="center">431</td>
<td align="center">790</td>
<td align="center">336</td>
</tr>
<tr>
<td align="center">2005</td>
<td align="center">422</td>
<td align="center">791</td>
<td align="center">329</td>
</tr>
<tr>
<td align="center">2006</td>
<td align="center">419</td>
<td align="center">799</td>
<td align="center">321</td>
</tr>
<tr>
<td align="center">2007</td>
<td align="center">424</td>
<td align="center">812</td>
<td align="center">316</td>
</tr>
<tr>
<td align="center">2008</td>
<td align="center">434</td>
<td align="center">817</td>
<td align="center">313</td>
</tr>
<tr>
<td align="center">2009</td>
<td align="center">445</td>
<td align="center">821</td>
<td align="center">310</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">447</td>
<td align="center">800</td>
<td align="center">305</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">432</td>
<td align="center">786</td>
<td align="center">300</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">412</td>
<td align="center">772</td>
<td align="center">295</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">391</td>
<td align="center">739</td>
<td align="center">289</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">360</td>
<td align="center">698</td>
<td align="center">282</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">349</td>
<td align="center">705</td>
<td align="center">280</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">331</td>
<td align="center">701</td>
<td align="center">271</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">320</td>
<td align="center">712</td>
<td align="center">267</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">320</td>
<td align="center">725</td>
<td align="center">271</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">324</td>
<td align="center">733</td>
<td align="center">275</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: OECD environmental statistics, air quality, and health: exposure to PM<sub>2.5</sub> fine particles&#x2014;countries and regions (Environment at a Glance&#x2014;Climate Change, Compare Your Country, n.d.).</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>
<xref ref-type="fig" rid="F3">Figure 3</xref> shows the welfare costs of mortality due to exposure to fine particulates.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Welfare costs of mortality due to exposure to fine particulates in Poland and Germany for the period 1990&#x2013;2020, as a percentage of GDP equivalent. Presented data are matched with the colours in the following order: Poland, Germany, and OECD overage. Source: OECD environmental statistics, air quality, and health: exposure to PM<sub>2.5</sub> fine particles&#x2014;countries and regions (Environment at a Glance&#x2014;Climate Change, Compare Your Country, n.d.).</p>
</caption>
<graphic xlink:href="fenvs-11-1220325-g003.tif"/>
</fig>
<p>The air pollution effect is reflected in the mortality rate per 1,000,000 inhabitants. In 2019, the cost estimate of inhaled fine particulate matter that causes serious health issues, like respiratory and cardiovascular diseases, exerts a significant effect on children and elderly people, increasing their risk of heart diseases and stroke in particular, eventually leading to death. The cost estimate only represents premature mortalities and is calculated using the estimates of the value of a statistical life (VSL) and the number of premature deaths attributable to ambient particulate matter.</p>
<p>Here, the correlations between mortality and air pollution in Poland and Germany were examined. <xref ref-type="table" rid="T5">Table 5</xref> shows welfare costs, mortality, and municipal waste.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Mortality, welfare costs, and municipal wastes of Germany (the developed economy) for the period 2000&#x2013;2019.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="4" align="center">Variable</th>
</tr>
<tr>
<th align="center">Year</th>
<th align="center">Welfare costs: mortality</th>
<th align="center">Mortality attributed to PM<sub>2.5</sub>
</th>
<th align="center">Municipal waste</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2000</td>
<td align="center">5</td>
<td align="center">505</td>
<td align="center">648.8</td>
</tr>
<tr>
<td align="center">2001</td>
<td align="center">4.8</td>
<td align="center">482</td>
<td align="center">639.3</td>
</tr>
<tr>
<td align="center">2002</td>
<td align="center">4.6</td>
<td align="center">468</td>
<td align="center">647.2</td>
</tr>
<tr>
<td align="center">2003</td>
<td align="center">4.5</td>
<td align="center">454</td>
<td align="center">608</td>
</tr>
<tr>
<td align="center">2004</td>
<td align="center">4.3</td>
<td align="center">431</td>
<td align="center">593.2</td>
</tr>
<tr>
<td align="center">2005</td>
<td align="center">4.2</td>
<td align="center">422</td>
<td align="center">570.5</td>
</tr>
<tr>
<td align="center">2006</td>
<td align="center">4.1</td>
<td align="center">419</td>
<td align="center">569.8</td>
</tr>
<tr>
<td align="center">2007</td>
<td align="center">4.1</td>
<td align="center">424</td>
<td align="center">569.2</td>
</tr>
<tr>
<td align="center">2008</td>
<td align="center">4.2</td>
<td align="center">434</td>
<td align="center">596.6</td>
</tr>
<tr>
<td align="center">2009</td>
<td align="center">4.4</td>
<td align="center">445</td>
<td align="center">599.1</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">4.2</td>
<td align="center">447</td>
<td align="center">609.2</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">4.2</td>
<td align="center">432</td>
<td align="center">621.3</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">4</td>
<td align="center">412</td>
<td align="center">614.5</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">3.8</td>
<td align="center">391</td>
<td align="center">610.7</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">3.5</td>
<td align="center">360</td>
<td align="center">627.4</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">3.4</td>
<td align="center">349</td>
<td align="center">631.2</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">3.2</td>
<td align="center">331</td>
<td align="center">634.2</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">3.1</td>
<td align="center">320</td>
<td align="center">626.6</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">3.1</td>
<td align="center">320</td>
<td align="center">604.6</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">3.1</td>
<td align="center">324</td>
<td align="center">606</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: Authors&#x2019; own elaboration based on OECD data.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The data were used to run multiple correlation tests of the following variables: welfare cost of mortality from exposure to fine particulates (PM<sub>2.5</sub>), mortality attributed to the exposure to fine particulates (PM<sub>2.5</sub>), and municipal wastes generated in Germany. <xref ref-type="table" rid="T6">Table 6</xref> shows the correlation results.</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Germany: correlation test.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center"/>
<th align="center">Year</th>
<th align="center">Welfare costs: mortality</th>
<th align="center">Mortality attributed to PM<sub>2.5</sub>
</th>
<th align="center">Municipal waste</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Year</td>
<td align="center">1</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Welfare costs: mortality</td>
<td align="center">&#x2212;0.935504954</td>
<td align="center">1</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Mortality attributed to PM<sub>2.5</sub>
</td>
<td align="center">&#x2212;0.925554468</td>
<td align="center">0.998156632</td>
<td align="center">1</td>
<td align="left"/>
</tr>
<tr>
<td align="center">Municipal waste</td>
<td align="center">0.028839823</td>
<td align="center">0.037511786</td>
<td align="center">0.03574846</td>
<td align="center">1</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: Authors&#x2019; own elaboration.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The results of the correlation test showed that there was a significant positive correlation between the mortality attributed to exposure to fine particulates (PM<sub>2.5</sub>) and the welfare cost of mortality from exposure to fine particulates (PM<sub>2.5</sub>). Compared to that, municipal waste had a less significant positive correlation with both the welfare cost of mortality from exposure to fine particulates (PM<sub>2.5</sub>) and the mortality attributed to exposure to fine particulates (PM<sub>2.5</sub>). The same tests were conducted for Poland, an emerging economy, and the results are shown in <xref ref-type="table" rid="T7">Table 7</xref>.</p>
<table-wrap id="T7" position="float">
<label>TABLE 7</label>
<caption>
<p>Correlation test data of mortality, welfare costs, and municipal wastes in Poland (emerging economy) for the period 2000&#x2013;2019.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="4" align="center">Variable</th>
</tr>
<tr>
<th align="center">Year</th>
<th align="center">Welfare costs: mortality</th>
<th align="center">Mortality attributed to PM<sub>2.5</sub>
</th>
<th align="center">Municipal waste</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2000</td>
<td align="center">836</td>
<td align="center">9.2</td>
<td align="center">317.1</td>
</tr>
<tr>
<td align="center">2001</td>
<td align="center">813</td>
<td align="center">8.9</td>
<td align="center">288.3</td>
</tr>
<tr>
<td align="center">2002</td>
<td align="center">793</td>
<td align="center">8.7</td>
<td align="center">273</td>
</tr>
<tr>
<td align="center">2003</td>
<td align="center">787</td>
<td align="center">8.6</td>
<td align="center">258.2</td>
</tr>
<tr>
<td align="center">2004</td>
<td align="center">790</td>
<td align="center">8.6</td>
<td align="center">254.1</td>
</tr>
<tr>
<td align="center">2005</td>
<td align="center">791</td>
<td align="center">8.6</td>
<td align="center">317.2</td>
</tr>
<tr>
<td align="center">2006</td>
<td align="center">799</td>
<td align="center">8.6</td>
<td align="center">319</td>
</tr>
<tr>
<td align="center">2007</td>
<td align="center">812</td>
<td align="center">8.7</td>
<td align="center">319.8</td>
</tr>
<tr>
<td align="center">2008</td>
<td align="center">817</td>
<td align="center">8.7</td>
<td align="center">317.9</td>
</tr>
<tr>
<td align="center">2009</td>
<td align="center">821</td>
<td align="center">9.2</td>
<td align="center">314.3</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">800</td>
<td align="center">8.9</td>
<td align="center">313.9</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">786</td>
<td align="center">8.7</td>
<td align="center">316.8</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">772</td>
<td align="center">8.5</td>
<td align="center">316.1</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">739</td>
<td align="center">8.1</td>
<td align="center">296</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">698</td>
<td align="center">7.6</td>
<td align="center">271.2</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">705</td>
<td align="center">7.6</td>
<td align="center">285.6</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">701</td>
<td align="center">7.6</td>
<td align="center">306.8</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">712</td>
<td align="center">7.6</td>
<td align="center">315.4</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">725</td>
<td align="center">7.7</td>
<td align="center">329.2</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">733</td>
<td align="center">7.7</td>
<td align="center">336.6</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: Authors&#x2019; own elaboration based on OECD data.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The data were used to run multiple correlation tests of the following variables: welfare cost of mortality from exposure to fine particulates (PM<sub>2.5</sub>), mortality attributed to the exposure to fine particulates (PM<sub>2.5</sub>), and municipal wastes generated in Poland. <xref ref-type="table" rid="T8">Table 8</xref> shows the correlation results.</p>
<table-wrap id="T8" position="float">
<label>TABLE 8</label>
<caption>
<p>Poland: correlation test.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center"/>
<th align="center">Year</th>
<th align="center">Welfare costs: mortality</th>
<th align="center">Mortality attributed to PM<sub>2.5</sub>
</th>
<th align="center">Municipal waste</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Year</td>
<td align="center">1</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Welfare costs: mortality</td>
<td align="center">&#x2212;0.8217</td>
<td align="center">1</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">Mortality attributed to PM<sub>2.5</sub>
</td>
<td align="center">&#x2212;0.82114</td>
<td align="center">0.973813</td>
<td align="center">1</td>
<td align="left"/>
</tr>
<tr>
<td align="center">Municipal waste</td>
<td align="center">0.391742</td>
<td align="center">0.069979</td>
<td align="center">&#x2212;0.00643</td>
<td align="center">1</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Source: Authors&#x2019; own elaboration.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The correlation tests showed a strong positive association between mortality attributed to fine particulate exposure (PM<sub>2.5</sub>) and the welfare cost of mortality from fine particulate exposure (PM<sub>2.5</sub>), whereas municipal wastes showed a less significant positive correlation. In addition, there was a slight negative correlation between the amount of municipal wastes generated and the cost of mortality due to fine particulate exposure.</p>
</sec>
<sec sec-type="discussion" id="s5">
<title>5 Discussion</title>
<p>In 2017, air pollution caused 4.9 million deaths worldwide according to the global burden of death (GBoD) (<xref ref-type="bibr" rid="B33">Stanaway et al., 2018</xref>). Owing to the diversity of its diameter and composition type, particulate matter may have multiple effects on the whole body of a person, especially on the cardiovascular and respiratory systems, such as systemic inflammation and oxidative stress, increase in blood pressure, and changes in serum metabolites (<xref ref-type="bibr" rid="B42">Wang et al., 2015</xref>; <xref ref-type="bibr" rid="B16">Goudarzi et al., 2018</xref>; <xref ref-type="bibr" rid="B22">Mirabelli et al., 2020</xref>).</p>
<p>The environmental, behavioural, and metabolic risks that drive injury and disease are the mechanisms through which public health efforts can most efficiently and effectively prevent health loss. Therefore, effective population health improvements require the understanding of not only the injuries and diseases that drive health burdens but also the risks that drive injury and diseases (<xref ref-type="bibr" rid="B20">Landrigan, 2016</xref>). Against the backdrop of increasingly diverse multi-contaminant air conditions, human welfare will be seriously threatened if air pollution cannot be scientifically and effectively quantified and properly controlled (<xref ref-type="bibr" rid="B37">Tan et al., 2021</xref>). The health effects of particulate matter in Italy have been widely investigated in multicentre projects, evaluating short-term associations between daily particulate matter concentrations and mortality/hospitalisations across several Italian cities (<xref ref-type="bibr" rid="B4">Biggeri et al., 2005</xref>). The research on France and Iran (<xref ref-type="bibr" rid="B31">Sicard et al., 2019</xref>) showed that long-term exposure to ambient PM<sub>2.5</sub> and PM<sub>10</sub> has substantially contributed to mortality and hospital admissions in the selected cities: non-accidental causes, cardiovascular diseases, respiratory diseases, and chronic obstructive pulmonary diseases caused approximately 8,200, 2,400, 540, and 220 deaths, respectively, in 2015; in addition, cardiovascular diseases and respiratory diseases caused 18,800 and 3,400 hospital admissions, respectively. A research, in a national cohort of approximately 2.5 million Canadians, covered pollution exposure and its associations with citizen mortality, which showed that exposure to PM<sub>2.5</sub>, alone, was not sufficient to fully characterise the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution (<xref ref-type="bibr" rid="B8">Crouse et al., 2015</xref>). Increasing evidence suggests that long-term exposure to ambient air pollution is associated with death from cardiopulmonary diseases. A 2002 pilot study reported clear indications that traffic-related air pollution was related to cardiopulmonary mortality in a randomly selected sub-cohort of 5,000 older adults participating in an ongoing Netherlands Cohort Study (<xref ref-type="bibr" rid="B5">Brunekreef et al., 2009</xref>). Another study, which was part of the ESCAPE project, measured the concentrations of PM<sub>(2.5)</sub>, PM<sub>(2.5)</sub> absorbance, PM<sub>(10)</sub>, and PM (coarse) in 20 European study areas at 20 sites per area. Predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model the spatial variation in the annual average concentrations for each study area. Examination of influential factors and skewed variable distributions was essential for developing stable LUR models, which can be used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in the ESCAPE project (<xref ref-type="bibr" rid="B10">Eeftens et al., 2023</xref>). The range and number of studies covered by these topics confirmed and underlined their importance.</p>
<p>For developing risk attribution for particulate matter pollution, the integrated exposure&#x2013;response curves combine epidemiological data from ambient, household, secondhand, and active smoking sources to construct a risk curve for the full exposure range (<xref ref-type="bibr" rid="B20">Landrigan, 2016</xref>). Evidence suggests that exposure to PM<sub>2.5</sub> might be mechanistically linked to type 2 diabetes through altered lung function, vascular inflammation, and insulin sensitivity (<xref ref-type="bibr" rid="B27">Rajagopalan and Brook, 2012</xref>).</p>
<p>Air pollution can affect businesses through reduced workforce productivity, absence from work, premature deaths, and lower crop yields. Air pollution negatively impacts the U.S. economy, costing the U.S. roughly 5% of its yearly gross domestic product (GDP) in damages ($790 billion in 2014) (<xref ref-type="bibr" rid="B22">Mirabelli et al., 2020</xref>). The highest costs were on account of early deaths, attributable to the exposure to fine particulate matter (PM<sub>2.5</sub>). While some PM<sub>2.5</sub> in the atmosphere is due to the result of natural processes, such as forest fires or windblown dust, most damage from PM<sub>2.5</sub> is related to human activities, most of which can be attributed to different sectors of the economy, such as manufacturing and agriculture. Combining damage with more traditional measures of economic production, such as GDP or gross value added, provides a more accurate picture of a sector&#x2019;s full economic impact (<xref ref-type="bibr" rid="B39">Tschofen et al., 2019</xref>). In 2015, the WHO and OECD estimated that the economic cost of premature deaths and disabilities due to air pollution in Europe was close to 1.6 trillion USD. Air pollution takes its toll on the economy in several ways: it costs human lives, reduces people&#x2019;s ability to work, affects vital products, such as food, damages cultural and historical monuments, reduces the ability of ecosystems to perform functions that societies need, and costs money in remediation or restoration (<xref ref-type="bibr" rid="B41">UNECE, 2023</xref>). With sustained economic growth, environmental pollution in China has become increasingly serious and displays certain regional differences. Economic growth, energy structure, and industrial pollution in China are spatially correlated, with different agglomeration areas in the spatial distribution. In terms of the temporal dimension, energy consumption and technological pollution effects in China have led to increased environmental pollution, while changes in the structural effect have indirectly improved the environmental situation (<xref ref-type="bibr" rid="B47">Zhang et al., 2020</xref>; <xref ref-type="bibr" rid="B48">Zhang et al., 2023</xref>). Research indicates that there is a gap between citizens and technologies, which requires the development of more interactive systems and improved citizen education based on the SDGs and air pollution levels (<xref ref-type="bibr" rid="B19">Jasi&#x144;ska-Biliczak, 2022</xref>). However, air quality is subjected to many observed and unobserved factors, with evident seasonal changes that may confound the potential impact of enacted policies. Simple before&#x2013;after comparisons would fail to differentiate the policy impact from such confounding changes and may thus lead to biased and inconsistent results (<xref ref-type="bibr" rid="B25">Qin et al., 2023</xref>).</p>
<p>This study summarises a commonly used air pollution measure, which is particulate matter, and the economic consequences of air pollution in countries representing both emerging and developed economies. The sustainability of the integrated approach in the environment and society remains debatable (<xref ref-type="bibr" rid="B13">Fang et al., 2023</xref>). It contributes to the discussion on sustainable development as an economic concept for the practical realisation of SDGs. It also contributes to the discussion on the necessity of immediate action toward lowering the welfare cost of mortality due to exposure to polluted air. At the same time, it addresses the research gap regarding the impact of air pollution on economic costs in EU economies. However, the study has a limitation: it considers only two countries (Germany and Poland) and data for the year 2020.</p>
</sec>
<sec sec-type="conclusion" id="s6">
<title>6 Conclusion</title>
<p>This study assesses the environmental risk caused by air pollution across two representative countries. Poland and Germany had similar air quality and GHG emissions <italic>per capita</italic> in 2020. Germany had a score of 7.040, while Poland had a score of 6.980 (approximately 7.000 tonnes <italic>per capita</italic>). This was due to the lockdown measures to prevent COVID-19 from spreading, resulting in a positive effect of COVID-19 in the form of clean air, but only for a short time. Similarly, between 1990 and 2000, the percentage of population of Germany and Poland exposed to fine particles (PM<sub>2.5</sub>) was roughly the same, with Germany having 100%, 99.7%, and 99.8% of its population exposed and Poland having 100%, 100%, and 100% of its population exposed in 1990, 1995, and 2000, respectively. The findings show that pollution poses a high risk of causing negative health effects in both countries&#x2019; populations; however, there are differences between the emerging and developed economies, with developed economies having an advantage.</p>
<p>In contrast, both countries increased their municipal waste (in kilogrammes <italic>per capita</italic>) from 2019 to 2020. Germany produced twice as much waste as Poland at the same time. It follows that in Germany, the COVID-19 pandemic had a greater impact on waste management than that in Poland. In contrast, from 2000 to 2019, Germany&#x2019;s air and GHG emissions of carbon dioxide (CO<sub>2</sub>) in tonnes <italic>per capita</italic> increased, whereas those of Poland decreased. Furthermore, from 2000 to 2019, Germany&#x2019;s average population exposure to fine particulates (PM<sub>2.5</sub>) in micrograms per cubic metre decreased, while that of Poland increased. This means that people in Poland experienced more negative health effects than those in Germany.</p>
<p>Similarly, mortality due to fine particulate matter (PM<sub>2.5</sub>) was lower in Germany than that in Poland, with annual deaths per million inhabitants being lower in Germany than that in Poland. As a result, Poland had a higher rate of attributed mortality per million inhabitants than Germany. The most recent data of 2020 are not available yet; however, the study indicates that the mortality attributable to COVID-19 and environmental effects will increase. In 2019, Germany had an almost three times higher mortality rate due to exposure than Poland. In contrast, Germany&#x2019;s welfare cost of mortality from fine particulate exposure (PM<sub>2.5</sub>), expressed as a percentage of GDP, was lower than that of Poland.</p>
<p>Moreover, mortality is a socio-economic factor associated with the concept of sustainable development. In addition, it is strictly connected to the environmental dimension of sustainable development because the state of the environment has a direct influence on the number of sick and dying people. It also generates an additional cost for the society, which has to bear these costs for the treatment of people.</p>
<p>Air quality and its influence on health, economically understood as both private and public goods, is the subject of the present study and discussions at different levels. The governments of Poland and Germany should take major steps toward a European green deal accord and zero-pollution policy.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s7">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s8">
<title>Author contributions</title>
<p>Conceptualisation and methodology: AJ-B; analysis: AJ-B and EI; data curation: EI; writing&#x2014;original draft preparation, review, and editing: AJ-B; visualisation and supervision: AJ-B. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s9">
<title>Funding</title>
<p>This study received funding from the Polish Ministry of Education and Science (Grant Number: 000-6-69-0).</p>
</sec>
<ack>
<p>The authors would like to thank Editage (<ext-link ext-link-type="uri" xlink:href="http://www.editage.com">www.editage.com</ext-link>) for English language editing.</p>
</ack>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of interest</title>
<p>Author EI was employed by Cognizant Technology Solutions.</p>
<p>The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s11">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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