Does energy efficiency-indoor air quality dilemma have an impact on the gross domestic product?

Does energy efficiency-indoor air quality dilemma have an impact on the gross domestic product?

Journal of Environmental Management 262 (2020) 110270 Contents lists available at ScienceDirect Journal of Environmental Management journal homepage...

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Journal of Environmental Management 262 (2020) 110270

Contents lists available at ScienceDirect

Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman

Research article

Does energy efficiency-indoor air quality dilemma have an impact on the gross domestic product? Liva Asere *, Andra Blumberga Institute of Energy Systems and Environment, Riga Technical University, Azenes Iela 12/1, Riga, LV-1048, Latvia

A R T I C L E I N F O

A B S T R A C T

Keywords: Energy efficiency Indoor air quality System dynamics modelling CO2 emissions Student performance Gross domestic product

Increased energy efficiency of the building stock is one of the main tools to reduce climate change. Improved airtightness of the building envelope has a side effect – the need for higher ventilation rates which, in turn, lead to a higher energy bill and reduced indoor air quality. This creates an energy efficiency – indoor air quality dilemma. This study evaluates the dilemma impact on the gross domestic product (GDP). System dynamics modelling is applied to answer this question. The education system and labour market of Latvia is used as the case study. Simulation results show that even if all education buildings in Latvia have improved energy efficiency performance and have a significant reduction of outdoor CO2 level, indoor CO2 is very high if no mechanical ventilation is used. The best solution is to increase energy efficiency while providing good indoor air quality by operating mechanical ventilation since the increase in GDP provides financial sources for further energy effi­ ciency measures.

1. Introduction There is a need for major changes in the European building sector in order to reach the 2050 low-carbon economy objective of the European Union. Greenhouse gas (GHG) emissions from buildings are planned to be reduced by around 90% in 2050 (European Commission, 2011). It is crucial to improve the energy performance of buildings thus reducing GHG emissions by introducing passive technologies in new buildings, refurbishing old buildings to improve energy efficiency, and substituting electricity production from fossil fuels with production from renewable sources (European Commission, 2011). The retrofitting of existing building stock leads to less harmful environmental indicators than would result from demolition and reconstruction (Marique and Rossi, 2018). The provision of good indoor air quality is an important criterion to consider during the planning and design process of new buildings, as well as during the renovation of existing buildings. Building design must include the right materials for the specific climate variables to reduce the energy used for heating or cooling (Albatayneh et al., 2018; Mediastika and Hariyono, 2017)). New and innovative air conditioning and ventilation solutions have been introduced by science, industry and with the implementation of bioclimatic architectural design principles (Bajcinovci and Jerliu, 2016). These products and techniques help to

improve indoor air quality and thermal comfort; however, the desire to provide optimal and comfortable climate conditions in buildings leads to a larger energy bill. Therefore a conflict evolves between two goals – the need to reduce CO2 level indoors and outdoors. So far, the inter-relation of impact between indoor air quality and energy efficiency is considered a dilemma that does not have a definite solution (Dascalaki and Serm­ €ldva �ry et al., 2017; Ghita and Catalina, 2015; Vasile petzoglou, 2011; Fo et al., 2016; Yang et al., 2009). This research aims to evaluate how the energy efficiency – indoor air quality dilemma affects the GDP. The study starts with the literature review about indoor air quality and its impacts. The methodology unfolds the causal loop diagram and the main part of the model as well as the application of the case study of Latvia. Results show a comparison of total energy consumption reduc­ tion and income part of GDP and a comparison of every qualification group’s impact on GDP. 2. Literature review 2.1. Air quality in educational environments The quality of built environments has a significant impact on human health. According to the Science Advisory Board of the United States

* Corresponding author. E-mail address: [email protected] (L. Asere). https://doi.org/10.1016/j.jenvman.2020.110270 Received 12 September 2019; Received in revised form 31 January 2020; Accepted 11 February 2020 Available online 21 February 2020 0301-4797/© 2020 Elsevier Ltd. All rights reserved.

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Journal of Environmental Management 262 (2020) 110270

ppm, in Portugal 950–5000 ppm, in Czech Republic 1000–2000 ppm, therefore, the ventilation rate is around 1–2 l/s/person (Csobod et al., 2014) even though the requirements in standards are 4.8–14 l/s/per person depending on the building quality class and type of materials (CEN, 1998). The review on ventilation rates and carbon dioxide con­ centrations in schools worldwide done by Fisk (2017) confirms the issue of the rates not meeting the requirements. The peak CO2 concentrations indicated average and median values during regular school days or when the classrooms were occupied (Fig. 1.) These peak concentrations exceed 1000 ppm and, in many cases – 2000 ppm. The CO2 concentra­ tion maximum peak values range from 3000 to 6000 ppm that techni­ cally is already close to the CO2 level in a submarine (Fisk, 2017). Fig. 2 shows that most average and median concentration values exceed 1000 ppm and that maximum values range from 1400 ppm to 5200 ppm. No significant differences are observed between concentra­ tions of CO2 in naturally and mechanically ventilated classrooms. Fisk concludes that these CO2 measurements and research data indicate a widespread failure to provide the minimum amount of ventilation specified in standards for classrooms. The finding that CO2 concentrations often far exceed 1000 ppm indicates that ventilation rates are often far lower than required by the building standards. These results coincide with evidence on the problem of excessive CO2 level in schools found by Bako-Brio et al. in UK (Bak� o-Bir� o et al., 2012), Bluyssen et al. (2018) in the Netherlands, Duarte et al. (2017) in Portugal, Stabile et al. (2017) in Italy, Ghita et al. in Romania (Ghita and Catalina, 2015), Theodosiou et al. in Greece (Theodosiou and Ordoumpozanis, 2008), Yang in Korea (Yang et al., 2009).

Fig. 1. Peak CO2 concentrations in classrooms (Fisk, 2017).

2.3. Natural versus mechanical ventilation CO2 concentrations are also above 1000 ppm in buildings with nat­ ural ventilation where the air exchange depends on the manual opening of windows and doors and infiltration through leakages of the building as presented in Fisk’s research. Costa et al. researched university class­ rooms in Brazil and state that adequate natural ventilation can be used as a strategy for thermal comfort in a warm climate (Costa et al., 2019). The appropriate ventilation by manual window opening can be provided if the outdoor running mean temperatures are higher than þ19 � C. (Duarte et al., 2017). Manual window-airing becomes inappropriate when outdoor running mean temperatures are lower than þ16 � C and the adequate manual window-airing depends on the indoor air tem­ perature for outdoor running mean temperatures between þ16 and þ 19 � C. This type of ventilation is appropriate for approximately 25% of the academic year in places with a warm climate (Portugal). The like­ lihood of ventilation rates lower than 4 l/s/person typically exceeds 10% for outdoor temperatures below þ16 � C and even 60% for tem­ peratures under þ11 � C. An improved and development-oriented learning environment is an important factor that affects student achievement (Laiz� ane, 2015). An investigation in English primary schools provides strong evidence that low ventilation rates in classrooms destructively affect memory and concentration, and meaningfully reduce pupils’ attention and vigilance. The authors conclude that the physical environment affects both �-Biro � et al., 2012). Studies carried out in teaching and learning (Bako Denmark and Sweden show that an increased outdoor air supply rate has a measurable effect on the performance of schoolchildren (Wargocki and Wyon, 2013). Similar research carried out in Finnish schools revealed that performance improved by 2.8% if ventilation rates are increased (Haverinen-Shaughnessy and Shaughnessy, 2015). The proportion of students passing a standardised test is expected to increase by 2.9% for math and 2.7% for reading for every unit (1 l/s per person) increase in the ventilation rate (Haverinen-Shaughnessy et al., 2011). Different air exchange rates can even reach a 19% improvement in human produc­ tivity (Asere et al., 2016b, 2016a) (Asere et al., 2016a, 2016b). A study carried out in California schools found only small positive associations between classroom ventilation rates and learning (Mendell et al., 2016).

Fig. 2. Time-average CO2 dioxide concentrations in classrooms (Fisk, 2017).

Environmental Protection Agency, the indoor environment stands among the top five environmental risks to public health (US EPA, n. d.). Indoor air quality influences human health as well as impacts the ability to learn and work. Poor indoor air quality in schools is common all around the world (Fisk, 2017). It adversely influences the performance and attendance of students, primarily through health effects from indoor pollutants (Mendell and Heath, 2005). The research findings show that, with increased classroom ventilation, there is a great opportunity to improve the attendance and health of elementary school students (Mendell et al., 2013). The operation of a mechanical ventilation system does not reach the mandatory thermal comfort and indoor air quality as research carried out in Latvia shows (Asere et al., 2016b, 2016a) (Asere et al., 2016a, 2016b) because it is almost impossible to reach planned energy effi­ ciency goals while mechanical ventilation is in operation. During the research conducted, CO2 level measurements were in the range of 1000 ppm–2500 ppm. The results of the study included a comprehensive assessment of how the dilemma affects human productivity. The results indicate a productivity increase of 19% if the indoor air quality is improved. This improved air quality calls for additional energy con­ sumption that lowers the respective buildings’ energy efficiency. 2.2. CO2 analysis The findings in Latvia correspond with other studies, for example, CO2 measurements in schools in Denmark fall in the range of 600–1800 ppm, in UK 850–1650 ppm, in the Italy 450–1850, in Poland 950–2600 2

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Journal of Environmental Management 262 (2020) 110270

Research on life cycle costs in educational buildings shows that consideration of students’ wellbeing and performance leads to signifi­ cant total net benefits in the short and long term. Authors suggest that the traditional metrics focusing solely on building energy and resource efficiency should be balanced with human factors (Shan et al., 2018).

+ Ventilation costs B1 Profitability

Operation of ventilation systems +

B2 + EE measures financing +

2.4. Social benefits Achievements during studies correlate with income level after graduation. The study in the United Kingdom points out that it is important from a policy perspective to understand the impact of quali­ fications in the labour market. The research investigated the wage and employment returns on specific qualifications by comparing the wage and employment outcomes of individuals who hold those qualifications to similar individuals qualified to the level below (in terms of academic qualification). After determining the returns, the returns were used to estimate the economic benefit to society generated by individuals achieving these qualifications. The results show that 1% of pupils who leave school at age 16 without any qualifications do so at a high eco­ nomic cost to themselves and society in terms of lost output. Another conclusion is that even modest improvements of General Certificates of Secondary Education deliver larger returns so there is a strong economic imperative that all children fulfil their educational potential (Hayward et al., 2014)). Italian scientists found evidence that employees with tertiary education have steeper experience–earnings profiles than em­ ployees with upper secondary or lower education. Research showed that education offers not only a preliminary labour market advantage but also a permanent advantage that increases with time in the labour market (Brunello and Comi, 2004). OECD report about Baltic countries shows a better experience–earnings profile for more educated em­ ployees (OECD, 2003). Academic performance in high school is impor­ tant for short term goals, such as college admission, but the study in the USA clearly demonstrates a link between high school GPA and labour market earnings many years later. An increase of one point in high school GPA raises annual earnings in adulthood by around 12 per cent for men and 14 per cent for women. The findings show that people with better grades were more likely to keep studying after high school. A one-point increase in GPA doubles the chances for both genders that the person will complete college. The results of the study show that high school GPA is a significant predictor of educational attainment and earnings in adulthood (French et al., 2015). The income of educated persons and added value that they create to the national economy has an impact on the Gross Domestic Product (GDP) thus every country has to pay attention to the quality of all levels of the education system. The Human Capital Index by the World Bank measures the consequences of neglecting investments in human capital in terms of the next generation’s lost productivity. The analysis suggests that the workforce of the future in countries with the lowest human capital investments will only be one-third to one-half as productive as it could be if people enjoyed full health and received a high-quality edu­ cation (World Bank, 2019).

+

Energy p erformance of buildings

+

Air tightness of buildings

+ GDP

GHG reduction

-

R

+ Indoor air quality

Fraction of low skilled workers -

Student academic + performance and achievements

Fig. 3. Causal loop diagram.

accumulations and they are filled in or depleted over time through in­ flows and outflows. The model is made as a generic structure that can be adapted and applied to different cases and countries with different education sys­ tems, salary levels, energy efficiency policies, building stocks, etc. In this study, it is applied to Latvia as the case study. The model was populated with data from Latvia. 3.2. Model structure 3.2.1. Causal loop diagram Three causal loops determine the dynamics of the studied system – one reinforcing and two balancing loops (see Fig. 3). Reinforcing loop R starts to work if financing for energy efficiency measures is provided. This, in turn, will increase the energy performance of buildings and GHG reduction rate. The higher the energy performance of buildings (in the case energy efficiency of the building envelope is improved), the higher the airtightness of buildings and a higher need for operation of mechanical ventilation (2.2. CO2 analysis). Operation of ventilation systems will provide better indoor air quality thus having a positive impact on students’ academic performance and achievements (2.3. natural versus mechanical ventilation). It will reduce the fraction of low skilled workers (2.4. social benefits) in the population and in­ crease the amount of high skilled worker salaries that constitute a part of GDP. The higher the GDP, the higher financing rate allocated for energy efficiency measures in public buildings. The behaviour generated by reinforcing causal loop is balanced out with balancing loops B1 and B2. B1 loop shows that if the operation of ventilation systems is increasing, the energy and operation and main­ tenance costs also increase. These costs are high, and this leads to reduced working hours of ventilation systems (2.1. air quality in educational environments); therefore lowering indoor air quality, leading to decreased students’ academic performance and achieve­ ments. The decreased academic performance and achievements, in turn, increases the fraction of low skilled workers in the population and re­ duces the amount of ‘wages’ that constitute a part of GDP. The lower the GDP, the lower the financing rate allocated for energy efficiency mea­ sures in public buildings. The second balancing loop B2 has an impact of growth rate gener­ ated by R loop. The higher the ventilation costs, the lower the profit­ ability of energy efficiency measures and the lower the financing rate. Low funding limits building energy performance improvement and GHG reduction, therefore, lowering the need to operate mechanical ventila­ tion systems and cover its costs.

3. Methodology 3.1. System dynamics modelling System dynamics modelling is used. This mathematical modelling approach created by Jay Forrester (1961) is used to study the dynamics of complex systems with feedbacks, nonlinearities and delays. Powersim Studio has been used as the software tool for building stock and flow structure and simulation of the system’s behaviour. Causal loop diagram (CLD) represents the feedback structure of the system. CLD helps to explain the causes of dynamics, the mental models of model builders, and determine feedbacks that are responsible for a problem. CLD allows analysing systems qualitatively. Stock and flow structure of systems helps to carry out a quantitative analysis. Stocks are 3

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Journal of Environmental Management 262 (2020) 110270

Fig. 4. Main parts of the stock and flow model.

be implemented in the historic buildings built before 1940 due to their heritage value. Buildings constructed between 1940 and 1992 have been built according to the former Soviet Union’s building standards. Build­ ings constructed between 1992 and 2014 have been built using different energy efficiency standards. The last sub-group based on building standards contains low energy buildings that are constructed after 2014. Input data are presented in Table 1. Sub-model ‘The total benefits’ includes energy costs, energy con­ sumption and the energy efficiency measures’ benefits. ‘Construction companies’ capacity’ sub-model consists of construction company ca­ pacity and its building process as well as external funding and profit­ ability ratio that impacts the share of funding assigned to different building stock subgroups. ‘Policy instruments’ sub-model contains five different policy instruments – regulatory requirements, extra funding, penalties, CO2 tax and support for science and research. The dotted box represents sub-models created within this study (Fig. 4). It is assumed that prior to the addition of implementation of energy efficiency measures, only natural ventilation has been installed and operated (air exchange rate of 0.5 h 1). After the energy efficiency measures and mechanical ventilation have been implemented (air ex­ change rate of 6 h 1).

Table 1 Input data. Building sub-model

<1940

1940–1992

1992–2014

>2014

Heated floor area, m2

1 208 085 224 280

4065 019

970 000

10 000

784 980

112 140

10 000

Floor area with energy efficiency measures implemented before 2014, m2

3.2.2. The stock and flow structure A system dynamics model of energy efficiency for public buildings improvements (Asere and Blumberga, 2018, 2015) was praxis and further developed in this research. The model consisted of five main sub-models that are accompanied by three additional ones (Fig. 4). The building stock sub-model shows the ageing chain of the public building stock, beginning with the building stock with high energy consumption up to the building stock with decreased energy consump­ tion. Total public building stock based on construction periods is split into four sub-groups. Only a partial set of energy efficiency measures can

Air exchange rate

Begin w ith grade X

Supply air rate

End w ith grade X

entrance rate

Performance increase due to ventilation

Students that end w ith grade X plus1

Students that begin w ith grade X Rate of students w ho changed grades graduation rate w ith grade X

graduation rate w ith grade X plus 1

Duration of education level

Fig. 5. The generic structure of academic performance and achievements of students sub-model. 4

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Table 2 Average results (%). Elementary school Secondary school University

Number of students in particular education level

4

5

6

7

8

9

10

169 485 66 755 57 955

14.3 18.6 16.4

21.4 18.9 20.2

19.5 18.0 18.7

15.4 15.9 15.7

13.6 13.8 13.7

10.6 10.9 10.8

5.0 3.9 4.5

Fraction of secondary school graduates entering labour market

Duration of preschool

Duration of secondary school Elementary school Elementary school graduation rate pupils

Preschool pupils Birth rate

Fraction change rate secondary school graduates

Plate reschool graduation rate

Low skilled elementary school graduation rate

Fraction of low skilled Fraction change elementary school graduates rate low skilled

Se ondary school graduation rate

Duration of university

University dropout rate Duration of elementary school University students

Low skilled labour

Average work life duration

R

_25 Secondary school pupils

Secondary school graduation rate with grades 4 and 5 Medium skilled secondary school graduation rate1

Secondary school graduation rate with grades 6 and 7

University graduation rate 1 Medium skilled secondary school graduation rate2

Fraction of secondary school graduates entering labour market Medium skilled labour1

Medium skilled secondary school graduation rate3

Retirement rate medium skilled labour 2

Average work life duration

Medium skilled secondary school raduation rate4

Secondary school graduation rate with grade 10

University graduation rate 2

University graduation rate with grades 6 and 7

Se ondary school gradua tion rate with grades 8 and 9 Medium skilled labour2

Retirement rate medium skil ed labour 1

Fraction of secondary school graduates enter n labour market

University graduation rate with grades 4 and 5

Medium skilled labour4

Med um skilled labour3

Retirement rate medium skilled labour 3

Retirement rate medium skilled labour 4

Average work life duration

Retirement rate qualified labour 1

Qualified labour 1

Retirement rate qualified labour 2

Average work life duration

University graduation rate with grades 8 and 9

Qualified labour 2

University graduation rate 3

University graduation rate 4

University graduation rate with grade 10

Qualified labour 3

Retirement rate qualified labour 3

Qualified labour 4

Retirement rate qualified labour 4

Average work life duration

Fig. 6. The generic structure of study and work path sub-model.

results (Table 2). Stock ‘Students that begin with grade X’ can either stay in the same stock for all duration of education in particular education level or can move to stock ‘Students that end with grade X plus 1’. The first stock is regulated by one inflow and two outflows. The inflow ‘Entrance rate’ is the number of students entering a certain education level every year. The outflow ‘Graduation rate with grade X’ is the number of students who graduate annually with grade X. The outflow ‘Rate of students who changed grades’ is also inflow for the second stock and depends on the ventilation supply air rate which has an impact on the academic performance of students. Two ventilation regimes are used in the simulation (3 m3/h/person using natural ventilation and 36 m3/ h/person with operating mechanical ventilation. The academic perfor­ mance increases by 2.7% for every l/s/person based on (Haverinen-­ Shaughnessy and Shaughnessy, 2015). If the current grade is X and ventilation is poor and does not increase until a certain level, the student will graduate with grade X. If ventilation rate increases more, the stu­ dent moves from stock with grade X to the stock with grade Xþ1 and the student will graduate with grade Xþ1. This sub-model includes an ageing chain of life path from birth through pre-school, elementary school, secondary school, university, work and, finally, retirement. Fig. 6 illustrates stock and flow structure for this sub-model. People enter the system via birth rate and accumu­ late in preschool stock. After that, they move to elementary school. Elementary school graduates may decide to discontinue studies and enter the labour market as low-skilled labour or to continue studies in secondary schools or professional schools. After graduation from sec­ ondary vocational education, they can enter the labour market as medium-skilled employees or continue their studies at university. Uni­ versity graduates enter the labour market as qualified labour. Both secondary school graduates and university graduates are split into subcategories based on their grades (for more details see sub-model for

The study and work path sub-structure is built based on the current education system in Latvia. Five and six year old children first enter the education system by attending pre-school education institutions. Preschool education from the ages of 5–6 is compulsory in Latvia. Pri­ mary and lower secondary education (ISCED) is the next level that is usually completed by age 16. The two types of programmes at the sec­ ondary education level are academic secondary education programmes and vocational secondary education/training programmes. The aca­ demic education programmes aim to prepare for further studies at uni­ versity. A person graduating from a vocational programme and receiving professional qualification can enter the labour market and/or continue his/her education. Post-secondary education (ISCED level 4) or higher education (ISCED level 5) are the next education levels. Higher education comprises of both academic and professional study pro­ grammes that can be continued in master’s studies. If a person decides to study further in doctoral studies (PhD), a master’s degree or the equivalent is mandatory. The studies in a state or municipal pre-school, basic and secondary educational institution are funded from the national or municipal budget. A tuition fee may be set for a person who studies in a private educational institution. The admission to further study pro­ grammes is generally competitive, based on centralised examination results. Academic achievements are evaluated with a grading system from 1 to 10 (1 is poor and 10 is excellent) (Ministry of Education and Science, n. d.). The additional sub-models are explained in more detail in the next chapters. 3.2.3. Sub-model for academic performance and achievements of students The generic structure of academic performance and achievements of students sub-model is presented in Fig. 5. This structure is used for different education levels and different grades. The number of students at a particular education level is accumulated in stocks by the average 5

L. Asere and A. Blumberga

Journal of Environmental Management 262 (2020) 110270

Net rate of annual income change

Annual income of labour group A

Labour group A

Total annual income by labour group A

Fractional annual income change rate

Total compensation of employees

Gross Domestic Product

Fig. 7. The generic structure of sub-model of compensation of employees in gross domestic product.

academic performance and achievements of students).

Bureau of Latvia, n. d.). Secondary/Professional school duration in Latvia is three years. Survey data show that 75.8% of respondents in Latvia start their studies immediately after secondary education (Kor­ ol¸eva et al., 2018). During the last ten years, the university entrance rate decreased by 0.0045% per year in Latvia (Central Statistical Bureau of Latvia, n. d.). Studies in university last, on average, four years. Statistics from the National Centre for Education of the Republic of Latvia on exam results of 2013–2015 elementary school graduates and year 2014–2018 secondary school graduates are used as input data for this study (National Centre for Education, n. d.). The data of University graduate results was assumed as illustrated in Table 2. The research done in Latvia shows that secondary school graduates earn 13–14% more than elementary school graduates and university graduates earn around 44% more than high school graduates (OECD, 2003). Nevertheless, Eurostat data (mean and median income by educational attainment level) indicates an even larger wage gap be­ tween educational levels - 34–35% between upper secondary school and elementary school graduates and 55–59% between university and sec­ ondary school graduates. Average Eurostat data was used as a basis for generating input data on salary increases depending on minimum salaries. The low skilled worker salary’ used is the average minimum salary of the country (Latvia) and is assumed to increase by 3% each year. The simulation period is from 2014 to 2050.

3.2.4. Sub-model of compensation of employees in gross domestic product The gross domestic product (GDP) is measured by the income approach. Total GDP value is calculated as the sum of compensation of employees, gross operating surplus, gross mixed income, taxes minus subsidies on production and imports. In this study, compensation of employees and its impact on GDP is modelled. Compensation of em­ ployees includes total remuneration to employees for work done such as wages, salaries, and employer contributions to social security. Generic sub-model of compensation of employees in gross domestic product is presented in Fig. 7. Annual income (salary plus employer contributions to social security) of different labour groups described in the study and work path sub-model is changing by net rate of annual income change. Net rate of annual income change is annual income of particular labour group times fractional annual income change rate (statistic data input is 3%). Annual income is multiplied by the number of people in the particular labour stock. All groups are summed up to produce the total compensation of employees which is part of total GDP value. 3.3. Case study 3.3.1. Input data The average birth rate in Latvia is taken from the Central Statistical Bureau of Latvia (Central Statistical Bureau of Latvia, n. d.). Preschool training is mandatory from the age of 5, and at the age of 7 children begin elementary school in Latvia (European Commission, 2016). Elementary school duration in Latvia is 9 years. Data of the Central Statistical Bureau of Latvia show that the rate of elementary school graduates entering the labour market was 4.5% in 2014 and during the last ten years it increased by 0.0008% per year (Central Statistical

3.3.2. Simulation scenarios Six scenarios were simulated (Table 3) in which differences were defined for the air change rate and financial resources available for energy efficiency projects. If a building only has a natural ventilation system or it has a mechanical ventilation system that is not operated due to high energy costs, the air change rate is assumed to be 0.5 h 1. If a building has a mechanical ventilation system and it is operated during

Table 3 Simulation scenarios. Air change rate in buildings without energy efficiency improvements, h 1

Air change rate in buildings with energy efficiency improvements, h 1

Investments in energy efficiency measures

Scenario 0 (base scenario) Scenario 1

0.5

0.5

0.5

6

Scenario 2

0.5

0.5

Scenario 3

0.5

6

Scenario 4

0.5

0.5

Scenario 5

0.5

6

National energy efficiency programs available until 2022*. No further funding available National energy efficiency programs available until 2022*. No further funding available National energy efficiency programs available until 2022*. After that 2 mEUR/year funding available National energy efficiency programs available until 2022*. After that 2 mEUR/year funding available Additional 130 mEUR/year from national and municipal financing sources Additional 130 mEUR/year from national and municipal financing sources

*National funding scheme allocated for energy efficiency improvements in state-owned buildings from 2016 to 2022 with a total amount of 115.1 mEUR. 6

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Journal of Environmental Management 262 (2020) 110270

Fig. 8. Greenhouse gas emissions for different scenarios.

Fig. 9. Scenario 1–5 comparison according to pairs and with the base scenario (full lines are GDP income gains; dashed lines are GHG emission reduction).

Fig. 10. Scenario 4–6 comparison with the base scenario (full lines are GDP income gains; dashed lines are GHG emission reduction).

working hours, the air change rate is assumed to be 6 h 1. Air exchange rates show two extremes that can be applied for the buildings – only nature ventilation with low ventilation rate and very good ventilation rate that is defined as I category classroom in EN 15251. Scenario 0 is the base scenario and is the worst scenario from both indoor air quality and GHG emission reduction perspectives.

show higher reductions of GHG emissions. This is due to more financial sources allocated to energy efficiency projects. Similar to scenarios 0 and 1, the difference is caused by the operation of ventilation systems. Scenarios 4 and 5 present significant GHG reductions as a majority of the building stock has improved energy efficiency. The gap between build­ ings with mechanical and natural ventilation will reach 80 kt in 2050. Fig. 9 presents a comparison of different scenarios. GHG emission reduction and GDP income gain are used as indicators. Scenarios are compared in pairs as the main difference among the scenarios is air change rate (0.5 h 1 or 6 h 1): scenarios 0 and 1, scenarios 2 and 3, and scenarios 4 and 5. The comparison between scenarios helps to assess the impact of indoor quality on both GDP and GHG emissions. Each scenario from 1 to 5 is also compared to the base scenario (Scenario 0). Fig. 8 indicates the behaviour of two parameters over time – GDP generated by income and GHG emissions. Results show that with sufficient financing and operation of mechanical ventilation (Scenario 5), the GDP has the highest growth rate and reaches a value of 36 million EUR/year higher compared to Scenario 0. At the same time, the scenario 5 shows the second-highest GHG emission reduction rate compared to Scenario 0. While Scenario 4 has a major GHG reduction rate, it has almost no change in GDP. This scenario shows that if energy efficiency measures are implemented without having good indoor air quality, the climate change goals can be met while the GDP only has a marginal growth rate. A minimal GDP growth rate can be observed in Scenario 2 compared to the base scenario although the former scenario does provide GHG emission reduction. In Scenario 1, mechanical ventilation in buildings with improved energy efficiency is used and it results in higher GHG emissions than Scenario 0. On the other hand, Scenario 1 has a signifi­ cant increase in GDP due to better indoor air quality. The optimal scenario (Scenario 6) was created during the research by using the optimisation tool in Powersim Studio. An additional 74.66 mEUR/year from national and municipal funding sources and a 4.05 h 1 air change rate were used as input data in the optimal scenario. Fig. 10 illustrates the results of scenario 4–6 in comparison with the base scenario.

3.4. Limitations The key concern for all modelling studies is always data availability and quality. The simulated data within a model only creates a trend and does not generate exact numbers, thus there are no models that flaw­ lessly represent the studied systems. Data availability is not crucial to create a good system dynamics model, as Barlas explained (Barlas, 1996). John D. Sterman states that the complete verification and vali­ dation of the models is impossible. Models are simplified versions of the real world and they differ from reality in ways large and small, infinite in number. Pure analytical statements and propositions derived from the axioms of a closed logical system are the only statements that can be validated or shown to be true (Sterman, 2000). The structural and behavioural validations were carried out throughout the modelling process using statistics and the data from the literature. The historical behaviour validation test for study and work path, including the GDP income part was used to build confidence in the model. 4. Results Greenhouse gas emissions for different scenarios are presented in Fig. 8. The lowest GHG reduction is reached by scenario 1, followed by scenario 0. In both scenarios, implementation of energy efficiency measures is very slow due to the lack of finances after 2022. The dif­ ference between these two scenarios is caused by the air change rate – the higher the air change rate in the building, the higher the energy consumption and, correspondingly GHG emissions. Scenarios 2 and 3 7

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Journal of Environmental Management 262 (2020) 110270

Fig. 11. Dynamics of the qualified labour force in different sub-groups (Q1 – low-income labour; Q2 – medium low-income labour; Q3 – medium high-income labour; Q4 – high-income labour) for Scenarios 0, 1 and 5.

Fig. 11 shows the dynamics of the qualified labour force in different sub-groups for scenarios 0, 1 and 5. If indoor air quality is improved, the numbers of less paid sub-groups decrease as higher-paid sub-group numbers increase. This is due to higher achievements during the edu­ cation process if indoor air quality is improved. The same tendency is observed in the sub-groups of medium-skilled labour.

economy, which might have some impact on the final results. Another important aspect of the model will be the larger gap in salaries between different qualification levels, as this detail might have a much more significant impact on the income part of GDP. Liva Asere: Conceptualization, Methodology, Software, Writing Original Draft, Writing - Review & Editing. Andra Blumberga: Conceptualization, Methodology, Software, Writing - Review & Editing, Funding acquisition.

5. Conclusions Increased energy efficiency in buildings reduces global climate change; however, the operation of natural ventilation is not sufficient enough due to significantly improved airtightness of building envelope. Thus mechanical ventilation is crucial after improving these buildings to ensure indoor air quality. The operation of mechanical ventilation provides fresh air and reduces different pollutants in the room, which leads to increased productivity and study achievements. In long term, this has an impact on the qualification, salary and GDP. Our study results show that energy efficiency – indoor air quality dilemma has an impact on GDP in relation to the level of future salaries of young adults. The model structure created within this study is universal and can be applied to different countries with different education systems, salaries and other factors. Simulation results from the case study in Latvia show that even if all education buildings have improved energy efficiency performance and have a significant reduction of outdoor CO2 level, indoor CO2 will be very high if no mechanical ventilation is used. The best solution is to increase energy efficiency while providing good indoor air quality by operating mechanical ventilation since an increase in GDP provides financial sources for further energy efficiency measures. Results show a good correlation between air exchange rates used in standards ASHRAE and EN 15251 and modelled optimisation scenario (Scenario 6). Indoor air quality impact on student achievements during studies is based on scientific findings by other authors. There are factors such as student personality and abilities, teacher motivation, education pro­ gram, etc. that are not considered in this model. Further research will focus on education buildings as energy pro­ sumers using renewable energy sources, thus reducing GHG emissions by having high indoor air quality. It will also include analysis about unemployment and the shadow

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