Next Article in Journal
The Recent Progress of Natural Sources and Manufacturing Process of Biodiesel: A Review
Previous Article in Journal
Art in Urban Spaces
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Forest and Society’s Welfare: Impact Assessment in Lithuania

Lithuanian Research Centre for Agriculture and Forestry, Institute of Forestry, LT-53101 Girionys, Lithuania
Author to whom correspondence should be addressed.
Sustainability 2021, 13(10), 5598;
Received: 27 April 2021 / Revised: 14 May 2021 / Accepted: 15 May 2021 / Published: 17 May 2021
(This article belongs to the Section Economic and Business Aspects of Sustainability)


Effective formation and implementation of forest policy can only be achieved with orientation to the most important goal—increasing society’s welfare. The global problem is, at present, that the impact of forests on society welfare indexes have not been identified. The aim of the study is to design an assessment model and assess the impact of Lithuanian forests on the society welfare index. The impact of forests was determined by multiplying the country’s welfare of society index by the forest contribution coefficient. In this study, to assess the index of the welfare of Lithuanian society, a five-dimensional model with 16 indicators was applied. The study is based on the Eurostat database and on Lithuanian forestry statistics. The Lithuanian welfare of society index calculated according to the model was 51.4% and the contribution of forests in this index was 3.9%. It represented 7.6% of the index of the welfare of society. Forests have the greatest impact in the environmental dimension, according to the assessment results.

1. Introduction

Forests are one of the most significant world ecosystems. They provide wood and non-wood products as well as serve different social-ecological purposes, namely, recreation, absorption of greenhouse gas, biodiversity, water, soil, and air protection. The benefit of forests is expressed by various indicators such as a share within gross domestic product (GDP), volume of forest cutting, areas of protected forests, protection of biodiversity, the amount of absorbed greenhouse gas, the number of visitors in forests, etc. [1].
The Food and Agriculture Organization of the United Nations (FAO) stated that the economic benefits from the world forest is 1.1% of the global economy, 83.3 million forest-related workers and forest owners, 0.6% of food supply, and 6.1% energy. Forests indirectly contribute to human well-being by performing environment (water, air, soil) protection functions. Forests account for about 80% of the world’s biodiversity. They supply genetic material for the improvement of plants and animals [2].
A number of research papers were reviewed that mentioned that the relationship between forests and public welfare has been studied: forests and economic welfare [3], the welfare effects of forestry best management practices [4], forest ecosystem services—a cornerstone for human well-being [1,5]; forest and human health and well-being in light of climate change and urbanization [6], market and welfare economic impacts of sustainable forest management practices [7], the economic contribution of forests [8], the contribution of the forest sector to national economies [9], the contribution of forestry to well-being of mountain forest-dependent communities [10], the role of national-level indicators within sustainable development goals [11], the impacts of community forest management on human economic well-being [12], linking forest naturalness and human well-being [13], forest resources of nations in relation to human well-being [14], and others.
S. Kant et al. analyzed the effects of forests on welfare of society in a case study from West Bengal (India) and found that returns from non-timber forest products (NTFP) reduce income inequality, expressed by the Gini coefficient. To describe the welfare of society, average household income from the NTFP indicator was used as a descriptor. It was an attempt to link the impact of one group of forest products (NTFP) to one indicator of public welfare (Gini coefficient) [3].
In the United States, a multi-type equilibrium displacement model was constructed to examine how a forestry best management practices (BMP) program affected the welfare positions of consumers mills, loggers, and landowners. Forestry BMP programs are a combination of operational practices designed to protect stream water quality during timber harvesting operations. The welfare implication of forestry BMPs was measured by producer and consumer surplus. The results of the study showed that all agents experience higher or lower welfare losses [4]. As indicated by the study authors, the welfare measures did not capture nonmarket welfare effects of BMPs. Thus, the study evaluates the impact of BMP programs on forest sector welfare in terms of consumers and products surplus.
An ecosystems approach is proposed to analyze the impact of forests on the well-being of society in the Report of the Millennium Ecosystem Assessment. According to the report, ecosystems perform functions that benefit society by becoming services. Ecosystem services are divided into provisioning, regulating, supporting, and cultural. They affect elements of the well-being of society (basic material for good life, health, security, good social relations, freedom of choice). The main services from forest ecosystems include habitat provision, clean water, flood protection, carbon sequestration and storage, climate regulation, oxygen production, nutrient cycling, genetic and spiritual resources, cultural recreational, and tourism value [1]. The report describes the links between forest services and the well-being of society, and provides indicators that express them, but there are no studies on the impact of forest services on the welfare of society.
Chapter 12, “Forests, Human Health and Well-Being in the Light of Climate Change and Urbanizations” from the book Forest and Society—Responding to Global Drivers of Change states that many of the positive effects that forests have on human health and well-being may be altered as a result of climate change and subsequent changes in forest structure and forest cover. This book applies the broader definition of health, which embraces aspects of well-being. A quantitative interpretation of the impact of forests on human health and well-being provides data on forests as a source of health-promoting and bioactive components, vector-borne diseases in forested areas, and forest and human health in protected areas [6].
The economic welfare impacts of sustainable forest management (SFM) provide empirical evidence that there is a loss of economic welfare impacts in the timber industry in Peninsular Malaysia. If the forest area were managed according to the SFM practices, it would affect several economic elements such as harvested area, operational costs, and price and market changes in these elements influencing the economics of timber supply and demand. The total sum of the consumer and producer surplus was used as the most important indicator of economic welfare [7].
The contribution of forestry to the well-being of mountain forest-dependent communities in the Ukrainian Carpathian Mountains was investigated. Well-being there was a term used to describing the general condition of an individual or group, for example, their social, economic, ecological, psychological, spiritual, or medical state. High well-being means that, in some sense, the individual or group’s experience is positive. A questionnaire survey of business representatives, forestry specialists, and local community representatives found that in a broad sense, economic, environmental, social, cultural, and aesthetic functions of forests contribute considerably to the well-being of forest-depended communities in the Ukrainian Carpathians [10].
Evaluating the impact of communities’ forest management on human economic well-being across Madagascar determined that forest use restrictions had a negative impact on household well-being. In this study, human economic well-being was measured by per capita consumption expenditures [12].
The studies were conducted with the general goal to contribute to the empirical rationale for linking forest naturalness with human well-being in Malaysia. A subjective approach that takes into account individual experiences in nature and is able to reveal the synergistic well-being benefits of nature on physical, mental, and social well-being is adopted in this study. Well-being was assessed by respondents using the interview method. Respondents were asked about their perceived well-being, physical and mental health gained by visiting the forest, and experimental connection to nature. “Nature” was divided into botanical garden, abandoned rubber estate, secondary forest, and primary forest. The study determined a positive correlation between environmental preferences and concluded that naturalness is an important dimension of environmental experience that may benefit human well-being [13].
There are studies not only on the impact of forests on the welfare of society, but also on the impact of the welfare of society on forests. For a description of social welfare, the Human Development Index (HDI) stated that it entails forest resources of nations improving along with progress in human well-being. Highly developed countries apply modern agricultural methods on good farmlands and abandon marginal lands, which become available for forest expansion. Developed countries invest in sustainable programs of forest management and nature protection [14]. A similar conclusion was formulated by analyzing the socio-economic factors affecting global forest area changes. The results show that many socio-economic factors have a negative impact on forest area in countries at low levels of human development, but their impacts become positive in countries at higher levels of human development, such as the rate of rural population, the adult literacy rate, and GDP per capita [15].
However, forest benefit evaluations [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] are not linked to index assessments on the welfare of society, and do not show which part of the welfare of society is determined by forests.
There is a great variety of concepts describing the welfare of society (well-being, welfare, quality of life, sustainable development, etc.) and the methods of assessing them. From the traditional point of view, the size of GDP was considered to be the indicator of the welfare of society within a country. However, currently there is common agreement to express welfare not only by financial indicators but also by taking into consideration quality of life [18]. Quality of life is determined by material conditions, personal security, environmental quality, population health status, expression possibilities, and moral psychological climate [19]. In different methodologies, the welfare of society (quality of life) is described by indicators that are often grouped into economic, social, political, health, environmental, and other dimensions. Various aggregated indices are constructed on that basis. They include various constructions of composite index steps: selection and combination of variables, data selection, multivariate analysis, normalization, weighting, and aggregation [20].
Different methodologies to assess society’s quality of life (QL) have been created [21]: Physical QL Index (1970), Living Condition Index (1974), Michigan QL Assessment Methodology (1975), Ferrans and Powers QL Index (1984), Individual’s Social Development Index (1990), Genuine Progress Indicators (1995), World Health Organization QL Model (1997), Calvert-Henderson QL Assessment Methodology (2000), Economist Intelligent Unit QL Index (2005), Legatum Prosperity Index (2007), J. Stiglitz, A. Sen and J. P. Fitoussi QL index (2009), International Living QL Index (2010), Indirect QL Indicator (2010), and the Complex LQ Assessment Methodology (2011). Newer indexes include the European Deprivation Index [22], Global Sustainable Competitiveness Index [23], Sustainable Progress Index [24], index of the welfare of society [25], and the Sustainable Development Goals (SDG) index [26,27]. Following Brundtland’s report “Our common future” (1987), stating that “humankind development must be sustainable in order to assure welfare at present without diminishing possibilities of welfare in the future” [28], the concept of the welfare of society has been transformed into the concept of sustainable development of society. In the 70th session of the United Nations, the 2030 Agenda for Sustainable development was adopted and it includes the Sustainable Development Goals (SDG) and targets. The SDGs cover a broad range of social economic development issues (poverty, hunger, health, gender equality, water, sanitation, energy, environment, social justice, etc.). It includes 17 goals, 169 targets, and 247 indicators [29]. The analysis of correlation between forestry and the 2030 Agenda for Sustainable Development goals and targets was undertaken. The strongest correlation was identified with goal 15: “to preserve, restore and promote sustainable use of dryland ecosystems, to sustainable manage forests, fight with deserting, prevent the loss of biodiversity.” Moreover, forests have an impact on the implementation of goals related to increasing inhabitants’ income, food resources, healthy way of life, water access, climate change, etc. [16,17].
There is a tendency to increasingly integrate environmental indicators into public welfare assessments. For instance, the Genuine Progress Index (GPI) is one of the most widespread indexes and changes GDP in assessing the welfare of society by taking into consideration not only economic benefit but also social and environmental indicators. Environmental indicators show costs and losses due to water and air pollution [30]. A specialized Environmental Performance Index [31] and individual environmental dimensions have been created [22,23,24,25,26,27,30]. The lists of evaluation indicators are supplemented by forest-related indicators, for example, permanent deforestation, meaning an area that is protected in terrestrial sites important to biodiversity, greenhouse emissions, and the share of renewable energy in the total primary energy supply [26,27]. However, the overall impact of forests on indexes of the welfare of society has not been determined.
The aim of this study is to assess the impact of Lithuanian forests on the welfare of society on the basis of assessments of Lithuanian social welfare indexes. The area of Lithuanian forest land covers 2197 thousand ha, whereas forests occupy 33.7% of the territory (data from 1 January 2019) [31]. The proposed solutions of this study, which elaborate on the Lithuanian case, are important internationally because the developed methodology and assessment are replicable.

2. Materials and Methods

The impact of forests on the welfare of society was determined by multiplying the country’s index of the welfare of society by the forest contribution coefficient:
where SWIF—contribution of forests on the welfare of society index, %; SWI—welfare of society index for the country, %; and k—forest contribution coefficient.
The SWI calculation is based on the multidimensional welfare of society and sustainable development assessment method [20,21,25,27,32]: (1) selection of dimensions and indicators of the welfare of society, (2) defining indicator performance, (3) defining indicator performance threshold, (4) normalization, (5) weighting, and (6) aggregation.
Dimensions and indicators. The dimensions and indicators used for Lithuania in this study were suggested by J. Kisieliauskas. After analyzing various methodologies for assessing the level of the welfare of society, the 5 dimensions that met the objective needs of members of society were distinguished: economic, political, social, health, and environmental. Sixteen indicators of the welfare of society selected were based on statistical and expert methods. The dimensions were expressed by the following indicators: the economic dimension—indicators of GDP per capita, annual inflation, employment rate, and government debt; the social dimension—indicators of income inequality, poverty rate, divorce rate, and expected duration of education; the political dimension—indicators of perception of corruption and democracy; the health dimension—indicators of life expectancy, infant mortality, and suicide rate; and the environmental dimension—indicators of greenhouse gas emissions, energy from renewable sources, and water productivity [25].
Indicator performance. The performance of indicators was defined in the Eurostat database [33].
Indicators performance threshold. The majority of threshold performance (optimal and minimum values) was defined in the Europe Sustainable Development Report 2020 [27]. The missing optimal and minimum values of indicators were determined according the abovementioned report’s [27] methodology by using the average of the top two performers in Europe.
Normalization. To make the data comparable across indicators, each variable was normalized using the following formula [20,34]:
x = x min ( x ) max ( x ) min ( x ) 100
where x’—the normalized value after rescaling (the same as the SWI), %; x—indicator value; and max/min denote the bounds for best and worst performance, respectively.
Weighing. Equal weights for the indicators and dimensions were applied.
Aggregation. The country overall SWI index was estimated according to the following formula [20,34]:
SWI = j m 1 m i n SWI i j n i j
where SWI—welfare of society index for the country, SWIij—index of indicator i under dimension j, i—indicator, j—dimension, n—number of indicators for dimension j, and m—number of dimensions.
The contribution of forests coefficient (k) was measured by the ratio of the forest-related effect to its size in the country. The welfare of society indicators were divided into three groups according to the impact of forests. First were the indicators for which the impact of forests was clearly expressed and could be determined on the basis of statistics (GDP per capita, employment rate, greenhouse gas emissions, energy from renewable sources). The annual GDP per capita in the Lithuanian forest sector (forestry; wood, paper, and furniture industries) was EUR 618.7 in 2019 [35]. The contribution of the forest sector to the employment rate was 5.1% in 2019 [35]. The impact of forests on greenhouse gas emissions was determined by increasing the country’s total emissions by the amount of greenhouse gases absorbed in the forests. Forests in Lithuania absorbed 4743 kt of greenhouse gases in 2018, or 1.7 t per capita [36]. The contribution of energy from wood to the total energy consumption was 16% in 2019 [37]. Second were indicators for which forest impacts were possible but statistically not identified (poverty rate, health indicators, water productivity). Indicators were assessed based on various assumptions and known information. The forest contribution coefficient for the poverty rate was determined by the ratio of food products from forests (mushrooms, berries, and hunting game) to total agricultural production. In 2015, agricultural production in Lithuania was EUR 2530.4 million [37], and food from forests was EUR 72.7 million [35]. The contribution of forests was 2.9%. Forests certainly had an impact on health indicators. We assumed that the importance of forests to human health was proportional to the proportion of leisure time spent in forests. The contribution coefficient for indicators of the health dimension was determined as the ratio of the duration of leisure time in the forests to the total length of leisure time of the country’s population. In 2015, leisure time in Lithuania was 1788.5 million hours/year, of which 72.7 million hours/year were spent in forests [38]. Forest contribution was 4.3%. The forest contribution coefficient for water productivity was determined as the ratio of the transfer of forest-influenced surface water runoff to groundwater per year (279.3 million m3) [39] and the amount of Lithuanian groundwater (14,670 million m3) [40]. The forest contribution was 1.9%. Third were indicators for which there was no possibility or for which it was impossible to estimate forest impact (e.g., divorce rate, expected duration of education, corruption perception index, democracy index, inflation rate, government debt). The calculation of the forest contribution coefficients is presented in Table 1.
For other social and political indicators (Gini income inequality coefficient, divorce rate, expected duration of education, corruption perception index, democracy index), we assumed that they were not influenced by forests.

3. Results

The calculation of forests’ impact on the welfare index of Lithuanian society is presented in Table 2.
The calculation shows that the contribution of forests to the welfare index of Lithuanian society (51.4%) was 3.9%. It represented 7.6% of the SWI. Most of the SWIF had environmental dimensions—75.6% of the SWIF (Table 2). This was calculated by dividing the environmental dimension of the SWIF (14.9%) by the sum of all the SWIF (19.7%).

4. Discussion

Several problems remain in assessing the impact of forests on the welfare of society. First, the compilation of the list of dimensions and indicators already showed that different results are obtained from different lists.
Our assessment results from to the five-dimensional and 16-indicator model were compared to the European Sustainable Development Report 2020 [20] model, where the assessment is based on the 2030 Agenda for Sustainable Development SDGs (17 goals) with 106 indicators. According to the first model, SWI was 51.4% (Table 1), and according to the second, 64.4% (Appendix A). The SWIFT, on the other hand, was 3.9% and 4.9%, respectively. As the data sources of indicator performance and their thresholds overlapped, the same data normalization formula was applied, and the differences in the assessment were due to the differences in dimensions and indicators. For example, the suicide rate in Lithuania is large and its negative impact on the health dimension of the three indicators is more significant than in the other model, where the good health and well-being dimension has 20 indicators. However, models with fewer indicators due to information provision problems are more appropriate for assessing forest impacts. As regards studies on the impact of forests on the index of the welfare of society, it should be noted that a comprehensive analogous study such as the one done by the authors for Lithuania does not exist for other countries, so in this respect it is difficult to comment on the results obtained by the authors. In Lithuania, the issues of assessing the impact of agriculture on the welfare of society were studied [41]. The study concluded that interrelated economic, social, and environmental dimensions must be applied in assessing the impact of agriculture on the welfare of society.
In Lithuania, the share of forest impact in the country was assessed according to nine indicators, where forests influenced the overall indicators of the country: GDP, material investment, energy generation, protected territories, forest coverage, absorption of CO2, number of employees, leisure time, and food resources. It was established that on average this share in 2015 was 17.6%. This shows the share of forests only according to the abovementioned nine indicators, but does not show the share of forests in the system of indicators of the welfare of society in the country [38]. This study evaluated the share of forests according to the country’s indicators for the welfare of society. the evaluated impact of forests on the index of the welfare of society was 3.9%. The assessed index was significantly lower because some indicators of the welfare of society were not affected by forests or were unknown. The calculation of the total SWI, SWIF, and its share in the SWI only for the items from Table 2 that had a specific k showed a higher share of SWIF in SWI (SWI was 49.3%, SWIF was 5.5%, share was 11.2%).
Debatable is the weighting of dimensions and indicators in the calculation of the index of the welfare of society. The main approaches to designing weights include equal weights, mathematical weights, expert weights, and subjective weights [32]. Due to the difficulty and uncertainty in determining weighting factors based on experts’ opinion, equal weighting was suggested [20]. Equal weighting was used in methodology of the Europe Sustainable Development Report 2020. Equal weights were therefore retained and countered as the most suitable option [27]. This view was followed in this study.
Assessing the impact of forests on the welfare of society, it has become clear that many indicators are not quantified in relation to the welfare of society. Forests contribute to most SDGs. Forests contribute to the food supply. Wood fuel is an important source of energy. Forests generate employment in rural areas and in the wood industry. Wood is a renewable resource, and forests mitigate climate change, contributing to low carbon economies. Forests provide medicines and contribute to human health and a healthy environment. Forest ecosystems provide services, including climate regulation, social stabilization, regulation of water flows, and biodiversity, as well the gene pool and home of pollinators of agricultural crops [42]. The study “Sustainable development goals: their impacts on forest and people” found that all 17 SDGs are related to forests—for example, SDG1 (No Poverty): “Forests are both a mainstay of rural livelihoods and buffer and source of natural insurance,” SDG2 (Zero Hunger): “We need a reimagined food system that does not polarise agricultural production and the conservation of forest resources,” SDG3 (Health and Well-Being): “Forests are of crucial importance to global health and well-being,” etc. [43]. This justifies the need to assess the impact of forests on the SDGs. The United Nations Economic Commission for Europe (UNECE) Committee of Forests and the Forest Industry and the FAO European Commission’s 10 key targets for forests and trees in the 2030 Agenda for Sustainable Development are subdivided into three groups: (1) improving social and cultural benefit from forests and trees, (2) enhancing resilience and ecosystem benefits of forests, and (3) increasing green economy contribution of forest and trees [44]. However, the current statistical systems do not have sufficient data on the impact of forests on many indicators of the welfare of society. Our study reflects the most important forest-related indicators: the GDP of the forest sector, the number of its employees, the absorption of greenhouse gases, energy from wood fuel, food resources from forests, leisure time spend in forests, and groundwater recharge by forests.
Assessments of the impact of forests on the welfare of society are important in formulating and implementing forest policy. The assessments of the impact of forests on the welfare of society in this study highlight the feasibility of such assessments and raise issues for further research into improving assessment methods, such as estimation dimensions and indicators and their weighting, as well as the improvement of forest contribution coefficient determination.

5. Conclusions

The assessment of the welfare of society and sustainable development provided preconditions for assessing the impact of forests on the index of the welfare of society. The impact of forests on the welfare of society can by determined by multiplying the country’s index of the welfare of society by the forest contribution coefficient. The estimated contribution of forests to the index of the welfare of society in Lithuanian was about 8% in 2019. In Lithuania, forests have the greatest impact on the environmental dimension of the welfare of society. Statistical databases and other sources of information can identify the impact of forests on only a part of indicators of the welfare of society (GDP per capita, employment rate, greenhouse gas emissions per capita, share of energy from renewable sources). Other indicators of the welfare of society lack such information. Future research and efforts should focus on filling these data gaps.

Author Contributions

This research article is the result of a collaboration of the first contributing authors. These authors contributed to the conceptualization of the paper. S.M. wrote the original draft, and a draft was revised and edited by D.L. Both authors have read and agreed to the published version of the manuscript.


This research was supported by the Lithuanian Research Centre for Agriculture and Forestry’s long-term program “Sustainable forestry and global changes, 2017–2021.”

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that there is no conflict of interest.

Appendix A

Table A1. Lithuanian Sustainable Development indicators [27].
Table A1. Lithuanian Sustainable Development indicators [27].
SDGs and IndicatorsValue, xYearOptimal Value = 100, max (x)Lower Bound Value = 0, min (x)Normalized Value, SWI (x’)
SDG1—No Poverty
1.1 People at risk of income poverty after social transfers (%)22.92018025.610.5
1.2 Severely materially deprived people (%)9.42019031.470.1
1.3 Poverty headcount at USD 5.50/day (%)2.72020021.087.1
On average --55.9
SDG2—Zero Hunger
2.1 Prevalence of obesity, BMI ≥30 (% of adult population)26.320163.035.127.4
2.2 Human trophic level (best—2, 3—worst)2.520172.042.477.0
2.3 Yield gap closure (%)45.6201580.028.033.8
2.4 Gross nitrogen balance on agricultural land (kg/hectare)2520151020092.1
2.5 Ammonia emissions from agriculture (kg/hectare)8.8201786098.5
2.6 Exports of pesticides banned in the EU (kg per 1000 population)0.020190550100.0
On average --59.8
SDG3—Good Health and Well-Being
3.1 Life expectancy at birth (years)76.02018835475.9
3.2 Gap in life expectancy at birth among regions (years)0.4201801196.4
3.3 Population with good or very good perceived heath (% of population aged 16 or over)44.02018802534.5
3.4 Gap in self-reported health, by income (p.p.—percentage of people)35.4201806041.0
3.5 Self-reported unmet need for medical examination and care (%)2.2201803092.7
3.6 Gap in self-reported unmet need for medical examination and care, by income (p.p.)1.1201802094.5
3.7 Gap in self-reported unmet need for medical examination and care, urban vs. rural areas (p.p.) 0.0201801.2100.0
3.8 New reported cases of tuberculosis (per 100,000 population)37.820183.656193.9
3.9 Age-standardized death rate due to cardiovascular disease, cancer, diabetes, and chronic respiratory disease (per 100,000 population aged 30 to 70)20.720169.33147.5
3.10 Suicide rate (per 100,000 population)25.8201743016.2
3.11 Age standardized death rate attributable to household air pollution and ambient air pollution (per 100,000 population)342016036990.8
3.12 Mortality rate, under-5 (per 1000 live births) 4.020182.613098.9
3.13 People killed in road accidents (per 100,000 population)6.2201833489.7
3.14 Surviving infants who received 2 WHO-recommended vaccines (%)9220181004186.4
3.15 Alcohol consumption (liter/capita/year)11.2201871758.0
3.16 Smoking prevalence (%)292017125055.3
3.17 People covered by health insurance for a core set of services (%)98.720191005097.4
3.18 Share of total health spending financed by out-of-pocket payments (%)31.62018106661.4
3.19 Subjective well-being (average ladder score, worst—0, 10—best)6.320187.63.369.8
3.20 Cumulative COVID-19 tests performed, Feb–June 2020 (per 1000 population)41.1202050082.2
On average --74.1
SDG4—Quality Education
4.1 Participation in early childhood education (% of population aged 4 to 6)91.020181003586.2
4.2 Early leavers from education and training (% of population aged 18 to 24)4.02019431100.0
4.3 PISA score (worst—0, 600—best)479.72018525.635073.9
4.4 Underachievers in science (% of population aged 15)22.22018125375.1
4.5 Variation in science performance explained by students’ socio-economic status (%)12.520188.321.467.9
4.6 Resilient students (%)26.4201846.6551.4
4.7 Tertiary educational attainment (% of population aged 30 to 34)57.82019520100.0
4.8 Adults participation in learning (%)7.0201928025.0
4.9 Mean numeracy score in the Survey of Adults Skills (PIAAC) (worst—0, 500—best)267.2201928020084.0
On average --73.7
SDG5—Gender Equality
5.1 Unadjusted gender pay gap (% of gross male earnings)14.0201804065.0
5.2 Gender employment gap (p.p.)1.6201904196.1
5.3 Population inactive due to caring responsibilities (% of population aged 20 to 64)18.7201966678.8
5.4 Seats held by women in national parliaments (%)24.12019501231.8
5.5 Positions held by women in senior management positions (%)12.0201950024.0
5.6 Women who feel safe walking alone at night in the city or area where they live (%)652019903356.1
On average --58.6
SDG6—Clean Water and Sanitation
6.1 Population having neither a bath, nor a shower, nor an indoor flushing toilet in their household (%)9.1201803069.7
6.2 Population connected to at least secondary wastewater treatment (%)73.820171002067.3
6.3 Freshwater abstraction (% of long-term average available water)0.42017180100.0
6.4 Scarce water consumption embodied in imports (m3/capita)21.52013010078.5
6.5 Population using safely managed water services (%)92.0201710010.591.1
6.6 Population using safely managed sanitation services (%)91.3201710014.189.9
On average --82.8
SDG7—Affordable and Clean Energy
7.1 Population unable to keep home adequately warm (%)26.7201903523.7
7.2 Share of renewable energy in gross final energy consumption (%)24.4201850345.5
7.3 CO2 emission from fuel combustion per electricity output (MtCO2/TWh)3.5201705.940.7
On average --36.6
SDG8—Decent Work and Economic Growth
8.1 Gross disposable income (EUR/capita)18,391201830,000500053.6
8.2 Youth not in employment, education, or training (NEET) (% of population aged 15 to 29)10.9201982784.7
8.3 Employment rate (%)78.22019805592.8
8.4 Long-term unemployment rate (%)1.9201911493.1
8.5 People killed in accidents at work (per 100,000 population)2.820170544.0
8.6 In work at-risk-of-poverty rate (%)8.120183.318.668.6
8.7 Fatal work-related accidents embodied in imports (per 100,000 population)0.620100690.0
On average --75.3
SDG9—Industry, Innovation, and Infrastructure
9.1 Gross domestic expenditure on R&D (% of GDP)0.920183.30.417.2
9.2 R&D personnel (% of active population)0.8201820.329.4
9.3 Patent applications to the European Patent Office (per million population)10.4201924033.1
9.4 Households with broadband access (%)812019966058.3
9.5 Gap in broadband access, urban vs. rural areas (p.p.)9201902665.4
9.6 Individuals aged 55 to 74 years with basic or above digital skills (%)23201965530.0
9.7 Logistics performance index: quality of trade and transport-related infrastructure (worst—1, 5—best)2.720184.21.837.5
9.8 The Times Higher Education Universities Ranking: Average score of top 3 universities (worst—0, 100—best)19.3202050038.6
9.9 Scientific and technical journal articles (per 1000 population)0.820181.2066.7
On average --38.5
SDG10—Reduced Inequalities
10.1 Gini coefficient adjusted for top income44.2201527.56353.0
10.2 Palma ratio1.620170.92.556.3
10.3 Elderly poverty rate (%)28.220173.245.741.2
On average --50.2
SDG11—Sustainable Cities and Communities
11.1 Share of green space in urban areas (%)32.0201250064.0
11.2 Overcrowding rate among people living with below 60% of median equivalized income (%)23.8201866569.8
11.3 Recycling rate of municipal waste (%)52.5201862084.7
11.4 Population living in a dwelling with a leaking roof; damp walls, floors, or foundation; or rot in window frames or floor (%) 14.8201863063.3
11.5 Satisfaction with public transport (%)44.1201882.62137.5
11.6 Access to improved water source, piped (% of urban population)99.020171006.198.9
On average --69.7
SDG12—Responsible Consumption and Production
12.1 Circular material use rate (%)4.8201719121.1
12.2 Gross value added in environmental goods and services sector2.220175.5126.7
12.3 Production-based SO2 emissions (kg/capita)94.12012052582.1
12.4 Imported SO2 emissions (kg/capital)11.9201203060.3
12.5 Nitrogen production footprint (kg/capita)48.62010210052.4
12.6 Net imported emissions of reactive nitrogen (kg/capita)8.0201004582.2
On average --54.1
SDG13—Climate Action
13.1 Greenhouse gas emissions (t/capita)7.4201802063.0
13.2 CO2 emissions embodied in imports (tCO2/capita)1.8201503.243.8
13.3 CO2 emissions embodied in fossil fuel exports (kg/capita)0.02018044000100.0
On average --68.9
SDG14—Life Below Water
14.1 Excellent bathing site quality (%)84.620181002579.6
14.2 Fish caught by either trawling or dredging (%)1.4201609098.4
14.3 Fish caught that are then discarded (%)5.0201602075.0
14.4 Marine biodiversity threats embodied in imports (per million population)0.120180295.0
14.5 Mean area that is protected in marine sites important to biodiversity (%)83.42019100083.4
On average --86.3
SDG15—Life on Land
15.1 Mean area that is protected in terrestrial sites important to biodiversity (%)91.120191004.690.7
15.2 Mean area that is protected in freshwater sites important to biodiversity (%)95.22019100095.2
15.3 Biochemical oxygen demand in rivers (mg O2/litre)2.1201711087.7
15.4 Red List Index of species survival (worst—0, 1—best)1.0201910.6100.0
15.5 Terrestrial and freshwater biodiversity threats embodied in imports (per million population)0.8201801092.0
On average --93.1
SDG16—Peace, Justice, and Strong Institutions
16.1 Death rate due to homicide (per 100,000 population)2.820170.32389.0
16.2 Population reporting crime in their area (%)3.72018424100.0
16.3 Gap in population reporting crime in their area, by income (p.p.)1.0201801593.3
16.4 Corruption Perception Index (worst—0, 100—best)60201988.61362.2
16.5 Unsentenced detainees (% of prison population)9.1201877596.9
16.6 Exports of major conventional weapons (TIV constant 1990 million USD per 100,000 population)2.2201903.435.3
16.7 Press Freedom Index (best—0, 100—worst)22.12019108082.7
On average --79.9
SDG17—Partnership for the Goal
17.1 Official development assistance (% of GNI)0.1201910.10.0
17.2 Corporate Tax Haven Score (best—0, 100—worst)54.820194010075.3
On average --37.7
Total average --64.4


  1. MEA. Ecosystems and Human Well-Being: Synthesis; Island Press: Washington, DC, USA, 2005; 155p, Available online: (accessed on 10 January 2021).
  2. FAO. State of the World’s Forests. Enhancing the Socioeconomic Benefits from Forests; Food and Agriculture Organization of the United Nations: Rome, Italy, 2014; 120p, Available online: (accessed on 10 January 2021).
  3. Kant, S.; Nautiyal, J.C.; Berry, R.A. Forests and economic welfare. J. Econ. Stud. 1996, 23, 31–43. [Google Scholar] [CrossRef]
  4. Sun, C. Welfare effects of forestry best management practices in the United States. Can. J. For. Res. 2006, 36, 1674–1683. [Google Scholar] [CrossRef]
  5. Louman, B.; Fischling, A.; Glück, P.; Innes, J.; Lucier, A.; Parrotta, J.; Santoso, H.; Thompson, I.; Wreford, A. Forest Ecosystem Services: A Cornerstone for Human Well-Being; IUFRO World Series; IUFRO: Helsinki, Finland, 2009; Volume 22, pp. 15–27. Available online: (accessed on 14 February 2021).
  6. Hägerhäll, C.M.; Ode, A.; Tveit, M.S.; Velarde, M.D.; Colfer CJ, P.; Sarjala, T. Forests, Human Health and Well-Being in Light of Climate Change and Urbanisation; IUFRO World Series; No. 25; IUFRO: Helsinki, Finland, 2010; pp. 223–234. Available online: (accessed on 10 January 2021).
  7. Abdul-Rahim, A.S.; Mohd-Shahwahid, H.O.; Mad-Nasir, S.; Awang-Noor, A.G. Market and welfare economic impacts of sustainable forest management practices: An empirical analysis of timber market in Peninsular Malaysia. Afr. J. Bus. Manag. 2013, 7, 2951–2965. [Google Scholar]
  8. Agrawal, A.; Cashore, B.; Hardin, R.; Shepherd, G.; Benson, C.; Miller, D. Economic Contributions of Forests. In Background Paper, Proceedings of the United Nations Forum on Forests, Istanbul, Turkey, 8–19 April 2013; United Nations: New York, NY, USA, 2013; 121p. Available online: (accessed on 10 January 2021).
  9. FAO. Contribution of the Forestry Sector to National Economies, 1990–2011; Forest Finance Working Paper FSFM/ACC/09; FAO: Rome, Italy, 2014; 156p, Available online: (accessed on 10 January 2021).
  10. Melnykovych, M.; Soloviy, I. Contribution of Forestry to Well-being of Mountain Forest Dependent Communities’ in the Ukrainian Carpathians. Res. Pap. Ukr. Acad. Sci. 2014, 12, 233–241. [Google Scholar]
  11. Hoare, A. Improving Legality among Small-Scale Forest Enterprises. The Role of National-Level Indicators within the Sustainable Development Goals; Research paper; Royal Institute of International Affairs: London, UK, 2016; 28p, Available online: (accessed on 10 January 2021).
  12. Rasolofoson, R.A.; Ferraro, P.J.; Ruta, G.; Rasamoelina, M.S.; Randriankolona, P.L.; Larsen, H.O.; Jones, J.P.G. Impacts of Community Forest Management on Human Economic Well-Being across Madagascar. Conserv. Lett. 2016, 10, 346–353. [Google Scholar] [CrossRef][Green Version]
  13. Foo, C.H. Linking forest naturalness and human wellbeing—A study on public’s experiential connection to remnant forest within a highly urbanized region in Malaysia. Urban For. Urban Green. 2016, 16, 13–24. [Google Scholar] [CrossRef]
  14. Kauppi, P.E.; Sandström, V.; Lipponen, A. Forest resources of nations in relation to human well-being. PLoS ONE 2018, 13, 1–10. [Google Scholar] [CrossRef]
  15. Michinaka, T.; Miyamoto, M. Forests and Human Development: An Analysis of the Socio-Economic Factors Affecting Global Forest Area Changes. J. For. Plan. 2013, 18, 141–150. [Google Scholar]
  16. Gregersen, H.; El Lakany, H.; Blaser, J. Forests for sustainable development: A process approach to forest sector contributions to the UN 2030 Agenda for Sustainable Development. Int. For. Rev. 2017, 19, 10–22. [Google Scholar] [CrossRef]
  17. Baumgartner, R.J. Sustainable Development Goals and the Forest Sector—A Complex Relationship. Forests 2019, 10, 152. [Google Scholar] [CrossRef][Green Version]
  18. Samoška, M. Visuomenės gerovės ir verslo sąlygų palankumo vertinimo tyrimų analizė [Prosperity, human development and ease of doing business research analysis]. Moksl. Liet. Ateitis 2013, 5, 1–6. [Google Scholar] [CrossRef][Green Version]
  19. Ruževičius, V. Gyvenimo Kokybė [Quality of Life]. Kvalitetas, 17 October 2013; 10p. Available online: on 10 January 2021).
  20. OECD; JRC. Handbook on Constructing Composite Indicators. Methodology and User Guide; Organisation for Economic Co-Operation and Development: Paris, France; Joint Research Centre of the European Commission: Ispra, Italy, 2008; 162p, Available online: (accessed on 10 January 2021).
  21. Servetkienė, V. Gyvenimo Kokybės Daugiadimensis Vertinimas, Identifikuojant Kritines Sritis [Multidimensional Assessment of the Quality of Life Identifying Critical Areas]. Ph.D. Thesis, Mykolo Romerio Universitetas, Vilnius, Lithuania, 2013; 312p. Available online: https://mruni.en (accessed on 10 January 2021).
  22. Launoy, G.; Launay, L.; Dejardin, O.; Bryére, J.; Guillaume, E. European Deprivation Index: Designed to tackle socioeconomic inequalities in cancer in Europe. Eur. J. Public Health 2018, 28 (Suppl. 4), 214. [Google Scholar] [CrossRef]
  23. SolAbility. The Global Sustainable Competitiveness Index 2020. In The Sustainable Competitiveness Report, 9th ed.; Zurich: Seoul, Korea, 2020; 63p, Available online: (accessed on 23 March 2021).
  24. Clark, C.M.A.; Kavanagh, C.; Lenihan, N. Measuring Progress: The Sustainable Progress Index 2020; Social Justice Ireland: Dublin, Ireland, 2020; 79p, Available online: (accessed on 23 March 2021).
  25. Kisieliauskas, J. Assessment of Government Expenditure Effect on Welfare of Society in the EU Countries. Ph.D. Thesis, Vytautas Magnus University, Kaunas, Lithuania, 2017; 51p. Available online: (accessed on 20 March 2021).
  26. Sachs, J.; Schmidt-Traub, G.; Kroll, C.; Lafortune, G.; Fuller, G.; Woelm, F. The Sustainable Development Goals and COVID-19; Sustainable Development Report 2020; Cambridge University Press: Cambridge, UK, 2020; 520p, Available online: (accessed on 10 January 2021).
  27. SDSN; IEEP. The 2020 Europe Sustainable Development Report: Meeting the Sustainable Development Goals in the Face of the COVID-19 Pandemic; Sustainable Development Solutions Network: Paris, France; Institute for European Environmental Policy: Brussels, Belgium, 2020; 194p, Available online: (accessed on 10 January 2021).
  28. UN. Non-Legally Binding Authoritative Statement of Principles for a Global Consensus on the Management, Conservation and Sustainable Development of All Types of Forests; UN document; United Nations: New York, NY, USA, 1992; 6p. Available online: (accessed on 10 January 2021).
  29. UN. SDG Indicators. Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development; United Nations: New York, NY, USA, 2017; 21p. Available online: (accessed on 1 February 2021).
  30. Talberth, J.; Weisdorf, M. Genuine Progress Indicator 2.0: Pilot Accounts for the US, Maryland, and City of Baltimore 2012–2014. Ecol. Econ. 2017, 142, 1–11. [Google Scholar] [CrossRef]
  31. Wendling, Z.A.; Emerson, J.W.; de Sherbinin, A.; Esty, D.C.; Hoving, K.; Ospina, C.D.; Murray, J.-M.; Gunn, L.; Ferrato, M.; Schreck, M.; et al. Environmental Performance Index; Yale Center for Environmental Law & Policy: New Haven, CT, USA, 2020; Available online: (accessed on 9 April 2021).
  32. MRU. Gyvenimo Kokybės Matavimo Rodiklių Sistema ir Vertinimo Modelis [Quality of Life Measurement Indicator System and Assessment Model]; Mokslo Studija MRU: Vilnius, Lithuania, 2015; 760p, Available online: (accessed on 14 February 2021).
  33. Eurostat. European Statistical Recovery Dashboard. 2020. Available online: (accessed on 29 January 2021).
  34. Lafortune, G.; Fuller, G.; Moreno, J.; Schmidt-Traub, G.; Kroll, C. SDG Index and Dashboards; Detailed Methodological Paper; Bertelsmann Steftung: Gutersloh, Germany; Sustainable Development Solutions Network: Paris, France, 2018; 56p, Available online: https:// (accessed on 29 January 2021).
  35. Ministry of Environment, State Forest Service. Lithuanian Statistical Yearbook of Forestry 2019; VšĮ Lulutė; Ministry of Environment, State Forest Service: Vilnius, Lithuania, 2019; 184p. Available online: (accessed on 14 February 2021).
  36. Ministry of Environment. Lithuanian National Inventory Report 2020. Greenhouse Gas Emissions 1990–2018; Ministry of Environment: Vilnius, Lithuania, 2020; 567p. Available online: (accessed on 14 February 2021).
  37. Official Statistics Portal. Environment, Agriculture and Energy in Lithuania, 2020 ed.; Agricultural Production; Official Statistics Portal: Vilnius, Lithuania, 2020. Available online: (accessed on 9 April 2021).
  38. Mizaras, S.; Lukmine, D.; Doftarte, A. Miškų ūkio poveikio visuomenės gerovei vertinimas Lietuvoje [Assessment of forestry impact on society welfare in Lithuania]. Žemės Ūkio Moksl. 2019, 26, 135–144. [Google Scholar] [CrossRef]
  39. Mizaras, S.; Brukas, V.; Mizaraite, D. Miškų Tvarkymo Darnumo Vertinimas: Ekonominiai ir Socialiniai Aspektai [Evaluation of Forest Management Sustainability: Economic and Social Aspects]; Lututė: Kaunas, Lithuania, 2015; 256p, Available online: (accessed on 27 April 2021).
  40. Lietuvos Geologijos Tarnyba [Lithuanian Geological Survey]. Požeminis Vanduo [The Groundwater], 17 February 2020. Available online: on 9 April 2021).
  41. Stanytė, S.; Makutėnienė, D. Žemės ūkio reikšmės ir poveikio visuomenės gerovei vertinimo modelis [Model of the assessment of agriculture importance and impact upon public welfare]. Vadyb. Moksl. Stud. Kaimo Verslų Infrastruktūros Plėtrai 2012, 1, 113–121. [Google Scholar]
  42. FAO. Forest and the Sustainable Development Goals. In Proceedings of the Committee on Forestry, Twenty-Second Session, Rome, Italy, 23–27 June 2014; Available online: (accessed on 29 January 2021).
  43. Katila, P.; Colfer, C.J.P.; Jong, W.; Galloway, G.; Pacheco, P.; Winkel, G. Sustainable Development Goals: Their Impacts on Forests and People; Cambridge University Press: Cambridge, UK, 2019; 617p. [Google Scholar] [CrossRef][Green Version]
  44. IIED. Sustainable Development Goals and Forests; Report; International Institute for Environment and Development: London, UK, 2015; 31p, Available online: (accessed on 10 January 2021).
Table 1. The forest contribution coefficient in Lithuania.
Table 1. The forest contribution coefficient in Lithuania.
IndicatorsYearLithuaniaForest SectorForest Contribution Coefficient
1. GDP per capita, EUR201917460618.70.035
2. Employment rate, %201978.25.10.065
3. Greenhouse gas emissions per capita, t20187.41.70.230
4. Energy from renewable sources, %201925.516.00.627
5. Food resources, million t20152530.472.70.029
6. Citizens’ leisure time, million hours/year20151788.577.30.043
7. Groundwater recharge, million m3201914,670.0279.30.019
Table 2. Forest impact on the welfare of Lithuanian society.
Table 2. Forest impact on the welfare of Lithuanian society.
Dimension and IndicatorsValue for all Lithuania, x *YearOptimal Value = 100, max (x)Lower Bound Value = 0, min (x)Normalized Value, SWI (x’)Forest Contribution Coefficient (k)SWIF
1. Economic
1.1 GDP, EUR per capita17,460201930,000500049.80.0351.74
1.2 Inflation rate, %2.220190.53.441.4--
1.3 Employment rate, %78.22019805592.80.0656.03
1.4 Government debt, % of GDP35.920190157.677.2--
On average----65.3-1.94
2. Social
2.1 Poverty rate, %20.62019025.619.50.0290.6
2.2 Gini income inequality coefficient35.4201927.56377.7--
2.3 Divorce rate per 1000 persons3.120180.93.10.0--
2.4 Expected duration of education1920182115.563.6--
On average----40.2-0.15
3. Political
3.1 Corruption perception index60201988.61362.2--
3.2 Democracy index7.52019106.528.6--
On average----45.4--
4. Health
4.1 Life expectancy, years75.82019835475.20.0433.23
4.2 Infant mortality rate per 1000 born3.420182.613099.30.0434.27
4.3 Suicide death rate per 100,000 persons33.9201743015.00.0430.65
On average----63.2-2.72
5. Environmental
5.1 Greenhouse gas emissions per capita, metric tons7.4201802063.00.23014.5
5.2 Share of energy from renewable sources, %25.5201950347.90.62730.0
5.3 Water productivity, GDP EUR per m3131.22018664.59.718.60.0190.35
On average----43.1-14.9
Total on average----51.4-3.94
* Data from Eurostat database [36].
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mizaras, S.; Lukmine, D. Forest and Society’s Welfare: Impact Assessment in Lithuania. Sustainability 2021, 13, 5598.

AMA Style

Mizaras S, Lukmine D. Forest and Society’s Welfare: Impact Assessment in Lithuania. Sustainability. 2021; 13(10):5598.

Chicago/Turabian Style

Mizaras, Stasys, and Diana Lukmine. 2021. "Forest and Society’s Welfare: Impact Assessment in Lithuania" Sustainability 13, no. 10: 5598.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop