“Relationship between sustainable development indicators and SMEs’ development indicators: Evidence from the EU countries”

This study aims to identify whether achieving sustainable development goals influences SMEs’ development and assess its degree. The dataset on SMEs’ development indicators and SDGs 2, 8, 9, 12, and 13 for the panel of EU-27 countries in 2011–2020 was collected using Eurostat and OECD datasets. Breusch and Pagan Lagrangian multiplier test for pooled OLS/panel data random effects and Hausman test for fixed/random effects were utilized. The results were in favor of random effect GLS regression for SDG2 models, SDG9 models, and SDG12-13 (Model 1) and fixed effect GLS regression for SDG8 models and SDG12-13 (Model 2), respectively. Based on bibliometric analyses using VOSViewer 14 and a comprehensive literature review, 19 independent variables have been selected from the “Sustainable development indicators” catalog covering five sustainable development goals; SMEs’ turnover and SMEs’ employees employed are used as the dependent variables to reflect SMEs’ development. The empirical evidence suggests a significant relationship between individual sustainable development and SMEs’ development indicators. It was found that all seven sustainable development indicators of SDG 2 (Zero hunger) and SDG 12 (Responsible consumption and production) have a significant relationship with the indicators of SMEs’ development. Instead, only a part (8 out of 13) of the sustainable development indicators of SDG 8 (Decent work and economic growth), SDG 9 (Industry, innovation and infrastructure), and SDG 13 (Climate action) have a significant relationship with two or one of the SMEs’ development indicators. Therefore, achieving sustainability goals stimulates the development of SMEs itself.


INTRODUCTION
Today, considering sustainable development issues and the success of SMEs is becoming increasingly relevant.SMEs are catalysts for economic growth, providing employment and income distribution and facilitating innovation.Moreover, SMEs could participate in reducing the environmental impact, preserving biodiversity, and regenerating natural resources.In 2023, SMEs in the European Union employed around 84.75 million individuals (European Commission, 2023a).The collective contribution of SMEs to the European economy was estimated to be approximately 4.15 trillion euros in the same year, with micro-sized enterprises contributing roughly 1.5 trillion euros to this value (European Commission, 2023b).SMEs' development is pivotal to the implementation of sustainable development goals.Small businesses can address social, economic,

SMEs and sustainable development
SMEs are considered as a means of achieving various goals of sustainable development through the reduction of poverty by increasing quality of life and life satisfaction (SDG 1), new job generation and enhancing economic growth (SDG 8), cultivating sustainable consumption and production pattern (SDG 12), contributing to the industrial and innovational development (SDG 9), implementing sustainable and green practices for emissions reduction and climate action (SDG 13).
Abisuga-Oyekunle et al. (2020) emphasized the crucial role of SMEs in poverty reduction and employment generation in African countries.Lopes de Sousa Jabbour (2020) stated that SMEs play a vital role in generating employment and distributing income, and are essential for expanding Asian exports.Inegbedion et al. (2024) investigated the correlation between the competitiveness of SMEs and their role in generating employment.Their research sought to elucidate how the competitiveness of SMEs might bolster national income by fostering job creation, with SME growth serving as an intermediary factor.After conducting a cross-sectional survey involving 93 participants from SMEs, the study determined that product innovation and product differentiation exhibit notable associations with employment creation, as they can augment SME expansion.Jasińska-Biliczak (2023) and Horobchenko and Voronenko (2018) discovered that businesses acquainted with the principles of sustainable development and the sustainable development goals, integrated into their strategic plans, demonstrated a heightened likelihood of survival.Sonntag et al. (2022) explored implementing SDGs by SMEs in Germany and Poland.It was found that there is a national aspect (context) in the SDGs implementation by every country.The lack of financial funds is the primary barrier to implementing sustainable development goals in SMEs.Gomes and Pinho (2023) focus on the contribution of European SMEs to SDG 12 in terms of carbon neutrality.Their findings indicate that when SMEs implement resource-efficient practices at the individual company level, it positively affects their uptake of broader measures for decarbonization at the macro level.Additionally, the adoption of these micro-level practices is positively influenced by internal business investments, while being negatively influenced by external funding sources and regulatory/administrative obligations.
1.2.Factors of SMEs' development Tambunan (2009) states that economic development provides a supportive environment for the development and expansion of enterprises across all scales, encompassing micro, small, medium, and large entities.1).
The study identified eight keyword clusters with from nine to 25 items in each.
The first cluster (highlighted in red) includes 22 items with the main keyword "sustainable development."The group focuses on sustainable development, manufacture and supply chains (i.e., sustainable production, lean production, green manufacturing, sustainable business, and food supply), investments (i.e., in energy efficiency, renewable energy resources, and electric energy storage), and environmental sustainability and environmental impact (i.e., carbon dioxide).All variables were also logarithmized, which were used in regression models.This procedure reduces data variability and helps increase the stability of model parameter estimates.
Before performing the regression analysis, checking for multicollinearity between the variables was important.Pearson's correlation coefficients and VIF (variance inflation factor) were used.The multicollinearity assessment was carried out to identify and exclude redundant variables from the regression models.
The Bresch and Pagan Lagrangian multiplier test was performed for panel data.This test helped determine between the OLS (ordinary least squares) and GLS (generalized least squares) methods, which better accounts for heteroskedasticity in the data.Next, a Hausman test was performed to select between fixed and random effects in panel models.The STATA 18 software was used to run the regression analysis.

Equations for the theoretical concepts
The theoretical concepts for the SMEs' and SDG 2 indicators could be presented as follows:

Checking multicollinearity
No multicollinearity issues were found for the SDG2, the SDG12-13, and the SMEs' development variables.The analysis found multicollinearity for SDG8 models: a high negative correlation between EmpltmntRt and UnempYouth (coefficient equals -0.82), as well as EmpltmntRt and LngTrmNmpl (coefficient equals -0.77) were observed.Moreover, VIF values for EmpltmntRt (4.42-4.53)indicated moderate multicollinearity.Also, EmpltmntRt as an independent variable in the model resulted in lower significance and changing sign of the regression coefficients for UnempYouth and LngTrmNmpl.Thus, EmpltmntRt was excluded from the SDG8 regression models.
Next, a multicollinearity issue was found for the SDG9 models.Pearson correlation coefficient values were 0.81 and 0.86 between Patent_Inv and SMEturnperCa, as well as Patent_Inv and RDinGDP, respectively.VIF values for Patent_Inv (4.99-5.16)indicated moderate multicollinearity.Also, Patent_Inv as an independent variable in the model resulted in lower significance and changing sign of the regression coefficients for RDinGDP.Thus, the Patent_Inv variable was excluded from SDG9 regression models.Detailed results of calculations are presented in Appendix A (Tables A1-A12).

Selecting the regression method and the estimated results
The results favored random effect GLS regression for SDG2 models, SDG9 models, and SDG12-13 (Model 1).However, fixed effect GLS regression was fitted better for SDG8 models and SDG12-13 (Model 2).Detailed results of calculations are presented in Appendix B (Tables C1-C8) and Appendix C (Tables C1-C8).
The estimated results of GLS FE and RE for examining the relationship between the SDG 2, SDG 12-13 indicators, and the SMEs' development are presented in Table 1.
For both SDG2 models, all regression coefficients have statistically significant values at the 1% or 5% significance level, indicating the statistical importance of the relationship between dependent and independent variables.Specifically, the coefficients for the "Agricultural factor income per annual work unit," the "Government support to agricultural research and development," and the "Area under organic farming" indicators in both models are statistically significant at the 1% or 5% level.Additionally, it is worth noting that the coefficients for the "Ammonia emissions from agriculture" indicator in Model 2 are statistically significant at the 1% significance level.The positive signs of all regression coefficients indicate a direct relationship between both dependent and inde- For both SDG9 models, the regression coefficients for the "Air emission intensity from industry" indicator have statistically significant values at the 1% significance level.Also, the coefficient for the "Share of buses and trains in inland passenger transport" indicator is statistically significant at the 1% level for Model 1, and it is not statistically significant for Model 2. The negative signs of the coefficients for the "Air emission intensity from industry" and the "Share of buses and trains in inland passenger transport" indicators display an inverse relationship between dependent and independent variables.The constant values are -1.546 and -3.567 for Models 1 and 2, respectively, at the 1% significance level.

Relationship between SDG 2 and SMEs' development indicators
Hypothesis 1 was confirmed regarding a significant relationship between three of four SDG However, the regression results do not identify a significant relationship between net greenhouse gas emissions and SMEs' development.

Relationship between SDG 8 and SMEs' development indicators
Hypothesis 3 was confirmed regarding a significant relationship between SDG 8 indicators measuring inclusive employment and SMEs' development in EU-27 countries.
A significant negative relationship was found between both SMEs' development indicators and youth unemployment and the long-term unemployment rate.A 10% increase in youth unemployment results in a 1.9% and a 1.5% decrease in SMEs' turnover and employment, respectively.That proves the crucial contribution of young people aged 15 to 29 in SMEs' development as employees or entrepreneurs.Moreover, a 10% increase in long-term unemployment, a 0.6% increase in SMEs' unemployment, and a 1.2% decrease in turnover are observed.Thus, the segment of the workforce aged 15 to 74 who have experienced unemployment for 12 months or longer significantly influences SMEs' development.
It is worth mentioning that there was no significant relationship between the investment share of GDP, fatal accidents at work, and SMEs' development indicators.However, a significant relationship was not found between the "Share of rail and inland waterways in inland freight transport" indicator, the "Gross domestic expenditure on R&D" indicator, and SMEs' development indicators.

DISCUSSION
The The findings of a negative relationship between the share of collective transportation and SMEs' development align with Dėdelė et al. (2020), who found that individuals with a higher socioeconomic status (in terms of income, educational attainment, and employment status) exhibited a higher proto utilize cars for travel than those with a lower socioeconomic status.Moreover, Lunke (2022) proved that employment accessibility with vehicles is higher than with public transport.Also, Dobbs (2005) stated that women tend to have higher employment rates when they have unrestricted access to private transportation.Conversely, they are less prone to unemployment, and when employed, they are more likely to secure full-time positions.

CONCLUSION AND LIMITATIONS
The study aimed to determine whether there is a connection between indicators of sustainable development and indicators of SMEs' development in EU-27 countries.Four hypotheses designed to investigate within research were confirmed partially or entirely.
It was empirically proved that SMEs' development positively depends on sustainable agriculture development in terms of productivity, R&D funding, and spreading organic farming (hypothesis 1); sustainable sourcing, responsible manufacturing in terms of circular material use and new cars' CO2 emissions (hypothesis 2), inclusive employment in terms of youth and long-term unemployment (hypothesis 3), and responsible industrial practices, transport infrastructure in terms of industrial air emissions and the share of passenger cars (hypothesis 4).
However, this paper has several limitations enabling further research.First, co-occurrence analysis was limited to publications from two subject areas.Thus, further analysis could cover other subject areas that lead to identifying keywords referring to other SDGs.Second, sustainable development indicators were limited to SDG 2, 8, 9, 12, and 13.Further research needs to be conducted to determine the relationship between SME development and other SDGs.Third, dependent variables of SMEs' development reflected SMEs' turnover and employment.Further studies could investigate SMEs' development through other indicators, such as value added.Fourth, dependent variables for regression models were selected from the proposed list of sustainable development indicators in Eurostat.The availability of data for EU-28 countries in 2011-2020 limited the choice of indicators.The UK was not included in the sample as data were missing for many indicators in 2019 and 2020.Finally, this analysis was based on panel data for countries, but further research could focus on analyzing SME companies' data for one or more countries.
Source: Compiled using VOSViewer based on Scopus publications.
(Farja et al., 2017)21)ough innovation and technology adoption (da Silva et al., 2023;Hrytsenko et al., 2021), SMEs in agriculture enhance productivity and promote sustainable farming practices(Bucci et al., 2018), aligning to achieve food secu-rity.Furthermore, SMEs create employment opportunities in rural areas(Farja et al., 2017), supporting local communities and ensuring a more equitable distribution of resources.By empowering smallholder farmers, promoting resilient agricultural systems, and enhancing the efficiency of food supply chains, SMEs significantly contribute to the broader efforts to eliminate hunger.SDG 12 centers on promoting sustainable consumption and production practices, prioritizing the efficient utilization of resources, minimizing waste, and ensuring responsible business operations.For SMEs, aligning with these indicators involves adopting environmentally conscious practices, optimizing resource utilization, and integrating circular economy principles into their production processes(Kafel & Nowicki, 2023; Dey et al., 2022; Mishenin et al., 2015).In addition, SMEs prioritizing responsible consumption and production contribute to waste reduction, energy efficiency (Wang et al., 2023), and a lower ecological footprint.SDG 13 emphasizes the urgency of tackling climate change by taking significant action to diminish greenhouse gas emissions and expand resilience.To comply with climate indicators, SMEs implement ecological practices, minimize their carbon footprint, and implement strategies for the sustainable use of resources.Source: Compiled using VOSViewer based on Scopus publications.Figure 2. The "Sustainable development goals" keywords network visualization and infrastructure.Embracing innovation and advanced technologies enhances the capacity of SMEs to adapt, improve productivity, and remain competitive (Koblianska & Kalachevska, 2019).The focus on infrastructure development, including efficient logistics, connectivity, and employment accessibility, facilitates the growth of SMEs, enabling them to expand their market reach and operational capabilities.Based on the literature review and the co-occurrence analysis, the study aims to investigate the relationship between sustainable development indicators and SMEs' development in EU countries.Specifically, this paper explores the influence of SDGs 2, 8, 9, 12, and 13 indicators on SMEs' development indicators.The following research hypotheses were designed: H1: Indicators measuring sustainable agriculture relate positively to SMEs' development indicators.
SMEturnperCa -SMEs' turnover per capita; SMEpersperCa -SMEs` persons employed per capita; AgFaIn -agricultural factor income per annual work unit; AgriRnD -government support for agricultural research and development; OrgFarmArea -an area under organic farming; AmmonEmis -ammonia emissions from agriculture; InvInGDP -the investment share of GDP; UnempYouth -young people who are neither employed nor in education and training; EmpltmntRt -the employment rate; LngTrmNmpl -the longterm unemployment rate; FatAccid -fatal accidents at work per 100,000 workers; RDinGDP -gross domestic expenditure on R&D; Patent_ Inv -patent applications to the European Patent Office by inventors; ShrBssNTrn -the share of buses and trains in inland passenger transport; ShrRlNWtrWs -the share of rail and inland waterways in inland freight transport; AirEmis -air emission intensity from the industry; CrclrMtrlSRt -the circular material use rate; NewCarEmis -the average CO2 emissions per km from new passenger cars; RwMtrlCnsmptn -raw material consumption; NtGrnhsGsEms -net greenhouse gas emissions; PopCovMayAgr -a population covered by the Covenant of Mayors for Climate & Energy signatories.

Table A6 .
VIF values for SDG8 indicators and SMEs' turnover as the dependent variable (EmpltmntRt included)

Table A7 .
VIF values for SDG8 indicators and SMEs' turnover as the dependent variable (EmpltmntRt excluded)

Table A8 .
Pearson correlation matrix for SDG9 and SMEs indicators

Table A9 .
VIF values for SDG9 indicators and SMEs' persons employed as the dependent variable (Patent_Inn included)

Table A10 .
VIF values for SDG9 indicators and SMEs' persons employed as the dependent variable (Patent_Inn excluded)