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Abstract

In a study of 74 Russian regions over the period of 2013–2018, this paper demonstrates that economic, social, and ecological dimensions of the triple bottom line triad positively and significantly affect regional development, thus validating the growing emphasis on sustainable development found in the literature and among policymakers. Investments in research and development, the creation of new firms, reductions in poverty rates, and lowering emission intensity lead to regional development. The study develops four models to analyze the regional resource allocation efficiency in relation to the economic, social, and ecological goals of sustainable development. To assess regional development, this paper calculates the technical efficiency of the regions concerning technological frontiers using Data Envelopment Analysis (DEA). As one of our variables (the border region dummy) is time-invariant, the study chose to estimate models with the help of a random effects estimator. The effects of startup rates and ecological factors are moderated by the per capita income within the region. The results suggest that the triple bottom line agenda is justified as a regional development paradigm.

Keywords

The triple bottom line regional development startups ecological factors emission intensity

Introduction

The notion of the triple bottom line entered the lexicon of management scholars some thirty years ago (Elkington, 2018) and has inspired the philosophy of sustainable development at the macro level advocated by both academics (Carayannis & Campbell, 2018; York et al., 2018) and policymakers (United Nations, 2016). At its core, this notion implies the simultaneous pursuit of economic, social, and ecological agendas—the latter two dimensions typically seen as being at odds with economic development—and there is much debate in the literature as to whether these triple development goals may be truly aligned (Eisenmenger et al., 2020). Despite years of research, there is a dearth of knowledge with respect to the impact that the simultaneous pursuit of these goals is likely to exert on macroeconomic growth (Leal Filho et al., 2019).

We contend, however, that these three dimensions may be naturally aligned in promoting regional development. Specifically, new innovative efforts by existing firms and the creation of new startups are likely to bring about better overall efficiency within their regions directly (Vuković et al., 2012). Social improvements, such as poverty alleviation, and ecological advancements resulting in the reduction of emission intensity are likely to indirectly stimulate efficiency improvement as well. Taken together, the pursuit of the triple bottom line agenda by regional firms and policymakers may be expected to contribute to a visible improvement in resource allocation efficiency within the region.

The direct effect of innovative efforts on regional development is largely accepted within the literature (Gordon & McCann, 2005). The very goal of searching for new resource combinations—the essence of innovative efforts—is the identification of new ways of doing more with less (Radjou & Prabhu, 2015). Whether such improvements come from existing firms that funnel their resources into research and development (Shefer & Frenkel, 2005) or from de novo startups that are created to pursue opportunities left unaddressed by the existing firms (van Stel & Suddle, 2008), the end result is unambiguous: overall efficiency within the region improves (Cainelli et al., 2020), and regional well-being is promoted (Lenzi & Perucca, 2020). Innovation can be also interpreted in a broad sense, considering innovation in the economy, society, and ecology. However, based on the idea of this article, we explore innovation as an economic factor and select the appropriate variable that characterizes the impact of the economy. This article considers regional R&D investment as one of the test variables. We interpret it as an economic variable since most of the R&D investment is directed to technological product and process innovations. Of course, part of the R&D investment is directed to environmental innovation, but in Russia this share is low.

The effect of social goals’ pursuit, looked at from the viewpoint of poverty alleviation, is less clearly understood. Poverty alleviation is seen as a government’s responsibility, and many business owners may lack enthusiasm for addressing social ills within their regions because the money to deal with social challenges may be appropriated by the government from the businesses operating within the regional boundaries, a position long advocated by the proponents of the Friedman doctrine (1970). Yet, government failure at addressing social ills comes at a cost, and there is growing evidence that private initiative, including social entrepreneurial actions, is increasingly attentive to lifting people out of poverty (Anokhin et al., 2023 in press), thus signifying the shift in the thinking surrounding social challenges. Ultimately, however, the concerted efforts of regional players aimed at lifting people out of poverty increase the spending power of regional consumers (Chistyakova et al., 2023; Petrovic et al., 2017), which should have a positive impact on regional development. As such, we expect that addressing social ills within the region should promote regional development, albeit indirectly.

Finally, efforts to curb emissions are likely to indirectly promote regional development as well. First, reduction in emissions requires adoption of better technologies, and recent technologies often have the benefit of improved effectiveness in addition to reducing the environmental footprint (Swink et al., 2006; van Soest & Bulte, 2001). Second, investment in the development and deployment of better technologies, while constituting a cost to regional firms, results in higher within-region spending, thus growing the regional economy and contributing to development (Masduki et al., 2022). Third, improvements in the ecological situation within the region have a direct positive impact on the health of the population, which translates into higher productivity in the regional workforce (Cole & Neumayer, 2006). Overall, we expect the pursuit of the ecological agenda to positively affect regional development.

The study tests these expectations in a sample of 74 Russian regions over the course of 2013–2018. The Russian economy was going through a crisis caused by Western sanctions during this period, so the context of our study is conservative. Despite the crisis, Russia remains a major player in the global arena (World Bank, 2020), and understanding the factors affecting its development is essential. The paper is organized as follows: In the following section, we provide a brief overview of the relevant literature and formulate testable hypotheses that link the pursuit of economic, social, and ecological goals to regional development. Next, we introduce our data and present the methodology chosen to test the hypotheses. This is followed by the presentation of our results and the discussion of their implications for theory and practice. The paper concludes with an overview of the limitations of the study and suggestions for future research.

Conceptual framework and hypothesis development

Relationship between regional R&D investment and regional development

While different conceptualizations of regional development are possible and used in the literature (Pike et al., 2011), fundamentally, most metrics of development may be reduced to a simple maxim: the ability to do more with less (Higgins & Savoie, 2017). In the economics literature, this is typically captured with the notion of technical efficiency, with “technology” understood rather broadly as a way to combine certain inputs to produce a particular set of outputs (Mariano et al., 2015; Spitsin et al., 2021). Accordingly, to study the impact of economic, social, and ecological goals’ pursuit on regional development, it is essential to consider how these kinds of advancements are likely to affect the technical efficiency observed in the region.

It is logical to expect that the purposeful investment by the regional firms in research and development is likely to bring about improvements in resource allocation efficiency. While the nature of innovation is said to be ill-defined (Aghion & Tirole, 1994), and it may never be possible to guarantee a successful outcome of any given R&D project, taken as a whole, innovative activity is beneficial in terms of improving resource yield (Schulte, 2013), and firms expect to see such improvements when committing to various research and development projects. Indeed, had the positive association between systematic investment in R&D and efficiency improvement not been observed repeatedly, the innovative efforts of most rational firms would have stopped a long time ago. This, by extension, implies that regions with firms actively investing in research and development will necessarily see advancement with respect to resource utilization yield; that is, they will stand to benefit in terms of regional development. Importantly, regional firms initiating innovative projects are not the only recipients of the benefits that newly generated insights provide. As the knowledge spillover theory of entrepreneurship makes abundantly clear (Audretsch & Lehmann, 2005), many innovative ideas fail to be pursued by the corporations responsible for their discovery but will, instead, be acted upon by alert individuals and firms whose expectations of the value of new discoveries exceed those of the companies responsible for their discovery. That is, investment by regional firms in R&D promotes regional development both directly and indirectly. As such, we expect to see a pronounced positive association between regional R&D investment and regional development.

Stated formally, Hypothesis 1: There is a positive relationship between regional R&D investment and regional development.

Startup rates and regional development

Apart from relying on innovative ideas that spill over from corporations heavily investing in research and development, startups pursue their own technological insights as well as ideas created by the region’s universities (Audretsch & Lehmann, 2005). While some scholars express doubt as to the role of entrepreneurs in promoting the development agenda (Shane, 2009), the extant literature clearly indicates that entrepreneurship and innovation are positively associated (Anokhin & Schulze, 2009). Even if the majority of startups pursue opportunities that are not innovative in nature but rather have to do with arbitrage (Anokhin et al., 2011), it is important to notice that the very presence of arbitrage opportunities within the region indicates substantial inefficiencies in resource utilization. By exhausting arbitrage opportunities prevalent within their regions, young firms help address these inefficiencies and, as such, help close the gap between lagging and leading regions with respect to how resources are utilized.

Adoption of innovative resource combinations by startups along with the pursuit of arbitrage opportunities by regional entrepreneurs necessarily leads to improvements in the overall resource allocation efficiency within the region, thus promoting regional development. Accordingly, we expect startup rates within the region to be positively related to the overall development dynamics.

Stated formally, Hypothesis 2: There is a positive relationship between startup rates and regional development.

Poverty and regional development

Economic actors invest in the development and adoption of new technologies when they believe there is substantial market demand for their products and services (Zhu & Weyant, 2003). For this demand to manifest, it is essential that the region’s population is not afflicted by poverty, which remains one of the main social problems that governments and social entrepreneurs tackle together (Anokhin et al., 2023 in press). Poverty-ridden regions are unlikely to be seen by firms and entrepreneurs serving such markets as providing incentives to invest in better technologies because the probability of recouping such investments is prohibitively low (Mosley & Verschoor, 2005). Besides, poverty begets a host of issues like criminal activity that are detrimental to the economic environment and may lead to the flight of existing firms to other regions rather than entice the regional business population to invest in better resource utilization (Hipp et al., 2019). For this reason, certain regions may deal with a vicious problem where poverty begets poverty, and the intractability of this issue is widely acknowledged by both policymakers and social activists (Kraay & McKenzie, 2014).

On the other hand, poverty alleviation is likely to stimulate the demand for products and services offered by regional firms, which may result in retaining firms within the region and enticing them to invest in better technologies. In other words, regions that have successfully addressed the notion of poverty—to the extent possible—may expect to see improvements in resource allocation efficiency by regional firms as a result. Moreover, inasmuch as poverty alleviation reduces the intensity of the crime-related problems, regional businesses’ need to invest in security loses intensity, and the money previously diverted to safeguarding the operations may instead be funneled to more productive ends. Taken together, this implies that the relationship between poverty and regional development is negative, with regions demonstrating lower poverty rates displaying higher development trajectories.

Stated formally, Hypothesis 3: There is a negative relationship between prevalence of poverty and regional development.

Emission intensity and regional development

The ecological part of the triple bottom line agenda is also likely to positively affect regional development. Emission footprints are a side effect of using obsolete technologies. Curbing emissions necessarily requires adopting newer and better technologies, which typically come with improved economic effectiveness (van Soest & Bulte, 2001). That is, even if efforts to curb emissions are driven primarily by ecological considerations and not thoughts of regional development, improvement in resource utilization is likely to accompany the adoption of better technologies. It may be argued that upgrading technologies is costly (Liu et al., 2021). Yet these costs are not wasted. At the very least, investing the resources to reduce emissions contributes to gross regional product regardless of where the technology in question originates. Adopting sophisticated technology also requires access to qualified labor, and growing requirements for specialized labor within a region are likely to contribute to addressing many of the social ills otherwise permeating the regional environment. Besides, environmentally conscious technologies and business models often rely on the so-called circular economy paradigm (Parida et al., 2019), which, in addition to reducing emissions, improves resource yield (Lahti et al., 2018). Together, this implies that resource utilization efficiency improves when ecological goals are pursued.

Finally, as the ecological situation within the region improves, one may expect to see improvements in the health and life expectancy of the regional population. Improvements in health directly translate into higher productivity in the regional workforce (Kirsten, 2010). Reducing the incidence of sickness frees up the resources otherwise tied up in the local healthcare system and allows these resources to be reinvested in regional development. Overall, we expect the pursuit of the ecological agenda to positively affect regional development. By the same token, growing emissions are likely to exert a negative impact on regional development.

Stated formally, Hypothesis 4: There is a negative relationship between emission intensity and regional development.

Regions with above-average per capita income

While we believe that the relationships postulated above are true in most circumstances, we acknowledge that they are more likely to manifest in some regions than in others. Specifically, they are more likely to be observed in well-off regions that are already at or close to the technological forefront. Adopting sophisticated technologies requires a particular state of development by the regional firms, and firms that are close to their respective frontiers—and regions that have an abundance of such firms—are more likely to adopt technologies that yield the highest improvement in resource utilization efficiency (Mirvis et al., 1991). Durham et al. (1998) refer to this notion as the “rich get richer” phenomenon. If this line of reasoning is correct, it is likely that regional development is more intense in regions that already do quite well (Vuković & Wei, 2010; Vuković et al., 2022). Typically, this may be characterized by a relatively high per capita income. From an economic standpoint, investments in R&D and the creation of new firms will likely lead to the spread of truly superior technologies, which will generate the highest “bang for the buck” in terms of regional development, whereas similar efforts in less affluent regions will be sub-par (although still present).

We expect an amplifying moderation effect also from the interaction of the poverty level with the incomes of the population. Indeed, the clash of opposites (high poverty and high per capita income) points to strong disparities in the region’s economy. This means that there are several large super-successful firms operating in the region and their employees receive very high incomes. However, the rest of the region’s economy is poorly developed and the mechanisms for redistributing these incomes for the entire population of the region are not functioning well. Accordingly, this combination of factors will increase the negative impact of poverty on regional development. Such a situation may well be typical for Russian regions that are concentrated in the extractive industries.

Finally, curbing emissions is a very expensive proposition (Gillingham & Stock, 2018), and regions that boast higher per capita income are more likely to successfully adopt eco-friendly technologies that will contribute to regional development. In other words, in all cases, the relationships implied by Hypotheses 1–4 are likely to be more pronounced in regions with above-average per capita income.

As stated formally, hypotheses arise as follows: Hypothesis 5: The positive relationship between regional R&D investment and regional development is more pronounced in regions with above-average per capita income. Hypothesis 6: The positive relationship between startup rates and regional development is more pronounced in regions with above-average per capita income. Hypothesis 7: The negative relationship between the prevalence of poverty and regional development is more pronounced in regions with above-average per capita income. Hypothesis 8: The negative relationship between emission intensity and regional development is more pronounced in regions with above-average per capita income.

Data and methods

Data

The study employs a sample of the Russian regions over the period of 2013–2018. Only the regions comprising the Russian Federation as of 2013 were considered for inclusion in the database.

Two of the regions (Moscow and St Petersburg) are excluded because their economies are not representative of the rest of the country and may unduly distort the estimates of regional development. Russian laws formally classify them as regions, but in reality, they are large cities and agglomeration centers, not regions. The difference between these cities and regions is significant in terms of a number of economic, social, and environmental indicators, which can lead to distortions in the study results. A similar approach is used in the works (Kalabikhina et al., 2019; Mikheeva, 2021; Mokrensky, 2020; Solanko, 2008).

Russia also has a number of so-called autonomous districts that are unique in some important respects and were thus excluded from the analysis. Finally, we excluded the regions with missing values with respect to the key indicators analyzed in this paper. In all, our sample contains 74 regions. The data were sourced from the Russian Statistics Bureau, Rosstat (Federal State Statistics Service (Rosstat), 2022). Our database contains 444 observations (74 regions multiplied by 6 years).

Variables

Dependent variable

To assess regional development, we calculated the technical efficiency of the regions vis-à-vis the so-called technological frontier. The frontier has been calculated with the use of Data envelope Analysis (DEA) (Cooper et al., 2011). This approach has been widely used in the economics and development literature (e.g., Charnes et al., 1989; Emrouznejad et al., 2019). Fundamentally, DEA compares the regions with respect to how they combine certain inputs (e.g., labor and capital) to generate a specified output (e.g., gross regional product) to identify the leading regions that define the frontier. These leading regions will have their technical efficiency set to 1. Less efficient regions will find themselves at a distance from the frontier. Their efficiency estimates will vary in the interval from 0 to 1. Efficiency estimates were multiplied by 100% for ease of interpretation. The higher the efficiency estimate, the higher the level of regional development. In this study, we used labor and capital as inputs and gross regional product (calculated as regional sales) as an output. The calculations were performed in the R programming language. In any year, as few as 2 and as many as 4 regions were identified as having frontier efficiency, with the rest being at a distance from the frontier.

Independent and moderator variables

We use the logarithm of regional R&D investment as our first independent variable (Chen & Lee, 2020). Startup rates, calculated as a ratio of new firms to the region’s population, are our second independent variable capturing the impact of economic factors (Nakamura & Magani, 2020;Nakamura & Managi, 2020). The prevalence of poverty was assessed as the share of the population with income below the official poverty level (Korosteleva & Stpie-Baig, 2020). This represents the social dimension of the triple bottom line triad. Finally, emission intensity, calculated as the ratio of uncaptured air emissions from stationary sources within the region to the value of sales by regional firms (tons of emissions per 1 billion rubles of products sold), was used as a proxy for the ecological dimension of the triad (Dong et al., 2018). Per capita income (Behera & Dash, 2017), calculated as the average monthly income per capita in the region in rubles, was used as a moderator.

Control variables

We also controlled for a number of additional factors that may bear on regional development. Specifically, we accounted for the region’s export intensity (Cheng et al., 2019), defined as the ratio of the export-related revenue of regional firms to their overall sales multiplied by 100%, as this may substantially affect the dynamics of regional processes. We also controlled for higher education enrollment (Li et al., 2021), defined as the ratio of the number of students to the population of the region multiplied by 100%, because higher representation of an educated population may promote regional development in non-trivial ways (Faggian et al., 2019). Finally, because regional development may depend on cross-border cooperation (Basboga, 2020), we included a dummy variable border region that captures whether or not the region is located at the land border of Russia with foreign countries (Anokhin, 2013). Descriptive statistics and correlations are provided in Table 1.

Table 1.

Descriptive statistics and correlations between variables.

		Mean	SD	1	2	3	4	5	6	7	8
1	Export intensity	11.6	13.67	1
2	Higher education enrollment	2.8	.92	−.28***	1
3	Border region	.45	0.5	−.16***	0	1
4	Regional R&D investment	14.44	1.55	0	.18***	−.11*	1
5	Startup rates	0.2	.09	.06	.30***	−.12*	.43***	1
6	Prevalence of poverty	.14	.05	−.15**	−.15**	.18***	−.51***	−.30***	1
7	Emission intensity	304.92	265.9	.19***	−.12**	.07	−.27***	−.06	.38***	1
8	Per capita income	25.41	7.35	.47***	−.23***	−.12*	.39***	.21***	−.44***	−.13**	1
9	Regional development	51.68	23.32	.19***	.09	−.25***	.64***	.39***	−.54***	−.28***	.34***

Note. ***p < .001; **p < .01; *p < .05. Because correlation coefficients are within the conventional limits (below .7), it is possible to employ our variables in regression analysis.

Models and estimation

Working with the panel data necessitates employing proper econometric techniques. As one of our variables (border region dummy) is time-invariant, we chose to estimate our models with the help of a random effects estimator. The general expression for a regression model with random effects is as follows $Y i t = I n t e r c e p t + X_{i t} * β + μ_{i} + Ɛ_{i t}$ (1)where Intercept- is a constant, X_it- variables and β- coefficients for variables, μ_i- is a random error invariant in time for each object, Ɛ_it- model regression residual. We report three regression models with random effects. Model 1 is expressed as follows $R e g i o n a l d e v e l o p m e n t = I n t e r c e p t + β 1 * E x p o r t i n t e n s i t y + β 2 * H i g h e r e d u c a t i o n e n r o l l m e n t + β 3 * B o r d e r r e g i o n + μ_{i} + Ɛ_{i t}$ (2)

Models 2 and 3 are expressed as follows $R e g i o n a l d e v e l o p m e n t = I n t e r c e p t + β 1 * E x p o r t i n t e n s i t y + β 2 * H i g h e r e d u c a t i o n e n r o l l m e n t + β 3 * B o r d e r r e g i o n + β 4 * Regional R & D investment + β 5 * Startup rates + β 6 * Prevalence of poverty + β 7 * Emission intensity + μ_{i} + Ɛ_{i t}$ (3) $R e g i o n a l d e v e l o p m e n t = I n t e r c e p t + β 1 * E x p o r t i n t e n s i t y + β 2 * H i g h e r e d u c a t i o n e n r o l l m e n t + β 3 * B o r d e r r e g i o n + β 4 * Regional R & D investment + β 5 * Startup rates + β 6 * Prevalence of poverty + β 7 * Emission intensity + β 8 * Per capita income + β 9 * Regional R & D investment * Per capita income + β 10 * Startup rates * Per capita income + β 11 * Prevalence of poverty * Per capita income + β 12 * Emission intensity * Per capita income + μ_{i} + Ɛ_{i t}$ (4)

Model 1 is a comparison model that only includes control variables. Model 2 adds a set of independent variables to the set of predictors to test Hypotheses 1–4. Model 3 adds the moderator variable and its interactions with the independent variables and serves to test Hypotheses 5–8. To avoid non-essential ill-conditioning, predictor variables are standardized (Marquardt, 1980). Calculations were performed using the R programming language. All models are highly significant as indicated by the chi-squared statistics.

Results

The results of our calculations are presented in Table 2.

Table 2.

Regression results.

	Model 1	Model 2	Model 3
Intercept	51.68*** (2.56)	51.68*** (1.91)	51.69*** (1.91)
Export intensity	3.19*** (.96)	2.44** (.89)	2.37** (.89)
Higher education enrollment	2.36*** (.60)	2.58*** (.72)	3.02** (1.04)
Border region	−5.42* (2.57)	−3.87* (1.93)	−3.82* (1.90)
Regional R&D investment		5.08*** (1.47)	5.10*** (1.52)
Startup rates		2.60*** (.68)	2.98*** (.70)
Prevalence of poverty		−2.78** (1.05)	−2.40* (1.15)
Emission intensity		−4.72*** (.62)	−6.42*** (.99)
Per capita income			−.34 (1.27)
Regional R&D investment * per capita income			−.37 (.68)
Startup rates * per capita income			−1.12 (.62)
Prevalence of poverty * per capita income			−.37 (.72)
Emission intensity * per capita income			−1.48* (.74)
R ²	.054	.218	.231
ΔR²	-	.164	.013
Fit statistic	χ²₍₃₎ = 28.22	χ²₍₇₎ = 130.22	χ²₍₁₂₎ = 144.95
p	<.001	<.001	<.001

Note. Dependent variable: Regional development. Standard errors in parentheses. ***p < .001; **p < .01; *p < .05.

Based on the calculations (Table 2), Model 1 is described by the following expression $R e g i o n a l d e v e l o p m e n t = 51.68 + 3.19 * E x p o r t i n t e n s i t y + 2.36 * H i g h e r e d u c a t i o n e n r o l l m e n t - 5.42 * B o r d e r r e g i o n + μ_{i} + Ɛ_{i t}$ (5)

Expressions for models 2, 3, and 4 are constructed similarly according to the data in Table 2. Model 1 is highly significant and explains about 5.4% of the variance in the dependent variable. Both export intensity and higher education enrollment have a positive impact on regional development. At the same time, being located at the border with other countries exerts a negative impact on the level of regional development. While explaining this finding is beyond the scope of this paper, we suggest that the countries bordering Russian regions (mostly former Soviet Union republics) are less developed than Russia proper, such that sharing borders with those is less likely to result in positive spillovers to Russian regions.

Model 2, which adds the set of independent variables to Model 1, demonstrates a marked improvement in R² and explains 21.8% of the variance in the dependent variable. All direct effect hypotheses 1–4 are supported. R&D investments exert a significant positive impact on regional development (b = 5.08, p < .001) as do regional startup rates (b = 2.60, p.001). This lends support to hypotheses 1 and 2. The prevalence of poverty in the region negatively affects regional development (b = −2.78, p.01), thus lending support to Hypothesis 3. Regional development necessitates poverty alleviation. Emission intensity negatively affects regional development (b = −4.72, p < .001) thus providing support for Hypothesis 4. A reduction in emission intensity is likely to lead to regional development. Model 3 further improves the predictive power of our regression by bringing the R² to 23.1%, yet this improvement is fairly minor. Of the four interaction effects, only one attains statistical significance at the conventional level.

To ease the interpretation of the interaction effects, we plot the significant moderated relationship between the independent variable Emission intensity and the variable Per capita income in Figure 1. To plot the graph, we use the following technique: Figure 1 shows the effect of Emission intensity on regional development with the moderator of per capita income. The rest of the variables in Model 3 have average values. Because all variables except the dependent variable are standardized, their average values are equal to zero. We get the following function $R e g i o n a l d e v e l o p m e n t = 51.69 - 6.42 * E m i s s i o n i n t e n s i t y - 0.34 * P e r c a p i t a i n c o m e - 1.48 * E m i s s i o n i n t e n s i t y * P e r c a p i t a i n c o m e$ (6)

Figure 1.

Emission intensity and regional development in high- and low-income regions.

Since Per capita income is a moderator, we plot the resultant functions at one standard deviation above and below the mean for Per capita income, the graphical visualization of which is shown in Figure 1.

Specifically, the effect of emission intensity on regional development is more pronounced in regions with above-average per capita income. That is, our calculations confirmed Hypothesis 8: that the negative relationship between emission intensity and regional development is more pronounced in regions with above-average per capita income. At the same time, hypotheses 5, 6, and 7 do not receive support. Fundamentally, it implies that well-off regions are more likely to benefit from reducing emissions in terms of regional development than their less affluent counterparts. This makes the task of regional development particularly daunting for the relatively disadvantaged regions and necessitates careful consideration of ecological policies by their policymakers. At the same time, the fact that relative wealth does not moderate the effect of other independent variables indicates that both economic and social aspects of sustainable development are truly universal in their effectiveness and may be pursued by rich and poor regions alike with similar outcomes in terms of regional development improvements.

Discussion

Our results indicate that, in line with our conceptual development, the economic, social, and ecological goals of sustainable development may be well aligned and naturally lead to a noticeable improvement in regional resource allocation efficiency. Unlike prior literature that at times questioned whether all three facets of development goals may in fact reinforce one another, we demonstrate that indeed all the developmental dimensions may be seen as beneficial from a regional development standpoint, such that regional policy makers are fully justified in emphasizing all three simultaneously.

The positive effects of economic factors—investments in R&D and regional startup rates—are hardly surprising. Existing literature has long postulated these links, and the key role of these factors in bringing about regional advancement was expected. Perhaps less expected is the magnitude of these effects. The impact of a proportionate increase in R&D spending by established firms is almost twice as large as the impact of a similar growth in startup rates, based on the standardized regression coefficients. This dovetails with a more skeptical view of the role that entrepreneurs play in economic development found in Shane (2009). Still, in line with the voluminous literature on entrepreneurship and development dynamics, we confirm the overall positive impact of startup rates on regional development. Regarding hypothesis No. 2, the study tests without using temporal lags. The creation of new firms may not immediately affect the gross regional product or the technological frontier. However, the fact of higher entrepreneurial activity is rather important here. Indeed, newer firms are based on new technologies and are more efficient than older firms. We assume that the ranking of regions by entrepreneurial activity is stable and that it is acceptable to use its current values. On the other hand, the remaining variables do not imply temporal lags, and introducing them for this one variable seems inappropriate and will lead to the loss of one of the years of the study period.

The positive role of poverty alleviation and emission reduction in regional development is a welcome finding that is particularly important given the growing attention to sustainable development espoused by the United Nations and national and regional policymakers around the world. Far from being a “cost” for regional businesses, efforts at addressing social ills and curbing emissions are clearly beneficial from a regional development standpoint. Because regional development further perpetuates the development cycle, it is essential to communicate these results to not only policymakers but to decision-makers at the firm level. Through the concerted efforts of business owners, managers, and regional policymakers, it is possible to set regional development on a sustainable trajectory, which is a worthy goal in its own right.

It should be acknowledged, however, that without proper direction and attention, efforts by regional firms to improve efficiency may come at a cost to the environment. Care should be taken to ensure that businesses do not “cut corners” as they invest in their own research and development and that they keep the overall goal of sustainable development in mind when committing their funds to new products and services. Ultimately, all regional players stand to gain from concerted efforts aimed at sustainable development.

While the effects of economic and social agenda pursuit appear to be universal, attention to reducing emissions is more likely to bring about a pronounced improvement in regional development in wealthier regions. This suggests that less affluent regions would be wise to emphasize economic and social goals before turning their attention to addressing ecological challenges. Once per capita income increases, however, discerning policymakers should incorporate ecological considerations into their developmental agenda. In other words, sustainable development is subject to sequence and timing, and regional policymakers should focus their attention accordingly. At the same time, three hypotheses that are related to the moderation of the tested variables and the level of income have not been confirmed (hypotheses 5, 6, and 7). We associate this result with the fact that the “rich get richer” phenomenon (Durham et al., 1998) encounters active state regulation, which is aimed at smoothing out disparities between regions. Government regulation is aimed at equalizing, first of all, social and economic indicators. Environmental aspects have not yet received sufficient attention. Therefore, hypotheses 5, 6, and 7, which are associated with social and economic indicators, were not confirmed, but hypothesis 8, which is associated with the environmental factor, was successfully confirmed. Accordingly, this study emphasizes that sustainable development implies smoothing out regional disparities not only in terms of social and economic indicators but also in terms of environmental indicators. The Russian government needs to pay attention to this problem.

Finally, scholars propose to assess development at the level of countries and regions in three areas (economic, social, and ecological agendas) (Elkington, 2018). There are a large number of indicators that are proposed as criteria for assessing each of these areas (Gini coefficient, Belyavskiy (2023), human capital, environmental pollution, etc.). At the same time, the problem of developing a single criterion for evaluating all three areas has not yet been resolved, although attempts are made in the literature to form a complex criterion (e.g., the index method or the DEA method), but they can be distorted by the influence of the subjective factor. For this reason, it seems more appropriate to study the impact of economic, social, and environmental indicators on each of the triple development goals separately. In this paper, we have chosen the resulting indicator (dependent variable) to reflect the economic aspect of regional development. In further work, we plan to consider the impact of economic, social, and environmental indicators on two other development goals (social and ecological agendas), choosing the appropriate dependent variables.

Limitations, conclusions, and future research

Like any research, this study has certain limitations. The fact that the data come from a single country may limit the generalizability of our findings. Nevertheless, given the sheer size of the Russian territory, the role that Russia’s economy plays in the global arena, and the pronounced diversity in the level of development of the Russian regions analyzed in this study, we are confident that the results are relevant to a great number of other contexts.

The period under study has been characterized by a systemic crisis due to the imposition by Western countries of sanctions against Russia. We suggest that this provides for a conservative setting where regional development is harder to ensure. In this sense, the fact that we find support for our key hypotheses is noteworthy. Still, it will be important to replicate our study in different contexts not plagued by the economic or political crisis.

It may also be questioned whether the way we capture regional development with the help of Data envelope Analysis is sufficiently well-suited to make inferences with respect to sustainable development. We maintain that this technique is uniquely qualified to capture development regardless of the specific trajectories pursued and thus fits the goals of the study. Nevertheless, one may consider employing alternative indicators of sustainable development, including those offered by the United Nations, as an alternative dependent variable. Future research would be wise to consider such alternative indicators.

Overall, this study makes an important step in uncovering the interrelationship between economic, social, and ecological goals in the context of sustainable development. It is our hope that it initiates dialog among scholars and policymakers interested in promoting a sustainable development agenda.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by the Mendeleev Foundation No. 2.2.4.22 State Registration No. 122053100114-8 05.31.2022. This study was supported by the Tomsk State University Development Programme (Priority-2030).

ORCID iD

Darko B. Vukovic

Data availability statement

The data that support the findings of this study are available from the corresponding author upon request (

Author biographies

Darko B. Vukovic —serves as an associate professor (teaching courses in finance: CFA,EPAS,ACCA,PRMIA,GARP,QTEM) at the Department of Finance,Graduate School of Management,Saint Petersburg State University,Saint Petersburg,Russia. He is also the Head of the Regional geography department at Geographical Institute “Jovan Cvijic” of Serbian Academy of Sciences and Arts in Belgrade,Serbia. Prof. Vukovic is a visiting professor at La Salle University in Bogota,Colombia. Dr Vukovic has published more than 65 papers indexed in leading index databases: Web of Science,Scopus and ABS (CABS),including journals: Finance Research Letters,Expert Systems with Applications,Financial Innovations,Physics A: Statistical Mechanics and its Applications,Research in International Business and Finance,International Journal of Finance and Economics,North American Journal of Economics and Finance,Journal of Economic Studies,Annals of Tourism Research,and more. His research has been awarded multiple times,like from the Emerald Literati Award 2021 for “Outstanding Paper” in the British Food Journal. From publisher Wiley,his research has been awarded as Top Cited Article in 2020–2021,from the journal internet technology letters. He is Editor-In-Chief of the book—Digitalization and the Future of Financial Services,Springer (Nature),

. *Correspondence: vdarko@hotmail.rs ORCID: 0000-0002-1165-489X

Vladislav Spitsin is an associate professor at National Research Tomsk Polytechnic University and Senior Research Fellow,Center for Sustainable Development,National Research Tomsk State University. His professional interests are firms’ and regions’ growth and performance,high-tech industries and services,innovative development and technology transfer,regional economy,econometric analysis. He is a co-author of three Certificates of computer programs for solving economic problems. The results of research are presented in local and international publications. E-mail: spitsin_vv@mail.ru

Elena Akerman is a professor at National Research Tomsk State University. Her professional interests include regional economics,strategic planning and forecasting,innovation and regional competitiveness. The results of her research are presented in national and international publications. E-mail: aker@tomsk.gov.ru

Irina Gammerschmidt is an Associate Professor at National Research Tomsk State University,Director of the “Centre for Sustainable Development” at National Research Tomsk State University. Her professional interests include the study of regional environmental problems,pollution problems,emissions and environmental innovations in firms. The results of her research are presented in national and international publications. E-mail: igammer@mail.ru

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Triple Bottom Line,Sustainability,and Regional Development

Abstract

Keywords

Introduction

Conceptual framework and hypothesis development

Relationship between regional R&D investment and regional development

Startup rates and regional development

Poverty and regional development

Emission intensity and regional development

Regions with above-average per capita income

Data and methods

Data

Variables

Dependent variable

Independent and moderator variables

Control variables

Models and estimation

Results

Discussion

Limitations, conclusions, and future research

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Data availability statement

Author biographies

References