# A goal programming model to study the impact of R&D expenditures on sustainability-related criteria: the case of Kazakhstan

Cinzia Colapinto (Department of Management, Ca’ Foscari University of Venice, Venice, Italy)
Raja Jayaraman (Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates)
Davide La Torre ( SKEMA Business School, Université Côte Azur, Sophia Antipolis Campus, France)

ISSN: 0025-1747

Article publication date: 18 May 2020

Issue publication date: 11 December 2020

420

## Abstract

### Purpose

Most countries face important economic, social and environmental challenges and are strongly committed to invest in research and development (R&D) activities to help support the long-run economic sustainable growth. This paper aims to extend the previous research on macro-economic growth models and introduces endogenous variables to determine the amount of investments in R&D activities.

### Design/methodology/approach

The model considers four different criteria and six economic sectors and aims at finding the optimal allocation of labor across different sectors. The model also endogenously determines the amount of investments in pollution abatement activities together with energy-related R&D efforts. The paper presents an application to the case of Kazakhstan, an emerging Asian country, that aims to become one of the top 30 most developed countries in the world by 2050.

### Findings

The model shows the limits of the Kazakh agenda that identified too ambitious goals as the country has to go through a sociotechnical transition that involves a range of modifications in institutional structures, together with changes in user practices and the technological dimension. Kazakhstan should invest more in R&D activities able to develop sustainable energy sources to face the current electricity consumption demand and to reduce the greenhouse gas emission in the future.

### Originality/value

The paper provides valuable knowledge for researchers and policy makers interested in the impact of R&D on the long-run economic sustainable growth.

## Citation

Colapinto, C., Jayaraman, R. and La Torre, D. (2020), "A goal programming model to study the impact of R&D expenditures on sustainability-related criteria: the case of Kazakhstan", Management Decision, Vol. 58 No. 11, pp. 2497-2512. https://doi.org/10.1108/MD-09-2019-1334

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

The 21st century has been marked by turbulent transformations caused by social, economic, political and technological changes. In recent years, there is an increased attention by government agencies, policy analysts, economists and organizations to develop suitable implementation plans able to achieve sustainable development goals (SDGs). SDGs were adopted in the year 2015 by UN member countries to achieve overall prosperity addressing significant global challenges in 17 broad areas, including better health, climate action, clean energy, industry, innovation and infrastructure (United Nations, 2015). In developing an implementation plan, decision makers have to balance various priorities integrating economic, social and environmental objectives and energy use, among other criteria, that are often conflicting and incommensurable. For example, if each country aims to achieve a certain gross domestic product (GDP) growth rate and exploits available resources in the pursuit, they might pollute more than permissible levels, leading to major health issues for their citizens. To remedy the effects will require investments in environmental abatement efforts evaluated in consideration with various criteria, such as economic effectiveness, technical feasibility and environmental regulations. The ideal plan for sustainable development should foster the use of available resources in an optimal way preserving their availability for future generations. Traditionally, decision makers have used the triple bottom line (TBL) approach (Slaper and Hall, 2011). While the TBL framework provides a way to aggregate the three criteria, namely, social, economic and environment, it explicitly lacks to address the growing importance of energy consumption and its effects. It is widely evident that demographic growth contributes significantly to increase greenhouse gas emissions fueled by increased energy consumption. Another important drawback is several decision models fail to accommodate the role of innovation and productivity in developing potential solutions to address sustainability-related challenges.

This increasing degree of complexity is forcing decision makers to look out for new approaches and methodologies that facilitate and support decision-making. Multi-criteria decision analysis (MCDA) offers effective techniques to provide potential solutions for successful achievement of SDGs. The mathematical tractability, spurt in modeling and computational ease have made goal programming (GP) a popular MCDA technique to study sustainability issues. Several GP techniques have been used to study applications spanning from quality control (Sengupta, 1981), manufacturing (Satoglu and Suresh, 2009), supply chains (Selim and Ozkarahan, 2008), renewable energy planning (San Cristóbal, 2012a), workforce planning (Bastian et al., 2015) and others. We refer the readers to extensive reviews (Colapinto et al., 2017a, b; Aouni and Kettani, 2001) and books (Barichard et al., 2008; Jones and Tamiz, 2010) highlighting the role of GP models with applications.

The primary motivation of this paper is to develop a GP model to study the potential of achieving an SDG. To the best of our knowledge, no known approach has been used to study the effectiveness of research and development (R&D) investments and its influence in achieving sustainability-related goals. Our model has been validated using data from Republic of Kazakhstan. The global financial crisis of year 2008 and geopolitical changes have impacted the economy of Kazakhstan that was also challenged by the instability and decline in world oil prices. During the period 2000–2015, the commodity group “Mineral products” is the primary exported one and accounts for no less than 65.8 percent of the total exports. Kazakhstan has embraced a vision to become one of the world's most environmentally healthy countries, with sustainable energy at its foundation and diversified economic development as a key goal/objective. Kazakhstan 2050 development strategy (Linn, 2014) highlights a projected GDP growth of 3 percent, enabling the creation of 0.5 million jobs by transitioning to green economy. One effective and important step toward achieving an SDG related to the environmental objective is to increase the contribution of renewables in the energy mix, leading to efficient energy usage (Karatayev and Clarke, 2016). In addition, Kazakhstan aims at spend 3 percent of GDP for research, development and innovation in technology sectors (Mukhitdinova, 2015), preserving a steady employment, contributing to the social aspect of an SDG: this approach implies synergies and common goals between different stakeholders. Our paper adopts a broader approach with respect to previous research; past literature has concentrated mainly on energy policy and investments. MacGregor (2017) studied decision-making for electricity generation policy and investments in Kazakhstan and quantified the net present value of different policy options and suggested an alternative investment pathway. Ahmad et al. (2017) reviewed various potential local non-fossil fuel resources, including hydro, solar, wind, biomass and uranium, and established an assessment framework for prioritizing these resources via the analytic hierarchy process (AHP) based on expert opinion.

In this paper, we use a classical weighted GP (WGP) model to study the long-run sustainability of Kazakhstan toward achieving SDGs. We introduce the total amount of public investments in R&D as a constraint in the GP model considering investments in pollution abatement and energy consumption reduction. Our model is fully endogenous, and it allows to determine not only the optimal allocation of workforce in each economic sector but also the fraction of production to be devoted toward R&D investments, emission control and reduction in energy consumption. The original model presented in this paper enriches the work done by the same authors in previously (see Jayaraman et al., 2017a, b).

The rest of the paper is organized as follows. In the next section, we briefly discuss the mathematical formulations of the GP and the WGP models. In Section 3, we discuss the relevant literature, Section 4 presents the model formulation. Section 5 details model implementation with empirical data on the Kazakhstan economy and numerical simulations. The conclusions and recommendations for decision makers are presented in Section 6.

## 2. A brief review of goal programming models

In a classical MCDA framework, the decision maker (DM) has to deal with several and conflicting criteria F1, F2, …, Fp that have to be maximized or minimized simultaneously. Within the GP formulation, the DM tries to minimize any possible deviation from the objective goals, either positive or negative. In fact, the GP model is a distance-function model in which the obtained optimal solution represents the best compromise between different objectives.

There are two broad approaches in a GP. First, the GP with non-preemptive, weighted structure shows roughly comparable importance among conflicting criteria. The other approach is preemptive GP, where the goals must be ranked from the highest important to the least important. One of the simplest formulation of the GP model was introduced by Charnes and Cooper (1961) and Charnes et al. (1955), which reads as:

Min i=1pDi++Di

Subject to:

Fi(X1,X2,,Xn)+DiDi+=Gi, i=1pX=(X1,X2,,Xn) Ω
Di,Di+0, i=1p
where Di and Di+ are, respectively, the negative and positive deviations with respect to the aspirational goal levels Gi, i = 1, , p and Ω is the feasible set. Several different variants of this basic formulation have been introduced over the years (see Colapinto et al., 2017a, b). Among them, the most popular one is the WGP, which reads as follows: Given a set of weights ωi, ωi+chosen by the DM, solve the following program:
Min i=1pωiDi+ωi+Di+

Subject to:

Fi(X1,X2,,Xn)+DiDi+=Gi, i=1pX=(X1,X2,,Xn) Ω
Di,Di+0, i=1p
Xj0 and integer, j=1n

The weights ωi, ωi+allow us to introduce a system of priorities among the objectives, with the result that those having more importance for the DM will have a higher weight. In the sequel, we will utilize the WGP approach to formulate our model. To conclude this section, it is worth to recall that other GP models and variants are available in the extant literature.

## 3. Background literature

Belton and Stewart (2002) acknowledged structuring complex decision problems well and integrating multiple criteria explicitly in decision-making lead to better, more informed decisions. In this perspective, GP models have gained increased attention among decision analysts and policy makers. The ability of GP models to simplify complex economic scenarios and determine the long-run sustainability of government and macroeconomic policies have fueled several recent studies across different countries, including Canada (Nechi et al., 2019), Europe (Guijarro and Poyatos, 2018; Daim et al., 2010b) and Asia (Gupta et al., 2018). The application of GP models to social sciences, and in particular to economics, appears to start in the early 1970s, especially around the provision of public goods, and in special economic sectors. It has also been widely used to model environmental interactions (Linares and Romero, 2000) and macroeconomic policies (Colapinto et al., 2017b). The wide applicability of GP models and mathematical tractability has enhanced the scope of applications. GP models have been used in supply chain optimization problem, as they permit imprecise demand and information (Selim and Ozkarahan, 2008; Tsai and Hung, 2009). Other applications range from vendor selection (Kumar et al., 2004), production-distribution planning (Selim and Ozkarahan, 2008), manufacturing and production decision-making (Sheikhalishahi and Torabi, 2014; Taghizadeh et al., 2011). In management science, GP has a direct correspondence with decision-making, because business managers are constantly solving complex decision problems involving costs, returns and risk. The increasing popularity of GP models is also evident by the diversity of applications (Ignizio, 1982) such as finance (Aouni et al., 2014), marketing (Kwak et al., 1991), capital budgeting (Kalu, 1999) tourism management (Blancas et al., 2010) and others. GP models for R&D and project selection have been studied by several authors; some significant papers include: Keown et al. (1979) used a zero-one GP approach to allocation of R&D funds; Taylor et al. (1982) introduced a nonlinear integer GP for project selection and prioritizing the allocation of researchers, using both linear and nonlinear goal constraints; and Badri et al. (2001) developed a GP model for project selection incorporating R&D costs, capital budgeting and investment plans.

With respect to the aim of the current paper, we now focus on two major areas: problems relating to economic–energy–environmental (E3) criteria, either individually or as a combination that are studied under the GP ambit, and papers on sustainability applications that integrate various social aspects.

One of the earliest works on energy resource allocation via the MCDA technique is the one by Ramanathan and Ganesh (1995) who presented an integrated AHP-GP model. Their model considered nine quantitative and three qualitative criteria and was applied to the household sector of Madras (India). Daim et al. (2010a) develop an Fuzzy GP (FGP) model to determine the optimal mix of renewable energy with application to Oregon (USA). San Cristóbal (2012a) developed a GP model to determine the optimal mix and location of renewable energy plants applied to north of Spain. Jayaraman et al. (2015a, b) developed a WGP model considering energy, economic and environmental criteria applied to year 2030 sustainability goals of the UAE. Extending their previous work, Jayaraman et al. (2017a) also developed an FGP model applied to the UAE, and Jayaraman et al. (2017b) studied a WGP model applied to various Gulf Cooperation Council (GCC) countries. Jones and Wall (2016) studied an application of GP model for off-shore wind farm site selection in the UK. Zografidou et al. (2017) developed a GP model for installing solar power plants in Greece considering financial and energy criteria. Omrani et al. (2019) developed a WGP model for optimal workforce allocation in Iran considering economic, energy and environmental (greenhouse gas (GHG) emissions) criteria. Their results permit policy makers to develop suitable macroeconomic policies in planning for achieving year 2030 sustainability goals.

Models integrating social criteria emphasizing sustainability-related aspects due to climate change and adoption of SDGs have drawn significant attention among research and practice community in the past decade. Some significant works incorporating sustainability criteria using GP models include San Cristóbal (2012b) who developed a GP model combining economic, energy, social and environmental criteria to study sustainability-related goals for Spanish economy. His results provided insights on specific key economic sectors that needed significant attention to achieve sustainability-related goals. Zografidou et al. (2016) developed a data envelopment analysis and WGP model combining social, environmental and energy criteria to study the optimal renewable energy production toward meeting year 2020 EU mandated recommendations. Their results emphasized the allocation of more weightage to social and environmental aspects and reduced economic emphasis enables achieving maximum efficiency. Nomani et al. (2017) developed an FGP model to analyze the feasibility of achieving year 2030 environmental, energy and sustainability goals of India.

The growing body of literature concludes that altering energy mix with suitable substitutions from non-emitting renewable sources represents a viable option for meeting long-term energy sustainability. In addition, the focus on sustainability turns into developing well-diversified and robust economic policies that emphasize the need for innovation, entrepreneurship and R&D. This paper aims to bridge the gap from previous research by developing a fully endogenous model with explicit consideration of public investments in R&D as a constraint to determine: optimal allocation of workforce in each economic sector, fractions of production to be devoted toward R&D investments, pollution abatement and reduction in energy consumption. According to Johnstone et al. (2010), R&D expenditures and number of research personnel reflect the innovative capacity of a country, i.e. the resources available to develop new technologies.

We can conclude that a GP model has revealed useful insights on the performance and implementation of global sustainability policies involving multiple and conflicting objectives. Policy makers can use various sustainability assessment methods and multi-dimensional frameworks, based on the interactions and tradeoffs between economic, energy, environmental indicators and sustainability-related aspects.

A more moderate estimate of the 2050 Kazakh GDP is the average between the forecasted value of year 2025 and 2050, namely, US$442,800,000,000. This estimate may be more reasonable if one considers the high level of fluctuations of the Kazakh national currency, the Tenge. With these adjusted forecasts of the 2050 GDP presented in Table 4, the model shows a successful attainment of sustainability related targets because of a reasonable effort. The results are presented in Table 5. The results presented in Table 5 show very low levels for the deviation variables, and this combination of values shows a long-run sustainability of the identified goals. The model also allows to determine the investments in green energy and pollution reduction, which helps in achieving the long-run goals of macroeconomic policy. ## 6. Discussion and policy implication The year 2013 “National Concept for Transition to a Green Economy by the year 2050” focuses on transitioning the economy and power sector toward sustainable development and aims to bring the share of renewable energy in electricity generation to 3 percent by 2020 rising to 30 percent by 2030 and 50 percent by 2050. While the government is adopting new legal frameworks to encourage the transition toward renewables, there are still significant barriers to address, including a lack of awareness of the opportunities associated with renewable energy, a lack of technical expertise and capacity, insufficient governmental support to overcome high initial financial and capital requirements and investment disincentives due to subsidies of other energy sources (primarily fossil fuels). The financial barriers, including the low price of electricity in the country, uncertainties with the long-term power purchasing tariffs, difficulties in attracting foreign investment and a lack of access to credit for both consumers and investors, are currently acting against rapid adoption. Institutional barriers include the absence of a clear national program for renewable energy development, a lack of specific action plans and instruments, a lack of concrete competitive legislation and regulation relating to the newly developed renewable energy market. Given the increasing success of the oil and gas sector, Kazakhstan will require significant government leadership to meet its vision for 2050. Our paper presents a methodological approach to the formulation of an effective and sustainable macroeconomic policy aimed at supporting the development of Kazakhstan. At the first glance, our model shows that the GDP goal is unsustainable and not realizable. If instead, a more cautious forecast of the 2050 GDP is adopted, namely, US$442,800,000,000, our model demonstrates the attainment of sustainability goals, together with the concrete possibility to attain the year 2050 goals. The model also provides the amount of investments in the energy sector as well as in pollution abatement activities, described by the variables Δ2 and Δ3.

Combining these two elements, Kazakhstan should invest more in developing sustainable energy sources to face the current electricity consumption demand and rapidly reduce the GHG emission in the future. Implicitly, even R&D investment and the evolution of skill labor demand have to be narrowed toward green sustainability. The country has to put R&D at the service of sustainable development. Our findings are in line with the previous literature about the relationship between innovation and environmental quality. Many studies at the country or firm level yield similar results, showing that R&D expenditures lead to a reduction in emissions (i.e. Garrone and Grilli, 2010; Wang et al., 2009). And, the econometric model by Férnandez et al. (2018) proved that R&D spending can act as an engine of economic growth as well as a driver of sustainable development (lower CO2 emissions).

The model shows the limits of the agenda that identified too ambitious goals, as the country has to go through a sociotechnical transition that involves a range of modifications in institutional structures, together with changes in user practices and the technological dimension. Sustainability transitions represent “long-term multi-dimensional and fundamental transformation processes through which established socio-technical systems shift to more sustainable modes of production and consumption” (Markard et al., 2012). In favoring the year 2050 vision, it is evident that mixes of different policy instruments will perform better if all benefits and costs of complexity are taken into account. In Kazakhstan, we can observe an intrinsic complexity of the policy intervention that is highly context-dependent.

Further research should address and explore alternative criteria for the agenda-setting and the policy mixes; an estimation of the cost of single policy instrument implementation would also lead toward a more detailed efficiency analysis, taking into account also different energy policy options.

## Table 1

Aggregated data for Kazakhstan

Total GDPUS$221,400,000,000 Total population17,290,000 GDP per capitaUS$12,805
Total work force9,050 (in thousands)
Higher education (15% of population)2,590 (in thousands)
Electricity consumption96,820 Gwh
CO2 emission233,850 Kt
Total number of researchers in KZ = 0.196% of the workforce17,702
Investment in R&D = 0.167% GDPUS$36,973,800,000 ## Table 2 GDP per capita (reference year 2014), electricity consumption per capita (in GWh, reference year 2014), GHG emissions per capita (Kt of CO2 equivalent reference year 2012) and the number of employees (reference year 2014) per economic sector VariableSectorGDP per capitaElectricity consumptionGHG emissionsAmount of workforce (in thousands) X1Agriculture, fishing, forestry4,2800.000230.02681,605 X2Industry40,0200.02080.01611,090 X3Construction15,4800.02210.05461,650 X4Trade20,1700.0070.00661,833 X5Manufacturing, mining, oil, quarrying industry49,1300.01390.0431879 X6Education, health, social services28,0000.0050.01551,948 ## Table 3 Estimated goals by the year 2050 CriteriaYear 2050 goals values (growth%) [1] Expected GDPUS$1,346,820,000,000
Expected GDP per capitaUS$60,000 Expected electricity consumption151,000 GWh Expected goal for GHG emissions190,000 Kt (75% of 252,400 Kt in 1992) Expected total population22,447,000 Expected investments in R&D3% of the 2050 GDP = US$40,404,600,000

## Table 4

Feasible goals by the year 2050

CriteriaYear 2050 goals values (growth%) [2]
Expected GDPUS$442,800,000,000 Expected GDP per capitaUS$19,720
Expected electricity consumption151,000 GWh
Expected goal for GHG emissions190,000 Kt (75% of 252,400 Kt in 1992)
Expected total population22,447,000
Expected investments in R&D3% of the 2050 GDP = US\$13,284,000,000

## Table 5

Results by the year 2050

VariableValueVariableValue
D10.000000X12,439,385
D1+0.000000X21,090,000
D20.000000X30.1641025E+08
D2+0.000000X41,833,300
D30.000000X5879,000
D3+0.000000X61,948,000
D40.000000Δ20.6090579E-06
D4+2152937Δ30.1962744E-05

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## Corresponding author

Cinzia Colapinto can be contacted at: cinzia.colapinto@unive.it