A Weighted Goal Programming model for planning ... - Khalifa University

Applied Energy xxx (2016) xxx–xxx

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A Weighted Goal Programming model for planning sustainable development applied to Gulf Cooperation Council Countries Raja Jayaraman a,⇑, Cinzia Colapinto b, Davide La Torre c,d, Tufail Malik c a

Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates Department of Management, Ca’ Foscari University of Venice, Venice, Italy c Department of Applied Mathematics and Sciences, Khalifa University, Abu Dhabi, United Arab Emirates d Department of Economics, Management, and Quantitative Methods, University of Milan, Milan, Italy b

h i g h l i g h t s Application of multi-criteria optimization model for sustainable development. GHG emissions targets cannot be attainable due to reliance on hydrocarbon sources. Provides quantitative evidence for future investments in green energy. Application to Gulf Cooperation Countries.

a r t i c l e

i n f o

Article history: Received 22 August 2015 Received in revised form 22 March 2016 Accepted 10 April 2016 Available online xxxx Keywords: Multi-criteria analysis Goal Programming Energy–economic–environmental model Sustainability Gulf Cooperation Council

a b s t r a c t The United Nations agenda for sustainable development by the year 2030 proposes 17 sustainable development goals which include access to affordable, reliable and clean energy, sustained economic growth with full productive employment and, urgent action to mitigate environmental degradation. Planning for sustainable development requires integrating conflicting criteria on economy, energy, environment and social aspects. In this paper, we introduce a Weighted Goal Programming model involving criteria on the economic development (GDP), the electricity consumption, the greenhouse gas emissions, and the total number of employees to determine optimal labor allocation across various economic sectors. The proposed model is validated with data from the six members of the Gulf Cooperation Council (namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates). The results of the model aim to provide empirical evidence and insights to decision makers and policy analysts in developing optimal strategies able to simultaneously satisfy energy demand, economic growth, labor development and reduction in greenhouse gas emissions to achieve sustainability targets by the year 2030. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction The rapid economic development in recent decades of the six member Gulf Cooperation Council (GCC, comprising Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) has brought significant challenges due to increased energy consumption and the environmental impact associated with greenhouse gas (GHG) emissions. The GCC countries together represent over one-third of world’s oil and one fourth of natural gas reserves and have a pivotal role in the energy supply chain. Time-limited oil production, price volatility and other critical ⇑ Corresponding author. E-mail address: [email protected] (R. Jayaraman).

market factors present economic diversification as a key strategy in planning for sustainable development [1]. Economic growth, rise in population, increase in transportation, industrialization and, workforce expansion have strong interconnections with the increase in GHG levels. Economic growth encourages industrialization, boosts purchasing power and the demand for new goods and services, thereby increasing logistics and transportation. In turn economic growth also improves living standards, age and fertility, resulting in population growth. These together result in a nonlinear increase in energy consumption, which in turn contributes to the growing GHG emissions. The fast-tracked economic development of GCC countries has put incremental challenges on labor demand, development and infrastructure projects, electricity consumption, and GHG emissions to be addressed

http://dx.doi.org/10.1016/j.apenergy.2016.04.065 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Jayaraman R et al. A Weighted Goal Programming model for planning sustainable development applied to Gulf Cooperation Council Countries. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.04.065

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R. Jayaraman et al. / Applied Energy xxx (2016) xxx–xxx

through strong policy alternatives for achieving sustainable development. The energy production is feared to fall short of the anticipated demand unless the energy portfolio is altered. Currently majority of the electricity generation in GCC is based on hydrocarbon sources [2] putting constraints on the surplus production for domestic consumption instead of exports. To further exacerbate the problems GCC countries have yet to implement smart grids and adaptive demand management solutions for effective transmission and distribution. Fossil fuel combustion is widely recognized to be the most significant contributor to the GHG emissions. The increase in electricity demand in the GCC countries far exceeds the global average, due to the growing economic base and the associated development projects in the region. Three of the GCC countries are identified by the United Nations Environmental program as having the highest per capita energy consumption worldwide. The GCC country-wise contribution to the total GHG emission in the year 2005 was: 56% by Saudi Arabia, 18.75% by the UAE, 10.43% by Kuwait, 7.3% by Qatar, 4% by Oman and 3.4% by Bahrain respectively [3]. One potential option for GCC is the inclusion of renewable sources from solar and wind to augment electricity production. Some developments to integrate renewables in the energy mix can be seen in each GCC country. The UAE have developed 100 MW CSP (Shams) and 100 MW solar PV (Noor project), Mohammed Bin Rashed Solar Park with an anticipated capacity of 1000 MW by year 2030, and Masdar city promoting research in renewable energy technologies. Qatar plans to cool the stadiums using solar power in hosting the 2022 FIFA World Cup, and control GHG emissions. Bahrain and Oman have launched pilot projects for renewable energy generation. Kuwait is heading towards setting up a 60 MW solar power plant. Saudi Arabia’s recent investments in alternative energies include solar power to run anti-corrosion control panel devices, trials to power a village and a school using solar energy, the planned polysilicon production facilities in the country (IDEA polysilicon) and 100 MW and 600 MW solar projects in Makkah and Dibba respectively [4]. These projects in the GCC nations also entail numerous indirect benefits including creating a local skilled employment, reducing the dependence on fossil fuels for power generation and localized industrial development with huge domestic demand and export potential. On the other hand these targets for improving energy efficiency and reducing GHG emissions are not trivially achievable as they come with significant challenges. These include research and development relevant to the local needs associated with carrying out such projects (for instance dust, humidity and heat are unique to the location that can render the existing technologies inefficient). The subsidized use of hydrocarbons currently supported by the GCC governments also requires proper management to channel investments in renewable energy. The cost of solar power generation is at least six times higher than power generation using the subsidized gas [4]. In this paper we present a multi-criteria model using Weighted Goal Programming (WGP) technique to study optimal resource allocation with conflicting objectives related to GDP growth, electricity consumption, GHG emissions and number of employees towards achieving sustainability goals by the year 2030 for GCC countries. The results of the model offer policy makers options to explore trade-offs between competing objectives, assess strengths and weaknesses in simultaneously achieving sustainability goals and address the shortcomings through coordinated policy planning and development. Most existent literature on economic– environmental–energy model uses time series based approach, panel co-integration, and Granger causality tests. Our approach employs a multi-criteria model for GCC countries. Although goal programming (GP) approach and its variants have been used to study and plan policies for individual countries, to the best of our knowledge this is the first approach to use multi-criteria approach applied to GCC countries that allows comparative analysis.

The rest of the paper is organized as follows: in Section 2, we present relevant literature on multi-criteria models using goal programming applied to study energy–economic–environmental interactions. In Section 3 we introduce the mathematical formulation of multi-criteria model using GP technique, in Section 4 we present the data and estimated goals used to validate the model. In Section 5, we discuss the model results and their interpretation and present conclusions in Section 6.

2. Related literature Resource planning problems often involve economic, environmental and social objectives that are in conflict with one another. As pointed out by Dincer and Rosen [5] there is an intimate connection between energy, the environment and sustainable development. Multi-criteria decision models using goal programming techniques have been applied to a variety of energy planning, energy resource allocation, building energy management, transportation energy management, and planning for energy projects [6–12]. Indeed different stakeholders bring along different criteria and points of view, which must be resolved within a framework of understanding and mutual compromise. The traditional energy-resources allocation problem is concerned with the allocation of limited resources among the enduses such that the overall return is maximized. Mezher et al. [13] study the energy allocation process from two points of view: economy (costs, efficiency, energy conservation, and employment generation) and environment in Lebanon. The objective functions were transformed into mathematical language to obtain a multiobjective allocation model based on pre-emptive GP techniques. The proposed method allows decision makers to encourage or discourage specific energy resources for the various household end-uses. The review by Wang et al. [14] highlights the popularity of MCDA methods in decision-making for sustainable energy due to multi-dimensionality of the sustainability goals and the complexity of socio-economic and biophysical systems. Researchers emphasize that most countries are faced with important challenges concerning the definition of the policies to achieve energy and environmental targets, taking also into account the economic and social issues. Main recent contributions on this issue follow. Ren et al. [15] develop a multi-objective Mixed Integer Linear Programming (MILP) model to investigate an optimal operating strategy of a distributed energy system, considering the economic and environmental aspects. San Cristóbal [16] analyzes how targets for the emissions of GHGs may be reached and can affect the composition of production activity in Spain, considering a GP model across key economic sectors, minimizing GHG emissions, waste emissions, and energy requirements, and maximizing employment and output levels. Henriques and Antunes [17] construct an Input/Output multi-objective linear programming model for the Portuguese economy to assess the trade-offs between the maximization of GDP and employment level, and the minimization of energy imports and environmental impacts, tackling the uncertainty of the model coefficients using interval programming. Carvalho et al. [18] consider simultaneously economic and environmental criteria in the synthesis of a trigeneration system to be installed in a hospital. A MILP model provides a Pareto frontier set of solutions representing optimal trade-offs between the economic and environmental objectives. Chang [19] employs a GP model to identify the key CO2 emitting sectors for optimized production structure applied to emission reduction goals of China. Flores et al. [20] present a mathematical programming model that helps plan investment in energy sources. The model uses renewable and non-renewable demands and sources of new


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energy facilities and the current amount of fossil-fuel reserves in order to maximize the Net Present Value in time. An emerging literature stream is focusing on the Middle East and North Africa (MENA) and/or the GCC countries. Ozturk and Acaravci [21] investigate the short and long run causality issues between electricity consumption and economic growth in MENA countries by using Autoregressive Distributed Lag (ARDL) bounds testing approach of cointegration and vector error-correction models. The overall results indicate that there is no relationship between the electricity consumption and the economic growth in most of the MENA countries. Ozturk and Al-Mulali [22] investigate the relationship between natural gas energy consumption and economic growth by including trade openness, total labor force and gross fixed capital formation as major determinants of GDP growth within a multivariate framework model for GCC countries. Asif et al. [23] capture the relationships amongst energy consumption, urbanization, economic growth and environmental degradation in the GCC countries by using panel unit root and co-integration tests for the period 1980–2011. A MILP formulation is presented for the optimal design of UAE power system by Almansoori and Betancourt-Torcat [24]. They study the 2020 UAE power sector operations under three scenarios: domestic vs. international natural gas prices (considering different carbon tax levels), social benefits of using low emission power technologies (e.g., renewable and nuclear), and CO2 emission constraints. Recently, Jayaraman et al. [25] develop a polynomial GP model to study the effects of electricity consumption and GHG emissions on the economic growth of United Arab Emirates (UAE). Jayaraman et al. [26] propose a fuzzy GP model that integrates optimal resource allocation to simultaneously satisfy prospective goals on economic development, energy consumption, workforce, and greenhouse gas emission reduction applied to key economic sectors of the UAE. Jayaraman et al. [27,28] study optimal labor allocation for energy, economic and environmental sustainability of the UAE using a WGP approach. The current paper falls within this literature stream, as we extend the work presented in Jayaraman et al. [27] to compare policies across the GCC countries. This paper does not consider the perspective of a single country, as geographic and environmental reasons suggest that it is better to take into account the GCC area as a whole. Our model fits into multi-criteria models in line with the presented literature review: as we will show in the next section, our model includes four criteria namely gross domestic product, electricity consumption, GHG emissions, and the number of employees across major economic sectors. We present a WGP thus setting all goals equally important; obviously the presented model can be modified setting priorities among the criteria using Lexicographic GP model, introducing randomness in both goal values and criterion using a stochastic GP model and fuzzy values using a Fuzzy GP model.

have been applied to solve large-scale MCDM problems. Within the GP philosophy each criteria is provided with a goal or target value to be achieved, and unwanted deviations from this set of target values are then minimized by the objective function. The obtained solution through the GP model represents the best compromise that can be made by the decision maker. A positive feature of the GP philosophy is its simplicity and ease of use [29], which justify its wide popularity for solving MCDM models in diverse fields. GP model has been widely applied in several disciplines including: accounting and financial aspect of stock management, marketing, quality control, human resources, production and operations management [30–33]. A negative counterpart is the ability of the GP model to produce solutions that are not Pareto efficient, but there are methods to improve Pareto efficiency in solutions. The GP model was first introduced by Charnes and Cooper [34,35], and in the classical formulation it takes the following form:

Minimize

p X Dþi þ Di i¼1

Subject to n X Aij X j þ Di Dþi ¼ Gi ; i ¼ 1 . . . p

ð1Þ

j¼1

X2X Di ; Dþi P 0;

i ¼ 1...p

where X is the feasible set, Xj are the input variables representing the number of employees in each economic sector, the coefficient Aij states the contribution of the jth variable to the achievement P of the ith criterion F i ðX 1 ; X 2 ; . . . ; X n Þ ¼ nj¼1 Aij X j ; Dþ i and Di are, respectively, the positive and the negative deviations with respect to the aspirational goal levels Gi, i = 1. . .p. The interpretation of model (1) is quite straightforward: the smaller the deviations from the desired goals the better the solution X = (X1, . . ., Xn) is, meaning Pn that the achieved levels j¼1 Aij X j for each criterion are getting closer to the set goals Gi. Jones and Tamiz [36] discuss various GP variants and solution methodology. An alternative definition of GP Model is the Weighted GP represented as:

Minimize

p X W þi Dþi þ W i Di i¼1

Subject to n X Aij X j þ Di Dþi ¼ Gi ;

i ¼ 1...p

ð2Þ

j¼1

3. Model formulation using MCDM and goal programming

X2X Multi-criteria decision making (MCDM) refers to making decisions in the presence of multiple, usually conflicting and incommensurable criteria. MCDM problems are common in everyday life and very often real world problems are modeled using this framework. MCDM techniques provide solutions that can be considered the best compromise satisfying multiple criteria involved in the model. MCDM problems are more complicated than single criterion optimization and usually of large scale. Although MCDM problems are very well known since the 1950 s MCDM as a discipline only has a relatively short history and its development is closely related to the advancement of algorithms and computing systems. The GP techniques have become a widely used approach in Operations Research (OR): the classical GP model and its variants

Di ; Dþi P 0;

i ¼ 1...p

Wþ i

where and W i represent weights associated with positive and negative deviations of each goal. In this paper we formulate a macroeconomic WGP growth model that simultaneously considers the following four sustainability criteria: F1 = gross domestic product (in local currency); F2 = Electricity consumption (in Gwh); F3 = GHG emissions (in Gg of CO2 equivalent); F4 = Number of employees (in thousands)


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where criterion Fi is linear with respect to the decision variable Xj and takes the form

F 1 ðX 1 ; X 2 ; . . . ; X 8 Þ ¼ A11 X 1 þ A12 X 2 þ . . . þ A18 X 8 F 2 ðX 1 ; X 2 ; . . . ; X 8 Þ ¼ A21 X 1 þ A22 X 2 þ . . . þ A28 X 8 F 3 ðX 1 ; X 2 ; . . . ; X 8 Þ ¼ A31 X 1 þ A32 X 2 þ . . . þ A38 X 8 F 4 ðX 1 ; X 2 ; . . . ; X 8 Þ ¼ A41 X 1 þ A42 X 2 þ . . . þ A48 X 8 The decision variable in our proposed model Xi represents the number of employees in each economic sector, we consider the following eight economic sectors: X1 = Agriculture X2 = Crude oil, Natural Gas & Mining X3 = Manufacturing & Electricity X4 = Construction & Real Estate X5 = Trade & Transport X6 = Restaurants & Hotels X7 = Banking & Financial Services X8 = Government, Social & Personal Services For each criterion, four goals G1 ; G2 ; G3 and G4 are obtained by projecting the past trends. Finally each decision variable is bounded from below by a nonzero positive number Xj , meaning that each decision has to preserve at least the current number of employees in each sector. Summarizing, the following WGP model with weights W 1 ; W 2 ; W 3 ; W 4 is implemented as

Minimize W 1 ðD11 þ D12 Þ þ W 2 ðD21 þ D22 Þ þ W 3 ðD31 þ D32 Þ þ W 4 ðD41 þ D42 Þ; Subject to

A11 X 1 þ A12 X 2 þ þ A18 X 8 þ D11 D12 ¼ G1 A21 X 1 þ A22 X 2 þ þ A28 X 8 þ D21 D22 ¼ G2

ð3Þ

A31 X 1 þ A32 X 2 þ þ A38 X 8 þ D31 D32 ¼ G A41 X 1 þ A42 X 2 þ þ A48 X 8 þ D41 D42 ¼ G4 X 1 P X1 ; X 6 P X6 ;

X 2 P X2 ; X 7 P X7 ;

X 3 P X3 ; X 8 P X8 ;

X 4 P X4 ;

X 5 P X5 ;

X j ; j ¼ 1; 2 . . . 8 are integer Dij P 0 The variables D11, D12, D21, D22, D31, D32, D41, D42 represent the positive and negative deviations with the conventional notation Dj1 ¼ D 1 and Dj2 ¼ D2 . The input variables X1, X2, X3, X4, X5, X6, X7, X8 take integer values, thus the proposed WGP model takes the form of the Mixed Integer Linear Programming model. For each country we implement the model (3). The mathematical representation for each country is presented in Appendix A. The sectorial data and the goals for various criteria are discussed in Section 4.

each country is in US dollars. It is characteristic that GDP growth has been rapidly rising with a slight deflection in the year 2008. Total population and electricity consumption preserve a steadily increasing trend. For GHG emission, the trend of CO2 emissions is presented as it represents a major contributor to GHG emissions. Data for the trends on GDP, population growth and CO2 emissions were obtained from World Bank data bank and electricity consumption were obtained from U.S. Energy Information Administration. 4.1. Gross domestic product Sector wise GDP are published in annual statement of national accounts by Ministry of Economy, Central Bank of respective countries.1 In some cases the most updated entry was unavailable, and we used the estimated annual percentage growth rate of GDP based on constant growth in local currency from World Bank data bank.2 Table 1 presents the sector wise per capita estimates of GDP with reference to the year 2014. 4.2. Electricity consumption The per capita estimates for electricity consumption across the eight sectors in Gigawatt hour (Gwh) are summarized in Table 2. The sectorial data for electricity consumption was obtained from the International Energy Agency (IEA) with reference to the year 2011.3 The data from IEA did not provide sector specific estimates on electricity consumption; hence we estimate the percentile contribution of each sector relative to GDP and used it as measure of disaggregation to estimate sector specific values for electricity consumption with reference to year 2014. 4.3. GHG emissions GHG emission data was obtained from the United Nations Framework Convention on Climate Change4 (UNFCCC). Table 3 summarizes the sector specific per capita GHG emissions in Giga Grams of CO2 equivalent. All the six GCC countries are identified as non-annex I parties with no reporting burden. The most updated entry available for each country is as follows: Qatar (2007), UAE (2005), KSA (2000), Bahrain (2000), Oman (1994) and Kuwait (1994). The past trend of sector wise GHG were used to project the data with reference to year the 2012. 4.4. Number of employees

4. Model data and analysis

The number of employees across the eight economic sectors were obtained from Ministry of Labor, Statistics Planning and Development of respective countries.5 Table 4 presents the number of employees (in thousands) employed in each sector. The annual growth percentage of labor was used to project the data with reference to year 2014.

In validating the WGP model (3) for the six GCC countries, we consider the contribution of eight key economic sectors for the identified four criterion: gross domestic product (F 1 ), electricity consumption (F 2 ), GHG emissions (F 3 ), and total number of employees (F 4 ). Sectorial estimates were collected from several sources, in cases where the available data was missing or limited, reasonable values were estimated based on past trends. In this section we present the data discuss the assumptions and used in estimation of goals for numerical validation. Fig. 1 presents the historical trends of the four model criteria between the years 2000–2012. For comparison the GDP data for

1 GDP data sources: www.ncsi.gov.om, http://www.csb.gov.kw/Default_EN.aspx, http://www.economy.ae/English/Documents/Annual%20Economic%20Report% 202012-en.pdf, http://www.cdsi.gov.sa/english/, http://www.cbb.gov.bh/page-p-economic_indicators.htm, http://www.qsa.gov.qa/eng/index.htm. 2 World Bank: http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/countries/AE?display=default. 3 http://www.iea.org/statistics/. 4 http://unfccc.int/ghg_data/ghg_data_unfccc/ghg_profiles/items/4626.php. 5 Labor and population data: https://www.ncsi.gov.om/Pages/NCSI.aspx; http:// www.qsa.gov.qa/eng/index.htm; http://www.economy.ae/English/Documents/MOE% 20ANNUAL%20REPORT%202013%20ENG%20091013.pdf; http://www.cdsi.gov.sa/english/; http://www.cio.gov.bh/cio_eng/default.aspx; http://www.csb.gov.kw/Socan_ Statistic_EN.aspx?ID=64.


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Fig. 1. Historic trends in GDP, electricity consumption, GHG emissions and population growth. Energy Information Administration.

*

Data sources: World Development Indicators Database, World Bank; U.S.

Table 1 Gross domestic product per capita. Variable

Sector

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia

United Arab Emirates

X1 X2 X3 X4 X5 X6 X7 X8

Agriculture Crude oil, Natural Gas & Mining Manufacturing & Electricity Construction & Real Estate Trade & Transport Restaurants & Hotels Banking & Financial Services Government, Social & Personal Services

0.06285 0.23323 0.05118 0.01739 0.02089 0.04577 0.30844 0.02656

0.00299 6.78626 0.03058 0.07197 0.02542 0.01926 0.53933 0.07632

0.00470 1.00594 0.02162 0.00471 0.01904 0.00314 0.39240 0.02821

0.00715 0.40858 0.08348 0.03442 0.04852 0.13134 0.52286 0.03466

0.11608 9.37454 0.43333 0.17721 0.13206 0.49979 1.18960 0.08440

0.03571 4.76202 0.18385 0.08502 0.17935 0.08207 1.06595 0.09702

Table 2 Electricity consumption per capita (in Gwh, reference year 2014). Variable

Sector

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia


X1 X2 X3 X4 X5 X6 X7 X8


0.03688 0.00505 0.05018 0.03949 0.02047 0.04483 0.30233 0.02721

0.0015 1.4272 0.0016 0.037 0.0131 0.0099 0.2777 0.0393

0.00375 0.0477 0.00961 0.01725 0.00846 0.00139 0.17409 0.04499

0.0028 0.0333 0.1008 0.0206 0.0175 0.0456 0.2267 0.0133

0.009983 0.15103 0.017007 0.073282 0.005184 0.019588 0.046703 0.015529

0.00535774 0.0661707 0.02800659 0.0209694 0.01806757 0.00826637 0.1623983 0.00976069


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Table 3 Green house gas emissions per capita (in Giga Grams of CO2 equivalent, reference year 2012). Variable

Sector

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia


X1 X2 X3 X4 X5 X6 X7 X8


0.02138 0.42291 0.09047 0.02858 0.01375 0.02213 0.15673 0.01349

0.0054 13.663 0.0078 0.0185 0.0571 0.005 0.1386 0.0196

0.13522 0.76111 0.00857 0.00186 0.01285 0.00124 0.15466 0.01114

0.0061 0.6314 0.0661 0.004 0.0315 0.0553 0.2751 0.0165

0.04756 1.727105 0.048934 0.011533 0.049314 0.092628 0.22052 0.015672

0.01835571 1.82323275 0.07038974 0.00283748 0.00666301 0.00274052 0.03556374 0.00323856

Table 4 Number of employees (reference year 2014). Variable

Sector

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia


X1 X2 X3 X4 X5 X6 X7 X8


1,418 26,948 89,156 185,668 163,034 36,995 16,371 194,364

75,250 10,940 163,273 237,774 600,856 173,002 43,961 278,878

82,067 15,824 167,444 673,878 191,608 78,878 3663 185,688

19,433 88,929 118,204 545,587 234,428 34,459 12,260 381,776

461,948 120,896 776,809 1,808,873 2,096,694 264,920 122,752 5,247,497

258,867 74,284 687,686 1,505,931 1,403,509 236,357 81,037 810,366

Table 5 Estimated goals by the year 2030 for various model criteria. Year 2030 goals values (growth %)

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia


GDP Number of employees Electricity consumption GHG emissions

64347(5%) 1337222(3.5%) 90552(7.8%) 59237(3.3%)

299999(4%) 2966679(3.5%) 135470(5.5%) 1264670(10.72%)

190890(5%) 2620394(3.5%) 80767(7.6%) 571667(9.63%)

859855(14%) 2687870(3.5%) 190478(10.3%) 274311(6.71%)

1925490(6%) 20416224(3.5%) 584589(4.9%) 1346292(5.35%)

3110323(4%) 9115735(3.5%) 361513(9.2%) 286051(1.75%)

Table 5 presents the projected goal values for the year 2030 with the corresponding growth rates for the four criteria. The growth in population and rising labor force participation in GCC is estimated to be between 3% and 4%6 and we set the average growth rate of 3.5%. The GDP growth rates were estimated based on data from World Bank. Electricity consumption growth rates were estimated from IEA data and similarly GHG emissions were estimated based on past trends of data reported to UNFCCC. 5. Results and interpretation The WGP model was solved using optimization software LINGO 14Ò. The respective mathematical optimization model for each country is presented in the Appendix A. The results in Table 6 can be interpreted as follows: Regarding GDP growth, Qatar is the only country that shows a consistent negative deviation (D11 = 423637.6) with respect to its current trend, implying that the long-run macroeconomic scenario will not permit a GDP growth in line with the current trends. A positive deviation of D32 for the case of Bahrain, Saudi Arabia, Oman, and Qatar indicates that GHG emissions in these countries are expected to grow beyond the current trends. Regarding the energy consumption criterion, only the UAE shows a consistent nonzero positive deviation (D22 = 30606.7) which suggests that investments in green and renewable

6 ‘‘Labor Market Reforms to Boost Employment and Productivity in the GCC”, International Monetary Fund, Oct 20140.

energy resources have to be implemented to satisfy the current demand of energy consumption without any further increase in the total GHG emissions. This observation is consistent with findings in [28]. In addition the surplus in D21 for Bahrain, Oman and Qatar indicates that the long run energy requirements will be met. Interpreted together with D32 suggests a strong need to diversify energy production using renewables. The model provides policy makers a quantitative tool to simultaneously optimize and assess trade-offs between multiple sustainability criteria. Previous research has widely recognized the strong interdependencies between energy consumption, GDP growth and GHG emissions. The results of the WGP model indicate GDP growth (except Qatar) and number of employees goals are attainable but a combination of policy tools should encourages future investments in clean technologies, novel ways for energy storage [37], innovative ways to capture, store and transport CO2 emissions [38,39], promoting judicious use of energy resources goes hand-in-hand to enhance the future energy security, and permit healthy economic and environmental growth in GCC countries. 6. Conclusions Rapid economic development and industrialization go hand in hand with increased energy use, GHG emissions and labor demand require careful planning and exploring potential tradeoffs among competing objectives. The last few decades have witnessed a growing concern on resource consumption, the adequacy of energy resources and with the quality of the physical environment. Recently, the GCC countries have shown a keen interest in


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R. Jayaraman et al. / Applied Energy xxx (2016) xxx–xxx Table 6 Results of the WGP model. Variable

D11 D12 D21 D22 D31 D32 D41 D42 X1 X2 X3 X4 X5 X6 X7 X8

Bahrain

Kuwait

Oman

Qatar

Saudi Arabia


Value

Reduced cost

Value

Reduced cost

Value

Reduced cost

Value

Reduced cost

Value

Reduced cost

Value

Reduced cost

0 8.28E02 36457.12 0 0 15017.73 0 0 1418 26948 89156 672835 163034 36995 101958 194364

0.5 0 0 0.5 0.5 0 0.5 0 0.2618375 0.4127725 0.2728675 0.25162 0.2535425 0.2557675 0.29071 0.25321

0 1.03E02 100343.7 0 0 1.46E03 0 0 369439 10940 163273 237774 1355260 173015 266031 278878

0.5 0 0 0.5 0.5 0 0.5 0 0.2517072 5.005605 0.2592133 0.2633627 0.2673477 0.2535746 0.3500691 0.2641676

0 1.05E02 21561.07 0 0 4155.99 0 0 82067 15824 167444 673880 191608 78878 26069 1285637

0.5 0 0 0.5 0.5 0 0.5 0 0.2840436 0.6798382 0.255146 0.2473287 0.255857 0.2507459 0.3432408 0.2485913

423637.6 0 2.05E02 0 0 112476 0 0 19433 88929 118204 545587 234428 818887 379090 381776

0 0.5 0 0.5 0.5 0 0.5 0 0.2490527 0.2973712 0.2204534 0.2372553 0.2413764 0.2195911 0.1313715 0.2421242

0.2180172 0 6.55E02 0 0 401901.7 0 0 461948 426049 863130 1.02E+07 2096694 264920 122754 5247497

0 0.5 0 0.5 0.5 0 0 0.25 0.2696261 2.199617 0.3503525 0.3097375 0.2719832 0.3566881 0.5039455 0.2710654

0 6.32E03 0 30606.68 0 1.14E03 0 0 258867 78674 687692 1505932 3980533 236357 1557314 810366

0.5 0 0.5 0 0.5 0 0.5 0 0.2648548 1.912856 0.3205625 0.277206 0.3010215 0.27327 0.565978 0.277505

The bold values indicate significant deviation from the desired goals.

engaging in a more sustainable development path, with many of them are already pursuing strategic energy options beyond hydrocarbons by integrating nuclear power generation and renewable sources in their energy portfolio [40,41]. The WGP model results show that GHG emissions goal is not achievable by four out of six GCC countries thus there is a significant need to refocus their investments on energy efficiency, conservation and incrementing the share of renewable, non-emitting sources of energy production in order to achieve sustainable development in the long term. Furthermore the model provides insights that all other goals may be achieved with two exceptions (GDP for Qatar and electricity consumption for the UAE). It is well known that investments in pollution abatement activities impact on GDP: A portion of GDP has to be used to financing technology progress, innovation, and acquisition of skilled labor. Obviously, this scenario might have a negative impact on the achievement of all other goals and on the overall long-run sustainability. Acknowledgements The authors gratefully acknowledge the support and funding from Khalifa University Internal Research Fund (KUIRF) – Grant number 210032 to conduct this research. Cinzia Colapinto gratefully acknowledges financial support from Ca’ Foscari University of Venice.

A.1.2. Electricity consumption constraint

0:03688X 1 þ 0:00505X 2 þ 0:05018X 3 þ 0:03949X 4 þ 0:02047X 5 þ 0:04483X 6 þ 0:30233X 7 þ 0:02721X 8 þ D21 D22 ¼ 90552; A.1.3. GHG emission constraint

0:02138X 1 þ 0:42291X 2 þ 0:09047X 3 þ 0:02858X 4 þ 0:01375X 5 þ 0:02213X 6 þ 0:15673X 7 þ 0:01349X 8 þ D31 D32 ¼ 59237; A.1.4. Number of employees constraint

X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 1337222; X 1 P 1418; X 5 P 163034; D11 P 0; D41 P 0;

X 2 P 26948;

X 3 P 89156;

X 6 P 36995;

D12 P 0; D42 P 0;

D21 P 0;

X 4 P 185668;

X 7 P 16371; D22 P 0;

X 8 P 194364;

D31 P 0;

D32 P 0;

A.2. Kuwait

MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ þ 0:25ðD41 þ D42 Þ;

Appendix A

A.2.1. GDP constraint

A.1. Bahrain

0:0029949X 1 þ 6:786261X 2 þ 0:0305775X 3 þ 0:0719705X 4 þ 0:0254195X 5 þ 0:0192644X 6 þ 0:5393307X 7 þ 0:0763199X 8

MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ

þ D11 D12 ¼ 299999;

þ 0:25ðD41 þ D42 Þ; A.2.2. Electricity consumption constraint A.1.1. GDP constraint

0:00154206X 1 þ 1:427194698X 2 þ 0:001573683X 3 0:06285X 1 þ 0:23323X 2 þ 0:05118X 3 þ 0:01739X 4 þ 0:02089X 5 þ 0:04577X 6 þ 0:30844X 7 þ 0:02656X 8 þ D11 D12 ¼ 64347;

þ 0:03701313X 4 þ 0:01307929X 5 þ 0:009915955X 6 þ 0:277654967X 7 þ 0:039264768X 8 þ D21 D22 ¼ 135470;


8


A.2.3. GHG emission constraint


0:00537608X 1 þ 13:66335466X 2 þ 0:007849185X 3

0:0071491X 1 þ 0:4085752X 2 þ 0:0834765X 3 þ 0:034419X 4

þ 0:018493317X 4 þ 0:057050391X 5 þ 0:004950058X 6 þ 0:138600805X 7 þ 0:019615459X 8 þ D31 D32 ¼ 1264670;

þ 0:0485242X 5 þ 0:1313356X 6 þ 0:522864X 7 þ 0:034663X 8 þ D11 D12 ¼ 859855; A.4.2. Electricity consumption constraint

A.2.4. Number of employees

0:00278X 1 þ 0:03333X 2 þ 0:10085X 3 þ 0:02057X 4 þ 0:01749X 5 X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 2966679; X 1 P 75250; X 5 P 600856;

X 2 P 10940;

X 3 P 163273;

X 6 P 173002;

D11 P 0;

D12 P 0;

D41 P 0;

D42 P 0;

D21 P 0;

X 7 P 43961; D22 P 0;

þ 0:04555X 6 þ 0:22672X 7 þ 0:0133X 8 þ D21 D22 ¼ 190478;

X 4 P 237774; X 8 P 278878;

D31 P 0;

D32 P 0;



A.3. WGP model for OMAN

MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ

X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 2687870; X 1 P 19433;

þ 0:25ðD41 þ D42 Þ;

X 5 P 234428; A.3.1. GDP constraint

0:0046995X 1 þ 1:0059403X 2 þ 0:0216246X 3 þ 0:0047058X 4 þ 0:0190412X 5 þ 0:0031376X 6 þ 0:3923977X 7 þ 0:0282132X 8

X 2 P 88929;

X 3 P 118204;

X 4 P 545587;

X 7 P 12260;

X 8 P 381776;

X 6 P 34459;

D11 P 0;

D12 P 0;

D41 P 0;

D42 P 0;

D21 P 0;

D22 P 0;

D31 P 0;

D32 P 0;

A.5. WGP model for Saudi Arabia

þ D11 D12 ¼ 190890; MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ þ 0:25ðD41 þ D42 Þ;



0:00375X 1 þ 0:0477X 2 þ 0:00961X 3 þ 0:01725X 4 þ 0:00846X 5 þ 0:00139X 6 þ 0:17409X 7 þ 0:04499X 8 þ D21 D22 ¼ 80767;

0:1160845X 1 þ 9:3745368X 2 þ 0:43333X 3 þ 0:17720X 4 þ 0:1320627X 5 þ 0:4997924X 6 þ 1:1896018X 7 þ 0:0844018X 8 þ D11 D12


¼ 1925490; 0:135224938X 1 þ 0:761112323X 2 þ 0:008569463X 3 þ 0:001858961X 4 þ 0:012846627X 5 þ 0:001235809X 6 þ 0:15465536X 7 þ 0:011141805X 8 þ D31 D32 ¼ 571667;

¼ 584589;

X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 2620394; X 5 P 191608;

X 2 P 15824; X 6 P 78878;

D11 P 0;

D12 P 0;

D41 P 0;

D42 P 0;

D21 P 0;

X 3 P 167444; X 7 P 3663; D22 P 0;

X 4 P 673878; X 8 P 185688;

D31 P 0;

D32 P 0;

A.4. WGP model for Qatar

MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ þ 0:25ðD41 þ D42 Þ;

0:00998X 1 þ 0:15103X 2 þ 0:01701X 3 þ 0:07328X 4 þ 0:00518X 5 þ 0:01959X 6 þ 0:0467X 7 þ 0:01553X 8 þ D21 D22

A.3.4. Number of employees constraint

X 1 P 82067;




X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 20416224; X 1 P 461948; X 2 P 120896; X 3 P 776809; X 4 P 1808873; X 5 P 2096694; X 6 P 264920; X 7 P 122752; X 8 P 5247497;



D11 P 0;

D12 P 0;

D41 P 0;

D42 P 0;

D21 P 0;

D22 P 0;

D31 P 0;

D32 P 0;

A.6. WGP model for United Arab Emirates

MINIMIZE 0:25ðD11 þ D12 Þ þ 0:25ðD21 þ D22 Þ þ 0:25ðD31 þ D32 Þ þ 0:25ðD41 þ D42 Þ; A.6.1. GDP constraint

0:035705X 1 þ 4:762018X 2 þ 0:183853X 3 þ 0:085018X 4 þ 0:179355X 5 þ 0:082073X 6 þ 1:06595X 7 þ 0:09702X 8 þ D11 D12 ¼ 3110323; A.6.2. Electricity consumption constraint




A.6.4. Number of employees

X 1 þ X 2 þ X 3 þ X 4 þ X 5 þ X 6 þ X 7 þ X 8 þ D41 D42 ¼ 9115735; X 1 P 258867; X 5 P 1403509;

X 2 P 74284; X 6 P 236357;

D11 P 0;

D12 P 0;

D41 P 0;

D42 P 0;

D21 P 0;

X 3 P 687686; X 7 P 81037; D22 P 0;

X 4 P 1505931; X 8 P 810366;

D31 P 0;

D32 P 0;

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