- Email: [email protected]
Multicriteria-based decision aiding technique for assessing energy policy elements-demonstration to a case in Bangladesh
Applied Energy 164 (2016) 237–244
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Multicriteria-based decision aiding technique for assessing energy policy elements-demonstration to a case in Bangladesh Md. Mizanur Rahman a,b,⇑, Jukka V. Paatero b, Risto Lahdelma b, Mazlan A. Wahid a a b
Department of Thermo-Fluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia Department of Energy Technology, Aalto University School of Engineering, POB: 14100, FI-00076 Aalto, Finland
h i g h l i g h t s A multicriteria technique for assessing energy policy elements has been proposed. Energy policy elements have been examined based on assigned criteria. This assessment gives results which are representative of all stakeholders. Policy elements which are chosen by this method promote sustainability.
a r t i c l e
i n f o
Article history: Received 16 September 2015 Received in revised form 29 November 2015 Accepted 30 November 2015
Keywords: Multicriteria Long-range Sustainability Policy element Energy policy
a b s t r a c t The adverse environmental consequences and diminishing trend of fossil fuel reserves indicate a serious need for vibrant and judicious energy policy. Energy policy involves a number of stakeholders, and needs to incorporate the interests and requirements of all the key stakeholder groups. This paper presents a methodological technique to assist with formulating, evaluating, and promoting the energy policy of a country in a transparent and representative way with clear scientific justifications and balanced assessments. The multicriteria decision analysis approach has been a widely used technique for evaluating different alternatives based on the interests of a multitude of stakeholders, and goals. This paper utilizes the SMAA (Stochastic Multicriteria Acceptability Analysis) tool, which can evaluate different alternatives by incorporating multiple criteria, in order to examine the preferences of different policy elements. We further extend this technique by incorporating the LEAP model (Long-range Energy Alternatives Planning system) to assess the emission impacts of different policy elements. We demonstrate the application of this evaluation technique by an analysis of four hypothetical policy elements namely Business-as usual (BAU), Renewables (REN), Renewable-biomass only (REN-b), and Energy conservation and efficient technologies (ECET). These are applied to the case of sharing fuel sources for power generation for the Bangladesh power sector. We found that the REN-b and REN policy elements were the best and second best alternatives with 41% and 32% acceptability respectively. This technique gives transparent information for choosing appropriate policy elements that aimed at sustainable energy. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Due to the trend of diminishing fossil fuel reserves, the environmental consequences of fossil fuel combustion, and rapid economic growth, there is a serious need for a vibrant and judicious energy policy for long-term sustainability in the energy sector [1–3]. Setting policies that address increasing environmental concern and reduce dependency on fossil fuel sources are a continuous ⇑ Corresponding author at: Department of Thermo-Fluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia. Tel.: +60 (0) 176480491; fax: +60 7 5566159. E-mail address: [email protected] (Md.M. Rahman). http://dx.doi.org/10.1016/j.apenergy.2015.11.091 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
challenges for many countries [4–6]. The challenges arise mainly because energy policy involves a number of stakeholders and needs to incorporate the interests and requirements of all the major stakeholders to make energy policy viable [7]. The stakeholders’ interests and requirements are diverse and cannot be represented by a single criterion. Therefore, when aiming at sustainable and low-emission energy, strategic decision making arises from the multi-dimensionality of stakeholders interests, socioeconomic dynamics, sustainability goals, and the biophysical systems and long-range nature of the problems [8]. If energy policy is developed on the basis of political motives rather than careful scientific evaluation of multiple criteria, it eventually fails in terms of sustainability and acceptability.
238
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Fig. 1. Conceptual pathways for energy policy evolution.
Therefore, energy policy modeling needs to explicitly consider multiple objectives that can suitably meet the stakeholders’ interests and sustainability criteria. For that reason, policy-makers require detailed information and insights into multiple objectives to endorse appropriate policy measures [9]. Moreover, in addition to giving information on multiple objectives, the multicriteria method provides decision-makers with an opportunity to explore different energy options by trade-off their importance [10]. Complex interactions among multiple objectives (goals) require multicriteria technique to be integrated in the framing of appropriate policy directions [11–14]. Economic and environmental modeling techniques have been widely used by researchers to support developing energy and climate policies [15]. Policy frameworks for determining the optimal energy technology were also found in the existing literature [16,17]. Several studies examine the effects of different policy packages aimed at energy security, affordability, flexibility, extending services, and mitigating environmental pollution [18–23]. Studies also investigate the implications, social costs, benefits and climate-interactions for various policy supports and mechanisms [24–28]. Although significant efforts have been made in the development of energy and emission assessment methods, there are very few methods that include the multi-dimensionality of stakeholders’ interests and sustainability criteria through use of Multicriteria Decision Analysis (MCDA) technique. This paper presents a multicriteria-based decision aiding technique for evaluating and choosing energy policy elements in a representative way. Because this method incorporates the stakeholders’ interests and opinions through criteria values and weights, it eventually provides results which are relatively transparent and representative. This paper shows the relationship between energy-sector challenges, policy elements, and measures (regulatory, legislative or legal) in way to evolving of energy policy. Bangladesh, a developing country, is facing huge challenges in adopting renewable energy sources in its power generation mix, despite endowed with an abundant amount of evenly distributed renewable resources (e.g. average daily solar irradiation of 4.5 kW h/m2 and annual recoverable bio-wastes generation of 0.54 tonne/capita). The reserve of indigenous energy resources in Bangladesh is very limited (i.e. recoverable natural gas reserve 580 billion cubic meter, oil 0.84 million tonne, and coal 1.75 billion tonne) [29]. The objectives of this work are to present a multicriteria based decision aiding technique and to demonstrate the application of this technique to a case. This paper demonstrates the application of this technique through an analysis of four
assumed policy elements (i.e. four alternatives) against a particular aspect (i.e. share of fuel sources for power generation) of Bangladesh’s power sector as a case. The remainder of this paper is organized as follows: Section 2 describes the methodological framework for integrating multiple criteria and sustainability goal, and assessing the long-term emission impacts of policy elements. Section 3 presents datasets of four policy settings (elements), which will be applied to demonstrate the proposed technique. Section 4 presents the main results of this analysis according to the proposed technique and applied datasets. Finally Section 5 highlights the main conclusions obtained from this work. 2. Methodology 2.1. Conceptual pathways for evolution of energy policy Energy sector faces various challenges and barriers, which range from technical and behavioral to sociocultural and political challenges. Energy policy elements1 are the main ingredients of energy policy to deal with the energy-sector challenges and barriers through the undertaking of various policy measures or instruments2 (Fig. 1). The development of energy policy, in many cases, occurs as a ‘black box’ process rather than with clear reasoning and understanding by the stakeholders (e.g. decision-makers) [1]. Essentially, energy policy can be evolved by linking policy elements with different quantifiable parameters in a transparent and representative way [30]. This work presented the technique to choose appropriate policy elements through multicriteria and long-term emission assessments. The chosen policy elements should be implemented by enacting appropriate policy measures (e.g. incentive provisions, tax exemptions, legislations, treaties, etc.). 2.2. Multicriteria technique for analyzing policy elements Energy policy assessment models are usually developed in two ways–process analysis, and econometrics [31]. But the conflicts in sustainability are shared with broader environmental, socioeco1 Policy elements are the basic ingredients of energy policy, each element consists of a combination of purposes, objectives, ambitions, commitments, etc. on energy related issues. 2 Policy measures or instruments are the set of actions that lead to implementation of the tasks required by the chosen policy elements.
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
nomic, and natural resource issues. That means that, energy policy promotes sustainability only while it complies with all the major sustainability issues. The multicriteria decision analysis method, which engages economic, technology and social issues through participatory and analytical tools, is widely accepted for such a multidimensional task [8]. The participatory and multicriteria analysis along with a scenario-building tool can evaluate policy elements by reflecting different opinions, goals and constraints. This work adopted the following approaches to performing multicriteria-based evaluation of policy elements. Among different multicriteria decision analysis (MCDA) groups, value measurement methods are widely used, and these provide a decision by combining the criteria measurements with DMs’ (decision-makers) preferences [32]. However, traditional MCDA requires precise preference information for each of the criteria values, which is often unavailable in many real life problems involving multiple DMs (i.e. stakeholders) [33,34]. The criteria values may also have some uncertainty or imprecision. In this regard, the Stochastic Multicriteria Acceptability Analysis (SMAA) method evaluates alternatives based on inverse weight space that does not require preference weights [35]. SMAA identifies the preference weights that make each alternative preferable by stochastically simulating weights. Then the DMs will check whether they agree with the suggested preference weights or not. The main results of SMAA analysis are rank acceptability indices, central weight vectors, and confidence factors for different alternatives [36]. Let us consider a discrete multicriteria decisionmaking problem consisting of a set of m alternatives X = {x1, . . ., xi, . . ., xm} measured in terms of n criteria. The DMs’ preference structure is represented by a value function uðxi ; wÞ involving weighting coefficients w. The additive utility function is presented as Eq. (1).
uðxi ; wÞ ¼
n X wj uj ðxij Þ;
ð1Þ
j¼1
where xi is the ith alternative, xij is the criteria measurement of the ith alternative with respect to the jth criterion, and the partial utility function ui() converts criteria values to a scale from 0 to 1, where 1 represents the best outcome and 0 represents the worst. The vector of weights w shows the DM’s preferences for each criterion. The weights values are non-negative and normalized. The feasible weight space can be written as Eq. (2).
W ¼ fw 2 Rn : w P 0 and
n X wj ¼ 1g
ð2Þ
j¼1
2.2.1. Rank acceptability index r The relative size of Wri is the rank acceptability index bi : The rank acceptability indices describe the variety of weights that place alternative i in rank r. It determines the sensitivity ranges of the weight preferences to put an alternative to the respective ranks. The most preferable alternatives are those with high acceptability in their top rank (rank 1). The rank acceptability index can be computed as Eq. (3). r
bi ¼ E½VolðW ri ðxÞÞ=VolðW r Þ
ð3Þ
2.2.2. Central weight vector The central weight vector characterizes the typical weights that make an alternative most preferred. The central weight vector for alternative i can be computed as Eq. (4).
wci ¼ E w : w 2 W ri ðxÞ
ð4Þ
239
2.2.3. Confidence factor The confidence factor is another term to check the extent of certainty of the results. The confidence factor embodies a type of sensitivity analysis. It coincides with the first rank acceptability index subject to precise weight preferences w ¼ wci . Where, wci is the central weight vector for alternative i. A high confidence factor indicates high level of certainty of an alternative to get to the given rank and acceptability over the sensitivity variables (i.e. weight preferences). 2.3. Scenario evaluation tool A key challenge of sustainability is to incorporate the range of plausible future impacts of uncertainty in technology evolution, climate change, human behavior and economics [19,37]. This work integrates LEAP (the Long-range Energy Alternatives Planning system) modeling tool to build long-term scenarios, which can assess the impacts of different policy elements on energy issues such as security, supply, and emissions [38]. The LEAP modeling tool is a widely used software for energy policy analysis and climate change mitigation assessment [39]. The LEAP tool calculates the long-term effects of different existing and prospective policy elements, and facilitates evaluation of their long-term implications. LEAP utilizes the following approach to calculate the long-term effects of various policy elements. 2.3.1. Accounting of GHG (greenhouse gas) emissions The GHGs are gasses that trap heat in the atmosphere, and are released as a result of fossil fuel combustion, and industrial and other processes. The major GHGs in the earth atmosphere are water vapor, CO2, CH4, N2O, O3, and CFCs. A power generation technology may (in rare cases) use multiple fuels, but we consider here that each technology type uses a single fuel and has a unique emission factor. The emissions from each technology are determined from Eq. (5). Then the total GHG emissions in terms of the equivalent amount of carbon dioxide (CO2e) from 100-year GWP (global warming potential) factors of all the individual GHG are determined as in the following Eq. (6).
EMm;y ¼ EC m;y EF m;v EDm;y—v EMCO2e ¼
I X
EMi GWPi
ð5Þ ð6Þ
i
where EM is the amount of emissions, EC is the energy consumption, EF is the emission factor, ED is the change of emission factor due to technology degradation, CO2e is the 100-year carbon dioxide equivalent, m is the technology type, v is the vintage (i.e. the year when the technology was added), y is the calendar year, i is the GHG type among all possible types (I), and GWP is the global warming potential. 2.3.2. Energy consumption The energy consumption is calculated by modeling the energy demand as a function of activity and energy intensity for each technology. Activity is defined as the measure of social or economic activity for which energy is consumed (e.g. number of households, floor space, devices, etc.). Energy intensity is a measure of energy use per unit of activity per year. Energy consumption is calculated for the current year and for each future year in each scenario. Mathematically,
Ds;y ¼
J X TAs;y;j EIs;y;j
ð7Þ
j
where D is energy consumption, TA is total activity of a particular sector, EI is the energy intensity, s is the scenario (policy element
240
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
in this work), y is year, and j is the activity type among all J activities. 2.3.3. Determination of GHG threshold limit The majority (about 65%) of global GHGs are produced from energy production from fossil fuel sources. These GHGs act like a blanket around the earth, trapping the heat, and causing the earth to warm. A small increase in the average temperature of the planet’ atmosphere translates to a large degree of weather and climate change. Earth’s average temperature has risen by 0.78 °C over the past century, and is projected to increase another 1.1–6.4 °C over the next hundred years [40]. We have proposed the following formulas to estimate the per capita GHG emissions (t/cap) in a year from energy activities.
TEM country;y ¼
EMglobal;2000 Pcountry;2000 SSene;y Pglobal;2000
ð8Þ
PEMT country;y ¼ TEM country;y =P country;y
ð9Þ
where TEM country;y is the total GHG emission of a country in year y, EMglobal;2000 is the global total GHG emission in 2000, Pglobal;2000 is the global total population in 2000, Pcountry;2000 is the population of a country in 2000, SSene;y is the GHG emission share assigned to energy sector, Pcountry;y is the population of a country in year y, PEMT country;y is the GHG emission threshold for the energy sector of a country in year y. 2.4. Framework for integrating multicriteria and scenario issues The first step is to identify goals/targets based on the national level (highest level) political ambitions and commitments. The second step is to determine a series of criteria under five sustainability dimensions (i.e. economic, technical, social, environmental, and institutional) in such a way that these criteria potentially lead to an energy system which ensures sustainability and satisfies the requirements of all the major stakeholders. Based on the goals and criteria set, SMAA generates the decision values (i.e. central weights, acceptability indices, and confidence factors) for different policy elements. Then LEAP tool performs long-range scenario analysis of the policy elements in terms of demand, supply, and
emissions, etc. (Fig. 2). Then the resulting multicriteria and scenario results will be available for policy-makers to choose a policy element, and update or undertake new policy measures. Effective policy measures require regular reassessment of each policy elements with updated criteria values, preference weights and scenario results. 2.5. Criteria selection A joint UN publication [41] has recommended 39 well-defined indicators and these are suitable measures for five dimensions of sustainability–technical, economic, social, environmental and institutional sustainability. Based on the 39 indicators, the authors in their previous work [36] compiled 24 criteria under five sustainability dimensions, which can incorporate sustainability goal and interests of the major stakeholders. These criteria potentially direct the energy policy elements toward sustainability and low carbon energy paths (Table 1) (please refer to Rahman et al. [36] for a description of each criterion). 3. Demonstration of the proposed technique in the case of the Bangladesh power sector 3.1. Policy elements The presented framework has been demonstrated by an analysis of four policy elements (sometimes referred to as ‘alternatives’ in SMAA results) for a particular aspect (i.e. share of fuel sources) for the Bangladesh power sector. The main purpose of this work was to choose policy elements that address the energy sector challenges (please refer to Fig. 1). The prospective policy elements and their variables are presented in Table 1. This study examines the merits of these policy elements based on multiple criteria and long-range effects. The policy-makers are expected to take the policy measures against chosen policy elements. 3.2. Criteria values The compilation of criteria values is very important and significantly affects evaluation results. Among the 24 selected criteria,
Fig. 2. Frameworks for energy policy evaluation.
241
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244 Table 1 Policy elements with corresponding variable settings. Sources: [42–44]. Policy element
Short name
Assumptions in key variable setting for each policy scenario
Business as usual
BAU
Fossil fuel 96%, (natural gas 83.5%, coal steam turbine 1.75%, crude oil steam 10.75%), renewable 4%, Overall efficiency 35%
Renewables
REN
2020: Fossil fuel 80%, renewables 20% (biomass 9%, hydro 4%, solar 6%, wind 5%) 2030: Fossil fuel 60%, renewables 40% (biomass 18%, hydro 4%, solar 12%, wind 6%) 2040: Fossil fuel 50%, renewables 50% (biomass 20%, hydro 4%, solar 20%, wind 6%) Efficiency: biomass 26%, solar 12%, wind 25%
Renewable-biomass only
REN-b
2020: Fossil fuel 90%, biomass 10% 2030: Fossil fuel 80%, biomass 20% 2040: Fossil fuel 70%, biomass 30% Efficiency: biomass 26%
Energy conservation and efficient technologies
ECET
Fossil fuel 96%, (natural gas 83.5%, coal steam turbine 1.75%, crude oil steam 10.75%), renewable 4% Efficiency – gas turbine 60%, coal steam turbine 45%, crude oil 45%, hydro 90%
Table 2 Criteria names and their agreed values. Sl.
Criteria
Policy elements (or alternatives) BAU
REN
REN-b
ECET
Technical dimension 1 Capacity utilization factor, % 2 Compatibility with future capacity expansion 3 Compatibility with existing infrastructure 4 Availability of local skills and resources 5 Weather and climate condition dependence 6 Annual resource availability duration (h/y)
60 3 1 3 1 6000–8760
Criteria values 40–80 1 2 2 4 6000–8760
40–80 2 4 1 3 4320–8760
80 4 3 4 2 6000–8760
Economic dimension 7 Capital cost, US$/W 8 Annual operation and maintenance costs (fixed), US$/kW y 9 Lifespan of the system, year 10 Learning rate, % 11 Current market share, % 12 Dependence on fossil fuel, %
1.5–2.0 8–48 30 90 85 94
2.0–4.0 20–50 30 80 8 50
0.5–1.0 20–40 20 80 2 50
2.5–4.0 20–50 30 50 5 94
Social dimension 13 Public and political acceptance 14 Scope for local employment 15 Public awareness and willingness 16 Conflict with other applications
2 3 1 4
1 2 3 2
3 1 2 1
4 4 4 3
Environmental dimension 17 Lifecycle GHG emissions, kg CO2e/kW h 18 Local environmental impact
0.50–0.80 4
0.01–0.05 2
0.013–0.040 1
0.2–0.4 3
Policy/regulation dimension 19 Land requirement and acquisition 20 Emphasis on use of local resources 21 Opportunity for private participation 22 Tax incentives, % 23 Degree of local ownership 24 Interference with other utilities
1 3 3 4 3 1
3 2 2 1 2 4
4 1 1 2 1 3
2 4 4 3 4 2
Note: Criteria values are to be minimized except serial numbers 1, 6, 9, 10 and 11.
nine criteria are extracted from national and international reports compiled by the authors in their previous work [36]. The remaining 15 criteria values are qualitative, and cannot be measured using a quantitative scale. However, these criteria can be scored on an ordinal scale on the basis of their merits. In this illustration, we scored them on an ordinal scale (1–4, where 1 is the best value and 4 is poor) based on perspective knowledge and with justifiable reasons. The criteria and their agreed values are presented in Table 2.
set the GHG limit so as to ensure a 50% reduction in 2040 compared to the 2000 level. The GHG emission data are presented in Table 3 [45–48]. These data were used to determine the emission target in a year based on the global GHG emissions and population in 2000.
3.3. GHG emission data
The SMAA tool determined the rank, acceptability indices and central weight vectors for four policy elements in terms of 24 criteria values (Fig. 3). The rank acceptability indices are the measure of how often an alternative can achieve the given rank with any preference weights. Table 4 shows that REN and REN-b policy elements are the most attractive alternatives for the first rank with
To keep the global mean temperature increase within 2–2.4 °C above the pre-industrial level, the reduction of global CO2 emissions in 2050 has to be between 80% and 50% of the 2000 world GHGs emissions [45,46]. Based on the above prediction, we have
4. Selected analytical results 4.1. Multicriteria results
242
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Table 3 GHG emission data.
Table 6 Emission threshold limits.
Parameter
Value
Unit
Year
2010
2015
2020
2025
2030
2035
2040
World’s total GHG emission in 2000 World’s total population in 2000 Population of studied country in 2000 Total emission share assigned to energy sector Population of studied country in 2010
43 6071 130 80 145
Gt CO2e Million persons Million persons % Million persons
Emission limit (tCO2e/cap)
2.53
2.39
2.24
2.11
1.99
1.87
1.77
preferences weights are agreed by the DMs. Table 5 presents the central weights for the policy elements against all of the criteria (alternative ECET is inefficient and thus is not presented in the table). Table 5 shows that the policy elements REN and REN-b are favoured by the weight preferences that are uniformly distributed among the 24 criteria. These two alternatives are likely to get DMs’ consent. On the other hand, the BAU element has a high preference weight for criterion 11. The SMAA tool also determined the confidence factor, which is another term to check the level of certainty of the results. The confidence factors are based on a kind of sensitivity or robustness analysis. A high confidence factor indicates that the alternative is almost certain to obtain the given rank over the range of sensitivity variables (i.e. weight preferences). The BAU, REN and REN-b elements are obtained good confidence factors of 94%, 83% and 89% respectively, for their ranks and acceptability indices (Table 5). This means, the alternatives BAU, REN and REN-b are almost certain to get to their respective ranks with achieved acceptability values. The criteria weights assist with understanding the relative importance of each criterion. Criteria 11, 9 and 7 were found to be the most important for BAU, REN and REN-b elements, respectively. That means, to keep them acceptable in their top ranks, these three criteria deserve to have high weights. The policymakers can gain insights into the importance of different criteria on making each alternative (i.e. policy element) to be preferred. The alternatives REN and REN-b obtain higher acceptability in their first ranks while supported by the uniformly distributed weights. Although the alternative BAU has obtained acceptability which is closer to REN and REN-b, BAU has also obtained higher acceptability in its lower rank (third rank) which eventually disqualifies it. Based on the multicriteria results, the DMs might choose REN-b (best option) or REN (second best option) for deciding the share of power generation fuels. These two options are still dominated by fossil fuels, thus they do not lead to a power generation setting which is completely sustainable. Rather these two options retain better sustainability in terms of all the assigned criteria and their values.
100% REN REN-b ECET
90% 80% 70% 60% 50% 40% 30% 20%
Rank acceptability indices
BAU
10% 0% ECET REN-b REN Rank 4
BAU
Rank 3
Rank 2
Ranks
Rank 1
Fig. 3. Rank acceptability indices.
Table 4 Confidence factors (CF) and rank acceptability indices. Policy element
CF
Acceptability
BAU REN REN-b ECET
0.94 0.83 0.89 1.00
Rank 1 (%)
Rank 2 (%)
Rank 3 (%)
Rank 4 (%)
28 32 41 0
24 43 32 0
47 25 25 3
1 0 2 97
32% and 41% acceptability, respectively. Among others, the ECET policy element obtained zero acceptability in the first rank. The alternative ECET is inefficient and is unlikely to be the most preferred alternative based on the assumed decision model. The above results are stochastically determined based on randomized weight preferences and it is necessary to check that the
4.2. Emission threshold limit and projected emission To check the possibility of achieving of the low emission target, we have determined CO2e production scenarios for each of the policy elements. The emission limits are calculated through Eqs. (8)
Table 5 Distribution of central weights for efficient alternatives. Policy Element
CF
Central weights BAU REN REN-b
0.94 0.83 0.89
Policy Element
CF
Central weights BAU REN REN-b
0.94 0.83 0.89
Criteria 1
2
3
4
5
6
7
8
9
10
11
12
0.040 0.037 0.045
0.034 0.051 0.039
0.049 0.042 0.035
0.036 0.038 0.048
0.053 0.036 0.040
0.045 0.046 0.037
0.041 0.029 0.053
0.043 0.040 0.042
0.050 0.056 0.025
0.048 0.039 0.040
0.065 0.032 0.032
0.026 0.050 0.046
13
14
15
16
17
18
19
20
21
22
23
24
0.040 0.052 0.035
0.037 0.038 0.047
0.050 0.036 0.041
0.035 0.038 0.049
0.028 0.047 0.046
0.034 0.051 0.039
0.051 0.040 0.037
0.036 0.039 0.048
0.037 0.038 0.048
0.033 0.052 0.040
0.037 0.038 0.049
0.052 0.035 0.039
Criteria
243
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Emissions (tCO2e/cap)
0.080 0.070
BAU
0.060
REN
0.050
REN-b
0.040
ECET
0.030 0.020 0.010 -
2010
2015
2020
2025
2030
2035
2040
Years Fig. 4. Projected emissions against different policy elements.
and (9) and by utilizing data from Table 3. The per capita emission limits in different years are depicted in Table 6. Fig. 4 shows the per capita CO2e production for each of the policy elements for the period of 2010–2040. As shown in Fig. 4, all the policy elements are found to produce per capita emissions within the threshold limit (i.e. below 1.77 tCO2e/cap) every year. The ECET has the lowest per capita CO2e emission (i.e. lowest environmental impact), followed by the REN and REN-b policy elements. ECET element would produce half the level of CO2e emission compared to the BAU element in 2040. The ECET element employed efficient technologies, which significantly improve the power generating efficiencies, thus resulting in lower emissions than the other elements. Although ECET produce lower emissions, it has already been disqualified in the prior multicriteria assessment due to e.g. high costs, lower acceptance, etc. Based on these multicriteria assessment and long-term environmental impacts (i.e. CO2e emissions), only REN-b and REN are the acceptable policy elements. 5. Conclusions Energy policy has multiple objectives and involves the interests of several stakeholder groups. The requirements of the diverse stakeholder groups and multiple objectives cannot be met by a single criterion. Therefore, energy policy needs to be developed on the basis of a balanced assessment of stakeholders’ interests and several sustainability objectives. This research presents a transparent technique for assessing different prospective elements of energy policy by representing stakeholders’ interests and several sustainability goals. Based on the assessment results, policy-makers are expected to take various measures (e.g. legislation, treaty, laws, etc.) that lead to sustainable energy sector. The technique presented in this research will provide policy-makers with essential information for planning and promoting energy policy to accomplish sustainable and low carbon energy paths. The government of a country can evaluate the merits of different elements of energy policy and take steps toward formulating further measures. Thus, this method provides a valuable tool to the policy-makers to guide energy policy that will achieve a sustainable and low carbon energy future. In our illustration for a specific aspect (i.e. share of fuels for power generation) in the case of the Bangladesh power sector, REN-b and REN policy elements were found to be preferable over other alternatives based on acceptability indices (41% and 32% respectively). These two alternatives also obtained good confidence factors (0.89 and 0.83 respectively) and are favoured with
uniformly distributed weights. The GHG emissions for all of the four hypothetical policy elements were found to be within their threshold limits of 1.77 tCO2e/cap. From the overall analysis, policy-makers may choose REN-b (best option) or REN (second best option) policy element and enact relevant policy measures (legislation, laws, etc.). Further research is also necessary to evaluate other energy sector issues (e.g. long-term demands, supply and resources) and the authors would continue this effort. Acknowledgements The authors are grateful to Aalto University School of Engineering, Finland and Universiti Teknologi Malaysia UTM (PAS Grant, 01K95) for proving funding for conducting this research. The authors also thankful to the anonymous reviewers for kindly review this paper. References [1] Doukas H, Patlitzianas KD, Kagiannas AG, Psarras J. Energy policy making: an old concept or a modern challenge? Energy Sour Part B: Econ Plann Pol 2008;3:362–71. [2] Sanaeepur S, Sanaeepur H, Kargari A, Habibi MH. Renewable energies: climatechange mitigation and international climate policy. Int J Sustain Energy 2014;33:203–12. [3] Doukas H, Flamos A, Psarras J. Risks on the security of oil and gas supply. Energy Sour Part B: Econ Plann Pol 2011;6:417–25. [4] Løken E. Use of multicriteria decision analysis methods for energy planning problems. Renew Sustain Energy Rev 2007;11:1584–95. [5] Qudrat-Ullah H. Modelling and simulation in service of energy policy. Energy Proc 2015;75:2819–25. [6] Igos E, Rugani B, Rege S, Benetto E, Drouet L, Zachary DS. Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios. Appl Energy 2015;145:234–45. [7] Rahman MM, Paatero JV, Poudyal A, Lahdelma R. Driving and hindering factors for rural electrification in developing countries: lessons from Bangladesh. Energy Pol 2013;61:840–51. [8] Kowalski K, Stagl S, Madlener R, Omann I. Sustainable energy futures: methodological challenges in combining scenarios and participatory multicriteria analysis. Eur J Oper Res 2009;197:1063–74. [9] Strachan N, Pye S, Kannan R. The iterative contribution and relevance of modelling to UK energy policy. Energy Pol 2009;37:850–60. [10] Pokharel S, Chandrashekar M. A multiobjective approach to rural energy policy analysis. Energy 1998;23:325–36. [11] Kanellakis M, Martinopoulos G, Zachariadis T. European energy policy—a review. Energy Pol 2013;62:1020–30. [12] Laitner JA, DeCanio SJ, Koomey JG, Sanstad AH. Room for improvement: increasing the value of energy modeling for policy analysis. Util Pol 2003;11:87–94. [13] Pandey R. Energy policy modelling: agenda for developing countries. Energy Pol 2002;30:97–106. [14] Proença S, St Aubyn M. Hybrid modeling to support energy-climate policy: effects of feed-in tariffs to promote renewable energy in Portugal. Energy Econ 2013;38:176–85.
244
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
[15] Fortes P, Pereira R, Pereira A, Seixas J. Integrated technological–economic modeling platform for energy and climate policy analysis. Energy 2014;73:716–30. [16] Erol Ö, Kılkısß B. An energy source policy assessment using analytical hierarchy process. Energy Convers Manage 2012;63:245–52. [17] Richstein JC, Chappin ÉJL, de Vries LJ. Adjusting the CO2 cap to subsidised RES generation: can CO2 prices be decoupled from renewable policy? Appl Energy 2015;156:693–702. [18] Sovacool BK, Saunders H. Competing policy packages and the complexity of energy security. Energy 2014;67:641–51. [19] Zhang C, Chen J, Wen Z. Assessment of policy alternatives and key technologies for energy conservation and water pollution reduction in China’s synthetic ammonia industry. J Cleaner Prod 2012;25:96–105. [20] Zhang C, Chen J, Wen Z. Alternative policy assessment for water pollution control in China’s pulp and paper industry. Resour Conserv Recycl 2012;66:15–26. [21] Zhang C, Wen Z, Chen J. An integrated model for technology forecasting to reduce pollutant emission in China’s pulp industry. Resour Conserv Recycl 2009;54:62–72. [22] Zhang S, Andrews-Speed P, Perera P. The evolving policy regime for pumped storage hydroelectricity in China: a key support for low-carbon energy. Appl Energy 2015;150:15–24. [23] Reuter WH, Szolgayová J, Fuss S, Obersteiner M. Renewable energy investment: policy and market impacts. Appl Energy 2012;97:249–54. [24] Casisi M, De Nardi A, Pinamonti P, Reini M. Effect of different economic support policies on the optimal synthesis and operation of a distributed energy supply system with renewable energy sources for an industrial area. Energy Convers Manage 2015;95:131–9. [25] Lin B, Xie X. Energy conservation potential in China’s petroleum refining industry: evidence and policy implications. Energy Convers Manage 2015;91:377–86. [26] Sßekerciog˘lu S, Yılmaz M. Renewable energy perspectives in the frame of Turkey’s and the EU’s energy policies. Energy Convers Manage 2012;63:233–8. [27] Spyridaki N-A, Flamos A. A paper trail of evaluation approaches to energy and climate policy interactions. Renew Sustain Energy Rev 2014;40:1090–107. [28] Park T, Kim C, Kim H. A real option-based model to valuate CDM projects under uncertain energy policies for emission trading. Appl Energy 2014;131:288–96. [29] Hossain AK, Badr O. Prospects of renewable energy utilisation for electricity generation in Bangladesh. Renew Sustain Energy Rev 2007;11:1617–49. [30] Lee S-C, Shih L-H. Renewable energy policy evaluation using real option model—the case of Taiwan. Energy Econ 2010;32(S1):S67–78. [31] Hoffman KC, Jorgenson DW. Economic and technological models for evaluation of energy policy. Bell J Econ 1977;8:444–66.
[32] Kangas J, Kangas A, Leskinen P, Pykäläinen J. MCDM methods in strategic planning of forestry on state-owned lands in Finland: applications and experiences. J Multi-Crit Decis Anal 2001;10:257–71. [33] Hokkanen J, Lahdelma R, Salminen P. Multicriteria decision support in a technology competition for cleaning polluted soil in Helsinki. J Environ Manage 2000;60:339–48. [34] Lahdelma R, Salminen P. SMAA-2: stochastic multicriteria acceptability analysis for group decision making. Oper Res 2001;49:444–54. [35] Lahdelma R, Hokkanen J, Salminen P. SMAA – stochastic multiobjective acceptability analysis. Eur J Oper Res 1998;106:137–43. [36] Rahman MM, Paatero JV, Lahdelma R. Evaluation of choices for sustainable rural electrification in developing countries: a multicriteria approach. Energy Pol 2013;59:589–99. [37] Swart RJ, Raskin P, Robinson J. The problem of the future: sustainability science and scenario analysis. Global Environ Change 2004;14:137–46. [38] Heaps CG. LEAP (Long range Energy Alternatives Planning). Somerville (MA, USA): Stockholm Environment Institute; 2012. [39] Jebaraj S, Iniyan S. A review of energy models. Renew Sustain Energy Rev 2006;10:281–311. [40] US EPA CCD. Climate Change: Basic Information; 2014. [accessed 09.10.14]. [41] IAEA. Energy indicators for sustainable development: guidelines and methodologies. Vienna (Austria): International Atomic Energy Agency; 2005. [42] Diaf S, Notton G, Belhamel M, Haddadi M, Louche A. Design and technoeconomical optimization for hybrid PV/wind system under various meteorological conditions. Appl Energy 2008;85:968–87. [43] Erickson P, Heaps C, Lazarus M. Greenhouse gas mitigation in developing countries: promising options in China, Mexico, India, Brazil, South Africa, and South Korea. Somerville (MA, USA): Stockholm Environment Institute; 2009. [44] Lee YE, Jung YB. Challenges of nuclear power for sustainable role in Korean energy policy. Energy Convers Manage 2008;49:1951–9. [45] IPCC WGIII. Climate Change 2014 Mitigation of climate change working group III contribution to the fifth assessment report of the intergovernmental panel on climate change. IPCC; 2014. [46] Bautista S. A sustainable scenario for Venezuelan power generation sector in 2050 and its costs. Energy Pol 2012;44:331–40. [47] UN population division. World Population Prospects, the 2010 Revision 2013. [accessed 02.12.13]. [48] IEA. World energy outlook 2012. Paris: Organisation for Economic Cooperation and Development (OECD); 2012.
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Multicriteria-based decision aiding technique for assessing energy policy elements-demonstration to a case in Bangladesh Md. Mizanur Rahman a,b,⇑, Jukka V. Paatero b, Risto Lahdelma b, Mazlan A. Wahid a a b
Department of Thermo-Fluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia Department of Energy Technology, Aalto University School of Engineering, POB: 14100, FI-00076 Aalto, Finland
h i g h l i g h t s A multicriteria technique for assessing energy policy elements has been proposed. Energy policy elements have been examined based on assigned criteria. This assessment gives results which are representative of all stakeholders. Policy elements which are chosen by this method promote sustainability.
a r t i c l e
i n f o
Article history: Received 16 September 2015 Received in revised form 29 November 2015 Accepted 30 November 2015
Keywords: Multicriteria Long-range Sustainability Policy element Energy policy
a b s t r a c t The adverse environmental consequences and diminishing trend of fossil fuel reserves indicate a serious need for vibrant and judicious energy policy. Energy policy involves a number of stakeholders, and needs to incorporate the interests and requirements of all the key stakeholder groups. This paper presents a methodological technique to assist with formulating, evaluating, and promoting the energy policy of a country in a transparent and representative way with clear scientific justifications and balanced assessments. The multicriteria decision analysis approach has been a widely used technique for evaluating different alternatives based on the interests of a multitude of stakeholders, and goals. This paper utilizes the SMAA (Stochastic Multicriteria Acceptability Analysis) tool, which can evaluate different alternatives by incorporating multiple criteria, in order to examine the preferences of different policy elements. We further extend this technique by incorporating the LEAP model (Long-range Energy Alternatives Planning system) to assess the emission impacts of different policy elements. We demonstrate the application of this evaluation technique by an analysis of four hypothetical policy elements namely Business-as usual (BAU), Renewables (REN), Renewable-biomass only (REN-b), and Energy conservation and efficient technologies (ECET). These are applied to the case of sharing fuel sources for power generation for the Bangladesh power sector. We found that the REN-b and REN policy elements were the best and second best alternatives with 41% and 32% acceptability respectively. This technique gives transparent information for choosing appropriate policy elements that aimed at sustainable energy. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Due to the trend of diminishing fossil fuel reserves, the environmental consequences of fossil fuel combustion, and rapid economic growth, there is a serious need for a vibrant and judicious energy policy for long-term sustainability in the energy sector [1–3]. Setting policies that address increasing environmental concern and reduce dependency on fossil fuel sources are a continuous ⇑ Corresponding author at: Department of Thermo-Fluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia. Tel.: +60 (0) 176480491; fax: +60 7 5566159. E-mail address: [email protected] (Md.M. Rahman). http://dx.doi.org/10.1016/j.apenergy.2015.11.091 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
challenges for many countries [4–6]. The challenges arise mainly because energy policy involves a number of stakeholders and needs to incorporate the interests and requirements of all the major stakeholders to make energy policy viable [7]. The stakeholders’ interests and requirements are diverse and cannot be represented by a single criterion. Therefore, when aiming at sustainable and low-emission energy, strategic decision making arises from the multi-dimensionality of stakeholders interests, socioeconomic dynamics, sustainability goals, and the biophysical systems and long-range nature of the problems [8]. If energy policy is developed on the basis of political motives rather than careful scientific evaluation of multiple criteria, it eventually fails in terms of sustainability and acceptability.
238
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Fig. 1. Conceptual pathways for energy policy evolution.
Therefore, energy policy modeling needs to explicitly consider multiple objectives that can suitably meet the stakeholders’ interests and sustainability criteria. For that reason, policy-makers require detailed information and insights into multiple objectives to endorse appropriate policy measures [9]. Moreover, in addition to giving information on multiple objectives, the multicriteria method provides decision-makers with an opportunity to explore different energy options by trade-off their importance [10]. Complex interactions among multiple objectives (goals) require multicriteria technique to be integrated in the framing of appropriate policy directions [11–14]. Economic and environmental modeling techniques have been widely used by researchers to support developing energy and climate policies [15]. Policy frameworks for determining the optimal energy technology were also found in the existing literature [16,17]. Several studies examine the effects of different policy packages aimed at energy security, affordability, flexibility, extending services, and mitigating environmental pollution [18–23]. Studies also investigate the implications, social costs, benefits and climate-interactions for various policy supports and mechanisms [24–28]. Although significant efforts have been made in the development of energy and emission assessment methods, there are very few methods that include the multi-dimensionality of stakeholders’ interests and sustainability criteria through use of Multicriteria Decision Analysis (MCDA) technique. This paper presents a multicriteria-based decision aiding technique for evaluating and choosing energy policy elements in a representative way. Because this method incorporates the stakeholders’ interests and opinions through criteria values and weights, it eventually provides results which are relatively transparent and representative. This paper shows the relationship between energy-sector challenges, policy elements, and measures (regulatory, legislative or legal) in way to evolving of energy policy. Bangladesh, a developing country, is facing huge challenges in adopting renewable energy sources in its power generation mix, despite endowed with an abundant amount of evenly distributed renewable resources (e.g. average daily solar irradiation of 4.5 kW h/m2 and annual recoverable bio-wastes generation of 0.54 tonne/capita). The reserve of indigenous energy resources in Bangladesh is very limited (i.e. recoverable natural gas reserve 580 billion cubic meter, oil 0.84 million tonne, and coal 1.75 billion tonne) [29]. The objectives of this work are to present a multicriteria based decision aiding technique and to demonstrate the application of this technique to a case. This paper demonstrates the application of this technique through an analysis of four
assumed policy elements (i.e. four alternatives) against a particular aspect (i.e. share of fuel sources for power generation) of Bangladesh’s power sector as a case. The remainder of this paper is organized as follows: Section 2 describes the methodological framework for integrating multiple criteria and sustainability goal, and assessing the long-term emission impacts of policy elements. Section 3 presents datasets of four policy settings (elements), which will be applied to demonstrate the proposed technique. Section 4 presents the main results of this analysis according to the proposed technique and applied datasets. Finally Section 5 highlights the main conclusions obtained from this work. 2. Methodology 2.1. Conceptual pathways for evolution of energy policy Energy sector faces various challenges and barriers, which range from technical and behavioral to sociocultural and political challenges. Energy policy elements1 are the main ingredients of energy policy to deal with the energy-sector challenges and barriers through the undertaking of various policy measures or instruments2 (Fig. 1). The development of energy policy, in many cases, occurs as a ‘black box’ process rather than with clear reasoning and understanding by the stakeholders (e.g. decision-makers) [1]. Essentially, energy policy can be evolved by linking policy elements with different quantifiable parameters in a transparent and representative way [30]. This work presented the technique to choose appropriate policy elements through multicriteria and long-term emission assessments. The chosen policy elements should be implemented by enacting appropriate policy measures (e.g. incentive provisions, tax exemptions, legislations, treaties, etc.). 2.2. Multicriteria technique for analyzing policy elements Energy policy assessment models are usually developed in two ways–process analysis, and econometrics [31]. But the conflicts in sustainability are shared with broader environmental, socioeco1 Policy elements are the basic ingredients of energy policy, each element consists of a combination of purposes, objectives, ambitions, commitments, etc. on energy related issues. 2 Policy measures or instruments are the set of actions that lead to implementation of the tasks required by the chosen policy elements.
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
nomic, and natural resource issues. That means that, energy policy promotes sustainability only while it complies with all the major sustainability issues. The multicriteria decision analysis method, which engages economic, technology and social issues through participatory and analytical tools, is widely accepted for such a multidimensional task [8]. The participatory and multicriteria analysis along with a scenario-building tool can evaluate policy elements by reflecting different opinions, goals and constraints. This work adopted the following approaches to performing multicriteria-based evaluation of policy elements. Among different multicriteria decision analysis (MCDA) groups, value measurement methods are widely used, and these provide a decision by combining the criteria measurements with DMs’ (decision-makers) preferences [32]. However, traditional MCDA requires precise preference information for each of the criteria values, which is often unavailable in many real life problems involving multiple DMs (i.e. stakeholders) [33,34]. The criteria values may also have some uncertainty or imprecision. In this regard, the Stochastic Multicriteria Acceptability Analysis (SMAA) method evaluates alternatives based on inverse weight space that does not require preference weights [35]. SMAA identifies the preference weights that make each alternative preferable by stochastically simulating weights. Then the DMs will check whether they agree with the suggested preference weights or not. The main results of SMAA analysis are rank acceptability indices, central weight vectors, and confidence factors for different alternatives [36]. Let us consider a discrete multicriteria decisionmaking problem consisting of a set of m alternatives X = {x1, . . ., xi, . . ., xm} measured in terms of n criteria. The DMs’ preference structure is represented by a value function uðxi ; wÞ involving weighting coefficients w. The additive utility function is presented as Eq. (1).
uðxi ; wÞ ¼
n X wj uj ðxij Þ;
ð1Þ
j¼1
where xi is the ith alternative, xij is the criteria measurement of the ith alternative with respect to the jth criterion, and the partial utility function ui() converts criteria values to a scale from 0 to 1, where 1 represents the best outcome and 0 represents the worst. The vector of weights w shows the DM’s preferences for each criterion. The weights values are non-negative and normalized. The feasible weight space can be written as Eq. (2).
W ¼ fw 2 Rn : w P 0 and
n X wj ¼ 1g
ð2Þ
j¼1
2.2.1. Rank acceptability index r The relative size of Wri is the rank acceptability index bi : The rank acceptability indices describe the variety of weights that place alternative i in rank r. It determines the sensitivity ranges of the weight preferences to put an alternative to the respective ranks. The most preferable alternatives are those with high acceptability in their top rank (rank 1). The rank acceptability index can be computed as Eq. (3). r
bi ¼ E½VolðW ri ðxÞÞ=VolðW r Þ
ð3Þ
2.2.2. Central weight vector The central weight vector characterizes the typical weights that make an alternative most preferred. The central weight vector for alternative i can be computed as Eq. (4).
wci ¼ E w : w 2 W ri ðxÞ
ð4Þ
239
2.2.3. Confidence factor The confidence factor is another term to check the extent of certainty of the results. The confidence factor embodies a type of sensitivity analysis. It coincides with the first rank acceptability index subject to precise weight preferences w ¼ wci . Where, wci is the central weight vector for alternative i. A high confidence factor indicates high level of certainty of an alternative to get to the given rank and acceptability over the sensitivity variables (i.e. weight preferences). 2.3. Scenario evaluation tool A key challenge of sustainability is to incorporate the range of plausible future impacts of uncertainty in technology evolution, climate change, human behavior and economics [19,37]. This work integrates LEAP (the Long-range Energy Alternatives Planning system) modeling tool to build long-term scenarios, which can assess the impacts of different policy elements on energy issues such as security, supply, and emissions [38]. The LEAP modeling tool is a widely used software for energy policy analysis and climate change mitigation assessment [39]. The LEAP tool calculates the long-term effects of different existing and prospective policy elements, and facilitates evaluation of their long-term implications. LEAP utilizes the following approach to calculate the long-term effects of various policy elements. 2.3.1. Accounting of GHG (greenhouse gas) emissions The GHGs are gasses that trap heat in the atmosphere, and are released as a result of fossil fuel combustion, and industrial and other processes. The major GHGs in the earth atmosphere are water vapor, CO2, CH4, N2O, O3, and CFCs. A power generation technology may (in rare cases) use multiple fuels, but we consider here that each technology type uses a single fuel and has a unique emission factor. The emissions from each technology are determined from Eq. (5). Then the total GHG emissions in terms of the equivalent amount of carbon dioxide (CO2e) from 100-year GWP (global warming potential) factors of all the individual GHG are determined as in the following Eq. (6).
EMm;y ¼ EC m;y EF m;v EDm;y—v EMCO2e ¼
I X
EMi GWPi
ð5Þ ð6Þ
i
where EM is the amount of emissions, EC is the energy consumption, EF is the emission factor, ED is the change of emission factor due to technology degradation, CO2e is the 100-year carbon dioxide equivalent, m is the technology type, v is the vintage (i.e. the year when the technology was added), y is the calendar year, i is the GHG type among all possible types (I), and GWP is the global warming potential. 2.3.2. Energy consumption The energy consumption is calculated by modeling the energy demand as a function of activity and energy intensity for each technology. Activity is defined as the measure of social or economic activity for which energy is consumed (e.g. number of households, floor space, devices, etc.). Energy intensity is a measure of energy use per unit of activity per year. Energy consumption is calculated for the current year and for each future year in each scenario. Mathematically,
Ds;y ¼
J X TAs;y;j EIs;y;j
ð7Þ
j
where D is energy consumption, TA is total activity of a particular sector, EI is the energy intensity, s is the scenario (policy element
240
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
in this work), y is year, and j is the activity type among all J activities. 2.3.3. Determination of GHG threshold limit The majority (about 65%) of global GHGs are produced from energy production from fossil fuel sources. These GHGs act like a blanket around the earth, trapping the heat, and causing the earth to warm. A small increase in the average temperature of the planet’ atmosphere translates to a large degree of weather and climate change. Earth’s average temperature has risen by 0.78 °C over the past century, and is projected to increase another 1.1–6.4 °C over the next hundred years [40]. We have proposed the following formulas to estimate the per capita GHG emissions (t/cap) in a year from energy activities.
TEM country;y ¼
EMglobal;2000 Pcountry;2000 SSene;y Pglobal;2000
ð8Þ
PEMT country;y ¼ TEM country;y =P country;y
ð9Þ
where TEM country;y is the total GHG emission of a country in year y, EMglobal;2000 is the global total GHG emission in 2000, Pglobal;2000 is the global total population in 2000, Pcountry;2000 is the population of a country in 2000, SSene;y is the GHG emission share assigned to energy sector, Pcountry;y is the population of a country in year y, PEMT country;y is the GHG emission threshold for the energy sector of a country in year y. 2.4. Framework for integrating multicriteria and scenario issues The first step is to identify goals/targets based on the national level (highest level) political ambitions and commitments. The second step is to determine a series of criteria under five sustainability dimensions (i.e. economic, technical, social, environmental, and institutional) in such a way that these criteria potentially lead to an energy system which ensures sustainability and satisfies the requirements of all the major stakeholders. Based on the goals and criteria set, SMAA generates the decision values (i.e. central weights, acceptability indices, and confidence factors) for different policy elements. Then LEAP tool performs long-range scenario analysis of the policy elements in terms of demand, supply, and
emissions, etc. (Fig. 2). Then the resulting multicriteria and scenario results will be available for policy-makers to choose a policy element, and update or undertake new policy measures. Effective policy measures require regular reassessment of each policy elements with updated criteria values, preference weights and scenario results. 2.5. Criteria selection A joint UN publication [41] has recommended 39 well-defined indicators and these are suitable measures for five dimensions of sustainability–technical, economic, social, environmental and institutional sustainability. Based on the 39 indicators, the authors in their previous work [36] compiled 24 criteria under five sustainability dimensions, which can incorporate sustainability goal and interests of the major stakeholders. These criteria potentially direct the energy policy elements toward sustainability and low carbon energy paths (Table 1) (please refer to Rahman et al. [36] for a description of each criterion). 3. Demonstration of the proposed technique in the case of the Bangladesh power sector 3.1. Policy elements The presented framework has been demonstrated by an analysis of four policy elements (sometimes referred to as ‘alternatives’ in SMAA results) for a particular aspect (i.e. share of fuel sources) for the Bangladesh power sector. The main purpose of this work was to choose policy elements that address the energy sector challenges (please refer to Fig. 1). The prospective policy elements and their variables are presented in Table 1. This study examines the merits of these policy elements based on multiple criteria and long-range effects. The policy-makers are expected to take the policy measures against chosen policy elements. 3.2. Criteria values The compilation of criteria values is very important and significantly affects evaluation results. Among the 24 selected criteria,
Fig. 2. Frameworks for energy policy evaluation.
241
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244 Table 1 Policy elements with corresponding variable settings. Sources: [42–44]. Policy element
Short name
Assumptions in key variable setting for each policy scenario
Business as usual
BAU
Fossil fuel 96%, (natural gas 83.5%, coal steam turbine 1.75%, crude oil steam 10.75%), renewable 4%, Overall efficiency 35%
Renewables
REN
2020: Fossil fuel 80%, renewables 20% (biomass 9%, hydro 4%, solar 6%, wind 5%) 2030: Fossil fuel 60%, renewables 40% (biomass 18%, hydro 4%, solar 12%, wind 6%) 2040: Fossil fuel 50%, renewables 50% (biomass 20%, hydro 4%, solar 20%, wind 6%) Efficiency: biomass 26%, solar 12%, wind 25%
Renewable-biomass only
REN-b
2020: Fossil fuel 90%, biomass 10% 2030: Fossil fuel 80%, biomass 20% 2040: Fossil fuel 70%, biomass 30% Efficiency: biomass 26%
Energy conservation and efficient technologies
ECET
Fossil fuel 96%, (natural gas 83.5%, coal steam turbine 1.75%, crude oil steam 10.75%), renewable 4% Efficiency – gas turbine 60%, coal steam turbine 45%, crude oil 45%, hydro 90%
Table 2 Criteria names and their agreed values. Sl.
Criteria
Policy elements (or alternatives) BAU
REN
REN-b
ECET
Technical dimension 1 Capacity utilization factor, % 2 Compatibility with future capacity expansion 3 Compatibility with existing infrastructure 4 Availability of local skills and resources 5 Weather and climate condition dependence 6 Annual resource availability duration (h/y)
60 3 1 3 1 6000–8760
Criteria values 40–80 1 2 2 4 6000–8760
40–80 2 4 1 3 4320–8760
80 4 3 4 2 6000–8760
Economic dimension 7 Capital cost, US$/W 8 Annual operation and maintenance costs (fixed), US$/kW y 9 Lifespan of the system, year 10 Learning rate, % 11 Current market share, % 12 Dependence on fossil fuel, %
1.5–2.0 8–48 30 90 85 94
2.0–4.0 20–50 30 80 8 50
0.5–1.0 20–40 20 80 2 50
2.5–4.0 20–50 30 50 5 94
Social dimension 13 Public and political acceptance 14 Scope for local employment 15 Public awareness and willingness 16 Conflict with other applications
2 3 1 4
1 2 3 2
3 1 2 1
4 4 4 3
Environmental dimension 17 Lifecycle GHG emissions, kg CO2e/kW h 18 Local environmental impact
0.50–0.80 4
0.01–0.05 2
0.013–0.040 1
0.2–0.4 3
Policy/regulation dimension 19 Land requirement and acquisition 20 Emphasis on use of local resources 21 Opportunity for private participation 22 Tax incentives, % 23 Degree of local ownership 24 Interference with other utilities
1 3 3 4 3 1
3 2 2 1 2 4
4 1 1 2 1 3
2 4 4 3 4 2
Note: Criteria values are to be minimized except serial numbers 1, 6, 9, 10 and 11.
nine criteria are extracted from national and international reports compiled by the authors in their previous work [36]. The remaining 15 criteria values are qualitative, and cannot be measured using a quantitative scale. However, these criteria can be scored on an ordinal scale on the basis of their merits. In this illustration, we scored them on an ordinal scale (1–4, where 1 is the best value and 4 is poor) based on perspective knowledge and with justifiable reasons. The criteria and their agreed values are presented in Table 2.
set the GHG limit so as to ensure a 50% reduction in 2040 compared to the 2000 level. The GHG emission data are presented in Table 3 [45–48]. These data were used to determine the emission target in a year based on the global GHG emissions and population in 2000.
3.3. GHG emission data
The SMAA tool determined the rank, acceptability indices and central weight vectors for four policy elements in terms of 24 criteria values (Fig. 3). The rank acceptability indices are the measure of how often an alternative can achieve the given rank with any preference weights. Table 4 shows that REN and REN-b policy elements are the most attractive alternatives for the first rank with
To keep the global mean temperature increase within 2–2.4 °C above the pre-industrial level, the reduction of global CO2 emissions in 2050 has to be between 80% and 50% of the 2000 world GHGs emissions [45,46]. Based on the above prediction, we have
4. Selected analytical results 4.1. Multicriteria results
242
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Table 3 GHG emission data.
Table 6 Emission threshold limits.
Parameter
Value
Unit
Year
2010
2015
2020
2025
2030
2035
2040
World’s total GHG emission in 2000 World’s total population in 2000 Population of studied country in 2000 Total emission share assigned to energy sector Population of studied country in 2010
43 6071 130 80 145
Gt CO2e Million persons Million persons % Million persons
Emission limit (tCO2e/cap)
2.53
2.39
2.24
2.11
1.99
1.87
1.77
preferences weights are agreed by the DMs. Table 5 presents the central weights for the policy elements against all of the criteria (alternative ECET is inefficient and thus is not presented in the table). Table 5 shows that the policy elements REN and REN-b are favoured by the weight preferences that are uniformly distributed among the 24 criteria. These two alternatives are likely to get DMs’ consent. On the other hand, the BAU element has a high preference weight for criterion 11. The SMAA tool also determined the confidence factor, which is another term to check the level of certainty of the results. The confidence factors are based on a kind of sensitivity or robustness analysis. A high confidence factor indicates that the alternative is almost certain to obtain the given rank over the range of sensitivity variables (i.e. weight preferences). The BAU, REN and REN-b elements are obtained good confidence factors of 94%, 83% and 89% respectively, for their ranks and acceptability indices (Table 5). This means, the alternatives BAU, REN and REN-b are almost certain to get to their respective ranks with achieved acceptability values. The criteria weights assist with understanding the relative importance of each criterion. Criteria 11, 9 and 7 were found to be the most important for BAU, REN and REN-b elements, respectively. That means, to keep them acceptable in their top ranks, these three criteria deserve to have high weights. The policymakers can gain insights into the importance of different criteria on making each alternative (i.e. policy element) to be preferred. The alternatives REN and REN-b obtain higher acceptability in their first ranks while supported by the uniformly distributed weights. Although the alternative BAU has obtained acceptability which is closer to REN and REN-b, BAU has also obtained higher acceptability in its lower rank (third rank) which eventually disqualifies it. Based on the multicriteria results, the DMs might choose REN-b (best option) or REN (second best option) for deciding the share of power generation fuels. These two options are still dominated by fossil fuels, thus they do not lead to a power generation setting which is completely sustainable. Rather these two options retain better sustainability in terms of all the assigned criteria and their values.
100% REN REN-b ECET
90% 80% 70% 60% 50% 40% 30% 20%
Rank acceptability indices
BAU
10% 0% ECET REN-b REN Rank 4
BAU
Rank 3
Rank 2
Ranks
Rank 1
Fig. 3. Rank acceptability indices.
Table 4 Confidence factors (CF) and rank acceptability indices. Policy element
CF
Acceptability
BAU REN REN-b ECET
0.94 0.83 0.89 1.00
Rank 1 (%)
Rank 2 (%)
Rank 3 (%)
Rank 4 (%)
28 32 41 0
24 43 32 0
47 25 25 3
1 0 2 97
32% and 41% acceptability, respectively. Among others, the ECET policy element obtained zero acceptability in the first rank. The alternative ECET is inefficient and is unlikely to be the most preferred alternative based on the assumed decision model. The above results are stochastically determined based on randomized weight preferences and it is necessary to check that the
4.2. Emission threshold limit and projected emission To check the possibility of achieving of the low emission target, we have determined CO2e production scenarios for each of the policy elements. The emission limits are calculated through Eqs. (8)
Table 5 Distribution of central weights for efficient alternatives. Policy Element
CF
Central weights BAU REN REN-b
0.94 0.83 0.89
Policy Element
CF
Central weights BAU REN REN-b
0.94 0.83 0.89
Criteria 1
2
3
4
5
6
7
8
9
10
11
12
0.040 0.037 0.045
0.034 0.051 0.039
0.049 0.042 0.035
0.036 0.038 0.048
0.053 0.036 0.040
0.045 0.046 0.037
0.041 0.029 0.053
0.043 0.040 0.042
0.050 0.056 0.025
0.048 0.039 0.040
0.065 0.032 0.032
0.026 0.050 0.046
13
14
15
16
17
18
19
20
21
22
23
24
0.040 0.052 0.035
0.037 0.038 0.047
0.050 0.036 0.041
0.035 0.038 0.049
0.028 0.047 0.046
0.034 0.051 0.039
0.051 0.040 0.037
0.036 0.039 0.048
0.037 0.038 0.048
0.033 0.052 0.040
0.037 0.038 0.049
0.052 0.035 0.039
Criteria
243
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
Emissions (tCO2e/cap)
0.080 0.070
BAU
0.060
REN
0.050
REN-b
0.040
ECET
0.030 0.020 0.010 -
2010
2015
2020
2025
2030
2035
2040
Years Fig. 4. Projected emissions against different policy elements.
and (9) and by utilizing data from Table 3. The per capita emission limits in different years are depicted in Table 6. Fig. 4 shows the per capita CO2e production for each of the policy elements for the period of 2010–2040. As shown in Fig. 4, all the policy elements are found to produce per capita emissions within the threshold limit (i.e. below 1.77 tCO2e/cap) every year. The ECET has the lowest per capita CO2e emission (i.e. lowest environmental impact), followed by the REN and REN-b policy elements. ECET element would produce half the level of CO2e emission compared to the BAU element in 2040. The ECET element employed efficient technologies, which significantly improve the power generating efficiencies, thus resulting in lower emissions than the other elements. Although ECET produce lower emissions, it has already been disqualified in the prior multicriteria assessment due to e.g. high costs, lower acceptance, etc. Based on these multicriteria assessment and long-term environmental impacts (i.e. CO2e emissions), only REN-b and REN are the acceptable policy elements. 5. Conclusions Energy policy has multiple objectives and involves the interests of several stakeholder groups. The requirements of the diverse stakeholder groups and multiple objectives cannot be met by a single criterion. Therefore, energy policy needs to be developed on the basis of a balanced assessment of stakeholders’ interests and several sustainability objectives. This research presents a transparent technique for assessing different prospective elements of energy policy by representing stakeholders’ interests and several sustainability goals. Based on the assessment results, policy-makers are expected to take various measures (e.g. legislation, treaty, laws, etc.) that lead to sustainable energy sector. The technique presented in this research will provide policy-makers with essential information for planning and promoting energy policy to accomplish sustainable and low carbon energy paths. The government of a country can evaluate the merits of different elements of energy policy and take steps toward formulating further measures. Thus, this method provides a valuable tool to the policy-makers to guide energy policy that will achieve a sustainable and low carbon energy future. In our illustration for a specific aspect (i.e. share of fuels for power generation) in the case of the Bangladesh power sector, REN-b and REN policy elements were found to be preferable over other alternatives based on acceptability indices (41% and 32% respectively). These two alternatives also obtained good confidence factors (0.89 and 0.83 respectively) and are favoured with
uniformly distributed weights. The GHG emissions for all of the four hypothetical policy elements were found to be within their threshold limits of 1.77 tCO2e/cap. From the overall analysis, policy-makers may choose REN-b (best option) or REN (second best option) policy element and enact relevant policy measures (legislation, laws, etc.). Further research is also necessary to evaluate other energy sector issues (e.g. long-term demands, supply and resources) and the authors would continue this effort. Acknowledgements The authors are grateful to Aalto University School of Engineering, Finland and Universiti Teknologi Malaysia UTM (PAS Grant, 01K95) for proving funding for conducting this research. The authors also thankful to the anonymous reviewers for kindly review this paper. References [1] Doukas H, Patlitzianas KD, Kagiannas AG, Psarras J. Energy policy making: an old concept or a modern challenge? Energy Sour Part B: Econ Plann Pol 2008;3:362–71. [2] Sanaeepur S, Sanaeepur H, Kargari A, Habibi MH. Renewable energies: climatechange mitigation and international climate policy. Int J Sustain Energy 2014;33:203–12. [3] Doukas H, Flamos A, Psarras J. Risks on the security of oil and gas supply. Energy Sour Part B: Econ Plann Pol 2011;6:417–25. [4] Løken E. Use of multicriteria decision analysis methods for energy planning problems. Renew Sustain Energy Rev 2007;11:1584–95. [5] Qudrat-Ullah H. Modelling and simulation in service of energy policy. Energy Proc 2015;75:2819–25. [6] Igos E, Rugani B, Rege S, Benetto E, Drouet L, Zachary DS. Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios. Appl Energy 2015;145:234–45. [7] Rahman MM, Paatero JV, Poudyal A, Lahdelma R. Driving and hindering factors for rural electrification in developing countries: lessons from Bangladesh. Energy Pol 2013;61:840–51. [8] Kowalski K, Stagl S, Madlener R, Omann I. Sustainable energy futures: methodological challenges in combining scenarios and participatory multicriteria analysis. Eur J Oper Res 2009;197:1063–74. [9] Strachan N, Pye S, Kannan R. The iterative contribution and relevance of modelling to UK energy policy. Energy Pol 2009;37:850–60. [10] Pokharel S, Chandrashekar M. A multiobjective approach to rural energy policy analysis. Energy 1998;23:325–36. [11] Kanellakis M, Martinopoulos G, Zachariadis T. European energy policy—a review. Energy Pol 2013;62:1020–30. [12] Laitner JA, DeCanio SJ, Koomey JG, Sanstad AH. Room for improvement: increasing the value of energy modeling for policy analysis. Util Pol 2003;11:87–94. [13] Pandey R. Energy policy modelling: agenda for developing countries. Energy Pol 2002;30:97–106. [14] Proença S, St Aubyn M. Hybrid modeling to support energy-climate policy: effects of feed-in tariffs to promote renewable energy in Portugal. Energy Econ 2013;38:176–85.
244
M.M. Rahman et al. / Applied Energy 164 (2016) 237–244
[15] Fortes P, Pereira R, Pereira A, Seixas J. Integrated technological–economic modeling platform for energy and climate policy analysis. Energy 2014;73:716–30. [16] Erol Ö, Kılkısß B. An energy source policy assessment using analytical hierarchy process. Energy Convers Manage 2012;63:245–52. [17] Richstein JC, Chappin ÉJL, de Vries LJ. Adjusting the CO2 cap to subsidised RES generation: can CO2 prices be decoupled from renewable policy? Appl Energy 2015;156:693–702. [18] Sovacool BK, Saunders H. Competing policy packages and the complexity of energy security. Energy 2014;67:641–51. [19] Zhang C, Chen J, Wen Z. Assessment of policy alternatives and key technologies for energy conservation and water pollution reduction in China’s synthetic ammonia industry. J Cleaner Prod 2012;25:96–105. [20] Zhang C, Chen J, Wen Z. Alternative policy assessment for water pollution control in China’s pulp and paper industry. Resour Conserv Recycl 2012;66:15–26. [21] Zhang C, Wen Z, Chen J. An integrated model for technology forecasting to reduce pollutant emission in China’s pulp industry. Resour Conserv Recycl 2009;54:62–72. [22] Zhang S, Andrews-Speed P, Perera P. The evolving policy regime for pumped storage hydroelectricity in China: a key support for low-carbon energy. Appl Energy 2015;150:15–24. [23] Reuter WH, Szolgayová J, Fuss S, Obersteiner M. Renewable energy investment: policy and market impacts. Appl Energy 2012;97:249–54. [24] Casisi M, De Nardi A, Pinamonti P, Reini M. Effect of different economic support policies on the optimal synthesis and operation of a distributed energy supply system with renewable energy sources for an industrial area. Energy Convers Manage 2015;95:131–9. [25] Lin B, Xie X. Energy conservation potential in China’s petroleum refining industry: evidence and policy implications. Energy Convers Manage 2015;91:377–86. [26] Sßekerciog˘lu S, Yılmaz M. Renewable energy perspectives in the frame of Turkey’s and the EU’s energy policies. Energy Convers Manage 2012;63:233–8. [27] Spyridaki N-A, Flamos A. A paper trail of evaluation approaches to energy and climate policy interactions. Renew Sustain Energy Rev 2014;40:1090–107. [28] Park T, Kim C, Kim H. A real option-based model to valuate CDM projects under uncertain energy policies for emission trading. Appl Energy 2014;131:288–96. [29] Hossain AK, Badr O. Prospects of renewable energy utilisation for electricity generation in Bangladesh. Renew Sustain Energy Rev 2007;11:1617–49. [30] Lee S-C, Shih L-H. Renewable energy policy evaluation using real option model—the case of Taiwan. Energy Econ 2010;32(S1):S67–78. [31] Hoffman KC, Jorgenson DW. Economic and technological models for evaluation of energy policy. Bell J Econ 1977;8:444–66.
[32] Kangas J, Kangas A, Leskinen P, Pykäläinen J. MCDM methods in strategic planning of forestry on state-owned lands in Finland: applications and experiences. J Multi-Crit Decis Anal 2001;10:257–71. [33] Hokkanen J, Lahdelma R, Salminen P. Multicriteria decision support in a technology competition for cleaning polluted soil in Helsinki. J Environ Manage 2000;60:339–48. [34] Lahdelma R, Salminen P. SMAA-2: stochastic multicriteria acceptability analysis for group decision making. Oper Res 2001;49:444–54. [35] Lahdelma R, Hokkanen J, Salminen P. SMAA – stochastic multiobjective acceptability analysis. Eur J Oper Res 1998;106:137–43. [36] Rahman MM, Paatero JV, Lahdelma R. Evaluation of choices for sustainable rural electrification in developing countries: a multicriteria approach. Energy Pol 2013;59:589–99. [37] Swart RJ, Raskin P, Robinson J. The problem of the future: sustainability science and scenario analysis. Global Environ Change 2004;14:137–46. [38] Heaps CG. LEAP (Long range Energy Alternatives Planning). Somerville (MA, USA): Stockholm Environment Institute; 2012. [39] Jebaraj S, Iniyan S. A review of energy models. Renew Sustain Energy Rev 2006;10:281–311. [40] US EPA CCD. Climate Change: Basic Information; 2014.