energy consumption forecase-nepal_ranjan et.al

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Energy consumption projection of Nepal: An econometric approach

Ranjan Parajuli a , * , Poul Alberg stergaard a, Tommy Dalgaard b, Govind Raj Pokharel ca Department of Development and Planning, Aalborg University, Vestre Havnepromenade 9, 9000, Aalborg, Denmarkb Department of Agroecology, Aarhus University, Blichers All 20, 8830, Tjele, Denmarkc Alternative Energy Promotion Centre, Ministry of Environment, Science and Technology, Government of Nepal, Lalitpur, Nepal

a r t i c l e i n f o

Article history:Received 6 February 2013Accepted 27 September 2013Available online

Keywords:GDPTotal primary energy consumptionFossil fuelsRenewable energyNepal

a b s t r a c t

In energy dependent economies, energy consumption is often linked with the growth in Gross DomesticProduct (GDP). Energy intensity, de ned herewith, as the ratio of the total primary energy consumption(TPE) to the GDP, is a useful concept for understanding the relation between energy demand and eco-nomic development. The scope of this article is to assess the future primary energy consumption of Nepal, and the projection is carried out along with the formulation of simple linear logarithmic energyconsumption models. This initiates with a hypothesis that energy consumption is dependent with thenational macro-economic parameters. To test the hypothesis, nexus between energy consumption andpossible determinant variables are examined. Status of energy consumption between the period of 1996and 2009, and for the same period, growth of economic parameters are assessed. Three scenarios aredeveloped differing from each other on the basis of growth rates of economic indicators: total GDP, GDP-agriculture, GDP-trade, GDP-industry, and other variables including growth in private consumptions,population, transport vehicles numbers, prices of fossil fuels etc. Scenarios are: Business as Usual (BAU),Medium Growth Scenario (MGS) and High Growth Scenario (HGS). Energy consumption in all the sectorsand for all fuel types are not statistically correlated with every economic parameters tested in theassessment. Hence, the statistically correlated models are included in the prognosis of energy con-sumption. For example, the TPE consumption and electricity consumption, both are signi cantly

dependent with the total GDP and population growth. Likewise, fuel wood consumption is signi cantlydependent with the growth in rural population and private consumptions. In BAU the estimated elec-tricity consumption in 2030 would be 7.97 TWh, which is 3.47 times higher than that of 2009. In MGS,the total electricity consumption in 2030 is estimated to increase by a factor of 5.71 compared to 2009.Likewise, in HGS, electricity consumption would increase by 10-fold until 2030 compared to 2009,demanding installed capacity of power plant at 6600 MW, which is only from hydro power and othercentralised system.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

There have been signi cant debates on the causal relationshipbetween energy consumption and economic growth [1e 6]. The

understanding of the correlation between energy consumption andeconomy is highly relevant to policy makers. Realising this fact, thisarticle assesses short-run econometric models of primary energyconsumption of Nepal. Historical trends of the TPE consumptionhave a determinant role in analysing the energy situation of anyeconomy, whereas on the other hand, when the economic structurechanges it can also have a bearing on the energy supply and

demand [7,8] . Thus the historical energy consumption patterngenerally facilitates to delineate the future energy system. Assess-ment of the future energy consumption in relation to possiblegrowth in economy is also important in the process of formulating

conducive development plans and policies. Likewise, it is alsorelevant to identify the measures of energy diversi cation in suchevents.

Nepal is hugely dependent on the imported fossil fuel, despitethe country has massive hydro power potential [9] . During scalyear 2000/2001, import of the petroleum products was equivalentto 27% of the merchandise exports. With an annual average growthrate of 10%, spurred by rising power outage, in 2008/2009 thecountry spent NRs 1 41.4 billion or 61.5% of its export earnings (NRs

* Corresponding author. Present address: Department of Agroecology, AarhusUniversity, Blichers All 20, 8830 Tjele, Denmark.

E-mail addresses: [email protected] , [email protected](R. Parajuli). 1 1$ NRs 89.17 NRs, at June 18, 2012 price.

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http://dx.doi.org/10.1016/j.renene.2013.09.048

Renewable Energy 63 (2014) 432 e 444
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67.2 billion) just on petroleum products [10] . This actuallyexceededthe total export earnings of NRs 40.9 billion from India. Further-more, estimates show that if oil prices hit US$150 per barrel (upfrom around US$120 now), then export earnings should beincreased by 2-fold just to meet the demand of the petroleumproducts [10] . This situation reveals that the country has no choicerather than increasing the production capacity of electricity, uti-lising the available hydro power and renewable sources.

In this article, we discuss about the possible future primaryenergy consumption of Nepal. Forecasting of energy consumptionis based on the formulated simple logarithmic equations, which wediscuss in Section 5. Signi cance and importance of econometricmodels and as such formulated in this study are rationalised inSection 2. For instance, we discuss about the energy forecastingapproaches, including as those of using sophisticated tools andsimple linear logarithmic models, and their applicability indifferent economies. Energy models portrayed in Section 5 arebased on the economic indicators that we have discussed in Section3, and methods are elaborated in Section 4. In Section 6, prognosisof future primary energy consumption is discussed. It should benoted that the prognosis is carried out keeping in mind that theenergy consumption would follow the similar pattern as experi-enced in the last decade, and changes in the energy intensity thatwould most likely take place in future is determined by their cor-relation with the economic indicators considered in the models.Prognosis for future energy consumption is carried out for theperiod of 2010 e 2030, and 2009 is the base year.

2. Review on energy models and forecastings

There are some debates on the energy and economy relation-ships. The results on the relationship between energy consump-tion and economic parameters based on some studies can besummarised into mainly three main categories; no causality, uni-directional causality and bi-directional causality, which are pri-marily de ned on the basis of relation between energy

consumption and income [11,12] . Unidirectional causality resultscan be further divided into two categories: (i) energy consumptioncauses income, and (ii) income causes energy consumption [11 e13]. Furthermore, some distinct schools of thoughts found in un-derstanding the correlation between energy and economy are the growth hypothesis , Conservation hypothesis and neutral hy-pothesis [2,14 e 17] . The growth hypothesis often convey thatenergy consumption is a crucial component in economic growth,directly or indirectly as a supporting element to capital and labouras input factors of production. Analogously, a decrease in energyconsumption causes a decrease in GDP in an energy dependenteconomy [11,16 e 19]. In contrast, the conservation hypothesisstates that policies directed towards lower energy consumptionmay have little or no adverse impact on GDP [16,17,20] . Addi-

tionally, economic growth should be decoupled from energyconsumption to avoid a negative impact on economic develop-ment resulting from a reduction of energy use. The neutralityhypothesis holds that energy and economy are uncorrelated thusreducing energy consumption does not affect economic growth orvice versa [12,21 e 23] . Hence, energy conservation policies wouldnot have any impact on GDP [21e 23] . The relation of energyconsumption to GDP depends on the practice of energy genera-tion, transformation and use in an economy [2,22 e 24] . Whenconsidering country-speci c studies, the relation of energy con-sumption to the economic growth is not well attributed, as even inthe increased GDP, more or less energy consumption is constant,which concludes that electricity could be a limiting factor toeconomic growth, and hence, shocks to energy supply will have a

negative impact on economic growth [2,24] . However, more

con icting results come from high income and middle incomecountries, where energy consumption, and economic growth areclosely correlated [18,24,25] . Furthermore, unidirectional causalityrunning from economic growth to electricity consumption areargued in the context of developing countries in many studies[1,3,5,6] , whereas some studies also support that there can bechanges in the direction of electricity consumption and economicgrowth depending upon their long-run and short-run relation-ships [26] . One may observe energy intensity in relation to theGDP growth of Denmark that took place in the past three decades,for instance in 1980 and 2009 the energy intensity was 0.998 and0.595 TJ per DKK mil. GDP at 2000 price respectively, which alsoindicates that the annual average decrease of energy intensity was2% [27,28] , despite there was growth in economy.

Due to the constant increase in electricity consumption in thelast two decades, globally many energy planning and managementefforts have been made to avoid electricity shortage and guaranteeadequate infrastructures, where much effort have been made onthe forecasting of electricity demand using different techniques[29 e 32] . Yee Yan [32] presented residential consumption modelsusing climatic variables for Hong Kong. Egelioglu et al. [33] inves-tigated the in uence of economic variables on the annual elec-tricity consumption in Northern Cyprus and they found that amodel using number of customers, number of tourists and elec-tricity prices have strong predictive ability. Mohamed and Bodger[31] studied a model for electricity forecasting in New Zealand,which is based on multiple linear regression analysis, taking intoaccount economic and demographic variables. Similarly, Al-Ghan-door et al. [34] presented a model developed to forecast electricityconsumption of the Jordanian industrial sector based on multivar-iate linear regression of time series in order to identify the maindrivers behind electricity consumption. Electricity demand projections for Sri Lanka were carried out based on a time seriesanalysis to show how different time series estimation methodsperform; in terms of modelling past electricity demand, estimatingthe key income and price elasticities, and hence forecasting future

electricity consumption [30] .There are some debates, if simple models can produce accurate

results similar to those obtained from sophisticated models [35 e38] , but have been practiced in many developing countriesincluding Nepal, Sri Lanka, as discussed earlier. In Nepal, linearregression models were developed to show the short-run energyconsumption forecast for the period of 1988 e 2002 [7], and themodels were formulated using different variables such as popula-tion, income, price, growth factors and available energy conversiontechnologies.Realising these approaches of determining the energyconsumption models, and most importantly considering suchinitiative practiced in Nepal, this article also works with linearlogarithmic model to estimate the future primary energy con-sumption of the country.

3. Energy and economics in Nepal

Energy sources of Nepal are generally classi ed into mainlythree groups, traditional, commercial and renewable sources[7,9,39,40] . Traditional energy sources cover the energy suppliedfrom indigenous sources like fuel wood, animal waste and agri-culture residues [9,40 e 42] . Commercial energy sources are theimported fossil fuels and grid connected electricity. In this article,the grid connected system is de ned as electricity generated frombig hydro power ( > 5 MW) and thermal (diesel) plants [40] .Renewable energy is primarily rural energy solutions in thecontext of Nepal, as till date they have been playing an importantrole in increasing the energy access of the country. Solar photo-

voltaic technology and Micro/Mini hydro power plants are referred

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as off-grid source of electricity [43] , since till date these technol-ogies are serving as isolated/decentralised energy system in thecountry [9], and they do not have any connection with the nationalgrid of the country. In recent days, initiatives have been taken todevelop a rural electricity grid (also known as mini-grid) to connectelectricity generated from Micro/Mini hydro power plants in ruralareas of the country.

Table 1 shows the energy mix of Nepal of the year 1996, 2005and 2009, and one may observe that the share of traditional energysources was decreased marginally from 90% to 87% from 1996 to2009. Fuel wood consumption having the highest share in thegroup of traditional energy source was decreased from 81% to 78%.Despite there was a decrease in the share of traditional energysources, but was modest in absolute values. For example con-sumption of the traditional energy sources in 1996 was 264 PJ,whilst in 2005 and 2009, they were 322 PJ and 349 PJ respectively(Table 1 ). Similarly, commercial energy sources were increasedfrom 9% to 12% from the period of 1996 to 2009 against the TPEconsumption. High Speed Diesel (HSD) with the highest shareamong the commercial sources was increased from 3% to 4%, andLiquidPetroleum Gas (LPG) from 0.3% to 1.4%. The 4-fold increase inthe share of LPG was parallelled by the reduction of the keroseneconsumption i.e. from 2.6% to 0.63%. The share of electricity in theTPE consumption doubled from 1.04% to 2.03% from 1996 to 2009though still modest in absolute numbers.

Nepal has a total installed capacity of 690 MW of electricity,including the installed capacity of the grid connected hydro powersystem with 473 MW and isolated small hydro power with around4.5 MW. In addition to this, there is a number of small and mediumhydro power plants with a total installed capacity of 158 MW,which is connected to national grid and owned by IndependentPower Producers (IPP) [40,44 e 46] . Renewable energy technologies(RETs) started to serve noticeably in the country basically from1996 contributing about 0.14% (0.43 PJ) of the TPE consumption,and increased to 0.68% (2.73 PJ) of the TPE consumption by 2009.Biogas alone covered 0.14% in 1996 and 0.64% in 2009 of such

contributions [40] .The TPE consumption of the country in 2009 was 400 PJ, which

was 37% higher than that in 1996. The residential sector had thehighest consumption in both 1996 and 2009, with 92% and 89%

respectively of the TPE consumption. The TPE consumption in theresidential sector in 1996 was 267 PJ, which has increased by 33%until 2009. Similarly, the TPE consumption in the industrial sectorwas 12 PJ and 13 PJ respectively in 1996 and 2009. In the com-mercial, transport, agriculture and others sectors, increase in theenergy consumption from 1996 to 2009 was 80%, 139%, 428% and182% respectively. Fig. 1 shows the TPE consumption of Nepal indifferent sectors from 1996 to 2009. Table 2 indicates the economicindicators of the country for the same periods.

Fig. 2 shows the growth in the TPE consumption and GDP fromthe period from 1996 to 2009. This reveals that for every incrementof 1 bil. NRs in the GDP (at constant 2005 prices), about 3.83 PJ of TPE was consumed.

Similarly, in 1996 the GDP/capita was 1084 NRs, and the TPEconsumption per capita was 12.66 GJ. The GDP per capita in 2009has increased by 93% than of 1996, whereas per capita TPE con-sumption was increased by 11% ( Fig. 3).

4. Materials and methods

Status of energy consumption is based on the studies as those of Refs. [39,40,45 e 47,51 e 56] , and economic parameters are based onthe national reports including as those of Refs. [47 e 50,54] . Staticlog-linear Cobb e Douglas functions [57 e 59] are used to formulatethe econometric models of energy consumption. Models aredeveloped assessing the correlation of primary energy

Table 1TPE consumption and shares by fuel types of Nepal in 1996 and 2009.

Fuel types/Year 1996 2005 2009

% PJ % PJ % PJ

Traditional 90.4 263.63 88 322.11 87.11 348.87Agricultureresidue(Agr-residue)

3.62 10.57 4 13.96 3.67 14.68

Animal Dung 6.02 17.57 6 21.18 5.75 23.02Fuel wood 80.7 235.5 78 286.96 77.69 311.17

Commercial 9.51 27.76 12 43.22 12.21 48.90ATF 0.50 1.47 1 2.42 0.62 2.49Coal 1.06 3.08 2 6.46 1.93 7.75Electricity 1.05 3.06 2 6.67 2.03 8.14Fuel oil 0.117 0.34 0 0.00 0.00 0.00Gasoline 0.47 1.38 1 2.53 1.04 4.16HS Diesel 3.26 9.50 3 11.91 4.42 17.69Kerosene 2.59 7.57 2 8.66 0.63 2.54Light Diesel 0.060 0.17 0 0.00 0.004 0.01LPG 0.314 0.92 1 3.82 1.42 5.70OtherPetroleum

0.09 0.27 0 0.75 0.10 0.41

Renewable 0.15 0.43 1 1.91 0.68 2.73Biogas 0.14 0.41 1 1.85 0.65 2.59MHP 0.008 0.02 0 0.06 0.034 0.14Solar 0 0.00 0 0.00 0.001 0.01

Fig. 1. TPE consumption in different economic sectors of Nepal [40] .

Table 2Economic indicators of Nepal, in 1996 and 2009 (GDP in NRs).

Parameters 1996 2009

Total GDP sharesGDP (at producer price)_Constant-base

year 2005 (bil. NRs)249 592

Shares of Total GDP Agriculture/forestry (GDP_agri) 40% 35%Industrial (GDP_ind) 21% 13%Services 39% 51%Private Consumption (Pvt . Cons.)(NRs) 19,147 77,276Population (mil.) 23.04 28.36Urban (Urb) (mil.) 19.84 23.42Rural (Rur) (mil.) 3.2 4.94

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consumption with major macro-economic parameters like GDP(producer price), population (rural and urban) growth and othereconomic activities of the country observed during the period of 1996 e 2009. GDP growth rates (at 2005 constant price) are esti-mated based on the national budget reports published by Ministryof Finance, Government of Nepal [47e 49] .

Initially, all energy consuming sectors of the country (residen-tial, commercial, transport, industrial, agriculture and others) ascategorised by the government of Nepal [39,40] and fuel types wereconsidered for formulating the energy consumption models. Vari-ables that have been considered for developing energy models arepresented in Table 2 . Testingof energy consumption models follows

a statistical test criteria, where R2 values, t -stat, signi cance F -statand p-stat were considered. In a statistical analysis, the modelresulting with the signi cance F -values and the p-values 0.05were quali ed, assuming having a better correlation between en-ergy consumption and its determinant variables. Whilst models

with higher signi cance F -values and p-values than stated aboveweredis-regarded, thus forecasting of energy consumption of thosesectors and fuel types are not discussed in this article.

Scenario 1 is referred as Business As Usual (BAU), and the eco-nomic growth rate in this scenario follow the trend observed dur-ing the period of 1996 e 2009 ( Table 3 ). Scenario 2 is referred as theMedium Growth Scenario (MGS), and the growth rate of the eco-nomic parameters is estimated considering the growth that tookplace between the period of 2001 and 2009, and is assumed rep-resenting the latest economic growth experiences of the country.Furthermore, while estimating the economic growth rate in MGS,efforts have been given to represent the recent short term eco-nomic development plan of the government of Nepal (planned for

2013e

2014), which aims to meet the overall GDP growth at 6% andagriculture GDP at around 5%. Scenario 3, referred as the HighGrowth Scenario (HGS) represents the ambition of the governmentof Nepal to achieve the growth in the total GDP at least in doubledigits (i.e. equivalent to 10%). Growth rate of othersectoral GDP (e.g.agriculture, trade) and growth in private consumption are esti-mated proportionately considering the total GDP growth.

Population growth rate represents the medium fertility variantgrowth [54,60] estimated for the period of 1996 e 2009, and arekept same in all scenarios. In ation of the petroleum products

prices is estimated based on the annual reports of Nepal Oil Cor-poration [55] , and annual reports of Ministry of Finance [47 e 49] .Energy status and prospects of future energy interventions are alsoreviewed from literatures including, Parajuli [9] and publications of WECS [39,40] . Transportation data are based on MoPPWTM [61] .Other data source for energy related variables presented in themodels is based on different literatures including those of Refs.[51e 54,62] .

In this study, we have not considered other potential energysources and technologies such as, geothermal sources and windpower, considering the status and use of such energy carriers/technologies, and since their exploitation yet being questionablefrom economic perspectives in Nepal.

5. Formulation of energy consumption models

Energy consumption models are formulated in series of steps,primarily to identify the correlation of energy consumption (sec-toral and by fuel types) with possible determinant variables. Sta-tistical test procedures and criterion for the test are elaborated inSection 4. Summary of statistical tests of the formulated energymodels is presented in Table 4 and further elaborated in Section 5.1.Likewise, the formulated energy consumption models are tested bybackcasting the energy consumption from 2010 e 1996 andcompared with the actual values for the same period ( Fig. 4), whichis latter presented in section 6.1.

5.1. Energy sector model

5.1.1. Residential sector Initially, we made a hypothesis that the residential energy

consumption may show a signi cant relation with the independent

variables such as total GDP, private consumption, and urban and

Fig. 2. Growth trend of GDP and TPE consumption, Nepal (1996 e 2009) [39,40,47 e 50] .

Fig. 3. Energy consumption and GDP relation from the period of 1996 to 2009s

[39,40,47e

50] .

Table 3Parameters for scenario development.

Parameters BAU (%) MGS (%) HGS (%)

GDP_constant 2005 price 3.31 6.38 10Growth in Pvt. Cons. 9.23 10.48 16.42GDP_trade_constant price 0.23 7.7 12.07GDP_agri (Constant price) 2.91 5.37 8.41



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rural population. In such hypothesis, we found that p-stat is notsigni cant (i.e. higher than 0.05), hence the model was testedfurther. In the next step, we found that the residential energyconsumption is highly dependent on the urban and rural popula-tion, which was signi ed by the statistical test criterion as shown inTable 4 . Relation (1) shows the logarithmic model for the residen-tial energy consumption.

lnRsd 21 : 982 0 : 23ln Rur_pop 0 : 76ln Urb_pop

0 : 221ln Pvt : Cons : (1)

5.1.2. Industrial sector The industrial sector energy consumption model is unable to

establish a good correlation with the economic variable, such asindustrial GDP, which we assumed would exist. We attempted toformulate the model in a number of steps: (i) testing the signi -cance of the energy consumption with determinant variables, suchas total GDP, private consumption, population and GDP-trade, butshowed insigni cant relations, (ii) assessing correlation betweenindustrial energy consumption and GDP-industry and privateconsumption, which was still insigni cant, and (iii) assessing thecorrelation between industrial energy consumption and the GDP-industry, where still lacked of signi cant relations. The industrialenergy consumption was thus dif cult to trace the nexus with themacro-economic parameters of the country. However, the averageannual growth of energy consumption in the sector during theperiod of 1996 e 2009 was 0.05%. In terms of end-uses, the con-sumption for motive power and process heat was almost 60% of theenergy consumption in the sector. MoICS [63] highlighted about theinadequacy of energy inputs, in particular electricity in differentmanufacturing industries of Nepal, and from that study we alsofound that about 60% of the manufacturing rms had their owngenerators. The reason of having a poor connectivity of energyconsumption and GDP-industry hence may also be due to lack of considering fuel consumption in those rms in regard to theireconomic outputs.

5.1.3. Commercial sector The commercial energy consumption shows a better correlation

with GDP-trade of the country, where the R2 value is 0.88, and p-stat and signi cance F -values are within the accepted limits(Table 4 ). Relation (2) shows the energy consumption model of thecommercial sector.

lnCom : 0: 989 0 : 576ln GDP_trade (2)

5.1.4. Transport sector Among the different trials for the transport sector, energy con-

sumption is relatively better correlated when urban population,rural population and vehicle numbers are considered as the deter-minant variables ( Table 4 ). It is found that the energy consumptionin this sector may decrease with the decrease in the rural popula-tion. This could be due to decrease in travel distance in rural areascompared to urban areas, with more migration from rural areas tourban areas, as experienced in the last decades [54] . Similarly, thetransport sector energy consumption is expected to change by afactorof 0.446 per unit increase in the vehicle number(relation (3) ).

lntrans 64 : 242 6 : 571ln Urb_pop 2 : 564ln Rur_pop

0 : 446ln Vehicles number (3)

5.1.5. AgricultureEnergy consumption model of the agriculture sector shows a

better correlation with agricultural and forestry GDP (relation (4)and Table 4 ). The changes in number of tractors and in the pum-ped irrigation area are also tested to observe the correlation, butsigni cant relations with them were not established. The model(relation (4) ), hence indicates that the energy consumption in theagriculture sector is expected to increase by a factor of 2.264 with aunit increase in the agricultural GDP ( Table 4 ), provided that itfollows the similar pattern of energy consumption, as observedduring the period of 1996 e 2009.

ln Agr 43 : 774 2 : 264ln GDP_agri (4)

5.2. Fuel model

5.2.1. Fuel wood modelIn the process of formulating the model of fuel wood con-

sumption, it was initially assumed that with the increase in theprivate consumption (Pvt. Cons.), fuel wood consumption may

decrease. Such assumption could be argued from the perspectivethat if there would be increase in private consumption, conse-quences of it could be shifting towards more ef cient energy de-vices and possibly lowering the energy intensity than before [7] .The fuel wood consumption is found signi cantly dependent withrural population and private consumption ( Table 4 ).

ln Fuel wood 12 : 097 0 : 12ln Rur_pop

0 : 176ln Pvt : Cons (5)

5.2.2. Agri-residues modelConsumption of agri-residues is found related with the changes

in the rural population (relation (6) ), and is also justi ed from thefact that 91% of the total agri-residues consumed in 2009 was in theresidential sector alone, and were primarily consumed in the ruralsettlements [39,40] . However, 9% of the total agri-residues wasconsumed in the industrial sector, and still now having similartrend, but the relation between the energy consumption and in-dustrial GDP was unable to signify the assumption. The reasonbehind such limitations could be due to lack of auditing of agri-residues production, conversion of them in different energy sec-tors, and most importantly measurement of energy intensity insuch sectors and end-uses in the past.

ln Agri_residue 8: 888 0 : 495ln Rur_pop (6)

5.2.3. LPG model

In 2009, of total LPG consumption in the country the residentialsector had a share of 56% and the commercial 40% [39,40] . The restof the LPG consumption was made in the transport sector, partic-ularly for running auto-rickshaws in the Kathmandu Valley. Suchaspects are justi ed in the LPG model, for example, a better cor-relation between LPG consumption with the urban population andGDP-trade ( Table 4 ). Furthermore, the model also shows that theLPG consumption in the country may not be in uenced by itsincreasing price. The past records also signify this assumption, forinstance in the last few decades (1996 e 2009), even when the priceof LPG was increasing annually at 8% [55] , annual consumption of LPG increased by 40% ( Table 1 ). The reason behind this could bebecause of the absence of alternatives to this technology in ful llingurban energy demand of the country. It may also indicate that LPG

would still be an important energy carrier to satisfy the energy



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demand of the urban population and in the commercial sector of the country.

lnLPG 46 : 929 2 : 457ln Urb_pop 0 : 831ln LPG_price

0 : 597ln GDP_trade (7)

5.2.4. HSD modelDiesel consumption in Nepal increased at an average annual

rate of 7% between 1996 and 2009 [55] , despite increase in theHSD price was 11% [55] . While assessing the HSD energy model,initially statistical tests were carried out to identify the signi -cance level of HSD consumption with economic parameters,such as changes in the diesel vehicles numbers and increase inthe road kilometres. The average rate of increase in diesel ve-hicles and road kilometres was respectively, 8% [61] and 5% [47]between the period 1996 and 2009. In such trial signi cantrelation to one another was not established. Even diesel priceelasticity did not show better correlation with the HSDconsumption.

5.2.5. Petrol modelThe average annual growth of petrol consumption in the

country between 1996 and 2009 was 15% [40] , where the estimatedgrowth in the number of vehicles using petrol is 12%. Petrol con-sumption is found not affected even by the rise in its price (esti-mated to be 6% between these periods) [40,55] , which can also be justi ed with the increase in the petrol vehicles during these pe-riods [61] . The econometric model of the petrol consumptionshows that its consumption is expected to increase by a factor of 0.69 with every unit increase in the number of vehicles consumingpetrol ( Table 4 ).

lnPetrol 6: 689 0 : 629ln petrol_vehicles (8)

5.2.6. Kerosene modelConsumption of kerosene was increased between 1996 and

2009, whereas in the later years from 2003 to 2009 it has decreasedat an annual average rate of 20% [40] . Hence, the annual averagedecrease in the kerosene consumption from 1996 to 2009 was 5%.Kerosene consumption model (relation (9)) shows that its con-sumption could further decrease in future with the increase in theurban population ( Table 4 ). A reason for the decline may be due tosubstitution of it by LPG, as in urban areas LPG is found alternativesource of cooking fuels [56] .

lnKerosene 288 : 982 4 : 226ln Rur_pop

20ln Urb_pop (9)

5.2.7. Biogas modelBiogas is one of the important source of energy for ful lling

residential energy needs, particularly for cooking in rural areas of the country, where agriculture is the mainstay of the population[40] . Biogas consumption model (relation (10) ) shows that itsconsumption is signi cantlydependent on the ruralpopulation andagricultural GDP of the country, where R2 values of the model areestimated at 0.99 ( Table 4 ).

lnBiogas 49 : 423 0 : 558ln Rur_pop

2 : 119ln GDP_agri (10)

5.2.8. Electricity modelIn uence of economic growth on the electricity consumption is

presented in a number of studies particularly in developing econ-omies [6,14,16,26,64,65] , whereas studies such as Altinay and Kar-agol [66] and Squalli [67] reported in an opposite direction. On theother hand, studies such as Morimoto and Hope [68] found bi-directional causality between electricity consumption and eco-nomic growth (GDP) in Korea and Sri Lanka. The reason behindthese debates could be because of improved energy ef ciency thathave been integrated in the developed economies, where even if GDP is increasing the energy intensity is fairly constant [21 e 23] ,such as in Denmark [28] . Whilst, in the developing economies theenergy ef ciency is not improved along with the growth in eco-nomic activities [28] , and increasing access to electricity is still oneof the development agendas.

In Nepal 55% of the total population in 2009 had access toelectricity suppliedfrom the grid and off-grid (solar and Micro/Minihydro) connected power systems [51e 53] . The annual averagegrowth in the electricity consumption supplied from all types of generation system between 1996 and 2009 was 7.31%, and theelectricity consumption in the grid connected areas alonewas 7.24%[40] . Grid connected electricity consumption shows signi cantrelation with the total GDP and the total population of the country(relation (11) and Table 4 ).

ln elec_grid 38 : 134 0 : 822ln GDP

1 : 1857ln Total_pop (11)

Off-grid electricity sources are primarily renewable energy tech-nologies like Micro/Mini hydro and Solar PV, which have been pro-moted tomeetthe country s rural lightingenergydemand [51,52,62] .The off-grid electricity consumption model is thus developedconsidering the rural population and electri cation ratio of thecountry, as the determinant variables. It is found that the con-sumption of the off-grid electricity is signi cantly dependent on therural population and the electri cationratio of thecountry ( Table 4 ).This indicates that to increase the energy access of the country, Mi-cro/Mini hydro and Solar PV technologies have prominent roles.

lnelec_off grid 15 : 185 1 : 787ln Rur_pop

1 : 147ln electrification_ratio (12)

5.3. TPE consumption model

We have found that all the sectors and fuels are not signi cantlycorrelated with the economic parameters, thus the sectoral and fuelmodels cannot be aggregated to calculate the TPE consumption.Hence, the TPE consumption model is developed separately, whereit is found that the TPE consumption of the country is signi cantlydependent on the total GDP and total population of the country(Table 4 ). With every unit increment in the total GDPof the country,energy consumption may increases by a factor of 0.282, and withthe increase in the total population its consumption may increaseby a factor of 0.413 (relation (13) ).

lnTPE 5: 072 0 : 282ln GDP 0 : 413ln Total_pop (13)



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Table 4 shows the results of statistical analysis, which we havediscussed while explaining the relation of sectoral and fuel con-sumption models.

6. Results and discussions

6.1. Backcasting to test the models

The objective of backcasting the energy consumption from theyear 2010 to 1996 is to validate the approach that we haveconsidered while formulating energy models. The validation isdone by comparing the actual primary energy consumption of thecountry observed between the period of 1996 and 2009 with thesimulated values estimated with the aid of formulated energymodels. Energy consumption in all sectors and fuel types is thusbackcasted from the year 2010 to 1996 using the economic pa-rameters, as presented in BAU, where the backcasted energyconsumption and actual consumption showed better R2 values(Fig. 4).

6.2. TPE consumption and GDP forecast

GDP and TPE consumption for the period of 1996 e 2009, asshown in Fig. 5 refer the actualvalues observed during the period of 1996 e 2009, whilst values beyond 2009 are the simulated (usingthe relation (13) ). GDP growth rates are as such described for BAU,MGS and HGS ( Table 3 ). In BAU scenario, the GDP is expected toincrease by 2-fold in 2030 compared to 2009. In BAU scenario, theTPE consumption in 2020 is estimated to be higher by 1.4-foldcompared to the base year.

In MGS, the total GDP would increase by a factor of 3.67 by 2030in comparison to 2009, which would facilitate for higher energyconsumption. For instance, the TPE consumptionwould increase bya factor of 1.63 to meet the economic growth anticipated in MGS.Similarly, in HGS the total GDP is expected to increase by a factor of 7.4 in 2030 compared to 2009, and in such growth the primaryenergy consumption would lead to increase by 2-fold compared to2009.

6.3. Sectoral energy consumption forecast

Fig. 6 shows the primary energy consumption in the residentialsector in different scenarios, and indicates that would increase by afactor of 1.45, 1.5 and 2 in 2030 compared to 2009, respectively inBAU, MGS and HGS. Absolute values of energy consumption areshown in Table 5 .

Likewise, the TPE consumption in the commercial sector wouldlead toincrease bya factor of 1.02, 2.45 and 4 in 2030 along with theanticipated growth of the determinant economic variables in theBAU, MGS and HGS respectively ( Fig. 7). The determinant variableof the commercial sector (i.e. GDP-trade) is expected to increase bya factorof 1.5 in BAU. In MGS and HGS the GDP-trade could increase

by 4.8- and 11-foldrespectively with respect to the situation as thatof in 2009.In the same manner, the primary energy consumption in the

agriculture sector is expected to increase by a factor of 4, 12 and 48in BAU, MGS and HGS (Fig. 8), along with the anticipated growth inthe agricultural GDP of the country ( Table 3 ), provided that thechanges in the cultivation area, agricultural productivity and en-ergy use would follow the similar pattern as observed during theperiod of 1996 e 2009. Thereason behind the higher primary energyconsumption in the agriculture sector is because of the increasinggrowth in the diesel consumption. For instance,between the periodof 1996 and 2009, HSD had an average share of 95% of the totalenergy consumed in this sector [40] . Despite this there are oppor-tunities to reduce the HSD consumption in the sector by promoting

rural electri cation entities to facilitate pumped irrigations.

Table 4Statistical test for energy consumption models.

ln(Rsd.) 21.982 0.230 ln(Rur_pop) 0.760 ln(Urb_pop) 0.221 ln(Pvt.Cons.)

(t -stat) (7.988) (3.022) ( 3.429) (11.897)( p-stat) (1.19E-05) (0.0128) (0.00645) (3.17E-07)Adjusted R2 0.99Signi cance F -stat 9.68E-11ln(Com) 0.989 0.576 ln(GDP_trade)(t -stat) (0.683) (9.849)( p-stat) (0.507) (4.22E-07)Adjusted R2 0.88Signi cance F 4.22E-07ln(trans) 64.242 6.571 ln(Urb_pop) 2.564 ln(Rur_pop) 0.446

ln(Vehicles number)(t -stat) ( 2.026) (2.7) ( 2.669) (2.043)( p-stat) (0.0703) (0.0223) (0.0235) (0.0682)Adjusted R2 0.72Signi cance F 0.0039ln(Agri) 43.774 2.264 ln(GDP_agri)(t -stat) ( 4.619) (6.155)( p-stat) (0.00059) (4.91E-05)Adjusted R2 0.74Signi cance F 4,91E-05ln(Fuel wood) 12.097 0.12 ln(Rur_pop) 0.176 ln(Pvt. Cons.)(t -stat) (43.371) (2.386) (8.783)

( p-stat) (1.19E-13) (0.036) (2.66E-06)Adjusted R2 0.98Signi cance F 1.39E-11ln(Agri_resd.) 8.888 0.495 ln(Rur_pop)(t -stat) (7.526) (6.344)( p-stat ) (6.99E-06) (3.69E-05)Adjusted R2 0.75Signi cance F 3.69E-05ln(LPG.) 46.929 2.457 ln(Urb_pop) 0.831 ln(LPG_price) 0.597 ln(GDP-

trade)(t -stat) ( 3.063) (2.674) (4.797) (7.493)( p-stat) (0.0119) (0.0233) (0.00073) (2.08E-05)Adjusted R2 0.99Signi cance F 5.44E-11ln(Petrol) 6.689 0.629 ln(Petrol_vehicles)(t -stat) (18.734) (22.215)( p-stat) (2.99E-10) (4.08E-11)

Adjusted R2

0.97Signi cance F 4.08E-11ln(Kerosene) 288.982 4.226 ln(Rur_pop) 20 ln(Urb_pop)(t -stat) (4.997) (2.917) ( 4.27)( p-stat) (0.0004) (0.014) (0.0013)Adjusted R2 0.80Signi cance F 0.000122ln(Biogas) 49.423 0.588 ln(Rur_pop) 2.119 ln(GDP_agri)(t -stat) ( 31.318) (3.886) (19.237)( p-stat ) (4.175E-12) (0.00253) (8.109E-10)Adjusted R2 0.99Signi cance F 1.04E-12ln(elec_grid) 38.134 0.822 ln(GDP) 1.857 ln(Total_pop)(t -stat) ( 10.023) (10.043) (5.838)( p-stat ) (7.223E-07) (7.083E-07) (0.00011)Adjusted R2 0.98Signi cance F 8.3E-11ln(off-grid_elec) 15.185 1.787 ln(Rur_pop) 1.147

ln(electri cation_ratio)(t -stat) ( 2.158) (3.999) (3.668)( p-stat) (0.053) (0.002) (0.0037)Adjusted R2 0.97Signi cance F 1.57E-09ln(TPE) 5.072 0.282 ln(GDP) 0.413 ln(Total_pop)(t -stat) (6.667) (17.283) (6.504)( p-stat) (3.48E-05) (2.54E-09) (4.4E-05)Adjusted R2 0.99Signi cance F 9.52E-13



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6.4. Fuel consumption forecast

In BAU scenario, it is estimated that fuel wood would cover 86%(474 PJ) of the projected TPE in 2030. In the same year, fuel wood

consumption in MGS and HGS is most likely to cover 76% (495 PJ)

and 75% (600 PJ) of the TPE consumption. The sustainable supply of fuel wood from reachable area of all land resources type in Nepalfor the year 2008/2009 was 12.5 million tons (about 1837 PJ-pri-mary), which includes yields from the community forest, as the

managed forest regime having higher yield of wood resources, and

Fig. 4. Comparison of actual and simulated values of primary energy consumption, sectoral and fuel types.



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a l s of r om t h e unm a n a g e d n a t ur a l f or e s t s

[ 4 0 ] .T h i s i n d i c a t e s t h a t

f or t h e s h or t r un , a v a i l a b i l i t y of s u s t a i n a b l e f u e l w o o d s u p pl ym a y

n o t b e a pr o b l e m , b u t i t c o ul d b e i m

p or t a n t t or e d u c e f u e l w o o d

c on s um p t i on a l on gwi t h t h e ur b a ni s a t i on gr ow t h , a n d a l s of ul -

l l i n g t h e c o un t r y s d e v e l o pm e n t g o a l s wh e r e i n c r e a s i n g a c c e s s t o

m o d e r nf or m s of e n e r g y a n d

i m pr ovi n g e n e r g y e f c i e n c y a r e t a k e n

i n t o c on s i d e r a t i on s .A s s t a t e d e a r l i e r , d e s pi t e t h e r e w a s d e c r e a s e i n

t h e s h a r e of f u e l w o o d c on s um p t i oni n t h e pr e vi o u s y e a r s , b u t i n

a b s ol u t e v a l u e s t h e yw e r e m o d e s t .R e d u c t i oni n t h e c on s um p t i on of

f u e l w o o d c o ul d b e t r i g g e r e d wi t h a c on c e n t r a t e d pr om o t i on of

e f c i e n t c o ok s t ov e s a n d b i o g a s .T h e s e r ur a l e n e r g y t e c h n ol o gi e s

a r e p o p ul a r a n d pl a yi n g a n o t i c e a b l e r ol e i nr e d u c i n gf u e l w o o d

c on s um p t i oni n d e v e l o pi n g

c o un t r i e s , s u c h a s i nr ur a l a r e a s of

N e p a l [ 9 ] , a n d a l s oi nB a n gl a d e s h a n d I n d i a

[ 6 9 e7 2 ] .R e d u c t i oni n

t h e s h a r e of f u e l w o o d c on s um p t i on c o ul d a l s o b e j u s t i e d i f t h e

F i g . 6 .

P r o j e c t e d T P E c on s um p t i oni n t h e r e s i d e n t i a l s e c t or i n t h e s c e n a r i o s d e v e l o p e d .

Table 5Energy consumption by fuel types based on developed model.

Fuels/Years Unit 1996 2009 2015 2020 2025

BAU MGS HGS BAU MGS HGS BAU MGS H

Fuel Wood % 80.70 77.69 81.48 78.47 78.37 83.11 77.57 77.39 84.77 76.68 76.42 PJ (235) (311) (355) (360) (380) (391) (400) (443) (431) (445) (515)

Agri-residues % 3.62 3.67 3.74 3.56 3.37 3.75 3.42 3.08 3.75 3.28 2.82 PJ (11) (15) (16) (18) (19)

LPG % 0.31 1.42 2.37 2.92 3.18 3.51 5.14 6.01 5.19 9.04 11.36PJ (1) (6) (10) (13) (15) (17) (26) (34) (26) (52) (77)

Petrol % 0.47 1.04 1.34 1.27 1.20 1.79 1.63 1.47 2.38 2.09 1.80 PJ (1) (4) (5.83) (5.83) (5.83) (8.40) (8.40) (8.40) (12.11) (12.11) (12.11)

Kerosene % 2.59 0.63 0.31 0.29 0.27 0.17 0.15 0.14 0.09 0.08 0.07 PJ (8) (3) (1.33) (0.79 (0.46)

Biogas % 0.14 0.65 0.98 1.26 1.71 1.35 2.14 3.74 1.86 3.62 8.17 PJ e (3) (4.28) (5.77) (8.29) (6.35) (11.01) (21.37) (9.43) (20.99) (55.09)

Elec_total % 1.06 2.07 2.91 3.19 3.55 3.54 4.19 5.10 4.30 5.51 7.33 PJ (3) (8) (13) (14.62) (17) (17) (22) (29) (22) (32) (49)

TPE PJ 292 401 436 458 485 471 516 572 508 580 674

F i g . 5 .

E n e r g y c on s um p t i onf or e c a s t i n g i nr e l a t i on t o t o t a l G D P

g r ow t h i n t h r e e

s c e n a r i o s .


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share of biogas on the TPE consumption of the country is observed.For example, in 1996 and 2009 biogas had a share of 0.14% and0.65%respectivelyof the TPE consumption. Furthermore, the biogasmodel (relation (10) ) shows that biogas could cover 3% (14 PJ), 6%(40 PJ), and 18% (142 PJ) of TPE consumption in 2030 in BAU, MGSand HGS respectively ( Table 5 ). The theoretical potential of biogasplant in Nepal is estimated to be 43 PJ, which are estimated basedon animal manure as a feedstock input and generated from a familysize biodigester of 4 m 3 in size [40] . This reveals that if the biogasplant has to be coupled with the growth in agriculture GDP andruralpopulation, the supply potential will be far below the demand,particular for HGS. In such case, alternative feedstock should be

identi ed and used. Furthermore, commercial scale of biogasshould be initiated to support the economic growth anticipated inMGS and HGS. Likewise, in BAU, MGS and HGS, electricity con-sumption is expected to cover 5%, 7%, and 11% of the TPE con-sumption of 2030 ( Table 5 ).

Agri-residue consumption in BAU, MGS and HGS in 2030 is ex-pected to cover 3.75%, 3.15% and 2.59% of the TPE of the same year,representing about 21 PJ. The agricultural residue production po-tential in the country is estimated to be 19.4 million ton [40] . Oneof the important aspects that needs to be taken into account with theutilisation of agri-residues (rice straw, husk, maize cobs, bagasse, jute sticks etc.) are that they are generally burnt as a loose biomassin rural dwellings as cooking fuels, and also in industrial boilers. Onaverage 4% of total energy consumed comes from agriculture resi-dues in the manufacturing industries [63] . This signi es the ne-cessity of optimising the consumption of agri-residues, which mayinclude modi cations in its forms of utilisation-densifying theavailable residues i.e. converting into bio-briquettes for ful llingthe residential and small scale demand, and converting to bales sothat can be used in industrial boilers and so on. Such diversi cationon the use of agri-residues in manufacturing sector is also high-lighted in the Industrial Development Perspective Plan of Nepal[63] . Most importantly, it could be an important decision toencourage industrial entities to use these prudent sources of renewable energy in a sustainable manner, such as in a cogenera-tion units, producing both heat and electricity that can also meettheir own short term or local energy demand.

In terms of industrial application, rice husk has been used as aboiler fuel to generate steam or to produce electricity in parboilingrice mills, strawboard factories and solvent mills [63] . Recently,some factories have been set up to produce briquettes out of ricehusks. Bagasse is used in sugar mills to raise steam in the boiler andis considered a cheap reliable source of energy. This highlightsabout the potential opportunities that can be integrated in futureenergy structure of the country.

We have not discussed about the animal waste consumptionmodel, we are not in favour of burning animal waste in a traditionalmanner, instead we urge that the government should bring aconducive plans to maximise the production of biogas and optimisethe production cost to ensure commercial expansion of biogastechnology. Biogas energy consumption model, as discussed in

Section 5.2.7 also shows higher demand of feedstocks, includinganimal waste, agri-residues, and hence it could be necessary tothink on such aspects, while embarking ahead with bettereconomy.

In the same context, kerosene consumption is expected todecrease in all scenarios, but the decrease is expected to be with ahigher rate in MGS and HGS as indicated in Table 5 . An increase inthe urban population, since is directly linked with LPG consump-tion, as also justi ed by the LPG model, it is expected that itsconsumption would increase in the three scenarios, which is also inparallel to decrease in the consumption of kerosene. The LPGconsumption, which was 1% of the TPE in 2009, isexpected to be 8%(42 PJ), 16% (104 PJ) and 21% (171 PJ) by 2030 in BAU, MGS and HGSrespectively.

6.5. Electricity production capacity

Fig. 9 shows the electri cation coverage of Nepal, and are basedon the electricity access observed during the period of 1996 e 2006[45,51 e 53,56] . It isestimated that by 2030, on an average 80% of thetotal households of Nepal could have access to electricity throughgrid and off-grid electri cation facilities, which was 28% in 1996and 49% in 2006 [45,51 e 53,56] .

Grid connected electricity has been the primary means of supplytill 2000, whereas from 2001 RETs such as Micro/Mini hydro andSolar PV have been promoted. In 2009 the share of renewable en-ergy was 3.37% of the total installed capacity. In 2009, the installed

capacity of the grid connected system was 645 MW, whereas Micro/

Fig. 7. Projected TPE consumption in the commercial sector in the three scenarios.

Fig. 8. Projected TPE consumption in agricultural sector in the three scenarios.



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Mini hydro and Solar PV had the capacity of 21.56 MW and0.93 MW respectively.

Electricity consumption model (relation (11) ) shows that it isdirectly related to the total GDP and the total population growth of the country. Hence, along with the growth of the determinantvariables in uencing the electricity consumption, as understood forBAU, MGS and HGS, consumption of electricity would lead to in-crease by a factor of 3.47, 5.71 and 10.12 in 2030 compared to 2009(Table 6 ).

In BAU the estimated electricity consumption in 2030 is7.97 TWh, which is 3.47 times higher than that of 2009. The esti-mated production capacity to cover this consumption is 2235 MWfrom the grid connected and from off-grid is 82 MW. Of the off-grid

production capacity, 95% is expected to be covered by Micro/Minihydro and rest by the solar PV system. In BAU situation for a unitincrease in the per capita GDP, the increase in the per capita elec-tricity consumption would take place by a factor of 3.85 units.

In MGS, the total electricity consumption in 2030 is estimated toincrease by a factor of 5.71 compared to that of in 2009. The totalelectricity consumption in 2009 was 2.3 TWh, whereas its con-sumption in 2030 is estimated to be 13.13 TWh. The required ca-pacity of the grid connected power in the country to satisfy theconsumption in 2030 is estimated to increase by a factor of 5.75compared to 2009, demanding the installed capacity of 3706 MW.Similarly, both Micro/Mini hydro and Solar PV should be increasedby a factor of 3.63 than that of in 2009 to meet the demand of 2030,demanding the installed capacity of 78.29 and 3.39 MW respec-

tively ( Fig. 10 ).In HGS, it is estimated that electricity consumption would in-crease by 10-fold in 2030 compared to the consumption that tookplace in 2009. The projected increase in the electricity consumption

thus requires the installed capacity of about 6600 MW (the gridconnected system), whereas the renewable energy technologieswould be in the similar proportion as discussed in BAU and MGS.The reason behind the similar proportion of renewable energy inHGS is because the model shows that its consumption is signi -cantly dependent on the rural population and electri cation ratiorather than the GDP of Nepal. In all scenarios, it is obvious to claimthat additional production capacity of electricity should take placeto cope the future demand of the country. The Government of Nepalhas initiated some steps in improving electricity demand andsupply at the policy level. This includes the declaration of an energy

emergency in 2010 and bringing a plan for requiring a mandatorydeposit of Rs 100,000 deposit per MW to be generated, which isaimed to discourage developers forholding hydro power licence fora long time. The government has also announced plans to produce2,500 MWof electricity within ve years. It has proposed an EnergyCrisis Control Commission to oversee government programs forpower production [10]. Nevertheless, until and unless, policies arenot brought into action and production capacity is not enhanced, itwould just be a myth.

7. Summary and conclusion

We have found that most of the sectors and fuel types could beaptly assessed using the methodology adopted in this article. This

shows the importance and necessity of understanding the nexusbetween energy and economy in formulating energy and economicdevelopment plans of Nepal. It should be noted that the futureprognosis presented in the article is based on the status-quo situ-ations of the energy development and consumption pattern of thecountry.

In this study, we have estimated that fuel wood consumption isexpected to increase by 1.52 times compared to 2009 in the BAUscenario, whereas 1.59 and 1.93 times in MGS and HGS. Con-sumption of agri-residues is expected to increase with the growthin the rural population. Biogas consumption is dependent with therural population and agricultural GDP of the country, hence withthe increase in the agricultural GDP, the rate of promotion of biogasis expected to increase, thereby increasing the biogas consumption

particularly for meeting the rural residential cooking energy needs.

Fig. 9. Projected electri cation coverage (2009 e 2030).

Table 6Electricity consumption (kWh) and GDP (NRs) per capita in different scenarios.

Years BAU MGS HGS

kWh/cap GDP/cap kWh/cap GDP/cap kWh/cap GDP/cap

2015 114 23,212 131 27,673 154 33,8202020 139 25,362 180 35,009 242 50,5702025 169 27,712 247 44,289 382 75,6142030 206 30,280 339 56,028 601 113,062

Fig. 10. Production capacity required to meet the future electricity consumption.



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Compared to 2009, in 2030 biogas consumption is expected to in-crease by 3.47 times in the BAU scenario, whereas with MGS andHGS it is 15.44 and 54.76 times than that of 2009. LPG consumptionis related to the urban population and the GDP-trade of the country,the consumption of it in 2030 is expected to increase by a factor of 7.39, 18.21, 29.26 than that of 2009 under BAU, MGS and HGS sce-narios. It is also concluded that the consumption of fuel woodchanges with the promotion of alternative energy technologies likeimproved cook stoves and biogas, and also with the increasedaccessibility of LPG in remote villages of the country. Assuming thatwith the improvement in the access of alternative energy tech-nologies, fuel wood consumption could decrease at least at the rateequivalent to the reduction of population using traditional stoves.The model has incorporated these dynamics, since the scenariosconsidered in this article are based on the present growth of alternative energy technologies (ICS, biogas) and increase in theshares of population using LPG. Petrol consumption is found to bedependent on the number of vehicles using petrol, which revealsthat with the present growth of the vehicles, its consumption isexpected toincrease bya factorof 4.2 in 2030 than that of 2009. Thepotential opportunity to reduce its consumption couldbe the use of fuel ethanol partially substituting the petrol, but commercialavailability of which is still questionable.

From the entire set of analysis and issues regarding level of available data, re ection to and fro between macro-economy andenergy consumption activities, and unability to comprehend withthe econometric models (sectoral and fuel types) of the country, wecould aptly recommend for additional future research perspectives.These may include assessment of energy models with updatedenergy and economic indicators that could possibly re ect the realnexus of energy inputs vs output of economic sub-sectors. Thedatabase considered in this study, even though are based on na-tional publications of government of Nepal, but may not necessarilyhave updates on recent changes, particularly at the micro level: (i)changes in the energy intensity, speci c energy consumptions,resource ow of energy carriers to a particular economic sector and

end-uses, (ii) measurement of effects of energy inputs with respectto economic outputs, and (iii) degree of changes in the quality of machineries and devices that could possibly bring changes in theenergyconsumption, etc. In addition to these,it couldbe relevant toexamine effects on the future energy consumption due to changesin technical interventions and policy such as: maximisation of theconsumption of electricity instead of conventional fuels in indus-trial, commercial and residential end-uses; partial substitution of fossil fuel with alternative fuels including biodiesel and bio-ethanol; up-scaling of biogas production in a commercial scale, andutilising municipal waste and other biomass feedstocks in the en-ergy conversion system of the country.

Acknowledgement

This is an independent study based on the existing energydatabase and consumption patterns of the Nepal. We extend ourgratitude to the reviewers for proving necessary suggestions.

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