modelling the adoption of industrial cogeneration in japan using manufacturing plant survey data

Energy Policy 31 (2003) 895–910

Modelling the adoption of industrial cogeneration in Japan usingmanufacturing plant survey data

David Bonillaa,*, Atsushi Akisawab, Takao Kashiwagib

aMechanical Systems Engineering Department, Tokyo University of Agriculture and Technology, 2-24-16 Naka-machi Koganei-shi,

Tokyo 184-8588, JapanbBio-Applications and Systems Engineering Department, Tokyo University of Agriculture and Technology, 2-24-16 Naka-machi Koganei-shi, Tokyo

184-8588, Japan

Abstract

Electric power deregulation in Japan opens opportunity for further penetration of on-site generation (cogeneration) otherwise

known as distributed generation. In the paper the authors present a survey on Japanese industrial plants to fill existing gaps for the

assessment of modern cogeneration (combined heat and power, CHP). The objective of the paper is to empirically examine CHP

systems based on cross-sectional binary models; second to review diffusion trends of CHP by system vintage during the 1980–2000

period in the manufacturing sector. The econometric results point that the probabilities of embracing this technology increase, in

declining importance, with on-site power consumption, and steam demand, operational hours as well as with payback period,

purchased power. For example the survey shows that the CHP is used for the purpose of exporting power rather than meeting the

plant’s own consumption. Some of our results are in line with those of Dismukes and Kleit (Resource Energy Econ. 21 (1999) 153) as

well with Rose and Macdonald (Energy J. 12(12) (1991) 47). We also find that a unit increase in satisfaction with CHP will lead to a

54% in CHP capacity. We find significant evidence on the cost effectiveness of CHP under conservative assumptions. Regarding the

influence of satisfaction and performance indicators for the several plants, the survey threw some unexpected evidence on the nature

of CHP.

r 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Cogeneration; Adoption survey

1. Introduction

Combined heat and power (CHP) has been deemed tobe a powerful carbon abating power technology and it isconsidered a feasible alternative for industrial opera-tions since it saves large amounts of primary energyinputs.The necessity of assessing energy efficiency, as based

on CHP, in the domain of industrial energy demandersin order to assist carbon mitigation policies cannot bedisregarded. Manufacturing industry continues to exertconsiderable weight in final energy consumption. Thesector consumed 42% of total final energy demand inJapan in 1998 (EDMC, 2000). Rapid diffusion of CHPin industry could further reduce this level of energy

demand as well as CO2 emissions in the short to mediumterms.We were originally led to the analysis of distributed

generation by the following question: How is theprobability of adopting CHP affected by its expectedprofitability, as well as by other technological factors ofa given manufacturing plant?Second, how is a reduction (increase) in adoption

rates of CHP, by manufacturing plants, induced byhigher (lower) levels of: on-site power consumption,power grid or improved power reliability in a factory?CHP performance is greatly affected by the market andtechnical actions of the centralised power plant, and it isextremely complex to accurately model the interactionof the former with the latter.We examine the above-described issues supported by

survey data and consequently carry out the econometricanalysis of CHP, based on cross-sectional analysis.Rather than looking at anecdotal evidence on theperformance of CHP, or on aggregated behaviour of

*Corresponding author. Tel.: +81-42-388-7412; fax: +81-42-388-

7076.

E-mail address: [email protected] (D. Bonilla).

0301-4215/03/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 0 3 0 1 - 4 2 1 5 ( 0 2 ) 0 0 1 3 6 - 2

manufacturing industry towards CHP we examinemanufacturing plant specific data. The economic analy-sis of CHP should be an integral part of environmentalpolicy. Moreover, as argued by Reinhardt (1999),environmental policy needs to be based on the economicfundamentals of the business such as the structure of thebusiness of the industry in which it operates, its positionin that structure, and its organisational capabilities;there is no one unique policy for all firms. Therefore, weplace the analysis of CHP within the microeconomic andenvironment energy nexus.

1.1. Review of the literature

Based on a discretionary regression and reflectingwhether or not a firm had joined the US EnvironmentalProtection Agency’s Green Lights Programme, (Decan-io and Watkins, 1998) finds that firm’s financialcharacteristics affect the decision whether or not to jointhe programme. Such programme is a type of energyefficiency policy initiative. Whilst the decision whetheror not to cogenerate is determined by regulatory andpopulation predictors, and to a lesser extent byelectricity and gas prices, in a cross-sectional modelextending to different regions in the USA (Fox-Penner,1990). In so far the industrial self-generation action, it isdetermined by the effects of price of purchasedelectricity, derived demand for electricity and themarginal cost of self-generation (Rose and Macdonald,1991). That research focuses on highly energy intensivesectors. In contrast, in our study we consider morediverse mix of sectors in our data sample.The competitive bidding to price CHP reduces the

drive to expand capacity beyond the firm’s thermalenergy requirements, thus making CHP supply match itsefficiency benefits and giving consumers a share of thebenefits through lower power rates (Serot, 1991). Inaddition the supply of cogenerated power, using cross-sectional data in various states of the USA, changesdirectly with operating cost savings linked to CHP;while also the latter changes with electricity prices andinversely with fuel prices (Joskow, 1984).Lastly the decision not to generate, to self-generate, or

to commercially generate represent different industrialgeneration decisions. Technical capabilities of a plantincrease the probability of either selling the cogeneratedpower or self-generating (Dismukes and Kleit, 1999).That research focused on three energy intensive in-dustries (petroleum refining, chemicals and paper andpulp). In a panel analysis for the 1985–1998 years,undertaken for 7 sectors in Japanese industry, it wasfound that purchased power, the electricity retail priceto gas price ratio, manufacturing value added hadpositively impacted on CHP deployment (Bonilla et al.,2001). Regarding carbon abatement achieved by CHPthe lowest carbon mitigation costs are obtained when

the CHP is operated by the utility and when the CHPplant can sell the excess power and steam in thewholesale market (Krushch et al., 1999).In the manufacturing sector competition and high

energy end use prices have led to improvements inenergy efficiency in dynamic firms. Smaller firms,however, may be less inclined to cut energy costs. Theymay not be able to afford the needed expertise, or energymay represent an insignificant fraction of their costs.Improved profitability in a given industry such aschemicals would translate into more rapid adoption ofnewer boilers or CHP systems both of which are likelyto help to reduce energy use in manufacturing plants.Schipper (1993) argued that energy intensities increaseduring recessions because capital is under-utilised andthe transition towards more efficient technologies slows.It seems that this is exactly the case in recession hitJapan. Nonetheless the adoption of CHP technologies,albeit a slow one, continues.Why examine CHP adoption by manufacturing

plants? It is important to empirically model the technicaland economic determinants that induce firms to adoptenergy efficiency measures. Second the study of techno-logical diffusion in the context of climate change and theenergy efficiency gap, in this case focusing on CHP, is animportant issue in the short and medium terms asJacobsen (2000) has argued. In the paper, we do notfocus however on diffusion over time of CHP. Ad-ditionally, it can be asserted that there has been arevolution in plant size reflecting a change in theeconomies of scale, previously linked to large powerprojects (Dunsky, 2000). According to Dunsky, thecombined impact of changes in technology, marketstructure and economic imperatives are affecting theevolution of electric power market. We believe Japan isnot an exception to this.We examine the decision to cogenerate for two

CHP sizes and find that the probability of adoptionincreases with on-site power demand, steam capacity,purchased power and operating hours. Expectedprofitability of CHP systems does not confirm ourprediction for explaining the probability of adoptingCHP.The effect of the satisfaction of managers with CHP

equipment at manufacturing plants and the improve-ment of power reliability is found to increase theprobability of adopting CHP in the Japanese manufac-turing sector.The paper is an enquiry into (1) reasons for adopting

small scale CHP technologies; (2) empirical analysis offactors affecting the expansion of CHP; (3) costeffectiveness of the CHP systems in Japanese manufac-turing plants; (4) estimation of the energy savings andprofitability conferred by CHP in a sample of plants;and (5) description of the survey of manufacturingplants that have adopted CHP.

D. Bonilla et al. / Energy Policy 31 (2003) 895–910896

The paper is organised as follows: in Section 2 wediscuss the penetration of gas turbine-CHP, and othersystems, as well as the entire steam demand of industryof Japanese industry. Without looking at a particularsector, we look at issues of manufacturing plant scaleaffecting CHP. In Section 3 we discuss the data for theeconometric analysis. In Section 4, further divided inthree parts, we examine the decision whether or not toadopt CHP; second we investigate how CHP installmentchanges and under what conditions from the point ofview of the environment within which the plant operatesbased on satisfaction, Section 4.3. In Section 5 we shallreview the survey undertaken by the Japan Cogenera-tion Research Centre, in collaboration with the authors.

2. Overall trends in the deployment of modern CHP in

Japanese industry

Fig. 1 describes the growth in annual CHP installa-tions by type of engine and shows that gas turbinesapplications for CHP purposes have been gainingmarket share as a proportion of total installations ofCHP systems. There were two peaks in the annualinstallations pattern of CHP investments. The first peakappeared during the 1988–1990 years following theimpact of the bubble years—the high economic growthera. The second peak took place in 1996 as a result ofthe deregulation drive.

2.1. The distribution of CHP in manufacturing plants by

application

In the section we further discuss the distribution ofCHP by engine type. The data on CHP covers the years1985–2000. In Figs. 2–4 the distribution of gas turbine-CHP, diesel-CHP and gas engine-CHP capacity isplotted against the number of manufacturing plants.

We observe that the mass of diesel engine CHP and gasengine CHP is clustered in 300 and 200 sites, respec-tively, with the distribution for gas turbines being highlyskewed in favour of the first 100 manufacturing plants.Hence, diesel and gas engine-CHP instalments are moreuniformly distributed as well as being on average smallerunits.

2.2. CHP and steam generation facilities in Japanese

industry

Fig. 5 shows the breakdown of non-CHP steamcapacity and the number of manufacturing plants. It

Fig. 1. Annual installations of CHP systems by vintage in the Japan’s manufacturing sector. Source: Japan Cogeneration Research Centre.

0

50

100

150

200

250

0 50 100 150 200 250 300

No. of CHP plants

Cum

ulat

ive

capa

city

(M

W)

Fig. 2. Gas- engine CHP capacity. Source: Japan Cogeneration

Research Centre.

0500

10001500200025003000

0 100 200 300 400

No. of CHP plants

Cum

ulat

ive

capa

city

(M

W)

Fig. 3. Gas-turbine CHP capacity. Source: Japan Cogeneration

Research Centre.

D. Bonilla et al. / Energy Policy 31 (2003) 895–910 897

can be seen that 826 sites cluster the mass of non-CHPsteam capacity which together account for 40% of non-CHP steam capacity. The sites tend to be largemanufacturing plants, while the remaining account for60% of industrial steam capacity. Such steam capacity

may yet be replaced by CHP should a more aggressiveimplementation policy be adopted by policy actors; it isfeasible that these sites may be the future clients of CHPsystems.Fig. 6 shows the non-CHP steam capacity and the

distribution of energy inputs at each manufacturingplant size.To investigate how CHP is linked to the scale of

manufacturing plants, reflecting a given production levelof these manufacturing plants, we plot both steamgeneration facilities and CHP energy consumption, as afunction of the size class distribution of manufacturingplants. The distribution of CHP energy consumption inJapanese manufacturing plants is shown in the right-hand scale. The figure also shows non-CHP steamcapacity, on the left-hand side scale. Such figure revealsthe frequency of CHP at a given plant size. Thus Fig. 6indicates CHP is frequent at an average steam capacitylevel of 11 (tons/h) at the 100–199 size range of

Fig. 5. Distribution of steam capacity per site in Japanese Industry. Source : MITI (1999).

0

20

40

60

80

100

120

30-49 50-99 100-199 200-299 300-499 500-999 1000>

Ave

rage

ste

am c

apac

ity

per

site

(to

ns/n

our)

0

200

400

600

800

1000

1200

1400

1600

Energy consum

ption of CH

P plants

average steam capacity

CHP (million) litres of crude oil equivalent

Industrial Plants ranked by workforce size

Fig. 6. Average non-CHP steam capacity and primary energy consumption of CHP ranked against firm size. Source: MITI (1999).

0200400600800

10001200140016001800

0 100 200 300 400 500 600 700 800

No. of CHP plants

Cum

ulat

ive

capa

city

(M

W)

Fig. 4. Diesel-engine CHP. Source: Japan Cogeneration Research

Centre.


manufacturing sites. The following is noted from theFig. 6:

(1) The first plant scales within the 30–199 workforcesize, on the x-axis, show that the larger the plantsize the larger the energy consumption of CHP,reflecting a higher number of CHP installations at agiven range. Since CHP is not adopted at everyscale of manufacturing plants a drop, in the energyconsumption at the next range (200–299 workforcesize), shows that this size of plants have not adoptedCHP as much as the large plants of 1000 and above.

(2) At medium-sized plants CHP is nearly as frequentas at the largest plants, i.e. at the range of 1000 andabove, reflecting the adoption of smaller CHPsystems such as the ones we discuss in the sectionsbelow.

3. Data

In the section a description is made of some key inputparameters for the subsequent econometric analysis ofCHP adoption. The degree to which a given manufac-turing plant clusters power demand and purchased canbe observed in the figures below. We will examine theserelationships affecting CHP in Section 5.Survey data on power demand (including CHP

electricity and mains grid) is shown in Figs. 7 and 8.Fig. 7 shows the distribution of power consumptionas a function of manufacturing plants. We observethat the first 20 plants, as selected from the survey,are responsible for the largest chunk of total powerdemand. In Fig. 8, the first 60MW and 159 (MW) ofthe cumulative CHP capacity account for more than60% and 80% of total power demand respectively.Hence the distribution shows that one fourth of installedCHP capacity is associated with the largest powerdemands.

As Rose and Macdonald (1991) have stated there is asignificant distinction between industrial cogeneratorsand other non-utility power producers. Industrialcogenerators that produce heat or steam with powerdiffer from facilities that produce only power.In our sample, again based on the survey of CHP, we

found that manufacturing sites cogenerated 50%, onaverage, of their total power demand. 74 plantscogenerated more than half of their power needs and77 purchased their power from the grid. Thus themanufacturing plants surveyed are evenly distributed, asshown in Fig. 9.Fig. 10 depicts the frequency of manufacturing plants

according to payback periods. The most frequentexpected payback period range of CHP in our sub-sample, in declining importance, are the 5–9 years and1–4 brackets. Out of 159 plants 80% reported expectedpayback periods of between 1 and 9 years. This isexpected not realised payback. Nonetheless, we find thatseveral plants reported large payback periods, from 10years onwards; there may be respondents that mista-kenly reported these large payback values. Althoughsome manufacturing plants may be adopting CHP forother reasons, such as the need for reliable power, evenif it is uneconomic. For some industries or firms it is

0

5000

10000

15000

20000

25000

30000

35000

40000

0 20 40 60 80 100 120 140 160Number of industrial plants

Pow

er d

eman

d (G

wh)

Fig. 7. Cumulative power demand as a function of industrial plants. Source: Survey.

0%

20%

40%

60%

80%

100%

60 103 159 322 405 542 765

Shar

e of

pow

er d

eman

d

Cumulative CHP capacity (MW)

Fig. 8. Power demand as a function of CHP capacity. Source: this

survey.


necessary to have continuous power supply and hencethis factor may outweigh the investment cost in CHP. InSection 4.2 we will discuss an alternative analysis of theprofitability of CHP investments.

4. The econometric model of CHP adoption

The econometric analysis is broken down in threeparts. First we examine whether or not a given CHP sizeis to be adopted and why. Second, based on a selectedsample of manufacturing plants we obtain somemeasures of profitability for CHP investments. Finally,we examine some qualitative variables and their effecton additional CHP, such as engine type i.e. diesel enginesor gas turbines and satisfaction of plant managers withCHP.We assembled a cross-sectional model using a data set

of 300 manufacturing plants, which after filtering,shortens to 104. Some items examined here have beendescribed in Section 3. The majority of plants pertain tothe following sectors: chemicals (18%), steel (14%),food (10%), fibre (10%), machinery (4%) and electrical

machinery (7%); the sectors to which the rest of theplants belong to are unknown. The chemicals, steel andfood sectors are some of the most energy intensivesectors in manufacturing industry. The majority of theCHP systems in this case, are based on diesel engines onthe basis of the number of sites, whilst on capacity, gasturbines had the largest presence of all the systems in thesample.We examined econometrically these issues via a cross-

sectional data set. We attempted to investigate theeffects on CHP adoption rates based on engineering andeconomic indicators for the sampled manufacturingplants. Unlike the ordinary least-squares technique, thecoefficients given by the Tobit technique would indicatethe effect the independent variables on the latent(unobserved) dependent variable of a Tobit model, i.e.potential CHP additions, they do not represent theinfluence of independent variables on the originaldependent variable (Roncek, 1992).A cross-sectional model on the decision to adopt CHP

is developed below. The objective is to examine whetherCHP installations above 5000 kW are affected bydifferent dynamics, such as scale economies, as opposed

0

2

4

6

8

10

12

14

0 <.10 <.20 <.30 <.40 <.50 <.60 <.70 <.80 <.90 <1Ratio of CHP power production to total power consumption

Num

ber

of in

dust

rial

pla

nts

Fig. 9. Frequency of industrial cogeneration in sample. Source: this survey.

20-28 15-20 10-14 5-9 1-4 0

10

20

30

40

50

60

70

No.

of

indu

stri

al p

lant

s

payback ranges (years)

Fig. 10. Frequency of industrial plants and expected payback period for CHP investment. Source: this survey.


to larger systems. Thus, systems above 5000 kW areassumed to be found in the larger plants, while systemsbelow this benchmark are assumed to be affected byother dynamics: we examine the decision whether or notto install CHP larger than 5000 and 10,000 kW. Theanalysis is based on the Tobit technique. One keyassumption of this non-linear estimation technique isthat it assumes that zero observations as well as negativeobservations on CHP are not observed. In the samplewe have many observations of CHP capacity with5000 kW. In our cross-sectional model, the limit for thenon-positive observations, is assumed to be 5000 kW ofCHP capacity. Thus any manufacturing sites with aCHP capacity above 5000 kW are treated as positive andanything below as negative. Two equations are com-puted under Tobit and thus we treat the dependentvariable as: Say CHP is

chp ¼1 if chpi * > 0;

0 if chpi *o0:

(ð1Þ

The objective is to estimate the b parameters and sigma.Sigma is the estimated standard deviation of the residualof the CHP equation.Therefore, our model for determining the CHP

decision:1

chpi ¼chpi ¼ bxi þ ui if chpi * > 5000 kw cogenerate;

0 if chpi *o0 do not cogenerate:

ð2Þ

Definition of variables

pb expected payback at plant i;ph plant operating hours (h/year) at plant i

elus plant power consumption, purchased pluscogenerated power (MWh/year)

gpkw purchased power (kW) at manufacturingplant i;

th non-chp steam capacity (tons/h) at plant i;CHP CHP installed at plant i

chpi ¼ aþ b1pb þ b2ph þ b3elus þ b4gp þ b5th þ e ð3Þ

we shall label Eq. (3) the small scale CHP model.The typical manufacturing plant in our survey installs

CHP systems with an electrical capacity of 4646 kW,installs 31(tons/h) of steam capacity, operates for 5029 hin a year, buys 41251 (MWh) electricity from the grid,shows a payback period of 5.5 years and consumes91036 (MWh) of power. In addition half of is powerconsumption is cogenerated, while the other half ispurchased from the mains grid. Does the statistic

indicate anything about the average industrial cogen-erator? In all likelihood it does.The exercise was based on 104 observations. The

following variables are assumed to increase the prob-abilities of installing CHP:

(1) Expected payback time;(2) operational hours;(3) total power (on-site power consumption) consump-

tion;(4) purchased power;(5) non-CHP steam capacity;

all the calculated coefficients are expected to be positive,except for payback time, which should show an inverserelationship with respect to CHP.

Plant hours reflect the ability to capture scaleeconomies among manufacturing plants, as Rose andMacdonald (1991) explained. Purchased power verifieswhether CHP applications export power to the grid;some researchers have stated that the sizing of a CHPsystem for base load applications is determined bycapacity requirements in the wholesale energy market.2

But the wholesale energy market in Japan is still in itsinfancy and, therefore, we cannot argue that sizing isdetermined by such market.Hence, we expect a higher probability of CHP

diffusion with higher purchased power. Should themanufacturing plants use CHP uniquely for peak loadreduction, then the likelihood of CHP diffusion wouldalso increase with purchased power and with on-sitepower demand. CHP should be a substitute forpurchased power but not if purchased power reflectshigher on-site power demand. We expect that steamcapacity, reflecting the existence of steam demand,increase the probability of installing CHP.The model, Eq. (3), incorporates the assumption that

the plant can combine conventional and CHP technol-ogy to satisfy both its heat and power demands.Furthermore, such model captures whether the firmwishes to consume purchased power or cogeneratedpower. This effect enters through the impact of totalpower consumption or through purchased power.Tables 1 and 2 present parameter estimates. We fit twomodels one representing the behaviour of small scaleCHP and another representing medium-sized CHPinstallations. Our objective is to make conclusionsregarding the entire population of manufacturing plantsand hence the calculated coefficients are important.In the second analysis we examine the medium size

plants segment of our sample of 104 plants. Theeconometric model, labeled the medium sized CHP

model, resembles the small scale CHP model with the1Fox-Penner (1990) develops a similar model but explains the

probability of cogeneration as a function of pre-purpa capacity, state

population, industrial value added, buyback rates, electricity prices

and some regulatory variables; see pp. 130–131.

2They assert that ‘‘rather than meeting the demand of an end-user,

such plants are dispatched to the grid along with other generating as a

function of cost of generation’’ pp. 84 (Onsite Sycom, 2000).


only difference that the new cut-off value for thedependent variable is now 10,000 kW. This assumesthat any manufacturing plant with installed CHP below10,000 kW is treated as zero. Unlike in the previousmodel, this time the degree of truncation in the sample is11.6%.

4.1. Discussion of results

We ran ordinary least-squares regressions initiallyusing CHP as the dependent variable, disregarding thesize aspect of CHP systems installed per manufacturingplant. The right-hand side variables were the same as inthe Tobit models. The results are omitted here. None-theless, we found similar coefficients, in terms of sizeand statistical significance, to those given by the small

scale CHP model. Table 2 shows the Tobit regressioncoefficients. Results for the small scale CHP modelconfirmed that the probability of adopting CHP systemssized above 5000 kW improve with higher operatinghours and most significantly with total power consump-tion and steam capacity. Also, power demand showed

the highest statistical significance from zero. Sucheconometric results are confirmed, for instance: toinstall or not to install is dominated by on-site electricityusage, plant hours, steam temperature, fuel switchingability and steam capacity, among others (Dismukes andKleit, 1999) and also by Rose and Macdonald (1991)who report a positive and statistical significant coeffi-cient for on-site electricity use in the analysis of CHP.Our model shows also that purchased power magnifiesthe probability of adopting this size of CHP.Pay back time is statistically insignificant but it shows

the wrong sign: the probabilities of adopting expandwith longer payback periods. In reality the coefficientfor payback should be negative: higher payback yearswould lower the attractiveness of investing in CHP. Thisresult is confirmed by examining the actual reportedpayback periods for a given CHP systems size. The tableincludes a larger sample than the one use in the cross-sectional models. Table 3 shows actual reported pay-back periods, the number of sites and the average systemsize of CHP. Ninety-three plants reported paybacklevels above 5 years. It appears that the ratio of CHP to

Table 1

Summary statistics of model structure

Mean Minimum Maximum

Variables

Number of observations: 104

CHP (kW)

PB (years) 5.67 1.0 25

OPH (annual plant hours) 4909.88 0.0 8784

ELUS (MWh) 91,036.43 3.9 3,176,047

GPKW (kW) 8249.87 4.4 84,000

TH (tons/h) 30.92 0.16 674.5

Table 2

Results for the Tobit equations of CHP

Parameter Equation: medium-sized CHP Equation: small scale CHP

Dependent variable

CHP>10,000 kW CHP>5000 kW

Estimate t-statistic Estimate t-statistic

C �17,939.1 �3.6* �11,302 �4.01234PB 696.78 2.26** 259 1.07

OPH 0.20 0.50 0.58 2.03**

ELUS 0.01 3.05* 0.01 3.04*

GPKW 0.17 2.24** 0.15 2.51*

TH 35.7 2.78* 31.43 3.03*

SIGMA 7291.5 4.11* 7031.36 6.83*

Fraction of positive obs. 0.11 0.29

Number of positive observations. 12 30

sample size 104 104

Log likelihood �950.274 �337.734Schwarz BIC 964.207 155.355

*Significant at more than 99%, ** significant at 95%.


purchased power, A=B; rises as average paybacklevels worsen, however, many sites report ratiosbelow unity. The sites in the 4–5 year payback groupshow a high ratio: signaling that CHP is mildlyprofitable. However, it should be noted that small ratiois found at the apparently profitable sites of the 1–3payback group.As shown in Table 3 as the average CHP size

increases, as economies of scale begin to take effect,payback periods fall. Thus systems beyond 6000 kW arehighly profitable, while systems below this thresholdshow long payback periods. Although 5 plants reportedpayback periods of 8 years.Table 2 tabulates the calculated values for the second

model. Equally in our second model, the probabilities ofinstalling a system larger than 10,000 kw increase withlonger payback, higher power demand, purchasedpower, and steam capacity; although higher operationalhours are not significant. This is odd since larger plantsshould be able to exploit economies of scale more easily.The result may be related to the sample data where thenumber of large CHP installations is not large. Thistime, however, payback does increase the probabilityand it is statistically significant.In the medium sized CHP model the coefficient

for steam capacity was larger than in the small scale

CHP model. The model, however, may not be capturingany substitution effects between the conventionalpractice (separate production of steam or power) andCHP: less purchased power and non-CHP steamcapacity for more CHP output. Alternatively, bothsteam capacity and CHP may have expanded simulta-neously, since some plants may find it feasible tocombine the two systems. Hence the positive coefficientfor steam capacity. In summary, both models for largeand small manufacturing plants do show one thing:expected payback by plant managers is not a reliableindicator for adopting CHP and power demand doesmagnify the probabilities of adoption in combinationwith steam capacity.

4.2. Calculating the energy savings of CHP and

profitability of CHP

In the section we focus on the economic potential ofCHP. Data on profitability, using payback years as theyardstick, as provided by the survey and used in theforgone econometric analysis may not be sufficientlyaccurate as some mis-reporting of payback may haveoccurred. As said in Section 3, it seems that largenumber of plants reported poor payback levels of CHP.However, we do not estimate profitability for the samedata set of manufacturing plants given the absence ofsome items required to calculate the primary energysaved.We opted for calculating profitability by an alter-

native measure based on the internal rate of return (irr).At this juncture, the question arises regarding expectedpayback data provided in the survey, which shows thatsome factory managers do not expect CHP to be highlyprofitable; why do these firms adopt CHP? Is theexpectation of large payback evidence that firms are notrational in their investment behaviour thus contra-dicting the neo-classical theory of investment? We thinknot since we believe CHP is profitable.We did not have data for all sites that were used in the

sample for the econometrics analysis. Preventing us tomake direct comparisons between the actual expectedpayback and our alternative measure of profitability ofCHP. Using the filtered data on manufacturing plants,however, we obtain a measure of the profitability ofCHP. The sample size is 80 plants.Next we decided to calculate the energy savings and the

monetary savings per plant generated by our micro data.We also obtain a measure of profitability. We use thefollowing formula as Metcalf and Hassett (1999) did.3

Table 3

Relationship between profitabilitya and system size of CHP

Average CHP size (kW) Payback (years) Average purchased power (kW) Ratio CHP to purchased power No. sites

8836.9 1–3 17,556 0.53 14

5980.9 3–4 11,284 6.1 29

7818.0 4–5 1281 0.52 19

3658.4 5 7013 0.52 28

3514.2 6 4194 0.84 12

3296.7 7 4567 0.72 17

7600.0 8 3107 2.45 5

2648.33 9 1905 1.39 6

2456.444 10 or > 1430 1.71 18

1804.571 15 or > 1450 1.24 7

aExpected pay back data by some of the industrial plants surveyed.

3Metcalf and Hassett (1999) applied their analysis to the energy

savings following investments in attic insulation of 1136 households

and provide evidence against the energy efficiency paradox based in a

representative household.


The advantage is that this measure accounts for theeffects of energy prices of a given investment in energyefficiency.Should the returns on the CHP investment continue

forever, (t ¼ N), and r > g The irr, r; will be

r ¼chpð1þ gÞ

Iþ g: ð4Þ

Let CHP be the cost of energy savings at plant I

expressed in US$ dollar terms, g is the rate of growth ofenergy prices and i is the investment cost in US$ per kWof gas turbine.While the energy savings were calculated as:Peheat primary energy input for power assuming an

efficiency of 35% (based on power output data of the ithCHP, as reported in the survey).Pepower primary energy input for heat and hot water

or steam assuming efficiency of 85% (based on hotwater and steam supply data of the ith CHP, as reportedin the survey).tpesc total primary energy supply under conventional

practice.TpesCHP primary energy inputs for heat, hot water

and power under CHP as reported in the survey.EsCHP energy savings conferred by CHP in plant i:

tpesc ¼ peheat þ pepower; ð5Þ

eschp ¼ tpesc � tpeschp ð6Þ

primary energy consumption under conventional practiceat plant i (generation efficiency of 35% and steam boilerefficiency of 85%) minus the primary energy consumptionat plant i: Eq. (6) gives the primary energy savings.Energy savings times the price of electricity gives the

financial savings (CHP). Assuming an electricity price of0.15 US$ per kWh (EDMC, 2000), gas turbine costs of650 US$ per kW (Krushch et al., 1999).

The ratio of CHP energy savings to its capital cost(c=k) for 80 plants are given in Table 4. One weakness ofthe irr formula, Eq. (4), often mentioned is that it avoidsthe question of expressing an absolute value by using arate to indicate the profitability of an energy efficientinvestment. Another problem is that it ignores thelifetime of the CHP project.To illustrate the impact of changes in energy prices on

the profitability of CHP investments for these plants wetabulated the different internal rates of return for 80plants. The size of the installed CHP in a givenmanufacturing plant varies from as little as 200 kW toas high as 50MW.In the size range of 4–5MW for CHP systems, for

instance, should firms assume that energy prices will notrise in the future then the mean return on thisinvestment will be 96%, with an equivalent meanpayback of 1 year. If however firms believe that priceswill increase by 10%, profitability of investing in CHPwould increase to 116%. The median irr for all size classdistribution under falling energy prices at �3% is 106%on the low end, while at the high end 123% assuming a10% increase in energy prices.All plants show an irr larger under higher growth in

energy prices. Second, for the most frequent range, 1–2MW the irr ranges from 121% to 151%, thus movinginto even healthier levels. Third, the larger systems areless frequent. Fourth, small systems, i.e. 1–7MW, basedon the irr yardstick are extremely profitable; speciallythe 1–2MW range which shows an irr above 120%under any price scenario.Hence, we can argue that CHP investments at least in

the light of the assumptions made here, are highlyprofitable, contrary to the reported payback levelsfound in our survey. Sensitivity analysis done elsewherebased on the irr indicates that profitability of industrialCHP, its economic potential, is inferior than in the case of

Table 4

Profitability of investment in CHP assuming different growth rates in energy prices (average internal rate of return per range of CHP installed) in a

given industrial plant

CHP installed (MW) �3% �1% 0% 1% 3% 5% 10% c=k payback (years) (I/CHP)

Internal rates of return in percent

0–1 23 24 24 26 25 26 28 0.25 0.24

1–2 121 126 128 140 135 140 151 1.32 1.28

2–3 90 94 96 106 102 106 115 0.99 0.96

3k–4 81 85 87 95 93 96 106 0.90 0.87

4–5 90 94 96 106 102 106 116 0.99 0.96

5–6 49 52 54 62 59 62 69 0.56 0.54

6–7 112 116 118 129 125 129 140 1.22 1.18

7–8 23 26 27 33 31 33 40 0.29 0.27

8–9 133 138 140 152 147 152 164 1.44 1.40

10–11 103 108 110 120 116 120 131 1.13 1.10

16–17 188 194 197 212 206 212 226 2.01 1.97

25–26 89 93 95 105 101 105 115 0.98 0.95

50 253 260 264 282 275 282 300 2.70 2.64

Median 106 101 103 113 109 95 123 1.06 1


utility operation and at low irr the economic potential of autility adopting CHP is theoretically unlimited. Moreover,overall the effect of increasing the irr is significant,specially in the pulp and paper industry (Krushch et al.,1999). In contrast in our study we only consider industrialCHP, yet we obtain extremely high irr levels.

4.3. CHP satisfaction model

We examine how satisfaction of the different technicaland human factors, as listed below, have an effect onadding CHP to a manufacturing facility. A few dummyvariables are used as independent variables. We discussin more detail these variables in Section 6.Based on ordinary least squares, we regressed CHP

capacity against dummies for: economic performance,energy efficiency, reliability of CHP, and managementdifficulty. We obtained no statistical significant results.4

The model was based on cross-sectional data as given bythe survey.Instead we ran a semi-log model (taking logarithm in

the dependent variable). The objective was to estimatetheir effect on CHP capacity based on:

lnðCHPÞ ¼ aþ b1 satisfactionCHP þ b2 Grid power

þ b3 Share of CHP

þ b4 Improved power reliability

þ b5 Gas turbinCHP þ b5 DECHPþ b5CHP reliabilityþ e: ð7Þ

The semi-log model, Eq. (7), is used to estimate growthrates (Greene, 2000) and it is linear in the coefficientsand in the disturbance e: The coefficients represent thepercentage change in CHP for a unit change in the x

independent variables. Econometric results are given inTable 5. All of the coefficients are expected to affectCHP growth positively.Definition of variables:

Sat Total satisfaction by factory managers withCHP, dummy variable (1 if satisfied or morethan satisfied, 0 if not satisfied).

GPKW Purchased power (in kW) in plant i;SH Share of CHP power output of total power

consumption at plant i (percentage;IPREL Improved power reliability at plant i dummy

variable (1 if normal or higher satisfactionwith power reliability following the intro-duction of CHP, 0 otherwise);

GT Gas turbine application dummy variable (1 ifgas turbine CHP, 0 otherwise);

DE diesel engine system dummy variable (1 ifdiesel engine-CHP, 0 otherwise)

CHREL reliability with CHP systems (1 if satisfied ormore than satisfied, 0 if not satisfied).

4.4. Discussion of results on the CHP satisfaction model

We judge the model as follows. First, we find somecorrelation amongst the independent variables. It isfeasible that overall satisfaction with a CHP system andthe reliability of power at the whole manufacturingplant are correlated. The same can be said of thereliability of CHP systems. We obtained positivecoefficient for these variables.

Table 5

Cross-sectional CHP model (logarithmic dependent variable)

Dependent variable: ln (CHP)

Variable Estimated coefficient Standard error t-statistic P-value

C 5.63366 0.330868 17.0269 [0.000]

SAT 0.542531 0.248586 2.18247 [0.032]

GPKW 0.0001 0.00001 5.4744 [0.000]

SH 1.69641 0.371984 4.56043 [0.000]

IPREL 0.195283 0.195116 1.0008 [0.320]

DE 0.519125 0.290058 1.78973 [0.077]

GT 0.709138 0.325933 2.17572 [0.032]

CHREL �0.236642 0.207324 �1.14141 [0.257]

Number of observations: 91

Adjusted R2=0.44

Mean of dep. var.=2574

LM het. test=2.75731 [0.097]

SD of dep. var.=1.20

Jarque–Bera test (normality in residuals)=3.13 [0.209]

Ramsey’s RESET2=1.44 [0.234]

F (zero slopes)=11.12 [0.000]

4The independent variables included the following dummies for

indications of satisfaction: economic performance, energy efficiency;

carbon reduction; emergency power; improved power reliability at the

whole industrial plant; CHP system reliability; CHP management

quality; Overall satisfaction with CHP.


A unit increase in the overall satisfaction with CHP,purchased power, diesel engine CHP, and gas turbineCHP installations will increase CHP by their reportedcoefficients of Table 5. For example, a unit increase inimproved power reliability is associated with an increasein CHP of 39%; an increase of 34% from an increase inoverall satisfaction with CHP and a 54% increase frompurchased power. The higher the share of CHP in totalpower consumption the higher the additional CHP.Higher CHP reliability depresses additional CHP.Purchased power is found positive, it shows a small

percentage change effect on CHP, indicating that CHPexports power, or that the ith plant does not dependsolely on on-site power generation. The dummiesaccounting for improved power reliability and CHPreliability showed no statistical significance from zerobut both reported positive coefficients. Therefore, theempirical results show that CHP growth will be affectedby the total satisfaction of the manufacturing plant withit and with other factors such as the power market, viathe effect of purchased power.

5. The survey on manufacturing plants with CHP systems

The survey on manufacturing plants that cogeneratewas carried out in the year 2000 in collaborationbetween the Japan Cogeneration Centre and theauthors. The success rate was 30%. We obtained dataas explained before on (Table 6):The data collected represented nearly 19% (765mw)

of total installed capacity of (4200MW) of modern CHPsystems, in the nation’s manufacturing sector, that wereadopted in 1985 until the year 2000 Table 7.The following industries were reviewed as shown in

Fig. 11.The power to heat ratio of the industrial energy

demanders reflects in what proportions power and heatis used, it reflects steam, hot water and power demandsof an manufacturing plant. Overall, fuel efficiencyfluctuates depending on system features. Total energyefficiency changes as power to heat ratio varies. Theintroduction of cogeneration is usually thought to beideal in plants where heat and power demands arematched. Thus a ratio of 1 means that the plant isconsuming power and heat in equal amounts.In our survey The power to heat ratio (see Fig. 12) of

the manufacturing plants, that cogenerate, showed that19% of plants had ratios of 0.5 or below. Kaarsberget al. (1999) argued that the most efficient system havepower to heat ratios of less than 0.5. Hence, it appearsthat these plants operate the system in a CHP mode andare highly energy efficient, whereas the rest of the plants,81% of them, operate under a power only mode. This israther surprising: if CHP plants are being under utilisedthen it means that energy efficiency is being sacrificed

and hence carbon emissions could also be higher thanthey need be. In terms of energy efficiency, the electricalefficiency of CHP is not yet as high as that of theconventional power plants.Among the factors affecting the purpose of adopting

CHP we find that economic factors, followed by energyefficiency and carbon abatement, were the most cited bythe plants surveyed. Thus on this basis it would appearthat CHP is adopted by manufacturing plants because itsaves on energy costs. And secondly because of the needto reduce energy efficiency (Fig. 13).The following tables summarise the performance of

CHP that was reported by the managers at manufactur-ing plants that were surveyed. We note that theresponses were not uniform for all items and thereforethe number of plants that responded to certain items inthe questionnaire varies. As it would be expected of anycapital investment, the economic effectiveness of CHPreported the highest percentage of respondents with thehighest levels of satisfaction, reflecting the optimaleconomic performance of CHP, out of all the items(Tables 8–14).

Table 6

Survey items on industrial CHP in Japanese industry

CHP capacity installed

CHP electricity generation

Non CHP steam capacity

Steam pressure

Steam temperature

Inlet and outlet temperature of steam

Inlet and outlet temperature of hot water

Power purchased from grid

Annual operating hours

Waste heat recovery

Heat to power ratio

Purpose for adopting CHP system

(several reasons)

Carbon reduction

Energy efficiency

Performance (satisfaction) of CHP

Economic

Energy efficiency

Carbon reduction, among others

Management of CHP

Improved power reliability

Among others

Table 7

CHP capacity by engine type (kW)

Engine type CHP capacity % Share

Gas turbine 428,389 57

Diesel engine 301,370 40

Gas engine 18,985 3

Total 748,744


Fig. 11. industrial CHP by subsectors. Source: this survey.

05

1015

2025

3035

40

2 1 to 2 0.7 to 1 0.5 to 0.66 0.4 to 0.5 0.3 to .4

Power to heat ratio

CH

P p

lan

ts (

%)

Fig. 12. power to heat ratio of cogenerators (factories). Source: this survey.

Fig. 13. Reasons for adopting CHP. Source: this survey.


6. Conclusion

CHP has been deemed to be a powerful carbonabating power technology and it is considered a feasiblealternative for manufacturing operations since it saves

large amounts of primary energy inputs. Gas turbineCHP continues to outpace all other CHP applications.In 1980 to 2000 CHP has grown tremendously, andcontinues to increase albeit at a far lower pace given thenegative investment climate. We undertook a survey ofJapanese manufacturing plants to examine empiricallythe decision to adopt small and medium-sized CHPapplications, and to estimate its cost effectiveness basedon energy price assumptions. Considerations of scale ofa manufacturing plant should not be ignored in anyempirical analysis of manufacturing CHP. Mediumsized manufacturing plants seem to have a bias to adoptCHP.Based on a cross-sectional econometric model we

found that the decision whether to adopt CHP systems

Table 8

Satisfaction with CHP systems by plant managers respondents

breakdown

Level of satisfaction with CHP system % Share

Total No. of plants 143

Do not know 6

Between normal and not satisfied 24

Normally satisfied 35

Highly satisfied 9

Table 9

Satisfaction with improved power reliability by plant managers;

breakdown of respondents

Improved power reliability of whole plant No. of plants % Share

Do not know 32 30

Not satisfied 6 6

Between normal and unsatisfied 7 7

Normally satisfied 34 32

Between satisfied and normal 17 16

Highly satisfied 9 9

Total plants 105

Table 10

Satisfaction with regard to carbon reduction by plant managers;

breakdown of respondents

Carbon reduction effectiveness No. of plants % Share

Do not know 22 17

Not satisfied 5 4





Total plants 132

Table 11

Satisfaction with CHP reliability by plant managers; breakdown of

respondents

CHP reliability No. of plants % Share

Do not know 4 3

Not satisfied 6 4



Between sat and normal 32 23


Total plants 139

Table 12

Satisfaction with economic performance of CHP systems reported by

plant managers; breakdown of respondents

Economic efficiency of CHP No. of plants % Share

Do not know 2 1

Not satisfied 19 12





Total plants 162

Table 13

Satisfaction with the energy efficiency given by CHP by plant

managers; breakdown by respondents

Energy efficiency No. of plants % Share

Do not know 8 5

Not satisfied 5 3





Total plants 153

Table 14

Overall satisfaction with CHP reported by plant managers; breakdown

of respondents

Overall satisfaction with CHP No. of plants % Share

Do not know 1 1

Not satisfied 3 2





Total plants 133


above 5000 kW is conditioned by power consumption,through the power market, steam demand, on-sitepower needs, among others factors. A key question isthe influence of the power grid on CHP growth andadoption since this variable may reflect that CHP plantsdepend on the need to export power or import. Furtherevidence is required to establish whether this is so.Nonetheless, the results confirmed that purchased powerpositively increases the probabilities of adopting this sizeof CHP in the two models. Similar results were obtainedfor the medium size CHP model: all of the estimatesbehaved similarly as in the small scale CHP. Theempirical results show no differences in the scale ofCHP that can be adopted. Research by Dismukes andKleit (1999) partially confirm some of our results,specially regarding the influence of on-site powerconsumption, steam capacity and operational hours.Our models failed, however, to capture technologicalsubstitution between the separate production of power(and steam) and CHP technologies.We proceeded to estimate the cost effectiveness of

CHP in the manufacturing sector based on the internalrates of return of CHP averaging of 106% under �3%fall in energy prices for the size range of CHP systems 4–5(MW). Hence, CHP is demonstrated to be a highly costeffective energy efficiency measure. The reported ex-pected payback levels as given in the survey did notmatch with the results of the estimates given in section4.2: there may be under estimation of the payback datagiven by the manufacturing plants.In the satisfaction model we show that a unit increase

in the overall satisfaction with CHP, purchasedpower, diesel engine CHP, and gas turbine CHPinstallations expand CHP. For example, a unit increasein improved power reliability is associated with andincrease in CHP of 39%; an increase of 34% from anincrease in overall satisfaction with CHP and a 54%increase from purchased power. The higher the share ofCHP in total power consumption the higher theadditional CHP. Higher CHP reliability depressesadditional CHP.Finally our survey shows that half of the plants

depend partially on cogenerated power, and that manyplants operate CHP in power only mode, which couldmean that plants operate less than optimally. Furtherresearch should investigate the factors that leadmanufacturing plants to adopt the practice of poweronly CHP.We found that the main reason inducing firms to

adopt CHP is economic followed by energy efficiencyconsiderations; the economic effectiveness of CHP wasthe most frequently cited reason by the plant managersthat were questioned. An unexplored issue was theinfluence of power tariffs on manufacturing plants.Unfortunately our survey was unsuccessful in thisrespect. Environmental strategists, energy economists

and experts on distributed generation should beconcerned with developments of small scale distributedgeneration as the world’s electricity markets becomeincreasingly decentralised.

Acknowledgements

The authors are grateful for the support in conductingthe survey of cogenerators, given by the JapanCogeneration Research Centre, Tokyo Japan duringyear 2000.

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modelling the adoption of industrial cogeneration in japan using manufacturing plant survey data

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