WPS6920
Policy Research Working Paper 6920
Why Has Energy Efficiency Not Scaled-up
in the Industrial and Commercial Sectors
in Ukraine?
An Empirical Analysis
Gal Hochman
Govinda R. Timilsina
The World Bank
Development Research Group
Environment and Energy Team
June 2014
Policy Research Working Paper 6920
Abstract
Improvement of energy efficiency is one of the main higher costs of finance, and higher opportunity costs
options to reduce energy demand and to reduce of energy efficiency investment are key barriers to the
greenhouse gas emissions in Ukraine. However, large- adoption of energy efficiency measures in Ukraine.
scale deployment of energy efficient technologies Institutional barriers particularly lack government
has been constrained by several financial, technical, policies, which also contributes to the slow adoption of
information, behavioral, and institutional barriers. This energy efficient technologies in the country. The results
study assesses these barriers through a survey of 500 suggest targeted policy and credit enhancements could
industrial and commercial firms throughout Ukraine. help trigger adoption of energy efficient measures. The
The results from the survey were used in a cumulative empirical analysis shows strong inter-linkages among the
multi-logit model to understand the importance of the barriers and finds heterogeneity between industrial and
barriers. The analysis shows that financial barriers caused commercial sectors on the realization of the barriers.
by high upfront costs of energy efficient technologies,
This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by
the World Bank to provide open access to its research and make a contribution to development policy discussions around
the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be
contacted at gtimilsina@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Why Has Energy Efficiency Not Scaled-up in the Industrial and Commercial
Sectors in Ukraine? An Empirical Analysis *
Gal Hochman and Govinda R. Timilsina§
JEL Classification: Q4
Keywords: Energy efficiency, Ukraine, Barriers, Adoption, Discrete choice models, Cumulative Logit
Model
Sector: Energy
*The authors woud like to thank Mook Bangalore, Michael Traux and Mike Toman for very helpful comments
and suggestions. We also acknowledge the financial support of the World Bank’s Knowledge for Change
Program (KCP). The views and interpretations are of authors and should not be attributed to the World Bank
Group and the organizations they are affiliated with.
§ Hochman (gal.hochman@rutgers.edu) is an Associate Professor, Department of Economics, Rutgers
University and Timilsina (gtimilsina@worldbank.org) is a Senior Research Economist, Development
Research Group, World Bank.
1. Introduction
The adoption of energy efficiency measures has been touted as a major policy option to
curtail energy demand in response to increasing price volatility. Its importance has been further
lauded to reduce greenhouse gas (GHG) emissions. The International Energy Agency (IEA)
estimates that energy efficiency measures account for the highest potential of the total GHG
mitigation required to limit global temperature rise by 2050 to 2°C above pre-industrial levels
(IEA, 2012). Many studies that develop marginal abatement cost curves for GHG mitigation
show energy efficiency measures entail negative costs (i.e., value of energy savings exceeds
investment costs even if GHG mitigation benefits are not accounted for) and therefore these
options are interpreted as ‘low hanging fruits’ for climate mitigation (McKinsey & Company,
2009; ESMAP, 2012; ADB, 1998).
In practice, however, the scale of implementation of such seemingly win-win options is
small relative to their apparent economic potential. The rationale for this disparity is that
implementation of these options is constrained by financial, institutional, and information
barriers (Jaffe and Stavins 1994; Howarth and Sanstad 1995; Sorell et al. 2004; Mundaca et al.
2013). Moreover, the economics of energy efficiency measures is normally evaluated using
engineering benefit-cost approaches (e.g., Goldstein et al. 1990; Blumstein and Stoft 1995;
Brown et al. 1998; McKinsey & Company 2009; Gillingham and Sweeney 2012) and such an
analysis usually omits variables such as opportunity costs (Allcott and Greenstone, 2014). If the
costs imposed by barriers are accounted for, energy efficiency measures would be expensive, and
firms lose interest to adopt (Anderson and Newell, 2004). The gap between cost efficiency of
energy efficiency measures and their implementation is also coined as the “energy efficiency
gap” (Blumstein et al., 1980; DeCanio, 1993; Jaffe and Stavins 1994; Sanstad and Howarth,
1994; Schleich, 2009; Sorrell et al., 2004).
A number of studies have attempted, through empirical analysis, to understand the energy
efficiency barriers in different countries, economic sectors and energy end-uses (see e.g., Rohdin
and Thollander, 2006; Sardianou, 2008; Schleich, 2009). Using semi-structured interviews of the
largest 8 non-energy intensive manufacturing firms in Oskarshamn municipality in Sweden that
had participated in government sponsored energy audits around 2000, Rohdin and Thollander
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(2006) find that cost/risk of production disruption, other priorities not related to energy
consumption, information search costs related to energy efficient appliances/devices, higher
opportunity costs of investment, lack of sub-metering and split incentives with energy service
companies all are barriers to adopt increased energy efficiency. Sardianou (2008) investigates the
determinants of industrial decision-making with respect to energy efficiency investments in
Greece through a survey of 779 industrial firms around 2005 followed by an empirical analysis
using a Probit model. A majority (62%) of firms surveyed reported they did not consider energy
saving a first priority although 52% of the sample reported energy saving as a decision criterion
when installing new machines or buildings. Fifty-six percent of the sample reported that they
would develop an energy conservation policy if a competitor industry had implemented relevant
actions; while 70% of firms indicated they were not aware of existing new technologies. Based
on a sample of 2,848 German commercial and services sector firms that were surveyed during
the 1990s, Schleich (2009) econometrically assesses the relevance of various types of barriers to
energy efficiency at the sectoral level and across fifteen sub-sectors. The analysis suggests the
lack of information and priority-setting of upper management, who often do not consider energy
efficiency as a strategic priority as the main barrier to energy efficiency improvement.
Historically, energy consumption has remained inefficient in Ukraine due to ageing
infrastructure and prolonged consumption subsidies (Ogaranko and Hubacek, 2013) These
developments have strengthened calls for Ukraine to increase its clean energy base and improve
its energy efficiency. The government has indeed enacted several policies to promote the
adoption of clean and energy-efficient technologies (Trypolska, 2012) – however, the adoption
of energy-efficient technologies remains slow. While barriers to clean energy adoption and
options to improve investment have been examined by OECD (2012), to our knowledge, the
same analysis has not been done for energy efficiency despite its strategic importance. It is
therefore important to investigate the barriers to adoption of energy efficient technologies in the
country.
This study aims to empirically examine the energy efficiency barriers in Ukraine in the
commercial and industrial establishments. Specifically, the study attempts to examine key
questions related to energy efficiency barriers including:
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• Size: do larger firms have greater incentives to invest in energy efficiency?
• Energy in total production costs: do energy-intensive firms (i.e., firms with higher share
of energy costs in total production costs) have greater incentives to invest in energy
efficiency?
• Ownership: are private firms more energy efficient than public firms?
• Employment: are labor-intensive firms less energy efficient than capital-intensive ones?
• Financing: have high upfront capital costs hindered the adoption of energy efficiency
technologies?
• Split incentives: are rented spaces where landlords pay energy bills less energy efficient
than self-owned spaces?
• Knowledge: is lack of knowledge about energy efficient technologies one of the key
barriers?
• Technical barriers: are there any technical barriers preventing scaling-up of energy
efficiency measures?
• Existing rules/regulations: have existing rules and regulations helped improve energy
efficiency?
• Firm’s bureaucracy: have a convoluted and complex internal decision process slowed
down adoption of energy efficiency measures?
The study employed a sample survey of 500 commercial/service and industrial
establishments throughout the country done in 2012. The data collected were then used in a
cumulative Logit model to estimate the importance of the various barriers to the adoption of
energy efficiency measures in Ukraine. Our analysis shows financial barriers (e.g., high upfront
costs or high costs of financing) are the key factors impeding firms’ investments in energy
efficiency measures in Ukraine. Knowledge and technical barriers follow this. We find mixed
results regarding split incentives, whereby the building is rented and/or jointly owned. Contrary
to general intuition, the study does not find evidence to support the energy-intensity hypothesis
that assumes energy-intensive firms are more likely to adopt energy efficient technologies
compared to non-energy-intensive firms. Instead, firms with higher revenue per unit of energy
consumption tend to invest more on energy efficiency improvements. The separation of
industrial and commercial firms allow us to introduce heterogeneity among sectors that lead us to
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suggest that the commercial sector, which includes the public sector, is less likely to invest in
energy efficiency measures in the absence of policy interventions.
The paper is organized as follows. The next section (Section 2) briefly discusses the
methodology used to derive the results. Section 3 presents the data used in the analysis, while
Section 4 presents preliminary results. The main results are presented in Section 5, and their
robustness assessed in Section 6. We offer concluding remarks in Section 7.
2. The methodology
To investigate the hypotheses specified in the introduction and to better understand the
barriers to the adoption of cost-effective energy efficiency technologies in Ukraine, we estimate
a discrete choice model using the sample of Ukrainian firms that participated in our survey. We
choose a discrete choice model to estimate the factors influencing the adoption of energy
efficiency measures. We first estimate a binary choice model. The dependent variable is a binary
variable with possible values of invested/did not invest in energy efficiency measures in the past
five years. The covariate vector includes several factors that in theory facilitate the adoption of
energy efficiency measures or keep investments in such measures at bay.
The log-likelihood function of our binary choice model is of the standard type. We look
at the log-likelihood function due to convenience, while noting that the natural logarithm of the
likelihood function is a monotonic transformation of the likelihood function, and that the log-
likelihood function achieves its maximum value at the same point as the likelihood function
itself. Let xi denote the vector of independent variables and let yi denote a dummy that equals 1 if
the firm invested in energy efficiency technologies in the past five years and 0 otherwise. An
observation is denoted with i and there are N observations:
Where F is the cumulative distribution function associated with either a logit or a probit
specification and β denotes the parameters estimated. This formulation results in the following
log-likelihood function:
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Our covariate vector includes firm revenues, share of energy cost in total production cost,
firm privately owned, and several measures of potential barriers to the adoption of energy
efficiency measures. Specifically, we use the survey data collected to construct the independent
variables capturing the barriers to adoption of energy efficiency measures. The survey section
used to collect this data focuses on the perceived barriers to adoption.. The questions of that
section in the questionnaire quantify firms’ perceived barriers to the adoption of energy
efficiency measures. We grouped the barriers into seven categories: financial, split,
informational, technical, existing rules and regulation, low energy prices, and firm’s
bureaucracy. Each category included questions pertaining to specific barriers in the category,
where each question asked the respondent answering the questionnaire to quantify the
importance of a specific barrier on the scale from 0 (no influence) to 3 (strong influence).
The binary choice model discussed above quantifies the factors that affect firms’ decision
whether to invest in energy efficiency measures. However, because of the ordinal response of our
questionnaire where the response implicitly captures ever-increasing levels of investment in
energy efficiency measures, we also employed the cumulative logit model. These models are
defined for the probability of having an ordinal response that is less than or equal to the value R,
relative to the probability of having a response greater than the value R:
Where for an ordinal variable with 7 categories, 6 cumulative logit functions are defined.
Each of these cumulative logit functions includes a “cutpoint” (i.e., its own intercept), , but
all of the cumulative logit functions share the same set of parameters for the k predictors, i.e.,
. Note that the number of estimated parameters is significantly lower than that of a
multinomial logit model, and is equal to (R-1)+k, as opposed to (R-1)*(k+1) that are required in
a multinomial logit model.
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This framework suggests that the following transformation estimates the cumulative
probability, denoted , that a given response y is less than or equal to the ordinal
category k:
The estimated cumulative probability, denoted , is just the difference in the estimates’
cumulative probability between response category k and (k-1):
where .
DeMaris (2004) identified conditions under which linear regression treatment of ordinal
response leads to robust analysis. These include more than 5 levels, large sample, and a response
distribution that is not highly skewed across the ordinal range. Although once introducing into
the calculations missing observations the sample is not too large and our sample does not meet
all of these conditions, we elected to keep the linear regression and add it to our analysis. The
linear regression treats the ordinal response as a continuous variable, and the estimated results
are used to assess the robustness of the estimates of the cumulative Logit model.
3. The survey and data processing
The study employed a sample survey of 500 commercial/service and industrial
establishments throughout the country. To map the sample back to an unbiased representation of
the survey population we weighted the survey data using the prevalence of different firms in the
overall economy for each sample observation.
The respondents rated each barrier on a scale from 0 to 3 based on its perceived influence
on the implementation of energy efficiency measures to the firm. If a barrier’s influence was
strong, it was given 3 points; 2 points were given for considerable influence and 1 point for little
influence. If a barrier had no influence, it was given 0 points, and if it was not applicable for a
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firm, it was marked as “No answer/Not applicable”. The specific questions pertaining to the
barriers of each group are included in Table 1 below.
Table 1: Specific questions asked to analyze energy efficiency barriers
Financial barriers
High upfront costs: Are upfront capital costs of energy efficient appliances and devices high?
Lack of capital: Do financial institutions (Banks and other financial institutions) perceive energy efficiency
investment as risky and therefore charge high premium?
Low opportunity costs: Are there other priorities for capital investment, which can produce high returns?
Low opportunity costs of appliances to be replaced: Is there any resale value of the replaced appliances, which
still has a long operational life?
Long payback period: Is payback period of efficient appliances/devices too long to discourage their
implementation?
Split incentives
Bills paid by landlord: No incentives for the firm to reduce energy consumption as energy bills are paid by
building/facility owners
Split bills: No incentives for the firm to reduce energy consumption as energy bills are split among the
building/facility tenants
Knowledge, information and experience
Metering: Lack of gas, electric and heat metering
Awareness: Lack of awareness of the availability and/or benefits of deploying energy efficient processes and
devices
Information: Difficulties with obtaining necessary information
Confidence: Lack of confidence on energy efficient devices and processes (they do not deliver the services at the
level their promoters advocate)
Experience: Lack of experience in energy efficiency measures
Technical barriers
Skilled personnel: Lack of skilled personnel to handle the efficient devices and processes
Supplies: Lack of local supplies for equipment parts and very expensive purchasing from abroad, as well as long
lead time to get equipment parts
Reconfiguration: Installation of energy efficiency measures needs substantial reconfiguration of production process
Malfunction and poor performance: Higher probability of malfunction or poor performance thereby disrupting
production process
Existing Rules and Regulation
Government permits: Need to obtain government permits to deploy energy efficient devices and processes
Property rights: Lack of legal protection of property rights
Policy instruments: Administrative price setting, subsidies and cross subsidies
Government policy: Lack of effective government policies to facilitate energy efficiency programs
Unofficial payments: Unofficial payments demanded to receive government permits
Institutional barriers
Decision chain: Long decision chain on the firm
The future: Uncertainty about the firm’s future
Conflict of interest: Conflict of interests inside the firm
Economic barriers
Low priority: Low priority of the firm to reduce energy consumption; energy cost is not a big component of
production costs due to low energy prices
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To this set of variables, we also introduced variables that capture firm characteristics.
These are (i) revenues, (ii) ownership structure (public or private), (iii) share of energy costs to
total production costs, (iv) number of employees and (v) facility rented or owned.
Before estimating the binary choice model, we reduced the dimensionality of the model.
There are 25 questions assessing the importance of the different barriers to the adoption of
energy efficiency measures. In addition, there are 5 variables that capture firms’ characteristics.
There are another 6 variables when estimating the cumulative logit model. On the other hand,
because of missing observations, in some of the runs we ended up with only 98 observations. We
had too many variables. We therefore reduced the number of variables/factors using factor
2
analysis tools. Specifically, we used principle-component analysis. While using principle-
component analysis we managed to reduce the number of parameters estimated from 36 to about
12, and thus increased precision when estimating the various factors affecting the adoption of
energy efficiency measures.
We employ these techniques to aggregate the various independent variables and compute
the common factors. The eigenvalue is proportional to the portion of the sum of the squared
distances of the points from their multidimensional mean. The principle-component analysis
essentially rotates the set of points around their mean in order to align with the principal
components. This moves as much of the variance as possible (using an orthogonal
transformation) into the first few dimensions. The values in the remaining dimensions tend to be
small and may be dropped with minimal loss of information. To this end, we use the rule of
thumb that requires the eigenvalue to be greater than 1 for the factor to be included in the
empirical analysis. The common factors were then used to aggregate the specific barriers to those
mentioned in Section 4 and that are used in the regression analysis. The results of the principle
component analysis are depicted in Appendix A.
2 Principal component analysis employs orthogonal transformation to convert observations of correlated variables
(variables that belong to a certain group – e.g., financial barriers) into a set of values of linearly uncorrelated
variables that are called principal components. It is used in macroeconomics to aggregate multi-dimension indicators
and to clean the noise from observed series in the panel, which is poorly correlated with the rest of the panel (e.g.,
Avesani et al. 2006, and Forni et al. 2000). It has also being applied to complex dataset, which included multiple
indicators, to construct social capital indices (Sabatini, 2005). For more on the asymptotic characteristics of factor
analysis, see Bai (2003).
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In sum, we substantially reduced the dimensionality of the data (from about 36 to 12
variables). We obtained one common factor with eigenvalue greater than 1 for each of the
barriers analyzed bellow, as illustrated in Table 1A to 6A in Appendix A. Using the loading
factors we computed the aggregate level explanatory variable and tested the importance of
financial (hypothesis vi); knowledge (hypothesis vii); technical (hypothesis viii); whether
existing rules and regulations (hypothesis ix) serve as barriers to the adoption of energy
efficiency measures in Ukraine; the barriers created by the internal structure of the firm
(hypothesis x); and energy prices (hypothesis xi). When assessing the barriers to the adoption of
energy efficiency measures, we also included a seventh barrier: energy prices. This set of
explanatory variables was then augmented as follows:
1. With variables that capture the size of the firm and are used to test hypothesis (i).
2. To test hypothesis (ii) we included in the regression the share of energy cost to the firm
relative to total production costs.
3. A dummy variable that equals 1 if the facility is rented and 0 otherwise is used to test the
split incentive hypothesis (hypothesis (iii)).
4. An ownership dummy that equals one if the firm is privately owned, and zero otherwise
(i.e., public or foreign owned), is introduced into the analysis. The parameter is used to
evaluate hypothesis (iv).
5. An employment variable is introduced to assess hypothesis (v).
4. The empirical analysis
The key data used in the empirical analysis are summarized in Table 2 – barriers include
financial, split, knowledge information and experience, technical, existing rules and regulations,
institutional, and economic barriers. Overall, factors are normalized, such that the lowest value
assigned to a barrier is zero. When modeling firm characteristics, we have one categorical (total
revenues), one dummy variable (private ownership), and one continuous variable (share of
energy costs).
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Table 2. Data summary
Variable Obs Mean Std. Dev. Min Max
Firm characteristics
Invest 389 0.7609254 0.4270676 0 1
Total revenues 334 2.730539 1.63827 1 8
Share of energy costs 316 11.43358 10.21259 0.3 70
Facility rented 499 1.817635 0.3865323 1 2
Private ownership 491 0.694501 0.4610882 0 1
Employment 233 2.613734 1.375957 0 6
Barriers to the adoption of energy efficiency measures
Financial barriers 356 6.583883 2.884173 0 14.59367
Split Barriers 382 1.025171 1.562384 0 5.50279
Knowledge barriers 433 3.667689 3.445591 0 12.38596
Technical barriers 420 3.758199 2.553517 0 11.82016
Existing laws and regulation 296 4.832675 2.878283 0 10.71962
Internal institutions 401 0.6719663 0.6634264 0 2.093352
Energy prices 443 0.9006772 1.039524 0 3
We present the Pearson correlation coefficients among the various factors in Table 3.
Although principle-component analysis controls for correlation within groups of variables, we
wanted to better understand correlation between groups of variables. Some of the correlations
suggest we should add an interaction term among the factors, which we do below. But before
studying the importance of the interaction terms, we focus on our baseline model, which is
without the interaction term.
Table 3. The Pearson correlation coefficient
Financial Split Knowledge Technical Existing rules Internal Energy
Column1 barriers Barriers Barriers Barriers and regulations institutions prices
Financial barriers 1.00
Split Barriers 0.30 1.00
Knowledge
Barriers 0.48 0.57 1.00
Technical Barriers 0.62 0.29 0.59 1.00
Existing rules and
regulations 0.46 0.56 0.60 0.54 1.00
Internal institutions 0.46 0.42 0.49 0.50 0.55 1.00
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Energy prices 0.31 0.54 0.57 0.46 0.60 0.52 1.00
We begin with a linear binary model that investigates and evaluates the factors that might
impede firms from making any investment in energy efficiency measures. Recall that the
dependent variable in our binary model receives a value of 1 if the firm invested in energy
efficiency technologies in the past 5 years and 0 otherwise. The parameters estimated are
depicted in Table 4, where we depict both the Probit and the Logit model. Although the fit of the
models is not very good, the outcome does suggest financial barriers are the key obstacles to the
adoption of energy efficiency measures (i.e., we cannot reject hypothesis vi at a 5% significant
level).
Table 4. The binary model
Column1 Column2 Column3
Variable Probit Logit
Total revenues 0.0258 -0.0380
Share of energy cost 0.0246 0.0529
Private owned -1.0039 -2.3658
Financial factor -0.1186** -0.1892**
Split factor -0.0203 -0.1294
Knowledge and information factor 0.0040 0.0380
Technical factor 0.2083 0.3800
Existing rules and regulation factor 0.0259 0.0652
Energy prices 0.0902 0.0020
Constant 1.2752*** 2.7960*
N 98 98
F 6.1630 6.343536844
Legend: * p<0.10; ** p<0.05; *** p<0.01
We now present the baseline cumulative logit model, where we try and better understand
the factors that guide firms in Ukraine when deciding if and how much to invest in energy
efficiency measures. The results are depicted in Table 5, where the cumulative logit model is
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depicted in addition to the linear regression model that assumes the investment decisions are a
continuous variable. For most of the results we find similar results across the cumulative logit
and the linear models, except for the technical barriers, which are significant under the linear
model at a 10%, level but not significant under the cumulative logit model. While the F-statistic
of the cumulative Logit model is 3028.94, it is less than 100 for the linear model. Thus, in what
follows we focus on the cumulative logit model.
Financial Barriers
The analysis suggests financial barriers are the key barriers not only when contemplating
whether to make an investment in energy efficiency measures, but also when firms decide how
much to invest. While reviewing the literature on energy efficiency barriers in the industrial
sector, Worrell (2009) also finds similar results.
Table 5. The baseline model
Column1 Column2 Column3
Variable Cumulative Logit Linear
Log of revenues 2.4257*** 1.7974***
Log of energy cost share 0.1728 0.0146
Private owned -0.4782* -0.3466*
Log of financial factor -0.9205** -0.6200**
Log of split factor -0.2368 -0.1860
Log of knowledge and information factor -0.9285 -0.5954
Log of technical factor 1.3632 0.8870*
Log of existing rules and regulation factor 0.2303 0.1157
Log of energy prices 0.4577* 0.3768*
Constant -0.0494
Cutoff value
Constant: cut 1 0.5802
Constant: cut 2 2.8455*
Constant: cut 3 4.2898**
Constant: cut 4 5.4113***
Constant: cut 5 5.6107***
Constant: cut 6 6.7458***
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Statistics
N 98 98
F 3028.94 302.96
legend: * p<0.10; ** p<0.05; *** p<0.01
Moreover, with our dataset, we are able to split the observations into industrial and
commercial firms to examine barriers specific to each sector. The barriers are ranked in the
industrial sector as follows (we report in parenthesis the rank score that respondents put on the
questionnaire): high upfront capital costs that are needed to invest in energy efficient appliances
and devices (2.1), lack of capital (1.9), long payback period (1.8), low opportunity costs (1.4)
and small monetary value of the replaced appliances (1.2). This ranking is illustrated in Figure 1.
Similarly, the commercial firms rank the barriers as follows: high upfront capital costs (1.9), lack
of capital (1.8), long payback period (1.5), low opportunity cost (1.4) and small monetary value
of the replaced appliances (1.2). Overall, the industrial sector ranks various financial barriers
higher, although the differences are not large.
While analyzing conservation tax credits of the early 1980s in the U.S., Carpenter &
Chester (1984) found that although 86% of those surveyed knew about the credit, only 35% used
it, and of those firms that used it, 94% of investments made into energy efficiency would have
been done regardless of the financial incentives (e.g. in the absence of policy). In other words,
Carpenter & Chester (1984) do not find the role of financial barriers in inhibiting energy
efficiency investments. Our findings contradict those reported in Carpenter & Chester (1984).
Our cumulative logit model suggests that at the mean, an increase of 1 in the log of the financial
barriers results in an increase of the probability that a firm not invest in energy efficiency
measures by 0.92. The linear model finds a similar, yet smaller impact: an increase of 1 in the
log of the financial barriers results in the investment variable declining by 0.62.
Split barriers
Split barriers combine two barriers that show the influence of splitting the responsibility
of using energy resources with another side: “No incentives for the firm to reduce energy
consumption as energy bills are paid by building/facility owners” and “No incentives for the firm
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to reduce energy consumption as energy bills are split among the building/facility tenants.” Our
baseline analysis rejects hypothesis (iii). It rejects the hypothesis that imperfect information
yields underinvestment in energy efficiency measures. We return to this hypothesis below, where
interaction terms among the various factors are introduced.
Technical barriers, and knowledge and information barriers
Technical barriers are the other major barriers. The installation of energy efficiency
measures needs substantial reconfiguration of production processes, and lack of local supplies
for equipment parts and very expensive purchasing from abroad are seen as most important
technical barriers (1.5). Besides, industrial producers in Ukraine do not trust new devices: they
name high probability of their malfunction or poor performance, which can result in disrupting
production process as an important barrier (1.1). Commercial firms also report having little
experience in energy efficiency measures (the factor having 1 point on average) and say they
lack local supplies of equipment parts that are too expensive to purchase from abroad (1.1 on
average). Otherwise, respondents of commercial sector did not indicate significant informational
or technical barriers. Although the linear regression outcome suggests technical barriers impact
adoption of energy efficiency measures, the baseline cumulative Logit model does not hold this
claim (i.e., while the linear model cannot reject hypothesis viii at a 10% significant level, the
cumulative logit model does reject this hypothesis). We also do not find support for information
barriers (hypothesis vii) under the baseline analysis.
Existing Rules and Regulations
A lack of effective government policies to facilitate energy efficiency programs ranks
highest among rules and regulation factors with an average assessment of 2.2 points (Figure 1).
Most important rules and regulation barriers for commercial firms are the lack of effective
government policies to facilitate energy efficiency programs (1.8), need to obtain government
permits to introduce energy efficient devices and processes (1.5), and long decision chain on the
firm (1.3). However, once firm characteristics are controlled, existing rules and regulations do
not seem to result in large barriers to the adoption of energy efficiency measures. That is, we do
not find support for hypothesis ix.
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Energy Prices
On the other hand, energy prices do play an important role, as predicted by theoretical
models and documented in other work that investigated various economies and focused on
durable goods (Gillingham et al., 2009) – hypothesis xi. Hughes (1991) suggests that the energy
sector is very important to Eastern European economies for two reasons: Eastern European
countries have higher energy prices than countries with equivalent levels of income but are also
some of the most energy intensive economies in the world. Our results suggest that high-energy
prices might be affecting firms’ demand for cost-effective energy efficiency measures.
Energy Costs
On average, the firms surveyed reveal that they would decrease their energy costs by one-
third, if there were no barriers to energy efficiency measures. There is almost no difference
between estimates of different sectors. Industrial firms expect to reduce their energy
consumption costs by 35.8% on average, and commercial firms say that if not for the barriers,
their energy expenditures would be lower by 38.3%.
Firm Revenues
We cannot reject hypothesis (i): size matters. Firms with higher revenues are more likely
to invest in energy efficiency measures. In the sample population, industrial firms have more
revenues. Further, there is no correlation between firms that earn more revenues and financial
barriers (the pairwise correlation coefficient equals -0.04).
Private Ownership
Hypothesis (ii) is rejected: Privately owned firms in Ukraine invest less in energy
efficiency measures. While in our sample population industrial firms make on average more
revenues than commercial firms, relatively more industrial firms are privately owned.
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17
Figure 1. Rating of barriers for energy efficiency by industrial and commercial
firms
Industrial/commercial investment levels
While using the cumulative logit model and the estimated cutoff values, we calculated the
predicted probability that a firm not invest (0), make a small investment (<20k), make a slightly
larger investment (20k-100k), make an average investment (100k-500k), make a larger
investment (500k-1M), make a large investment (1M-10M), or make a very large investment
18
(>10M). We depict the predicted probabilities while separating between industrial and
commercial firms. Our analysis suggests that while 24% of commercial firms will not invest in
energy efficiency measures, only 15% of industrial firms will not invest. Further, the calculated
predicted probabilities suggest that industrial firms are more likely to have larger investments in
energy efficiency measures than commercial firms. This conclusion is interesting given that, on
average, commercial firms are more energy intensive: while the average share of energy costs in
total production costs is slightly larger for commercial firms (19.9% versus 17.7%, respectively),
industrial firms are more likely to invest in energy efficiency measures.
Figure 2. Predicted probabilities of investment using the baseline model
Industrial firms are more likely to be privately owned, while commercial firms are more
likely to be publicly owned (Table 6). Our baseline model suggests that, on average, privately
owned firms are less likely to invest in energy efficiency measures (Table 5).
Table 6. Ownership
% private % public % foreign % Total
Commercial 59 38 4 100
Industrial 81 12 7 100
19
Interactions
How do the various barriers interact? And what is their impact on the adoption of energy
efficiency measures? While focusing on the interaction among the various barriers whose
correlation coefficient is larger than 0.5, we investigate, for example, the impact of lack of
knowledge on firm’s perception of technical barriers to the adoption of energy efficiency
measures. Although we suspected interaction terms might convey new information, we could not
add them to the baseline specification because of data limitations. Therefore, and to investigate
the importance of the various interactions, we dropped the private owned firm dummy variable
and included an interaction term, one at a time. The various models were estimated assuming a
cumulative logit model, and the results are depicted in Appendix B. In Table 7 we present the
model that has the greatest explanatory power – its F-Statistic is more than 1,000,000.
Introducing other interaction terms resulted in models with substantially lower explanatory
power.
Table 7. Baseline model with an interaction of knowledge and technical barriers
Variable Model I
Log of revenues 2.741325190***
Log of energy cost share 0.130654024
Log of financial factor -0.915981282***
Log of split factor -0.235681133
Log of knowledge and information factor -2.100629047**
Log of technical factor 0.394984634
Log of existing regulation factor 0.200068708
Log of energy cost 0.162249862
Knowledge * technical 0.913821119**
Cutoff parameters omitted
Statistics
N 98
F 1.04E+06
20
Although we do observe some fluctuation in the coefficient values, overall, the
significance of firms’ revenues and financial barriers is maintained among the various
specifications (see Appendix B). Further, as long as we do not introduce an interaction term,
which interacts with the financial barriers, the magnitude of the estimated revenue and financial
barriers parameters remains relatively stable.
When introducing various interaction terms, one at a time, we get mixed results with
respect to split, knowledge and information, and technical barriers. The coefficient of these
parameters is significant under some of the specifications modeled in Appendix B but not others.
The introduction of an interaction term between information and technical barriers
suggests that less informed firms (higher information barrier) results in firms underestimating the
importance of the technical barrier (Table 7). In Appendix B Model VI we also depict a model
that shows knowledge affects the impact of split incentives on adoption of energy efficiency
measures and reduces the negative impact split incentives have on the amount invested in these
measures.
We also computed the predicted probability, when an interaction between knowledge and
technical barriers is introduced into the empirical analysis. Introducing an interaction term
skewed the predicted probabilities of the commercial firms’ investment patterns toward more
investment but yielded less investment for the industrial firms. However, the results still suggest
it is more likely to observe investment in energy efficiency measures by industrial firms than
commercial firms.
How do the results change when we address the missing data problem? Do we gain
information when imputing data or does it just introduce noise and affect the precision of the
parameters estimated? The robustness analysis below explores these questions.
5. Robustness
In the main analysis we introduced weights to compensate for potential biases. We now
further investigate ways of correcting for the missing data while evaluating the benefits of
imputing data. Because of the large portions of data missing, as well as the absence of questions
21
answered by all respondents that can be used to impute the data, poor results were obtained when
using the multiple imputation models.
However, two simple imputations proved useful in further understanding our results. For
the first we substituted missing observations pertaining to firms’ perception regarding barriers to
the adoption of energy efficiency measures with 0, while for the second we substituted it with the
mean value. Overall, the results supported our main findings although the size of the parameters
estimated did change (see Appendix C).
6. Concluding remarks
This study examines energy efficiency barriers to the industrial and commercial
(including public) sectors in Ukraine by conducting a survey of 500 firms throughout the
country. The results from the survey are then used in empirical (i.e., Logit and Probit) models to
understand the importance of various barriers to the adoption of energy efficiency.
The study finds that financial barriers, such as higher upfront investment costs of energy
efficiency technologies, lack of capital and long pay-back period are the strongest barriers to the
deployment of energy efficiency technologies in the both industrial and commercial sectors in
Ukraine. Lack of effective government policies and existing regulation such as government
permits required for the adoption of energy efficiency technologies are other key barriers.
Predicted probabilities estimated by our study suggest that industrial firms are more likely to
have larger investments than commercial firms despite the fact that the latter have, on average,
slightly higher share of energy costs in the total production costs. Our study also suggests that
energy price rises would yield more adoption of energy efficiency measures, as would the
introduction of credit enhancement instruments.
Although the study suggests policy that reduces upfront costs and risk will result in more
adoption of energy efficiency measures in Ukraine, the analysis also suggests heterogeneity
among firms and sectors. That is, our analysis finds differences in levels of investment in energy
efficiency measures among sectors, with industrial firms investing more. This raises the question
whether sectoral heterogeneity be accounted for while designing energy efficiency policy
instruments. We plan to further investigate this in future research.
22
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25
Appendix A:
In computing the common factor attributed to “financial” barriers to the adoption of
energy efficiency measures, we obtained the eigenvalues of the various relevant factors. The
analysis suggests we retain only one factor (Table 1A-a). We also obtained the extracted sum of
the squared loading (Table 1A-b). We use the loading coefficients to calculate the financial
barrier factor that we employ in the empirical analysis. The loading coefficients and eigenvalues
enabled us to transition from five variables to one that explains more than 53% of the financial
barriers data variability.
Table 1A-a. Principle component analysis of financial barriers
Factor analysis/correlation Number of obs 356
Method: Principal-component Factors retained 1
Rotation: (unrotated) Number of parameters 5
Factor Eigenvalue Difference Proportion Cumulative
Factor1 2.68267 1.97536 0.5365 0.5365
Factor2 0.70732 0.03498 0.1415 0.678
Factor3 0.67234 0.13459 0.1345 0.8125
Factor4 0.53776 0.13785 0.1076 0.92
Factor5 0.39991 . 0.08 1
LR test: independent vs. saturated chi2(10)= 457.19
Table 1A-b. Factor loading (pattern matrix) and unique variance for financial
barriers
Variable Factor1 Uniqueness
High Up front costs 0.7184 0.484
Lack of Capital 0.7952 0.3677
Low opportunity cost 0.7205 0.4809
Zero or very small monetary value 0.723 0.4773
Long pay back period 0.7018 0.5074
26
Next, we computed the eigenvalue of the split incentives (Table 2A-a). The eigenvalues
suggest one common factor, with the loading factors depicted in Table 2A-b. This resulted in
moving from two variables to one, but note that the single factor used explains more than 80% of
the variability in the split barriers data.
Table 2A-a. Principle component analysis of split barriers
Factor analysis/correlation Number of obs 382
Method: Principal-component Factors retained 1
Rotation: (unrotated) Number of parameters 1
Factor Eigenvalue Difference Proportion Cumulative
Factor1 1.68226 1.36452 0.8411 0.8411
Factor2 0.31774 . 0.1589 1
LR test: independent vs. saturated chi2(10)= 238.34
Table 2A-b. Factor loading (pattern matrix) and unique variance for split barriers
Variable Factor1 Uniqueness
Energy bills paid by building/facility
owner 0.9171 0.1589
Energy bills shared among
building/facility owner and firm 0.9171 0.1589
Next, we compute the eigenvalue of the information and knowledge incentives (Table
3A-a). The eigenvalues suggest one common factor, with the loading factors depicted in Table
3A-b. Here we reduced the number of variables in the empirical analysis from five to one, while
the the factor chosen explains more than 68% of the variability in the data.
Table 3A-a. Principle component analysis of information and knowledge barriers
Factor analysis/correlation Number of obs 433
27
Method: principal-component Factors retained 1
Rotation: (unrotated) Number of parameters 5
Factor Eigenvalue Difference Proportion Cumulative
Factor1 3.41871 2.84219 0.6837 0.6837
Factor2 0.57652 0.13668 0.1153 0.799
Factor3 0.43985 0.08195 0.088 0.887
Factor4 0.35789 0.15086 0.0716 0.9586
Factor5 0.20703 . 0.0414 1
LR test: independent vs. saturated chi2(10)= 1181.82
Table 3A-b. Factor loading (pattern matrix) and unique variance for split barriers
Variable Factor1 Uniqueness
No metering 0.7696 0.4077
Lack of awareness 0.8895 0.2088
Difficulty obtaining information 0.8623 0.2564
Lack of confidence in these measures 0.8003 0.3596
Lack of experience 0.8069 0.3489
We computed the eigenvalue of the technical barriers (Table 4A-a). The eigenvalues
suggest one common factor, with the loading factors depicted in Table 4A-b. When focusing on
the technical barriers, the single factor explains more than 57% of the variability.
Table 4A-a. Principle component analysis of technical barriers
Factor analysis/correlation Number of obs 420
Method: principal-component factors retained 1
Rotation: (unrotated) Number of parameters 4
Factor Eigenvalue Difference Proportion Cumulative
Factor1 2.31472 1.56538 0.5787 0.5787
Factor2 0.74934 0.2142 0.1873 0.766
28
Factor3 0.53514 0.13434 0.1338 0.8998
Factor4 0.4008 . 0.1002 1
LR test: independent vs. saturated chi2(10)= 413.15
Table 4A-b. Factor loading (pattern matrix) and unique variance for technical
barriers
Variable Factor1 Uniqueness
Skilled labor 0.7026 0.5064
Expensive imports and lack of
domestic supply 0.7938 0.3699
Requires substantial changes
to the production process 0.8116 0.3413
High probability of
malfunction 0.7296 0.4676
When computing the eigenvalue of questions pertaining rules and regulations (Table 5A-
a), we retain one common factor, with the loading factors depicted in Table 5A-b. We used the
rule of thumb, that requires the eigenvalue to be greater than 1 for the factor to be included in the
empirical analysis. Although the second factor is close to one, its contribution to explaining the
variability is much smaller than the first factor (0.5153 versus 0.182). We therefore elected to use
only the first factor in our empirical analysis.
Table 5A-a. Principle component analysis of existing rules and regulations
Factor analysis/correlation Number of obs 296
Method: principal-component factors retained 1
Rotation: (unrotated) Number of parameters 5
Factor Eigenvalue Difference Proportion Cumulative
29
Factor1 2.57632 1.66628 0.5153 0.5153
Factor2 0.91004 0.31833 0.182 0.6973
Factor3 0.59171 0.07042 0.1183 0.8156
Factor4 0.52128 0.12064 0.1043 0.9199
Factor5 0.40065 . 0.0801 1
LR test: independent vs. saturated chi2(10)= 363.58
Table 5A-b. Factor loading (pattern matrix) and unique variance for existing rules
and regulations
Variable Factor1 Uniqueness
Government permits
required 0.7873 0.3802
Lack of property
rights protection 0.7467 0.4425
Administrative price
setting 0.7174 0.4853
Government policy
not effective 0.5878 0.6544
Unofficial payments
demanded 0.7341 0.4612
Finally, we compute the eigenvalue of the internal barriers, i.e., the firms’ administration
and bureaucratic barriers to the adoption of energy efficiency measures (Table 6A-a). The
eigenvalues suggest one common factor, with the loading factors depicted in Table 6A-b. The
common factor explains more than 62% of the variability.
Table 6A-a. Principle component analysis of firm’s administrative barriers
30
Factor analysis/correlation Number of obs 401
Method: principal-component factors retained 1
Rotation: (unrotated) Number of parameters 3
Factor Eigenvalue Difference Proportion Cumulative
Factor1 1.88518 1.19138 0.6284 0.6284
Factor2 0.69381 0.27279 0.2313 0.8597
Factor3 0.42101 . 0.1403 1
LR test: independent vs. saturated chi2(10)= 238.16
Table 6A-b. Factor loading (pattern matrix) and unique variance for firm’s
administrative barriers
Variable Factor1 Uniqueness
Long decision chains 0.7044 0.5039
Uncertainty about firm's future 0.8139 0.3375
Conflict of interest inside the firm 0.8524 0.2734
Appendix B:
Table 1B. Baseline model with an interaction term
Variable Model I Model II Model III
Log of revenues 2.741325190*** 2.182619969*** 2.465261230***
Log of energy cost share 0.130654024 0.24513135 0.169066623
Log of financial factor -0.915981282*** -1.148505292** -0.969937005**
Log of split factor -0.235681133 0.176517929 -0.111932736
Log of knowledge and information factor -2.100629047** -1.313373421 -0.818492276
Log of technical factor 0.394984634 1.627786066* 1.327466515
Log of existing regulation factor 0.200068708 0.087423073 0.182224142
Log of energy cost 0.162249862 0.743714209** 0.623607336
Knowledge * technical 0.913821119**
Knowledge * regulation -0.188530449
knowledge*energy price -0.119880911
regulation*technical
regulation*energy price
31
Cutoff parameters omitted
Statistics
N 98 91 98
F 1.04E+06 38.62230397 7.65E+03
Variable Model IV Model V
Log of revenues 2.100542807*** 2.028502915***
Log of energy cost share 0.270295821 0.236193307
Log of financial factor-1.149360445** -1.208104546**
Log of split factor 0.180178453 0.400469767*
Log of knowledge and information factor -1.443025467 -1.398589357
Log of technical factor 1.811733302 1.786825604*
Log of existing regulation factor 0.062758406 0.172567756
Log of energy cost 0.755600114** 1.375906460**
Knowledge * technical
Knowledge * regulation
knowledge*energy price
regulation*technical -0.058099965
regulation*energy price -0.964637983
Cutoff parameters omitted
Statistics
N 91 91
F 16.96402005 18.93379284
Variable Model VI Model VII
Log of revenues 2.431694291*** 2.393070223***
Log of energy cost share 0.19165519 0.320244444
Log of financial factor -1.018054681** -1.228787952***
Log of split factor -3.135320904*** -0.786425154
Log of knowledge and information factor -1.217403092 -1.417070951
Log of technical factor 1.411140358 1.571963524*
Log of existing regulation factor 0.137219632 -0.131083337
Log of energy cost 0.381446147 0.669916849**
knowledge*split 1.561192111***
regulation*split 0.723553286
split*energy price
32
financial*technical
Cutoff parameters omitted
Statistics
N 98 91
F 1.69E+02 1.32E+02
Variable Model VIII Model IX
Log of revenues 2.642495979*** 2.708161900***
Log of energy cost share 0.151742152 0.122248513
Log of financial factor -0.953537008*** -2.322088947***
Log of split factor -1.590755663*** -0.289456429
Log of knowledge and information factor -0.807606837 -0.841238136
Log of technical factor 1.15309456 -0.587985858
Log of existing regulation factor 0.113266895 0.33348211
Log of energy cost 0.088761721 0.389469817
knowledge*split
regulation*split
split*energy price 1.427506905***
financial*technical 1.055441141**
Cutoff parameters omitted
Statistics
N 98 98
F 55.46015871 2.36E+02
Appendix C:
We began by re-estimating the baseline model presented in Table 4, but now missing data
regarding firms’ perceived barriers to the adoption were replaced with a zero (Table 1C). The
importance of most of the barriers increased by 20% or more relative to the baseline model, but
the importance of revenues declined by more than 10%. However, the magnitude of revenues
and financial barriers still remained far greater than any of the other barriers estimated.
Table 1C. Replacing missing observations in the data with zeros
33
Column1 Column2 Column3
Variable Linear Ologit
Total revenues 1.6221*** 2.1925***
Share of energy cost 0.0527 0.1994
Private owned -0.3677*** -0.5879**
Financial factor -0.9723*** -1.5305***
Split factor -0.0076 0.0565
Knowledge and information factor -0.6150** -0.8400***
Technical factor 0.6161** 1.0282**
Existing rules and regulation factor 0.2425 0.2489
Price of energy 0.2823*** 0.2155*
Constant 0.8770***
Cutpoints
Cutpoint1 -0.8082*
Cutpoint2 1.1169**
Cutpoint3 2.4062***
Cutpoint4 3.2745***
Cutpoint5 3.9332***
Cutpoint6 5.2864***
Statistics
N 214 214
F 95.0368 15.9024
legend: * p<0.10; ** p<0.05; *** p<0.01
Next, instead of replacing missing-data with zeros, we replaced them with the mean value
(Table 2C). The estimated parameters are similar to those obtained when replacing missing-data
with zeros.
When substituting missing-data with either zero or mean of variable, firm’s revenues,
financial barriers, lack of knowledge and low energy prices are the main factors affecting the
adoption of energy efficiency measures.
Table 2C. Replacing missing observations with mean of variable
34
Column1 Column2 Column3
Variable Linear Ologit
Total revenues 1.6088*** 2.1656***
Share of energy cost 0.0396 0.1969
Private owned -0.3308** -0.5381**
Financial factor -0.8557*** -1.2845***
Split factor -0.0358 -0.0460
Knowledge and information factor -0.6512*** -0.8892***
Technical factor 0.5396* 0.8963**
Existing rules and regulation factor 0.1874 0.1934
Price of energy 0.3082*** 0.2566**
Constant 0.9907***
Cutpoints
Cutpoint1 -0.8119*
Cutpoint2 1.0992*
Cutpoint3 2.3917***
Cutpoint4 3.2309***
Cutpoint5 3.8991***
Cutpoint6 5.2299***
Statistics
N 213 213
F 90.7612 21.5872
legend: * p<0.10; ** p<0.05; *** p<0.01
35