Journal of Financial Risk Management
2013. Vol.2, No.2, 33-37
Published Online June 2013 in SciRes (http://www.scirp.org/journal/jfrm) http://dx.doi.org/10.4236/jfrm.2013.22005
On the Evaluation of Performance System Incorporating “Green
Credit” Policies in China’s Financial Industry
Lan Xu
Business School, East China University of Science and Technology, Shanghai, China
Received March 29th, 2013; revised May 1st, 2013; accepted May 8th, 2013
Copyright © 2013 Lan Xu. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
The main work of this paper is aimed, through utilizing the two-stage optimization theory, to estimate the
green distance functions and Malmquist green growth indexes for the China’s main commercial banks
and relevant financial institutions, and to further set up an overall appraisal index system to assess their
performance in implementing the “Green Credit” principles. The paper also analyzes effects of the “Green
Credit” policies influential on revenue achievements, as well as performs a decomposition analysis of the
above impacts. The carry-outs of the paper may serve as useful references and guiding means in achieving
China’s economic transitional strategies of sustainable development.
Keywords: Green Credit; Sustainable Development; Financial Distance Function; Performance Evaluation
Research Background
Since reform and opening-up, China’s economy develops
with remarkable speed, having achieved a successful result.
However, in the process of such rapid economic development,
China has also paid a heavy price. An extensively developed
economy has featured a blind expansion of the “highly energy
consuming and highly polluted” industries, with numerous
construction projects and illegal environmental phenomenon by
corporations and enterprises in many areas. It becomes obvious
that the environment and resources have been seriously de-
structed. In fact, the situation has an unfavorable impact on the
social stability and sustainable economic development, and thus
become the core issue of economic problems currently. The
“China’s 12th five-year plan” suggested that it would be an
important point of strength to build a resource-conserving and
environmental-friendly society as to speed up the transition of
economic developing mode. It is repeatedly proved by the his-
tory of human being’s progresses that, important technical in-
novations and economic transitions rely heavily on a well-
functioned financial leverage accompanied by the reasonable
allocation of funds and the formation of incentive mechanisms.
In the processes, the operation of financial system can effect-
tively promote the economical innovation and transformation.
In the sense, the development of “Green Finance” provides
necessary prerequisites currently in realizing China’s economic
transition and maintaining sustainable development strategies.
The core content of “Green Finance” is the principle of
“Green Credit”, which refers to a series of administrative means
requiring that commercial banks and other financial institutions,
according to the national environmental economic policies and
industrial policies, carry on researches and developments to
produce pollution treatment facilities, be engaged in the eco-
logical protection and restoration, develop and utilize new en-
ergy resources, focus on the circular economic production,
green goods production, and ecological agricultural production,
provide loans to support relevant enterprises and institutions
and implement concessionary low interest rates, but restrict
new project investments of polluting enterprises accompanied
with some punishable interest rates. The principle of “Green
Credit” is aimed at linking the sustainable development strate-
gies with the financial sector, making funds and loans effect-
tively flow into environment-friendly enterprises and institu-
tions, cutting off the disordered and blinded expansion of the
“highly polluted and highly energy consuming” industries from
the very source of the production, in order to achieve a green
configuration and an allocation of social resources.
In the early 1990s, a publication of “the Financial Environ-
ment and Sustainable Development Declaration” by the De-
partment of Environmental Program of the United Nations
(UNEP) stressed that it is necessary to take into account the
environmental factors in the process of the standard risk as-
sessment, urged the banking industry to consider environmental
factors in the business of management activities, and to en-
courage private sectors to invest production, services, and
technologies beneficial to the environment. Nowadays, the
environmental protection finance and “Green Credit” ideas
have become an international trend, among which, one of the
most influential policies are the “Equatorial Principles”. That is,
the financial institutions should stipulate separately the social
and environmental risk assessment policies, and the implement
procedures and baseline standards which are related to project
financing appraisals and management activities. By the end of
2008, over the global five continents, a total of 63 world fa-
mous institutions, including Citigroup, Netherlands Bank,
HSBC, Standard Chartered Bank, and the Bank of America, all
have adopted the “Equatorial Principles”, their businesses cover
all over the world with more than 100 countries, and the total
value of those projects financed accounts for more than 85% of

L. XU
the total global market shares of financing projects (Zheng,
2008). Thus so far, typical “Green Credit” products in the
global banking system mainly include: the structural energy
saving collateral loans offered for instance by Citigroup and
Fannie Mae, the ecological home loans offered i.e. by British
Joint Financial Service, the green car loans offered i.e. by the
Canadian Van City Bank and Australian MECU Bank, and the
climate credit card loans offered i.e. by the European Robobank,
and etc.
In the progress of implementing the “Green Credit” policies,
China shows a lagged pace, but also reflects a huge potential
for a future development. In July 2007, the China’s Environ-
mental Protection Administration, the People’s Bank of China,
and the China’s Banking Regulatory Commission jointly issued
the bulletin of “Opinions on the Implementation of Environ-
mental Protection Policies and Regulations to Protect Credit
Risks”, putting forward to implementing the “Green Credit”
policies and some specific provisions of requirements, clarify-
ing the necessities and urgencies of implementing the policies,
which marks the starting stage of China’s “Green Credit” par-
ticipation (CEPA, PBC, & CBRC, 2007). According to income-
plete statistics, in recently five years, China’s total amount of
project loans concerning energy conservation and environ-
mental protection has increased every year, from 202.89 billion
yuan in 2006 to 964.74 billion yuan in 2010, increasing at an
average rate of about 75% annually. The total numbers of pro-
ject loans concerning the energy conservation and environ-
mental protection rises from 1999 items in 2006 to 6634 items
in 2010, increasing at an average rate of 46% annually (CBA,
2010).
The adoption of “Green Credit” policies can reduce resource
depletions and pollution production. Even more, it is an inevi-
tably choice in fulfilling the sustainable economic and social
development. However, there still exist many obstacles in the
development process of the “Green Credit” policies, so to ex-
tensively promote the “Green Credit” rules also faces with
many difficulties. The main reasons may due that there are
incomplete informational symmetries in the “Green Credit”
market, thus in turn leading to an unequal interest among three
parties of the market executive bodies (banks, enterprises, and
governments), and causing mutual game strategies between
interest groups, which eventually leads to difficulty in imple-
menting the “Green Credit” principles, and makes the whole
society suffer with economic losses. In view of the context, this
paper puts forward that, the top priority to implement and de-
velop the “Green Credit” principles is to establish a set of fea-
sible evaluation system, strengthening the standardized assess-
ment on the effects of “Green Credit” implementation across
the commercial banks and other relevant financial institutions,
thus in turn to band together with laws, regulations, restraints,
and incentive and punishment mechanisms, making sure that
the “Green Credit” system achieve a realistic and real effective
result.
Research Reviews
So far, most researches on the green credit issues are nearly
based on qualitative analysis, limited to the introductory and
comparative studies on the implementing status and policy
regime of the “Green Credit” system across nations over the
world. The goal of these researches are basically to strengthen
and recognize the purposes of “Green Credit” principles, that is,
to reduce environmental financial risks, call on social response-
bilities for the corporations, disclose the environmental infor-
mation, and enhance the effectiveness in implementing the
“Green Credit” rules. Research results of this respect may in-
clude early definitions and progressive researches of environ-
mental finances. Some scholars defined the environmental fi-
nance as a financial innovation by the financial industry de-
manding for the environmental production industry. For exam-
ple, Marcel (Jeucken, 2001) analyzed the relationship between
financial industry and sustainable development in the book of
“Financial Sustainable Development and Banking Industry”,
emphasizing an important role that the bank may take in the
environmental issues. Sonia (Labatt & White, 2002) in her
masterpiece of “Environmental Finance” discussed mainly
about a relationship between the financial innovation and the
environment, pointing out how financial services should carry
on environmental risk assessments and provide environmental
financial products. Recent studies of domestic scholars involve-
ing in this area include journal articles by Huitong (Wang &
Chen, 2006), Huayou (Li & Feng, 2007), and etc.
In view of difficulties in implementing the “Green Credit”
policies in recent years, more researchers believe that the ef-
fects of the “Green Credit” implementation should be included
in the banking and financial appraisal system, and it should
formulate a set of standardized comprehensive evaluation
methods at three different stages (before, in the middle of, and
after) credit loans, and establish an information sharing plat-
form for the executive bodies of governments, banks, and en-
terprises. Accordingly, research literatures relating to the three
stages of the “Green Credit” implementation assessments can
also be classified into the following three categories respect-
tively: 1) Environmental risk management evaluations prior to
the “Green Credit” implementation; 2) Potential cost-benefit
analysis on the process of implementing the “Green Credit”
principles; and 3) Performance evaluations after the implement-
tation of the “Green Credit” principles (Chen & Lu, 2011; Dang,
2009; Fei, 2008; Zhu & Yu, 2011; Zhu & Wang, 2009; Zuo &
Guo, 2010).
The purpose of this paper is to study and assess on the over-
all effects of implementing the “Green Credit” policies for the
China’s banking and financial industry through establishing a
set of comprehensive assessment index system using distance
functions and Malmquist productivity growth index, in addition
to analyze the effects of “Green Credit” policies on the finan-
cial performances and conduct a decomposition analysis of
such impacts (Grosskopf, 2003).
Establishment of the “Green” Growth Index
A traditional performance evaluation mainly focuses on com-
paring the operational costs of enterprises and the revenues of
product sales, thus net gains come from the difference of these
two items, such approach is usually called “cost-benefit analy-
sis”. For considering efficiencies and technical progresses, we
can apply “Malmquist productivity growth index” to evaluate
the operational performances enterprises, which is actually
reflecting the abilities of resource allocation, technological
transformation, and labor productivity, and the overall profit-
ability of business sectors. Similarly, when we evaluate the
comprehensive ability of making profits for the banking and
financial system after implementing the “Green Credit” policies,
we can also calculate the “Malmquist productivity growth in-
34

L. XU
dex”, called the “Malmquist green credit productivity growth
index”, that can be used to measure the overall competitive
capacities of the banking and financial industry when consider-
ing the environmental resources as one of production inputs.
For the environmental resources as investment input elements
cannot be measured by the price mechanism of a unified stan-
dard, we need to introduce and set up the “input-output distance
function” before defining and calculating the “Malmquist green
credit growth index”. This is exactly the main work this paper
intends to undertake.
“Green credit distance function” and thus “Malmquist green
credit growth index” are of great importance in realizing the
sustainable economic development and economic transition,
and it provides practical means in constructing and improving
the “green credit assessment system” of the financial industry.
Setup of the Distance Function for the Green Credit
In order to evaluate the operating performance for the com-
mercial banks and other institutions, we use the “Malmquist
green credit productivity growth index” to measure the overall
competitive capacities of the banking and financial industry. To
this purpose, we need first to construct a production technology
frontier as a benchmark of the financial industry, then to com-
pare the output level of each commercial bank and institution in
the system to such benchmark reference of production frontier
of the financial industry, thus in turn to obtain each institutional
distance function of its own. In this sense, the “Green Credit”
distance function is calculated through a set-up of the “input-
output technology frontier” of the production operation process
for the whole banking and financial system, when the environ-
mental factors are taken into consideration as one of production
inputs. In the progress of calculating the distance function for
the financial institutions, we adopt the two-stage optimizational
theory. First, we make use of non-parametric linear program-
ming technique to recuperate each bank or institution’s original
production technology. Then, we utilize the parametric linear
programming technique with a translog functional form to es-
timate a smooth production technological frontier for the finan-
cial industry, which is as well as best-practiced to the actual
data (Xu, Bao, & Mai, 2011).
Finally, through the above two-stage optimization linear pro-
gramming approaches to obtain the estimated “Green Credit”
distance function, we can further calculate the “Malmquist
green credit productivity growth index” which gives explana-
tion of the comprehensive competencies of productivity of the
commercial banks and financial institutions. Afterwards, we
can also perform a comparison and decomposition analysis
among various financial institutions to show different status of
implementation of the “Green Credit” policies.
Two-Stage Estimation of Distance Function
In the estimation of the benchmark distance function for the
financial industry, we usually need to construct a reference
technology frontier, so that we can compare output level of
each financial institution to that reference frontier. To this pur-
pose, we apply the first-stage of the non-parametric technology
to set up such a reference frontline. In order to reflect the char-
acteristics that there is certain dependency among production
sets consecutive for several years, this research assumes a se-
quential technological set so that data of past history across
time accumulate rather than uncorrelated within the examined
time range. Specifically, for each time period, , the
reference production technology models an input-output mix,
(xt–s, yt–s) with
1, ,tT
0,1,2,s

, t = 1, using observations of pre-
vious several years as part of the technology in period t. This
means successive production sets as to form a sequential refer-
ence frontier are nested one another (Pastor & Lovell, 2007). In
particular, the formulation of non-parametric linear program-
ming problem for


,
tt
o
Dxy
t
is presented as follows:



1
,,
,, ,
1
,, ,
1
,
,max
..
1, ,
1, ,
01
tktkt k
o
Kkt ktk kt
mm
k
Kkt ktkt
nn
k
kt
Dx y
st
zy y mM
zx xnN
zk




 







,,.K
where z is an intensity variable of inputs and outputs.
In the second stage, we specify a functional form of Translog
distance equation to estimate a smoothly best-practiced techno-
logical frontier for the financial sector as a whole. The advan-
tage of parameterizing Translog functional form is that this
specification is most flexible and consistent with the actual
technology. More importantly, the estimated production tech-
nology enables us to deduce parameters of the input-output
combination. Specifically, the functional form of the Translog
distance equation in the second stage can be expressed as fol-
lows:



 
0
11
1'1
1'1
11
ln, ααln βln
1αlnln
2
1βln ln
2
γlnln .
MN
omm
mn
MM
mm mm
mm
NN
nn nn
nn
NM
nm nm
nm
Dxyy x
yy
xx
xy






 







nn
With the restrictions for symmetry and homogeneity imposed,
we can estimate input parameters via the means of linear pro-
gramming optimization as discussed previously.
Calculatin g Malmquist “Gre en” Growth Index
In order to define the “Malmquist green credit growth index”,
we have to specify two different time periods for the “Green
Credit” distance equation, denoted as t and t + 1, then to take
geometric mean value of the Malmquist index (the ratio of the
distance equation) of two consecutive time periods. After ma-
nipulation, the output-based Malmquist green credit growth
index can be expressed in the following form:



  


111
1
111 112
111 1
,,,
,,,
.
,,,
ttttt
o
ttt tttttt
ooo
tttt ttt tt
oo o
Mxyxy
Dxy DxyDxy
DxyDxyD xy

 
 




 








So, the Malmquist “Green Credit” growth index can be de-
composed into two components: The efficiency changes
(EFFCH) and technological changes (TECHCH) of the per-

L. XU
formance of financial institutions. A ratio outside the bracket is
the efficiency change component, which describes relatively
efficiency catch-up between two periods, t and t + 1, or some-
times called “the effect of catching-up”. A geometric mean of
the two ratios inside the bracket captures the shifting effect of
frontiers representing the change of technology, or sometimes
called “the effect of technological innovation”.
Empirical Researches
The empirical ducted based on
th
put data as costs
an
f input and output data
fo
major indicators explaining the en-
vi
part of the “Green Financial” efforts mainly refers to
th
ng the GAMS programming software and the
SP
Summary and Discussion
This article lquist “Green
C
on, the paper carries on an in-depth study and dis-
cu
analysis of this research is con
e above illustration of theoretical approaches. First of all, we
apply the two-stage linear programming method of optimiza-
tion to estimate a benchmark production frontier reflecting the
overall technological effects for the entire financial industry
after implementing the “Green Credit” policy. Then we can
estimate the “Green Credit” distance function for an individual
financial institution, and further to specify the estimated dis-
tance functions as main components in defining and calculating
the Malmquist green credit growth index that explains the
overall performance of the financial industry.
In addition to utilize the often-applying in
d the output data as revenues, we also need to introduce and
gather the “Green Credit” data for the financial sector as part of
the input costs in the production operation, through which we
can estimate and calculate the “Green Credit” distance func-
tions and the “Green Growth” index.
For processing the basic indicators o
r the banks and financial institutions, we follow the methods
of the intermediary approach and the capital valuation approach,
existing in most financial literatures, such as Shiyu (Hou, 2006),
so that various sources of deposits are regarded as part of the
bank’s investment inputs. In addition, human resources in the
banking and financial industry account for a great amount of
input costs, and the business operation expenses are also em-
bodied by the same category as input costs. In aggregation,
except for the costs incurred by the “green credit” efforts, input
factors include the total amount of bank’s deposits, the net
value of fixed assets, and various operational expenses. On the
other hand, the output data consist mainly of two parts, the
interest income and the non-interest income. The first part
represents the scale effect of production, the term structure of
interest rates, and the industrial structure of the banking and
financial system, while the latter part of non-interest income
shows the profits gaining from the intermediary businesses
within the financial system. The aggregation of the above two
sources of revenues explains the overall abilities of profit-
making and technological innovation for the commercial banks
and financial institutions.
Moreover, there are two
ronmental input costs of the banking and financial industry,
one is the total amount of credit loans regarding to the energy
conservation and environmental protection projects, which
summed up to the total costs of the “Green Credit” activities.
Currently to speak, the environmental project loans in China
generally include credit loans of the renewable energy projects
and the environmental protection projects. However, the total
amount of specified “Green Credit” loans accounts for a very
small portion of the total amount of credit loans for the entire
financial system in China, showing that China still remains a
big gap by the international experiences of some developed
countries.
Another
e improvement of regulations on the environmental protection
system and the participation of environmental executive actions
of the individual financial sector. Specifically, it includes the
following aspects of contents: possession of two environmental
permits (license of “Green” business operation and certificate
of national standard discharge) annually, annual reports on the
environmental executive carry-outs, participation rates of the
environmental protection activities at various administrative
levels (national, provincial, and municipal), and other various
supporting activities for the implementation of environmental
protection rules.
Finally, by usi
SS statistical software that are commonly accepted in the
academia, we can estimate the ten-year (2001-2010) “Green
Credit” productivity growth index that reflects the overall per-
formance of the Chinese banking and financial industry, and
furthermore we can analyze the scale effect and technological
effect that the “Green Credit” activities may have on the com-
prehensive performance of the financial system.
is intended to construct a Mam
redit” growth index to assess a comprehensive productivity
performance for the banking and financial industry of China,
incorporating activities of environmental financing. Since the
environmental indicators cannot be measured by market prices
of uniform standards, we adopt the input-output distance func-
tion to define the Malmquist “Green Credit” productivity
growth index, so that it can be avoided the disadvantage of
using input prices as measures for the environmental indicators.
In estimating the “Green Credit” distance functions, we first set
up a benchmark technological frontier of the whole banking
and financial system, and then through comparison to the ref-
erence frontier as an industrial benchmark, we can estimate the
individual “Green Credit” distance functions by time and by
financial institutions. Furthermore by definition, we can also
calculate an integrated “Green Credit” productivity growth
index for the financial sector as a whole across various time
periods. Based on the above approaches, we can further discuss
the effects of efficiency change and technological progress that
may have impacts on the overall productivity growth in the
financial industry while incorporating the environmental per-
formance.
In additi
ssion on the establishment and application of the appraisal
system of the “Green Credit” productivity growth index both
theoretically and empirically. When estimating the “Green
Credit” distance functions, we apply a two-stage linear pro-
gramming optimization principle to smoothen out the estimated
benchmark distance functions of the entire banking and finan-
cial system, characterized with the most consistent to the actual
data, but free of excessive discontinuity, periodic, and error
deviation. Besides, the parameter values of the input-output
combination generated from the second stage are potentially
valuable, which provides the theoretical basis and practical
means of computing the environmental financial expenses.
Lastly, in estimating the “Green Credit” distance function and
thus the Malmquist “Green Credit” productivity growth index,
we apply the sequential linear programming method to stack up
the historical data in constructing a continuous production
36

L. XU
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