On the Evaluation of Performance System Incorporating “Green Credit” Policies in China’s Financial Industry

doi:10.4236/jfrm.2013.22005

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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

Email: [email protected]

Received March 29th, 2013; revised May 1st, 2013; accepted May 8th, 2013

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

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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-

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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





Dxy

is presented as follows:







,, ,

,max

1, ,

tktkt k

Kkt ktk kt

Kkt ktkt

Dx y

zy y mM

zx xnN







 









,,.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:





 

1'1

ln, ααln βln

1αlnln

1βln ln

γlnln .

omm

mm mm

nn nn

nm nm

Dxyy x











 





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

111 112

111 1

,,,

ttttt

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-

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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

put data as costs

f input and output data

major indicators explaining the en-

part of the “Green Financial” efforts mainly refers to

ng the GAMS programming software and the

Summary and Discussion

This article lquist “Green

on, the paper carries on an in-depth study and dis-

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

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