企業(yè)環(huán)境污染強度研究

蔡慧穎

（日本上智大學 地球環(huán)境研究生學院，日本 102-8554）

1 背景介紹

在過去的10年中，中國經濟高速發(fā)展，遠超過經濟合作發(fā)展機構成員（Organization for Economic Cooperation and Development，OEDC），成為世界第二大經濟體[1]。改革開放以來，中國經濟已經經歷了一個“奇跡發(fā)展時期”。在過去的34年，名義GDP增幅甚至達到15.7%，實際GDP增長率達到了9.6%。而與此同時，全球GDP增長率僅2.8%。在1978年，中國經濟出口僅排世界第10位。

改革開放初期，中國政府宣布要不惜一切代價大力發(fā)展經濟[2]。從圖1可以看出，30年過后，中國GDP快速增長。2010年，中國取代日本，成為世界第二大經濟體。沒有任何一個國家能像中國一樣，在如此短的時間里有如此大的經濟發(fā)展。

在20世紀80年代，因為國際經濟競爭，全世界國有企業(yè)經濟發(fā)展形勢不容樂觀[3]。國有企業(yè)不佳的經濟績效使得中國不得不尋找提高國有企業(yè)經濟效率的辦法：企業(yè)重組、剝離或者破產。隨著私有企業(yè)的崛起，研究者相信私有企業(yè)擁有更好的經濟績效和競爭力。因此，國有企業(yè)私有化成了包括中國在內的很多發(fā)展中國家發(fā)展經濟的首選方式。在過去30年中，中國政府為了提高生產力、利潤率和競爭力，采取了將部分國有企業(yè)私有化，并大力提倡發(fā)展私營企業(yè)，導致私有企業(yè)大量出現(xiàn)，成為了中國最受歡迎的所有制結構[4]。如圖2所示。

圖1 GDP增速對比（1970—2015年）Fig.1 Comparison of GDP growth rate(1970—2015)

圖2 中國企業(yè)數(shù)量（百萬）Fig.2 Number of firms in China(in million)

但是，隨著工業(yè)化和城市化進程的不斷加快，中國的環(huán)境現(xiàn)狀出現(xiàn)了一些新的問題。污染物的長期累積已經對公眾健康產生了巨大的影響。環(huán)境惡化已經給中國帶來了不容忽視的經濟損失。中國已經公布了包括環(huán)境稅在內的各項治理空氣和水污染的法律法規(guī)，加大了環(huán)境保護的執(zhí)法力度。

在中國，國有企業(yè)規(guī)模一般較大，而私有企業(yè)更多的是中小企業(yè)。因此，探究國有企業(yè)和私有企業(yè)對環(huán)境的承受能力從某種角度來說就是探討大型企業(yè)和中小企業(yè)對環(huán)境的承受能力。Wang和Wheeler在2003年建立了關于大型國有企業(yè)和環(huán)境績效關系的模型[5]，并表明國有企業(yè)相對于中小型私營企業(yè)對環(huán)境污染的影響更大。但是國有企業(yè)因為資金雄厚、有政府支持，環(huán)境承受力可能相對較強。相反，中小企業(yè)因規(guī)模較小、投資環(huán)境治理的能力有限，再加上中小企業(yè)習慣了“搭便車”（free rides），因此，它們的環(huán)境污染承受力可能較弱?；谏鲜鲇懻?，筆者從國有企業(yè)和私有企業(yè)對環(huán)境的影響來探討中國不同企業(yè)的環(huán)境承受能力。

2 數(shù)據(jù)與模型

本文數(shù)據(jù)均來自《中國統(tǒng)計年鑒》（2007—2015）和國家統(tǒng)計局，覆蓋31個?。ㄗ灾螀^(qū)/直轄市）（北京、天津、河北、山西、內蒙古、遼寧、吉林、黑龍江、上海、江蘇、浙江、江西、安徽、福建、山東、河南、湖北、湖南、廣東、廣西、海南、重慶、四川、貴州、云南、西藏、陜西、甘肅、青海、寧夏和新疆），包含248個觀測數(shù)據(jù)和2006—2014年共計31個橫截面的面板數(shù)據(jù)。

筆者選擇Wang和Wheeler在1999年所建立的公式，建立“三廢”（工業(yè)廢氣、工業(yè)廢水和工業(yè)固體廢棄物）指標[6]來衡量企業(yè)污染指標：

其次，筆者運用Wang和Wheeler在2003年提出的方法，通過收集31個省份國有企業(yè)和私有企業(yè)的年產值來衡量企業(yè)規(guī)模。為了得到更準確的模型結果，還將引入以下控制變量（這些變量同時影響污染指標和企業(yè)規(guī)模）：（1）人均GDP，（2）城市化程度（各省份城市人口比例），（3）交通情況（鐵路、高速和水路乘客占各省份總人口的比例），（4）科技水平（各省份專利數(shù)）。

根據(jù)上述討論，建立以下雙向固定效應模型：

式中：ln(PI)it表示污染強度在時間t=1，2，3，…，9和地區(qū)i=1，2，3，…，31上的自然對數(shù)；lnSOEsit和lnPOEsit表示國有企業(yè)和私有企業(yè)在時間t=1，2，3，…，9和地區(qū)i=1，2，3，…，31上的工業(yè)生產總值；γ表示包括人均GDP、城市化程度、交通情況和科技水平在內的4個控制變量向量；εit表示各變量的殘差。還建立了包含滯后項的動態(tài)模型：

3 結果與討論

運用Eviews 9.0軟件建立雙向固定效應模型得出國有企業(yè)和私有企業(yè)的污染強度，結果見表1。

根據(jù)表1的結果可以發(fā)現(xiàn)，國有企業(yè)工業(yè)產出在模型1中顯著（-0.307 6），意味著大型國有企業(yè)每增加百分之一的產出將會導致0.3%的污染強度的減少。同樣，模型3也證實了大型企業(yè)和污染強度的負相關關系。這表明國家對國有企業(yè)施加的壓力迫使國有企業(yè)更加關注環(huán)境污染問題。另外，模型2中，中小私有企業(yè)工業(yè)產出在5%顯著水平上顯著（0.125 6），意味著中小企業(yè)每增加百分之一的產出將會直接導致0.18%的污染強度增加。大多私有企業(yè)以營利為目的，治理環(huán)境污染的成本與投入使得它們不愿意在現(xiàn)有階段加大環(huán)境治理。但加入了滯后變量的模型4中，私有中小企業(yè)工業(yè)產出變量并不顯著，潛在原因可能是因為中小私有企業(yè)對環(huán)境污染的影響無法在短時間體現(xiàn)。

4 結論

本文采用中國31個?。ㄗ灾螀^(qū)/直轄市）的面板數(shù)據(jù)，建立了從2006年到2014年的雙向固定因素模型，來探討國企和私有企業(yè)對環(huán)境的影響力，進而探討中國不同企業(yè)規(guī)模對環(huán)境的影響力。從結果來看，目前在中國，大型國企促進環(huán)境保護而中小私營企業(yè)對環(huán)境污染有較大影響。規(guī)模較大的國有企業(yè)，資金相對充足，更加注重社會責任與經濟共同發(fā)展，因此會相對嚴格地執(zhí)行國家環(huán)境政策、嚴控廢物排放；而中小私有企業(yè)更加追求利潤，較弱的資金背景讓它們在考慮利潤最大化的同時很難兼顧環(huán)境保護。如今，中國中小企業(yè)發(fā)展迅速，國家政策逐漸完善，如何加強中小企業(yè)環(huán)保意識值得探討。中小企業(yè)是國家產業(yè)的重要組成部分，可以在行業(yè)內成立產業(yè)鏈或者企業(yè)集群來共同面對環(huán)境問題，在某種程度上促進中小企業(yè)對環(huán)境保護的作用。

表1 污染強度模型結果Tab.1 Results of pollution intensity models

1.Introduction

Over the last decade,China has witnessed a rapid economic growth rate,far beyond that of(Organization for Economic Cooperation and Development,OECD)members,and is assumed to be the largest economy in the world[1].It can be seen from Figure 1 that since Reform and Opening-up,China's economic growth has experienced a“miracle period”.In the past 34 years,the nominal annual GDP growth rate even reached 15.7%and real GDP growth rate has reached 9.6%.Yet,the average GDP growth rate in the globe was only 2.8%over the same period（World Bank Open Database）.In 1978,China's economic outputs ranked only at the tenth in the world while after 30 years,in 2008,China's GDP surpassed Germany's to rank at the third in the world.In 2010,China took Japan's place to become the second largest economy in the globe.There is no other country in the world which has achieved such substantial economic growth in such a short period of time.

Fig.1 Comparison of GDP growth rate(1970-2015)

At the beginning of the Reform and Open-up,Chinese government stated to pursue a high economic growth at any cost[2].However,the economic performance of state-owned enterprises(SOEs)deteriorated over the world in 1980s because of the fierce international competitiveness[3].The poor economic performance of SOEs motivates Chinese government to explore other methods to promote economic growth.There are three ways that have been issued to improve economic efficiency of SOEs:corporation and re-structuring,divestiture,and bankruptcy.With the belief that private enterprises have better economic performance and better competitiveness than public sectors,privatization has emerged in most disadvantageous countries including China to boost their economic growth.Over the past decades,in order to improve competitiveness,boost profits and enhance productivities,China has witnessed privatization[4],with the result of the diminishing presence of SOEs,which can be observed in the Fig.2.After 1998,due to“privatization Chinese style”,the number of privateowned enterprises(POEs)surpassed that of SOEs approximately 2002,and thus POEs became the most popular ownership structure in China.Then,the number of POEs increased considerably and had as more than ten times as that of SOEs in 2010.

Fig.2 Number of firms in China(in million)

The changes in the number of SOEs and POEs also bring to changes in the industrial outputs of SOEs and POEs.For instance,more large-scale SOEs exist in the most developed cities with severe pollution problems,such as Beijing and Shanghai,than other regions while more relatively small-scale POEs are seen in coastal regions,such as Zhejiang and Guangdong[5].Apart from the differences in the distribution of various types of corporate ownership structures,conditions such as GDP per capita,levels of private sectors as well as openness to foreign sectors,also show the uneven economic development across China.

But with the process of industrialization and urbanization,environmental situation in China has become more and more complex,and the long-term effects of accumulated pollutant have posed a tremendous threat to the public health.The past 30 years has not only witnessed the Golden Ages for China's rapid economic growth,but also marked the threshold of high resource consumption and serious environmental pollution.The effects brought by environmental deterioration have resulted in a huge economic loss forChina.Chinesegovernmentareestimated to spend approximately 3 to 10 percent of national income on tackling environmental pollution every year.China has issued various laws to deal with air and water pollution in recent years,and pollution management has become the top priority in government agenda[6].Although numerous pollution control regulations,such as Pollution Discharge Levy Law,have been issued in 1990s in China,enterprises still have no incentives to control their pollution.Apart from that,the current situation in China also makes environmental regulations less effective than in other countries[7].

The relationship between industrial corporate ownership structures and their environmental performance is still unconducive.Wang and Wheeler in 2003 constructed a model to figure out the relationship between the outputs of large state-owned enterprises and environment,revealing that corporates under state's control polluted much more than private sectors[8].Also,in 2012,some scholars argued that size of enterprises was an essential determinant in terms of corporate's environmental performance[9].The increased privatizations in Chinese corporates have led to the increasing number of small-and medium-enterprises[10].With the depth of development policy for SMEs in China,this study is likely to give SMEs the suggestions for their future environmental development.

2 Data and Models

All data used in this study come from China Statistical Yearbook(2007—2015)and National Bureau of Statistics of the People's Republic of China.The testing period in this study is from 2006 to 2014,covering the main 31 administrative regions in China(Beijing,Tianjin,Hebei,Shanxi,Inner Mongolia,Liaoning,Jilin,Heilongjiang,Shanghai,Jiangsu,Zhejiang,Anhui,Fujian,Jiangxi,Shandong,Henan,Hubei,Hunan,Guangdong,Guangxi,Hainan,Chongqing,Sichuan,Guizhou,Yunnan,Tibet,Shaanxi,Gansu,Qinghai,Ningxia,Xinjiang).All data selected are strongly balanced.In the sum,there are 248 observations and 31 cross-sections in the panel data from year 2006 to 2014.

Some scholars claim that it is inappropriate to discuss environmental affordability by measuring the current environmental performance.Therefore,this study introduces the method proposed Wang and Wheeler in 1999 to consider“three wastes” (industrial waste gases,industrial waste water and industrial solid wastes)into one pollutant index by employing pollution intensity to measure the gross industrial pollution[11].

The pollution intensity includes main industrial pollutant to measure its environmental effects,and this study puts“three waste”together to figure out the clear correlation between corporate ownership structures and environmental pollution.

The essential task in this study is to study environmental effects of different corporate ownership structures at different scales.It is unreasonable to simply use the total volume of SOEs and POEs industrial outputs in a region directly because of the different pollution levies and regulations among these three corporate ownerships in China[12].For instance,in 1997,Pargal and Hettige found the evidence that in most Asian countries,SOEs resisted regulations more successfully than POEs[13].What's more,SOEs are more likely to generate more pollutant per unit of output due to their inefficiency in utilization.As such,the study controls these unexpected influences by introducing the method of Wang and Wheeler that applies the provincial share of productions in SOEs and POEs[13].More specifically,using the volume of SOEs,POEs,and FOEs divided by the total industrial outputs in a provincial level,respectively.

When considering the provincial data in China,it is crucial to acknowledge the huge regional differences among these 31 administrative regions.In or-der to recognize the corporate ownership structure effects and eliminate internal correlations among different provinces,it is significant to recognize control variables that tend to be related to both industrial ownerships and environment pollution.

2.1 GDP per capita

GDP per capita is a perfect representative to measure the level of development in one region.China is the country that has huge economic differences across regions.It seems that higher GDP are always found in the regions where have more private sectors[14].So in the model,GDP per capita should be included as a control variable,as it is not only related to the volume output of industries but also to corporate ownership structures.

2.2 Level of Urbanization

Urbanization has a great impact on industrial waste gas emissions,and one percentage rises in urbanization rates(the number of non-agriculture population in one area divided by the total number of residences in this area)will lead to more than one percentage increased in industrial waste gas emissions.Therefore,this study controls the level of urbanization by introducing the percentage of urban population as it is a popular and proper measurement to estimate the level of urbanization.

2.3 Traffic Situation

Exhausted emissions from urban traffic have become one of the major threats to air quality in China.From the research[15],corporate ownerships may have some relations to transportation.POEs are likely to reduce their operating costs and maximize their profits,thus wanting to seek methods to reduce their transport costs,such as planning a good transportation route to reduce petrol costs.The traffic control variable is estimated by using the number of passenger in railways,highways and waterways,divided by the total population in the specific regions to remove the size effect of population.

2.4 Level of Technology

Wang and Wheeler point out that“state-owned plants may simply generate more waste residuals per unit of output because these plants are less efficient”[16].The level of technology estimates the equivalent level of development,income and foreign direct investment in the region[17].The empirical study conducted by Watson,Jonstone and Hascic in 2009 introduced the number of patents granted as a proxy to measure the scale of technology[18].It is an appropriate measurement for China where the technological statistical data are not transparent enough owing to the number of patent applications reported insufficiently in databases.

Based on the above analysis and tests,this study specifies the equations of estimating pollution intensity as follows:

Here Models(1)and(2)are two-way fixed effects models,whereΔis the first difference,andln(PI)it is the natural logarithm of pollution intensity across time(t=1,2,3,…,9)and regions(i=1,2,3,…,31).lnSOEsitandlnPOEsitare industrial output shares of SOEs and POEs in natural logarithms form in regioni(i=1,2,3,…,31)from periodt(t=1,2,3,…,9).γis a vector of all control variables,including GDP per capita(GDPpc),percentage of urban population(Urbanization),patents granted per capita(Patents)and traffic per capita(Traffic),in regioni(i=1,2,3,…,31)and timet(t=1,2,3,…,9).εitis the unobservable disturbance that is unexplained by variables.

Besides,this study also develops dynamic models by introducing a lagged variable,ln(PI)it-1.Including lagged dependent variables can reduce the occurrence of autocorrelation,so helps us to defend the existence of autocorrelation in the model.The adjusted models of(1)and(2)which include one-year lagged variable have the form of

3 Results

This section gives the results of the four models provided in the previous section.Table 1 represents the results of model 1,2,3 and 4 by employing time-and city-fixed effects method,and model 3 and 4 are dynamic two-way fixed effects models.

Tab.1 Results of pollution intensity models

From the table,SOEs share is significant at confidence level 5%in non-dynamic model 1 and its coefficient is negative at-0.307 6,indicating the larger share of SOEs leads to the lower level of pollution intensity.That is,one-percent increase in share of SOEs would lead to about 0.3%decrease in environmental pollution intensity,provided other variables kept at constant.Not surprisingly,in model 3,the dynamic counterpart of model 1,SOEs is also statistically significant and presents a negative relationship with environmental pollution.Taking together,these two models present that the larger output share of state-owned enterprises has positive effects on environment in China.

With respect to the independent variables,POEs share is significant in model 2 and 4,suggesting that one-percent increase in the output share in POEs is associated with 0.12%increase and 0.18%decrease respectively in pollution intensity.This result implies that the larger output share of POEs would give rise to the larger pollution intensity.Results here document that output of POEs deteriorates to the environment.

Despite the fact that remaining control variables are not the central topics in this study,they should also be noted.In all models(exclude model 2),GDP per capital(GDPpc)is significant and has positively related to pollution.On the other hand,the volume of passenger per capita(Traffic)is significant in model 1 and 2 and directly related to pollution intensity.

The adding of lagged dependent variable may reduce the sample size effects,and it is still arguable whether lagged dependent variables should be added in fixed-effect models.Because of the time lag in economic operation,it is statistically reasonable to add lagged variables on the right of model.After adding lagged variables,all of them are significant at 1%confidence level,and have positive relationship with pollution intensity.The coefficients of lagged variables are positive,which are 0.7949 and 0.8185 respectively.This result is surprising at first glance,but the positive effects in all lagged variables can be explained as the failure of governments'environmental policies,and all of SOEs and POEs are potentially put more pressures on environmental situation in China in the long haul.Furthermore,SOEs share in model 3 is negatively correlated with pollution intensity,which suggests one-percent increase in output share of SOEs would have 0.2%decrease in pollution intensity.This negative relationship between SOEs share and pollution intensity indicates that SOEs still have beneficial effects on the environment.Nevertheless,similar tendency is also observed for POEs in model 4.The reason for this phenomenon could be that the effects tof POEs on environment are unable to be observed in a short time.

4 Conclusions

Distinct from the previous works,this study investigates environmental impacts of different industrial ownerships at the provincial level in China.With a database of 31 provinces from the year 2006 to 2014,this study finds that privately owned enterprises lack the incentive to reduce their emissions of pollutants.This is mostly related to the short-sighted decisions of privately owned enterprises when pursuing economic profits.

Additional implications are observed from the findings.Firstly,there is a necessity for managers in private firms to improve their engagement in environmental protection.The stakeholders'engagement in a small company has been regarded as the most essential contributor to corporate social responsibility[18].Policies that can push stakeholders to lean towards more environmentally-friendly practices should be developed.Secondly,since most private sectors mostly lack financial resources and environmental management involving large-scale investments,government policies which give rewards or incentives for private firms may be considered.Finally,as previously discussed,because the small private sectors often have limited resources,encouraging private sectors to cooperate with state enterprises to form private-public partnership is one method to help them seek for more resources.

There are currently no accurate studies done undoubtedly.The methods used and the data gathered in this study are limited in some aspects.One limitation of the models is their control variables.It should be noted that only the GDP per capita,level of urbanization,level of technology,and traffic situation are employed as the main control variables.However,these four factors do not represent all control variables in the real world.In 1994,Grossman and Krueger figured out the relationship between squared GDP per capita and environmental pollution,which they referred to as the Environmental Kuznets Curve(EKC),implying that squared GDP per capita might also be an option of the control variable in the model[19].It is,however,of great importance to realize that some scholars believe that there is no relationship between economic growth and environmental pollution in China,and thus they do not think that there exists an EKC in the country[20].It is because of this controversy that the squared GDP per capita is not included in the models used in this study.

Furthermore,Dasgupta and Wheeler in 1996 figured out the potential relationship between human capital and environmental pollution.Specifically,they found that higher levels of education had positive impactson environmentalimprovement.Areaswith higher educational levels(less illiteracy rates)were more easily aware of and responded more quickly to environmental problems.Additionally,corporate ownership structures have been shown to be positively related to human capitals.For instance,SOEs and FOEs have more skilled workers than POEs since SOEs and FOEs tend to have higher salaries and welfares for their employees in developing countries[5].Since human capitals may be a potential variable,it is logical to be taken into account in the models.However,because there is no known standard proxy to accurately measure the level of human capitals in China and there exists missing data in some provinces,human capitals is ignored in this study.

Results from this study are useful to explain the current corporate ownership structure and environmental performance in China.However,there are still questions left unanswered.For one,this study has discussed the social responsibility of state-owned enterprises but what we do not know at this stage is how the social responsibilities of these public enterprises work in real world.Furthermore,the different proxies for environmental pollution in China lead enterprises to have different reflections in environmental pollution.As firms under different ownership structures face different financial difficulties,they may choose various indexes to accurately measure environmental pollution and set targets accordingly.Thus,further research should be conducted in order to eradicate the importance for firms to comply to different environmental measures,especially for those in the private sectors as they usually face difficult economic constraints and generally have poor choices when it comes to environmental pollution.