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Under the Shadow of Asian Brown Clouds: Unbalanced Regional Productivities in China and Environmental Concerns

Posted on: Friday, 17 March 2006, 06:00 CST

By Hu, Jin-Li; Sheu, Her-Jiun; Lo, Shih-Fang

Keywords: Data envelopment analysis (DEA), Malmquist productivity index, environment, sustainable development

SUMMARY

China has seen rapid economic growth over the past two decades, but severe environmental problems have accompanied this, such as the looming danger of Asian Brown Clouds. This paper analyzes the regional development of China by examining economic performance and environmental factors. Technical efficiency and productivity changes in 31 regions of China are computed for the period 1997-2001. In the case of regional GDP, the fast-developing eastern (coastal) regions experience higher technical efficiency and productivity growth than the inland central and western regions. When environmental factors are incorporated, the eastern regions still perform better than inland regions, both from static and dynamic analysis. This phenomenon is termed the 'double deterioration' of the inland areas in China. Double deterioration is attributed to the lack of economic resources to replace highly-polluting production equipment and technology in those less developed regions.

INTRODUCTION

A 3-km thick cloud of toxic pollution looming over Asia, known as 'Asian Brown Clouds', caught global concern at the 2002 World Summit on Sustainable Development in South Africa. This thick layer of haze hangs over a wide area, covering Asia from south to east (south Asia, India, Pakistan, Southeast Asia and China) and is a direct result of damaging development trends (CNN News 2002), for which the world now has to work together so as to help reverse it. Asian Brown Clouds are made of soot, ash, dust and airborne chemicals, which are all products of manmade pollution. This toxic haze could kill hundreds of thousands of people prematurely and cause deadly flooding and drought. Scientists warn that the impact could be global since winds can push pollutants halfway around the world, including to Europe and even the Americas in a week, according to the Concept Paper on Asian Brown Clouds (2001). Therefore, Asian Brown Clouds are not only an important subject for China and its people, but also for all the people of the world.

Ever since China adopted the policy of economic reform and opened up to the outside world in the late 1970s, it has experienced double- digit growth. Although China has experienced rapid economic growth for more than a decade, its environment is rapidly deteriorating. Soot, dust and sulfur dioxide, the main components of Asian Brown Clouds, are the major pollutants being emitted. Only recently has the Chinese government taken action to cope with these environmental problems, especially on air and water pollution (World Bank 2001). Although the dust emission has declined, sulfur dioxide and soot emissions have been climbing in recent years (Liu 2001), and these problems can be attributed to old-fashioned and inefficient technology, as well as highly polluting engines and fuels (Ramanathan and Crutzen 2001).

There are numerous theoretical and empirical studies considering the relationship between economic development and environmental quality the famous Environmental Kuznets Curve (EKC) postulates an inverted U relationship between economic growth and pollution. It suggests that environmental degradation would increase at low incomes, reach a peak (turning point), and eventually decrease with high incomes. EKC implies that persistent economic growth can be accompanied by reductions in environmental degradation in the long term (Neumayer 1999). The other optimistic view, the Porter hypothesis, states that reducing environmental impacts of production will improve productivity, hence simultaneously benefiting economic growth and the environment (Porter and van der Linde 1995). Furthermore, more profitable firms are more likely to adopt clear technologies (Dasgupta et al. 2002). This aroused our curiosity: Do China's fast-developing eastern regions both economically and environmentally perform better than the less-developed inland ones? Do their rankings in regional productivities drastically change after taking into account environmental factors? After its entrance into the World Trade Organization (WTO) in 2001, problems of rising regional economic disparities and environmental protection have become more imminent for China.

For OECD members, the objective to pursue a balance between pro- development and proenvironment has received considerable attention. Lovell et al. (1995) studied the macroeconomic performance of 19 OECD countries by analysing four services, real GDP, low rate of inflation, low rate of unemployment, and favorable trade balance. When two environmental disamenities (carbon and nitrogen emissions) are included into the service list, the rankings change while the relative scores of the European countries decline. Environmental indicators do seem to have crucial effects on a nation's relative performance.

Incorporating the economy and the environment together, the concept of sustainable development has become a key element of policies not only at national levels, but also at regional levels (Gibbs 1998). One can recall the old radical green slogan 'think globally, act locally.' In other words, development towards sustainability can be introduced by starting from areas on a local or regional level (Wallner et al. 1996; Dryzek 1997). This type of sub-national scale can be emphasized as a key site for the integration of economic and environment policy (Gibbs 2000). This would seem to be of particular importance to various regions in China, in light of their geographical and economic diversity.

In this paper we will examine the overall performance of each region in China by comparing the relative technical efficiency and productivity change before and after incorporating environmental impacts. All major forms of emission for Asian Brown Clouds will be included in our analysis. We use a linear programming technique known as Data Envelopment Analysis (DEA) to analyze the relative macroeconomic performance of regions in China. DEA, first developed by Charnes, Cooper, and Rhodes ( 1978), is a methodology for constructing a best practice frontier, which tightly envelops observed data on producers' inputs and outputs. The relative performance of a decision-making unit is evaluated in terms of its proximity to the best practice frontier. DEA was originally intended for use in microeconomic environments to measure the performance of schools, hospitals and the like, and it is also ideally suited to macroeconomic performance analysis (Lovell et al. 1995).

Although DEA is useful to identify the best performers in a certain year, performance improvement over time (including productivity changes) is not considered. Productivity changes can be measured by the Malmquist productivity index, introduced by Caves et al. (1982), which takes panel data into account. Sten Malmquist was the first person to construct quantity indices as ratios of distance functions. This method was applied by Fare et al. (1994) to analyze productivity growth of OECD countries, by considering labour and capital as inputs and GDP as an output. Chang and Luh (2000) adopted the same method to analyze the productivity growth often Asian economies.

REGIONAL ECONOMIC DISPARITIES IN CHINA

From the perspective of China's development and political factors, its provinces, autonomous regions and municipalities are usually divided into three major areas: the east, central, and west. The eastern area stretches from the province of Liaoning to Guangxi, including Shandong, Hebei, Jiangsu, Zhejiang, Fujian, Guandong, and Hainan, and the municipalities of Beijing, Tianjin, and Shanghai. Among the three major areas, the eastern area has experienced the most rapid economic growth. In the early 1980s, the Chinese government established and opened up four special economic zones and 14 coastal cities to foreign investment and trade. Since then, the special economic zones and the coastal open areas have enjoyed considerable autonomy, special tax treatment and preferential resource allocations (Litwack and Qian 1998). They have attracted the most foreign capital, technology and managerial know-how. Rapid economic growth has made this area a magnet for attracting investment and migrant workers. The central area consists of Heilongjiang, Jilin, Inner Mongolia, Henan, Shanxi, Anhui, Hubei, Hunan, and Jiangxi. This area has a large population and is a home base of farming. Foreign investment in this area is not as high as in the eastern coastal regions, and existing equipment relatively lags behind. The western area covers more than half of China, including the provinces of Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Tibet, Yunnan, Xinjiang, Sichuan, and the municipality of Chongqing. Compared to the other two, this area generally has a low population density and is the least developed.

The high economic inequality, which can be mainly attributed to the growing inland-coastal disparity (Chang 2002; Yang 2002) in China, has caught considerable attention and research recently. For instance, the rich coastal provinces performed better with respect to per capita production and consumption than the inland ones during the reform period (Kanbur and Zhang 1999; Yao and Zhang 2001). The total factor productivity of the coastal pro\vinces is roughly twice as high as that of the non-coastal provinces (Fleisher and Chen 1997). General explanations for these disparity issues are from the advantageous geographic factors which will reduce transportation cost and the government's preferential policies for the coastal areas (Yang 2002).

The locations of the provinces and municipalities and the average per capita nominal GDP of each region in China are shown in Figure 1. There is an apparent economic disparity between the coastal and inland areas. Regional economic disparities are related to greater access to world markets, better infrastructure, higher-educated labour force and the government's preferential policies on foreign investment for the eastern area (World Bank 1997). Figure 2 presents the industry composition (percentage of an industry's output value of GDP for primary, secondary, and tertian' industry) of these three areas in 1997. Primary industries include agriculture (farming, forestry, animal husbandry and fishery). secondary industries include mining and quarrying, manufacturing, production and supply of electricity, water and gas and construction. Tertiary industries include all other industries not included in the primary or secondary industry. This is a percentage of an industry's output value of GDP. Compared to the inland central and western areas, the eastern area has higher proportions of secondary and tertiary industries and a far lower proportion of primaiy industry.

METHODS

In this section the data envelopment analysis (DEA) approach and Malmquist productivity index will be used to measure technical efficiency and productivity changes of regions in China, without and with the incorporation of environmental impacts.

Measuring technical efficiency: the Data Envelopment Analysis (DEA) approach

Figure 1 Regions of China and average per capita nominal GDP 1997- 2001 (RMB)

Figure 2 The industry composition among areas (% of GDP in 1997)

Measurement of productivity change: the Malmquist index

The efficiency measured from the above procedure is static in nature, as the performance of a production unit is evaluated with reference to the best practice in a given year. The shift of the frontier over time cannot be obtained from DEA. To account for dynamic shifts in the frontier, we use the Malmquist productivity index (MALM) developed by Fare et al. (1994). This method is also capable of decomposing the productivity change into efficiency and technical changes, which are components of productivity change.

The analytical process

The growth of a nation's output depends on capital formation as well as efficiency and productivity improvement. Labour and capital are two major inputs in production. When measuring a nation's overall output, gross domestic product (GDP) is commonly used. For a nation, while GDP (income) is desirable, emissions (pollution) are undesirable. The change of income and pollution are two-way relations: first, increasing of income deteriorates the environmental condition directly because pollution is generally a by- product of a production process and is costly to dispose of. In reverse, growth of income is accompanied by increasing public demand for better environmental quality through driving forces such as control measures, technological progress and structural change of consumption. Desirable GDP and undesirable pollution should be both taken into account in order to correct a nation's output. This concept is called 'green GDP.' Green GDP is derived from the GDP through a deduction of negative environmental and social impacts.

In this study, we treat pollution as a negative externality which directly reduces output and productivity of capital and labour (Gates and Schwab 1988; Lopez 1994; Smulders 1999; de Bruyn 2000). In other words, the emission proxies used in our analysis are acted as cost of loss, e.g. the health problem caused, corrosion of industrial equipment due to polluted air, and other related social expenses. In the following analytical process, soot, dust and sulfur dioxide, the main components of Asian Brown Clouds, are considered as input terms to evaluate macroeconomic performance in terms of the regions with BCC and Malmquist models.

DATA SELECTION

From the China Statistical Yearbook, we established a data set for 31 regions in China (27 provinces and 4 municipalities) from 1997 to 2001. In the analysis without environmental impacts, there are two inputs and one output. The two inputs are capital stock and number of employed persons. Capital stock cannot be directly obtained from China Statistical Yearbook, and in this study, every regional capital stock in a specific year is calculated by the authors according to the following formula: capital stock in the previous year + capital formation in the current year - capital depreciation in the current year. All the nominal values are deflated in 1997 prices before summations and deductions. We find the initial capital stock (capital stock data in 1996) from the research of Li (2003). The one output is GDP of a specific region. These are aggregated input and output proxies. The analysis of environmental impact involves five inputs and one output. In addition to those two inputs and one output, three inputs of emissions, which are treated as cost of production, are added: volumes of sulfur dioxide emission, industrial soot emission and industrial dust emission. These are China's three most serious emissions and constitute the major components of Asian Brown Clouds.

Macroeconomic performance is evaluated in terms of the ability of a region to maximize the one desirable output, GDP, and to minimize the three environmental disamenities. The value of monetary inputs and outputs such as GDP and capital are in 1997 prices. Summary statistics of these inputs and outputs ordered by year and area are shown in Tables 1 and 2, respectively. We use freeware Deap 2.1, kindly provided by Coelli (1996), to solve the linear programming problems.

Table 1 Summary statistics of inputs and outputs by year

Table 2 Summary statistics of inputs and outputs by area

RESULTS AND DISCUSSION

Efficiency frontier

The efficiency frontier consists of the most efficient regions for each particular year. Regions on the frontier are assigned an efficiency score of one. Regions with scores approximating to one are those that are closer to the frontier. Compositions of efficiency frontiers without and with environmental factors from 1997 to 2001 are shown in Table 3.

Generally speaking, about one-sixth of the regions in the sample are on the frontier at least once for the time period from 1997 to 2001 when environmental factors are not considered. With environmental factors, about one-third of the regions are on the frontier. With or without environmental factors, Shanghai (09), Hunan (18), Guangdong (19) and Tibet (26) are on the frontier every year. Fujian (13) is on the frontier in some years without environmental factors and is on the frontier for every year with environmental factors. Heilongjiang (08), Jiangsu (10) and Hainan (21) behave most efficiently after taking the environmental factors into account. Two municipalities, Beijing (01) and Tianjin (02), are on the frontier for some years with environmental factors. Most of the best performers are in the highly developing areas of China.

Table 3 Technical efficiency score of region for variable returns to scale

Composition of the efficiency frontier sorted by areas of China is in Table 4. The eastern coastal regions are, on average, in a better position, no matter whether these are with or without environmental factors. Taking into account environmental factors makes the number of regions on the frontier increase. The total amount of regions gained on the frontier mainly results from the eastern area. The efficiency frontier derived from technical efficiency is a relative concept. We cannot conclude that those eastern coastal regions in the frontier have absolutely good environmental conditions. However, these provinces perform better than their inland peers when both economic and environmental factors are concerned.

Productivity change

In the above analysis, the efficiency frontier for each year is constructed from the efficient regions of the given year. This is a kind of static analysis that disregards movements of the frontier, and regions on the frontier have the same efficiency score of one. Geometric means of the Malmquist productivity change summary indices and the components of growth for each sample region are listed (Table 5), without/with environmental factors. On the left side of Table 5, the Malmquist indices and components without environmental factors are listed. The average Malmquist index is 0.955, with four regions' indices exceeding unity, implying that they have positive production growth. The eastern regions generally perform better than inland ones. The sources of productivity growth for those eastern regions are technical change rather than efficiency change. Most western regions and some central ones lie at the bottom of the list. This result is consistent with the developing disparity in China (World Bank 2001 ) whereby the eastern areas have better economic conditions.

Table 4 Composition of the efficiency frontier for variable returns to scale

After incorporating the case of the three undesirable and costly emissions as inputs, regional performance rankings on average do not change: the Malmquist indices and components with environmental factors are listed on the right side of Table 5. The average Malmquist index is 0.957, with six regions showing positive productivity growth. The overall rankings of Malmquist indices change slightly with and without environmental factors. Productivities of three big cities, Shanghai (09), Beijing (01) and Tianjin (02), improve to a large extent when environmental factors are considered. The regions which improve their rank \position for more than five positions are: Hainan (21) in the east; Heilongjiang (8) in the centre. The regions which regress more than five places are: Jiangsu (10) and Zhejiang (11) in the east; Jiangxi (14) and Hubei (17) in the centre.

In order to examine whether an association exits between the two rank lists without/with environmental factors, Spearman rank correlation coefficient test is used. This is a nonparametric rank correlation procedure for making inferences about the association between two rank series. The Spearman correlation coefficient for the Malmquist indices is 0.9108 at a 1% significance level which strongly rejects the null hypothesis that there is no association between the two rank lists. Therefore, it can be generally concluded that those regions with higher productivity while GDP is solely concerned are still ranked superior when both GDP and environmental factors are considered.

In Lovell et al. (1995), for OECD countries, the inclusion of two environmental indicators did change the ranking, reflecting that the environment is a decisive variable when assessing a nation's relative performance. However, this is not to say that environmental factors are not of importance to Chinese regional comparisons, because of this unchanged productivity ranking. It is rather a warning of the extreme developing disparity in China, whereby the non-coastal areas are frail in economic growth as well as in environmental protection. We call this phenomenon the 'double deterioration' of regional development in China.

Table 5 Decomposition of the Malmquist index without/with environmental factors by region

The double deterioration in China can also be clearly observed through the regional indices changes without/with environmental factors (Figure 3). Figure 3 presents the decomposition of the Malmquist index by area. There appears to be an obvious difference between the eastern and the inland-central-western areas: the productivity growth (MALM) of the east dominates that of the central and western areas without/with environmental factors. With respect to technical changes (TECHCH), the east still leads the central and western areas without/with environmental factors. For efficiency changes (EFFCH) without environmental factors, the east performs worse than the centre and west. However, this gap is narrowed after accounting for environmental factors.

Figure 3 Decomposition of Malmquist index without/with environmental factors by area

One may wonder whether or not industry composition creates the disparities since the pollution emitted is mainly from the secondary industry. Recall Figure 2, which presents the industry composition of the three areas, the percentage of secondary industry in the east is higher than that of the other two areas. The postulate that an area with a higher percentage of secondary industry performs even worse under environmental concerns is definitely not supported. A possible explanation is that secondary industry in the inland area is pollution-intensive, such as basic metals and chemicals; the production equipment and environmental control skills are less developed, hence inducing higher pollution. 'Double deterioration' is a consequence of insufficient funds to replace dirty equipment and fuel for the poor regions.

CONCLUSIONS

Two decades of rapid economic growth have brought about a steady deterioration in the environment in China. Air pollution alone contributes to the premature death of more than a quarter of a million people each year (World Bank 1997). With the threat of Asian Brown Clouds, this problem is starting to prompt global attention. In this paper we have provided an evaluation of the performance of those regions responsible for the conduct of economic development and environmental problems in China.

This study may be the first to incorporate environmental considerations accompanying rapid economic issues in China from a sub-national perspective. We believe that regional development performances would be biased when neglecting a number of important aspects such as environmental factors. In this paper three severe air emissions (soot, dust and sulfur dioxide) are included as proxies of undesirable externalities. We treat these forms of pollution as costly inputs (expenses) which the whole society has to bear. A 1997-2001 panel data set of 31 regions in China is used. The relative technical efficiency and productivity change of these thirty-one regions in China without/with environmental factors are discussed. The empirical results can be summarized:

1. The fast-developing eastern coastal regions experience comparatively higher technical efficiency and productivity growth than the other inland regions when GDP is solely considered as a region's output.

2. In static analysis, taking into account environmental factors makes the number of regions on the frontier increase. The total amount of regions gained on the frontier mainly results from the progress of the eastern area.

3. In dynamic analysis, the ranking lists without/with environmental factors change only slightly. This result is statistically significant and provides evidence that these two rank series without/with environmental factors are highly related. The possible interpretation for this phenomenon is that those regions with inferior productivity suffer from costly environmental problems at the same time. In this study, we called this a 'double deterioration' in China.

4. In the comparison of the Malmquist index and its components, the eastern area performs better than the inland central and western ones, after adjustment adding in environmental factors. The above phenomenon should be attributed to highly polluting production processes rather than the industrial composition.

Receiving $45 billion in 1998, China was the largest FDI (Foreign Direct Investment) host country among the developing Asian economies (United Nations 1999). However, per capita FDI in the west is only 8% of that in the east. Traditional rules, such as 'economy first, environment later' or 'the coast first, inland later,' still dominate national development policy. Furthermore, China opened up for all industries without discrimination after it entered the WTO in 2001. People in China, especially in areas with lower income, may welcome dirtier industries so as to increase their income. Hence, China faces a dilemma of economic growth versus environmental protection.

Our empirical findings are consistent with EKC theory: while the poorer inland areas are in the increasing stage in terms of output pollution, the richer east is in the decreasing stage in terms of output pollution. Better environmental performance has accompanied economic achievement for the fast-developing area. On the other side, double deterioration of the inland area is indeed a warning for China to pursue more balanced regional development. The inland regions produce and mine using a lower grade of equipment that is highly polluting, and they still cannot afford better equipment to treat the pollutants. According to EKC theory, with persistent economic growth, the environment of inland China will sooner or later improve. However, before this turning point occurs, they are now suffering from a double deterioration of economic performance and environment.

The following principles may serve as inspiration to speed up the development of inland China:

1. Diminish transportation expenses in these areas. Most western regions are relatively disadvantaged in not only having a longer distance to market, but also higher transportation costs, which are also obstacles to import the latest pollution abatement technologies and information.

2. Ask for domestic and international assistance in financing local environmental policy reforms and education.

In the long term, growth without environmental protection could lead industry to be less competitive under pressure from a world that needs to adhere to environmental protection. Our warning of a 'double deterioration' may be beneficial in promoting sustainable development of China's economy as well as that of the global village.

Environmental disamenities are frequently trans-regional, and may not be entirely under the control of a particular region. However, this study can serve as a starting point to inspire attention towards the balance between economic growth and environmental protection. For future research, we may study the effects of a region's industrial structure, environmental policies and the local government's power over its performance. The efficiency and productivity approaches used in this paper can be applied to other regional holistic development studies.

ACKNOWLEDGEMENTS

The authors would like to thank the Editor-in-Chief and two anonymous referees for their valuable comments. We are also indebted to Joseph Plasmans, Daigee Shaw and seminar participants at Remin University of China for their helpful suggestions. Partial financial support for the first and second authors from Taiwan's National Science Council (NSC 93-2415-H-009-001 and NSC90-2621-Z-009-004) is gratefully acknowledged.

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Jin-Li Hu1, Her-Jiun Sheu2 and Shih-Fang Lo3'

1 Institute of Business and Management, National Chiao Tung University, Taiwan

2 Department of Management Science and Institute of Finance and Banking, National Chiao Tung University, Taiwan

3 International Division, Chung-Hua Institute for Economic Research

Correspondence: Jin-Li Hu, Institute of Business and Management, National Chiao Tung University, 4F, 114 Chung-Hsiao W. Rd., Sec. 1, Taipei City 100, Taiwan. Email: jinlihu@mail.nctu.edu.tw

Copyright Taylor & Francis Ltd. Dec 2005

Source: International Journal of Sustainable Development and World Ecology

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